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bded93adc301a0fcc3dd01dc4f3e259ca71231cc421fd7bee8443d206b1ce143 | #!/usr/bin/env python
# Licensed under a 3-clause BSD style license - see LICENSE.rst
# NOTE: The configuration for the package, including the name, version, and
# other information are set in the setup.cfg file.
import sys
# First provide helpful messages if contributors try and run legacy commands
# for tests or docs.
TEST_HELP = """
Note: running tests is no longer done using 'python setup.py test'. Instead
you will need to run:
tox -e test
If you don't already have tox installed, you can install it with:
pip install tox
If you only want to run part of the test suite, you can also use pytest
directly with::
pip install -e .[test]
pytest
For more information, see:
https://docs.astropy.org/en/latest/development/testguide.html#running-tests
"""
if 'test' in sys.argv:
print(TEST_HELP)
sys.exit(1)
DOCS_HELP = """
Note: building the documentation is no longer done using
'python setup.py build_docs'. Instead you will need to run:
tox -e build_docs
If you don't already have tox installed, you can install it with:
pip install tox
You can also build the documentation with Sphinx directly using::
pip install -e .[docs]
cd docs
make html
For more information, see:
https://docs.astropy.org/en/latest/install.html#builddocs
"""
if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv:
print(DOCS_HELP)
sys.exit(1)
# Only import these if the above checks are okay
# to avoid masking the real problem with import error.
from setuptools import setup # noqa
from extension_helpers import get_extensions # noqa
setup(ext_modules=get_extensions())
|
38130b17f976bb6e9362cab7c0b3b3194c6644a357928a4401fd5a46c34d13c7 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# This file is the main file used when running tests with pytest directly,
# in particular if running e.g. ``pytest docs/``.
import os
import tempfile
import hypothesis
from astropy import __version__
try:
from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS
except ImportError:
PYTEST_HEADER_MODULES = {}
TESTED_VERSIONS = {}
# This has to be in the root dir or it will not display in CI.
def pytest_configure(config):
PYTEST_HEADER_MODULES['PyERFA'] = 'erfa'
PYTEST_HEADER_MODULES['Cython'] = 'cython'
PYTEST_HEADER_MODULES['Scikit-image'] = 'skimage'
PYTEST_HEADER_MODULES['asdf'] = 'asdf'
PYTEST_HEADER_MODULES['pyarrow'] = 'pyarrow'
TESTED_VERSIONS['Astropy'] = __version__
# This has to be in the root dir or it will not display in CI.
def pytest_report_header(config):
# This gets added after the pytest-astropy-header output.
return (f'ARCH_ON_CI: {os.environ.get("ARCH_ON_CI", "undefined")}\n'
f'IS_CRON: {os.environ.get("IS_CRON", "undefined")}\n')
# Tell Hypothesis that we might be running slow tests, to print the seed blob
# so we can easily reproduce failures from CI, and derive a fuzzing profile
# to try many more inputs when we detect a scheduled build or when specifically
# requested using the HYPOTHESIS_PROFILE=fuzz environment variable or
# `pytest --hypothesis-profile=fuzz ...` argument.
hypothesis.settings.register_profile(
'ci', deadline=None, print_blob=True, derandomize=True
)
hypothesis.settings.register_profile(
'fuzzing', deadline=None, print_blob=True, max_examples=1000
)
default = 'fuzzing' if (os.environ.get('IS_CRON') == 'true' and os.environ.get('ARCH_ON_CI') not in ('aarch64', 'ppc64le')) else 'ci' # noqa: E501
hypothesis.settings.load_profile(os.environ.get('HYPOTHESIS_PROFILE', default))
# Make sure we use temporary directories for the config and cache
# so that the tests are insensitive to local configuration.
os.environ['XDG_CONFIG_HOME'] = tempfile.mkdtemp('astropy_config')
os.environ['XDG_CACHE_HOME'] = tempfile.mkdtemp('astropy_cache')
os.mkdir(os.path.join(os.environ['XDG_CONFIG_HOME'], 'astropy'))
os.mkdir(os.path.join(os.environ['XDG_CACHE_HOME'], 'astropy'))
# Note that we don't need to change the environment variables back or remove
# them after testing, because they are only changed for the duration of the
# Python process, and this configuration only matters if running pytest
# directly, not from e.g. an IPython session.
|
849d26898fd469a494890a464c47e1076f6d55df28b54c2619cc895ab57340d1 | # NOTE: First try _dev.scm_version if it exists and setuptools_scm is installed
# This file is not included in astropy wheels/tarballs, so otherwise it will
# fall back on the generated _version module.
try:
try:
from ._dev.scm_version import version
except ImportError:
from ._version import version
except Exception:
import warnings
warnings.warn(
f'could not determine {__name__.split(".")[0]} package version; '
f'this indicates a broken installation')
del warnings
version = '0.0.0'
# We use Version to define major, minor, micro, but ignore any suffixes.
def split_version(version):
pieces = [0, 0, 0]
try:
from packaging.version import Version
v = Version(version)
pieces = [v.major, v.minor, v.micro]
except Exception:
pass
return pieces
major, minor, bugfix = split_version(version)
del split_version # clean up namespace.
release = 'dev' not in version
|
f3a0aeb97076b413c5b12bf54c9d21687bfae4f546d809811d8fd5110a7aa5eb | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Astropy is a package intended to contain core functionality and some
common tools needed for performing astronomy and astrophysics research with
Python. It also provides an index for other astronomy packages and tools for
managing them.
"""
import os
import sys
from .version import version as __version__
def _is_astropy_source(path=None):
"""
Returns whether the source for this module is directly in an astropy
source distribution or checkout.
"""
# If this __init__.py file is in ./astropy/ then import is within a source
# dir .astropy-root is a file distributed with the source, but that should
# not installed
if path is None:
path = os.path.join(os.path.dirname(__file__), os.pardir)
elif os.path.isfile(path):
path = os.path.dirname(path)
source_dir = os.path.abspath(path)
return os.path.exists(os.path.join(source_dir, '.astropy-root'))
# The location of the online documentation for astropy
# This location will normally point to the current released version of astropy
if 'dev' in __version__:
online_docs_root = 'https://docs.astropy.org/en/latest/'
else:
online_docs_root = f'https://docs.astropy.org/en/{__version__}/'
from . import config as _config # noqa: E402
class Conf(_config.ConfigNamespace):
"""
Configuration parameters for `astropy`.
"""
unicode_output = _config.ConfigItem(
False,
'When True, use Unicode characters when outputting values, and '
'displaying widgets at the console.')
use_color = _config.ConfigItem(
sys.platform != 'win32',
'When True, use ANSI color escape sequences when writing to the console.',
aliases=['astropy.utils.console.USE_COLOR', 'astropy.logger.USE_COLOR'])
max_lines = _config.ConfigItem(
None,
description='Maximum number of lines in the display of pretty-printed '
'objects. If not provided, try to determine automatically from the '
'terminal size. Negative numbers mean no limit.',
cfgtype='integer(default=None)',
aliases=['astropy.table.pprint.max_lines'])
max_width = _config.ConfigItem(
None,
description='Maximum number of characters per line in the display of '
'pretty-printed objects. If not provided, try to determine '
'automatically from the terminal size. Negative numbers mean no '
'limit.',
cfgtype='integer(default=None)',
aliases=['astropy.table.pprint.max_width'])
conf = Conf()
# Define a base ScienceState for configuring constants and units
from .utils.state import ScienceState # noqa: E402
class base_constants_version(ScienceState):
"""
Base class for the real version-setters below
"""
_value = 'test'
_versions = dict(test='test')
@classmethod
def validate(cls, value):
if value not in cls._versions:
raise ValueError(f'Must be one of {list(cls._versions.keys())}')
return cls._versions[value]
@classmethod
def set(cls, value):
"""
Set the current constants value.
"""
import sys
if 'astropy.units' in sys.modules:
raise RuntimeError('astropy.units is already imported')
if 'astropy.constants' in sys.modules:
raise RuntimeError('astropy.constants is already imported')
return super().set(value)
class physical_constants(base_constants_version):
"""
The version of physical constants to use
"""
# Maintainers: update when new constants are added
_value = 'codata2018'
_versions = dict(codata2018='codata2018', codata2014='codata2014',
codata2010='codata2010', astropyconst40='codata2018',
astropyconst20='codata2014', astropyconst13='codata2010')
class astronomical_constants(base_constants_version):
"""
The version of astronomical constants to use
"""
# Maintainers: update when new constants are added
_value = 'iau2015'
_versions = dict(iau2015='iau2015', iau2012='iau2012',
astropyconst40='iau2015', astropyconst20='iau2015',
astropyconst13='iau2012')
# Create the test() function
from .tests.runner import TestRunner # noqa: E402
test = TestRunner.make_test_runner_in(__path__[0]) # noqa: F821
# if we are *not* in setup mode, import the logger and possibly populate the
# configuration file with the defaults
def _initialize_astropy():
try:
from .utils import _compiler # noqa: F401
except ImportError:
if _is_astropy_source():
raise ImportError('You appear to be trying to import astropy from '
'within a source checkout or from an editable '
'installation without building the extension '
'modules first. Either run:\n\n'
' pip install -e .\n\nor\n\n'
' python setup.py build_ext --inplace\n\n'
'to make sure the extension modules are built ')
else:
# Outright broken installation, just raise standard error
raise
# Set the bibtex entry to the article referenced in CITATION.
def _get_bibtex():
citation_file = os.path.join(os.path.dirname(__file__), 'CITATION')
with open(citation_file, 'r') as citation:
refs = citation.read().split('@ARTICLE')[1:]
if len(refs) == 0:
return ''
bibtexreference = f'@ARTICLE{refs[0]}'
return bibtexreference
__citation__ = __bibtex__ = _get_bibtex()
from .logger import _init_log, _teardown_log # noqa: E402, F401
log = _init_log()
_initialize_astropy()
from .utils.misc import find_api_page # noqa: E402, F401
def online_help(query):
"""
Search the online Astropy documentation for the given query.
Opens the results in the default web browser. Requires an active
Internet connection.
Parameters
----------
query : str
The search query.
"""
import webbrowser
from urllib.parse import urlencode
version = __version__
if 'dev' in version:
version = 'latest'
else:
version = 'v' + version
url = f"https://docs.astropy.org/en/{version}/search.html?{urlencode({'q': query})}"
webbrowser.open(url)
__dir_inc__ = ['__version__', '__githash__',
'__bibtex__', 'test', 'log', 'find_api_page', 'online_help',
'online_docs_root', 'conf', 'physical_constants',
'astronomical_constants']
from types import ModuleType as __module_type__ # noqa: E402
# Clean up top-level namespace--delete everything that isn't in __dir_inc__
# or is a magic attribute, and that isn't a submodule of this package
for varname in dir():
if not ((varname.startswith('__') and varname.endswith('__')) or
varname in __dir_inc__ or
(varname[0] != '_' and
isinstance(locals()[varname], __module_type__) and
locals()[varname].__name__.startswith(__name__ + '.'))):
# The last clause in the the above disjunction deserves explanation:
# When using relative imports like ``from .. import config``, the
# ``config`` variable is automatically created in the namespace of
# whatever module ``..`` resolves to (in this case astropy). This
# happens a few times just in the module setup above. This allows
# the cleanup to keep any public submodules of the astropy package
del locals()[varname]
del varname, __module_type__
|
e3970894c3f13086681508803f5f44c92c2fd9b69c5f237606b04099de0ac0dd | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This file contains pytest configuration settings that are astropy-specific
(i.e. those that would not necessarily be shared by affiliated packages
making use of astropy's test runner).
"""
import builtins
import os
import sys
import tempfile
import warnings
try:
from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS
except ImportError:
PYTEST_HEADER_MODULES = {}
TESTED_VERSIONS = {}
import pytest
from astropy import __version__
# This is needed to silence a warning from matplotlib caused by
# PyInstaller's matplotlib runtime hook. This can be removed once the
# issue is fixed upstream in PyInstaller, and only impacts us when running
# the tests from a PyInstaller bundle.
# See https://github.com/astropy/astropy/issues/10785
if getattr(sys, 'frozen', False) and hasattr(sys, '_MEIPASS'):
# The above checks whether we are running in a PyInstaller bundle.
warnings.filterwarnings("ignore", "(?s).*MATPLOTLIBDATA.*",
category=UserWarning)
# Note: while the filterwarnings is required, this import has to come after the
# filterwarnings above, because this attempts to import matplotlib:
from astropy.utils.compat.optional_deps import HAS_MATPLOTLIB # noqa: E402
if HAS_MATPLOTLIB:
import matplotlib
matplotlibrc_cache = {}
@pytest.fixture
def ignore_matplotlibrc():
# This is a fixture for tests that use matplotlib but not pytest-mpl
# (which already handles rcParams)
from matplotlib import pyplot as plt
with plt.style.context({}, after_reset=True):
yield
@pytest.fixture
def fast_thread_switching():
"""Fixture that reduces thread switching interval.
This makes it easier to provoke race conditions.
"""
old = sys.getswitchinterval()
sys.setswitchinterval(1e-6)
yield
sys.setswitchinterval(old)
def pytest_configure(config):
from astropy.utils.iers import conf as iers_conf
# Disable IERS auto download for testing
iers_conf.auto_download = False
builtins._pytest_running = True
# do not assign to matplotlibrc_cache in function scope
if HAS_MATPLOTLIB:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
matplotlibrc_cache.update(matplotlib.rcParams)
matplotlib.rcdefaults()
matplotlib.use('Agg')
# Make sure we use temporary directories for the config and cache
# so that the tests are insensitive to local configuration. Note that this
# is also set in the test runner, but we need to also set it here for
# things to work properly in parallel mode
builtins._xdg_config_home_orig = os.environ.get('XDG_CONFIG_HOME')
builtins._xdg_cache_home_orig = os.environ.get('XDG_CACHE_HOME')
os.environ['XDG_CONFIG_HOME'] = tempfile.mkdtemp('astropy_config')
os.environ['XDG_CACHE_HOME'] = tempfile.mkdtemp('astropy_cache')
os.mkdir(os.path.join(os.environ['XDG_CONFIG_HOME'], 'astropy'))
os.mkdir(os.path.join(os.environ['XDG_CACHE_HOME'], 'astropy'))
config.option.astropy_header = True
PYTEST_HEADER_MODULES['PyERFA'] = 'erfa'
PYTEST_HEADER_MODULES['Cython'] = 'cython'
PYTEST_HEADER_MODULES['Scikit-image'] = 'skimage'
PYTEST_HEADER_MODULES['asdf'] = 'asdf'
TESTED_VERSIONS['Astropy'] = __version__
def pytest_unconfigure(config):
from astropy.utils.iers import conf as iers_conf
# Undo IERS auto download setting for testing
iers_conf.reset('auto_download')
builtins._pytest_running = False
# do not assign to matplotlibrc_cache in function scope
if HAS_MATPLOTLIB:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
matplotlib.rcParams.update(matplotlibrc_cache)
matplotlibrc_cache.clear()
if builtins._xdg_config_home_orig is None:
os.environ.pop('XDG_CONFIG_HOME')
else:
os.environ['XDG_CONFIG_HOME'] = builtins._xdg_config_home_orig
if builtins._xdg_cache_home_orig is None:
os.environ.pop('XDG_CACHE_HOME')
else:
os.environ['XDG_CACHE_HOME'] = builtins._xdg_cache_home_orig
def pytest_terminal_summary(terminalreporter):
"""Output a warning to IPython users in case any tests failed."""
try:
get_ipython()
except NameError:
return
if not terminalreporter.stats.get('failed'):
# Only issue the warning when there are actually failures
return
terminalreporter.ensure_newline()
terminalreporter.write_line(
'Some tests may fail when run from the IPython prompt; '
'especially, but not limited to tests involving logging and warning '
'handling. Unless you are certain as to the cause of the failure, '
'please check that the failure occurs outside IPython as well. See '
'https://docs.astropy.org/en/stable/known_issues.html#failing-logging-'
'tests-when-running-the-tests-in-ipython for more information.',
yellow=True, bold=True)
|
ab94da6ed51f8e247cb5bf4f29bd54aa85a12e98494aebf149298826f5daa769 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""This module defines a logging class based on the built-in logging module.
.. note::
This module is meant for internal ``astropy`` usage. For use in other
packages, we recommend implementing your own logger instead.
"""
import inspect
import os
import sys
import logging
import warnings
from contextlib import contextmanager
from . import config as _config
from . import conf as _conf
from .utils import find_current_module
from .utils.exceptions import AstropyWarning, AstropyUserWarning
__all__ = ['Conf', 'conf', 'log', 'AstropyLogger', 'LoggingError']
# import the logging levels from logging so that one can do:
# log.setLevel(log.DEBUG), for example
logging_levels = ['NOTSET', 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL',
'FATAL', ]
for level in logging_levels:
globals()[level] = getattr(logging, level)
__all__ += logging_levels
# Initialize by calling _init_log()
log = None
class LoggingError(Exception):
"""
This exception is for various errors that occur in the astropy logger,
typically when activating or deactivating logger-related features.
"""
class _AstLogIPYExc(Exception):
"""
An exception that is used only as a placeholder to indicate to the
IPython exception-catching mechanism that the astropy
exception-capturing is activated. It should not actually be used as
an exception anywhere.
"""
class Conf(_config.ConfigNamespace):
"""
Configuration parameters for `astropy.logger`.
"""
log_level = _config.ConfigItem(
'INFO',
"Threshold for the logging messages. Logging "
"messages that are less severe than this level "
"will be ignored. The levels are ``'DEBUG'``, "
"``'INFO'``, ``'WARNING'``, ``'ERROR'``.")
log_warnings = _config.ConfigItem(
True,
"Whether to log `warnings.warn` calls.")
log_exceptions = _config.ConfigItem(
False,
"Whether to log exceptions before raising "
"them.")
log_to_file = _config.ConfigItem(
False,
"Whether to always log messages to a log "
"file.")
log_file_path = _config.ConfigItem(
'',
"The file to log messages to. If empty string is given, "
"it defaults to a file ``'astropy.log'`` in "
"the astropy config directory.")
log_file_level = _config.ConfigItem(
'INFO',
"Threshold for logging messages to "
"`log_file_path`.")
log_file_format = _config.ConfigItem(
"%(asctime)r, "
"%(origin)r, %(levelname)r, %(message)r",
"Format for log file entries.")
log_file_encoding = _config.ConfigItem(
'',
"The encoding (e.g., UTF-8) to use for the log file. If empty string "
"is given, it defaults to the platform-preferred encoding.")
conf = Conf()
def _init_log():
"""Initializes the Astropy log--in most circumstances this is called
automatically when importing astropy.
"""
global log
orig_logger_cls = logging.getLoggerClass()
logging.setLoggerClass(AstropyLogger)
try:
log = logging.getLogger('astropy')
log._set_defaults()
finally:
logging.setLoggerClass(orig_logger_cls)
return log
def _teardown_log():
"""Shut down exception and warning logging (if enabled) and clear all
Astropy loggers from the logging module's cache.
This involves poking some logging module internals, so much if it is 'at
your own risk' and is allowed to pass silently if any exceptions occur.
"""
global log
if log.exception_logging_enabled():
log.disable_exception_logging()
if log.warnings_logging_enabled():
log.disable_warnings_logging()
del log
# Now for the fun stuff...
try:
logging._acquireLock()
try:
loggerDict = logging.Logger.manager.loggerDict
for key in loggerDict.keys():
if key == 'astropy' or key.startswith('astropy.'):
del loggerDict[key]
finally:
logging._releaseLock()
except Exception:
pass
Logger = logging.getLoggerClass()
class AstropyLogger(Logger):
'''
This class is used to set up the Astropy logging.
The main functionality added by this class over the built-in
logging.Logger class is the ability to keep track of the origin of the
messages, the ability to enable logging of warnings.warn calls and
exceptions, and the addition of colorized output and context managers to
easily capture messages to a file or list.
'''
def makeRecord(self, name, level, pathname, lineno, msg, args, exc_info,
func=None, extra=None, sinfo=None):
if extra is None:
extra = {}
if 'origin' not in extra:
current_module = find_current_module(1, finddiff=[True, 'logging'])
if current_module is not None:
extra['origin'] = current_module.__name__
else:
extra['origin'] = 'unknown'
return Logger.makeRecord(self, name, level, pathname, lineno, msg,
args, exc_info, func=func, extra=extra,
sinfo=sinfo)
_showwarning_orig = None
def _showwarning(self, *args, **kwargs):
# Bail out if we are not catching a warning from Astropy
if not isinstance(args[0], AstropyWarning):
return self._showwarning_orig(*args, **kwargs)
warning = args[0]
# Deliberately not using isinstance here: We want to display
# the class name only when it's not the default class,
# AstropyWarning. The name of subclasses of AstropyWarning should
# be displayed.
if type(warning) not in (AstropyWarning, AstropyUserWarning):
message = f'{warning.__class__.__name__}: {args[0]}'
else:
message = str(args[0])
mod_path = args[2]
# Now that we have the module's path, we look through sys.modules to
# find the module object and thus the fully-package-specified module
# name. The module.__file__ is the original source file name.
mod_name = None
mod_path, ext = os.path.splitext(mod_path)
for name, mod in list(sys.modules.items()):
try:
# Believe it or not this can fail in some cases:
# https://github.com/astropy/astropy/issues/2671
path = os.path.splitext(getattr(mod, '__file__', ''))[0]
except Exception:
continue
if path == mod_path:
mod_name = mod.__name__
break
if mod_name is not None:
self.warning(message, extra={'origin': mod_name})
else:
self.warning(message)
def warnings_logging_enabled(self):
return self._showwarning_orig is not None
def enable_warnings_logging(self):
'''
Enable logging of warnings.warn() calls
Once called, any subsequent calls to ``warnings.warn()`` are
redirected to this logger and emitted with level ``WARN``. Note that
this replaces the output from ``warnings.warn``.
This can be disabled with ``disable_warnings_logging``.
'''
if self.warnings_logging_enabled():
raise LoggingError("Warnings logging has already been enabled")
self._showwarning_orig = warnings.showwarning
warnings.showwarning = self._showwarning
def disable_warnings_logging(self):
'''
Disable logging of warnings.warn() calls
Once called, any subsequent calls to ``warnings.warn()`` are no longer
redirected to this logger.
This can be re-enabled with ``enable_warnings_logging``.
'''
if not self.warnings_logging_enabled():
raise LoggingError("Warnings logging has not been enabled")
if warnings.showwarning != self._showwarning:
raise LoggingError("Cannot disable warnings logging: "
"warnings.showwarning was not set by this "
"logger, or has been overridden")
warnings.showwarning = self._showwarning_orig
self._showwarning_orig = None
_excepthook_orig = None
def _excepthook(self, etype, value, traceback):
if traceback is None:
mod = None
else:
tb = traceback
while tb.tb_next is not None:
tb = tb.tb_next
mod = inspect.getmodule(tb)
# include the the error type in the message.
if len(value.args) > 0:
message = f'{etype.__name__}: {str(value)}'
else:
message = str(etype.__name__)
if mod is not None:
self.error(message, extra={'origin': mod.__name__})
else:
self.error(message)
self._excepthook_orig(etype, value, traceback)
def exception_logging_enabled(self):
'''
Determine if the exception-logging mechanism is enabled.
Returns
-------
exclog : bool
True if exception logging is on, False if not.
'''
try:
ip = get_ipython()
except NameError:
ip = None
if ip is None:
return self._excepthook_orig is not None
else:
return _AstLogIPYExc in ip.custom_exceptions
def enable_exception_logging(self):
'''
Enable logging of exceptions
Once called, any uncaught exceptions will be emitted with level
``ERROR`` by this logger, before being raised.
This can be disabled with ``disable_exception_logging``.
'''
try:
ip = get_ipython()
except NameError:
ip = None
if self.exception_logging_enabled():
raise LoggingError("Exception logging has already been enabled")
if ip is None:
# standard python interpreter
self._excepthook_orig = sys.excepthook
sys.excepthook = self._excepthook
else:
# IPython has its own way of dealing with excepthook
# We need to locally define the function here, because IPython
# actually makes this a member function of their own class
def ipy_exc_handler(ipyshell, etype, evalue, tb, tb_offset=None):
# First use our excepthook
self._excepthook(etype, evalue, tb)
# Now also do IPython's traceback
ipyshell.showtraceback((etype, evalue, tb), tb_offset=tb_offset)
# now register the function with IPython
# note that we include _AstLogIPYExc so `disable_exception_logging`
# knows that it's disabling the right thing
ip.set_custom_exc((BaseException, _AstLogIPYExc), ipy_exc_handler)
# and set self._excepthook_orig to a no-op
self._excepthook_orig = lambda etype, evalue, tb: None
def disable_exception_logging(self):
'''
Disable logging of exceptions
Once called, any uncaught exceptions will no longer be emitted by this
logger.
This can be re-enabled with ``enable_exception_logging``.
'''
try:
ip = get_ipython()
except NameError:
ip = None
if not self.exception_logging_enabled():
raise LoggingError("Exception logging has not been enabled")
if ip is None:
# standard python interpreter
if sys.excepthook != self._excepthook:
raise LoggingError("Cannot disable exception logging: "
"sys.excepthook was not set by this logger, "
"or has been overridden")
sys.excepthook = self._excepthook_orig
self._excepthook_orig = None
else:
# IPython has its own way of dealing with exceptions
ip.set_custom_exc(tuple(), None)
def enable_color(self):
'''
Enable colorized output
'''
_conf.use_color = True
def disable_color(self):
'''
Disable colorized output
'''
_conf.use_color = False
@contextmanager
def log_to_file(self, filename, filter_level=None, filter_origin=None):
'''
Context manager to temporarily log messages to a file.
Parameters
----------
filename : str
The file to log messages to.
filter_level : str
If set, any log messages less important than ``filter_level`` will
not be output to the file. Note that this is in addition to the
top-level filtering for the logger, so if the logger has level
'INFO', then setting ``filter_level`` to ``INFO`` or ``DEBUG``
will have no effect, since these messages are already filtered
out.
filter_origin : str
If set, only log messages with an origin starting with
``filter_origin`` will be output to the file.
Notes
-----
By default, the logger already outputs log messages to a file set in
the Astropy configuration file. Using this context manager does not
stop log messages from being output to that file, nor does it stop log
messages from being printed to standard output.
Examples
--------
The context manager is used as::
with logger.log_to_file('myfile.log'):
# your code here
'''
encoding = conf.log_file_encoding if conf.log_file_encoding else None
fh = logging.FileHandler(filename, encoding=encoding)
if filter_level is not None:
fh.setLevel(filter_level)
if filter_origin is not None:
fh.addFilter(FilterOrigin(filter_origin))
f = logging.Formatter(conf.log_file_format)
fh.setFormatter(f)
self.addHandler(fh)
yield
fh.close()
self.removeHandler(fh)
@contextmanager
def log_to_list(self, filter_level=None, filter_origin=None):
'''
Context manager to temporarily log messages to a list.
Parameters
----------
filename : str
The file to log messages to.
filter_level : str
If set, any log messages less important than ``filter_level`` will
not be output to the file. Note that this is in addition to the
top-level filtering for the logger, so if the logger has level
'INFO', then setting ``filter_level`` to ``INFO`` or ``DEBUG``
will have no effect, since these messages are already filtered
out.
filter_origin : str
If set, only log messages with an origin starting with
``filter_origin`` will be output to the file.
Notes
-----
Using this context manager does not stop log messages from being
output to standard output.
Examples
--------
The context manager is used as::
with logger.log_to_list() as log_list:
# your code here
'''
lh = ListHandler()
if filter_level is not None:
lh.setLevel(filter_level)
if filter_origin is not None:
lh.addFilter(FilterOrigin(filter_origin))
self.addHandler(lh)
yield lh.log_list
self.removeHandler(lh)
def _set_defaults(self):
'''
Reset logger to its initial state
'''
# Reset any previously installed hooks
if self.warnings_logging_enabled():
self.disable_warnings_logging()
if self.exception_logging_enabled():
self.disable_exception_logging()
# Remove all previous handlers
for handler in self.handlers[:]:
self.removeHandler(handler)
# Set levels
self.setLevel(conf.log_level)
# Set up the stdout handler
sh = StreamHandler()
self.addHandler(sh)
# Set up the main log file handler if requested (but this might fail if
# configuration directory or log file is not writeable).
if conf.log_to_file:
log_file_path = conf.log_file_path
# "None" as a string because it comes from config
try:
_ASTROPY_TEST_
testing_mode = True
except NameError:
testing_mode = False
try:
if log_file_path == '' or testing_mode:
log_file_path = os.path.join(
_config.get_config_dir('astropy'), "astropy.log")
else:
log_file_path = os.path.expanduser(log_file_path)
encoding = conf.log_file_encoding if conf.log_file_encoding else None
fh = logging.FileHandler(log_file_path, encoding=encoding)
except OSError as e:
warnings.warn(
f'log file {log_file_path!r} could not be opened for writing: {str(e)}',
RuntimeWarning)
else:
formatter = logging.Formatter(conf.log_file_format)
fh.setFormatter(formatter)
fh.setLevel(conf.log_file_level)
self.addHandler(fh)
if conf.log_warnings:
self.enable_warnings_logging()
if conf.log_exceptions:
self.enable_exception_logging()
class StreamHandler(logging.StreamHandler):
"""
A specialized StreamHandler that logs INFO and DEBUG messages to
stdout, and all other messages to stderr. Also provides coloring
of the output, if enabled in the parent logger.
"""
def emit(self, record):
'''
The formatter for stderr
'''
if record.levelno <= logging.INFO:
stream = sys.stdout
else:
stream = sys.stderr
if record.levelno < logging.DEBUG or not _conf.use_color:
print(record.levelname, end='', file=stream)
else:
# Import utils.console only if necessary and at the latest because
# the import takes a significant time [#4649]
from .utils.console import color_print
if record.levelno < logging.INFO:
color_print(record.levelname, 'magenta', end='', file=stream)
elif record.levelno < logging.WARN:
color_print(record.levelname, 'green', end='', file=stream)
elif record.levelno < logging.ERROR:
color_print(record.levelname, 'brown', end='', file=stream)
else:
color_print(record.levelname, 'red', end='', file=stream)
record.message = f"{record.msg} [{record.origin:s}]"
print(": " + record.message, file=stream)
class FilterOrigin:
'''A filter for the record origin'''
def __init__(self, origin):
self.origin = origin
def filter(self, record):
return record.origin.startswith(self.origin)
class ListHandler(logging.Handler):
'''A handler that can be used to capture the records in a list'''
def __init__(self, filter_level=None, filter_origin=None):
logging.Handler.__init__(self)
self.log_list = []
def emit(self, record):
self.log_list.append(record)
|
6f600f3b21fec07e8bd0aecdf780f73a4b028ba7cacacb37ef41fecbcb1b874f | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
#
# Astropy documentation build configuration file.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this file.
#
# All configuration values have a default. Some values are defined in
# the global Astropy configuration which is loaded here before anything else.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
# sys.path.insert(0, os.path.abspath('..'))
# IMPORTANT: the above commented section was generated by sphinx-quickstart, but
# is *NOT* appropriate for astropy or Astropy affiliated packages. It is left
# commented out with this explanation to make it clear why this should not be
# done. If the sys.path entry above is added, when the astropy.sphinx.conf
# import occurs, it will import the *source* version of astropy instead of the
# version installed (if invoked as "make html" or directly with sphinx), or the
# version in the build directory.
# Thus, any C-extensions that are needed to build the documentation will *not*
# be accessible, and the documentation will not build correctly.
# See sphinx_astropy.conf for which values are set there.
import os
import sys
import configparser
from datetime import datetime
from importlib import metadata
import doctest
from packaging.requirements import Requirement
from packaging.specifiers import SpecifierSet
# -- Check for missing dependencies -------------------------------------------
missing_requirements = {}
for line in metadata.requires('astropy'):
if 'extra == "docs"' in line:
req = Requirement(line.split(';')[0])
req_package = req.name.lower()
req_specifier = str(req.specifier)
try:
version = metadata.version(req_package)
except metadata.PackageNotFoundError:
missing_requirements[req_package] = req_specifier
if version not in SpecifierSet(req_specifier, prereleases=True):
missing_requirements[req_package] = req_specifier
if missing_requirements:
print('The following packages could not be found and are required to '
'build the documentation:')
for key, val in missing_requirements.items():
print(f' * {key} {val}')
print('Please install the "docs" requirements.')
sys.exit(1)
from sphinx_astropy.conf.v1 import * # noqa
# -- Plot configuration -------------------------------------------------------
plot_rcparams = {}
plot_rcparams['figure.figsize'] = (6, 6)
plot_rcparams['savefig.facecolor'] = 'none'
plot_rcparams['savefig.bbox'] = 'tight'
plot_rcparams['axes.labelsize'] = 'large'
plot_rcparams['figure.subplot.hspace'] = 0.5
plot_apply_rcparams = True
plot_html_show_source_link = False
plot_formats = ['png', 'svg', 'pdf']
# Don't use the default - which includes a numpy and matplotlib import
plot_pre_code = ""
# -- General configuration ----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
needs_sphinx = '1.7'
# To perform a Sphinx version check that needs to be more specific than
# major.minor, call `check_sphinx_version("X.Y.Z")` here.
check_sphinx_version("1.2.1") # noqa: F405
# The intersphinx_mapping in sphinx_astropy.sphinx refers to astropy for
# the benefit of other packages who want to refer to objects in the
# astropy core. However, we don't want to cyclically reference astropy in its
# own build so we remove it here.
del intersphinx_mapping['astropy'] # noqa: F405
# add any custom intersphinx for astropy
intersphinx_mapping['astropy-dev'] = ('https://docs.astropy.org/en/latest/', None) # noqa: F405
intersphinx_mapping['pyerfa'] = ('https://pyerfa.readthedocs.io/en/stable/', None) # noqa: F405
intersphinx_mapping['pytest'] = ('https://docs.pytest.org/en/stable/', None) # noqa: F405
intersphinx_mapping['ipython'] = ('https://ipython.readthedocs.io/en/stable/', None) # noqa: F405
intersphinx_mapping['pandas'] = ('https://pandas.pydata.org/pandas-docs/stable/', None) # noqa: F405, E501
intersphinx_mapping['sphinx_automodapi'] = ('https://sphinx-automodapi.readthedocs.io/en/stable/', None) # noqa: F405, E501
intersphinx_mapping['packagetemplate'] = ('https://docs.astropy.org/projects/package-template/en/latest/', None) # noqa: F405, E501
intersphinx_mapping['h5py'] = ('https://docs.h5py.org/en/stable/', None) # noqa: F405
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns.append('_templates') # noqa: F405
exclude_patterns.append('changes') # noqa: F405
exclude_patterns.append('_pkgtemplate.rst') # noqa: F405
exclude_patterns.append('**/*.inc.rst') # .inc.rst mean *include* files, don't have sphinx process them # noqa: F405, E501
# Add any paths that contain templates here, relative to this directory.
if 'templates_path' not in locals(): # in case parent conf.py defines it
templates_path = []
templates_path.append('_templates')
extensions += ["sphinx_changelog"] # noqa: F405
# Grab minversion from setup.cfg
setup_cfg = configparser.ConfigParser()
setup_cfg.read(os.path.join(os.path.pardir, 'setup.cfg'))
__minimum_python_version__ = setup_cfg['options']['python_requires'].replace('>=', '')
project = u'Astropy'
min_versions = {}
for line in metadata.requires('astropy'):
req = Requirement(line.split(';')[0])
min_versions[req.name.lower()] = str(req.specifier)
# This is added to the end of RST files - a good place to put substitutions to
# be used globally.
with open("common_links.txt", "r") as cl:
rst_epilog += cl.read().format(minimum_python=__minimum_python_version__,
**min_versions)
# Manually register doctest options since matplotlib 3.5 messed up allowing them
# from pytest-doctestplus
IGNORE_OUTPUT = doctest.register_optionflag('IGNORE_OUTPUT')
REMOTE_DATA = doctest.register_optionflag('REMOTE_DATA')
FLOAT_CMP = doctest.register_optionflag('FLOAT_CMP')
# Whether to create cross-references for the parameter types in the
# Parameters, Other Parameters, Returns and Yields sections of the docstring.
numpydoc_xref_param_type = True
# Words not to cross-reference. Most likely, these are common words used in
# parameter type descriptions that may be confused for classes of the same
# name. The base set comes from sphinx-astropy. We add more here.
numpydoc_xref_ignore.update({
"mixin",
"Any", # aka something that would be annotated with `typing.Any`
# needed in subclassing numpy # TODO! revisit
"Arguments", "Path",
# TODO! not need to ignore.
"flag", "bits",
})
# Mappings to fully qualified paths (or correct ReST references) for the
# aliases/shortcuts used when specifying the types of parameters.
# Numpy provides some defaults
# https://github.com/numpy/numpydoc/blob/b352cd7635f2ea7748722f410a31f937d92545cc/numpydoc/xref.py#L62-L94
# and a base set comes from sphinx-astropy.
# so here we mostly need to define Astropy-specific x-refs
numpydoc_xref_aliases.update({
# python & adjacent
"Any": "`~typing.Any`",
"file-like": ":term:`python:file-like object`",
"file": ":term:`python:file object`",
"path-like": ":term:`python:path-like object`",
"module": ":term:`python:module`",
"buffer-like": ":term:buffer-like",
"hashable": ":term:`python:hashable`",
# for matplotlib
"color": ":term:`color`",
# for numpy
"ints": ":class:`python:int`",
# for astropy
"number": ":term:`number`",
"Representation": ":class:`~astropy.coordinates.BaseRepresentation`",
"writable": ":term:`writable file-like object`",
"readable": ":term:`readable file-like object`",
"BaseHDU": ":doc:`HDU </io/fits/api/hdus>`"
})
# Add from sphinx-astropy 1) glossary aliases 2) physical types.
numpydoc_xref_aliases.update(numpydoc_xref_astropy_aliases)
# -- Project information ------------------------------------------------------
author = u'The Astropy Developers'
copyright = f'2011–{datetime.utcnow().year}, ' + author
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
# The full version, including alpha/beta/rc tags.
release = metadata.version(project)
# The short X.Y version.
version = '.'.join(release.split('.')[:2])
# Only include dev docs in dev version.
dev = 'dev' in release
if not dev:
exclude_patterns.append('development/*') # noqa: F405
exclude_patterns.append('testhelpers.rst') # noqa: F405
# -- Options for the module index ---------------------------------------------
modindex_common_prefix = ['astropy.']
# -- Options for HTML output ---------------------------------------------------
# A NOTE ON HTML THEMES
#
# The global astropy configuration uses a custom theme,
# 'bootstrap-astropy', which is installed along with astropy. The
# theme has options for controlling the text of the logo in the upper
# left corner. This is how you would specify the options in order to
# override the theme defaults (The following options *are* the
# defaults, so we do not actually need to set them here.)
# html_theme_options = {
# 'logotext1': 'astro', # white, semi-bold
# 'logotext2': 'py', # orange, light
# 'logotext3': ':docs' # white, light
# }
# A different theme can be used, or other parts of this theme can be
# modified, by overriding some of the variables set in the global
# configuration. The variables set in the global configuration are
# listed below, commented out.
# Add any paths that contain custom themes here, relative to this directory.
# To use a different custom theme, add the directory containing the theme.
# html_theme_path = []
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes. To override the custom theme, set this to the
# name of a builtin theme or the name of a custom theme in html_theme_path.
# html_theme = None
# Custom sidebar templates, maps document names to template names.
# html_sidebars = {}
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = ''
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
# html_last_updated_fmt = ''
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
html_title = f'{project} v{release}'
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
# A dictionary of values to pass into the template engine’s context for all pages.
html_context = {
'to_be_indexed': ['stable', 'latest'],
'is_development': dev
}
# -- Options for LaTeX output --------------------------------------------------
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [('index', project + '.tex', project + u' Documentation',
author, 'manual')]
latex_logo = '_static/astropy_logo.pdf'
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [('index', project.lower(), project + u' Documentation',
[author], 1)]
# Setting this URL is requited by sphinx-astropy
github_issues_url = 'https://github.com/astropy/astropy/issues/'
edit_on_github_branch = 'main'
# Enable nitpicky mode - which ensures that all references in the docs
# resolve.
nitpicky = True
# This is not used. See docs/nitpick-exceptions file for the actual listing.
nitpick_ignore = []
for line in open('nitpick-exceptions'):
if line.strip() == "" or line.startswith("#"):
continue
dtype, target = line.split(None, 1)
target = target.strip()
nitpick_ignore.append((dtype, target))
# -- Options for the Sphinx gallery -------------------------------------------
try:
import warnings
import sphinx_gallery # noqa: F401
extensions += ["sphinx_gallery.gen_gallery"] # noqa: F405
sphinx_gallery_conf = {
'backreferences_dir': 'generated/modules', # path to store the module using example template # noqa: E501
'filename_pattern': '^((?!skip_).)*$', # execute all examples except those that start with "skip_" # noqa: E501
'examples_dirs': f'..{os.sep}examples', # path to the examples scripts
'gallery_dirs': 'generated/examples', # path to save gallery generated examples
'reference_url': {
'astropy': None,
'matplotlib': 'https://matplotlib.org/stable/',
'numpy': 'https://numpy.org/doc/stable/',
},
'abort_on_example_error': True
}
# Filter out backend-related warnings as described in
# https://github.com/sphinx-gallery/sphinx-gallery/pull/564
warnings.filterwarnings("ignore", category=UserWarning,
message='Matplotlib is currently using agg, which is a'
' non-GUI backend, so cannot show the figure.')
except ImportError:
sphinx_gallery = None
# -- Options for linkcheck output -------------------------------------------
linkcheck_retry = 5
linkcheck_ignore = ['https://journals.aas.org/manuscript-preparation/',
'https://maia.usno.navy.mil/',
'https://www.usno.navy.mil/USNO/time/gps/usno-gps-time-transfer',
'https://aa.usno.navy.mil/publications/docs/Circular_179.php',
'http://data.astropy.org',
'https://doi.org/10.1017/S0251107X00002406', # internal server error
'https://doi.org/10.1017/pasa.2013.31', # internal server error
r'https://github\.com/astropy/astropy/(?:issues|pull)/\d+']
linkcheck_timeout = 180
linkcheck_anchors = False
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
html_extra_path = ['robots.txt']
def rstjinja(app, docname, source):
"""Render pages as a jinja template to hide/show dev docs. """
# Make sure we're outputting HTML
if app.builder.format != 'html':
return
files_to_render = ["index", "install"]
if docname in files_to_render:
print(f"Jinja rendering {docname}")
rendered = app.builder.templates.render_string(
source[0], app.config.html_context)
source[0] = rendered
def resolve_astropy_and_dev_reference(app, env, node, contnode):
"""
Reference targets for ``astropy:`` and ``astropy-dev:`` are special cases.
Documentation links in astropy can be set up as intersphinx links so that
affiliate packages do not have to override the docstrings when building
the docs.
If we are building the development docs it is a local ref targeting the
label ``astropy-dev:<label>``, but for stable docs it should be an
intersphinx resolution to the development docs.
See https://github.com/astropy/astropy/issues/11366
"""
# should the node be processed?
reftarget = node.get('reftarget') # str or None
if str(reftarget).startswith('astropy:'):
# This allows Astropy to use intersphinx links to itself and have
# them resolve to local links. Downstream packages will see intersphinx.
# TODO! deprecate this if sphinx-doc/sphinx/issues/9169 is implemented.
process, replace = True, 'astropy:'
elif dev and str(reftarget).startswith('astropy-dev:'):
process, replace = True, 'astropy-dev:'
else:
process, replace = False, ''
# make link local
if process:
reftype = node.get('reftype')
refdoc = node.get('refdoc', app.env.docname)
# convert astropy intersphinx targets to local links.
# there are a few types of intersphinx link patters, as described in
# https://docs.readthedocs.io/en/stable/guides/intersphinx.html
reftarget = reftarget.replace(replace, '')
if reftype == "doc": # also need to replace the doc link
node.replace_attr("reftarget", reftarget)
# Delegate to the ref node's original domain/target (typically :ref:)
try:
domain = app.env.domains[node['refdomain']]
return domain.resolve_xref(app.env, refdoc, app.builder,
reftype, reftarget, node, contnode)
except Exception:
pass
# Otherwise return None which should delegate to intersphinx
def setup(app):
if sphinx_gallery is None:
msg = ('The sphinx_gallery extension is not installed, so the '
'gallery will not be built. You will probably see '
'additional warnings about undefined references due '
'to this.')
try:
app.warn(msg)
except AttributeError:
# Sphinx 1.6+
from sphinx.util import logging
logger = logging.getLogger(__name__)
logger.warning(msg)
# Generate the page from Jinja template
app.connect("source-read", rstjinja)
# Set this to higher priority than intersphinx; this way when building
# dev docs astropy-dev: targets will go to the local docs instead of the
# intersphinx mapping
app.connect("missing-reference", resolve_astropy_and_dev_reference,
priority=400)
|
b083f49c5a623cf28e75b25cd79688276dbe7ed19355a301169da9235586360e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# This file needs to be included here to make sure commands such
# as ``pytest docs/...`` works, since this
# will ignore the conftest.py file at the root of the repository
# and the one in astropy/conftest.py
import os
import tempfile
import pytest
# Make sure we use temporary directories for the config and cache
# so that the tests are insensitive to local configuration.
os.environ['XDG_CONFIG_HOME'] = tempfile.mkdtemp('astropy_config')
os.environ['XDG_CACHE_HOME'] = tempfile.mkdtemp('astropy_cache')
os.mkdir(os.path.join(os.environ['XDG_CONFIG_HOME'], 'astropy'))
os.mkdir(os.path.join(os.environ['XDG_CACHE_HOME'], 'astropy'))
# Note that we don't need to change the environment variables back or remove
# them after testing, because they are only changed for the duration of the
# Python process, and this configuration only matters if running pytest
# directly, not from e.g. an IPython session.
@pytest.fixture(autouse=True)
def _docdir(request):
"""Run doctests in isolated tmpdir so outputs do not end up in repo"""
# Trigger ONLY for doctestplus
doctest_plugin = request.config.pluginmanager.getplugin("doctestplus")
if isinstance(request.node.parent, doctest_plugin._doctest_textfile_item_cls):
# Don't apply this fixture to io.rst. It reads files and doesn't write
if "io.rst" not in request.node.name:
tmpdir = request.getfixturevalue('tmpdir')
with tmpdir.as_cwd():
yield
else:
yield
else:
yield
|
73b018608b35beb850df948ebe0315cbe4f8019618c62ccea33612d1f828bdf7 | import os
import shutil
import sys
import erfa # noqa
import pytest
import astropy # noqa
if len(sys.argv) == 3 and sys.argv[1] == '--astropy-root':
ROOT = sys.argv[2]
else:
# Make sure we don't allow any arguments to be passed - some tests call
# sys.executable which becomes this script when producing a pyinstaller
# bundle, but we should just error in this case since this is not the
# regular Python interpreter.
if len(sys.argv) > 1:
print("Extra arguments passed, exiting early")
sys.exit(1)
for root, dirnames, files in os.walk(os.path.join(ROOT, 'astropy')):
# NOTE: we can't simply use
# test_root = root.replace('astropy', 'astropy_tests')
# as we only want to change the one which is for the module, so instead
# we search for the last occurrence and replace that.
pos = root.rfind('astropy')
test_root = root[:pos] + 'astropy_tests' + root[pos + 7:]
# Copy over the astropy 'tests' directories and their contents
for dirname in dirnames:
final_dir = os.path.relpath(os.path.join(test_root, dirname), ROOT)
# We only copy over 'tests' directories, but not astropy/tests (only
# astropy/tests/tests) since that is not just a directory with tests.
if dirname == 'tests' and not root.endswith('astropy'):
shutil.copytree(os.path.join(root, dirname), final_dir, dirs_exist_ok=True)
else:
# Create empty __init__.py files so that 'astropy_tests' still
# behaves like a single package, otherwise pytest gets confused
# by the different conftest.py files.
init_filename = os.path.join(final_dir, '__init__.py')
if not os.path.exists(os.path.join(final_dir, '__init__.py')):
os.makedirs(final_dir, exist_ok=True)
with open(os.path.join(final_dir, '__init__.py'), 'w') as f:
f.write("#")
# Copy over all conftest.py files
for file in files:
if file == 'conftest.py':
final_file = os.path.relpath(os.path.join(test_root, file), ROOT)
shutil.copy2(os.path.join(root, file), final_file)
# Add the top-level __init__.py file
with open(os.path.join('astropy_tests', '__init__.py'), 'w') as f:
f.write("#")
# Remove test file that tries to import all sub-packages at collection time
os.remove(os.path.join('astropy_tests', 'utils', 'iers', 'tests', 'test_leap_second.py'))
# Remove convolution tests for now as there are issues with the loading of the C extension.
# FIXME: one way to fix this would be to migrate the convolution C extension away from using
# ctypes and using the regular extension mechanism instead.
shutil.rmtree(os.path.join('astropy_tests', 'convolution'))
os.remove(os.path.join('astropy_tests', 'modeling', 'tests', 'test_convolution.py'))
os.remove(os.path.join('astropy_tests', 'modeling', 'tests', 'test_core.py'))
os.remove(os.path.join('astropy_tests', 'visualization', 'tests', 'test_lupton_rgb.py'))
# FIXME: The following tests rely on the fully qualified name of classes which
# don't seem to be the same.
os.remove(os.path.join('astropy_tests', 'table', 'mixins', 'tests', 'test_registry.py'))
# Copy the top-level conftest.py
shutil.copy2(os.path.join(ROOT, 'astropy', 'conftest.py'),
os.path.join('astropy_tests', 'conftest.py'))
# We skip a few tests, which are generally ones that rely on explicitly
# checking the name of the current module (which ends up starting with
# astropy_tests rather than astropy).
SKIP_TESTS = ['test_exception_logging_origin',
'test_log',
'test_configitem',
'test_config_noastropy_fallback',
'test_no_home',
'test_path',
'test_rename_path',
'test_data_name_third_party_package',
'test_pkg_finder',
'test_wcsapi_extension',
'test_find_current_module_bundle',
'test_minversion',
'test_imports',
'test_generate_config',
'test_generate_config2',
'test_create_config_file',
'test_download_parallel_fills_cache']
# Run the tests!
sys.exit(pytest.main(['astropy_tests',
'-k ' + ' and '.join('not ' + test for test in SKIP_TESTS)],
plugins=['pytest_doctestplus.plugin',
'pytest_openfiles.plugin',
'pytest_remotedata.plugin',
'pytest_astropy_header.display']))
|
bfa48512aada02006c25f249d8b46593f5a541cd7244d7f675f740dab06a9099 | # -*- coding: utf-8 -*-
"""
========================
Title of Example
========================
This example <verb> <active tense> <does something>.
The example uses <packages> to <do something> and <other package> to <do other
thing>. Include links to referenced packages like this: `astropy.io.fits` to
show the astropy.io.fits or like this `~astropy.io.fits`to show just 'fits'
*By: <names>*
*License: BSD*
"""
##############################################################################
# Make print work the same in all versions of Python, set up numpy,
# matplotlib, and use a nicer set of plot parameters:
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
# uncomment if including figures:
# import matplotlib.pyplot as plt
# from astropy.visualization import astropy_mpl_style
# plt.style.use(astropy_mpl_style)
##############################################################################
# This code block is executed, although it produces no output. Lines starting
# with a simple hash are code comment and get treated as part of the code
# block. To include this new comment string we started the new block with a
# long line of hashes.
#
# The sphinx-gallery parser will assume everything after this splitter and that
# continues to start with a **comment hash and space** (respecting code style)
# is text that has to be rendered in
# html format. Keep in mind to always keep your comments always together by
# comment hashes. That means to break a paragraph you still need to comment
# that line break.
#
# In this example the next block of code produces some plotable data. Code is
# executed, figure is saved and then code is presented next, followed by the
# inlined figure.
x = np.linspace(-np.pi, np.pi, 300)
xx, yy = np.meshgrid(x, x)
z = np.cos(xx) + np.cos(yy)
plt.figure()
plt.imshow(z)
plt.colorbar()
plt.xlabel('$x$')
plt.ylabel('$y$')
###########################################################################
# Again it is possible to continue the discussion with a new Python string. This
# time to introduce the next code block generates 2 separate figures.
plt.figure()
plt.imshow(z, cmap=plt.cm.get_cmap('hot'))
plt.figure()
plt.imshow(z, cmap=plt.cm.get_cmap('Spectral'), interpolation='none')
##########################################################################
# There's some subtle differences between rendered html rendered comment
# strings and code comment strings which I'll demonstrate below. (Some of this
# only makes sense if you look at the
# :download:`raw Python script <plot_notebook.py>`)
#
# Comments in comment blocks remain nested in the text.
def dummy():
"""Dummy function to make sure docstrings don't get rendered as text"""
pass
# Code comments not preceded by the hash splitter are left in code blocks.
string = """
Triple-quoted string which tries to break parser but doesn't.
"""
############################################################################
# Output of the script is captured:
print('Some output from Python')
############################################################################
# Finally, I'll call ``show`` at the end just so someone running the Python
# code directly will see the plots; this is not necessary for creating the docs
plt.show()
|
9035692fe1ca2a75b94864183c99f6dfb14e7c446c8fa7bdbb53ec46b965569a | # -*- coding: utf-8 -*-
"""
========================================================================
Transforming positions and velocities to and from a Galactocentric frame
========================================================================
This document shows a few examples of how to use and customize the
`~astropy.coordinates.Galactocentric` frame to transform Heliocentric sky
positions, distance, proper motions, and radial velocities to a Galactocentric,
Cartesian frame, and the same in reverse.
The main configurable parameters of the `~astropy.coordinates.Galactocentric`
frame control the position and velocity of the solar system barycenter within
the Galaxy. These are specified by setting the ICRS coordinates of the
Galactic center, the distance to the Galactic center (the sun-galactic center
line is always assumed to be the x-axis of the Galactocentric frame), and the
Cartesian 3-velocity of the sun in the Galactocentric frame. We'll first
demonstrate how to customize these values, then show how to set the solar motion
instead by inputting the proper motion of Sgr A*.
Note that, for brevity, we may refer to the solar system barycenter as just "the
sun" in the examples below.
*By: Adrian Price-Whelan*
*License: BSD*
"""
##############################################################################
# Make `print` work the same in all versions of Python, set up numpy,
# matplotlib, and use a nicer set of plot parameters:
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
##############################################################################
# Import the necessary astropy subpackages
import astropy.coordinates as coord
import astropy.units as u
##############################################################################
# Let's first define a barycentric coordinate and velocity in the ICRS frame.
# We'll use the data for the star HD 39881 from the `Simbad
# <https://simbad.u-strasbg.fr/simbad/>`_ database:
c1 = coord.SkyCoord(ra=89.014303*u.degree, dec=13.924912*u.degree,
distance=(37.59*u.mas).to(u.pc, u.parallax()),
pm_ra_cosdec=372.72*u.mas/u.yr,
pm_dec=-483.69*u.mas/u.yr,
radial_velocity=0.37*u.km/u.s,
frame='icrs')
##############################################################################
# This is a high proper-motion star; suppose we'd like to transform its position
# and velocity to a Galactocentric frame to see if it has a large 3D velocity
# as well. To use the Astropy default solar position and motion parameters, we
# can simply do:
gc1 = c1.transform_to(coord.Galactocentric)
##############################################################################
# From here, we can access the components of the resulting
# `~astropy.coordinates.Galactocentric` instance to see the 3D Cartesian
# velocity components:
print(gc1.v_x, gc1.v_y, gc1.v_z)
##############################################################################
# The default parameters for the `~astropy.coordinates.Galactocentric` frame
# are detailed in the linked documentation, but we can modify the most commonly
# changes values using the keywords ``galcen_distance``, ``galcen_v_sun``, and
# ``z_sun`` which set the sun-Galactic center distance, the 3D velocity vector
# of the sun, and the height of the sun above the Galactic midplane,
# respectively. The velocity of the sun can be specified as an
# `~astropy.units.Quantity` object with velocity units and is interpreted as a
# Cartesian velocity, as in the example below. Note that, as with the positions,
# the Galactocentric frame is a right-handed system (i.e., the Sun is at negative
# x values) so ``v_x`` is opposite of the Galactocentric radial velocity:
v_sun = [11.1, 244, 7.25] * (u.km / u.s) # [vx, vy, vz]
gc_frame = coord.Galactocentric(
galcen_distance=8*u.kpc,
galcen_v_sun=v_sun,
z_sun=0*u.pc)
##############################################################################
# We can then transform to this frame instead, with our custom parameters:
gc2 = c1.transform_to(gc_frame)
print(gc2.v_x, gc2.v_y, gc2.v_z)
##############################################################################
# It's sometimes useful to specify the solar motion using the `proper motion
# of Sgr A* <https://arxiv.org/abs/astro-ph/0408107>`_ instead of Cartesian
# velocity components. With an assumed distance, we can convert proper motion
# components to Cartesian velocity components using `astropy.units`:
galcen_distance = 8*u.kpc
pm_gal_sgrA = [-6.379, -0.202] * u.mas/u.yr # from Reid & Brunthaler 2004
vy, vz = -(galcen_distance * pm_gal_sgrA).to(u.km/u.s, u.dimensionless_angles())
##############################################################################
# We still have to assume a line-of-sight velocity for the Galactic center,
# which we will again take to be 11 km/s:
vx = 11.1 * u.km/u.s
v_sun2 = u.Quantity([vx, vy, vz]) # List of Quantity -> a single Quantity
gc_frame2 = coord.Galactocentric(galcen_distance=galcen_distance,
galcen_v_sun=v_sun2,
z_sun=0*u.pc)
gc3 = c1.transform_to(gc_frame2)
print(gc3.v_x, gc3.v_y, gc3.v_z)
##############################################################################
# The transformations also work in the opposite direction. This can be useful
# for transforming simulated or theoretical data to observable quantities. As
# an example, we'll generate 4 theoretical circular orbits at different
# Galactocentric radii with the same circular velocity, and transform them to
# Heliocentric coordinates:
ring_distances = np.arange(10, 25+1, 5) * u.kpc
circ_velocity = 220 * u.km/u.s
phi_grid = np.linspace(90, 270, 512) * u.degree # grid of azimuths
ring_rep = coord.CylindricalRepresentation(
rho=ring_distances[:,np.newaxis],
phi=phi_grid[np.newaxis],
z=np.zeros_like(ring_distances)[:,np.newaxis])
angular_velocity = (-circ_velocity / ring_distances).to(u.mas/u.yr,
u.dimensionless_angles())
ring_dif = coord.CylindricalDifferential(
d_rho=np.zeros(phi_grid.shape)[np.newaxis]*u.km/u.s,
d_phi=angular_velocity[:,np.newaxis],
d_z=np.zeros(phi_grid.shape)[np.newaxis]*u.km/u.s
)
ring_rep = ring_rep.with_differentials(ring_dif)
gc_rings = coord.SkyCoord(ring_rep, frame=coord.Galactocentric)
##############################################################################
# First, let's visualize the geometry in Galactocentric coordinates. Here are
# the positions and velocities of the rings; note that in the velocity plot,
# the velocities of the 4 rings are identical and thus overlaid under the same
# curve:
fig,axes = plt.subplots(1, 2, figsize=(12,6))
# Positions
axes[0].plot(gc_rings.x.T, gc_rings.y.T, marker='None', linewidth=3)
axes[0].text(-8., 0, r'$\odot$', fontsize=20)
axes[0].set_xlim(-30, 30)
axes[0].set_ylim(-30, 30)
axes[0].set_xlabel('$x$ [kpc]')
axes[0].set_ylabel('$y$ [kpc]')
# Velocities
axes[1].plot(gc_rings.v_x.T, gc_rings.v_y.T, marker='None', linewidth=3)
axes[1].set_xlim(-250, 250)
axes[1].set_ylim(-250, 250)
axes[1].set_xlabel(f"$v_x$ [{(u.km / u.s).to_string('latex_inline')}]")
axes[1].set_ylabel(f"$v_y$ [{(u.km / u.s).to_string('latex_inline')}]")
fig.tight_layout()
plt.show()
##############################################################################
# Now we can transform to Galactic coordinates and visualize the rings in
# observable coordinates:
gal_rings = gc_rings.transform_to(coord.Galactic)
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
for i in range(len(ring_distances)):
ax.plot(gal_rings[i].l.degree, gal_rings[i].pm_l_cosb.value,
label=str(ring_distances[i]), marker='None', linewidth=3)
ax.set_xlim(360, 0)
ax.set_xlabel('$l$ [deg]')
ax.set_ylabel(fr'$\mu_l \, \cos b$ [{(u.mas/u.yr).to_string("latex_inline")}]')
ax.legend()
plt.show()
|
12bef7dc219d41692d2cbc9fe75a49900ea1f0df6e831e5d70e413d075a0fb12 | # -*- coding: utf-8 -*-
"""
===================================================================
Determining and plotting the altitude/azimuth of a celestial object
===================================================================
This example demonstrates coordinate transformations and the creation of
visibility curves to assist with observing run planning.
In this example, we make a `~astropy.coordinates.SkyCoord` instance for M33.
The altitude-azimuth coordinates are then found using
`astropy.coordinates.EarthLocation` and `astropy.time.Time` objects.
This example is meant to demonstrate the capabilities of the
`astropy.coordinates` package. For more convenient and/or complex observation
planning, consider the `astroplan <https://astroplan.readthedocs.org/>`_
package.
*By: Erik Tollerud, Kelle Cruz*
*License: BSD*
"""
##############################################################################
# Let's suppose you are planning to visit picturesque Bear Mountain State Park
# in New York, USA. You're bringing your telescope with you (of course), and
# someone told you M33 is a great target to observe there. You happen to know
# you're free at 11:00 pm local time, and you want to know if it will be up.
# Astropy can answer that.
#
# Import numpy and matplotlib. For the latter, use a nicer set of plot
# parameters and set up support for plotting/converting quantities.
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style, quantity_support
plt.style.use(astropy_mpl_style)
quantity_support()
##############################################################################
# Import the packages necessary for finding coordinates and making
# coordinate transformations
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation, AltAz
##############################################################################
# `astropy.coordinates.SkyCoord.from_name` uses Simbad to resolve object
# names and retrieve coordinates.
#
# Get the coordinates of M33:
m33 = SkyCoord.from_name('M33')
##############################################################################
# Use `astropy.coordinates.EarthLocation` to provide the location of Bear
# Mountain and set the time to 11pm EDT on 2012 July 12:
bear_mountain = EarthLocation(lat=41.3*u.deg, lon=-74*u.deg, height=390*u.m)
utcoffset = -4*u.hour # Eastern Daylight Time
time = Time('2012-7-12 23:00:00') - utcoffset
##############################################################################
# `astropy.coordinates.EarthLocation.get_site_names` and
# `~astropy.coordinates.EarthLocation.get_site_names` can be used to get
# locations of major observatories.
#
# Use `astropy.coordinates` to find the Alt, Az coordinates of M33 at as
# observed from Bear Mountain at 11pm on 2012 July 12.
m33altaz = m33.transform_to(AltAz(obstime=time,location=bear_mountain))
print(f"M33's Altitude = {m33altaz.alt:.2}")
##############################################################################
# This is helpful since it turns out M33 is barely above the horizon at this
# time. It's more informative to find M33's airmass over the course of
# the night.
#
# Find the alt,az coordinates of M33 at 100 times evenly spaced between 10pm
# and 7am EDT:
midnight = Time('2012-7-13 00:00:00') - utcoffset
delta_midnight = np.linspace(-2, 10, 100)*u.hour
frame_July13night = AltAz(obstime=midnight+delta_midnight,
location=bear_mountain)
m33altazs_July13night = m33.transform_to(frame_July13night)
##############################################################################
# convert alt, az to airmass with `~astropy.coordinates.AltAz.secz` attribute:
m33airmasss_July13night = m33altazs_July13night.secz
##############################################################################
# Plot the airmass as a function of time:
plt.plot(delta_midnight, m33airmasss_July13night)
plt.xlim(-2, 10)
plt.ylim(1, 4)
plt.xlabel('Hours from EDT Midnight')
plt.ylabel('Airmass [Sec(z)]')
plt.show()
##############################################################################
# Use `~astropy.coordinates.get_sun` to find the location of the Sun at 1000
# evenly spaced times between noon on July 12 and noon on July 13:
from astropy.coordinates import get_sun
delta_midnight = np.linspace(-12, 12, 1000)*u.hour
times_July12_to_13 = midnight + delta_midnight
frame_July12_to_13 = AltAz(obstime=times_July12_to_13, location=bear_mountain)
sunaltazs_July12_to_13 = get_sun(times_July12_to_13).transform_to(frame_July12_to_13)
##############################################################################
# Do the same with `~astropy.coordinates.get_moon` to find when the moon is
# up. Be aware that this will need to download a 10MB file from the internet
# to get a precise location of the moon.
from astropy.coordinates import get_moon
moon_July12_to_13 = get_moon(times_July12_to_13)
moonaltazs_July12_to_13 = moon_July12_to_13.transform_to(frame_July12_to_13)
##############################################################################
# Find the alt,az coordinates of M33 at those same times:
m33altazs_July12_to_13 = m33.transform_to(frame_July12_to_13)
##############################################################################
# Make a beautiful figure illustrating nighttime and the altitudes of M33 and
# the Sun over that time:
plt.plot(delta_midnight, sunaltazs_July12_to_13.alt, color='r', label='Sun')
plt.plot(delta_midnight, moonaltazs_July12_to_13.alt, color=[0.75]*3, ls='--', label='Moon')
plt.scatter(delta_midnight, m33altazs_July12_to_13.alt,
c=m33altazs_July12_to_13.az, label='M33', lw=0, s=8,
cmap='viridis')
plt.fill_between(delta_midnight, 0*u.deg, 90*u.deg,
sunaltazs_July12_to_13.alt < -0*u.deg, color='0.5', zorder=0)
plt.fill_between(delta_midnight, 0*u.deg, 90*u.deg,
sunaltazs_July12_to_13.alt < -18*u.deg, color='k', zorder=0)
plt.colorbar().set_label('Azimuth [deg]')
plt.legend(loc='upper left')
plt.xlim(-12*u.hour, 12*u.hour)
plt.xticks((np.arange(13)*2-12)*u.hour)
plt.ylim(0*u.deg, 90*u.deg)
plt.xlabel('Hours from EDT Midnight')
plt.ylabel('Altitude [deg]')
plt.show()
|
169d08766b27d0e8c3edf0512170a6796246d917db295c0b3463aa7bfb1c4330 | # -*- coding: utf-8 -*-
"""
================================================================
Convert a radial velocity to the Galactic Standard of Rest (GSR)
================================================================
Radial or line-of-sight velocities of sources are often reported in a
Heliocentric or Solar-system barycentric reference frame. A common
transformation incorporates the projection of the Sun's motion along the
line-of-sight to the target, hence transforming it to a Galactic rest frame
instead (sometimes referred to as the Galactic Standard of Rest, GSR). This
transformation depends on the assumptions about the orientation of the Galactic
frame relative to the bary- or Heliocentric frame. It also depends on the
assumed solar velocity vector. Here we'll demonstrate how to perform this
transformation using a sky position and barycentric radial-velocity.
*By: Adrian Price-Whelan*
*License: BSD*
"""
################################################################################
# Make print work the same in all versions of Python and import the required
# Astropy packages:
import astropy.units as u
import astropy.coordinates as coord
################################################################################
# Use the latest convention for the Galactocentric coordinates
coord.galactocentric_frame_defaults.set('latest')
################################################################################
# For this example, let's work with the coordinates and barycentric radial
# velocity of the star HD 155967, as obtained from
# `Simbad <https://simbad.u-strasbg.fr/simbad/>`_:
icrs = coord.SkyCoord(ra=258.58356362*u.deg, dec=14.55255619*u.deg,
radial_velocity=-16.1*u.km/u.s, frame='icrs')
################################################################################
# We next need to decide on the velocity of the Sun in the assumed GSR frame.
# We'll use the same velocity vector as used in the
# `~astropy.coordinates.Galactocentric` frame, and convert it to a
# `~astropy.coordinates.CartesianRepresentation` object using the
# ``.to_cartesian()`` method of the
# `~astropy.coordinates.CartesianDifferential` object ``galcen_v_sun``:
v_sun = coord.Galactocentric().galcen_v_sun.to_cartesian()
################################################################################
# We now need to get a unit vector in the assumed Galactic frame from the sky
# position in the ICRS frame above. We'll use this unit vector to project the
# solar velocity onto the line-of-sight:
gal = icrs.transform_to(coord.Galactic)
cart_data = gal.data.to_cartesian()
unit_vector = cart_data / cart_data.norm()
################################################################################
# Now we project the solar velocity using this unit vector:
v_proj = v_sun.dot(unit_vector)
################################################################################
# Finally, we add the projection of the solar velocity to the radial velocity
# to get a GSR radial velocity:
rv_gsr = icrs.radial_velocity + v_proj
print(rv_gsr)
################################################################################
# We could wrap this in a function so we can control the solar velocity and
# re-use the above code:
def rv_to_gsr(c, v_sun=None):
"""Transform a barycentric radial velocity to the Galactic Standard of Rest
(GSR).
The input radial velocity must be passed in as a
Parameters
----------
c : `~astropy.coordinates.BaseCoordinateFrame` subclass instance
The radial velocity, associated with a sky coordinates, to be
transformed.
v_sun : `~astropy.units.Quantity`, optional
The 3D velocity of the solar system barycenter in the GSR frame.
Defaults to the same solar motion as in the
`~astropy.coordinates.Galactocentric` frame.
Returns
-------
v_gsr : `~astropy.units.Quantity`
The input radial velocity transformed to a GSR frame.
"""
if v_sun is None:
v_sun = coord.Galactocentric().galcen_v_sun.to_cartesian()
gal = c.transform_to(coord.Galactic)
cart_data = gal.data.to_cartesian()
unit_vector = cart_data / cart_data.norm()
v_proj = v_sun.dot(unit_vector)
return c.radial_velocity + v_proj
rv_gsr = rv_to_gsr(icrs)
print(rv_gsr)
|
547790e0143fe9a8b1996e4cecd461eb313bd82630ddb4630845753fd1343eef | # -*- coding: utf-8 -*-
r"""
==========================================================
Create a new coordinate class (for the Sagittarius stream)
==========================================================
This document describes in detail how to subclass and define a custom spherical
coordinate frame, as discussed in :ref:`astropy:astropy-coordinates-design` and
the docstring for `~astropy.coordinates.BaseCoordinateFrame`. In this example,
we will define a coordinate system defined by the plane of orbit of the
Sagittarius Dwarf Galaxy (hereafter Sgr; as defined in Majewski et al. 2003).
The Sgr coordinate system is often referred to in terms of two angular
coordinates, :math:`\Lambda,B`.
To do this, we need to define a subclass of
`~astropy.coordinates.BaseCoordinateFrame` that knows the names and units of the
coordinate system angles in each of the supported representations. In this case
we support `~astropy.coordinates.SphericalRepresentation` with "Lambda" and
"Beta". Then we have to define the transformation from this coordinate system to
some other built-in system. Here we will use Galactic coordinates, represented
by the `~astropy.coordinates.Galactic` class.
See Also
--------
* The `gala package <http://gala.adrian.pw/>`_, which defines a number of
Astropy coordinate frames for stellar stream coordinate systems.
* Majewski et al. 2003, "A Two Micron All Sky Survey View of the Sagittarius
Dwarf Galaxy. I. Morphology of the Sagittarius Core and Tidal Arms",
https://arxiv.org/abs/astro-ph/0304198
* Law & Majewski 2010, "The Sagittarius Dwarf Galaxy: A Model for Evolution in a
Triaxial Milky Way Halo", https://arxiv.org/abs/1003.1132
* David Law's Sgr info page https://www.stsci.edu/~dlaw/Sgr/
*By: Adrian Price-Whelan, Erik Tollerud*
*License: BSD*
"""
##############################################################################
# Make `print` work the same in all versions of Python, set up numpy,
# matplotlib, and use a nicer set of plot parameters:
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
##############################################################################
# Import the packages necessary for coordinates
from astropy.coordinates import frame_transform_graph
from astropy.coordinates.matrix_utilities import rotation_matrix, matrix_product, matrix_transpose
import astropy.coordinates as coord
import astropy.units as u
##############################################################################
# The first step is to create a new class, which we'll call
# ``Sagittarius`` and make it a subclass of
# `~astropy.coordinates.BaseCoordinateFrame`:
class Sagittarius(coord.BaseCoordinateFrame):
"""
A Heliocentric spherical coordinate system defined by the orbit
of the Sagittarius dwarf galaxy, as described in
https://ui.adsabs.harvard.edu/abs/2003ApJ...599.1082M
and further explained in
https://www.stsci.edu/~dlaw/Sgr/.
Parameters
----------
representation : `~astropy.coordinates.BaseRepresentation` or None
A representation object or None to have no data (or use the other keywords)
Lambda : `~astropy.coordinates.Angle`, optional, must be keyword
The longitude-like angle corresponding to Sagittarius' orbit.
Beta : `~astropy.coordinates.Angle`, optional, must be keyword
The latitude-like angle corresponding to Sagittarius' orbit.
distance : `~astropy.units.Quantity`, optional, must be keyword
The Distance for this object along the line-of-sight.
pm_Lambda_cosBeta : `~astropy.units.Quantity`, optional, must be keyword
The proper motion along the stream in ``Lambda`` (including the
``cos(Beta)`` factor) for this object (``pm_Beta`` must also be given).
pm_Beta : `~astropy.units.Quantity`, optional, must be keyword
The proper motion in Declination for this object (``pm_ra_cosdec`` must
also be given).
radial_velocity : `~astropy.units.Quantity`, optional, keyword-only
The radial velocity of this object.
"""
default_representation = coord.SphericalRepresentation
default_differential = coord.SphericalCosLatDifferential
frame_specific_representation_info = {
coord.SphericalRepresentation: [
coord.RepresentationMapping('lon', 'Lambda'),
coord.RepresentationMapping('lat', 'Beta'),
coord.RepresentationMapping('distance', 'distance')]
}
##############################################################################
# Breaking this down line-by-line, we define the class as a subclass of
# `~astropy.coordinates.BaseCoordinateFrame`. Then we include a descriptive
# docstring. The final lines are class-level attributes that specify the
# default representation for the data, default differential for the velocity
# information, and mappings from the attribute names used by representation
# objects to the names that are to be used by the ``Sagittarius`` frame. In this
# case we override the names in the spherical representations but don't do
# anything with other representations like cartesian or cylindrical.
#
# Next we have to define the transformation from this coordinate system to some
# other built-in coordinate system; we will use Galactic coordinates. We can do
# this by defining functions that return transformation matrices, or by simply
# defining a function that accepts a coordinate and returns a new coordinate in
# the new system. Because the transformation to the Sagittarius coordinate
# system is just a spherical rotation from Galactic coordinates, we'll just
# define a function that returns this matrix. We'll start by constructing the
# transformation matrix using pre-determined Euler angles and the
# ``rotation_matrix`` helper function:
SGR_PHI = (180 + 3.75) * u.degree # Euler angles (from Law & Majewski 2010)
SGR_THETA = (90 - 13.46) * u.degree
SGR_PSI = (180 + 14.111534) * u.degree
# Generate the rotation matrix using the x-convention (see Goldstein)
D = rotation_matrix(SGR_PHI, "z")
C = rotation_matrix(SGR_THETA, "x")
B = rotation_matrix(SGR_PSI, "z")
A = np.diag([1.,1.,-1.])
SGR_MATRIX = matrix_product(A, B, C, D)
##############################################################################
# Since we already constructed the transformation (rotation) matrix above, and
# the inverse of a rotation matrix is just its transpose, the required
# transformation functions are very simple:
@frame_transform_graph.transform(coord.StaticMatrixTransform, coord.Galactic, Sagittarius)
def galactic_to_sgr():
""" Compute the transformation matrix from Galactic spherical to
heliocentric Sgr coordinates.
"""
return SGR_MATRIX
##############################################################################
# The decorator ``@frame_transform_graph.transform(coord.StaticMatrixTransform,
# coord.Galactic, Sagittarius)`` registers this function on the
# ``frame_transform_graph`` as a coordinate transformation. Inside the function,
# we simply return the previously defined rotation matrix.
#
# We then register the inverse transformation by using the transpose of the
# rotation matrix (which is faster to compute than the inverse):
@frame_transform_graph.transform(coord.StaticMatrixTransform, Sagittarius, coord.Galactic)
def sgr_to_galactic():
""" Compute the transformation matrix from heliocentric Sgr coordinates to
spherical Galactic.
"""
return matrix_transpose(SGR_MATRIX)
##############################################################################
# Now that we've registered these transformations between ``Sagittarius`` and
# `~astropy.coordinates.Galactic`, we can transform between *any* coordinate
# system and ``Sagittarius`` (as long as the other system has a path to
# transform to `~astropy.coordinates.Galactic`). For example, to transform from
# ICRS coordinates to ``Sagittarius``, we would do:
icrs = coord.SkyCoord(280.161732*u.degree, 11.91934*u.degree, frame='icrs')
sgr = icrs.transform_to(Sagittarius)
print(sgr)
##############################################################################
# Or, to transform from the ``Sagittarius`` frame to ICRS coordinates (in this
# case, a line along the ``Sagittarius`` x-y plane):
sgr = coord.SkyCoord(Lambda=np.linspace(0, 2*np.pi, 128)*u.radian,
Beta=np.zeros(128)*u.radian, frame='sagittarius')
icrs = sgr.transform_to(coord.ICRS)
print(icrs)
##############################################################################
# As an example, we'll now plot the points in both coordinate systems:
fig, axes = plt.subplots(2, 1, figsize=(8, 10),
subplot_kw={'projection': 'aitoff'})
axes[0].set_title("Sagittarius")
axes[0].plot(sgr.Lambda.wrap_at(180*u.deg).radian, sgr.Beta.radian,
linestyle='none', marker='.')
axes[1].set_title("ICRS")
axes[1].plot(icrs.ra.wrap_at(180*u.deg).radian, icrs.dec.radian,
linestyle='none', marker='.')
plt.show()
##############################################################################
# This particular transformation is just a spherical rotation, which is a
# special case of an Affine transformation with no vector offset. The
# transformation of velocity components is therefore natively supported as
# well:
sgr = coord.SkyCoord(Lambda=np.linspace(0, 2*np.pi, 128)*u.radian,
Beta=np.zeros(128)*u.radian,
pm_Lambda_cosBeta=np.random.uniform(-5, 5, 128)*u.mas/u.yr,
pm_Beta=np.zeros(128)*u.mas/u.yr,
frame='sagittarius')
icrs = sgr.transform_to(coord.ICRS)
print(icrs)
fig, axes = plt.subplots(3, 1, figsize=(8, 10), sharex=True)
axes[0].set_title("Sagittarius")
axes[0].plot(sgr.Lambda.degree,
sgr.pm_Lambda_cosBeta.value,
linestyle='none', marker='.')
axes[0].set_xlabel(r"$\Lambda$ [deg]")
axes[0].set_ylabel(
fr"$\mu_\Lambda \, \cos B$ [{sgr.pm_Lambda_cosBeta.unit.to_string('latex_inline')}]")
axes[1].set_title("ICRS")
axes[1].plot(icrs.ra.degree, icrs.pm_ra_cosdec.value,
linestyle='none', marker='.')
axes[1].set_ylabel(
fr"$\mu_\alpha \, \cos\delta$ [{icrs.pm_ra_cosdec.unit.to_string('latex_inline')}]")
axes[2].set_title("ICRS")
axes[2].plot(icrs.ra.degree, icrs.pm_dec.value,
linestyle='none', marker='.')
axes[2].set_xlabel("RA [deg]")
axes[2].set_ylabel(
fr"$\mu_\delta$ [{icrs.pm_dec.unit.to_string('latex_inline')}]")
plt.show()
|
f060aa9837a0df597dbb32aa17d9db8fda514facc6f269012d3fc9611751b903 | # -*- coding: utf-8 -*-
"""
=====================================================
Create a multi-extension FITS (MEF) file from scratch
=====================================================
This example demonstrates how to create a multi-extension FITS (MEF)
file from scratch using `astropy.io.fits`.
*By: Erik Bray*
*License: BSD*
"""
import os
##############################################################################
# HDUList objects are used to hold all the HDUs in a FITS file. This
# ``HDUList`` class is a subclass of Python's builtin `list`. and can be
# created from scratch. For example, to create a FITS file with
# three extensions:
from astropy.io import fits
new_hdul = fits.HDUList()
new_hdul.append(fits.ImageHDU())
new_hdul.append(fits.ImageHDU())
##############################################################################
# Write out the new file to disk:
new_hdul.writeto('test.fits')
##############################################################################
# Alternatively, the HDU instances can be created first (or read from an
# existing FITS file).
#
# Create a multi-extension FITS file with two empty IMAGE extensions (a
# default PRIMARY HDU is prepended automatically if one is not specified;
# we use ``overwrite=True`` to overwrite the file if it already exists):
hdu1 = fits.PrimaryHDU()
hdu2 = fits.ImageHDU()
new_hdul = fits.HDUList([hdu1, hdu2])
new_hdul.writeto('test.fits', overwrite=True)
##############################################################################
# Finally, we'll remove the file we created:
os.remove('test.fits')
|
fbb510c5e0186d5e16961c613d3831dbd37a4d215fe262121af70c4b3d053210 | # -*- coding: utf-8 -*-
"""
==================
Edit a FITS header
==================
This example describes how to edit a value in a FITS header
using `astropy.io.fits`.
*By: Adrian Price-Whelan*
*License: BSD*
"""
from astropy.io import fits
##############################################################################
# Download a FITS file:
from astropy.utils.data import get_pkg_data_filename
fits_file = get_pkg_data_filename('tutorials/FITS-Header/input_file.fits')
##############################################################################
# Look at contents of the FITS file
fits.info(fits_file)
##############################################################################
# Look at the headers of the two extensions:
print("Before modifications:")
print()
print("Extension 0:")
print(repr(fits.getheader(fits_file, 0)))
print()
print("Extension 1:")
print(repr(fits.getheader(fits_file, 1)))
##############################################################################
# `astropy.io.fits` provides an object-oriented interface for reading and
# interacting with FITS files, but for small operations (like this example) it
# is often easier to use the
# `convenience functions <https://docs.astropy.org/en/latest/io/fits/index.html#convenience-functions>`_.
#
# To edit a single header value in the header for extension 0, use the
# `~astropy.io.fits.setval()` function. For example, set the OBJECT keyword
# to 'M31':
fits.setval(fits_file, 'OBJECT', value='M31')
##############################################################################
# With no extra arguments, this will modify the header for extension 0, but
# this can be changed using the ``ext`` keyword argument. For example, we can
# specify extension 1 instead:
fits.setval(fits_file, 'OBJECT', value='M31', ext=1)
##############################################################################
# This can also be used to create a new keyword-value pair ("card" in FITS
# lingo):
fits.setval(fits_file, 'ANEWKEY', value='some value')
##############################################################################
# Again, this is useful for one-off modifications, but can be inefficient
# for operations like editing multiple headers in the same file
# because `~astropy.io.fits.setval()` loads the whole file each time it
# is called. To make several modifications, it's better to load the file once:
with fits.open(fits_file, 'update') as f:
for hdu in f:
hdu.header['OBJECT'] = 'CAT'
print("After modifications:")
print()
print("Extension 0:")
print(repr(fits.getheader(fits_file, 0)))
print()
print("Extension 1:")
print(repr(fits.getheader(fits_file, 1)))
|
78cf5c60f74125abbfa26c0a951f75863b38c484d06ca630714937cedada6e19 | # -*- coding: utf-8 -*-
"""
=======================================
Read and plot an image from a FITS file
=======================================
This example opens an image stored in a FITS file and displays it to the screen.
This example uses `astropy.utils.data` to download the file, `astropy.io.fits` to open
the file, and `matplotlib.pyplot` to display the image.
*By: Lia R. Corrales, Adrian Price-Whelan, Kelle Cruz*
*License: BSD*
"""
##############################################################################
# Set up matplotlib and use a nicer set of plot parameters
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
##############################################################################
# Download the example FITS files used by this example:
from astropy.utils.data import get_pkg_data_filename
from astropy.io import fits
image_file = get_pkg_data_filename('tutorials/FITS-images/HorseHead.fits')
##############################################################################
# Use `astropy.io.fits.info()` to display the structure of the file:
fits.info(image_file)
##############################################################################
# Generally the image information is located in the Primary HDU, also known
# as extension 0. Here, we use `astropy.io.fits.getdata()` to read the image
# data from this first extension using the keyword argument ``ext=0``:
image_data = fits.getdata(image_file, ext=0)
##############################################################################
# The data is now stored as a 2D numpy array. Print the dimensions using the
# shape attribute:
print(image_data.shape)
##############################################################################
# Display the image data:
plt.figure()
plt.imshow(image_data, cmap='gray')
plt.colorbar()
|
042abe04a704fa7bcd949b1e95727d07e411096873c00d61d30b3d539d20354a | # -*- coding: utf-8 -*-
"""
==========================================
Create a very large FITS file from scratch
==========================================
This example demonstrates how to create a large file (larger than will fit in
memory) from scratch using `astropy.io.fits`.
*By: Erik Bray*
*License: BSD*
"""
##############################################################################
# Normally to create a single image FITS file one would do something like:
import os
import numpy as np
from astropy.io import fits
data = np.zeros((40000, 40000), dtype=np.float64)
hdu = fits.PrimaryHDU(data=data)
##############################################################################
# Then use the `astropy.io.fits.writeto()` method to write out the new
# file to disk
hdu.writeto('large.fits')
##############################################################################
# However, a 40000 x 40000 array of doubles is nearly twelve gigabytes! Most
# systems won't be able to create that in memory just to write out to disk. In
# order to create such a large file efficiently requires a little extra work,
# and a few assumptions.
#
# First, it is helpful to anticipate about how large (as in, how many keywords)
# the header will have in it. FITS headers must be written in 2880 byte
# blocks, large enough for 36 keywords per block (including the END keyword in
# the final block). Typical headers have somewhere between 1 and 4 blocks,
# though sometimes more.
#
# Since the first thing we write to a FITS file is the header, we want to write
# enough header blocks so that there is plenty of padding in which to add new
# keywords without having to resize the whole file. Say you want the header to
# use 4 blocks by default. Then, excluding the END card which Astropy will add
# automatically, create the header and pad it out to 36 * 4 cards.
#
# Create a stub array to initialize the HDU; its
# exact size is irrelevant, as long as it has the desired number of
# dimensions
data = np.zeros((100, 100), dtype=np.float64)
hdu = fits.PrimaryHDU(data=data)
header = hdu.header
while len(header) < (36 * 4 - 1):
header.append() # Adds a blank card to the end
##############################################################################
# Now adjust the NAXISn keywords to the desired size of the array, and write
# only the header out to a file. Using the ``hdu.writeto()`` method will cause
# astropy to "helpfully" reset the NAXISn keywords to match the size of the
# dummy array. That is because it works hard to ensure that only valid FITS
# files are written. Instead, we can write just the header to a file using the
# `astropy.io.fits.Header.tofile` method:
header['NAXIS1'] = 40000
header['NAXIS2'] = 40000
header.tofile('large.fits')
##############################################################################
# Finally, grow out the end of the file to match the length of the
# data (plus the length of the header). This can be done very efficiently on
# most systems by seeking past the end of the file and writing a single byte,
# like so:
with open('large.fits', 'rb+') as fobj:
# Seek past the length of the header, plus the length of the
# Data we want to write.
# 8 is the number of bytes per value, i.e. abs(header['BITPIX'])/8
# (this example is assuming a 64-bit float)
# The -1 is to account for the final byte that we are about to
# write:
fobj.seek(len(header.tostring()) + (40000 * 40000 * 8) - 1)
fobj.write(b'\0')
##############################################################################
# More generally, this can be written:
shape = tuple(header[f'NAXIS{ii}'] for ii in range(1, header['NAXIS']+1))
with open('large.fits', 'rb+') as fobj:
fobj.seek(len(header.tostring()) + (np.product(shape) * np.abs(header['BITPIX']//8)) - 1)
fobj.write(b'\0')
##############################################################################
# On modern operating systems this will cause the file (past the header) to be
# filled with zeros out to the ~12GB needed to hold a 40000 x 40000 image. On
# filesystems that support sparse file creation (most Linux filesystems, but not
# the HFS+ filesystem used by most Macs) this is a very fast, efficient
# operation. On other systems your mileage may vary.
#
# This isn't the only way to build up a large file, but probably one of the
# safest. This method can also be used to create large multi-extension FITS
# files, with a little care.
##############################################################################
# Finally, we'll remove the file we created:
os.remove('large.fits')
|
f8e82631004deaaab5e0800e207f27d16652fb1494aaeb6bec42c040fe4f5c35 | # -*- coding: utf-8 -*-
"""
=====================================================================
Accessing data stored as a table in a multi-extension FITS (MEF) file
=====================================================================
FITS files can often contain large amount of multi-dimensional data and
tables. This example opens a FITS file with information
from Chandra's HETG-S instrument.
The example uses `astropy.utils.data` to download multi-extension FITS (MEF)
file, `astropy.io.fits` to investigate the header, and
`astropy.table.Table` to explore the data.
*By: Lia Corrales, Adrian Price-Whelan, and Kelle Cruz*
*License: BSD*
"""
##############################################################################
# Use `astropy.utils.data` subpackage to download the FITS file used in this
# example. Also import `~astropy.table.Table` from the `astropy.table` subpackage
# and `astropy.io.fits`
from astropy.utils.data import get_pkg_data_filename
from astropy.table import Table
from astropy.io import fits
##############################################################################
# Download a FITS file
event_filename = get_pkg_data_filename('tutorials/FITS-tables/chandra_events.fits')
##############################################################################
# Display information about the contents of the FITS file.
fits.info(event_filename)
##############################################################################
# Extension 1, EVENTS, is a Table that contains information about each X-ray
# photon that hit Chandra's HETG-S detector.
#
# Use `~astropy.table.Table` to read the table
events = Table.read(event_filename, hdu=1)
##############################################################################
# Print the column names of the Events Table.
print(events.columns)
##############################################################################
# If a column contains unit information, it will have an associated
# `astropy.units` object.
print(events['energy'].unit)
##############################################################################
# Print the data stored in the Energy column.
print(events['energy'])
|
93f258becc4ec5da3768b6fd02d801854a4cb29f419856cfe08914b80cd312c8 | # -*- coding: utf-8 -*-
"""
=====================================================
Convert a 3-color image (JPG) to separate FITS images
=====================================================
This example opens an RGB JPEG image and writes out each channel as a separate
FITS (image) file.
This example uses `pillow <https://python-pillow.org>`_ to read the image,
`matplotlib.pyplot` to display the image, and `astropy.io.fits` to save FITS files.
*By: Erik Bray, Adrian Price-Whelan*
*License: BSD*
"""
import numpy as np
from PIL import Image
from astropy.io import fits
##############################################################################
# Set up matplotlib and use a nicer set of plot parameters
import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
##############################################################################
# Load and display the original 3-color jpeg image:
image = Image.open('Hs-2009-14-a-web.jpg')
xsize, ysize = image.size
print(f"Image size: {ysize} x {xsize}")
print(f"Image bands: {image.getbands()}")
ax = plt.imshow(image)
##############################################################################
# Split the three channels (RGB) and get the data as Numpy arrays. The arrays
# are flattened, so they are 1-dimensional:
r, g, b = image.split()
r_data = np.array(r.getdata()) # data is now an array of length ysize*xsize
g_data = np.array(g.getdata())
b_data = np.array(b.getdata())
print(r_data.shape)
##############################################################################
# Reshape the image arrays to be 2-dimensional:
r_data = r_data.reshape(ysize, xsize) # data is now a matrix (ysize, xsize)
g_data = g_data.reshape(ysize, xsize)
b_data = b_data.reshape(ysize, xsize)
print(r_data.shape)
##############################################################################
# Write out the channels as separate FITS images.
# Add and visualize header info
red = fits.PrimaryHDU(data=r_data)
red.header['LATOBS'] = "32:11:56" # add spurious header info
red.header['LONGOBS'] = "110:56"
red.writeto('red.fits')
green = fits.PrimaryHDU(data=g_data)
green.header['LATOBS'] = "32:11:56"
green.header['LONGOBS'] = "110:56"
green.writeto('green.fits')
blue = fits.PrimaryHDU(data=b_data)
blue.header['LATOBS'] = "32:11:56"
blue.header['LONGOBS'] = "110:56"
blue.writeto('blue.fits')
from pprint import pprint
pprint(red.header)
##############################################################################
# Delete the files created
import os
os.remove('red.fits')
os.remove('green.fits')
os.remove('blue.fits')
|
dfc13bac93535762aa3d6012610dcf92120ad2d6ce7bf1a42700d457bad21447 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import warnings
import numpy as np
from numpy.core.multiarray import normalize_axis_index
from astropy.units import Quantity
from astropy.utils import isiterable
from astropy.utils.exceptions import AstropyUserWarning
from astropy.stats._fast_sigma_clip import _sigma_clip_fast
from astropy.stats.funcs import mad_std
from astropy.utils.compat.optional_deps import HAS_BOTTLENECK
if HAS_BOTTLENECK:
import bottleneck
__all__ = ['SigmaClip', 'sigma_clip', 'sigma_clipped_stats']
def _move_tuple_axes_first(array, axis):
"""
Bottleneck can only take integer axis, not tuple, so this function
takes all the axes to be operated on and combines them into the
first dimension of the array so that we can then use axis=0.
"""
# Figure out how many axes we are operating over
naxis = len(axis)
# Add remaining axes to the axis tuple
axis += tuple(i for i in range(array.ndim) if i not in axis)
# The new position of each axis is just in order
destination = tuple(range(array.ndim))
# Reorder the array so that the axes being operated on are at the
# beginning
array_new = np.moveaxis(array, axis, destination)
# Collapse the dimensions being operated on into a single dimension
# so that we can then use axis=0 with the bottleneck functions
array_new = array_new.reshape((-1,) + array_new.shape[naxis:])
return array_new
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmean(array, axis=axis))
else:
return bottleneck.nanmean(array, axis=axis)
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanmedian(array, axis=axis))
else:
return bottleneck.nanmedian(array, axis=axis)
def _nanstd(array, axis=None, ddof=0):
"""Bottleneck nanstd function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
if isinstance(array, Quantity):
return array.__array_wrap__(bottleneck.nanstd(array, axis=axis,
ddof=ddof))
else:
return bottleneck.nanstd(array, axis=axis, ddof=ddof)
def _nanmadstd(array, axis=None):
"""mad_std function that ignores NaNs by default."""
return mad_std(array, axis=axis, ignore_nan=True)
class SigmaClip:
"""
Class to perform sigma clipping.
The data will be iterated over, each time rejecting values that are
less or more than a specified number of standard deviations from a
center value.
Clipped (rejected) pixels are those where::
data < center - (sigma_lower * std)
data > center + (sigma_upper * std)
where::
center = cenfunc(data [, axis=])
std = stdfunc(data [, axis=])
Invalid data values (i.e., NaN or inf) are automatically clipped.
For a functional interface to sigma clipping, see
:func:`sigma_clip`.
.. note::
`scipy.stats.sigmaclip` provides a subset of the functionality
in this class. Also, its input data cannot be a masked array
and it does not handle data that contains invalid values (i.e.,
NaN or inf). Also note that it uses the mean as the centering
function. The equivalent settings to `scipy.stats.sigmaclip`
are::
sigclip = SigmaClip(sigma=4., cenfunc='mean', maxiters=None)
sigclip(data, axis=None, masked=False, return_bounds=True)
Parameters
----------
sigma : float, optional
The number of standard deviations to use for both the lower
and upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is 3.
sigma_lower : float or None, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or None, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or None, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute
the center value for the clipping. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanmean`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'median'``.
stdfunc : {'std', 'mad_std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanstd`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
grow : float or `False`, optional
Radius within which to mask the neighbouring pixels of those
that fall outwith the clipping limits (only applied along
``axis``, if specified). As an example, for a 2D image a value
of 1 will mask the nearest pixels in a cross pattern around each
deviant pixel, while 1.5 will also reject the nearest diagonal
neighbours and so on.
See Also
--------
sigma_clip, sigma_clipped_stats
Notes
-----
The best performance will typically be obtained by setting
``cenfunc`` and ``stdfunc`` to one of the built-in functions
specified as as string. If one of the options is set to a string
while the other has a custom callable, you may in some cases see
better performance if you have the `bottleneck`_ package installed.
.. _bottleneck: https://github.com/pydata/bottleneck
Examples
--------
This example uses a data array of random variates from a Gaussian
distribution. We clip all points that are more than 2 sample
standard deviations from the median. The result is a masked array,
where the mask is `True` for clipped data::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=2, maxiters=5)
>>> filtered_data = sigclip(randvar)
This example clips all points that are more than 3 sigma relative
to the sample *mean*, clips until convergence, returns an unmasked
`~numpy.ndarray`, and modifies the data in-place::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> from numpy import mean
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=3, maxiters=None, cenfunc='mean')
>>> filtered_data = sigclip(randvar, masked=False, copy=False)
This example sigma clips along one axis::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import normal
>>> from numpy import arange, diag, ones
>>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))
>>> sigclip = SigmaClip(sigma=2.3)
>>> filtered_data = sigclip(data, axis=0)
Note that along the other axis, no points would be clipped, as the
standard deviation is higher.
"""
def __init__(self, sigma=3., sigma_lower=None, sigma_upper=None,
maxiters=5, cenfunc='median', stdfunc='std', grow=False):
self.sigma = sigma
self.sigma_lower = sigma_lower or sigma
self.sigma_upper = sigma_upper or sigma
self.maxiters = maxiters or np.inf
self.cenfunc = cenfunc
self.stdfunc = stdfunc
self._cenfunc_parsed = self._parse_cenfunc(cenfunc)
self._stdfunc_parsed = self._parse_stdfunc(stdfunc)
self._min_value = np.nan
self._max_value = np.nan
self._niterations = 0
self.grow = grow
# This just checks that SciPy is available, to avoid failing
# later than necessary if __call__ needs it:
if self.grow:
from scipy.ndimage import binary_dilation
self._binary_dilation = binary_dilation
def __repr__(self):
return ('SigmaClip(sigma={}, sigma_lower={}, sigma_upper={}, '
'maxiters={}, cenfunc={}, stdfunc={}, grow={})'
.format(self.sigma, self.sigma_lower, self.sigma_upper,
self.maxiters, repr(self.cenfunc), repr(self.stdfunc),
self.grow))
def __str__(self):
lines = ['<' + self.__class__.__name__ + '>']
attrs = ['sigma', 'sigma_lower', 'sigma_upper', 'maxiters', 'cenfunc',
'stdfunc', 'grow']
for attr in attrs:
lines.append(f' {attr}: {repr(getattr(self, attr))}')
return '\n'.join(lines)
@staticmethod
def _parse_cenfunc(cenfunc):
if isinstance(cenfunc, str):
if cenfunc == 'median':
if HAS_BOTTLENECK:
cenfunc = _nanmedian
else:
cenfunc = np.nanmedian # pragma: no cover
elif cenfunc == 'mean':
if HAS_BOTTLENECK:
cenfunc = _nanmean
else:
cenfunc = np.nanmean # pragma: no cover
else:
raise ValueError(f'{cenfunc} is an invalid cenfunc.')
return cenfunc
@staticmethod
def _parse_stdfunc(stdfunc):
if isinstance(stdfunc, str):
if stdfunc == 'std':
if HAS_BOTTLENECK:
stdfunc = _nanstd
else:
stdfunc = np.nanstd # pragma: no cover
elif stdfunc == 'mad_std':
stdfunc = _nanmadstd
else:
raise ValueError(f'{stdfunc} is an invalid stdfunc.')
return stdfunc
def _compute_bounds(self, data, axis=None):
# ignore RuntimeWarning if the array (or along an axis) has only
# NaNs
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
self._max_value = self._cenfunc_parsed(data, axis=axis)
std = self._stdfunc_parsed(data, axis=axis)
self._min_value = self._max_value - (std * self.sigma_lower)
self._max_value += std * self.sigma_upper
def _sigmaclip_fast(self, data, axis=None,
masked=True, return_bounds=False,
copy=True):
"""
Fast C implementation for simple use cases.
"""
if isinstance(data, Quantity):
data, unit = data.value, data.unit
else:
unit = None
if copy is False and masked is False and data.dtype.kind != 'f':
raise Exception("cannot mask non-floating-point array with NaN "
"values, set copy=True or masked=True to avoid "
"this.")
if axis is None:
axis = -1 if data.ndim == 1 else tuple(range(data.ndim))
if not isiterable(axis):
axis = normalize_axis_index(axis, data.ndim)
data_reshaped = data
transposed_shape = None
else:
# The gufunc implementation does not handle non-scalar axis
# so we combine the dimensions together as the last
# dimension and set axis=-1
axis = tuple(normalize_axis_index(ax, data.ndim) for ax in axis)
transposed_axes = tuple(ax for ax in range(data.ndim)
if ax not in axis) + axis
data_transposed = data.transpose(transposed_axes)
transposed_shape = data_transposed.shape
data_reshaped = data_transposed.reshape(
transposed_shape[:data.ndim - len(axis)] + (-1,))
axis = -1
if data_reshaped.dtype.kind != 'f' or data_reshaped.dtype.itemsize > 8:
data_reshaped = data_reshaped.astype(float)
mask = ~np.isfinite(data_reshaped)
if np.any(mask):
warnings.warn('Input data contains invalid values (NaNs or '
'infs), which were automatically clipped.',
AstropyUserWarning)
if isinstance(data_reshaped, np.ma.MaskedArray):
mask |= data_reshaped.mask
data = data.view(np.ndarray)
data_reshaped = data_reshaped.view(np.ndarray)
mask = np.broadcast_to(mask, data_reshaped.shape).copy()
bound_lo, bound_hi = _sigma_clip_fast(
data_reshaped, mask, self.cenfunc == 'median',
self.stdfunc == 'mad_std',
-1 if np.isinf(self.maxiters) else self.maxiters,
self.sigma_lower, self.sigma_upper, axis=axis)
with np.errstate(invalid='ignore'):
mask |= data_reshaped < np.expand_dims(bound_lo, axis)
mask |= data_reshaped > np.expand_dims(bound_hi, axis)
if transposed_shape is not None:
# Get mask in shape of data.
mask = mask.reshape(transposed_shape)
mask = mask.transpose(tuple(transposed_axes.index(ax)
for ax in range(data.ndim)))
if masked:
result = np.ma.array(data, mask=mask, copy=copy)
else:
if copy:
result = data.astype(float, copy=True)
else:
result = data
result[mask] = np.nan
if unit is not None:
result = result << unit
bound_lo = bound_lo << unit
bound_hi = bound_hi << unit
if return_bounds:
return result, bound_lo, bound_hi
else:
return result
def _sigmaclip_noaxis(self, data, masked=True, return_bounds=False,
copy=True):
"""
Sigma clip when ``axis`` is None and ``grow`` is not >0.
In this simple case, we remove clipped elements from the
flattened array during each iteration.
"""
filtered_data = data.ravel()
# remove masked values and convert to ndarray
if isinstance(filtered_data, np.ma.MaskedArray):
filtered_data = filtered_data.data[~filtered_data.mask]
# remove invalid values
good_mask = np.isfinite(filtered_data)
if np.any(~good_mask):
filtered_data = filtered_data[good_mask]
warnings.warn('Input data contains invalid values (NaNs or '
'infs), which were automatically clipped.',
AstropyUserWarning)
nchanged = 1
iteration = 0
while nchanged != 0 and (iteration < self.maxiters):
iteration += 1
size = filtered_data.size
self._compute_bounds(filtered_data, axis=None)
filtered_data = filtered_data[
(filtered_data >= self._min_value)
& (filtered_data <= self._max_value)]
nchanged = size - filtered_data.size
self._niterations = iteration
if masked:
# return a masked array and optional bounds
filtered_data = np.ma.masked_invalid(data, copy=copy)
# update the mask in place, ignoring RuntimeWarnings for
# comparisons with NaN data values
with np.errstate(invalid='ignore'):
filtered_data.mask |= np.logical_or(data < self._min_value,
data > self._max_value)
if return_bounds:
return filtered_data, self._min_value, self._max_value
else:
return filtered_data
def _sigmaclip_withaxis(self, data, axis=None, masked=True,
return_bounds=False, copy=True):
"""
Sigma clip the data when ``axis`` or ``grow`` is specified.
In this case, we replace clipped values with NaNs as placeholder
values.
"""
# float array type is needed to insert nans into the array
filtered_data = data.astype(float) # also makes a copy
# remove invalid values
bad_mask = ~np.isfinite(filtered_data)
if np.any(bad_mask):
filtered_data[bad_mask] = np.nan
warnings.warn('Input data contains invalid values (NaNs or '
'infs), which were automatically clipped.',
AstropyUserWarning)
# remove masked values and convert to plain ndarray
if isinstance(filtered_data, np.ma.MaskedArray):
filtered_data = np.ma.masked_invalid(filtered_data).astype(float)
filtered_data = filtered_data.filled(np.nan)
if axis is not None:
# convert negative axis/axes
if not isiterable(axis):
axis = (axis,)
axis = tuple(filtered_data.ndim + n if n < 0 else n for n in axis)
# define the shape of min/max arrays so that they can be broadcast
# with the data
mshape = tuple(1 if dim in axis else size
for dim, size in enumerate(filtered_data.shape))
if self.grow:
# Construct a growth kernel from the specified radius in
# pixels (consider caching this for re-use by subsequent
# calls?):
cenidx = int(self.grow)
size = 2 * cenidx + 1
indices = np.mgrid[(slice(0, size),) * data.ndim]
if axis is not None:
for n, dim in enumerate(indices):
# For any axes that we're not clipping over, set
# their indices outside the growth radius, so masked
# points won't "grow" in that dimension:
if n not in axis:
dim[dim != cenidx] = size
kernel = (sum(((idx - cenidx)**2 for idx in indices))
<= self.grow**2)
del indices
nchanged = 1
iteration = 0
while nchanged != 0 and (iteration < self.maxiters):
iteration += 1
self._compute_bounds(filtered_data, axis=axis)
if not np.isscalar(self._min_value):
self._min_value = self._min_value.reshape(mshape)
self._max_value = self._max_value.reshape(mshape)
with np.errstate(invalid='ignore'):
# Since these comparisons are always False for NaNs, the
# resulting mask contains only newly-rejected pixels and
# we can dilate it without growing masked pixels more
# than once.
new_mask = ((filtered_data < self._min_value)
| (filtered_data > self._max_value))
if self.grow:
new_mask = self._binary_dilation(new_mask, kernel)
filtered_data[new_mask] = np.nan
nchanged = np.count_nonzero(new_mask)
del new_mask
self._niterations = iteration
if masked:
# create an output masked array
if copy:
filtered_data = np.ma.MaskedArray(data,
~np.isfinite(filtered_data),
copy=True)
else:
# ignore RuntimeWarnings for comparisons with NaN data values
with np.errstate(invalid='ignore'):
out = np.ma.masked_invalid(data, copy=False)
filtered_data = np.ma.masked_where(np.logical_or(
out < self._min_value, out > self._max_value),
out, copy=False)
if return_bounds:
return filtered_data, self._min_value, self._max_value
else:
return filtered_data
def __call__(self, data, axis=None, masked=True, return_bounds=False,
copy=True):
"""
Perform sigma clipping on the provided data.
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
The data to be sigma clipped.
axis : None or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If
`None`, then the flattened data will be used. ``axis`` is
passed to the ``cenfunc`` and ``stdfunc``. The default is
`None`.
masked : bool, optional
If `True`, then a `~numpy.ma.MaskedArray` is returned, where
the mask is `True` for clipped values. If `False`, then a
`~numpy.ndarray` is returned. The default is `True`.
return_bounds : bool, optional
If `True`, then the minimum and maximum clipping bounds are
also returned.
copy : bool, optional
If `True`, then the ``data`` array will be copied. If
`False` and ``masked=True``, then the returned masked array
data will contain the same array as the input ``data`` (if
``data`` is a `~numpy.ndarray` or `~numpy.ma.MaskedArray`).
If `False` and ``masked=False``, the input data is modified
in-place. The default is `True`.
Returns
-------
result : array-like
If ``masked=True``, then a `~numpy.ma.MaskedArray` is
returned, where the mask is `True` for clipped values and
where the input mask was `True`.
If ``masked=False``, then a `~numpy.ndarray` is returned.
If ``return_bounds=True``, then in addition to the masked
array or array above, the minimum and maximum clipping
bounds are returned.
If ``masked=False`` and ``axis=None``, then the output
array is a flattened 1D `~numpy.ndarray` where the clipped
values have been removed. If ``return_bounds=True`` then the
returned minimum and maximum thresholds are scalars.
If ``masked=False`` and ``axis`` is specified, then the
output `~numpy.ndarray` will have the same shape as the
input ``data`` and contain ``np.nan`` where values were
clipped. If the input ``data`` was a masked array, then the
output `~numpy.ndarray` will also contain ``np.nan`` where
the input mask was `True`. If ``return_bounds=True`` then
the returned minimum and maximum clipping thresholds will be
be `~numpy.ndarray`\\s.
"""
data = np.asanyarray(data)
if data.size == 0:
if masked:
result = np.ma.MaskedArray(data)
else:
result = data
if return_bounds:
return result, self._min_value, self._max_value
else:
return result
if isinstance(data, np.ma.MaskedArray) and data.mask.all():
if masked:
result = data
else:
result = np.full(data.shape, np.nan)
if return_bounds:
return result, self._min_value, self._max_value
else:
return result
# Shortcut for common cases where a fast C implementation can be
# used.
if (self.cenfunc in ('mean', 'median')
and self.stdfunc in ('std', 'mad_std')
and axis is not None and not self.grow):
return self._sigmaclip_fast(data, axis=axis, masked=masked,
return_bounds=return_bounds,
copy=copy)
# These two cases are treated separately because when
# ``axis=None`` we can simply remove clipped values from the
# array. This is not possible when ``axis`` or ``grow`` is
# specified.
if axis is None and not self.grow:
return self._sigmaclip_noaxis(data, masked=masked,
return_bounds=return_bounds,
copy=copy)
else:
return self._sigmaclip_withaxis(data, axis=axis, masked=masked,
return_bounds=return_bounds,
copy=copy)
def sigma_clip(data, sigma=3, sigma_lower=None, sigma_upper=None, maxiters=5,
cenfunc='median', stdfunc='std', axis=None, masked=True,
return_bounds=False, copy=True, grow=False):
"""
Perform sigma-clipping on the provided data.
The data will be iterated over, each time rejecting values that are
less or more than a specified number of standard deviations from a
center value.
Clipped (rejected) pixels are those where::
data < center - (sigma_lower * std)
data > center + (sigma_upper * std)
where::
center = cenfunc(data [, axis=])
std = stdfunc(data [, axis=])
Invalid data values (i.e., NaN or inf) are automatically clipped.
For an object-oriented interface to sigma clipping, see
:class:`SigmaClip`.
.. note::
`scipy.stats.sigmaclip` provides a subset of the functionality
in this class. Also, its input data cannot be a masked array
and it does not handle data that contains invalid values (i.e.,
NaN or inf). Also note that it uses the mean as the centering
function. The equivalent settings to `scipy.stats.sigmaclip`
are::
sigma_clip(sigma=4., cenfunc='mean', maxiters=None, axis=None,
... masked=False, return_bounds=True)
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
The data to be sigma clipped.
sigma : float, optional
The number of standard deviations to use for both the lower
and upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is 3.
sigma_lower : float or None, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or None, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or None, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute
the center value for the clipping. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanmean`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'median'``.
stdfunc : {'std', 'mad_std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanstd`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
axis : None or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If `None`,
then the flattened data will be used. ``axis`` is passed to the
``cenfunc`` and ``stdfunc``. The default is `None`.
masked : bool, optional
If `True`, then a `~numpy.ma.MaskedArray` is returned, where
the mask is `True` for clipped values. If `False`, then a
`~numpy.ndarray` and the minimum and maximum clipping thresholds
are returned. The default is `True`.
return_bounds : bool, optional
If `True`, then the minimum and maximum clipping bounds are also
returned.
copy : bool, optional
If `True`, then the ``data`` array will be copied. If `False`
and ``masked=True``, then the returned masked array data will
contain the same array as the input ``data`` (if ``data`` is a
`~numpy.ndarray` or `~numpy.ma.MaskedArray`). If `False` and
``masked=False``, the input data is modified in-place. The
default is `True`.
grow : float or `False`, optional
Radius within which to mask the neighbouring pixels of those
that fall outwith the clipping limits (only applied along
``axis``, if specified). As an example, for a 2D image a value
of 1 will mask the nearest pixels in a cross pattern around each
deviant pixel, while 1.5 will also reject the nearest diagonal
neighbours and so on.
Returns
-------
result : array-like
If ``masked=True``, then a `~numpy.ma.MaskedArray` is returned,
where the mask is `True` for clipped values and where the input
mask was `True`.
If ``masked=False``, then a `~numpy.ndarray` is returned.
If ``return_bounds=True``, then in addition to the masked array
or array above, the minimum and maximum clipping bounds are
returned.
If ``masked=False`` and ``axis=None``, then the output array
is a flattened 1D `~numpy.ndarray` where the clipped values
have been removed. If ``return_bounds=True`` then the returned
minimum and maximum thresholds are scalars.
If ``masked=False`` and ``axis`` is specified, then the output
`~numpy.ndarray` will have the same shape as the input ``data``
and contain ``np.nan`` where values were clipped. If the input
``data`` was a masked array, then the output `~numpy.ndarray`
will also contain ``np.nan`` where the input mask was `True`.
If ``return_bounds=True`` then the returned minimum and maximum
clipping thresholds will be be `~numpy.ndarray`\\s.
See Also
--------
SigmaClip, sigma_clipped_stats
Notes
-----
The best performance will typically be obtained by setting
``cenfunc`` and ``stdfunc`` to one of the built-in functions
specified as as string. If one of the options is set to a string
while the other has a custom callable, you may in some cases see
better performance if you have the `bottleneck`_ package installed.
.. _bottleneck: https://github.com/pydata/bottleneck
Examples
--------
This example uses a data array of random variates from a Gaussian
distribution. We clip all points that are more than 2 sample
standard deviations from the median. The result is a masked array,
where the mask is `True` for clipped data::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> filtered_data = sigma_clip(randvar, sigma=2, maxiters=5)
This example clips all points that are more than 3 sigma relative
to the sample *mean*, clips until convergence, returns an unmasked
`~numpy.ndarray`, and does not copy the data::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import randn
>>> from numpy import mean
>>> randvar = randn(10000)
>>> filtered_data = sigma_clip(randvar, sigma=3, maxiters=None,
... cenfunc=mean, masked=False, copy=False)
This example sigma clips along one axis::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import normal
>>> from numpy import arange, diag, ones
>>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))
>>> filtered_data = sigma_clip(data, sigma=2.3, axis=0)
Note that along the other axis, no points would be clipped, as the
standard deviation is higher.
"""
sigclip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower,
sigma_upper=sigma_upper, maxiters=maxiters,
cenfunc=cenfunc, stdfunc=stdfunc, grow=grow)
return sigclip(data, axis=axis, masked=masked,
return_bounds=return_bounds, copy=copy)
def sigma_clipped_stats(data, mask=None, mask_value=None, sigma=3.0,
sigma_lower=None, sigma_upper=None, maxiters=5,
cenfunc='median', stdfunc='std', std_ddof=0,
axis=None, grow=False):
"""
Calculate sigma-clipped statistics on the provided data.
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
Data array or object that can be converted to an array.
mask : `numpy.ndarray` (bool), optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are excluded when computing the statistics.
mask_value : float, optional
A data value (e.g., ``0.0``) that is ignored when computing the
statistics. ``mask_value`` will be masked in addition to any
input ``mask``.
sigma : float, optional
The number of standard deviations to use for both the lower
and upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is 3.
sigma_lower : float or None, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or None, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or None, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute
the center value for the clipping. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanmean`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'median'``.
stdfunc : {'std', 'mad_std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If using a callable
function/object and the ``axis`` keyword is used, then it must
be able to ignore NaNs (e.g., `numpy.nanstd`) and it must have
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
std_ddof : int, optional
The delta degrees of freedom for the standard deviation
calculation. The divisor used in the calculation is ``N -
std_ddof``, where ``N`` represents the number of elements. The
default is 0.
axis : None or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If `None`,
then the flattened data will be used. ``axis`` is passed to the
``cenfunc`` and ``stdfunc``. The default is `None`.
grow : float or `False`, optional
Radius within which to mask the neighbouring pixels of those
that fall outwith the clipping limits (only applied along
``axis``, if specified). As an example, for a 2D image a value
of 1 will mask the nearest pixels in a cross pattern around each
deviant pixel, while 1.5 will also reject the nearest diagonal
neighbours and so on.
Notes
-----
The best performance will typically be obtained by setting
``cenfunc`` and ``stdfunc`` to one of the built-in functions
specified as as string. If one of the options is set to a string
while the other has a custom callable, you may in some cases see
better performance if you have the `bottleneck`_ package installed.
.. _bottleneck: https://github.com/pydata/bottleneck
Returns
-------
mean, median, stddev : float
The mean, median, and standard deviation of the sigma-clipped
data.
See Also
--------
SigmaClip, sigma_clip
"""
if mask is not None:
data = np.ma.MaskedArray(data, mask)
if mask_value is not None:
data = np.ma.masked_values(data, mask_value)
if isinstance(data, np.ma.MaskedArray) and data.mask.all():
return np.ma.masked, np.ma.masked, np.ma.masked
sigclip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower,
sigma_upper=sigma_upper, maxiters=maxiters,
cenfunc=cenfunc, stdfunc=stdfunc, grow=grow)
data_clipped = sigclip(data, axis=axis, masked=False, return_bounds=False,
copy=True)
if HAS_BOTTLENECK:
mean = _nanmean(data_clipped, axis=axis)
median = _nanmedian(data_clipped, axis=axis)
std = _nanstd(data_clipped, ddof=std_ddof, axis=axis)
else: # pragma: no cover
mean = np.nanmean(data_clipped, axis=axis)
median = np.nanmedian(data_clipped, axis=axis)
std = np.nanstd(data_clipped, ddof=std_ddof, axis=axis)
return mean, median, std
|
bc8d3a39057c8b8bd873134b92c3d48b38660d331937e2def02f2dab1d513a3a | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Bayesian Blocks for Time Series Analysis
========================================
Dynamic programming algorithm for solving a piecewise-constant model for
various datasets. This is based on the algorithm presented in Scargle
et al 2013 [1]_. This code was ported from the astroML project [2]_.
Applications include:
- finding an optimal histogram with adaptive bin widths
- finding optimal segmentation of time series data
- detecting inflection points in the rate of event data
The primary interface to these routines is the :func:`bayesian_blocks`
function. This module provides fitness functions suitable for three types
of data:
- Irregularly-spaced event data via the :class:`Events` class
- Regularly-spaced event data via the :class:`RegularEvents` class
- Irregularly-spaced point measurements via the :class:`PointMeasures` class
For more fine-tuned control over the fitness functions used, it is possible
to define custom :class:`FitnessFunc` classes directly and use them with
the :func:`bayesian_blocks` routine.
One common application of the Bayesian Blocks algorithm is the determination
of optimal adaptive-width histogram bins. This uses the same fitness function
as for irregularly-spaced time series events. The easiest interface for
creating Bayesian Blocks histograms is the :func:`astropy.stats.histogram`
function.
References
----------
.. [1] https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S
.. [2] https://www.astroml.org/ https://github.com//astroML/astroML/
.. [3] Bellman, R.E., Dreyfus, S.E., 1962. Applied Dynamic
Programming. Princeton University Press, Princeton.
https://press.princeton.edu/books/hardcover/9780691651873/applied-dynamic-programming
.. [4] Bellman, R., Roth, R., 1969. Curve fitting by segmented
straight lines. J. Amer. Statist. Assoc. 64, 1079–1084.
https://www.tandfonline.com/doi/abs/10.1080/01621459.1969.10501038
"""
import warnings
import numpy as np
from inspect import signature
from astropy.utils.exceptions import AstropyUserWarning
# TODO: implement other fitness functions from appendix C of Scargle 2013
__all__ = ['FitnessFunc', 'Events', 'RegularEvents', 'PointMeasures',
'bayesian_blocks']
def bayesian_blocks(t, x=None, sigma=None,
fitness='events', **kwargs):
r"""Compute optimal segmentation of data with Scargle's Bayesian Blocks
This is a flexible implementation of the Bayesian Blocks algorithm
described in Scargle 2013 [1]_.
Parameters
----------
t : array-like
data times (one dimensional, length N)
x : array-like, optional
data values
sigma : array-like or float, optional
data errors
fitness : str or object
the fitness function to use for the model.
If a string, the following options are supported:
- 'events' : binned or unbinned event data. Arguments are ``gamma``,
which gives the slope of the prior on the number of bins, or
``ncp_prior``, which is :math:`-\ln({\tt gamma})`.
- 'regular_events' : non-overlapping events measured at multiples of a
fundamental tick rate, ``dt``, which must be specified as an
additional argument. Extra arguments are ``p0``, which gives the
false alarm probability to compute the prior, or ``gamma``, which
gives the slope of the prior on the number of bins, or ``ncp_prior``,
which is :math:`-\ln({\tt gamma})`.
- 'measures' : fitness for a measured sequence with Gaussian errors.
Extra arguments are ``p0``, which gives the false alarm probability
to compute the prior, or ``gamma``, which gives the slope of the
prior on the number of bins, or ``ncp_prior``, which is
:math:`-\ln({\tt gamma})`.
In all three cases, if more than one of ``p0``, ``gamma``, and
``ncp_prior`` is chosen, ``ncp_prior`` takes precedence over ``gamma``
which takes precedence over ``p0``.
Alternatively, the fitness parameter can be an instance of
:class:`FitnessFunc` or a subclass thereof.
**kwargs :
any additional keyword arguments will be passed to the specified
:class:`FitnessFunc` derived class.
Returns
-------
edges : ndarray
array containing the (N+1) edges defining the N bins
Examples
--------
.. testsetup::
>>> np.random.seed(12345)
Event data:
>>> t = np.random.normal(size=100)
>>> edges = bayesian_blocks(t, fitness='events', p0=0.01)
Event data with repeats:
>>> t = np.random.normal(size=100)
>>> t[80:] = t[:20]
>>> edges = bayesian_blocks(t, fitness='events', p0=0.01)
Regular event data:
>>> dt = 0.05
>>> t = dt * np.arange(1000)
>>> x = np.zeros(len(t))
>>> x[np.random.randint(0, len(t), len(t) // 10)] = 1
>>> edges = bayesian_blocks(t, x, fitness='regular_events', dt=dt)
Measured point data with errors:
>>> t = 100 * np.random.random(100)
>>> x = np.exp(-0.5 * (t - 50) ** 2)
>>> sigma = 0.1
>>> x_obs = np.random.normal(x, sigma)
>>> edges = bayesian_blocks(t, x_obs, sigma, fitness='measures')
References
----------
.. [1] Scargle, J et al. (2013)
https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S
.. [2] Bellman, R.E., Dreyfus, S.E., 1962. Applied Dynamic
Programming. Princeton University Press, Princeton.
https://press.princeton.edu/books/hardcover/9780691651873/applied-dynamic-programming
.. [3] Bellman, R., Roth, R., 1969. Curve fitting by segmented
straight lines. J. Amer. Statist. Assoc. 64, 1079–1084.
https://www.tandfonline.com/doi/abs/10.1080/01621459.1969.10501038
See Also
--------
astropy.stats.histogram : compute a histogram using bayesian blocks
"""
FITNESS_DICT = {'events': Events,
'regular_events': RegularEvents,
'measures': PointMeasures}
fitness = FITNESS_DICT.get(fitness, fitness)
if type(fitness) is type and issubclass(fitness, FitnessFunc):
fitfunc = fitness(**kwargs)
elif isinstance(fitness, FitnessFunc):
fitfunc = fitness
else:
raise ValueError("fitness parameter not understood")
return fitfunc.fit(t, x, sigma)
class FitnessFunc:
"""Base class for bayesian blocks fitness functions
Derived classes should overload the following method:
``fitness(self, **kwargs)``:
Compute the fitness given a set of named arguments.
Arguments accepted by fitness must be among ``[T_k, N_k, a_k, b_k, c_k]``
(See [1]_ for details on the meaning of these parameters).
Additionally, other methods may be overloaded as well:
``__init__(self, **kwargs)``:
Initialize the fitness function with any parameters beyond the normal
``p0`` and ``gamma``.
``validate_input(self, t, x, sigma)``:
Enable specific checks of the input data (``t``, ``x``, ``sigma``)
to be performed prior to the fit.
``compute_ncp_prior(self, N)``: If ``ncp_prior`` is not defined explicitly,
this function is called in order to define it before fitting. This may be
calculated from ``gamma``, ``p0``, or whatever method you choose.
``p0_prior(self, N)``:
Specify the form of the prior given the false-alarm probability ``p0``
(See [1]_ for details).
For examples of implemented fitness functions, see :class:`Events`,
:class:`RegularEvents`, and :class:`PointMeasures`.
References
----------
.. [1] Scargle, J et al. (2013)
https://ui.adsabs.harvard.edu/abs/2013ApJ...764..167S
"""
def __init__(self, p0=0.05, gamma=None, ncp_prior=None):
self.p0 = p0
self.gamma = gamma
self.ncp_prior = ncp_prior
def validate_input(self, t, x=None, sigma=None):
"""Validate inputs to the model.
Parameters
----------
t : array-like
times of observations
x : array-like, optional
values observed at each time
sigma : float or array-like, optional
errors in values x
Returns
-------
t, x, sigma : array-like, float or None
validated and perhaps modified versions of inputs
"""
# validate array input
t = np.asarray(t, dtype=float)
# find unique values of t
t = np.array(t)
if t.ndim != 1:
raise ValueError("t must be a one-dimensional array")
unq_t, unq_ind, unq_inv = np.unique(t, return_index=True,
return_inverse=True)
# if x is not specified, x will be counts at each time
if x is None:
if sigma is not None:
raise ValueError("If sigma is specified, x must be specified")
else:
sigma = 1
if len(unq_t) == len(t):
x = np.ones_like(t)
else:
x = np.bincount(unq_inv)
t = unq_t
# if x is specified, then we need to simultaneously sort t and x
else:
# TODO: allow broadcasted x?
x = np.asarray(x, dtype=float)
if x.shape not in [(), (1,), (t.size,)]:
raise ValueError("x does not match shape of t")
x += np.zeros_like(t)
if len(unq_t) != len(t):
raise ValueError("Repeated values in t not supported when "
"x is specified")
t = unq_t
x = x[unq_ind]
# verify the given sigma value
if sigma is None:
sigma = 1
else:
sigma = np.asarray(sigma, dtype=float)
if sigma.shape not in [(), (1,), (t.size,)]:
raise ValueError('sigma does not match the shape of x')
return t, x, sigma
def fitness(self, **kwargs):
raise NotImplementedError()
def p0_prior(self, N):
"""
Empirical prior, parametrized by the false alarm probability ``p0``
See eq. 21 in Scargle (2013)
Note that there was an error in this equation in the original Scargle
paper (the "log" was missing). The following corrected form is taken
from https://arxiv.org/abs/1304.2818
"""
return 4 - np.log(73.53 * self.p0 * (N ** -0.478))
# the fitness_args property will return the list of arguments accepted by
# the method fitness(). This allows more efficient computation below.
@property
def _fitness_args(self):
return signature(self.fitness).parameters.keys()
def compute_ncp_prior(self, N):
"""
If ``ncp_prior`` is not explicitly defined, compute it from ``gamma``
or ``p0``.
"""
if self.gamma is not None:
return -np.log(self.gamma)
elif self.p0 is not None:
return self.p0_prior(N)
else:
raise ValueError("``ncp_prior`` cannot be computed as neither "
"``gamma`` nor ``p0`` is defined.")
def fit(self, t, x=None, sigma=None):
"""Fit the Bayesian Blocks model given the specified fitness function.
Parameters
----------
t : array-like
data times (one dimensional, length N)
x : array-like, optional
data values
sigma : array-like or float, optional
data errors
Returns
-------
edges : ndarray
array containing the (M+1) edges defining the M optimal bins
"""
t, x, sigma = self.validate_input(t, x, sigma)
# compute values needed for computation, below
if 'a_k' in self._fitness_args:
ak_raw = np.ones_like(x) / sigma ** 2
if 'b_k' in self._fitness_args:
bk_raw = x / sigma ** 2
if 'c_k' in self._fitness_args:
ck_raw = x * x / sigma ** 2
# create length-(N + 1) array of cell edges
edges = np.concatenate([t[:1],
0.5 * (t[1:] + t[:-1]),
t[-1:]])
block_length = t[-1] - edges
# arrays to store the best configuration
N = len(t)
best = np.zeros(N, dtype=float)
last = np.zeros(N, dtype=int)
# Compute ncp_prior if not defined
if self.ncp_prior is None:
ncp_prior = self.compute_ncp_prior(N)
else:
ncp_prior = self.ncp_prior
# ----------------------------------------------------------------
# Start with first data cell; add one cell at each iteration
# ----------------------------------------------------------------
for R in range(N):
# Compute fit_vec : fitness of putative last block (end at R)
kwds = {}
# T_k: width/duration of each block
if 'T_k' in self._fitness_args:
kwds['T_k'] = block_length[:R + 1] - block_length[R + 1]
# N_k: number of elements in each block
if 'N_k' in self._fitness_args:
kwds['N_k'] = np.cumsum(x[:R + 1][::-1])[::-1]
# a_k: eq. 31
if 'a_k' in self._fitness_args:
kwds['a_k'] = 0.5 * np.cumsum(ak_raw[:R + 1][::-1])[::-1]
# b_k: eq. 32
if 'b_k' in self._fitness_args:
kwds['b_k'] = - np.cumsum(bk_raw[:R + 1][::-1])[::-1]
# c_k: eq. 33
if 'c_k' in self._fitness_args:
kwds['c_k'] = 0.5 * np.cumsum(ck_raw[:R + 1][::-1])[::-1]
# evaluate fitness function
fit_vec = self.fitness(**kwds)
A_R = fit_vec - ncp_prior
A_R[1:] += best[:R]
i_max = np.argmax(A_R)
last[R] = i_max
best[R] = A_R[i_max]
# ----------------------------------------------------------------
# Now find changepoints by iteratively peeling off the last block
# ----------------------------------------------------------------
change_points = np.zeros(N, dtype=int)
i_cp = N
ind = N
while i_cp > 0:
i_cp -= 1
change_points[i_cp] = ind
if ind == 0:
break
ind = last[ind - 1]
if i_cp == 0:
change_points[i_cp] = 0
change_points = change_points[i_cp:]
return edges[change_points]
class Events(FitnessFunc):
r"""Bayesian blocks fitness for binned or unbinned events
Parameters
----------
p0 : float, optional
False alarm probability, used to compute the prior on
:math:`N_{\rm blocks}` (see eq. 21 of Scargle 2013). For the Events
type data, ``p0`` does not seem to be an accurate representation of the
actual false alarm probability. If you are using this fitness function
for a triggering type condition, it is recommended that you run
statistical trials on signal-free noise to determine an appropriate
value of ``gamma`` or ``ncp_prior`` to use for a desired false alarm
rate.
gamma : float, optional
If specified, then use this gamma to compute the general prior form,
:math:`p \sim {\tt gamma}^{N_{\rm blocks}}`. If gamma is specified, p0
is ignored.
ncp_prior : float, optional
If specified, use the value of ``ncp_prior`` to compute the prior as
above, using the definition :math:`{\tt ncp\_prior} = -\ln({\tt
gamma})`.
If ``ncp_prior`` is specified, ``gamma`` and ``p0`` is ignored.
"""
def fitness(self, N_k, T_k):
# eq. 19 from Scargle 2013
return N_k * (np.log(N_k / T_k))
def validate_input(self, t, x, sigma):
t, x, sigma = super().validate_input(t, x, sigma)
if x is not None and np.any(x % 1 > 0):
raise ValueError("x must be integer counts for fitness='events'")
return t, x, sigma
class RegularEvents(FitnessFunc):
r"""Bayesian blocks fitness for regular events
This is for data which has a fundamental "tick" length, so that all
measured values are multiples of this tick length. In each tick, there
are either zero or one counts.
Parameters
----------
dt : float
tick rate for data
p0 : float, optional
False alarm probability, used to compute the prior on :math:`N_{\rm
blocks}` (see eq. 21 of Scargle 2013). If gamma is specified, p0 is
ignored.
ncp_prior : float, optional
If specified, use the value of ``ncp_prior`` to compute the prior as
above, using the definition :math:`{\tt ncp\_prior} = -\ln({\tt
gamma})`. If ``ncp_prior`` is specified, ``gamma`` and ``p0`` are
ignored.
"""
def __init__(self, dt, p0=0.05, gamma=None, ncp_prior=None):
self.dt = dt
super().__init__(p0, gamma, ncp_prior)
def validate_input(self, t, x, sigma):
t, x, sigma = super().validate_input(t, x, sigma)
if not np.all((x == 0) | (x == 1)):
raise ValueError("Regular events must have only 0 and 1 in x")
return t, x, sigma
def fitness(self, T_k, N_k):
# Eq. C23 of Scargle 2013
M_k = T_k / self.dt
N_over_M = N_k / M_k
eps = 1E-8
if np.any(N_over_M > 1 + eps):
warnings.warn('regular events: N/M > 1. '
'Is the time step correct?', AstropyUserWarning)
one_m_NM = 1 - N_over_M
N_over_M[N_over_M <= 0] = 1
one_m_NM[one_m_NM <= 0] = 1
return N_k * np.log(N_over_M) + (M_k - N_k) * np.log(one_m_NM)
class PointMeasures(FitnessFunc):
r"""Bayesian blocks fitness for point measures
Parameters
----------
p0 : float, optional
False alarm probability, used to compute the prior on :math:`N_{\rm
blocks}` (see eq. 21 of Scargle 2013). If gamma is specified, p0 is
ignored.
ncp_prior : float, optional
If specified, use the value of ``ncp_prior`` to compute the prior as
above, using the definition :math:`{\tt ncp\_prior} = -\ln({\tt
gamma})`. If ``ncp_prior`` is specified, ``gamma`` and ``p0`` are
ignored.
"""
def __init__(self, p0=0.05, gamma=None, ncp_prior=None):
super().__init__(p0, gamma, ncp_prior)
def fitness(self, a_k, b_k):
# eq. 41 from Scargle 2013
return (b_k * b_k) / (4 * a_k)
def validate_input(self, t, x, sigma):
if x is None:
raise ValueError("x must be specified for point measures")
return super().validate_input(t, x, sigma)
|
09bbd38e466a98790d27444affa79c3418a6b8c8e451798b40883aaf1b3a2387 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This subpackage contains statistical tools provided for or used by Astropy.
While the `scipy.stats` package contains a wide range of statistical
tools, it is a general-purpose package, and is missing some that are
particularly useful to astronomy or are used in an atypical way in
astronomy. This package is intended to provide such functionality, but
*not* to replace `scipy.stats` if its implementation satisfies
astronomers' needs.
"""
from . import funcs
from .funcs import * # noqa
from . import biweight
from .biweight import * # noqa
from . import sigma_clipping
from .sigma_clipping import * # noqa
from . import jackknife
from .jackknife import * # noqa
from . import circstats
from .circstats import * # noqa
from . import bayesian_blocks as _bb
from .bayesian_blocks import * # noqa
from . import histogram as _hist
from .histogram import * # noqa
from . import info_theory
from .info_theory import * # noqa
from . import spatial
from .spatial import * # noqa
from .lombscargle import * # noqa
from .bls import * # noqa
# This is to avoid importing deprecated modules in subpackage star import
__all__ = []
__all__.extend(funcs.__all__)
__all__.extend(biweight.__all__)
__all__.extend(sigma_clipping.__all__)
__all__.extend(jackknife.__all__)
__all__.extend(circstats.__all__)
__all__.extend(_bb.__all__)
__all__.extend(_hist.__all__)
__all__.extend(info_theory.__all__)
__all__.extend(spatial.__all__)
|
40748fb8d5335457054df1a2845f23b17e313e59b9ad495a0578faeb965ea7bd | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
from setuptools import Extension
import numpy
SRCDIR = os.path.join(os.path.relpath(os.path.dirname(__file__)), 'src')
SRCFILES = ['wirth_select.c', 'compute_bounds.c', 'fast_sigma_clip.c']
SRCFILES = [os.path.join(SRCDIR, srcfile) for srcfile in SRCFILES]
def get_extensions():
_sigma_clip_ext = Extension(name='astropy.stats._fast_sigma_clip', sources=SRCFILES,
include_dirs=[numpy.get_include()],
language='c')
return [_sigma_clip_ext]
|
fbd3884fc3b873a9a2ba6e586358f76e01bf28198212fe4a8a8cd36b64128bf5 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains simple functions for dealing with circular statistics, for
instance, mean, variance, standard deviation, correlation coefficient, and so
on. This module also cover tests of uniformity, e.g., the Rayleigh and V tests.
The Maximum Likelihood Estimator for the Von Mises distribution along with the
Cramer-Rao Lower Bounds are also implemented. Almost all of the implementations
are based on reference [1]_, which is also the basis for the R package
'CircStats' [2]_.
"""
import numpy as np
from astropy.units import Quantity
__all__ = ['circmean', 'circstd', 'circvar', 'circmoment', 'circcorrcoef',
'rayleightest', 'vtest', 'vonmisesmle']
__doctest_requires__ = {'vtest': ['scipy']}
def _components(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized rectangular components
# of the circular data.
if weights is None:
weights = np.ones((1,))
try:
weights = np.broadcast_to(weights, data.shape)
except ValueError:
raise ValueError('Weights and data have inconsistent shape.')
C = np.sum(weights * np.cos(p * (data - phi)), axis)/np.sum(weights, axis)
S = np.sum(weights * np.sin(p * (data - phi)), axis)/np.sum(weights, axis)
return C, S
def _angle(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized sample mean angle
C, S = _components(data, p, phi, axis, weights)
# theta will be an angle in the interval [-np.pi, np.pi)
# [-180, 180)*u.deg in case data is a Quantity
theta = np.arctan2(S, C)
if isinstance(data, Quantity):
theta = theta.to(data.unit)
return theta
def _length(data, p=1, phi=0.0, axis=None, weights=None):
# Utility function for computing the generalized sample length
C, S = _components(data, p, phi, axis, weights)
return np.hypot(S, C)
def circmean(data, axis=None, weights=None):
""" Computes the circular mean angle of an array of circular data.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
axis : int, optional
Axis along which circular means are computed. The default is to compute
the mean of the flattened array.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``sum(weights, axis)``
equals the number of observations. See [1]_, remark 1.4, page 22, for
detailed explanation.
Returns
-------
circmean : ndarray or `~astropy.units.Quantity`
Circular mean.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import circmean
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circmean(data) # doctest: +FLOAT_CMP
<Quantity 48.62718088722989 deg>
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
"""
return _angle(data, 1, 0.0, axis, weights)
def circvar(data, axis=None, weights=None):
""" Computes the circular variance of an array of circular data.
There are some concepts for defining measures of dispersion for circular
data. The variance implemented here is based on the definition given by
[1]_, which is also the same used by the R package 'CircStats' [2]_.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
Dimensionless, if Quantity.
axis : int, optional
Axis along which circular variances are computed. The default is to
compute the variance of the flattened array.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``sum(weights, axis)``
equals the number of observations. See [1]_, remark 1.4, page 22,
for detailed explanation.
Returns
-------
circvar : ndarray or `~astropy.units.Quantity` ['dimensionless']
Circular variance.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import circvar
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circvar(data) # doctest: +FLOAT_CMP
<Quantity 0.16356352748437508>
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
Notes
-----
The definition used here differs from the one in scipy.stats.circvar.
Precisely, Scipy circvar uses an approximation based on the limit of small
angles which approaches the linear variance.
"""
return 1.0 - _length(data, 1, 0.0, axis, weights)
def circstd(data, axis=None, weights=None, method='angular'):
""" Computes the circular standard deviation of an array of circular data.
The standard deviation implemented here is based on the definitions given
by [1]_, which is also the same used by the R package 'CirStat' [2]_.
Two methods are implemented: 'angular' and 'circular'. The former is
defined as sqrt(2 * (1 - R)) and it is bounded in [0, 2*Pi]. The
latter is defined as sqrt(-2 * ln(R)) and it is bounded in [0, inf].
Following 'CircStat' the default method used to obtain the standard
deviation is 'angular'.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
If quantity, must be dimensionless.
axis : int, optional
Axis along which circular variances are computed. The default is to
compute the variance of the flattened array.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``sum(weights, axis)``
equals the number of observations. See [3]_, remark 1.4, page 22,
for detailed explanation.
method : str, optional
The method used to estimate the standard deviation:
- 'angular' : obtains the angular deviation
- 'circular' : obtains the circular deviation
Returns
-------
circstd : ndarray or `~astropy.units.Quantity` ['dimensionless']
Angular or circular standard deviation.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import circstd
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circstd(data) # doctest: +FLOAT_CMP
<Quantity 0.57195022>
Alternatively, using the 'circular' method:
>>> import numpy as np
>>> from astropy.stats import circstd
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circstd(data, method='circular') # doctest: +FLOAT_CMP
<Quantity 0.59766999>
References
----------
.. [1] P. Berens. "CircStat: A MATLAB Toolbox for Circular Statistics".
Journal of Statistical Software, vol 31, issue 10, 2009.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
.. [3] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
"""
if method not in ('angular', 'circular'):
raise ValueError("method should be either 'angular' or 'circular'")
if method == 'angular':
return np.sqrt(2. * (1. - _length(data, 1, 0.0, axis, weights)))
else:
return np.sqrt(-2. * np.log(_length(data, 1, 0.0, axis, weights)))
def circmoment(data, p=1.0, centered=False, axis=None, weights=None):
""" Computes the ``p``-th trigonometric circular moment for an array
of circular data.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
p : float, optional
Order of the circular moment.
centered : bool, optional
If ``True``, central circular moments are computed. Default value is
``False``.
axis : int, optional
Axis along which circular moments are computed. The default is to
compute the circular moment of the flattened array.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``sum(weights, axis)``
equals the number of observations. See [1]_, remark 1.4, page 22,
for detailed explanation.
Returns
-------
circmoment : ndarray or `~astropy.units.Quantity`
The first and second elements correspond to the direction and length of
the ``p``-th circular moment, respectively.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import circmoment
>>> from astropy import units as u
>>> data = np.array([51, 67, 40, 109, 31, 358])*u.deg
>>> circmoment(data, p=2) # doctest: +FLOAT_CMP
(<Quantity 90.99263082432564 deg>, <Quantity 0.48004283892950717>)
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
"""
if centered:
phi = circmean(data, axis, weights)
else:
phi = 0.0
return _angle(data, p, phi, axis, weights), _length(data, p, phi, axis,
weights)
def circcorrcoef(alpha, beta, axis=None, weights_alpha=None,
weights_beta=None):
""" Computes the circular correlation coefficient between two array of
circular data.
Parameters
----------
alpha : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
beta : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
axis : int, optional
Axis along which circular correlation coefficients are computed.
The default is the compute the circular correlation coefficient of the
flattened array.
weights_alpha : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights_alpha``
represents a weighting factor for each group such that
``sum(weights_alpha, axis)`` equals the number of observations.
See [1]_, remark 1.4, page 22, for detailed explanation.
weights_beta : numpy.ndarray, optional
See description of ``weights_alpha``.
Returns
-------
rho : ndarray or `~astropy.units.Quantity` ['dimensionless']
Circular correlation coefficient.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import circcorrcoef
>>> from astropy import units as u
>>> alpha = np.array([356, 97, 211, 232, 343, 292, 157, 302, 335, 302,
... 324, 85, 324, 340, 157, 238, 254, 146, 232, 122,
... 329])*u.deg
>>> beta = np.array([119, 162, 221, 259, 270, 29, 97, 292, 40, 313, 94,
... 45, 47, 108, 221, 270, 119, 248, 270, 45, 23])*u.deg
>>> circcorrcoef(alpha, beta) # doctest: +FLOAT_CMP
<Quantity 0.2704648826748831>
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
"""
if(np.size(alpha, axis) != np.size(beta, axis)):
raise ValueError("alpha and beta must be arrays of the same size")
mu_a = circmean(alpha, axis, weights_alpha)
mu_b = circmean(beta, axis, weights_beta)
sin_a = np.sin(alpha - mu_a)
sin_b = np.sin(beta - mu_b)
rho = np.sum(sin_a*sin_b)/np.sqrt(np.sum(sin_a*sin_a)*np.sum(sin_b*sin_b))
return rho
def rayleightest(data, axis=None, weights=None):
""" Performs the Rayleigh test of uniformity.
This test is used to identify a non-uniform distribution, i.e. it is
designed for detecting an unimodal deviation from uniformity. More
precisely, it assumes the following hypotheses:
- H0 (null hypothesis): The population is distributed uniformly around the
circle.
- H1 (alternative hypothesis): The population is not distributed uniformly
around the circle.
Small p-values suggest to reject the null hypothesis.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
axis : int, optional
Axis along which the Rayleigh test will be performed.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``np.sum(weights, axis)``
equals the number of observations.
See [1]_, remark 1.4, page 22, for detailed explanation.
Returns
-------
p-value : float or `~astropy.units.Quantity` ['dimensionless']
Examples
--------
>>> import numpy as np
>>> from astropy.stats import rayleightest
>>> from astropy import units as u
>>> data = np.array([130, 90, 0, 145])*u.deg
>>> rayleightest(data) # doctest: +FLOAT_CMP
<Quantity 0.2563487733797317>
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
.. [3] M. Chirstman., C. Miller. "Testing a Sample of Directions for
Uniformity." Lecture Notes, STA 6934/5805. University of Florida, 2007.
.. [4] D. Wilkie. "Rayleigh Test for Randomness of Circular Data". Applied
Statistics. 1983.
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.211.4762>
"""
n = np.size(data, axis=axis)
Rbar = _length(data, 1, 0.0, axis, weights)
z = n*Rbar*Rbar
# see [3] and [4] for the formulae below
tmp = 1.0
if(n < 50):
tmp = 1.0 + (2.0*z - z*z)/(4.0*n) - (24.0*z - 132.0*z**2.0 +
76.0*z**3.0 - 9.0*z**4.0)/(288.0 *
n * n)
p_value = np.exp(-z)*tmp
return p_value
def vtest(data, mu=0.0, axis=None, weights=None):
""" Performs the Rayleigh test of uniformity where the alternative
hypothesis H1 is assumed to have a known mean angle ``mu``.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
mu : float or `~astropy.units.Quantity` ['angle'], optional
Mean angle. Assumed to be known.
axis : int, optional
Axis along which the V test will be performed.
weights : numpy.ndarray, optional
In case of grouped data, the i-th element of ``weights`` represents a
weighting factor for each group such that ``sum(weights, axis)``
equals the number of observations. See [1]_, remark 1.4, page 22,
for detailed explanation.
Returns
-------
p-value : float or `~astropy.units.Quantity` ['dimensionless']
Examples
--------
>>> import numpy as np
>>> from astropy.stats import vtest
>>> from astropy import units as u
>>> data = np.array([130, 90, 0, 145])*u.deg
>>> vtest(data) # doctest: +FLOAT_CMP
<Quantity 0.6223678199713766>
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
.. [3] M. Chirstman., C. Miller. "Testing a Sample of Directions for
Uniformity." Lecture Notes, STA 6934/5805. University of Florida, 2007.
"""
from scipy.stats import norm
if weights is None:
weights = np.ones((1,))
try:
weights = np.broadcast_to(weights, data.shape)
except ValueError:
raise ValueError('Weights and data have inconsistent shape.')
n = np.size(data, axis=axis)
R0bar = np.sum(weights * np.cos(data - mu), axis)/np.sum(weights, axis)
z = np.sqrt(2.0 * n) * R0bar
pz = norm.cdf(z)
fz = norm.pdf(z)
# see reference [3]
p_value = 1 - pz + fz*((3*z - z**3)/(16.0*n) +
(15*z + 305*z**3 - 125*z**5 + 9*z**7)/(4608.0*n*n))
return p_value
def _A1inv(x):
# Approximation for _A1inv(x) according R Package 'CircStats'
# See http://www.scienceasia.org/2012.38.n1/scias38_118.pdf, equation (4)
if 0 <= x < 0.53:
return 2.0*x + x*x*x + (5.0*x**5)/6.0
elif x < 0.85:
return -0.4 + 1.39*x + 0.43/(1.0 - x)
else:
return 1.0/(x*x*x - 4.0*x*x + 3.0*x)
def vonmisesmle(data, axis=None):
""" Computes the Maximum Likelihood Estimator (MLE) for the parameters of
the von Mises distribution.
Parameters
----------
data : ndarray or `~astropy.units.Quantity`
Array of circular (directional) data, which is assumed to be in
radians whenever ``data`` is ``numpy.ndarray``.
axis : int, optional
Axis along which the mle will be computed.
Returns
-------
mu : float or `~astropy.units.Quantity`
The mean (aka location parameter).
kappa : float or `~astropy.units.Quantity` ['dimensionless']
The concentration parameter.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import vonmisesmle
>>> from astropy import units as u
>>> data = np.array([130, 90, 0, 145])*u.deg
>>> vonmisesmle(data) # doctest: +FLOAT_CMP
(<Quantity 101.16894320013179 deg>, <Quantity 1.49358958737054>)
References
----------
.. [1] S. R. Jammalamadaka, A. SenGupta. "Topics in Circular Statistics".
Series on Multivariate Analysis, Vol. 5, 2001.
.. [2] C. Agostinelli, U. Lund. "Circular Statistics from 'Topics in
Circular Statistics (2001)'". 2015.
<https://cran.r-project.org/web/packages/CircStats/CircStats.pdf>
"""
mu = circmean(data, axis=None)
kappa = _A1inv(np.mean(np.cos(data - mu), axis))
return mu, kappa
|
4a1d1cd842a3cbf45b3dc88ef31a3eecd1b675adfabc18e1097218cf00a7750d | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module implements functions and classes for spatial statistics.
"""
import numpy as np
import math
__all__ = ['RipleysKEstimator']
class RipleysKEstimator:
"""
Estimators for Ripley's K function for two-dimensional spatial data.
See [1]_, [2]_, [3]_, [4]_, [5]_ for detailed mathematical and
practical aspects of those estimators.
Parameters
----------
area : float
Area of study from which the points where observed.
x_max, y_max : float, float, optional
Maximum rectangular coordinates of the area of study.
Required if ``mode == 'translation'`` or ``mode == ohser``.
x_min, y_min : float, float, optional
Minimum rectangular coordinates of the area of study.
Required if ``mode == 'variable-width'`` or ``mode == ohser``.
Examples
--------
>>> import numpy as np
>>> from matplotlib import pyplot as plt # doctest: +SKIP
>>> from astropy.stats import RipleysKEstimator
>>> z = np.random.uniform(low=5, high=10, size=(100, 2))
>>> Kest = RipleysKEstimator(area=25, x_max=10, y_max=10,
... x_min=5, y_min=5)
>>> r = np.linspace(0, 2.5, 100)
>>> plt.plot(r, Kest.poisson(r)) # doctest: +SKIP
>>> plt.plot(r, Kest(data=z, radii=r, mode='none')) # doctest: +SKIP
>>> plt.plot(r, Kest(data=z, radii=r, mode='translation')) # doctest: +SKIP
>>> plt.plot(r, Kest(data=z, radii=r, mode='ohser')) # doctest: +SKIP
>>> plt.plot(r, Kest(data=z, radii=r, mode='var-width')) # doctest: +SKIP
>>> plt.plot(r, Kest(data=z, radii=r, mode='ripley')) # doctest: +SKIP
References
----------
.. [1] Peebles, P.J.E. *The large scale structure of the universe*.
<https://ui.adsabs.harvard.edu/abs/1980lssu.book.....P>
.. [2] Spatial descriptive statistics.
<https://en.wikipedia.org/wiki/Spatial_descriptive_statistics>
.. [3] Package spatstat.
<https://cran.r-project.org/web/packages/spatstat/spatstat.pdf>
.. [4] Cressie, N.A.C. (1991). Statistics for Spatial Data,
Wiley, New York.
.. [5] Stoyan, D., Stoyan, H. (1992). Fractals, Random Shapes and
Point Fields, Akademie Verlag GmbH, Chichester.
"""
def __init__(self, area, x_max=None, y_max=None, x_min=None, y_min=None):
self.area = area
self.x_max = x_max
self.y_max = y_max
self.x_min = x_min
self.y_min = y_min
@property
def area(self):
return self._area
@area.setter
def area(self, value):
if isinstance(value, (float, int)) and value > 0:
self._area = value
else:
raise ValueError(f'area is expected to be a positive number. Got {value}.')
@property
def y_max(self):
return self._y_max
@y_max.setter
def y_max(self, value):
if value is None or isinstance(value, (float, int)):
self._y_max = value
else:
raise ValueError('y_max is expected to be a real number '
'or None. Got {}.'.format(value))
@property
def x_max(self):
return self._x_max
@x_max.setter
def x_max(self, value):
if value is None or isinstance(value, (float, int)):
self._x_max = value
else:
raise ValueError('x_max is expected to be a real number '
'or None. Got {}.'.format(value))
@property
def y_min(self):
return self._y_min
@y_min.setter
def y_min(self, value):
if value is None or isinstance(value, (float, int)):
self._y_min = value
else:
raise ValueError(f'y_min is expected to be a real number. Got {value}.')
@property
def x_min(self):
return self._x_min
@x_min.setter
def x_min(self, value):
if value is None or isinstance(value, (float, int)):
self._x_min = value
else:
raise ValueError(f'x_min is expected to be a real number. Got {value}.')
def __call__(self, data, radii, mode='none'):
return self.evaluate(data=data, radii=radii, mode=mode)
def _pairwise_diffs(self, data):
npts = len(data)
diff = np.zeros(shape=(npts * (npts - 1) // 2, 2), dtype=np.double)
k = 0
for i in range(npts - 1):
size = npts - i - 1
diff[k:k + size] = abs(data[i] - data[i+1:])
k += size
return diff
def poisson(self, radii):
"""
Evaluates the Ripley K function for the homogeneous Poisson process,
also known as Complete State of Randomness (CSR).
Parameters
----------
radii : 1D array
Set of distances in which Ripley's K function will be evaluated.
Returns
-------
output : 1D array
Ripley's K function evaluated at ``radii``.
"""
return np.pi * radii * radii
def Lfunction(self, data, radii, mode='none'):
"""
Evaluates the L function at ``radii``. For parameter description
see ``evaluate`` method.
"""
return np.sqrt(self.evaluate(data, radii, mode=mode) / np.pi)
def Hfunction(self, data, radii, mode='none'):
"""
Evaluates the H function at ``radii``. For parameter description
see ``evaluate`` method.
"""
return self.Lfunction(data, radii, mode=mode) - radii
def evaluate(self, data, radii, mode='none'):
"""
Evaluates the Ripley K estimator for a given set of values ``radii``.
Parameters
----------
data : 2D array
Set of observed points in as a n by 2 array which will be used to
estimate Ripley's K function.
radii : 1D array
Set of distances in which Ripley's K estimator will be evaluated.
Usually, it's common to consider max(radii) < (area/2)**0.5.
mode : str
Keyword which indicates the method for edge effects correction.
Available methods are 'none', 'translation', 'ohser', 'var-width',
and 'ripley'.
* 'none'
this method does not take into account any edge effects
whatsoever.
* 'translation'
computes the intersection of rectangular areas centered at
the given points provided the upper bounds of the
dimensions of the rectangular area of study. It assumes that
all the points lie in a bounded rectangular region satisfying
x_min < x_i < x_max; y_min < y_i < y_max. A detailed
description of this method can be found on ref [4].
* 'ohser'
this method uses the isotropized set covariance function of
the window of study as a weight to correct for
edge-effects. A detailed description of this method can be
found on ref [4].
* 'var-width'
this method considers the distance of each observed point to
the nearest boundary of the study window as a factor to
account for edge-effects. See [3] for a brief description of
this method.
* 'ripley'
this method is known as Ripley's edge-corrected estimator.
The weight for edge-correction is a function of the
proportions of circumferences centered at each data point
which crosses another data point of interest. See [3] for
a detailed description of this method.
Returns
-------
ripley : 1D array
Ripley's K function estimator evaluated at ``radii``.
"""
data = np.asarray(data)
if not data.shape[1] == 2:
raise ValueError('data must be an n by 2 array, where n is the '
'number of observed points.')
npts = len(data)
ripley = np.zeros(len(radii))
if mode == 'none':
diff = self._pairwise_diffs(data)
distances = np.hypot(diff[:, 0], diff[:, 1])
for r in range(len(radii)):
ripley[r] = (distances < radii[r]).sum()
ripley = self.area * 2. * ripley / (npts * (npts - 1))
# eq. 15.11 Stoyan book page 283
elif mode == 'translation':
diff = self._pairwise_diffs(data)
distances = np.hypot(diff[:, 0], diff[:, 1])
intersec_area = (((self.x_max - self.x_min) - diff[:, 0]) *
((self.y_max - self.y_min) - diff[:, 1]))
for r in range(len(radii)):
dist_indicator = distances < radii[r]
ripley[r] = ((1 / intersec_area) * dist_indicator).sum()
ripley = (self.area**2 / (npts * (npts - 1))) * 2 * ripley
# Stoyan book page 123 and eq 15.13
elif mode == 'ohser':
diff = self._pairwise_diffs(data)
distances = np.hypot(diff[:, 0], diff[:, 1])
a = self.area
b = max((self.y_max - self.y_min) / (self.x_max - self.x_min),
(self.x_max - self.x_min) / (self.y_max - self.y_min))
x = distances / math.sqrt(a / b)
u = np.sqrt((x * x - 1) * (x > 1))
v = np.sqrt((x * x - b ** 2) * (x < math.sqrt(b ** 2 + 1)) * (x > b))
c1 = np.pi - 2 * x * (1 + 1 / b) + x * x / b
c2 = 2 * np.arcsin((1 / x) * (x > 1)) - 1 / b - 2 * (x - u)
c3 = (2 * np.arcsin(((b - u * v) / (x * x))
* (x > b) * (x < math.sqrt(b ** 2 + 1)))
+ 2 * u + 2 * v / b - b - (1 + x * x) / b)
cov_func = ((a / np.pi) * (c1 * (x >= 0) * (x <= 1)
+ c2 * (x > 1) * (x <= b)
+ c3 * (b < x) * (x < math.sqrt(b ** 2 + 1))))
for r in range(len(radii)):
dist_indicator = distances < radii[r]
ripley[r] = ((1 / cov_func) * dist_indicator).sum()
ripley = (self.area**2 / (npts * (npts - 1))) * 2 * ripley
# Cressie book eq 8.2.20 page 616
elif mode == 'var-width':
lt_dist = np.minimum(np.minimum(self.x_max - data[:, 0], self.y_max - data[:, 1]),
np.minimum(data[:, 0] - self.x_min, data[:, 1] - self.y_min))
for r in range(len(radii)):
for i in range(npts):
for j in range(npts):
if i != j:
diff = abs(data[i] - data[j])
dist = math.sqrt((diff * diff).sum())
if dist < radii[r] < lt_dist[i]:
ripley[r] = ripley[r] + 1
lt_dist_sum = (lt_dist > radii[r]).sum()
if not lt_dist_sum == 0:
ripley[r] = ripley[r] / lt_dist_sum
ripley = self.area * ripley / npts
# Cressie book eq 8.4.22 page 640
elif mode == 'ripley':
hor_dist = np.zeros(shape=(npts * (npts - 1)) // 2,
dtype=np.double)
ver_dist = np.zeros(shape=(npts * (npts - 1)) // 2,
dtype=np.double)
for k in range(npts - 1):
min_hor_dist = min(self.x_max - data[k][0],
data[k][0] - self.x_min)
min_ver_dist = min(self.y_max - data[k][1],
data[k][1] - self.y_min)
start = (k * (2 * (npts - 1) - (k - 1))) // 2
end = ((k + 1) * (2 * (npts - 1) - k)) // 2
hor_dist[start: end] = min_hor_dist * np.ones(npts - 1 - k)
ver_dist[start: end] = min_ver_dist * np.ones(npts - 1 - k)
diff = self._pairwise_diffs(data)
dist = np.hypot(diff[:, 0], diff[:, 1])
dist_ind = dist <= np.hypot(hor_dist, ver_dist)
w1 = (1 - (np.arccos(np.minimum(ver_dist, dist) / dist) +
np.arccos(np.minimum(hor_dist, dist) / dist)) / np.pi)
w2 = (3 / 4 - 0.5 * (
np.arccos(ver_dist / dist * ~dist_ind) +
np.arccos(hor_dist / dist * ~dist_ind)) / np.pi)
weight = dist_ind * w1 + ~dist_ind * w2
for r in range(len(radii)):
ripley[r] = ((dist < radii[r]) / weight).sum()
ripley = self.area * 2. * ripley / (npts * (npts - 1))
else:
raise ValueError(f'mode {mode} is not implemented.')
return ripley
|
b3d62a5e1cea6aa287da76cab039739becf11914fa630a63874ca745a62c0355 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains simple statistical algorithms that are
straightforwardly implemented as a single python function (or family of
functions).
This module should generally not be used directly. Everything in
`__all__` is imported into `astropy.stats`, and hence that package
should be used for access.
"""
import math
import numpy as np
import astropy.units as u
from . import _stats
__all__ = ['gaussian_fwhm_to_sigma', 'gaussian_sigma_to_fwhm',
'binom_conf_interval', 'binned_binom_proportion',
'poisson_conf_interval', 'median_absolute_deviation', 'mad_std',
'signal_to_noise_oir_ccd', 'bootstrap', 'kuiper', 'kuiper_two',
'kuiper_false_positive_probability', 'cdf_from_intervals',
'interval_overlap_length', 'histogram_intervals', 'fold_intervals']
__doctest_skip__ = ['binned_binom_proportion']
__doctest_requires__ = {'binom_conf_interval': ['scipy'],
'poisson_conf_interval': ['scipy']}
gaussian_sigma_to_fwhm = 2.0 * math.sqrt(2.0 * math.log(2.0))
"""
Factor with which to multiply Gaussian 1-sigma standard deviation to
convert it to full width at half maximum (FWHM).
"""
gaussian_fwhm_to_sigma = 1. / gaussian_sigma_to_fwhm
"""
Factor with which to multiply Gaussian full width at half maximum (FWHM)
to convert it to 1-sigma standard deviation.
"""
# NUMPY_LT_1_18
def _expand_dims(data, axis):
"""
Expand the shape of an array.
Insert a new axis that will appear at the `axis` position in the
expanded array shape.
This function allows for tuple axis arguments.
``numpy.expand_dims`` currently does not allow that, but it will in
numpy v1.18 (https://github.com/numpy/numpy/pull/14051).
``_expand_dims`` can be replaced with ``numpy.expand_dims`` when the
minimum support numpy version is v1.18.
Parameters
----------
data : array-like
Input array.
axis : int or tuple of int
Position in the expanded axes where the new axis (or axes) is
placed. A tuple of axes is now supported. Out of range axes as
described above are now forbidden and raise an `AxisError`.
Returns
-------
result : ndarray
View of ``data`` with the number of dimensions increased.
"""
if isinstance(data, np.matrix):
data = np.asarray(data)
else:
data = np.asanyarray(data)
if not isinstance(axis, (tuple, list)):
axis = (axis,)
out_ndim = len(axis) + data.ndim
axis = np.core.numeric.normalize_axis_tuple(axis, out_ndim)
shape_it = iter(data.shape)
shape = [1 if ax in axis else next(shape_it) for ax in range(out_ndim)]
return data.reshape(shape)
def binom_conf_interval(k, n, confidence_level=0.68269, interval='wilson'):
r"""Binomial proportion confidence interval given k successes,
n trials.
Parameters
----------
k : int or numpy.ndarray
Number of successes (0 <= ``k`` <= ``n``).
n : int or numpy.ndarray
Number of trials (``n`` > 0). If both ``k`` and ``n`` are arrays,
they must have the same shape.
confidence_level : float, optional
Desired probability content of interval. Default is 0.68269,
corresponding to 1 sigma in a 1-dimensional Gaussian distribution.
Confidence level must be in range [0, 1].
interval : {'wilson', 'jeffreys', 'flat', 'wald'}, optional
Formula used for confidence interval. See notes for details. The
``'wilson'`` and ``'jeffreys'`` intervals generally give similar
results, while 'flat' is somewhat different, especially for small
values of ``n``. ``'wilson'`` should be somewhat faster than
``'flat'`` or ``'jeffreys'``. The 'wald' interval is generally not
recommended. It is provided for comparison purposes. Default is
``'wilson'``.
Returns
-------
conf_interval : ndarray
``conf_interval[0]`` and ``conf_interval[1]`` correspond to the lower
and upper limits, respectively, for each element in ``k``, ``n``.
Notes
-----
In situations where a probability of success is not known, it can
be estimated from a number of trials (n) and number of
observed successes (k). For example, this is done in Monte
Carlo experiments designed to estimate a detection efficiency. It
is simple to take the sample proportion of successes (k/n)
as a reasonable best estimate of the true probability
:math:`\epsilon`. However, deriving an accurate confidence
interval on :math:`\epsilon` is non-trivial. There are several
formulas for this interval (see [1]_). Four intervals are implemented
here:
**1. The Wilson Interval.** This interval, attributed to Wilson [2]_,
is given by
.. math::
CI_{\rm Wilson} = \frac{k + \kappa^2/2}{n + \kappa^2}
\pm \frac{\kappa n^{1/2}}{n + \kappa^2}
((\hat{\epsilon}(1 - \hat{\epsilon}) + \kappa^2/(4n))^{1/2}
where :math:`\hat{\epsilon} = k / n` and :math:`\kappa` is the
number of standard deviations corresponding to the desired
confidence interval for a *normal* distribution (for example,
1.0 for a confidence interval of 68.269%). For a
confidence interval of 100(1 - :math:`\alpha`)%,
.. math::
\kappa = \Phi^{-1}(1-\alpha/2) = \sqrt{2}{\rm erf}^{-1}(1-\alpha).
**2. The Jeffreys Interval.** This interval is derived by applying
Bayes' theorem to the binomial distribution with the
noninformative Jeffreys prior [3]_, [4]_. The noninformative Jeffreys
prior is the Beta distribution, Beta(1/2, 1/2), which has the density
function
.. math::
f(\epsilon) = \pi^{-1} \epsilon^{-1/2}(1-\epsilon)^{-1/2}.
The justification for this prior is that it is invariant under
reparameterizations of the binomial proportion.
The posterior density function is also a Beta distribution: Beta(k
+ 1/2, n - k + 1/2). The interval is then chosen so that it is
*equal-tailed*: Each tail (outside the interval) contains
:math:`\alpha`/2 of the posterior probability, and the interval
itself contains 1 - :math:`\alpha`. This interval must be
calculated numerically. Additionally, when k = 0 the lower limit
is set to 0 and when k = n the upper limit is set to 1, so that in
these cases, there is only one tail containing :math:`\alpha`/2
and the interval itself contains 1 - :math:`\alpha`/2 rather than
the nominal 1 - :math:`\alpha`.
**3. A Flat prior.** This is similar to the Jeffreys interval,
but uses a flat (uniform) prior on the binomial proportion
over the range 0 to 1 rather than the reparametrization-invariant
Jeffreys prior. The posterior density function is a Beta distribution:
Beta(k + 1, n - k + 1). The same comments about the nature of the
interval (equal-tailed, etc.) also apply to this option.
**4. The Wald Interval.** This interval is given by
.. math::
CI_{\rm Wald} = \hat{\epsilon} \pm
\kappa \sqrt{\frac{\hat{\epsilon}(1-\hat{\epsilon})}{n}}
The Wald interval gives acceptable results in some limiting
cases. Particularly, when n is very large, and the true proportion
:math:`\epsilon` is not "too close" to 0 or 1. However, as the
later is not verifiable when trying to estimate :math:`\epsilon`,
this is not very helpful. Its use is not recommended, but it is
provided here for comparison purposes due to its prevalence in
everyday practical statistics.
This function requires ``scipy`` for all interval types.
References
----------
.. [1] Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban (2001).
"Interval Estimation for a Binomial Proportion". Statistical
Science 16 (2): 101-133. doi:10.1214/ss/1009213286
.. [2] Wilson, E. B. (1927). "Probable inference, the law of
succession, and statistical inference". Journal of the American
Statistical Association 22: 209-212.
.. [3] Jeffreys, Harold (1946). "An Invariant Form for the Prior
Probability in Estimation Problems". Proc. R. Soc. Lond.. A 24 186
(1007): 453-461. doi:10.1098/rspa.1946.0056
.. [4] Jeffreys, Harold (1998). Theory of Probability. Oxford
University Press, 3rd edition. ISBN 978-0198503682
Examples
--------
Integer inputs return an array with shape (2,):
>>> binom_conf_interval(4, 5, interval='wilson') # doctest: +FLOAT_CMP
array([0.57921724, 0.92078259])
Arrays of arbitrary dimension are supported. The Wilson and Jeffreys
intervals give similar results, even for small k, n:
>>> binom_conf_interval([1, 2], 5, interval='wilson') # doctest: +FLOAT_CMP
array([[0.07921741, 0.21597328],
[0.42078276, 0.61736012]])
>>> binom_conf_interval([1, 2,], 5, interval='jeffreys') # doctest: +FLOAT_CMP
array([[0.0842525 , 0.21789949],
[0.42218001, 0.61753691]])
>>> binom_conf_interval([1, 2], 5, interval='flat') # doctest: +FLOAT_CMP
array([[0.12139799, 0.24309021],
[0.45401727, 0.61535699]])
In contrast, the Wald interval gives poor results for small k, n.
For k = 0 or k = n, the interval always has zero length.
>>> binom_conf_interval([1, 2], 5, interval='wald') # doctest: +FLOAT_CMP
array([[0.02111437, 0.18091075],
[0.37888563, 0.61908925]])
For confidence intervals approaching 1, the Wald interval for
0 < k < n can give intervals that extend outside [0, 1]:
>>> binom_conf_interval([1, 2], 5, interval='wald', confidence_level=0.99) # doctest: +FLOAT_CMP
array([[-0.26077835, -0.16433593],
[ 0.66077835, 0.96433593]])
""" # noqa
if confidence_level < 0. or confidence_level > 1.:
raise ValueError('confidence_level must be between 0. and 1.')
alpha = 1. - confidence_level
k = np.asarray(k).astype(int)
n = np.asarray(n).astype(int)
if (n <= 0).any():
raise ValueError('n must be positive')
if (k < 0).any() or (k > n).any():
raise ValueError('k must be in {0, 1, .., n}')
if interval == 'wilson' or interval == 'wald':
from scipy.special import erfinv
kappa = np.sqrt(2.) * min(erfinv(confidence_level), 1.e10) # Avoid overflows.
k = k.astype(float)
n = n.astype(float)
p = k / n
if interval == 'wilson':
midpoint = (k + kappa ** 2 / 2.) / (n + kappa ** 2)
halflength = (kappa * np.sqrt(n)) / (n + kappa ** 2) * \
np.sqrt(p * (1 - p) + kappa ** 2 / (4 * n))
conf_interval = np.array([midpoint - halflength,
midpoint + halflength])
# Correct intervals out of range due to floating point errors.
conf_interval[conf_interval < 0.] = 0.
conf_interval[conf_interval > 1.] = 1.
else:
midpoint = p
halflength = kappa * np.sqrt(p * (1. - p) / n)
conf_interval = np.array([midpoint - halflength,
midpoint + halflength])
elif interval == 'jeffreys' or interval == 'flat':
from scipy.special import betaincinv
if interval == 'jeffreys':
lowerbound = betaincinv(k + 0.5, n - k + 0.5, 0.5 * alpha)
upperbound = betaincinv(k + 0.5, n - k + 0.5, 1. - 0.5 * alpha)
else:
lowerbound = betaincinv(k + 1, n - k + 1, 0.5 * alpha)
upperbound = betaincinv(k + 1, n - k + 1, 1. - 0.5 * alpha)
# Set lower or upper bound to k/n when k/n = 0 or 1
# We have to treat the special case of k/n being scalars,
# which is an ugly kludge
if lowerbound.ndim == 0:
if k == 0:
lowerbound = 0.
elif k == n:
upperbound = 1.
else:
lowerbound[k == 0] = 0
upperbound[k == n] = 1
conf_interval = np.array([lowerbound, upperbound])
else:
raise ValueError(f'Unrecognized interval: {interval:s}')
return conf_interval
def binned_binom_proportion(x, success, bins=10, range=None,
confidence_level=0.68269, interval='wilson'):
"""Binomial proportion and confidence interval in bins of a continuous
variable ``x``.
Given a set of datapoint pairs where the ``x`` values are
continuously distributed and the ``success`` values are binomial
("success / failure" or "true / false"), place the pairs into
bins according to ``x`` value and calculate the binomial proportion
(fraction of successes) and confidence interval in each bin.
Parameters
----------
x : sequence
Values.
success : sequence of bool
Success (`True`) or failure (`False`) corresponding to each value
in ``x``. Must be same length as ``x``.
bins : int or sequence of scalar, optional
If bins is an int, it defines the number of equal-width bins
in the given range (10, by default). If bins is a sequence, it
defines the bin edges, including the rightmost edge, allowing
for non-uniform bin widths (in this case, 'range' is ignored).
range : (float, float), optional
The lower and upper range of the bins. If `None` (default),
the range is set to ``(x.min(), x.max())``. Values outside the
range are ignored.
confidence_level : float, optional
Must be in range [0, 1].
Desired probability content in the confidence
interval ``(p - perr[0], p + perr[1])`` in each bin. Default is
0.68269.
interval : {'wilson', 'jeffreys', 'flat', 'wald'}, optional
Formula used to calculate confidence interval on the
binomial proportion in each bin. See `binom_conf_interval` for
definition of the intervals. The 'wilson', 'jeffreys',
and 'flat' intervals generally give similar results. 'wilson'
should be somewhat faster, while 'jeffreys' and 'flat' are
marginally superior, but differ in the assumed prior.
The 'wald' interval is generally not recommended.
It is provided for comparison purposes. Default is 'wilson'.
Returns
-------
bin_ctr : ndarray
Central value of bins. Bins without any entries are not returned.
bin_halfwidth : ndarray
Half-width of each bin such that ``bin_ctr - bin_halfwidth`` and
``bin_ctr + bins_halfwidth`` give the left and right side of each bin,
respectively.
p : ndarray
Efficiency in each bin.
perr : ndarray
2-d array of shape (2, len(p)) representing the upper and lower
uncertainty on p in each bin.
Notes
-----
This function requires ``scipy`` for all interval types.
See Also
--------
binom_conf_interval : Function used to estimate confidence interval in
each bin.
Examples
--------
Suppose we wish to estimate the efficiency of a survey in
detecting astronomical sources as a function of magnitude (i.e.,
the probability of detecting a source given its magnitude). In a
realistic case, we might prepare a large number of sources with
randomly selected magnitudes, inject them into simulated images,
and then record which were detected at the end of the reduction
pipeline. As a toy example, we generate 100 data points with
randomly selected magnitudes between 20 and 30 and "observe" them
with a known detection function (here, the error function, with
50% detection probability at magnitude 25):
>>> from scipy.special import erf
>>> from scipy.stats.distributions import binom
>>> def true_efficiency(x):
... return 0.5 - 0.5 * erf((x - 25.) / 2.)
>>> mag = 20. + 10. * np.random.rand(100)
>>> detected = binom.rvs(1, true_efficiency(mag))
>>> bins, binshw, p, perr = binned_binom_proportion(mag, detected, bins=20)
>>> plt.errorbar(bins, p, xerr=binshw, yerr=perr, ls='none', marker='o',
... label='estimate')
.. plot::
import numpy as np
from scipy.special import erf
from scipy.stats.distributions import binom
import matplotlib.pyplot as plt
from astropy.stats import binned_binom_proportion
def true_efficiency(x):
return 0.5 - 0.5 * erf((x - 25.) / 2.)
np.random.seed(400)
mag = 20. + 10. * np.random.rand(100)
np.random.seed(600)
detected = binom.rvs(1, true_efficiency(mag))
bins, binshw, p, perr = binned_binom_proportion(mag, detected, bins=20)
plt.errorbar(bins, p, xerr=binshw, yerr=perr, ls='none', marker='o',
label='estimate')
X = np.linspace(20., 30., 1000)
plt.plot(X, true_efficiency(X), label='true efficiency')
plt.ylim(0., 1.)
plt.title('Detection efficiency vs magnitude')
plt.xlabel('Magnitude')
plt.ylabel('Detection efficiency')
plt.legend()
plt.show()
The above example uses the Wilson confidence interval to calculate
the uncertainty ``perr`` in each bin (see the definition of various
confidence intervals in `binom_conf_interval`). A commonly used
alternative is the Wald interval. However, the Wald interval can
give nonsensical uncertainties when the efficiency is near 0 or 1,
and is therefore **not** recommended. As an illustration, the
following example shows the same data as above but uses the Wald
interval rather than the Wilson interval to calculate ``perr``:
>>> bins, binshw, p, perr = binned_binom_proportion(mag, detected, bins=20,
... interval='wald')
>>> plt.errorbar(bins, p, xerr=binshw, yerr=perr, ls='none', marker='o',
... label='estimate')
.. plot::
import numpy as np
from scipy.special import erf
from scipy.stats.distributions import binom
import matplotlib.pyplot as plt
from astropy.stats import binned_binom_proportion
def true_efficiency(x):
return 0.5 - 0.5 * erf((x - 25.) / 2.)
np.random.seed(400)
mag = 20. + 10. * np.random.rand(100)
np.random.seed(600)
detected = binom.rvs(1, true_efficiency(mag))
bins, binshw, p, perr = binned_binom_proportion(mag, detected, bins=20,
interval='wald')
plt.errorbar(bins, p, xerr=binshw, yerr=perr, ls='none', marker='o',
label='estimate')
X = np.linspace(20., 30., 1000)
plt.plot(X, true_efficiency(X), label='true efficiency')
plt.ylim(0., 1.)
plt.title('The Wald interval can give nonsensical uncertainties')
plt.xlabel('Magnitude')
plt.ylabel('Detection efficiency')
plt.legend()
plt.show()
"""
x = np.ravel(x)
success = np.ravel(success).astype(bool)
if x.shape != success.shape:
raise ValueError('sizes of x and success must match')
# Put values into a histogram (`n`). Put "successful" values
# into a second histogram (`k`) with identical binning.
n, bin_edges = np.histogram(x, bins=bins, range=range)
k, bin_edges = np.histogram(x[success], bins=bin_edges)
bin_ctr = (bin_edges[:-1] + bin_edges[1:]) / 2.
bin_halfwidth = bin_ctr - bin_edges[:-1]
# Remove bins with zero entries.
valid = n > 0
bin_ctr = bin_ctr[valid]
bin_halfwidth = bin_halfwidth[valid]
n = n[valid]
k = k[valid]
p = k / n
bounds = binom_conf_interval(k, n, confidence_level=confidence_level, interval=interval)
perr = np.abs(bounds - p)
return bin_ctr, bin_halfwidth, p, perr
def _check_poisson_conf_inputs(sigma, background, confidence_level, name):
if sigma != 1:
raise ValueError(f"Only sigma=1 supported for interval {name}")
if background != 0:
raise ValueError(f"background not supported for interval {name}")
if confidence_level is not None:
raise ValueError(f"confidence_level not supported for interval {name}")
def poisson_conf_interval(n, interval='root-n', sigma=1, background=0,
confidence_level=None):
r"""Poisson parameter confidence interval given observed counts
Parameters
----------
n : int or numpy.ndarray
Number of counts (0 <= ``n``).
interval : {'root-n','root-n-0','pearson','sherpagehrels','frequentist-confidence', 'kraft-burrows-nousek'}, optional
Formula used for confidence interval. See notes for details.
Default is ``'root-n'``.
sigma : float, optional
Number of sigma for confidence interval; only supported for
the 'frequentist-confidence' mode.
background : float, optional
Number of counts expected from the background; only supported for
the 'kraft-burrows-nousek' mode. This number is assumed to be determined
from a large region so that the uncertainty on its value is negligible.
confidence_level : float, optional
Confidence level between 0 and 1; only supported for the
'kraft-burrows-nousek' mode.
Returns
-------
conf_interval : ndarray
``conf_interval[0]`` and ``conf_interval[1]`` correspond to the lower
and upper limits, respectively, for each element in ``n``.
Notes
-----
The "right" confidence interval to use for Poisson data is a
matter of debate. The CDF working group `recommends
<https://web.archive.org/web/20210222093249/https://www-cdf.fnal.gov/physics/statistics/notes/pois_eb.txt>`_
using root-n throughout, largely in the interest of
comprehensibility, but discusses other possibilities. The ATLAS
group also `discusses
<http://www.pp.rhul.ac.uk/~cowan/atlas/ErrorBars.pdf>`_ several
possibilities but concludes that no single representation is
suitable for all cases. The suggestion has also been `floated
<https://ui.adsabs.harvard.edu/abs/2012EPJP..127...24A>`_ that error
bars should be attached to theoretical predictions instead of
observed data, which this function will not help with (but it's
easy; then you really should use the square root of the theoretical
prediction).
The intervals implemented here are:
**1. 'root-n'** This is a very widely used standard rule derived
from the maximum-likelihood estimator for the mean of the Poisson
process. While it produces questionable results for small n and
outright wrong results for n=0, it is standard enough that people are
(supposedly) used to interpreting these wonky values. The interval is
.. math::
CI = (n-\sqrt{n}, n+\sqrt{n})
**2. 'root-n-0'** This is identical to the above except that where
n is zero the interval returned is (0,1).
**3. 'pearson'** This is an only-slightly-more-complicated rule
based on Pearson's chi-squared rule (as `explained
<https://web.archive.org/web/20210222093249/https://www-cdf.fnal.gov/physics/statistics/notes/pois_eb.txt>`_ by
the CDF working group). It also has the nice feature that if your
theory curve touches an endpoint of the interval, then your data
point is indeed one sigma away. The interval is
.. math::
CI = (n+0.5-\sqrt{n+0.25}, n+0.5+\sqrt{n+0.25})
**4. 'sherpagehrels'** This rule is used by default in the fitting
package 'sherpa'. The `documentation
<https://cxc.harvard.edu/sherpa4.4/statistics/#chigehrels>`_ claims
it is based on a numerical approximation published in `Gehrels
(1986) <https://ui.adsabs.harvard.edu/abs/1986ApJ...303..336G>`_ but it
does not actually appear there. It is symmetrical, and while the
upper limits are within about 1% of those given by
'frequentist-confidence', the lower limits can be badly wrong. The
interval is
.. math::
CI = (n-1-\sqrt{n+0.75}, n+1+\sqrt{n+0.75})
**5. 'frequentist-confidence'** These are frequentist central
confidence intervals:
.. math::
CI = (0.5 F_{\chi^2}^{-1}(\alpha;2n),
0.5 F_{\chi^2}^{-1}(1-\alpha;2(n+1)))
where :math:`F_{\chi^2}^{-1}` is the quantile of the chi-square
distribution with the indicated number of degrees of freedom and
:math:`\alpha` is the one-tailed probability of the normal
distribution (at the point given by the parameter 'sigma'). See
`Maxwell (2011)
<https://ui.adsabs.harvard.edu/abs/2011arXiv1102.0822M>`_ for further
details.
**6. 'kraft-burrows-nousek'** This is a Bayesian approach which allows
for the presence of a known background :math:`B` in the source signal
:math:`N`.
For a given confidence level :math:`CL` the confidence interval
:math:`[S_\mathrm{min}, S_\mathrm{max}]` is given by:
.. math::
CL = \int^{S_\mathrm{max}}_{S_\mathrm{min}} f_{N,B}(S)dS
where the function :math:`f_{N,B}` is:
.. math::
f_{N,B}(S) = C \frac{e^{-(S+B)}(S+B)^N}{N!}
and the normalization constant :math:`C`:
.. math::
C = \left[ \int_0^\infty \frac{e^{-(S+B)}(S+B)^N}{N!} dS \right] ^{-1}
= \left( \sum^N_{n=0} \frac{e^{-B}B^n}{n!} \right)^{-1}
See `Kraft, Burrows, and Nousek (1991)
<https://ui.adsabs.harvard.edu/abs/1991ApJ...374..344K>`_ for further
details.
These formulas implement a positive, uniform prior.
`Kraft, Burrows, and Nousek (1991)
<https://ui.adsabs.harvard.edu/abs/1991ApJ...374..344K>`_ discuss this
choice in more detail and show that the problem is relatively
insensitive to the choice of prior.
This function has an optional dependency: Either `Scipy
<https://www.scipy.org/>`_ or `mpmath <http://mpmath.org/>`_ need
to be available (Scipy works only for N < 100).
This code is very intense numerically, which makes it much slower than
the other methods, in particular for large count numbers (above 1000
even with ``mpmath``). Fortunately, some of the other methods or a
Gaussian approximation usually work well in this regime.
Examples
--------
>>> poisson_conf_interval(np.arange(10), interval='root-n').T
array([[ 0. , 0. ],
[ 0. , 2. ],
[ 0.58578644, 3.41421356],
[ 1.26794919, 4.73205081],
[ 2. , 6. ],
[ 2.76393202, 7.23606798],
[ 3.55051026, 8.44948974],
[ 4.35424869, 9.64575131],
[ 5.17157288, 10.82842712],
[ 6. , 12. ]])
>>> poisson_conf_interval(np.arange(10), interval='root-n-0').T
array([[ 0. , 1. ],
[ 0. , 2. ],
[ 0.58578644, 3.41421356],
[ 1.26794919, 4.73205081],
[ 2. , 6. ],
[ 2.76393202, 7.23606798],
[ 3.55051026, 8.44948974],
[ 4.35424869, 9.64575131],
[ 5.17157288, 10.82842712],
[ 6. , 12. ]])
>>> poisson_conf_interval(np.arange(10), interval='pearson').T
array([[ 0. , 1. ],
[ 0.38196601, 2.61803399],
[ 1. , 4. ],
[ 1.69722436, 5.30277564],
[ 2.43844719, 6.56155281],
[ 3.20871215, 7.79128785],
[ 4. , 9. ],
[ 4.8074176 , 10.1925824 ],
[ 5.62771868, 11.37228132],
[ 6.45861873, 12.54138127]])
>>> poisson_conf_interval(
... np.arange(10), interval='frequentist-confidence').T
array([[ 0. , 1.84102165],
[ 0.17275378, 3.29952656],
[ 0.70818544, 4.63785962],
[ 1.36729531, 5.91818583],
[ 2.08566081, 7.16275317],
[ 2.84030886, 8.38247265],
[ 3.62006862, 9.58364155],
[ 4.41852954, 10.77028072],
[ 5.23161394, 11.94514152],
[ 6.05653896, 13.11020414]])
>>> poisson_conf_interval(
... 7, interval='frequentist-confidence').T
array([ 4.41852954, 10.77028072])
>>> poisson_conf_interval(
... 10, background=1.5, confidence_level=0.95,
... interval='kraft-burrows-nousek').T # doctest: +FLOAT_CMP
array([[ 3.47894005, 16.113329533]])
""" # noqa
if not np.isscalar(n):
n = np.asanyarray(n)
if interval == 'root-n':
_check_poisson_conf_inputs(sigma, background, confidence_level, interval)
conf_interval = np.array([n - np.sqrt(n),
n + np.sqrt(n)])
elif interval == 'root-n-0':
_check_poisson_conf_inputs(sigma, background, confidence_level, interval)
conf_interval = np.array([n - np.sqrt(n),
n + np.sqrt(n)])
if np.isscalar(n):
if n == 0:
conf_interval[1] = 1
else:
conf_interval[1, n == 0] = 1
elif interval == 'pearson':
_check_poisson_conf_inputs(sigma, background, confidence_level, interval)
conf_interval = np.array([n + 0.5 - np.sqrt(n + 0.25),
n + 0.5 + np.sqrt(n + 0.25)])
elif interval == 'sherpagehrels':
_check_poisson_conf_inputs(sigma, background, confidence_level, interval)
conf_interval = np.array([n - 1 - np.sqrt(n + 0.75),
n + 1 + np.sqrt(n + 0.75)])
elif interval == 'frequentist-confidence':
_check_poisson_conf_inputs(1., background, confidence_level, interval)
import scipy.stats
alpha = scipy.stats.norm.sf(sigma)
conf_interval = np.array([0.5 * scipy.stats.chi2(2 * n).ppf(alpha),
0.5 * scipy.stats.chi2(2 * n + 2).isf(alpha)])
if np.isscalar(n):
if n == 0:
conf_interval[0] = 0
else:
conf_interval[0, n == 0] = 0
elif interval == 'kraft-burrows-nousek':
# Deprecation warning in Python 3.9 when N is float, so we force int,
# see https://github.com/astropy/astropy/issues/10832
if np.isscalar(n):
if not isinstance(n, int):
raise TypeError('Number of counts must be integer.')
elif not issubclass(n.dtype.type, np.integer):
raise TypeError('Number of counts must be integer.')
if confidence_level is None:
raise ValueError('Set confidence_level for method {}. (sigma is '
'ignored.)'.format(interval))
confidence_level = np.asanyarray(confidence_level)
if np.any(confidence_level <= 0) or np.any(confidence_level >= 1):
raise ValueError('confidence_level must be a number between 0 and 1.')
background = np.asanyarray(background)
if np.any(background < 0):
raise ValueError('Background must be >= 0.')
conf_interval = np.vectorize(_kraft_burrows_nousek,
cache=True)(n, background, confidence_level)
conf_interval = np.vstack(conf_interval)
else:
raise ValueError(f"Invalid method for Poisson confidence intervals: {interval}")
return conf_interval
def median_absolute_deviation(data, axis=None, func=None, ignore_nan=False):
"""
Calculate the median absolute deviation (MAD).
The MAD is defined as ``median(abs(a - median(a)))``.
Parameters
----------
data : array-like
Input array or object that can be converted to an array.
axis : None, int, or tuple of int, optional
The axis or axes along which the MADs are computed. The default
(`None`) is to compute the MAD of the flattened array.
func : callable, optional
The function used to compute the median. Defaults to `numpy.ma.median`
for masked arrays, otherwise to `numpy.median`.
ignore_nan : bool
Ignore NaN values (treat them as if they are not in the array) when
computing the median. This will use `numpy.ma.median` if ``axis`` is
specified, or `numpy.nanmedian` if ``axis==None`` and numpy's version
is >1.10 because nanmedian is slightly faster in this case.
Returns
-------
mad : float or `~numpy.ndarray`
The median absolute deviation of the input array. If ``axis``
is `None` then a scalar will be returned, otherwise a
`~numpy.ndarray` will be returned.
Examples
--------
Generate random variates from a Gaussian distribution and return the
median absolute deviation for that distribution::
>>> import numpy as np
>>> from astropy.stats import median_absolute_deviation
>>> rand = np.random.default_rng(12345)
>>> from numpy.random import randn
>>> mad = median_absolute_deviation(rand.standard_normal(1000))
>>> print(mad) # doctest: +FLOAT_CMP
0.6829504282771885
See Also
--------
mad_std
"""
if func is None:
# Check if the array has a mask and if so use np.ma.median
# See https://github.com/numpy/numpy/issues/7330 why using np.ma.median
# for normal arrays should not be done (summary: np.ma.median always
# returns an masked array even if the result should be scalar). (#4658)
if isinstance(data, np.ma.MaskedArray):
is_masked = True
func = np.ma.median
if ignore_nan:
data = np.ma.masked_where(np.isnan(data), data, copy=True)
elif ignore_nan:
is_masked = False
func = np.nanmedian
else:
is_masked = False
func = np.median # drops units if result is NaN
else:
is_masked = None
data = np.asanyarray(data)
# np.nanmedian has `keepdims`, which is a good option if we're not allowing
# user-passed functions here
data_median = func(data, axis=axis)
# this conditional can be removed after this PR is merged:
# https://github.com/astropy/astropy/issues/12165
if (isinstance(data, u.Quantity) and func is np.median
and data_median.ndim == 0 and np.isnan(data_median)):
data_median = data.__array_wrap__(data_median)
# broadcast the median array before subtraction
if axis is not None:
data_median = _expand_dims(data_median, axis=axis) # NUMPY_LT_1_18
result = func(np.abs(data - data_median), axis=axis, overwrite_input=True)
# this conditional can be removed after this PR is merged:
# https://github.com/astropy/astropy/issues/12165
if (isinstance(data, u.Quantity) and func is np.median
and result.ndim == 0 and np.isnan(result)):
result = data.__array_wrap__(result)
if axis is None and np.ma.isMaskedArray(result):
# return scalar version
result = result.item()
elif np.ma.isMaskedArray(result) and not is_masked:
# if the input array was not a masked array, we don't want to return a
# masked array
result = result.filled(fill_value=np.nan)
return result
def mad_std(data, axis=None, func=None, ignore_nan=False):
r"""
Calculate a robust standard deviation using the `median absolute
deviation (MAD)
<https://en.wikipedia.org/wiki/Median_absolute_deviation>`_.
The standard deviation estimator is given by:
.. math::
\sigma \approx \frac{\textrm{MAD}}{\Phi^{-1}(3/4)}
\approx 1.4826 \ \textrm{MAD}
where :math:`\Phi^{-1}(P)` is the normal inverse cumulative
distribution function evaluated at probability :math:`P = 3/4`.
Parameters
----------
data : array-like
Data array or object that can be converted to an array.
axis : None, int, or tuple of int, optional
The axis or axes along which the robust standard deviations are
computed. The default (`None`) is to compute the robust
standard deviation of the flattened array.
func : callable, optional
The function used to compute the median. Defaults to `numpy.ma.median`
for masked arrays, otherwise to `numpy.median`.
ignore_nan : bool
Ignore NaN values (treat them as if they are not in the array) when
computing the median. This will use `numpy.ma.median` if ``axis`` is
specified, or `numpy.nanmedian` if ``axis=None`` and numpy's version is
>1.10 because nanmedian is slightly faster in this case.
Returns
-------
mad_std : float or `~numpy.ndarray`
The robust standard deviation of the input data. If ``axis`` is
`None` then a scalar will be returned, otherwise a
`~numpy.ndarray` will be returned.
Examples
--------
>>> import numpy as np
>>> from astropy.stats import mad_std
>>> rand = np.random.default_rng(12345)
>>> madstd = mad_std(rand.normal(5, 2, (100, 100)))
>>> print(madstd) # doctest: +FLOAT_CMP
1.984147963351707
See Also
--------
biweight_midvariance, biweight_midcovariance, median_absolute_deviation
"""
# NOTE: 1. / scipy.stats.norm.ppf(0.75) = 1.482602218505602
MAD = median_absolute_deviation(
data, axis=axis, func=func, ignore_nan=ignore_nan)
return MAD * 1.482602218505602
def signal_to_noise_oir_ccd(t, source_eps, sky_eps, dark_eps, rd, npix,
gain=1.0):
"""Computes the signal to noise ratio for source being observed in the
optical/IR using a CCD.
Parameters
----------
t : float or numpy.ndarray
CCD integration time in seconds
source_eps : float
Number of electrons (photons) or DN per second in the aperture from the
source. Note that this should already have been scaled by the filter
transmission and the quantum efficiency of the CCD. If the input is in
DN, then be sure to set the gain to the proper value for the CCD.
If the input is in electrons per second, then keep the gain as its
default of 1.0.
sky_eps : float
Number of electrons (photons) or DN per second per pixel from the sky
background. Should already be scaled by filter transmission and QE.
This must be in the same units as source_eps for the calculation to
make sense.
dark_eps : float
Number of thermal electrons per second per pixel. If this is given in
DN or ADU, then multiply by the gain to get the value in electrons.
rd : float
Read noise of the CCD in electrons. If this is given in
DN or ADU, then multiply by the gain to get the value in electrons.
npix : float
Size of the aperture in pixels
gain : float, optional
Gain of the CCD. In units of electrons per DN.
Returns
-------
SNR : float or numpy.ndarray
Signal to noise ratio calculated from the inputs
"""
signal = t * source_eps * gain
noise = np.sqrt(t * (source_eps * gain + npix *
(sky_eps * gain + dark_eps)) + npix * rd ** 2)
return signal / noise
def bootstrap(data, bootnum=100, samples=None, bootfunc=None):
"""Performs bootstrap resampling on numpy arrays.
Bootstrap resampling is used to understand confidence intervals of sample
estimates. This function returns versions of the dataset resampled with
replacement ("case bootstrapping"). These can all be run through a function
or statistic to produce a distribution of values which can then be used to
find the confidence intervals.
Parameters
----------
data : ndarray
N-D array. The bootstrap resampling will be performed on the first
index, so the first index should access the relevant information
to be bootstrapped.
bootnum : int, optional
Number of bootstrap resamples
samples : int, optional
Number of samples in each resample. The default `None` sets samples to
the number of datapoints
bootfunc : function, optional
Function to reduce the resampled data. Each bootstrap resample will
be put through this function and the results returned. If `None`, the
bootstrapped data will be returned
Returns
-------
boot : ndarray
If bootfunc is None, then each row is a bootstrap resample of the data.
If bootfunc is specified, then the columns will correspond to the
outputs of bootfunc.
Examples
--------
Obtain a twice resampled array:
>>> from astropy.stats import bootstrap
>>> import numpy as np
>>> from astropy.utils import NumpyRNGContext
>>> bootarr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
>>> with NumpyRNGContext(1):
... bootresult = bootstrap(bootarr, 2)
...
>>> bootresult # doctest: +FLOAT_CMP
array([[6., 9., 0., 6., 1., 1., 2., 8., 7., 0.],
[3., 5., 6., 3., 5., 3., 5., 8., 8., 0.]])
>>> bootresult.shape
(2, 10)
Obtain a statistic on the array
>>> with NumpyRNGContext(1):
... bootresult = bootstrap(bootarr, 2, bootfunc=np.mean)
...
>>> bootresult # doctest: +FLOAT_CMP
array([4. , 4.6])
Obtain a statistic with two outputs on the array
>>> test_statistic = lambda x: (np.sum(x), np.mean(x))
>>> with NumpyRNGContext(1):
... bootresult = bootstrap(bootarr, 3, bootfunc=test_statistic)
>>> bootresult # doctest: +FLOAT_CMP
array([[40. , 4. ],
[46. , 4.6],
[35. , 3.5]])
>>> bootresult.shape
(3, 2)
Obtain a statistic with two outputs on the array, keeping only the first
output
>>> bootfunc = lambda x:test_statistic(x)[0]
>>> with NumpyRNGContext(1):
... bootresult = bootstrap(bootarr, 3, bootfunc=bootfunc)
...
>>> bootresult # doctest: +FLOAT_CMP
array([40., 46., 35.])
>>> bootresult.shape
(3,)
"""
if samples is None:
samples = data.shape[0]
# make sure the input is sane
if samples < 1 or bootnum < 1:
raise ValueError("neither 'samples' nor 'bootnum' can be less than 1.")
if bootfunc is None:
resultdims = (bootnum,) + (samples,) + data.shape[1:]
else:
# test number of outputs from bootfunc, avoid single outputs which are
# array-like
try:
resultdims = (bootnum, len(bootfunc(data)))
except TypeError:
resultdims = (bootnum,)
# create empty boot array
boot = np.empty(resultdims)
for i in range(bootnum):
bootarr = np.random.randint(low=0, high=data.shape[0], size=samples)
if bootfunc is None:
boot[i] = data[bootarr]
else:
boot[i] = bootfunc(data[bootarr])
return boot
def _scipy_kraft_burrows_nousek(N, B, CL):
'''Upper limit on a poisson count rate
The implementation is based on Kraft, Burrows and Nousek
`ApJ 374, 344 (1991) <https://ui.adsabs.harvard.edu/abs/1991ApJ...374..344K>`_.
The XMM-Newton upper limit server uses the same formalism.
Parameters
----------
N : int or np.int32/np.int64
Total observed count number
B : float or np.float32/np.float64
Background count rate (assumed to be known with negligible error
from a large background area).
CL : float or np.float32/np.float64
Confidence level (number between 0 and 1)
Returns
-------
S : source count limit
Notes
-----
Requires :mod:`~scipy`. This implementation will cause Overflow Errors for
about N > 100 (the exact limit depends on details of how scipy was
compiled). See `~astropy.stats.mpmath_poisson_upper_limit` for an
implementation that is slower, but can deal with arbitrarily high numbers
since it is based on the `mpmath <http://mpmath.org/>`_ library.
'''
from scipy.optimize import brentq
from scipy.integrate import quad
from scipy.special import factorial
from math import exp
def eqn8(N, B):
n = np.arange(N + 1, dtype=np.float64)
return 1. / (exp(-B) * np.sum(np.power(B, n) / factorial(n)))
# The parameters of eqn8 do not vary between calls so we can calculate the
# result once and reuse it. The same is True for the factorial of N.
# eqn7 is called hundred times so "caching" these values yields a
# significant speedup (factor 10).
eqn8_res = eqn8(N, B)
factorial_N = float(math.factorial(N))
def eqn7(S, N, B):
SpB = S + B
return eqn8_res * (exp(-SpB) * SpB**N / factorial_N)
def eqn9_left(S_min, S_max, N, B):
return quad(eqn7, S_min, S_max, args=(N, B), limit=500)
def find_s_min(S_max, N, B):
'''
Kraft, Burrows and Nousek suggest to integrate from N-B in both
directions at once, so that S_min and S_max move similarly (see
the article for details). Here, this is implemented differently:
Treat S_max as the optimization parameters in func and then
calculate the matching s_min that has has eqn7(S_max) =
eqn7(S_min) here.
'''
y_S_max = eqn7(S_max, N, B)
if eqn7(0, N, B) >= y_S_max:
return 0.
else:
return brentq(lambda x: eqn7(x, N, B) - y_S_max, 0, N - B)
def func(s):
s_min = find_s_min(s, N, B)
out = eqn9_left(s_min, s, N, B)
return out[0] - CL
S_max = brentq(func, N - B, 100)
S_min = find_s_min(S_max, N, B)
return S_min, S_max
def _mpmath_kraft_burrows_nousek(N, B, CL):
'''Upper limit on a poisson count rate
The implementation is based on Kraft, Burrows and Nousek in
`ApJ 374, 344 (1991) <https://ui.adsabs.harvard.edu/abs/1991ApJ...374..344K>`_.
The XMM-Newton upper limit server used the same formalism.
Parameters
----------
N : int or np.int32/np.int64
Total observed count number
B : float or np.float32/np.float64
Background count rate (assumed to be known with negligible error
from a large background area).
CL : float or np.float32/np.float64
Confidence level (number between 0 and 1)
Returns
-------
S : source count limit
Notes
-----
Requires the `mpmath <http://mpmath.org/>`_ library. See
`~astropy.stats.scipy_poisson_upper_limit` for an implementation
that is based on scipy and evaluates faster, but runs only to about
N = 100.
'''
from mpmath import mpf, factorial, findroot, fsum, power, exp, quad
# We convert these values to float. Because for some reason,
# mpmath.mpf cannot convert from numpy.int64
N = mpf(float(N))
B = mpf(float(B))
CL = mpf(float(CL))
tol = 1e-4
def eqn8(N, B):
sumterms = [power(B, n) / factorial(n) for n in range(int(N) + 1)]
return 1. / (exp(-B) * fsum(sumterms))
eqn8_res = eqn8(N, B)
factorial_N = factorial(N)
def eqn7(S, N, B):
SpB = S + B
return eqn8_res * (exp(-SpB) * SpB**N / factorial_N)
def eqn9_left(S_min, S_max, N, B):
def eqn7NB(S):
return eqn7(S, N, B)
return quad(eqn7NB, [S_min, S_max])
def find_s_min(S_max, N, B):
'''
Kraft, Burrows and Nousek suggest to integrate from N-B in both
directions at once, so that S_min and S_max move similarly (see
the article for details). Here, this is implemented differently:
Treat S_max as the optimization parameters in func and then
calculate the matching s_min that has has eqn7(S_max) =
eqn7(S_min) here.
'''
y_S_max = eqn7(S_max, N, B)
# If B > N, then N-B, the "most probable" values is < 0
# and thus s_min is certainly 0.
# Note: For small N, s_max is also close to 0 and root finding
# might find the wrong root, thus it is important to handle this
# case here and return the analytical answer (s_min = 0).
if (B >= N) or (eqn7(0, N, B) >= y_S_max):
return 0.
else:
def eqn7ysmax(x):
return eqn7(x, N, B) - y_S_max
return findroot(eqn7ysmax, [0., N - B], solver='ridder',
tol=tol)
def func(s):
s_min = find_s_min(s, N, B)
out = eqn9_left(s_min, s, N, B)
return out - CL
# Several numerical problems were found prevent the solvers from finding
# the roots unless the starting values are very close to the final values.
# Thus, this primitive, time-wasting, brute-force stepping here to get
# an interval that can be fed into the ridder solver.
s_max_guess = max(N - B, 1.)
while func(s_max_guess) < 0:
s_max_guess += 1
S_max = findroot(func, [s_max_guess - 1, s_max_guess], solver='ridder',
tol=tol)
S_min = find_s_min(S_max, N, B)
return float(S_min), float(S_max)
def _kraft_burrows_nousek(N, B, CL):
'''Upper limit on a poisson count rate
The implementation is based on Kraft, Burrows and Nousek in
`ApJ 374, 344 (1991) <https://ui.adsabs.harvard.edu/abs/1991ApJ...374..344K>`_.
The XMM-Newton upper limit server used the same formalism.
Parameters
----------
N : int or np.int32/np.int64
Total observed count number
B : float or np.float32/np.float64
Background count rate (assumed to be known with negligible error
from a large background area).
CL : float or np.float32/np.float64
Confidence level (number between 0 and 1)
Returns
-------
S : source count limit
Notes
-----
This functions has an optional dependency: Either :mod:`scipy` or `mpmath
<http://mpmath.org/>`_ need to be available. (Scipy only works for
N < 100).
'''
from astropy.utils.compat.optional_deps import HAS_SCIPY, HAS_MPMATH
if HAS_SCIPY and N <= 100:
try:
return _scipy_kraft_burrows_nousek(N, B, CL)
except OverflowError:
if not HAS_MPMATH:
raise ValueError('Need mpmath package for input numbers this '
'large.')
if HAS_MPMATH:
return _mpmath_kraft_burrows_nousek(N, B, CL)
raise ImportError('Either scipy or mpmath are required.')
def kuiper_false_positive_probability(D, N):
"""Compute the false positive probability for the Kuiper statistic.
Uses the set of four formulas described in Paltani 2004; they report
the resulting function never underestimates the false positive
probability but can be a bit high in the N=40..50 range.
(They quote a factor 1.5 at the 1e-7 level.)
Parameters
----------
D : float
The Kuiper test score.
N : float
The effective sample size.
Returns
-------
fpp : float
The probability of a score this large arising from the null hypothesis.
Notes
-----
Eq 7 of Paltani 2004 appears to incorrectly quote the original formula
(Stephens 1965). This function implements the original formula, as it
produces a result closer to Monte Carlo simulations.
References
----------
.. [1] Paltani, S., "Searching for periods in X-ray observations using
Kuiper's test. Application to the ROSAT PSPC archive",
Astronomy and Astrophysics, v.240, p.789-790, 2004.
.. [2] Stephens, M. A., "The goodness-of-fit statistic VN: distribution
and significance points", Biometrika, v.52, p.309, 1965.
"""
try:
from scipy.special import factorial, comb
except ImportError:
# Retained for backwards compatibility with older versions of scipy
# (factorial appears to have moved here in 0.14)
from scipy.misc import factorial, comb
if D < 0. or D > 2.:
raise ValueError("Must have 0<=D<=2 by definition of the Kuiper test")
if D < 2. / N:
return 1. - factorial(N) * (D - 1. / N)**(N - 1)
elif D < 3. / N:
k = -(N * D - 1.) / 2.
r = np.sqrt(k**2 - (N * D - 2.)**2 / 2.)
a, b = -k + r, -k - r
return 1 - (factorial(N - 1) * (b**(N - 1) * (1 - a) - a**(N - 1) * (1 - b))
/ N**(N - 2) / (b - a))
elif (D > 0.5 and N % 2 == 0) or (D > (N - 1.) / (2. * N) and N % 2 == 1):
# NOTE: the upper limit of this sum is taken from Stephens 1965
t = np.arange(np.floor(N * (1 - D)) + 1)
y = D + t / N
Tt = y**(t - 3) * (y**3 * N
- y**2 * t * (3 - 2 / N)
+ y * t * (t - 1) * (3 - 2 / N) / N
- t * (t - 1) * (t - 2) / N**2)
term1 = comb(N, t)
term2 = (1 - D - t / N)**(N - t - 1)
# term1 is formally finite, but is approximated by numpy as np.inf for
# large values, so we set them to zero manually when they would be
# multiplied by zero anyway
term1[(term1 == np.inf) & (term2 == 0)] = 0.
final_term = Tt * term1 * term2
return final_term.sum()
else:
z = D * np.sqrt(N)
# When m*z>18.82 (sqrt(-log(finfo(double))/2)), exp(-2m**2z**2)
# underflows. Cutting off just before avoids triggering a (pointless)
# underflow warning if `under="warn"`.
ms = np.arange(1, 18.82 / z)
S1 = (2 * (4 * ms**2 * z**2 - 1) * np.exp(-2 * ms**2 * z**2)).sum()
S2 = (ms**2 * (4 * ms**2 * z**2 - 3) * np.exp(-2 * ms**2 * z**2)).sum()
return S1 - 8 * D / 3 * S2
def kuiper(data, cdf=lambda x: x, args=()):
"""Compute the Kuiper statistic.
Use the Kuiper statistic version of the Kolmogorov-Smirnov test to
find the probability that a sample like ``data`` was drawn from the
distribution whose CDF is given as ``cdf``.
.. warning::
This will not work correctly for distributions that are actually
discrete (Poisson, for example).
Parameters
----------
data : array-like
The data values.
cdf : callable
A callable to evaluate the CDF of the distribution being tested
against. Will be called with a vector of all values at once.
The default is a uniform distribution.
args : list-like, optional
Additional arguments to be supplied to cdf.
Returns
-------
D : float
The raw statistic.
fpp : float
The probability of a D this large arising with a sample drawn from
the distribution whose CDF is cdf.
Notes
-----
The Kuiper statistic resembles the Kolmogorov-Smirnov test in that
it is nonparametric and invariant under reparameterizations of the data.
The Kuiper statistic, in addition, is equally sensitive throughout
the domain, and it is also invariant under cyclic permutations (making
it particularly appropriate for analyzing circular data).
Returns (D, fpp), where D is the Kuiper D number and fpp is the
probability that a value as large as D would occur if data was
drawn from cdf.
.. warning::
The fpp is calculated only approximately, and it can be
as much as 1.5 times the true value.
Stephens 1970 claims this is more effective than the KS at detecting
changes in the variance of a distribution; the KS is (he claims) more
sensitive at detecting changes in the mean.
If cdf was obtained from data by fitting, then fpp is not correct and
it will be necessary to do Monte Carlo simulations to interpret D.
D should normally be independent of the shape of CDF.
References
----------
.. [1] Stephens, M. A., "Use of the Kolmogorov-Smirnov, Cramer-Von Mises
and Related Statistics Without Extensive Tables", Journal of the
Royal Statistical Society. Series B (Methodological), Vol. 32,
No. 1. (1970), pp. 115-122.
"""
data = np.sort(data)
cdfv = cdf(data, *args)
N = len(data)
D = (np.amax(cdfv - np.arange(N) / float(N)) +
np.amax((np.arange(N) + 1) / float(N) - cdfv))
return D, kuiper_false_positive_probability(D, N)
def kuiper_two(data1, data2):
"""Compute the Kuiper statistic to compare two samples.
Parameters
----------
data1 : array-like
The first set of data values.
data2 : array-like
The second set of data values.
Returns
-------
D : float
The raw test statistic.
fpp : float
The probability of obtaining two samples this different from
the same distribution.
.. warning::
The fpp is quite approximate, especially for small samples.
"""
data1 = np.sort(data1)
data2 = np.sort(data2)
n1, = data1.shape
n2, = data2.shape
common_type = np.find_common_type([], [data1.dtype, data2.dtype])
if not (np.issubdtype(common_type, np.number)
and not np.issubdtype(common_type, np.complexfloating)):
raise ValueError('kuiper_two only accepts real inputs')
# nans, if any, are at the end after sorting.
if np.isnan(data1[-1]) or np.isnan(data2[-1]):
raise ValueError('kuiper_two only accepts non-nan inputs')
D = _stats.ks_2samp(np.asarray(data1, common_type),
np.asarray(data2, common_type))
Ne = len(data1) * len(data2) / float(len(data1) + len(data2))
return D, kuiper_false_positive_probability(D, Ne)
def fold_intervals(intervals):
"""Fold the weighted intervals to the interval (0,1).
Convert a list of intervals (ai, bi, wi) to a list of non-overlapping
intervals covering (0,1). Each output interval has a weight equal
to the sum of the wis of all the intervals that include it. All intervals
are interpreted modulo 1, and weights are accumulated counting
multiplicity. This is appropriate, for example, if you have one or more
blocks of observation and you want to determine how much observation
time was spent on different parts of a system's orbit (the blocks
should be converted to units of the orbital period first).
Parameters
----------
intervals : list of (3,) tuple
For each tuple (ai,bi,wi); ai and bi are the limits of the interval,
and wi is the weight to apply to the interval.
Returns
-------
breaks : (N,) array of float
The endpoints of a set of intervals covering [0,1]; breaks[0]=0 and
breaks[-1] = 1
weights : (N-1,) array of float
The ith element is the sum of number of times the interval
breaks[i],breaks[i+1] is included in each interval times the weight
associated with that interval.
"""
r = []
breaks = set()
tot = 0
for (a, b, wt) in intervals:
tot += (np.ceil(b) - np.floor(a)) * wt
fa = a % 1
breaks.add(fa)
r.append((0, fa, -wt))
fb = b % 1
breaks.add(fb)
r.append((fb, 1, -wt))
breaks.add(0.)
breaks.add(1.)
breaks = sorted(breaks)
breaks_map = dict([(f, i) for (i, f) in enumerate(breaks)])
totals = np.zeros(len(breaks) - 1)
totals += tot
for (a, b, wt) in r:
totals[breaks_map[a]:breaks_map[b]] += wt
return np.array(breaks), totals
def cdf_from_intervals(breaks, totals):
"""Construct a callable piecewise-linear CDF from a pair of arrays.
Take a pair of arrays in the format returned by fold_intervals and
make a callable cumulative distribution function on the interval
(0,1).
Parameters
----------
breaks : (N,) array of float
The boundaries of successive intervals.
totals : (N-1,) array of float
The weight for each interval.
Returns
-------
f : callable
A cumulative distribution function corresponding to the
piecewise-constant probability distribution given by breaks, weights
"""
if breaks[0] != 0 or breaks[-1] != 1:
raise ValueError("Intervals must be restricted to [0,1]")
if np.any(np.diff(breaks) <= 0):
raise ValueError("Breaks must be strictly increasing")
if np.any(totals < 0):
raise ValueError(
"Total weights in each subinterval must be nonnegative")
if np.all(totals == 0):
raise ValueError("At least one interval must have positive exposure")
b = breaks.copy()
c = np.concatenate(((0,), np.cumsum(totals * np.diff(b))))
c /= c[-1]
return lambda x: np.interp(x, b, c, 0, 1)
def interval_overlap_length(i1, i2):
"""Compute the length of overlap of two intervals.
Parameters
----------
i1, i2 : (float, float)
The two intervals, (interval 1, interval 2).
Returns
-------
l : float
The length of the overlap between the two intervals.
"""
(a, b) = i1
(c, d) = i2
if a < c:
if b < c:
return 0.
elif b < d:
return b - c
else:
return d - c
elif a < d:
if b < d:
return b - a
else:
return d - a
else:
return 0
def histogram_intervals(n, breaks, totals):
"""Histogram of a piecewise-constant weight function.
This function takes a piecewise-constant weight function and
computes the average weight in each histogram bin.
Parameters
----------
n : int
The number of bins
breaks : (N,) array of float
Endpoints of the intervals in the PDF
totals : (N-1,) array of float
Probability densities in each bin
Returns
-------
h : array of float
The average weight for each bin
"""
h = np.zeros(n)
start = breaks[0]
for i in range(len(totals)):
end = breaks[i + 1]
for j in range(n):
ol = interval_overlap_length((float(j) / n,
float(j + 1) / n), (start, end))
h[j] += ol / (1. / n) * totals[i]
start = end
return h
|
0c2df0e5560a15ac227de6205a97591834239d839a67df4f818fb8e6701363a7 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains simple functions for model selection.
"""
import numpy as np
__all__ = ['bayesian_info_criterion', 'bayesian_info_criterion_lsq',
'akaike_info_criterion', 'akaike_info_criterion_lsq']
__doctest_requires__ = {'bayesian_info_criterion_lsq': ['scipy'],
'akaike_info_criterion_lsq': ['scipy']}
def bayesian_info_criterion(log_likelihood, n_params, n_samples):
r""" Computes the Bayesian Information Criterion (BIC) given the log of the
likelihood function evaluated at the estimated (or analytically derived)
parameters, the number of parameters, and the number of samples.
The BIC is usually applied to decide whether increasing the number of free
parameters (hence, increasing the model complexity) yields significantly
better fittings. The decision is in favor of the model with the lowest
BIC.
BIC is given as
.. math::
\mathrm{BIC} = k \ln(n) - 2L,
in which :math:`n` is the sample size, :math:`k` is the number of free
parameters, and :math:`L` is the log likelihood function of the model
evaluated at the maximum likelihood estimate (i. e., the parameters for
which L is maximized).
When comparing two models define
:math:`\Delta \mathrm{BIC} = \mathrm{BIC}_h - \mathrm{BIC}_l`, in which
:math:`\mathrm{BIC}_h` is the higher BIC, and :math:`\mathrm{BIC}_l` is
the lower BIC. The higher is :math:`\Delta \mathrm{BIC}` the stronger is
the evidence against the model with higher BIC.
The general rule of thumb is:
:math:`0 < \Delta\mathrm{BIC} \leq 2`: weak evidence that model low is
better
:math:`2 < \Delta\mathrm{BIC} \leq 6`: moderate evidence that model low is
better
:math:`6 < \Delta\mathrm{BIC} \leq 10`: strong evidence that model low is
better
:math:`\Delta\mathrm{BIC} > 10`: very strong evidence that model low is
better
For a detailed explanation, see [1]_ - [5]_.
Parameters
----------
log_likelihood : float
Logarithm of the likelihood function of the model evaluated at the
point of maxima (with respect to the parameter space).
n_params : int
Number of free parameters of the model, i.e., dimension of the
parameter space.
n_samples : int
Number of observations.
Returns
-------
bic : float
Bayesian Information Criterion.
Examples
--------
The following example was originally presented in [1]_. Consider a
Gaussian model (mu, sigma) and a t-Student model (mu, sigma, delta).
In addition, assume that the t model has presented a higher likelihood.
The question that the BIC is proposed to answer is: "Is the increase in
likelihood due to larger number of parameters?"
>>> from astropy.stats.info_theory import bayesian_info_criterion
>>> lnL_g = -176.4
>>> lnL_t = -173.0
>>> n_params_g = 2
>>> n_params_t = 3
>>> n_samples = 100
>>> bic_g = bayesian_info_criterion(lnL_g, n_params_g, n_samples)
>>> bic_t = bayesian_info_criterion(lnL_t, n_params_t, n_samples)
>>> bic_g - bic_t # doctest: +FLOAT_CMP
2.1948298140119391
Therefore, there exist a moderate evidence that the increasing in
likelihood for t-Student model is due to the larger number of parameters.
References
----------
.. [1] Richards, D. Maximum Likelihood Estimation and the Bayesian
Information Criterion.
<https://hea-www.harvard.edu/astrostat/Stat310_0910/dr_20100323_mle.pdf>
.. [2] Wikipedia. Bayesian Information Criterion.
<https://en.wikipedia.org/wiki/Bayesian_information_criterion>
.. [3] Origin Lab. Comparing Two Fitting Functions.
<https://www.originlab.com/doc/Origin-Help/PostFit-CompareFitFunc>
.. [4] Liddle, A. R. Information Criteria for Astrophysical Model
Selection. 2008. <https://arxiv.org/pdf/astro-ph/0701113v2.pdf>
.. [5] Liddle, A. R. How many cosmological parameters? 2008.
<https://arxiv.org/pdf/astro-ph/0401198v3.pdf>
"""
return n_params*np.log(n_samples) - 2.0*log_likelihood
# NOTE: bic_t - bic_g doctest is skipped because it produced slightly
# different result in arm64 and big-endian s390x CI jobs.
def bayesian_info_criterion_lsq(ssr, n_params, n_samples):
r"""
Computes the Bayesian Information Criterion (BIC) assuming that the
observations come from a Gaussian distribution.
In this case, BIC is given as
.. math::
\mathrm{BIC} = n\ln\left(\dfrac{\mathrm{SSR}}{n}\right) + k\ln(n)
in which :math:`n` is the sample size, :math:`k` is the number of free
parameters and :math:`\mathrm{SSR}` stands for the sum of squared residuals
between model and data.
This is applicable, for instance, when the parameters of a model are
estimated using the least squares statistic. See [1]_ and [2]_.
Parameters
----------
ssr : float
Sum of squared residuals (SSR) between model and data.
n_params : int
Number of free parameters of the model, i.e., dimension of the
parameter space.
n_samples : int
Number of observations.
Returns
-------
bic : float
Examples
--------
Consider the simple 1-D fitting example presented in the Astropy
modeling webpage [3]_. There, two models (Box and Gaussian) were fitted to
a source flux using the least squares statistic. However, the fittings
themselves do not tell much about which model better represents this
hypothetical source. Therefore, we are going to apply to BIC in order to
decide in favor of a model.
>>> import numpy as np
>>> from astropy.modeling import models, fitting
>>> from astropy.stats.info_theory import bayesian_info_criterion_lsq
>>> # Generate fake data
>>> np.random.seed(0)
>>> x = np.linspace(-5., 5., 200)
>>> y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)
>>> y += np.random.normal(0., 0.2, x.shape)
>>> # Fit the data using a Box model.
>>> # Bounds are not really needed but included here to demonstrate usage.
>>> t_init = models.Trapezoid1D(amplitude=1., x_0=0., width=1., slope=0.5,
... bounds={"x_0": (-5., 5.)})
>>> fit_t = fitting.LevMarLSQFitter()
>>> t = fit_t(t_init, x, y)
>>> # Fit the data using a Gaussian
>>> g_init = models.Gaussian1D(amplitude=1., mean=0, stddev=1.)
>>> fit_g = fitting.LevMarLSQFitter()
>>> g = fit_g(g_init, x, y)
>>> # Compute the mean squared errors
>>> ssr_t = np.sum((t(x) - y)*(t(x) - y))
>>> ssr_g = np.sum((g(x) - y)*(g(x) - y))
>>> # Compute the bics
>>> bic_t = bayesian_info_criterion_lsq(ssr_t, 4, x.shape[0])
>>> bic_g = bayesian_info_criterion_lsq(ssr_g, 3, x.shape[0])
>>> bic_t - bic_g # doctest: +SKIP
30.644474706065466
Hence, there is a very strong evidence that the Gaussian model has a
significantly better representation of the data than the Box model. This
is, obviously, expected since the true model is Gaussian.
References
----------
.. [1] Wikipedia. Bayesian Information Criterion.
<https://en.wikipedia.org/wiki/Bayesian_information_criterion>
.. [2] Origin Lab. Comparing Two Fitting Functions.
<https://www.originlab.com/doc/Origin-Help/PostFit-CompareFitFunc>
.. [3] Astropy Models and Fitting
<https://docs.astropy.org/en/stable/modeling>
"""
return bayesian_info_criterion(-0.5 * n_samples * np.log(ssr / n_samples),
n_params, n_samples)
def akaike_info_criterion(log_likelihood, n_params, n_samples):
r"""
Computes the Akaike Information Criterion (AIC).
Like the Bayesian Information Criterion, the AIC is a measure of
relative fitting quality which is used for fitting evaluation and model
selection. The decision is in favor of the model with the lowest AIC.
AIC is given as
.. math::
\mathrm{AIC} = 2(k - L)
in which :math:`n` is the sample size, :math:`k` is the number of free
parameters, and :math:`L` is the log likelihood function of the model
evaluated at the maximum likelihood estimate (i. e., the parameters for
which L is maximized).
In case that the sample size is not "large enough" a correction is
applied, i.e.
.. math::
\mathrm{AIC} = 2(k - L) + \dfrac{2k(k+1)}{n - k - 1}
Rule of thumb [1]_:
:math:`\Delta\mathrm{AIC}_i = \mathrm{AIC}_i - \mathrm{AIC}_{min}`
:math:`\Delta\mathrm{AIC}_i < 2`: substantial support for model i
:math:`3 < \Delta\mathrm{AIC}_i < 7`: considerably less support for model i
:math:`\Delta\mathrm{AIC}_i > 10`: essentially none support for model i
in which :math:`\mathrm{AIC}_{min}` stands for the lower AIC among the
models which are being compared.
For detailed explanations see [1]_-[6]_.
Parameters
----------
log_likelihood : float
Logarithm of the likelihood function of the model evaluated at the
point of maxima (with respect to the parameter space).
n_params : int
Number of free parameters of the model, i.e., dimension of the
parameter space.
n_samples : int
Number of observations.
Returns
-------
aic : float
Akaike Information Criterion.
Examples
--------
The following example was originally presented in [2]_. Basically, two
models are being compared. One with six parameters (model 1) and another
with five parameters (model 2). Despite of the fact that model 2 has a
lower AIC, we could decide in favor of model 1 since the difference (in
AIC) between them is only about 1.0.
>>> n_samples = 121
>>> lnL1 = -3.54
>>> n1_params = 6
>>> lnL2 = -4.17
>>> n2_params = 5
>>> aic1 = akaike_info_criterion(lnL1, n1_params, n_samples)
>>> aic2 = akaike_info_criterion(lnL2, n2_params, n_samples)
>>> aic1 - aic2 # doctest: +FLOAT_CMP
0.9551029748283746
Therefore, we can strongly support the model 1 with the advantage that
it has more free parameters.
References
----------
.. [1] Cavanaugh, J. E. Model Selection Lecture II: The Akaike
Information Criterion.
<http://machinelearning102.pbworks.com/w/file/fetch/47699383/ms_lec_2_ho.pdf>
.. [2] Mazerolle, M. J. Making sense out of Akaike's Information
Criterion (AIC): its use and interpretation in model selection and
inference from ecological data.
<https://corpus.ulaval.ca/jspui/handle/20.500.11794/17461>
.. [3] Wikipedia. Akaike Information Criterion.
<https://en.wikipedia.org/wiki/Akaike_information_criterion>
.. [4] Origin Lab. Comparing Two Fitting Functions.
<https://www.originlab.com/doc/Origin-Help/PostFit-CompareFitFunc>
.. [5] Liddle, A. R. Information Criteria for Astrophysical Model
Selection. 2008. <https://arxiv.org/pdf/astro-ph/0701113v2.pdf>
.. [6] Liddle, A. R. How many cosmological parameters? 2008.
<https://arxiv.org/pdf/astro-ph/0401198v3.pdf>
"""
# Correction in case of small number of observations
if n_samples/float(n_params) >= 40.0:
aic = 2.0 * (n_params - log_likelihood)
else:
aic = (2.0 * (n_params - log_likelihood) +
2.0 * n_params * (n_params + 1.0) /
(n_samples - n_params - 1.0))
return aic
def akaike_info_criterion_lsq(ssr, n_params, n_samples):
r"""
Computes the Akaike Information Criterion assuming that the observations
are Gaussian distributed.
In this case, AIC is given as
.. math::
\mathrm{AIC} = n\ln\left(\dfrac{\mathrm{SSR}}{n}\right) + 2k
In case that the sample size is not "large enough", a correction is
applied, i.e.
.. math::
\mathrm{AIC} = n\ln\left(\dfrac{\mathrm{SSR}}{n}\right) + 2k +
\dfrac{2k(k+1)}{n-k-1}
in which :math:`n` is the sample size, :math:`k` is the number of free
parameters and :math:`\mathrm{SSR}` stands for the sum of squared residuals
between model and data.
This is applicable, for instance, when the parameters of a model are
estimated using the least squares statistic.
Parameters
----------
ssr : float
Sum of squared residuals (SSR) between model and data.
n_params : int
Number of free parameters of the model, i.e., the dimension of the
parameter space.
n_samples : int
Number of observations.
Returns
-------
aic : float
Akaike Information Criterion.
Examples
--------
This example is based on Astropy Modeling webpage, Compound models
section.
>>> import numpy as np
>>> from astropy.modeling import models, fitting
>>> from astropy.stats.info_theory import akaike_info_criterion_lsq
>>> np.random.seed(42)
>>> # Generate fake data
>>> g1 = models.Gaussian1D(.1, 0, 0.2) # changed this to noise level
>>> g2 = models.Gaussian1D(.1, 0.3, 0.2) # and added another Gaussian
>>> g3 = models.Gaussian1D(2.5, 0.5, 0.1)
>>> x = np.linspace(-1, 1, 200)
>>> y = g1(x) + g2(x) + g3(x) + np.random.normal(0., 0.2, x.shape)
>>> # Fit with three Gaussians
>>> g3_init = (models.Gaussian1D(.1, 0, 0.1)
... + models.Gaussian1D(.1, 0.2, 0.15)
... + models.Gaussian1D(2.4, .4, 0.1))
>>> fitter = fitting.LevMarLSQFitter()
>>> g3_fit = fitter(g3_init, x, y)
>>> # Fit with two Gaussians
>>> g2_init = (models.Gaussian1D(.1, 0, 0.1) +
... models.Gaussian1D(2, 0.5, 0.1))
>>> g2_fit = fitter(g2_init, x, y)
>>> # Fit with only one Gaussian
>>> g1_init = models.Gaussian1D(amplitude=2., mean=0.3, stddev=.5)
>>> g1_fit = fitter(g1_init, x, y)
>>> # Compute the mean squared errors
>>> ssr_g3 = np.sum((g3_fit(x) - y)**2.0)
>>> ssr_g2 = np.sum((g2_fit(x) - y)**2.0)
>>> ssr_g1 = np.sum((g1_fit(x) - y)**2.0)
>>> akaike_info_criterion_lsq(ssr_g3, 9, x.shape[0]) # doctest: +FLOAT_CMP
-634.5257517810961
>>> akaike_info_criterion_lsq(ssr_g2, 6, x.shape[0]) # doctest: +FLOAT_CMP
-662.83834510232043
>>> akaike_info_criterion_lsq(ssr_g1, 3, x.shape[0]) # doctest: +FLOAT_CMP
-647.47312032659499
Hence, from the AIC values, we would prefer to choose the model g2_fit.
However, we can considerably support the model g3_fit, since the
difference in AIC is about 2.4. We should reject the model g1_fit.
References
----------
.. [1] Akaike Information Criterion.
<https://en.wikipedia.org/wiki/Akaike_information_criterion>
.. [2] Origin Lab. Comparing Two Fitting Functions.
<https://www.originlab.com/doc/Origin-Help/PostFit-CompareFitFunc>
"""
return akaike_info_criterion(-0.5 * n_samples * np.log(ssr / n_samples),
n_params, n_samples)
|
d2a3de647d8a8bd5580a17bbf673074333e74c6744949cbacf63fbe6a9e53372 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains functions for computing robust statistics using
Tukey's biweight function.
"""
import numpy as np
from .funcs import _expand_dims, median_absolute_deviation
__all__ = ['biweight_location', 'biweight_scale', 'biweight_midvariance',
'biweight_midcovariance', 'biweight_midcorrelation']
def _stat_functions(data, ignore_nan=False):
if isinstance(data, np.ma.MaskedArray):
median_func = np.ma.median
sum_func = np.ma.sum
elif ignore_nan:
median_func = np.nanmedian
sum_func = np.nansum
else:
median_func = np.median
sum_func = np.sum
return median_func, sum_func
def biweight_location(data, c=6.0, M=None, axis=None, *, ignore_nan=False):
r"""
Compute the biweight location.
The biweight location is a robust statistic for determining the
central location of a distribution. It is given by:
.. math::
\zeta_{biloc}= M + \frac{\sum_{|u_i|<1} \ (x_i - M) (1 - u_i^2)^2}
{\sum_{|u_i|<1} \ (1 - u_i^2)^2}
where :math:`x` is the input data, :math:`M` is the sample median
(or the input initial location guess) and :math:`u_i` is given by:
.. math::
u_{i} = \frac{(x_i - M)}{c * MAD}
where :math:`c` is the tuning constant and :math:`MAD` is the
`median absolute deviation
<https://en.wikipedia.org/wiki/Median_absolute_deviation>`_. The
biweight location tuning constant ``c`` is typically 6.0 (the
default).
If :math:`MAD` is zero, then the median will be returned.
Parameters
----------
data : array-like
Input array or object that can be converted to an array.
``data`` can be a `~numpy.ma.MaskedArray`.
c : float, optional
Tuning constant for the biweight estimator (default = 6.0).
M : float or array-like, optional
Initial guess for the location. If ``M`` is a scalar value,
then its value will be used for the entire array (or along each
``axis``, if specified). If ``M`` is an array, then its must be
an array containing the initial location estimate along each
``axis`` of the input array. If `None` (default), then the
median of the input array will be used (or along each ``axis``,
if specified).
axis : None, int, or tuple of int, optional
The axis or axes along which the biweight locations are
computed. If `None` (default), then the biweight location of
the flattened input array will be computed.
ignore_nan : bool, optional
Whether to ignore NaN values in the input ``data``.
Returns
-------
biweight_location : float or `~numpy.ndarray`
The biweight location of the input data. If ``axis`` is `None`
then a scalar will be returned, otherwise a `~numpy.ndarray`
will be returned.
See Also
--------
biweight_scale, biweight_midvariance, biweight_midcovariance
References
----------
.. [1] Beers, Flynn, and Gebhardt (1990; AJ 100, 32) (https://ui.adsabs.harvard.edu/abs/1990AJ....100...32B)
.. [2] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/biwloc.htm
Examples
--------
Generate random variates from a Gaussian distribution and return the
biweight location of the distribution:
>>> import numpy as np
>>> from astropy.stats import biweight_location
>>> rand = np.random.default_rng(12345)
>>> biloc = biweight_location(rand.standard_normal(1000))
>>> print(biloc) # doctest: +FLOAT_CMP
0.01535330525461019
"""
median_func, sum_func = _stat_functions(data, ignore_nan=ignore_nan)
if isinstance(data, np.ma.MaskedArray) and ignore_nan:
data = np.ma.masked_where(np.isnan(data), data, copy=True)
data = np.asanyarray(data).astype(np.float64)
if M is None:
M = median_func(data, axis=axis)
if axis is not None:
M = _expand_dims(M, axis=axis) # NUMPY_LT_1_18
# set up the differences
d = data - M
# set up the weighting
mad = median_absolute_deviation(data, axis=axis, ignore_nan=ignore_nan)
# mad = 0 means data is constant or mostly constant
# mad = np.nan means data contains NaNs and ignore_nan=False
if axis is None and (mad == 0. or np.isnan(mad)):
return M
if axis is not None:
mad = _expand_dims(mad, axis=axis) # NUMPY_LT_1_18
with np.errstate(divide='ignore', invalid='ignore'):
u = d / (c * mad)
# now remove the outlier points
# ignore RuntimeWarnings for comparisons with NaN data values
with np.errstate(invalid='ignore'):
mask = np.abs(u) >= 1
u = (1 - u ** 2) ** 2
u[mask] = 0
# If mad == 0 along the specified ``axis`` in the input data, return
# the median value along that axis.
# Ignore RuntimeWarnings for divide by zero
with np.errstate(divide='ignore', invalid='ignore'):
value = M.squeeze() + (sum_func(d * u, axis=axis) /
sum_func(u, axis=axis))
if np.isscalar(value):
return value
where_func = np.where
if isinstance(data, np.ma.MaskedArray):
where_func = np.ma.where # return MaskedArray
return where_func(mad.squeeze() == 0, M.squeeze(), value)
def biweight_scale(data, c=9.0, M=None, axis=None, modify_sample_size=False,
*, ignore_nan=False):
r"""
Compute the biweight scale.
The biweight scale is a robust statistic for determining the
standard deviation of a distribution. It is the square root of the
`biweight midvariance
<https://en.wikipedia.org/wiki/Robust_measures_of_scale#The_biweight_midvariance>`_.
It is given by:
.. math::
\zeta_{biscl} = \sqrt{n} \ \frac{\sqrt{\sum_{|u_i| < 1} \
(x_i - M)^2 (1 - u_i^2)^4}} {|(\sum_{|u_i| < 1} \
(1 - u_i^2) (1 - 5u_i^2))|}
where :math:`x` is the input data, :math:`M` is the sample median
(or the input location) and :math:`u_i` is given by:
.. math::
u_{i} = \frac{(x_i - M)}{c * MAD}
where :math:`c` is the tuning constant and :math:`MAD` is the
`median absolute deviation
<https://en.wikipedia.org/wiki/Median_absolute_deviation>`_. The
biweight midvariance tuning constant ``c`` is typically 9.0 (the
default).
If :math:`MAD` is zero, then zero will be returned.
For the standard definition of biweight scale, :math:`n` is the
total number of points in the array (or along the input ``axis``, if
specified). That definition is used if ``modify_sample_size`` is
`False`, which is the default.
However, if ``modify_sample_size = True``, then :math:`n` is the
number of points for which :math:`|u_i| < 1` (i.e. the total number
of non-rejected values), i.e.
.. math::
n = \sum_{|u_i| < 1} \ 1
which results in a value closer to the true standard deviation for
small sample sizes or for a large number of rejected values.
Parameters
----------
data : array-like
Input array or object that can be converted to an array.
``data`` can be a `~numpy.ma.MaskedArray`.
c : float, optional
Tuning constant for the biweight estimator (default = 9.0).
M : float or array-like, optional
The location estimate. If ``M`` is a scalar value, then its
value will be used for the entire array (or along each ``axis``,
if specified). If ``M`` is an array, then its must be an array
containing the location estimate along each ``axis`` of the
input array. If `None` (default), then the median of the input
array will be used (or along each ``axis``, if specified).
axis : None, int, or tuple of int, optional
The axis or axes along which the biweight scales are computed.
If `None` (default), then the biweight scale of the flattened
input array will be computed.
modify_sample_size : bool, optional
If `False` (default), then the sample size used is the total
number of elements in the array (or along the input ``axis``, if
specified), which follows the standard definition of biweight
scale. If `True`, then the sample size is reduced to correct
for any rejected values (i.e. the sample size used includes only
the non-rejected values), which results in a value closer to the
true standard deviation for small sample sizes or for a large
number of rejected values.
ignore_nan : bool, optional
Whether to ignore NaN values in the input ``data``.
Returns
-------
biweight_scale : float or `~numpy.ndarray`
The biweight scale of the input data. If ``axis`` is `None`
then a scalar will be returned, otherwise a `~numpy.ndarray`
will be returned.
See Also
--------
biweight_midvariance, biweight_midcovariance, biweight_location, astropy.stats.mad_std, astropy.stats.median_absolute_deviation
References
----------
.. [1] Beers, Flynn, and Gebhardt (1990; AJ 100, 32) (https://ui.adsabs.harvard.edu/abs/1990AJ....100...32B)
.. [2] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/biwscale.htm
Examples
--------
Generate random variates from a Gaussian distribution and return the
biweight scale of the distribution:
>>> import numpy as np
>>> from astropy.stats import biweight_scale
>>> rand = np.random.default_rng(12345)
>>> biscl = biweight_scale(rand.standard_normal(1000))
>>> print(biscl) # doctest: +FLOAT_CMP
1.0239311812635818
"""
return np.sqrt(
biweight_midvariance(data, c=c, M=M, axis=axis,
modify_sample_size=modify_sample_size,
ignore_nan=ignore_nan))
def biweight_midvariance(data, c=9.0, M=None, axis=None,
modify_sample_size=False, *, ignore_nan=False):
r"""
Compute the biweight midvariance.
The biweight midvariance is a robust statistic for determining the
variance of a distribution. Its square root is a robust estimator
of scale (i.e. standard deviation). It is given by:
.. math::
\zeta_{bivar} = n \ \frac{\sum_{|u_i| < 1} \
(x_i - M)^2 (1 - u_i^2)^4} {(\sum_{|u_i| < 1} \
(1 - u_i^2) (1 - 5u_i^2))^2}
where :math:`x` is the input data, :math:`M` is the sample median
(or the input location) and :math:`u_i` is given by:
.. math::
u_{i} = \frac{(x_i - M)}{c * MAD}
where :math:`c` is the tuning constant and :math:`MAD` is the
`median absolute deviation
<https://en.wikipedia.org/wiki/Median_absolute_deviation>`_. The
biweight midvariance tuning constant ``c`` is typically 9.0 (the
default).
If :math:`MAD` is zero, then zero will be returned.
For the standard definition of `biweight midvariance
<https://en.wikipedia.org/wiki/Robust_measures_of_scale#The_biweight_midvariance>`_,
:math:`n` is the total number of points in the array (or along the
input ``axis``, if specified). That definition is used if
``modify_sample_size`` is `False`, which is the default.
However, if ``modify_sample_size = True``, then :math:`n` is the
number of points for which :math:`|u_i| < 1` (i.e. the total number
of non-rejected values), i.e.
.. math::
n = \sum_{|u_i| < 1} \ 1
which results in a value closer to the true variance for small
sample sizes or for a large number of rejected values.
Parameters
----------
data : array-like
Input array or object that can be converted to an array.
``data`` can be a `~numpy.ma.MaskedArray`.
c : float, optional
Tuning constant for the biweight estimator (default = 9.0).
M : float or array-like, optional
The location estimate. If ``M`` is a scalar value, then its
value will be used for the entire array (or along each ``axis``,
if specified). If ``M`` is an array, then its must be an array
containing the location estimate along each ``axis`` of the
input array. If `None` (default), then the median of the input
array will be used (or along each ``axis``, if specified).
axis : None, int, or tuple of int, optional
The axis or axes along which the biweight midvariances are
computed. If `None` (default), then the biweight midvariance of
the flattened input array will be computed.
modify_sample_size : bool, optional
If `False` (default), then the sample size used is the total
number of elements in the array (or along the input ``axis``, if
specified), which follows the standard definition of biweight
midvariance. If `True`, then the sample size is reduced to
correct for any rejected values (i.e. the sample size used
includes only the non-rejected values), which results in a value
closer to the true variance for small sample sizes or for a
large number of rejected values.
ignore_nan : bool, optional
Whether to ignore NaN values in the input ``data``.
Returns
-------
biweight_midvariance : float or `~numpy.ndarray`
The biweight midvariance of the input data. If ``axis`` is
`None` then a scalar will be returned, otherwise a
`~numpy.ndarray` will be returned.
See Also
--------
biweight_midcovariance, biweight_midcorrelation, astropy.stats.mad_std, astropy.stats.median_absolute_deviation
References
----------
.. [1] https://en.wikipedia.org/wiki/Robust_measures_of_scale#The_biweight_midvariance
.. [2] Beers, Flynn, and Gebhardt (1990; AJ 100, 32) (https://ui.adsabs.harvard.edu/abs/1990AJ....100...32B)
Examples
--------
Generate random variates from a Gaussian distribution and return the
biweight midvariance of the distribution:
>>> import numpy as np
>>> from astropy.stats import biweight_midvariance
>>> rand = np.random.default_rng(12345)
>>> bivar = biweight_midvariance(rand.standard_normal(1000))
>>> print(bivar) # doctest: +FLOAT_CMP
1.0484350639638342
"""
median_func, sum_func = _stat_functions(data, ignore_nan=ignore_nan)
if isinstance(data, np.ma.MaskedArray) and ignore_nan:
data = np.ma.masked_where(np.isnan(data), data, copy=True)
data = np.asanyarray(data).astype(np.float64)
if M is None:
M = median_func(data, axis=axis)
if axis is not None:
M = _expand_dims(M, axis=axis) # NUMPY_LT_1_18
# set up the differences
d = data - M
# set up the weighting
mad = median_absolute_deviation(data, axis=axis, ignore_nan=ignore_nan)
if axis is None:
# data is constant or mostly constant OR
# data contains NaNs and ignore_nan=False
if mad == 0. or np.isnan(mad):
return mad ** 2 # variance units
else:
mad = _expand_dims(mad, axis=axis) # NUMPY_LT_1_18
with np.errstate(divide='ignore', invalid='ignore'):
u = d / (c * mad)
# now remove the outlier points
# ignore RuntimeWarnings for comparisons with NaN data values
with np.errstate(invalid='ignore'):
mask = np.abs(u) < 1
if isinstance(mask, np.ma.MaskedArray):
mask = mask.filled(fill_value=False) # exclude masked data values
u = u ** 2
if modify_sample_size:
n = sum_func(mask, axis=axis)
else:
# set good values to 1, bad values to 0
include_mask = np.ones(data.shape)
if isinstance(data, np.ma.MaskedArray):
include_mask[data.mask] = 0
if ignore_nan:
include_mask[np.isnan(data)] = 0
n = np.sum(include_mask, axis=axis)
f1 = d * d * (1. - u)**4
f1[~mask] = 0.
f1 = sum_func(f1, axis=axis)
f2 = (1. - u) * (1. - 5.*u)
f2[~mask] = 0.
f2 = np.abs(np.sum(f2, axis=axis))**2
# If mad == 0 along the specified ``axis`` in the input data, return
# 0.0 along that axis.
# Ignore RuntimeWarnings for divide by zero.
with np.errstate(divide='ignore', invalid='ignore'):
value = n * f1 / f2
if np.isscalar(value):
return value
where_func = np.where
if isinstance(data, np.ma.MaskedArray):
where_func = np.ma.where # return MaskedArray
return where_func(mad.squeeze() == 0, 0., value)
def biweight_midcovariance(data, c=9.0, M=None, modify_sample_size=False):
r"""
Compute the biweight midcovariance between pairs of multiple
variables.
The biweight midcovariance is a robust and resistant estimator of
the covariance between two variables.
This function computes the biweight midcovariance between all pairs
of the input variables (rows) in the input data. The output array
will have a shape of (N_variables, N_variables). The diagonal
elements will be the biweight midvariances of each input variable
(see :func:`biweight_midvariance`). The off-diagonal elements will
be the biweight midcovariances between each pair of input variables.
For example, if the input array ``data`` contains three variables
(rows) ``x``, ``y``, and ``z``, the output `~numpy.ndarray`
midcovariance matrix will be:
.. math::
\begin{pmatrix}
\zeta_{xx} & \zeta_{xy} & \zeta_{xz} \\
\zeta_{yx} & \zeta_{yy} & \zeta_{yz} \\
\zeta_{zx} & \zeta_{zy} & \zeta_{zz}
\end{pmatrix}
where :math:`\zeta_{xx}`, :math:`\zeta_{yy}`, and :math:`\zeta_{zz}`
are the biweight midvariances of each variable. The biweight
midcovariance between :math:`x` and :math:`y` is :math:`\zeta_{xy}`
(:math:`= \zeta_{yx}`). The biweight midcovariance between
:math:`x` and :math:`z` is :math:`\zeta_{xz}` (:math:`=
\zeta_{zx}`). The biweight midcovariance between :math:`y` and
:math:`z` is :math:`\zeta_{yz}` (:math:`= \zeta_{zy}`).
The biweight midcovariance between two variables :math:`x` and
:math:`y` is given by:
.. math::
\zeta_{xy} = n_{xy} \ \frac{\sum_{|u_i| < 1, \ |v_i| < 1} \
(x_i - M_x) (1 - u_i^2)^2 (y_i - M_y) (1 - v_i^2)^2}
{(\sum_{|u_i| < 1} \ (1 - u_i^2) (1 - 5u_i^2))
(\sum_{|v_i| < 1} \ (1 - v_i^2) (1 - 5v_i^2))}
where :math:`M_x` and :math:`M_y` are the medians (or the input
locations) of the two variables and :math:`u_i` and :math:`v_i` are
given by:
.. math::
u_{i} = \frac{(x_i - M_x)}{c * MAD_x}
v_{i} = \frac{(y_i - M_y)}{c * MAD_y}
where :math:`c` is the biweight tuning constant and :math:`MAD_x`
and :math:`MAD_y` are the `median absolute deviation
<https://en.wikipedia.org/wiki/Median_absolute_deviation>`_ of the
:math:`x` and :math:`y` variables. The biweight midvariance tuning
constant ``c`` is typically 9.0 (the default).
If :math:`MAD_x` or :math:`MAD_y` are zero, then zero will be
returned for that element.
For the standard definition of biweight midcovariance,
:math:`n_{xy}` is the total number of observations of each variable.
That definition is used if ``modify_sample_size`` is `False`, which
is the default.
However, if ``modify_sample_size = True``, then :math:`n_{xy}` is the
number of observations for which :math:`|u_i| < 1` and/or :math:`|v_i|
< 1`, i.e.
.. math::
n_{xx} = \sum_{|u_i| < 1} \ 1
.. math::
n_{xy} = n_{yx} = \sum_{|u_i| < 1, \ |v_i| < 1} \ 1
.. math::
n_{yy} = \sum_{|v_i| < 1} \ 1
which results in a value closer to the true variance for small
sample sizes or for a large number of rejected values.
Parameters
----------
data : 2D or 1D array-like
Input data either as a 2D or 1D array. For a 2D array, it
should have a shape (N_variables, N_observations). A 1D array
may be input for observations of a single variable, in which
case the biweight midvariance will be calculated (no
covariance). Each row of ``data`` represents a variable, and
each column a single observation of all those variables (same as
the `numpy.cov` convention).
c : float, optional
Tuning constant for the biweight estimator (default = 9.0).
M : float or 1D array-like, optional
The location estimate of each variable, either as a scalar or
array. If ``M`` is an array, then its must be a 1D array
containing the location estimate of each row (i.e. ``a.ndim``
elements). If ``M`` is a scalar value, then its value will be
used for each variable (row). If `None` (default), then the
median of each variable (row) will be used.
modify_sample_size : bool, optional
If `False` (default), then the sample size used is the total
number of observations of each variable, which follows the
standard definition of biweight midcovariance. If `True`, then
the sample size is reduced to correct for any rejected values
(see formula above), which results in a value closer to the true
covariance for small sample sizes or for a large number of
rejected values.
Returns
-------
biweight_midcovariance : ndarray
A 2D array representing the biweight midcovariances between each
pair of the variables (rows) in the input array. The output
array will have a shape of (N_variables, N_variables). The
diagonal elements will be the biweight midvariances of each
input variable. The off-diagonal elements will be the biweight
midcovariances between each pair of input variables.
See Also
--------
biweight_midvariance, biweight_midcorrelation, biweight_scale, biweight_location
References
----------
.. [1] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/biwmidc.htm
Examples
--------
Compute the biweight midcovariance between two random variables:
>>> import numpy as np
>>> from astropy.stats import biweight_midcovariance
>>> # Generate two random variables x and y
>>> rng = np.random.default_rng(1)
>>> x = rng.normal(0, 1, 200)
>>> y = rng.normal(0, 3, 200)
>>> # Introduce an obvious outlier
>>> x[0] = 30.0
>>> # Calculate the biweight midcovariances between x and y
>>> bicov = biweight_midcovariance([x, y])
>>> print(bicov) # doctest: +FLOAT_CMP
[[0.83435568 0.02379316]
[0.02379316 7.15665769]]
>>> # Print standard deviation estimates
>>> print(np.sqrt(bicov.diagonal())) # doctest: +FLOAT_CMP
[0.91343072 2.67519302]
"""
data = np.asanyarray(data).astype(np.float64)
# ensure data is 2D
if data.ndim == 1:
data = data[np.newaxis, :]
if data.ndim != 2:
raise ValueError('The input array must be 2D or 1D.')
# estimate location if not given
if M is None:
M = np.median(data, axis=1)
M = np.asanyarray(M)
if M.ndim > 1:
raise ValueError('M must be a scalar or 1D array.')
# set up the differences
d = (data.T - M).T
# set up the weighting
mad = median_absolute_deviation(data, axis=1)
with np.errstate(divide='ignore', invalid='ignore'):
u = (d.T / (c * mad)).T
# now remove the outlier points
# ignore RuntimeWarnings for comparisons with NaN data values
with np.errstate(invalid='ignore'):
mask = np.abs(u) < 1
u = u ** 2
if modify_sample_size:
maskf = mask.astype(float)
n = np.inner(maskf, maskf)
else:
n = data[0].size
usub1 = (1. - u)
usub5 = (1. - 5. * u)
usub1[~mask] = 0.
with np.errstate(divide='ignore', invalid='ignore'):
numerator = d * usub1 ** 2
denominator = (usub1 * usub5).sum(axis=1)[:, np.newaxis]
numerator_matrix = np.dot(numerator, numerator.T)
denominator_matrix = np.dot(denominator, denominator.T)
value = n * (numerator_matrix / denominator_matrix)
idx = np.where(mad == 0)[0]
value[idx, :] = 0
value[:, idx] = 0
return value
def biweight_midcorrelation(x, y, c=9.0, M=None, modify_sample_size=False):
r"""
Compute the biweight midcorrelation between two variables.
The `biweight midcorrelation
<https://en.wikipedia.org/wiki/Biweight_midcorrelation>`_ is a
measure of similarity between samples. It is given by:
.. math::
r_{bicorr} = \frac{\zeta_{xy}}{\sqrt{\zeta_{xx} \ \zeta_{yy}}}
where :math:`\zeta_{xx}` is the biweight midvariance of :math:`x`,
:math:`\zeta_{yy}` is the biweight midvariance of :math:`y`, and
:math:`\zeta_{xy}` is the biweight midcovariance of :math:`x` and
:math:`y`.
Parameters
----------
x, y : 1D array-like
Input arrays for the two variables. ``x`` and ``y`` must be 1D
arrays and have the same number of elements.
c : float, optional
Tuning constant for the biweight estimator (default = 9.0). See
`biweight_midcovariance` for more details.
M : float or array-like, optional
The location estimate. If ``M`` is a scalar value, then its
value will be used for the entire array (or along each ``axis``,
if specified). If ``M`` is an array, then its must be an array
containing the location estimate along each ``axis`` of the
input array. If `None` (default), then the median of the input
array will be used (or along each ``axis``, if specified). See
`biweight_midcovariance` for more details.
modify_sample_size : bool, optional
If `False` (default), then the sample size used is the total
number of elements in the array (or along the input ``axis``, if
specified), which follows the standard definition of biweight
midcovariance. If `True`, then the sample size is reduced to
correct for any rejected values (i.e. the sample size used
includes only the non-rejected values), which results in a value
closer to the true midcovariance for small sample sizes or for a
large number of rejected values. See `biweight_midcovariance`
for more details.
Returns
-------
biweight_midcorrelation : float
The biweight midcorrelation between ``x`` and ``y``.
See Also
--------
biweight_scale, biweight_midvariance, biweight_midcovariance, biweight_location
References
----------
.. [1] https://en.wikipedia.org/wiki/Biweight_midcorrelation
Examples
--------
Calculate the biweight midcorrelation between two variables:
>>> import numpy as np
>>> from astropy.stats import biweight_midcorrelation
>>> rng = np.random.default_rng(12345)
>>> x = rng.normal(0, 1, 200)
>>> y = rng.normal(0, 3, 200)
>>> # Introduce an obvious outlier
>>> x[0] = 30.0
>>> bicorr = biweight_midcorrelation(x, y)
>>> print(bicorr) # doctest: +FLOAT_CMP
-0.09203238319481295
"""
x = np.asanyarray(x)
y = np.asanyarray(y)
if x.ndim != 1:
raise ValueError('x must be a 1D array.')
if y.ndim != 1:
raise ValueError('y must be a 1D array.')
if x.shape != y.shape:
raise ValueError('x and y must have the same shape.')
bicorr = biweight_midcovariance([x, y], c=c, M=M,
modify_sample_size=modify_sample_size)
return bicorr[0, 1] / (np.sqrt(bicorr[0, 0] * bicorr[1, 1]))
|
6cb82580a2881915b30ac59e24eb082e0240d18ef503ae4495f3c5694e623719 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Methods for selecting the bin width of histograms
Ported from the astroML project: https://www.astroml.org/
"""
import numpy as np
from . import bayesian_blocks
__all__ = ['histogram', 'scott_bin_width', 'freedman_bin_width',
'knuth_bin_width', 'calculate_bin_edges']
def calculate_bin_edges(a, bins=10, range=None, weights=None):
"""
Calculate histogram bin edges like ``numpy.histogram_bin_edges``.
Parameters
----------
a : array-like
Input data. The bin edges are calculated over the flattened array.
bins : int, list, or str, optional
If ``bins`` is an int, it is the number of bins. If it is a list
it is taken to be the bin edges. If it is a string, it must be one
of 'blocks', 'knuth', 'scott' or 'freedman'. See
`~astropy.stats.histogram` for a description of each method.
range : tuple or None, optional
The minimum and maximum range for the histogram. If not specified,
it will be (a.min(), a.max()). However, if bins is a list it is
returned unmodified regardless of the range argument.
weights : array-like, optional
An array the same shape as ``a``. If given, the histogram accumulates
the value of the weight corresponding to ``a`` instead of returning the
count of values. This argument does not affect determination of bin
edges, though they may be used in the future as new methods are added.
"""
# if range is specified, we need to truncate the data for
# the bin-finding routines
if range is not None:
a = a[(a >= range[0]) & (a <= range[1])]
# if bins is a string, first compute bin edges with the desired heuristic
if isinstance(bins, str):
a = np.asarray(a).ravel()
# TODO: if weights is specified, we need to modify things.
# e.g. we could use point measures fitness for Bayesian blocks
if weights is not None:
raise NotImplementedError("weights are not yet supported "
"for the enhanced histogram")
if bins == 'blocks':
bins = bayesian_blocks(a)
elif bins == 'knuth':
da, bins = knuth_bin_width(a, True)
elif bins == 'scott':
da, bins = scott_bin_width(a, True)
elif bins == 'freedman':
da, bins = freedman_bin_width(a, True)
else:
raise ValueError(f"unrecognized bin code: '{bins}'")
if range:
# Check that the upper and lower edges are what was requested.
# The current implementation of the bin width estimators does not
# guarantee this, it only ensures that data outside the range is
# excluded from calculation of the bin widths.
if bins[0] != range[0]:
bins[0] = range[0]
if bins[-1] != range[1]:
bins[-1] = range[1]
elif np.ndim(bins) == 0:
# Number of bins was given
bins = np.histogram_bin_edges(a, bins, range=range, weights=weights)
return bins
def histogram(a, bins=10, range=None, weights=None, **kwargs):
"""Enhanced histogram function, providing adaptive binnings
This is a histogram function that enables the use of more sophisticated
algorithms for determining bins. Aside from the ``bins`` argument allowing
a string specified how bins are computed, the parameters are the same
as ``numpy.histogram()``.
Parameters
----------
a : array-like
array of data to be histogrammed
bins : int, list, or str, optional
If bins is a string, then it must be one of:
- 'blocks' : use bayesian blocks for dynamic bin widths
- 'knuth' : use Knuth's rule to determine bins
- 'scott' : use Scott's rule to determine bins
- 'freedman' : use the Freedman-Diaconis rule to determine bins
range : tuple or None, optional
the minimum and maximum range for the histogram. If not specified,
it will be (x.min(), x.max())
weights : array-like, optional
An array the same shape as ``a``. If given, the histogram accumulates
the value of the weight corresponding to ``a`` instead of returning the
count of values. This argument does not affect determination of bin
edges.
other keyword arguments are described in numpy.histogram().
Returns
-------
hist : array
The values of the histogram. See ``density`` and ``weights`` for a
description of the possible semantics.
bin_edges : array of dtype float
Return the bin edges ``(length(hist)+1)``.
See Also
--------
numpy.histogram
"""
bins = calculate_bin_edges(a, bins=bins, range=range, weights=weights)
# Now we call numpy's histogram with the resulting bin edges
return np.histogram(a, bins=bins, range=range, weights=weights, **kwargs)
def scott_bin_width(data, return_bins=False):
r"""Return the optimal histogram bin width using Scott's rule
Scott's rule is a normal reference rule: it minimizes the integrated
mean squared error in the bin approximation under the assumption that the
data is approximately Gaussian.
Parameters
----------
data : array-like, ndim=1
observed (one-dimensional) data
return_bins : bool, optional
if True, then return the bin edges
Returns
-------
width : float
optimal bin width using Scott's rule
bins : ndarray
bin edges: returned if ``return_bins`` is True
Notes
-----
The optimal bin width is
.. math::
\Delta_b = \frac{3.5\sigma}{n^{1/3}}
where :math:`\sigma` is the standard deviation of the data, and
:math:`n` is the number of data points [1]_.
References
----------
.. [1] Scott, David W. (1979). "On optimal and data-based histograms".
Biometricka 66 (3): 605-610
See Also
--------
knuth_bin_width
freedman_bin_width
bayesian_blocks
histogram
"""
data = np.asarray(data)
if data.ndim != 1:
raise ValueError("data should be one-dimensional")
n = data.size
sigma = np.std(data)
dx = 3.5 * sigma / (n ** (1 / 3))
if return_bins:
Nbins = np.ceil((data.max() - data.min()) / dx)
Nbins = max(1, Nbins)
bins = data.min() + dx * np.arange(Nbins + 1)
return dx, bins
else:
return dx
def freedman_bin_width(data, return_bins=False):
r"""Return the optimal histogram bin width using the Freedman-Diaconis rule
The Freedman-Diaconis rule is a normal reference rule like Scott's
rule, but uses rank-based statistics for results which are more robust
to deviations from a normal distribution.
Parameters
----------
data : array-like, ndim=1
observed (one-dimensional) data
return_bins : bool, optional
if True, then return the bin edges
Returns
-------
width : float
optimal bin width using the Freedman-Diaconis rule
bins : ndarray
bin edges: returned if ``return_bins`` is True
Notes
-----
The optimal bin width is
.. math::
\Delta_b = \frac{2(q_{75} - q_{25})}{n^{1/3}}
where :math:`q_{N}` is the :math:`N` percent quartile of the data, and
:math:`n` is the number of data points [1]_.
References
----------
.. [1] D. Freedman & P. Diaconis (1981)
"On the histogram as a density estimator: L2 theory".
Probability Theory and Related Fields 57 (4): 453-476
See Also
--------
knuth_bin_width
scott_bin_width
bayesian_blocks
histogram
"""
data = np.asarray(data)
if data.ndim != 1:
raise ValueError("data should be one-dimensional")
n = data.size
if n < 4:
raise ValueError("data should have more than three entries")
v25, v75 = np.percentile(data, [25, 75])
dx = 2 * (v75 - v25) / (n ** (1 / 3))
if return_bins:
dmin, dmax = data.min(), data.max()
Nbins = max(1, np.ceil((dmax - dmin) / dx))
try:
bins = dmin + dx * np.arange(Nbins + 1)
except ValueError as e:
if 'Maximum allowed size exceeded' in str(e):
raise ValueError(
'The inter-quartile range of the data is too small: '
'failed to construct histogram with {} bins. '
'Please use another bin method, such as '
'bins="scott"'.format(Nbins + 1))
else: # Something else # pragma: no cover
raise
return dx, bins
else:
return dx
def knuth_bin_width(data, return_bins=False, quiet=True):
r"""Return the optimal histogram bin width using Knuth's rule.
Knuth's rule is a fixed-width, Bayesian approach to determining
the optimal bin width of a histogram.
Parameters
----------
data : array-like, ndim=1
observed (one-dimensional) data
return_bins : bool, optional
if True, then return the bin edges
quiet : bool, optional
if True (default) then suppress stdout output from scipy.optimize
Returns
-------
dx : float
optimal bin width. Bins are measured starting at the first data point.
bins : ndarray
bin edges: returned if ``return_bins`` is True
Notes
-----
The optimal number of bins is the value M which maximizes the function
.. math::
F(M|x,I) = n\log(M) + \log\Gamma(\frac{M}{2})
- M\log\Gamma(\frac{1}{2})
- \log\Gamma(\frac{2n+M}{2})
+ \sum_{k=1}^M \log\Gamma(n_k + \frac{1}{2})
where :math:`\Gamma` is the Gamma function, :math:`n` is the number of
data points, :math:`n_k` is the number of measurements in bin :math:`k`
[1]_.
References
----------
.. [1] Knuth, K.H. "Optimal Data-Based Binning for Histograms".
arXiv:0605197, 2006
See Also
--------
freedman_bin_width
scott_bin_width
bayesian_blocks
histogram
"""
# import here because of optional scipy dependency
from scipy import optimize
knuthF = _KnuthF(data)
dx0, bins0 = freedman_bin_width(data, True)
M = optimize.fmin(knuthF, len(bins0), disp=not quiet)[0]
bins = knuthF.bins(M)
dx = bins[1] - bins[0]
if return_bins:
return dx, bins
else:
return dx
class _KnuthF:
r"""Class which implements the function minimized by knuth_bin_width
Parameters
----------
data : array-like, one dimension
data to be histogrammed
Notes
-----
the function F is given by
.. math::
F(M|x,I) = n\log(M) + \log\Gamma(\frac{M}{2})
- M\log\Gamma(\frac{1}{2})
- \log\Gamma(\frac{2n+M}{2})
+ \sum_{k=1}^M \log\Gamma(n_k + \frac{1}{2})
where :math:`\Gamma` is the Gamma function, :math:`n` is the number of
data points, :math:`n_k` is the number of measurements in bin :math:`k`.
See Also
--------
knuth_bin_width
"""
def __init__(self, data):
self.data = np.array(data, copy=True)
if self.data.ndim != 1:
raise ValueError("data should be 1-dimensional")
self.data.sort()
self.n = self.data.size
# import here rather than globally: scipy is an optional dependency.
# Note that scipy is imported in the function which calls this,
# so there shouldn't be any issue importing here.
from scipy import special
# create a reference to gammaln to use in self.eval()
self.gammaln = special.gammaln
def bins(self, M):
"""Return the bin edges given M number of bins"""
return np.linspace(self.data[0], self.data[-1], int(M) + 1)
def __call__(self, M):
return self.eval(M)
def eval(self, M):
"""Evaluate the Knuth function
Parameters
----------
M : int
Number of bins
Returns
-------
F : float
evaluation of the negative Knuth loglikelihood function:
smaller values indicate a better fit.
"""
M = int(M)
if M <= 0:
return np.inf
bins = self.bins(M)
nk, bins = np.histogram(self.data, bins)
return -(self.n * np.log(M) +
self.gammaln(0.5 * M) -
M * self.gammaln(0.5) -
self.gammaln(self.n + 0.5 * M) +
np.sum(self.gammaln(nk + 0.5)))
|
81b632ee3068c5783fd5ea3c9246c9dc844ff16632bd960cecf39c3a36f6b936 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
__all__ = ['jackknife_resampling', 'jackknife_stats']
__doctest_requires__ = {'jackknife_stats': ['scipy']}
def jackknife_resampling(data):
"""Performs jackknife resampling on numpy arrays.
Jackknife resampling is a technique to generate 'n' deterministic samples
of size 'n-1' from a measured sample of size 'n'. Basically, the i-th
sample, (1<=i<=n), is generated by means of removing the i-th measurement
of the original sample. Like the bootstrap resampling, this statistical
technique finds applications in estimating variance, bias, and confidence
intervals.
Parameters
----------
data : ndarray
Original sample (1-D array) from which the jackknife resamples will be
generated.
Returns
-------
resamples : ndarray
The i-th row is the i-th jackknife sample, i.e., the original sample
with the i-th measurement deleted.
References
----------
.. [1] McIntosh, Avery. "The Jackknife Estimation Method".
<https://arxiv.org/abs/1606.00497>
.. [2] Efron, Bradley. "The Jackknife, the Bootstrap, and other
Resampling Plans". Technical Report No. 63, Division of Biostatistics,
Stanford University, December, 1980.
.. [3] Jackknife resampling <https://en.wikipedia.org/wiki/Jackknife_resampling>
""" # noqa
n = data.shape[0]
if n <= 0:
raise ValueError("data must contain at least one measurement.")
resamples = np.empty([n, n-1])
for i in range(n):
resamples[i] = np.delete(data, i)
return resamples
def jackknife_stats(data, statistic, confidence_level=0.95):
"""Performs jackknife estimation on the basis of jackknife resamples.
This function requires `SciPy <https://www.scipy.org/>`_ to be installed.
Parameters
----------
data : ndarray
Original sample (1-D array).
statistic : function
Any function (or vector of functions) on the basis of the measured
data, e.g, sample mean, sample variance, etc. The jackknife estimate of
this statistic will be returned.
confidence_level : float, optional
Confidence level for the confidence interval of the Jackknife estimate.
Must be a real-valued number in (0,1). Default value is 0.95.
Returns
-------
estimate : float or `~numpy.ndarray`
The i-th element is the bias-corrected "jackknifed" estimate.
bias : float or `~numpy.ndarray`
The i-th element is the jackknife bias.
std_err : float or `~numpy.ndarray`
The i-th element is the jackknife standard error.
conf_interval : ndarray
If ``statistic`` is single-valued, the first and second elements are
the lower and upper bounds, respectively. If ``statistic`` is
vector-valued, each column corresponds to the confidence interval for
each component of ``statistic``. The first and second rows contain the
lower and upper bounds, respectively.
Examples
--------
1. Obtain Jackknife resamples:
>>> import numpy as np
>>> from astropy.stats import jackknife_resampling
>>> from astropy.stats import jackknife_stats
>>> data = np.array([1,2,3,4,5,6,7,8,9,0])
>>> resamples = jackknife_resampling(data)
>>> resamples
array([[2., 3., 4., 5., 6., 7., 8., 9., 0.],
[1., 3., 4., 5., 6., 7., 8., 9., 0.],
[1., 2., 4., 5., 6., 7., 8., 9., 0.],
[1., 2., 3., 5., 6., 7., 8., 9., 0.],
[1., 2., 3., 4., 6., 7., 8., 9., 0.],
[1., 2., 3., 4., 5., 7., 8., 9., 0.],
[1., 2., 3., 4., 5., 6., 8., 9., 0.],
[1., 2., 3., 4., 5., 6., 7., 9., 0.],
[1., 2., 3., 4., 5., 6., 7., 8., 0.],
[1., 2., 3., 4., 5., 6., 7., 8., 9.]])
>>> resamples.shape
(10, 9)
2. Obtain Jackknife estimate for the mean, its bias, its standard error,
and its 95% confidence interval:
>>> test_statistic = np.mean
>>> estimate, bias, stderr, conf_interval = jackknife_stats(
... data, test_statistic, 0.95)
>>> estimate
4.5
>>> bias
0.0
>>> stderr # doctest: +FLOAT_CMP
0.95742710775633832
>>> conf_interval
array([2.62347735, 6.37652265])
3. Example for two estimates
>>> test_statistic = lambda x: (np.mean(x), np.var(x))
>>> estimate, bias, stderr, conf_interval = jackknife_stats(
... data, test_statistic, 0.95)
>>> estimate
array([4.5 , 9.16666667])
>>> bias
array([ 0. , -0.91666667])
>>> stderr
array([0.95742711, 2.69124476])
>>> conf_interval
array([[ 2.62347735, 3.89192387],
[ 6.37652265, 14.44140947]])
IMPORTANT: Note that confidence intervals are given as columns
"""
# jackknife confidence interval
if not (0 < confidence_level < 1):
raise ValueError("confidence level must be in (0, 1).")
# make sure original data is proper
n = data.shape[0]
if n <= 0:
raise ValueError("data must contain at least one measurement.")
# Only import scipy if inputs are valid
from scipy.special import erfinv
resamples = jackknife_resampling(data)
stat_data = statistic(data)
jack_stat = np.apply_along_axis(statistic, 1, resamples)
mean_jack_stat = np.mean(jack_stat, axis=0)
# jackknife bias
bias = (n-1)*(mean_jack_stat - stat_data)
# jackknife standard error
std_err = np.sqrt((n-1)*np.mean((jack_stat - mean_jack_stat)*(jack_stat -
mean_jack_stat), axis=0))
# bias-corrected "jackknifed estimate"
estimate = stat_data - bias
z_score = np.sqrt(2.0)*erfinv(confidence_level)
conf_interval = estimate + z_score*np.array((-std_err, std_err))
return estimate, bias, std_err, conf_interval
|
4b83259e6207d52d8d9b693d36812f8c57b56e520ae8e00263d32e7baa712a94 | """
Table property for providing information about table.
"""
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import sys
import os
from contextlib import contextmanager
from inspect import isclass
import numpy as np
from astropy.utils.data_info import DataInfo
__all__ = ['table_info', 'TableInfo', 'serialize_method_as']
def table_info(tbl, option='attributes', out=''):
"""
Write summary information about column to the ``out`` filehandle.
By default this prints to standard output via sys.stdout.
The ``option`` argument specifies what type of information
to include. This can be a string, a function, or a list of
strings or functions. Built-in options are:
- ``attributes``: basic column meta data like ``dtype`` or ``format``
- ``stats``: basic statistics: minimum, mean, and maximum
If a function is specified then that function will be called with the
column as its single argument. The function must return an OrderedDict
containing the information attributes.
If a list is provided then the information attributes will be
appended for each of the options, in order.
Examples
--------
>>> from astropy.table.table_helpers import simple_table
>>> t = simple_table(size=2, kinds='if')
>>> t['a'].unit = 'm'
>>> t.info()
<Table length=2>
name dtype unit
---- ------- ----
a int64 m
b float64
>>> t.info('stats')
<Table length=2>
name mean std min max
---- ---- --- --- ---
a 1.5 0.5 1 2
b 1.5 0.5 1 2
Parameters
----------
option : str, callable, list of (str or callable)
Info option, defaults to 'attributes'.
out : file-like, None
Output destination, default is sys.stdout. If None then a
Table with information attributes is returned
Returns
-------
info : `~astropy.table.Table` if out==None else None
"""
from .table import Table
if out == '':
out = sys.stdout
descr_vals = [tbl.__class__.__name__]
if tbl.masked:
descr_vals.append('masked=True')
descr_vals.append(f'length={len(tbl)}')
outlines = ['<' + ' '.join(descr_vals) + '>']
cols = list(tbl.columns.values())
if tbl.colnames:
infos = []
for col in cols:
infos.append(col.info(option, out=None))
info = Table(infos, names=list(infos[0]))
else:
info = Table()
if out is None:
return info
# Since info is going to a filehandle for viewing then remove uninteresting
# columns.
if 'class' in info.colnames:
# Remove 'class' info column if all table columns are the same class
# and they are the default column class for that table.
uniq_types = set(type(col) for col in cols)
if len(uniq_types) == 1 and isinstance(cols[0], tbl.ColumnClass):
del info['class']
if 'n_bad' in info.colnames and np.all(info['n_bad'] == 0):
del info['n_bad']
# Standard attributes has 'length' but this is typically redundant
if 'length' in info.colnames and np.all(info['length'] == len(tbl)):
del info['length']
for name in info.colnames:
if info[name].dtype.kind in 'SU' and np.all(info[name] == ''):
del info[name]
if tbl.colnames:
outlines.extend(info.pformat(max_width=-1, max_lines=-1, show_unit=False))
else:
outlines.append('<No columns>')
out.writelines(outline + os.linesep for outline in outlines)
class TableInfo(DataInfo):
def __call__(self, option='attributes', out=''):
return table_info(self._parent, option, out)
__call__.__doc__ = table_info.__doc__
@contextmanager
def serialize_method_as(tbl, serialize_method):
"""Context manager to temporarily override individual
column info.serialize_method dict values. The serialize_method
attribute is an optional dict which might look like ``{'fits':
'jd1_jd2', 'ecsv': 'formatted_value', ..}``.
``serialize_method`` is a str or dict. If str then it the the value
is the ``serialize_method`` that will be used for all formats.
If dict then the key values can be either:
- Column name. This has higher precedence than the second option of
matching class.
- Class (matches any column which is an instance of the class)
This context manager is expected to be used only within ``Table.write``.
It could have been a private method on Table but prefer not to add
clutter to that class.
Parameters
----------
tbl : Table object
Input table
serialize_method : dict, str
Dict with key values of column names or types, or str
Returns
-------
None (context manager)
"""
def get_override_sm(col):
"""
Determine if the ``serialize_method`` str or dict specifies an
override of column presets for ``col``. Returns the matching
serialize_method value or ``None``.
"""
# If a string then all columns match
if isinstance(serialize_method, str):
return serialize_method
# If column name then return that serialize_method
if col.info.name in serialize_method:
return serialize_method[col.info.name]
# Otherwise look for subclass matches
for key in serialize_method:
if isclass(key) and isinstance(col, key):
return serialize_method[key]
return None
# Setup for the context block. Set individual column.info.serialize_method
# values as appropriate and keep a backup copy. If ``serialize_method``
# is None or empty then don't do anything.
# Original serialize_method dict, keyed by column name. This only
# gets used and set if there is an override.
original_sms = {}
if serialize_method:
# Go through every column and if it has a serialize_method info
# attribute then potentially update it for the duration of the write.
for col in tbl.itercols():
if hasattr(col.info, 'serialize_method'):
override_sm = get_override_sm(col)
if override_sm:
# Make a reference copy of the column serialize_method
# dict which maps format (e.g. 'fits') to the
# appropriate method (e.g. 'data_mask').
original_sms[col.info.name] = col.info.serialize_method
# Set serialize method for *every* available format. This is
# brute force, but at this point the format ('fits', 'ecsv', etc)
# is not actually known (this gets determined by the write function
# in registry.py). Note this creates a new temporary dict object
# so that the restored version is the same original object.
col.info.serialize_method = {fmt: override_sm
for fmt in col.info.serialize_method}
# Finally yield for the context block
try:
yield
finally:
# Teardown (restore) for the context block. Be sure to do this even
# if an exception occurred.
if serialize_method:
for name, original_sm in original_sms.items():
tbl[name].info.serialize_method = original_sm
|
063b408848582eabfe45f09a87c6613ed8319d53254aabea0b6810bce4266a4c | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
The Index class can use several implementations as its
engine. Any implementation should implement the following:
__init__(data, row_index) : initialize index based on key/row list pairs
add(key, row) -> None : add (key, row) to existing data
remove(key, data=None) -> boolean : remove data from self[key], or all of
self[key] if data is None
shift_left(row) -> None : decrement row numbers after row
shift_right(row) -> None : increase row numbers >= row
find(key) -> list : list of rows corresponding to key
range(lower, upper, bounds) -> list : rows in self[k] where k is between
lower and upper (<= or < based on bounds)
sort() -> None : make row order align with key order
sorted_data() -> list of rows in sorted order (by key)
replace_rows(row_map) -> None : replace row numbers based on slice
items() -> list of tuples of the form (key, data)
Notes
-----
When a Table is initialized from another Table, indices are
(deep) copied and their columns are set to the columns of the new Table.
Column creation:
Column(c) -> deep copy of indices
c[[1, 2]] -> deep copy and reordering of indices
c[1:2] -> reference
array.view(Column) -> no indices
"""
from copy import deepcopy
import numpy as np
from .bst import MinValue, MaxValue
from .sorted_array import SortedArray
class QueryError(ValueError):
'''
Indicates that a given index cannot handle the supplied query.
'''
pass
class Index:
'''
The Index class makes it possible to maintain indices
on columns of a Table, so that column values can be queried
quickly and efficiently. Column values are stored in lexicographic
sorted order, which allows for binary searching in O(log n).
Parameters
----------
columns : list or None
List of columns on which to create an index. If None,
create an empty index for purposes of deep copying.
engine : type, instance, or None
Indexing engine class to use (from among SortedArray, BST,
and SCEngine) or actual engine instance.
If the supplied argument is None (by default), use SortedArray.
unique : bool (defaults to False)
Whether the values of the index must be unique
'''
def __init__(self, columns, engine=None, unique=False):
# Local imports to avoid import problems.
from .table import Table, Column
from astropy.time import Time
if columns is not None:
columns = list(columns)
if engine is not None and not isinstance(engine, type):
# create from data
self.engine = engine.__class__
self.data = engine
self.columns = columns
return
# by default, use SortedArray
self.engine = engine or SortedArray
if columns is None: # this creates a special exception for deep copying
columns = []
data = []
row_index = []
elif len(columns) == 0:
raise ValueError("Cannot create index without at least one column")
elif len(columns) == 1:
col = columns[0]
row_index = Column(col.argsort())
data = Table([col[row_index]])
else:
num_rows = len(columns[0])
# replace Time columns with approximate form and remainder
new_columns = []
for col in columns:
if isinstance(col, Time):
new_columns.append(col.jd)
remainder = col - col.__class__(col.jd, format='jd', scale=col.scale)
new_columns.append(remainder.jd)
else:
new_columns.append(col)
# sort the table lexicographically and keep row numbers
table = Table(columns + [np.arange(num_rows)], copy_indices=False)
sort_columns = new_columns[::-1]
try:
lines = table[np.lexsort(sort_columns)]
except TypeError: # arbitrary mixins might not work with lexsort
lines = table[table.argsort()]
data = lines[lines.colnames[:-1]]
row_index = lines[lines.colnames[-1]]
self.data = self.engine(data, row_index, unique=unique)
self.columns = columns
def __len__(self):
'''
Number of rows in index.
'''
return len(self.columns[0])
def replace_col(self, prev_col, new_col):
'''
Replace an indexed column with an updated reference.
Parameters
----------
prev_col : Column
Column reference to replace
new_col : Column
New column reference
'''
self.columns[self.col_position(prev_col.info.name)] = new_col
def reload(self):
'''
Recreate the index based on data in self.columns.
'''
self.__init__(self.columns, engine=self.engine)
def col_position(self, col_name):
'''
Return the position of col_name in self.columns.
Parameters
----------
col_name : str
Name of column to look up
'''
for i, c in enumerate(self.columns):
if c.info.name == col_name:
return i
raise ValueError(f"Column does not belong to index: {col_name}")
def insert_row(self, pos, vals, columns):
'''
Insert a new row from the given values.
Parameters
----------
pos : int
Position at which to insert row
vals : list or tuple
List of values to insert into a new row
columns : list
Table column references
'''
key = [None] * len(self.columns)
for i, col in enumerate(columns):
try:
key[self.col_position(col.info.name)] = vals[i]
except ValueError: # not a member of index
continue
num_rows = len(self.columns[0])
if pos < num_rows:
# shift all rows >= pos to the right
self.data.shift_right(pos)
self.data.add(tuple(key), pos)
def get_row_specifier(self, row_specifier):
'''
Return an iterable corresponding to the
input row specifier.
Parameters
----------
row_specifier : int, list, ndarray, or slice
'''
if isinstance(row_specifier, (int, np.integer)):
# single row
return (row_specifier,)
elif isinstance(row_specifier, (list, np.ndarray)):
return row_specifier
elif isinstance(row_specifier, slice):
col_len = len(self.columns[0])
return range(*row_specifier.indices(col_len))
raise ValueError("Expected int, array of ints, or slice but "
"got {} in remove_rows".format(row_specifier))
def remove_rows(self, row_specifier):
'''
Remove the given rows from the index.
Parameters
----------
row_specifier : int, list, ndarray, or slice
Indicates which row(s) to remove
'''
rows = []
# To maintain the correct row order, we loop twice,
# deleting rows first and then reordering the remaining rows
for row in self.get_row_specifier(row_specifier):
self.remove_row(row, reorder=False)
rows.append(row)
# second pass - row order is reversed to maintain
# correct row numbers
for row in reversed(sorted(rows)):
self.data.shift_left(row)
def remove_row(self, row, reorder=True):
'''
Remove the given row from the index.
Parameters
----------
row : int
Position of row to remove
reorder : bool
Whether to reorder indices after removal
'''
# for removal, form a key consisting of column values in this row
if not self.data.remove(tuple([col[row] for col in self.columns]), row):
raise ValueError(f"Could not remove row {row} from index")
# decrement the row number of all later rows
if reorder:
self.data.shift_left(row)
def find(self, key):
'''
Return the row values corresponding to key, in sorted order.
Parameters
----------
key : tuple
Values to search for in each column
'''
return self.data.find(key)
def same_prefix(self, key):
'''
Return rows whose keys contain the supplied key as a prefix.
Parameters
----------
key : tuple
Prefix for which to search
'''
return self.same_prefix_range(key, key, (True, True))
def same_prefix_range(self, lower, upper, bounds=(True, True)):
'''
Return rows whose keys have a prefix in the given range.
Parameters
----------
lower : tuple
Lower prefix bound
upper : tuple
Upper prefix bound
bounds : tuple (x, y) of bools
Indicates whether the search should be inclusive or
exclusive with respect to the endpoints. The first
argument x corresponds to an inclusive lower bound,
and the second argument y to an inclusive upper bound.
'''
n = len(lower)
ncols = len(self.columns)
a = MinValue() if bounds[0] else MaxValue()
b = MaxValue() if bounds[1] else MinValue()
# [x, y] search corresponds to [(x, min), (y, max)]
# (x, y) search corresponds to ((x, max), (x, min))
lower = lower + tuple((ncols - n) * [a])
upper = upper + tuple((ncols - n) * [b])
return self.data.range(lower, upper, bounds)
def range(self, lower, upper, bounds=(True, True)):
'''
Return rows within the given range.
Parameters
----------
lower : tuple
Lower prefix bound
upper : tuple
Upper prefix bound
bounds : tuple (x, y) of bools
Indicates whether the search should be inclusive or
exclusive with respect to the endpoints. The first
argument x corresponds to an inclusive lower bound,
and the second argument y to an inclusive upper bound.
'''
return self.data.range(lower, upper, bounds)
def replace(self, row, col_name, val):
'''
Replace the value of a column at a given position.
Parameters
----------
row : int
Row number to modify
col_name : str
Name of the Column to modify
val : col.info.dtype
Value to insert at specified row of col
'''
self.remove_row(row, reorder=False)
key = [c[row] for c in self.columns]
key[self.col_position(col_name)] = val
self.data.add(tuple(key), row)
def replace_rows(self, col_slice):
'''
Modify rows in this index to agree with the specified
slice. For example, given an index
{'5': 1, '2': 0, '3': 2} on a column ['2', '5', '3'],
an input col_slice of [2, 0] will result in the relabeling
{'3': 0, '2': 1} on the sliced column ['3', '2'].
Parameters
----------
col_slice : list
Indices to slice
'''
row_map = dict((row, i) for i, row in enumerate(col_slice))
self.data.replace_rows(row_map)
def sort(self):
'''
Make row numbers follow the same sort order as the keys
of the index.
'''
self.data.sort()
def sorted_data(self):
'''
Returns a list of rows in sorted order based on keys;
essentially acts as an argsort() on columns.
'''
return self.data.sorted_data()
def __getitem__(self, item):
'''
Returns a sliced version of this index.
Parameters
----------
item : slice
Input slice
Returns
-------
SlicedIndex
A sliced reference to this index.
'''
return SlicedIndex(self, item)
def __repr__(self):
col_names = tuple(col.info.name for col in self.columns)
return f'<{self.__class__.__name__} columns={col_names} data={self.data}>'
def __deepcopy__(self, memo):
'''
Return a deep copy of this index.
Notes
-----
The default deep copy must be overridden to perform
a shallow copy of the index columns, avoiding infinite recursion.
Parameters
----------
memo : dict
'''
# Bypass Index.__new__ to create an actual Index, not a SlicedIndex.
index = super().__new__(self.__class__)
index.__init__(None, engine=self.engine)
index.data = deepcopy(self.data, memo)
index.columns = self.columns[:] # new list, same columns
memo[id(self)] = index
return index
class SlicedIndex:
'''
This class provides a wrapper around an actual Index object
to make index slicing function correctly. Since numpy expects
array slices to provide an actual data view, a SlicedIndex should
retrieve data directly from the original index and then adapt
it to the sliced coordinate system as appropriate.
Parameters
----------
index : Index
The original Index reference
index_slice : tuple, slice
The slice to which this SlicedIndex corresponds
original : bool
Whether this SlicedIndex represents the original index itself.
For the most part this is similar to index[:] but certain
copying operations are avoided, and the slice retains the
length of the actual index despite modification.
'''
def __init__(self, index, index_slice, original=False):
self.index = index
self.original = original
self._frozen = False
if isinstance(index_slice, tuple):
self.start, self._stop, self.step = index_slice
elif isinstance(index_slice, slice): # index_slice is an actual slice
num_rows = len(index.columns[0])
self.start, self._stop, self.step = index_slice.indices(num_rows)
else:
raise TypeError('index_slice must be tuple or slice')
@property
def length(self):
return 1 + (self.stop - self.start - 1) // self.step
@property
def stop(self):
'''
The stopping position of the slice, or the end of the
index if this is an original slice.
'''
return len(self.index) if self.original else self._stop
def __getitem__(self, item):
'''
Returns another slice of this Index slice.
Parameters
----------
item : slice
Index slice
'''
if self.length <= 0:
# empty slice
return SlicedIndex(self.index, slice(1, 0))
start, stop, step = item.indices(self.length)
new_start = self.orig_coords(start)
new_stop = self.orig_coords(stop)
new_step = self.step * step
return SlicedIndex(self.index, (new_start, new_stop, new_step))
def sliced_coords(self, rows):
'''
Convert the input rows to the sliced coordinate system.
Parameters
----------
rows : list
Rows in the original coordinate system
Returns
-------
sliced_rows : list
Rows in the sliced coordinate system
'''
if self.original:
return rows
else:
rows = np.array(rows)
row0 = rows - self.start
if self.step != 1:
correct_mod = np.mod(row0, self.step) == 0
row0 = row0[correct_mod]
if self.step > 0:
ok = (row0 >= 0) & (row0 < self.stop - self.start)
else:
ok = (row0 <= 0) & (row0 > self.stop - self.start)
return row0[ok] // self.step
def orig_coords(self, row):
'''
Convert the input row from sliced coordinates back
to original coordinates.
Parameters
----------
row : int
Row in the sliced coordinate system
Returns
-------
orig_row : int
Row in the original coordinate system
'''
return row if self.original else self.start + row * self.step
def find(self, key):
return self.sliced_coords(self.index.find(key))
def where(self, col_map):
return self.sliced_coords(self.index.where(col_map))
def range(self, lower, upper):
return self.sliced_coords(self.index.range(lower, upper))
def same_prefix(self, key):
return self.sliced_coords(self.index.same_prefix(key))
def sorted_data(self):
return self.sliced_coords(self.index.sorted_data())
def replace(self, row, col, val):
if not self._frozen:
self.index.replace(self.orig_coords(row), col, val)
def get_index_or_copy(self):
if not self.original:
# replace self.index with a new object reference
self.index = deepcopy(self.index)
return self.index
def insert_row(self, pos, vals, columns):
if not self._frozen:
self.get_index_or_copy().insert_row(self.orig_coords(pos), vals, columns)
def get_row_specifier(self, row_specifier):
return [self.orig_coords(x) for x in
self.index.get_row_specifier(row_specifier)]
def remove_rows(self, row_specifier):
if not self._frozen:
self.get_index_or_copy().remove_rows(row_specifier)
def replace_rows(self, col_slice):
if not self._frozen:
self.index.replace_rows([self.orig_coords(x) for x in col_slice])
def sort(self):
if not self._frozen:
self.get_index_or_copy().sort()
def __repr__(self):
slice_str = '' if self.original else f' slice={self.start}:{self.stop}:{self.step}'
return (f'<{self.__class__.__name__} original={self.original}{slice_str}'
f' index={self.index}>')
def replace_col(self, prev_col, new_col):
self.index.replace_col(prev_col, new_col)
def reload(self):
self.index.reload()
def col_position(self, col_name):
return self.index.col_position(col_name)
def get_slice(self, col_slice, item):
'''
Return a newly created index from the given slice.
Parameters
----------
col_slice : Column object
Already existing slice of a single column
item : list or ndarray
Slice for retrieval
'''
from .table import Table
if len(self.columns) == 1:
index = Index([col_slice], engine=self.data.__class__)
return self.__class__(index, slice(0, 0, None), original=True)
t = Table(self.columns, copy_indices=False)
with t.index_mode('discard_on_copy'):
new_cols = t[item].columns.values()
index = Index(new_cols, engine=self.data.__class__)
return self.__class__(index, slice(0, 0, None), original=True)
@property
def columns(self):
return self.index.columns
@property
def data(self):
return self.index.data
def get_index(table, table_copy=None, names=None):
"""
Inputs a table and some subset of its columns as table_copy.
List or tuple containing names of columns as names,and returns an index
corresponding to this subset or list or None if no such index exists.
Parameters
----------
table : `Table`
Input table
table_copy : `Table`, optional
Subset of the columns in the ``table`` argument
names : list, tuple, optional
Subset of column names in the ``table`` argument
Returns
-------
Index of columns or None
"""
if names is not None and table_copy is not None:
raise ValueError('one and only one argument from "table_copy" or'
' "names" is required')
if names is None and table_copy is None:
raise ValueError('one and only one argument from "table_copy" or'
' "names" is required')
if names is not None:
names = set(names)
else:
names = set(table_copy.colnames)
if not names <= set(table.colnames):
raise ValueError(f'{names} is not a subset of table columns')
for name in names:
for index in table[name].info.indices:
if set([col.info.name for col in index.columns]) == names:
return index
return None
def get_index_by_names(table, names):
'''
Returns an index in ``table`` corresponding to the ``names`` columns or None
if no such index exists.
Parameters
----------
table : `Table`
Input table
nmaes : tuple, list
Column names
'''
names = list(names)
for index in table.indices:
index_names = [col.info.name for col in index.columns]
if index_names == names:
return index
else:
return None
class _IndexModeContext:
'''
A context manager that allows for special indexing modes, which
are intended to improve performance. Currently the allowed modes
are "freeze", in which indices are not modified upon column modification,
"copy_on_getitem", in which indices are copied upon column slicing,
and "discard_on_copy", in which indices are discarded upon table
copying/slicing.
'''
_col_subclasses = {}
def __init__(self, table, mode):
'''
Parameters
----------
table : Table
The table to which the mode should be applied
mode : str
Either 'freeze', 'copy_on_getitem', or 'discard_on_copy'.
In 'discard_on_copy' mode,
indices are not copied whenever columns or tables are copied.
In 'freeze' mode, indices are not modified whenever columns are
modified; at the exit of the context, indices refresh themselves
based on column values. This mode is intended for scenarios in
which one intends to make many additions or modifications on an
indexed column.
In 'copy_on_getitem' mode, indices are copied when taking column
slices as well as table slices, so col[i0:i1] will preserve
indices.
'''
self.table = table
self.mode = mode
# Used by copy_on_getitem
self._orig_classes = []
if mode not in ('freeze', 'discard_on_copy', 'copy_on_getitem'):
raise ValueError("Expected a mode of either 'freeze', "
"'discard_on_copy', or 'copy_on_getitem', got "
"'{}'".format(mode))
def __enter__(self):
if self.mode == 'discard_on_copy':
self.table._copy_indices = False
elif self.mode == 'copy_on_getitem':
for col in self.table.columns.values():
self._orig_classes.append(col.__class__)
col.__class__ = self._get_copy_on_getitem_shim(col.__class__)
else:
for index in self.table.indices:
index._frozen = True
def __exit__(self, exc_type, exc_value, traceback):
if self.mode == 'discard_on_copy':
self.table._copy_indices = True
elif self.mode == 'copy_on_getitem':
for col in reversed(self.table.columns.values()):
col.__class__ = self._orig_classes.pop()
else:
for index in self.table.indices:
index._frozen = False
index.reload()
def _get_copy_on_getitem_shim(self, cls):
"""
This creates a subclass of the column's class which overrides that
class's ``__getitem__``, such that when returning a slice of the
column, the relevant indices are also copied over to the slice.
Ideally, rather than shimming in a new ``__class__`` we would be able
to just flip a flag that is checked by the base class's
``__getitem__``. Unfortunately, since the flag needs to be a Python
variable, this slows down ``__getitem__`` too much in the more common
case where a copy of the indices is not needed. See the docstring for
``astropy.table._column_mixins`` for more information on that.
"""
if cls in self._col_subclasses:
return self._col_subclasses[cls]
def __getitem__(self, item):
value = cls.__getitem__(self, item)
if type(value) is type(self):
value = self.info.slice_indices(value, item, len(self))
return value
clsname = f'_{cls.__name__}WithIndexCopy'
new_cls = type(str(clsname), (cls,), {'__getitem__': __getitem__})
self._col_subclasses[cls] = new_cls
return new_cls
class TableIndices(list):
'''
A special list of table indices allowing
for retrieval by column name(s).
Parameters
----------
lst : list
List of indices
'''
def __init__(self, lst):
super().__init__(lst)
def __getitem__(self, item):
'''
Retrieve an item from the list of indices.
Parameters
----------
item : int, str, tuple, or list
Position in list or name(s) of indexed column(s)
'''
if isinstance(item, str):
item = [item]
if isinstance(item, (list, tuple)):
item = list(item)
for index in self:
try:
for name in item:
index.col_position(name)
if len(index.columns) == len(item):
return index
except ValueError:
pass
# index search failed
raise IndexError(f"No index found for {item}")
return super().__getitem__(item)
class TableLoc:
"""
A pseudo-list of Table rows allowing for retrieval
of rows by indexed column values.
Parameters
----------
table : Table
Indexed table to use
"""
def __init__(self, table):
self.table = table
self.indices = table.indices
if len(self.indices) == 0:
raise ValueError("Cannot create TableLoc object with no indices")
def _get_rows(self, item):
"""
Retrieve Table rows indexes by value slice.
"""
if isinstance(item, tuple):
key, item = item
else:
key = self.table.primary_key
index = self.indices[key]
if len(index.columns) > 1:
raise ValueError("Cannot use .loc on multi-column indices")
if isinstance(item, slice):
# None signifies no upper/lower bound
start = MinValue() if item.start is None else item.start
stop = MaxValue() if item.stop is None else item.stop
rows = index.range((start,), (stop,))
else:
if not isinstance(item, (list, np.ndarray)): # single element
item = [item]
# item should be a list or ndarray of values
rows = []
for key in item:
p = index.find((key,))
if len(p) == 0:
raise KeyError(f'No matches found for key {key}')
else:
rows.extend(p)
return rows
def __getitem__(self, item):
"""
Retrieve Table rows by value slice.
Parameters
----------
item : column element, list, ndarray, slice or tuple
Can be a value of the table primary index, a list/ndarray
of such values, or a value slice (both endpoints are included).
If a tuple is provided, the first element must be
an index to use instead of the primary key, and the
second element must be as above.
"""
rows = self._get_rows(item)
if len(rows) == 0: # no matches found
raise KeyError(f'No matches found for key {item}')
elif len(rows) == 1: # single row
return self.table[rows[0]]
return self.table[rows]
def __setitem__(self, key, value):
"""
Assign Table row's by value slice.
Parameters
----------
key : column element, list, ndarray, slice or tuple
Can be a value of the table primary index, a list/ndarray
of such values, or a value slice (both endpoints are included).
If a tuple is provided, the first element must be
an index to use instead of the primary key, and the
second element must be as above.
value : New values of the row elements.
Can be a list of tuples/lists to update the row.
"""
rows = self._get_rows(key)
if len(rows) == 0: # no matches found
raise KeyError(f'No matches found for key {key}')
elif len(rows) == 1: # single row
self.table[rows[0]] = value
else: # multiple rows
if len(rows) == len(value):
for row, val in zip(rows, value):
self.table[row] = val
else:
raise ValueError(f'Right side should contain {len(rows)} values')
class TableLocIndices(TableLoc):
def __getitem__(self, item):
"""
Retrieve Table row's indices by value slice.
Parameters
----------
item : column element, list, ndarray, slice or tuple
Can be a value of the table primary index, a list/ndarray
of such values, or a value slice (both endpoints are included).
If a tuple is provided, the first element must be
an index to use instead of the primary key, and the
second element must be as above.
"""
rows = self._get_rows(item)
if len(rows) == 0: # no matches found
raise KeyError(f'No matches found for key {item}')
elif len(rows) == 1: # single row
return rows[0]
return rows
class TableILoc(TableLoc):
'''
A variant of TableLoc allowing for row retrieval by
indexed order rather than data values.
Parameters
----------
table : Table
Indexed table to use
'''
def __init__(self, table):
super().__init__(table)
def __getitem__(self, item):
if isinstance(item, tuple):
key, item = item
else:
key = self.table.primary_key
index = self.indices[key]
rows = index.sorted_data()[item]
table_slice = self.table[rows]
if len(table_slice) == 0: # no matches found
raise IndexError(f'Invalid index for iloc: {item}')
return table_slice
|
40c5ce95c5fb602e61894827ea7fc4bc730178129daa2acda4e156eb7e99bb33 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from os.path import abspath, dirname, join
from .table import Table
import astropy.io.registry as io_registry
import astropy.config as _config
from astropy import extern
class Conf(_config.ConfigNamespace):
"""
Configuration parameters for `astropy.table.jsviewer`.
"""
jquery_url = _config.ConfigItem(
'https://code.jquery.com/jquery-3.1.1.min.js',
'The URL to the jquery library.')
datatables_url = _config.ConfigItem(
'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min.js',
'The URL to the jquery datatables library.')
css_urls = _config.ConfigItem(
['https://cdn.datatables.net/1.10.12/css/jquery.dataTables.css'],
'The URLs to the css file(s) to include.', cfgtype='string_list')
conf = Conf()
EXTERN_JS_DIR = abspath(join(dirname(extern.__file__), 'jquery', 'data', 'js'))
EXTERN_CSS_DIR = abspath(join(dirname(extern.__file__), 'jquery', 'data', 'css'))
_SORTING_SCRIPT_PART_1 = """
var astropy_sort_num = function(a, b) {{
var a_num = parseFloat(a);
var b_num = parseFloat(b);
if (isNaN(a_num) && isNaN(b_num))
return ((a < b) ? -1 : ((a > b) ? 1 : 0));
else if (!isNaN(a_num) && !isNaN(b_num))
return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));
else
return isNaN(a_num) ? -1 : 1;
}}
"""
_SORTING_SCRIPT_PART_2 = """
jQuery.extend( jQuery.fn.dataTableExt.oSort, {{
"optionalnum-asc": astropy_sort_num,
"optionalnum-desc": function (a,b) {{ return -astropy_sort_num(a, b); }}
}});
"""
IPYNB_JS_SCRIPT = """
<script>
%(sorting_script1)s
require.config({{paths: {{
datatables: '{datatables_url}'
}}}});
require(["datatables"], function(){{
console.log("$('#{tid}').dataTable()");
%(sorting_script2)s
$('#{tid}').dataTable({{
order: [],
pageLength: {display_length},
lengthMenu: {display_length_menu},
pagingType: "full_numbers",
columnDefs: [{{targets: {sort_columns}, type: "optionalnum"}}]
}});
}});
</script>
""" % dict(sorting_script1=_SORTING_SCRIPT_PART_1,
sorting_script2=_SORTING_SCRIPT_PART_2)
HTML_JS_SCRIPT = _SORTING_SCRIPT_PART_1 + _SORTING_SCRIPT_PART_2 + """
$(document).ready(function() {{
$('#{tid}').dataTable({{
order: [],
pageLength: {display_length},
lengthMenu: {display_length_menu},
pagingType: "full_numbers",
columnDefs: [{{targets: {sort_columns}, type: "optionalnum"}}]
}});
}} );
"""
# Default CSS for the JSViewer writer
DEFAULT_CSS = """\
body {font-family: sans-serif;}
table.dataTable {width: auto !important; margin: 0 !important;}
.dataTables_filter, .dataTables_paginate {float: left !important; margin-left:1em}
"""
# Default CSS used when rendering a table in the IPython notebook
DEFAULT_CSS_NB = """\
table.dataTable {clear: both; width: auto !important; margin: 0 !important;}
.dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{
display: inline-block; margin-right: 1em; }
.paginate_button { margin-right: 5px; }
"""
class JSViewer:
"""Provides an interactive HTML export of a Table.
This class provides an interface to the `DataTables
<https://datatables.net/>`_ library, which allow to visualize interactively
an HTML table. It is used by the `~astropy.table.Table.show_in_browser`
method.
Parameters
----------
use_local_files : bool, optional
Use local files or a CDN for JavaScript libraries. Default False.
display_length : int, optional
Number or rows to show. Default to 50.
"""
def __init__(self, use_local_files=False, display_length=50):
self._use_local_files = use_local_files
self.display_length_menu = [[10, 25, 50, 100, 500, 1000, -1],
[10, 25, 50, 100, 500, 1000, "All"]]
self.display_length = display_length
for L in self.display_length_menu:
if display_length not in L:
L.insert(0, display_length)
@property
def jquery_urls(self):
if self._use_local_files:
return ['file://' + join(EXTERN_JS_DIR, 'jquery-3.1.1.min.js'),
'file://' + join(EXTERN_JS_DIR, 'jquery.dataTables.min.js')]
else:
return [conf.jquery_url, conf.datatables_url]
@property
def css_urls(self):
if self._use_local_files:
return ['file://' + join(EXTERN_CSS_DIR,
'jquery.dataTables.css')]
else:
return conf.css_urls
def _jstable_file(self):
if self._use_local_files:
return 'file://' + join(EXTERN_JS_DIR, 'jquery.dataTables.min')
else:
return conf.datatables_url[:-3]
def ipynb(self, table_id, css=None, sort_columns='[]'):
html = f'<style>{css if css is not None else DEFAULT_CSS_NB}</style>'
html += IPYNB_JS_SCRIPT.format(
display_length=self.display_length,
display_length_menu=self.display_length_menu,
datatables_url=self._jstable_file(),
tid=table_id, sort_columns=sort_columns)
return html
def html_js(self, table_id='table0', sort_columns='[]'):
return HTML_JS_SCRIPT.format(
display_length=self.display_length,
display_length_menu=self.display_length_menu,
tid=table_id, sort_columns=sort_columns).strip()
def write_table_jsviewer(table, filename, table_id=None, max_lines=5000,
table_class="display compact", jskwargs=None,
css=DEFAULT_CSS, htmldict=None, overwrite=False):
if table_id is None:
table_id = f'table{id(table)}'
jskwargs = jskwargs or {}
jsv = JSViewer(**jskwargs)
sortable_columns = [i for i, col in enumerate(table.columns.values())
if col.info.dtype.kind in 'iufc']
html_options = {
'table_id': table_id,
'table_class': table_class,
'css': css,
'cssfiles': jsv.css_urls,
'jsfiles': jsv.jquery_urls,
'js': jsv.html_js(table_id=table_id, sort_columns=sortable_columns)
}
if htmldict:
html_options.update(htmldict)
if max_lines < len(table):
table = table[:max_lines]
table.write(filename, format='html', htmldict=html_options,
overwrite=overwrite)
io_registry.register_writer('jsviewer', Table, write_table_jsviewer)
|
632176446a2d126e44536efde415c8afe681d88ecb99aab614a2142a5ebb5b60 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from astropy.io import registry
from .info import serialize_method_as
__all__ = ['TableRead', 'TableWrite']
__doctest_skip__ = ['TableRead', 'TableWrite']
class TableRead(registry.UnifiedReadWrite):
"""Read and parse a data table and return as a Table.
This function provides the Table interface to the astropy unified I/O
layer. This allows easily reading a file in many supported data formats
using syntax such as::
>>> from astropy.table import Table
>>> dat = Table.read('table.dat', format='ascii')
>>> events = Table.read('events.fits', format='fits')
Get help on the available readers for ``Table`` using the``help()`` method::
>>> Table.read.help() # Get help reading Table and list supported formats
>>> Table.read.help('fits') # Get detailed help on Table FITS reader
>>> Table.read.list_formats() # Print list of available formats
See also: https://docs.astropy.org/en/stable/io/unified.html
Parameters
----------
*args : tuple, optional
Positional arguments passed through to data reader. If supplied the
first argument is typically the input filename.
format : str
File format specifier.
units : list, dict, optional
List or dict of units to apply to columns
descriptions : list, dict, optional
List or dict of descriptions to apply to columns
**kwargs : dict, optional
Keyword arguments passed through to data reader.
Returns
-------
out : `~astropy.table.Table`
Table corresponding to file contents
Notes
-----
"""
def __init__(self, instance, cls):
super().__init__(instance, cls, 'read', registry=None)
# uses default global registry
def __call__(self, *args, **kwargs):
cls = self._cls
units = kwargs.pop('units', None)
descriptions = kwargs.pop('descriptions', None)
out = self.registry.read(cls, *args, **kwargs)
# For some readers (e.g., ascii.ecsv), the returned `out` class is not
# guaranteed to be the same as the desired output `cls`. If so,
# try coercing to desired class without copying (io.registry.read
# would normally do a copy). The normal case here is swapping
# Table <=> QTable.
if cls is not out.__class__:
try:
out = cls(out, copy=False)
except Exception:
raise TypeError('could not convert reader output to {} '
'class.'.format(cls.__name__))
out._set_column_attribute('unit', units)
out._set_column_attribute('description', descriptions)
return out
class TableWrite(registry.UnifiedReadWrite):
"""
Write this Table object out in the specified format.
This function provides the Table interface to the astropy unified I/O
layer. This allows easily writing a file in many supported data formats
using syntax such as::
>>> from astropy.table import Table
>>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))
>>> dat.write('table.dat', format='ascii')
Get help on the available writers for ``Table`` using the``help()`` method::
>>> Table.write.help() # Get help writing Table and list supported formats
>>> Table.write.help('fits') # Get detailed help on Table FITS writer
>>> Table.write.list_formats() # Print list of available formats
The ``serialize_method`` argument is explained in the section on
`Table serialization methods
<https://docs.astropy.org/en/latest/io/unified.html#table-serialization-methods>`_.
See also: https://docs.astropy.org/en/stable/io/unified.html
Parameters
----------
*args : tuple, optional
Positional arguments passed through to data writer. If supplied the
first argument is the output filename.
format : str
File format specifier.
serialize_method : str, dict, optional
Serialization method specifier for columns.
**kwargs : dict, optional
Keyword arguments passed through to data writer.
Notes
-----
"""
def __init__(self, instance, cls):
super().__init__(instance, cls, 'write', registry=None)
# uses default global registry
def __call__(self, *args, serialize_method=None, **kwargs):
instance = self._instance
with serialize_method_as(instance, serialize_method):
self.registry.write(instance, *args, **kwargs)
|
8fbc137607dd786544e5b6eb12cd9c5af4315a5e24d3c71e1b17423a1110cc47 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import collections
from collections import OrderedDict
from operator import index as operator_index
import numpy as np
class Row:
"""A class to represent one row of a Table object.
A Row object is returned when a Table object is indexed with an integer
or when iterating over a table::
>>> from astropy.table import Table
>>> table = Table([(1, 2), (3, 4)], names=('a', 'b'),
... dtype=('int32', 'int32'))
>>> row = table[1]
>>> row
<Row index=1>
a b
int32 int32
----- -----
2 4
>>> row['a']
2
>>> row[1]
4
"""
def __init__(self, table, index):
# Ensure that the row index is a valid index (int)
index = operator_index(index)
n = len(table)
if index < -n or index >= n:
raise IndexError('index {} out of range for table with length {}'
.format(index, len(table)))
# Finally, ensure the index is positive [#8422] and set Row attributes
self._index = index % n
self._table = table
def __getitem__(self, item):
try:
# Try the most common use case of accessing a single column in the Row.
# Bypass the TableColumns __getitem__ since that does more testing
# and allows a list of tuple or str, which is not the right thing here.
out = OrderedDict.__getitem__(self._table.columns, item)[self._index]
except (KeyError, TypeError):
if self._table._is_list_or_tuple_of_str(item):
cols = [self._table[name] for name in item]
out = self._table.__class__(cols, copy=False)[self._index]
else:
# This is only to raise an exception
out = self._table.columns[item][self._index]
return out
def __setitem__(self, item, val):
if self._table._is_list_or_tuple_of_str(item):
self._table._set_row(self._index, colnames=item, vals=val)
else:
self._table.columns[item][self._index] = val
def _ipython_key_completions_(self):
return self.colnames
def __eq__(self, other):
if self._table.masked:
# Sent bug report to numpy-discussion group on 2012-Oct-21, subject:
# "Comparing rows in a structured masked array raises exception"
# No response, so this is still unresolved.
raise ValueError('Unable to compare rows for masked table due to numpy.ma bug')
return self.as_void() == other
def __ne__(self, other):
if self._table.masked:
raise ValueError('Unable to compare rows for masked table due to numpy.ma bug')
return self.as_void() != other
def __array__(self, dtype=None):
"""Support converting Row to np.array via np.array(table).
Coercion to a different dtype via np.array(table, dtype) is not
supported and will raise a ValueError.
If the parent table is masked then the mask information is dropped.
"""
if dtype is not None:
raise ValueError('Datatype coercion is not allowed')
return np.asarray(self.as_void())
def __len__(self):
return len(self._table.columns)
def __iter__(self):
index = self._index
for col in self._table.columns.values():
yield col[index]
def keys(self):
return self._table.columns.keys()
def values(self):
return self.__iter__()
@property
def table(self):
return self._table
@property
def index(self):
return self._index
def as_void(self):
"""
Returns a *read-only* copy of the row values in the form of np.void or
np.ma.mvoid objects. This corresponds to the object types returned for
row indexing of a pure numpy structured array or masked array. This
method is slow and its use is discouraged when possible.
Returns
-------
void_row : ``numpy.void`` or ``numpy.ma.mvoid``
Copy of row values.
``numpy.void`` if unmasked, ``numpy.ma.mvoid`` else.
"""
index = self._index
cols = self._table.columns.values()
vals = tuple(np.asarray(col)[index] for col in cols)
if self._table.masked:
mask = tuple(col.mask[index] if hasattr(col, 'mask') else False
for col in cols)
void_row = np.ma.array([vals], mask=[mask], dtype=self.dtype)[0]
else:
void_row = np.array([vals], dtype=self.dtype)[0]
return void_row
@property
def meta(self):
return self._table.meta
@property
def columns(self):
return self._table.columns
@property
def colnames(self):
return self._table.colnames
@property
def dtype(self):
return self._table.dtype
def _base_repr_(self, html=False):
"""
Display row as a single-line table but with appropriate header line.
"""
index = self.index if (self.index >= 0) else self.index + len(self._table)
table = self._table[index:index + 1]
descr_vals = [self.__class__.__name__,
f'index={self.index}']
if table.masked:
descr_vals.append('masked=True')
return table._base_repr_(html, descr_vals, max_width=-1,
tableid=f'table{id(self._table)}')
def _repr_html_(self):
return self._base_repr_(html=True)
def __repr__(self):
return self._base_repr_(html=False)
def __str__(self):
index = self.index if (self.index >= 0) else self.index + len(self._table)
return '\n'.join(self.table[index:index + 1].pformat(max_width=-1))
def __bytes__(self):
return str(self).encode('utf-8')
collections.abc.Sequence.register(Row)
|
06ad00fec8dcd0f8cfe753f63ff3657bf6074ec8911b60743ea075d8b3f2a0ca | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import platform
import warnings
import numpy as np
from .index import get_index_by_names
from astropy.utils.exceptions import AstropyUserWarning
__all__ = ['TableGroups', 'ColumnGroups']
def table_group_by(table, keys):
# index copies are unnecessary and slow down _table_group_by
with table.index_mode('discard_on_copy'):
return _table_group_by(table, keys)
def _table_group_by(table, keys):
"""
Get groups for ``table`` on specified ``keys``.
Parameters
----------
table : `Table`
Table to group
keys : str, list of str, `Table`, or Numpy array
Grouping key specifier
Returns
-------
grouped_table : Table object with groups attr set accordingly
"""
from .table import Table
from .serialize import represent_mixins_as_columns
# Pre-convert string to tuple of strings, or Table to the underlying structured array
if isinstance(keys, str):
keys = (keys,)
if isinstance(keys, (list, tuple)):
for name in keys:
if name not in table.colnames:
raise ValueError(f'Table does not have key column {name!r}')
if table.masked and np.any(table[name].mask):
raise ValueError(f'Missing values in key column {name!r} are not allowed')
# Make a column slice of the table without copying
table_keys = table.__class__([table[key] for key in keys], copy=False)
# If available get a pre-existing index for these columns
table_index = get_index_by_names(table, keys)
grouped_by_table_cols = True
elif isinstance(keys, (np.ndarray, Table)):
table_keys = keys
if len(table_keys) != len(table):
raise ValueError('Input keys array length {} does not match table length {}'
.format(len(table_keys), len(table)))
table_index = None
grouped_by_table_cols = False
else:
raise TypeError('Keys input must be string, list, tuple, Table or numpy array, but got {}'
.format(type(keys)))
# If there is not already an available index and table_keys is a Table then ensure
# that all cols (including mixins) are in a form that can sorted with the code below.
if not table_index and isinstance(table_keys, Table):
table_keys = represent_mixins_as_columns(table_keys)
# Get the argsort index `idx_sort`, accounting for particulars
try:
# take advantage of index internal sort if possible
if table_index is not None:
idx_sort = table_index.sorted_data()
else:
idx_sort = table_keys.argsort(kind='mergesort')
stable_sort = True
except TypeError:
# Some versions (likely 1.6 and earlier) of numpy don't support
# 'mergesort' for all data types. MacOSX (Darwin) doesn't have a stable
# sort by default, nor does Windows, while Linux does (or appears to).
idx_sort = table_keys.argsort()
stable_sort = platform.system() not in ('Darwin', 'Windows')
# Finally do the actual sort of table_keys values
table_keys = table_keys[idx_sort]
# Get all keys
diffs = np.concatenate(([True], table_keys[1:] != table_keys[:-1], [True]))
indices = np.flatnonzero(diffs)
# If the sort is not stable (preserves original table order) then sort idx_sort in
# place within each group.
if not stable_sort:
for i0, i1 in zip(indices[:-1], indices[1:]):
idx_sort[i0:i1].sort()
# Make a new table and set the _groups to the appropriate TableGroups object.
# Take the subset of the original keys at the indices values (group boundaries).
out = table.__class__(table[idx_sort])
out_keys = table_keys[indices[:-1]]
if isinstance(out_keys, Table):
out_keys.meta['grouped_by_table_cols'] = grouped_by_table_cols
out._groups = TableGroups(out, indices=indices, keys=out_keys)
return out
def column_group_by(column, keys):
"""
Get groups for ``column`` on specified ``keys``
Parameters
----------
column : Column object
Column to group
keys : Table or Numpy array of same length as col
Grouping key specifier
Returns
-------
grouped_column : Column object with groups attr set accordingly
"""
from .table import Table
from .serialize import represent_mixins_as_columns
if isinstance(keys, Table):
keys = represent_mixins_as_columns(keys)
keys = keys.as_array()
if not isinstance(keys, np.ndarray):
raise TypeError(f'Keys input must be numpy array, but got {type(keys)}')
if len(keys) != len(column):
raise ValueError('Input keys array length {} does not match column length {}'
.format(len(keys), len(column)))
idx_sort = keys.argsort()
keys = keys[idx_sort]
# Get all keys
diffs = np.concatenate(([True], keys[1:] != keys[:-1], [True]))
indices = np.flatnonzero(diffs)
# Make a new column and set the _groups to the appropriate ColumnGroups object.
# Take the subset of the original keys at the indices values (group boundaries).
out = column.__class__(column[idx_sort])
out._groups = ColumnGroups(out, indices=indices, keys=keys[indices[:-1]])
return out
class BaseGroups:
"""
A class to represent groups within a table of heterogeneous data.
- ``keys``: key values corresponding to each group
- ``indices``: index values in parent table or column corresponding to group boundaries
- ``aggregate()``: method to create new table by aggregating within groups
"""
@property
def parent(self):
return self.parent_column if isinstance(self, ColumnGroups) else self.parent_table
def __iter__(self):
self._iter_index = 0
return self
def next(self):
ii = self._iter_index
if ii < len(self.indices) - 1:
i0, i1 = self.indices[ii], self.indices[ii + 1]
self._iter_index += 1
return self.parent[i0:i1]
else:
raise StopIteration
__next__ = next
def __getitem__(self, item):
parent = self.parent
if isinstance(item, (int, np.integer)):
i0, i1 = self.indices[item], self.indices[item + 1]
out = parent[i0:i1]
out.groups._keys = parent.groups.keys[item]
else:
indices0, indices1 = self.indices[:-1], self.indices[1:]
try:
i0s, i1s = indices0[item], indices1[item]
except Exception as err:
raise TypeError('Index item for groups attribute must be a slice, '
'numpy mask or int array') from err
mask = np.zeros(len(parent), dtype=bool)
# Is there a way to vectorize this in numpy?
for i0, i1 in zip(i0s, i1s):
mask[i0:i1] = True
out = parent[mask]
out.groups._keys = parent.groups.keys[item]
out.groups._indices = np.concatenate([[0], np.cumsum(i1s - i0s)])
return out
def __repr__(self):
return f'<{self.__class__.__name__} indices={self.indices}>'
def __len__(self):
return len(self.indices) - 1
class ColumnGroups(BaseGroups):
def __init__(self, parent_column, indices=None, keys=None):
self.parent_column = parent_column # parent Column
self.parent_table = parent_column.parent_table
self._indices = indices
self._keys = keys
@property
def indices(self):
# If the parent column is in a table then use group indices from table
if self.parent_table:
return self.parent_table.groups.indices
else:
if self._indices is None:
return np.array([0, len(self.parent_column)])
else:
return self._indices
@property
def keys(self):
# If the parent column is in a table then use group indices from table
if self.parent_table:
return self.parent_table.groups.keys
else:
return self._keys
def aggregate(self, func):
from .column import MaskedColumn
i0s, i1s = self.indices[:-1], self.indices[1:]
par_col = self.parent_column
masked = isinstance(par_col, MaskedColumn)
reduceat = hasattr(func, 'reduceat')
sum_case = func is np.sum
mean_case = func is np.mean
try:
if not masked and (reduceat or sum_case or mean_case):
if mean_case:
vals = np.add.reduceat(par_col, i0s) / np.diff(self.indices)
else:
if sum_case:
func = np.add
vals = func.reduceat(par_col, i0s)
else:
vals = np.array([func(par_col[i0: i1]) for i0, i1 in zip(i0s, i1s)])
except Exception as err:
raise TypeError("Cannot aggregate column '{}' with type '{}'"
.format(par_col.info.name,
par_col.info.dtype)) from err
out = par_col.__class__(data=vals,
name=par_col.info.name,
description=par_col.info.description,
unit=par_col.info.unit,
format=par_col.info.format,
meta=par_col.info.meta)
return out
def filter(self, func):
"""
Filter groups in the Column based on evaluating function ``func`` on each
group sub-table.
The function which is passed to this method must accept one argument:
- ``column`` : `Column` object
It must then return either `True` or `False`. As an example, the following
will select all column groups with only positive values::
def all_positive(column):
if np.any(column < 0):
return False
return True
Parameters
----------
func : function
Filter function
Returns
-------
out : Column
New column with the aggregated rows.
"""
mask = np.empty(len(self), dtype=bool)
for i, group_column in enumerate(self):
mask[i] = func(group_column)
return self[mask]
class TableGroups(BaseGroups):
def __init__(self, parent_table, indices=None, keys=None):
self.parent_table = parent_table # parent Table
self._indices = indices
self._keys = keys
@property
def key_colnames(self):
"""
Return the names of columns in the parent table that were used for grouping.
"""
# If the table was grouped by key columns *in* the table then treat those columns
# differently in aggregation. In this case keys will be a Table with
# keys.meta['grouped_by_table_cols'] == True. Keys might not be a Table so we
# need to handle this.
grouped_by_table_cols = getattr(self.keys, 'meta', {}).get('grouped_by_table_cols', False)
return self.keys.colnames if grouped_by_table_cols else ()
@property
def indices(self):
if self._indices is None:
return np.array([0, len(self.parent_table)])
else:
return self._indices
def aggregate(self, func):
"""
Aggregate each group in the Table into a single row by applying the reduction
function ``func`` to group values in each column.
Parameters
----------
func : function
Function that reduces an array of values to a single value
Returns
-------
out : Table
New table with the aggregated rows.
"""
i0s = self.indices[:-1]
out_cols = []
parent_table = self.parent_table
for col in parent_table.columns.values():
# For key columns just pick off first in each group since they are identical
if col.info.name in self.key_colnames:
new_col = col.take(i0s)
else:
try:
new_col = col.groups.aggregate(func)
except TypeError as err:
warnings.warn(str(err), AstropyUserWarning)
continue
out_cols.append(new_col)
return parent_table.__class__(out_cols, meta=parent_table.meta)
def filter(self, func):
"""
Filter groups in the Table based on evaluating function ``func`` on each
group sub-table.
The function which is passed to this method must accept two arguments:
- ``table`` : `Table` object
- ``key_colnames`` : tuple of column names in ``table`` used as keys for grouping
It must then return either `True` or `False`. As an example, the following
will select all table groups with only positive values in the non-key columns::
def all_positive(table, key_colnames):
colnames = [name for name in table.colnames if name not in key_colnames]
for colname in colnames:
if np.any(table[colname] < 0):
return False
return True
Parameters
----------
func : function
Filter function
Returns
-------
out : Table
New table with the aggregated rows.
"""
mask = np.empty(len(self), dtype=bool)
key_colnames = self.key_colnames
for i, group_table in enumerate(self):
mask[i] = func(group_table, key_colnames)
return self[mask]
@property
def keys(self):
return self._keys
|
c42c0d5375112980d71b0cd6ee7070da392b7dabf5ff04f8a182d7475032a61b | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from .index import SlicedIndex, TableIndices, TableLoc, TableILoc, TableLocIndices
import sys
from collections import OrderedDict, defaultdict
from collections.abc import Mapping
import warnings
from copy import deepcopy
import types
import itertools
import weakref
import numpy as np
from numpy import ma
from astropy import log
from astropy.units import Quantity, QuantityInfo
from astropy.utils import isiterable, ShapedLikeNDArray
from astropy.utils.console import color_print
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils.masked import Masked
from astropy.utils.metadata import MetaData, MetaAttribute
from astropy.utils.data_info import BaseColumnInfo, MixinInfo, DataInfo
from astropy.utils.decorators import format_doc
from astropy.io.registry import UnifiedReadWriteMethod
from . import groups
from .pprint import TableFormatter
from .column import (BaseColumn, Column, MaskedColumn, _auto_names, FalseArray,
col_copy, _convert_sequence_data_to_array)
from .row import Row
from .info import TableInfo
from .index import Index, _IndexModeContext, get_index
from .connect import TableRead, TableWrite
from .ndarray_mixin import NdarrayMixin
from .mixins.registry import get_mixin_handler
from . import conf
_implementation_notes = """
This string has informal notes concerning Table implementation for developers.
Things to remember:
- Table has customizable attributes ColumnClass, Column, MaskedColumn.
Table.Column is normally just column.Column (same w/ MaskedColumn)
but in theory they can be different. Table.ColumnClass is the default
class used to create new non-mixin columns, and this is a function of
the Table.masked attribute. Column creation / manipulation in a Table
needs to respect these.
- Column objects that get inserted into the Table.columns attribute must
have the info.parent_table attribute set correctly. Beware just dropping
an object into the columns dict since an existing column may
be part of another Table and have parent_table set to point at that
table. Dropping that column into `columns` of this Table will cause
a problem for the old one so the column object needs to be copied (but
not necessarily the data).
Currently replace_column is always making a copy of both object and
data if parent_table is set. This could be improved but requires a
generic way to copy a mixin object but not the data.
- Be aware of column objects that have indices set.
- `cls.ColumnClass` is a property that effectively uses the `masked` attribute
to choose either `cls.Column` or `cls.MaskedColumn`.
"""
__doctest_skip__ = ['Table.read', 'Table.write', 'Table._read',
'Table.convert_bytestring_to_unicode',
'Table.convert_unicode_to_bytestring',
]
__doctest_requires__ = {'*pandas': ['pandas>=1.1']}
_pprint_docs = """
{__doc__}
Parameters
----------
max_lines : int or None
Maximum number of lines in table output.
max_width : int or None
Maximum character width of output.
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include a header row for column dtypes. Default is True.
align : str or list or tuple or None
Left/right alignment of columns. Default is right (None) for all
columns. Other allowed values are '>', '<', '^', and '0=' for
right, left, centered, and 0-padded, respectively. A list of
strings can be provided for alignment of tables with multiple
columns.
"""
_pformat_docs = """
{__doc__}
Parameters
----------
max_lines : int or None
Maximum number of rows to output
max_width : int or None
Maximum character width of output
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include a header row for column dtypes. Default is True.
html : bool
Format the output as an HTML table. Default is False.
tableid : str or None
An ID tag for the table; only used if html is set. Default is
"table{id}", where id is the unique integer id of the table object,
id(self)
align : str or list or tuple or None
Left/right alignment of columns. Default is right (None) for all
columns. Other allowed values are '>', '<', '^', and '0=' for
right, left, centered, and 0-padded, respectively. A list of
strings can be provided for alignment of tables with multiple
columns.
tableclass : str or list of str or None
CSS classes for the table; only used if html is set. Default is
None.
Returns
-------
lines : list
Formatted table as a list of strings.
"""
class TableReplaceWarning(UserWarning):
"""
Warning class for cases when a table column is replaced via the
Table.__setitem__ syntax e.g. t['a'] = val.
This does not inherit from AstropyWarning because we want to use
stacklevel=3 to show the user where the issue occurred in their code.
"""
pass
def descr(col):
"""Array-interface compliant full description of a column.
This returns a 3-tuple (name, type, shape) that can always be
used in a structured array dtype definition.
"""
col_dtype = 'O' if (col.info.dtype is None) else col.info.dtype
col_shape = col.shape[1:] if hasattr(col, 'shape') else ()
return (col.info.name, col_dtype, col_shape)
def has_info_class(obj, cls):
"""Check if the object's info is an instance of cls."""
# We check info on the class of the instance, since on the instance
# itself accessing 'info' has side effects in that it sets
# obj.__dict__['info'] if it does not exist already.
return isinstance(getattr(obj.__class__, 'info', None), cls)
def _get_names_from_list_of_dict(rows):
"""Return list of column names if ``rows`` is a list of dict that
defines table data.
If rows is not a list of dict then return None.
"""
if rows is None:
return None
names = set()
for row in rows:
if not isinstance(row, Mapping):
return None
names.update(row)
return list(names)
# Note to future maintainers: when transitioning this to dict
# be sure to change the OrderedDict ref(s) in Row and in __len__().
class TableColumns(OrderedDict):
"""OrderedDict subclass for a set of columns.
This class enhances item access to provide convenient access to columns
by name or index, including slice access. It also handles renaming
of columns.
The initialization argument ``cols`` can be a list of ``Column`` objects
or any structure that is valid for initializing a Python dict. This
includes a dict, list of (key, val) tuples or [key, val] lists, etc.
Parameters
----------
cols : dict, list, tuple; optional
Column objects as data structure that can init dict (see above)
"""
def __init__(self, cols={}):
if isinstance(cols, (list, tuple)):
# `cols` should be a list of two-tuples, but it is allowed to have
# columns (BaseColumn or mixins) in the list.
newcols = []
for col in cols:
if has_info_class(col, BaseColumnInfo):
newcols.append((col.info.name, col))
else:
newcols.append(col)
cols = newcols
super().__init__(cols)
def __getitem__(self, item):
"""Get items from a TableColumns object.
::
tc = TableColumns(cols=[Column(name='a'), Column(name='b'), Column(name='c')])
tc['a'] # Column('a')
tc[1] # Column('b')
tc['a', 'b'] # <TableColumns names=('a', 'b')>
tc[1:3] # <TableColumns names=('b', 'c')>
"""
if isinstance(item, str):
return OrderedDict.__getitem__(self, item)
elif isinstance(item, (int, np.integer)):
return list(self.values())[item]
elif (isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == 'i'):
return list(self.values())[item.item()]
elif isinstance(item, tuple):
return self.__class__([self[x] for x in item])
elif isinstance(item, slice):
return self.__class__([self[x] for x in list(self)[item]])
else:
raise IndexError('Illegal key or index value for {} object'
.format(self.__class__.__name__))
def __setitem__(self, item, value, validated=False):
"""
Set item in this dict instance, but do not allow directly replacing an
existing column unless it is already validated (and thus is certain to
not corrupt the table).
NOTE: it is easily possible to corrupt a table by directly *adding* a new
key to the TableColumns attribute of a Table, e.g.
``t.columns['jane'] = 'doe'``.
"""
if item in self and not validated:
raise ValueError("Cannot replace column '{}'. Use Table.replace_column() instead."
.format(item))
super().__setitem__(item, value)
def __repr__(self):
names = (f"'{x}'" for x in self.keys())
return f"<{self.__class__.__name__} names=({','.join(names)})>"
def _rename_column(self, name, new_name):
if name == new_name:
return
if new_name in self:
raise KeyError(f"Column {new_name} already exists")
# Rename column names in pprint include/exclude attributes as needed
parent_table = self[name].info.parent_table
if parent_table is not None:
parent_table.pprint_exclude_names._rename(name, new_name)
parent_table.pprint_include_names._rename(name, new_name)
mapper = {name: new_name}
new_names = [mapper.get(name, name) for name in self]
cols = list(self.values())
self.clear()
self.update(list(zip(new_names, cols)))
def __delitem__(self, name):
# Remove column names from pprint include/exclude attributes as needed.
# __delitem__ also gets called for pop() and popitem().
parent_table = self[name].info.parent_table
if parent_table is not None:
# _remove() method does not require that `name` is in the attribute
parent_table.pprint_exclude_names._remove(name)
parent_table.pprint_include_names._remove(name)
return super().__delitem__(name)
def isinstance(self, cls):
"""
Return a list of columns which are instances of the specified classes.
Parameters
----------
cls : class or tuple thereof
Column class (including mixin) or tuple of Column classes.
Returns
-------
col_list : list of `Column`
List of Column objects which are instances of given classes.
"""
cols = [col for col in self.values() if isinstance(col, cls)]
return cols
def not_isinstance(self, cls):
"""
Return a list of columns which are not instances of the specified classes.
Parameters
----------
cls : class or tuple thereof
Column class (including mixin) or tuple of Column classes.
Returns
-------
col_list : list of `Column`
List of Column objects which are not instances of given classes.
"""
cols = [col for col in self.values() if not isinstance(col, cls)]
return cols
class TableAttribute(MetaAttribute):
"""
Descriptor to define a custom attribute for a Table subclass.
The value of the ``TableAttribute`` will be stored in a dict named
``__attributes__`` that is stored in the table ``meta``. The attribute
can be accessed and set in the usual way, and it can be provided when
creating the object.
Defining an attribute by this mechanism ensures that it will persist if
the table is sliced or serialized, for example as a pickle or ECSV file.
See the `~astropy.utils.metadata.MetaAttribute` documentation for additional
details.
Parameters
----------
default : object
Default value for attribute
Examples
--------
>>> from astropy.table import Table, TableAttribute
>>> class MyTable(Table):
... identifier = TableAttribute(default=1)
>>> t = MyTable(identifier=10)
>>> t.identifier
10
>>> t.meta
OrderedDict([('__attributes__', {'identifier': 10})])
"""
class PprintIncludeExclude(TableAttribute):
"""Maintain tuple that controls table column visibility for print output.
This is a descriptor that inherits from MetaAttribute so that the attribute
value is stored in the table meta['__attributes__'].
This gets used for the ``pprint_include_names`` and ``pprint_exclude_names`` Table
attributes.
"""
def __get__(self, instance, owner_cls):
"""Get the attribute.
This normally returns an instance of this class which is stored on the
owner object.
"""
# For getting from class not an instance
if instance is None:
return self
# If not already stored on `instance`, make a copy of the class
# descriptor object and put it onto the instance.
value = instance.__dict__.get(self.name)
if value is None:
value = deepcopy(self)
instance.__dict__[self.name] = value
# We set _instance_ref on every call, since if one makes copies of
# instances, this attribute will be copied as well, which will lose the
# reference.
value._instance_ref = weakref.ref(instance)
return value
def __set__(self, instance, names):
"""Set value of ``instance`` attribute to ``names``.
Parameters
----------
instance : object
Instance that owns the attribute
names : None, str, list, tuple
Column name(s) to store, or None to clear
"""
if isinstance(names, str):
names = [names]
if names is None:
# Remove attribute value from the meta['__attributes__'] dict.
# Subsequent access will just return None.
delattr(instance, self.name)
else:
# This stores names into instance.meta['__attributes__'] as tuple
return super().__set__(instance, tuple(names))
def __call__(self):
"""Get the value of the attribute.
Returns
-------
names : None, tuple
Include/exclude names
"""
# Get the value from instance.meta['__attributes__']
instance = self._instance_ref()
return super().__get__(instance, instance.__class__)
def __repr__(self):
if hasattr(self, '_instance_ref'):
out = f'<{self.__class__.__name__} name={self.name} value={self()}>'
else:
out = super().__repr__()
return out
def _add_remove_setup(self, names):
"""Common setup for add and remove.
- Coerce attribute value to a list
- Coerce names into a list
- Get the parent table instance
"""
names = [names] if isinstance(names, str) else list(names)
# Get the value. This is the same as self() but we need `instance` here.
instance = self._instance_ref()
value = super().__get__(instance, instance.__class__)
value = [] if value is None else list(value)
return instance, names, value
def add(self, names):
"""Add ``names`` to the include/exclude attribute.
Parameters
----------
names : str, list, tuple
Column name(s) to add
"""
instance, names, value = self._add_remove_setup(names)
value.extend(name for name in names if name not in value)
super().__set__(instance, tuple(value))
def remove(self, names):
"""Remove ``names`` from the include/exclude attribute.
Parameters
----------
names : str, list, tuple
Column name(s) to remove
"""
self._remove(names, raise_exc=True)
def _remove(self, names, raise_exc=False):
"""Remove ``names`` with optional checking if they exist"""
instance, names, value = self._add_remove_setup(names)
# Return now if there are no attributes and thus no action to be taken.
if not raise_exc and '__attributes__' not in instance.meta:
return
# Remove one by one, optionally raising an exception if name is missing.
for name in names:
if name in value:
value.remove(name) # Using the list.remove method
elif raise_exc:
raise ValueError(f'{name} not in {self.name}')
# Change to either None or a tuple for storing back to attribute
value = None if value == [] else tuple(value)
self.__set__(instance, value)
def _rename(self, name, new_name):
"""Rename ``name`` to ``new_name`` if ``name`` is in the list"""
names = self() or ()
if name in names:
new_names = list(names)
new_names[new_names.index(name)] = new_name
self.set(new_names)
def set(self, names):
"""Set value of include/exclude attribute to ``names``.
Parameters
----------
names : None, str, list, tuple
Column name(s) to store, or None to clear
"""
class _Context:
def __init__(self, descriptor_self):
self.descriptor_self = descriptor_self
self.names_orig = descriptor_self()
def __enter__(self):
pass
def __exit__(self, type, value, tb):
descriptor_self = self.descriptor_self
instance = descriptor_self._instance_ref()
descriptor_self.__set__(instance, self.names_orig)
def __repr__(self):
return repr(self.descriptor_self)
ctx = _Context(descriptor_self=self)
instance = self._instance_ref()
self.__set__(instance, names)
return ctx
class Table:
"""A class to represent tables of heterogeneous data.
`~astropy.table.Table` provides a class for heterogeneous tabular data.
A key enhancement provided by the `~astropy.table.Table` class over
e.g. a `numpy` structured array is the ability to easily modify the
structure of the table by adding or removing columns, or adding new
rows of data. In addition table and column metadata are fully supported.
`~astropy.table.Table` differs from `~astropy.nddata.NDData` by the
assumption that the input data consists of columns of homogeneous data,
where each column has a unique identifier and may contain additional
metadata such as the data unit, format, and description.
See also: https://docs.astropy.org/en/stable/table/
Parameters
----------
data : numpy ndarray, dict, list, table-like object, optional
Data to initialize table.
masked : bool, optional
Specify whether the table is masked.
names : list, optional
Specify column names.
dtype : list, optional
Specify column data types.
meta : dict, optional
Metadata associated with the table.
copy : bool, optional
Copy the input data. If the input is a Table the ``meta`` is always
copied regardless of the ``copy`` parameter.
Default is True.
rows : numpy ndarray, list of list, optional
Row-oriented data for table instead of ``data`` argument.
copy_indices : bool, optional
Copy any indices in the input data. Default is True.
units : list, dict, optional
List or dict of units to apply to columns.
descriptions : list, dict, optional
List or dict of descriptions to apply to columns.
**kwargs : dict, optional
Additional keyword args when converting table-like object.
"""
meta = MetaData(copy=False)
# Define class attributes for core container objects to allow for subclass
# customization.
Row = Row
Column = Column
MaskedColumn = MaskedColumn
TableColumns = TableColumns
TableFormatter = TableFormatter
# Unified I/O read and write methods from .connect
read = UnifiedReadWriteMethod(TableRead)
write = UnifiedReadWriteMethod(TableWrite)
pprint_exclude_names = PprintIncludeExclude()
pprint_include_names = PprintIncludeExclude()
def as_array(self, keep_byteorder=False, names=None):
"""
Return a new copy of the table in the form of a structured np.ndarray or
np.ma.MaskedArray object (as appropriate).
Parameters
----------
keep_byteorder : bool, optional
By default the returned array has all columns in native byte
order. However, if this option is `True` this preserves the
byte order of all columns (if any are non-native).
names : list, optional:
List of column names to include for returned structured array.
Default is to include all table columns.
Returns
-------
table_array : array or `~numpy.ma.MaskedArray`
Copy of table as a numpy structured array.
ndarray for unmasked or `~numpy.ma.MaskedArray` for masked.
"""
masked = self.masked or self.has_masked_columns or self.has_masked_values
empty_init = ma.empty if masked else np.empty
if len(self.columns) == 0:
return empty_init(0, dtype=None)
dtype = []
cols = self.columns.values()
if names is not None:
cols = [col for col in cols if col.info.name in names]
for col in cols:
col_descr = descr(col)
if not (col.info.dtype.isnative or keep_byteorder):
new_dt = np.dtype(col_descr[1]).newbyteorder('=')
col_descr = (col_descr[0], new_dt, col_descr[2])
dtype.append(col_descr)
data = empty_init(len(self), dtype=dtype)
for col in cols:
# When assigning from one array into a field of a structured array,
# Numpy will automatically swap those columns to their destination
# byte order where applicable
data[col.info.name] = col
# For masked out, masked mixin columns need to set output mask attribute.
if masked and has_info_class(col, MixinInfo) and hasattr(col, 'mask'):
data[col.info.name].mask = col.mask
return data
def __init__(self, data=None, masked=False, names=None, dtype=None,
meta=None, copy=True, rows=None, copy_indices=True,
units=None, descriptions=None,
**kwargs):
# Set up a placeholder empty table
self._set_masked(masked)
self.columns = self.TableColumns()
self.formatter = self.TableFormatter()
self._copy_indices = True # copy indices from this Table by default
self._init_indices = copy_indices # whether to copy indices in init
self.primary_key = None
# Must copy if dtype are changing
if not copy and dtype is not None:
raise ValueError('Cannot specify dtype when copy=False')
# Specifies list of names found for the case of initializing table with
# a list of dict. If data are not list of dict then this is None.
names_from_list_of_dict = None
# Row-oriented input, e.g. list of lists or list of tuples, list of
# dict, Row instance. Set data to something that the subsequent code
# will parse correctly.
if rows is not None:
if data is not None:
raise ValueError('Cannot supply both `data` and `rows` values')
if isinstance(rows, types.GeneratorType):
# Without this then the all(..) test below uses up the generator
rows = list(rows)
# Get column names if `rows` is a list of dict, otherwise this is None
names_from_list_of_dict = _get_names_from_list_of_dict(rows)
if names_from_list_of_dict:
data = rows
elif isinstance(rows, self.Row):
data = rows
else:
data = list(zip(*rows))
# Infer the type of the input data and set up the initialization
# function, number of columns, and potentially the default col names
default_names = None
# Handle custom (subclass) table attributes that are stored in meta.
# These are defined as class attributes using the TableAttribute
# descriptor. Any such attributes get removed from kwargs here and
# stored for use after the table is otherwise initialized. Any values
# provided via kwargs will have precedence over existing values from
# meta (e.g. from data as a Table or meta via kwargs).
meta_table_attrs = {}
if kwargs:
for attr in list(kwargs):
descr = getattr(self.__class__, attr, None)
if isinstance(descr, TableAttribute):
meta_table_attrs[attr] = kwargs.pop(attr)
if hasattr(data, '__astropy_table__'):
# Data object implements the __astropy_table__ interface method.
# Calling that method returns an appropriate instance of
# self.__class__ and respects the `copy` arg. The returned
# Table object should NOT then be copied.
data = data.__astropy_table__(self.__class__, copy, **kwargs)
copy = False
elif kwargs:
raise TypeError('__init__() got unexpected keyword argument {!r}'
.format(list(kwargs.keys())[0]))
if (isinstance(data, np.ndarray)
and data.shape == (0,)
and not data.dtype.names):
data = None
if isinstance(data, self.Row):
data = data._table[data._index:data._index + 1]
if isinstance(data, (list, tuple)):
# Get column names from `data` if it is a list of dict, otherwise this is None.
# This might be previously defined if `rows` was supplied as an init arg.
names_from_list_of_dict = (names_from_list_of_dict
or _get_names_from_list_of_dict(data))
if names_from_list_of_dict:
init_func = self._init_from_list_of_dicts
n_cols = len(names_from_list_of_dict)
else:
init_func = self._init_from_list
n_cols = len(data)
elif isinstance(data, np.ndarray):
if data.dtype.names:
init_func = self._init_from_ndarray # _struct
n_cols = len(data.dtype.names)
default_names = data.dtype.names
else:
init_func = self._init_from_ndarray # _homog
if data.shape == ():
raise ValueError('Can not initialize a Table with a scalar')
elif len(data.shape) == 1:
data = data[np.newaxis, :]
n_cols = data.shape[1]
elif isinstance(data, Mapping):
init_func = self._init_from_dict
default_names = list(data)
n_cols = len(default_names)
elif isinstance(data, Table):
# If user-input meta is None then use data.meta (if non-trivial)
if meta is None and data.meta:
# At this point do NOT deepcopy data.meta as this will happen after
# table init_func() is called. But for table input the table meta
# gets a key copy here if copy=False because later a direct object ref
# is used.
meta = data.meta if copy else data.meta.copy()
# Handle indices on input table. Copy primary key and don't copy indices
# if the input Table is in non-copy mode.
self.primary_key = data.primary_key
self._init_indices = self._init_indices and data._copy_indices
# Extract default names, n_cols, and then overwrite ``data`` to be the
# table columns so we can use _init_from_list.
default_names = data.colnames
n_cols = len(default_names)
data = list(data.columns.values())
init_func = self._init_from_list
elif data is None:
if names is None:
if dtype is None:
# Table was initialized as `t = Table()`. Set up for empty
# table with names=[], data=[], and n_cols=0.
# self._init_from_list() will simply return, giving the
# expected empty table.
names = []
else:
try:
# No data nor names but dtype is available. This must be
# valid to initialize a structured array.
dtype = np.dtype(dtype)
names = dtype.names
dtype = [dtype[name] for name in names]
except Exception:
raise ValueError('dtype was specified but could not be '
'parsed for column names')
# names is guaranteed to be set at this point
init_func = self._init_from_list
n_cols = len(names)
data = [[]] * n_cols
else:
raise ValueError(f'Data type {type(data)} not allowed to init Table')
# Set up defaults if names and/or dtype are not specified.
# A value of None means the actual value will be inferred
# within the appropriate initialization routine, either from
# existing specification or auto-generated.
if dtype is None:
dtype = [None] * n_cols
elif isinstance(dtype, np.dtype):
if default_names is None:
default_names = dtype.names
# Convert a numpy dtype input to a list of dtypes for later use.
dtype = [dtype[name] for name in dtype.names]
if names is None:
names = default_names or [None] * n_cols
names = [None if name is None else str(name) for name in names]
self._check_names_dtype(names, dtype, n_cols)
# Finally do the real initialization
init_func(data, names, dtype, n_cols, copy)
# Set table meta. If copy=True then deepcopy meta otherwise use the
# user-supplied meta directly.
if meta is not None:
self.meta = deepcopy(meta) if copy else meta
# Update meta with TableAttributes supplied as kwargs in Table init.
# This takes precedence over previously-defined meta.
if meta_table_attrs:
for attr, value in meta_table_attrs.items():
setattr(self, attr, value)
# Whatever happens above, the masked property should be set to a boolean
if self.masked not in (None, True, False):
raise TypeError("masked property must be None, True or False")
self._set_column_attribute('unit', units)
self._set_column_attribute('description', descriptions)
def _set_column_attribute(self, attr, values):
"""Set ``attr`` for columns to ``values``, which can be either a dict (keyed by column
name) or a dict of name: value pairs. This is used for handling the ``units`` and
``descriptions`` kwargs to ``__init__``.
"""
if not values:
return
if isinstance(values, Row):
# For a Row object transform to an equivalent dict.
values = {name: values[name] for name in values.colnames}
if not isinstance(values, Mapping):
# If not a dict map, assume iterable and map to dict if the right length
if len(values) != len(self.columns):
raise ValueError(f'sequence of {attr} values must match number of columns')
values = dict(zip(self.colnames, values))
for name, value in values.items():
if name not in self.columns:
raise ValueError(f'invalid column name {name} for setting {attr} attribute')
# Special case: ignore unit if it is an empty or blank string
if attr == 'unit' and isinstance(value, str):
if value.strip() == '':
value = None
if value not in (np.ma.masked, None):
setattr(self[name].info, attr, value)
def __getstate__(self):
columns = OrderedDict((key, col if isinstance(col, BaseColumn) else col_copy(col))
for key, col in self.columns.items())
return (columns, self.meta)
def __setstate__(self, state):
columns, meta = state
self.__init__(columns, meta=meta)
@property
def mask(self):
# Dynamic view of available masks
if self.masked or self.has_masked_columns or self.has_masked_values:
mask_table = Table([getattr(col, 'mask', FalseArray(col.shape))
for col in self.itercols()],
names=self.colnames, copy=False)
# Set hidden attribute to force inplace setitem so that code like
# t.mask['a'] = [1, 0, 1] will correctly set the underlying mask.
# See #5556 for discussion.
mask_table._setitem_inplace = True
else:
mask_table = None
return mask_table
@mask.setter
def mask(self, val):
self.mask[:] = val
@property
def _mask(self):
"""This is needed so that comparison of a masked Table and a
MaskedArray works. The requirement comes from numpy.ma.core
so don't remove this property."""
return self.as_array().mask
def filled(self, fill_value=None):
"""Return copy of self, with masked values filled.
If input ``fill_value`` supplied then that value is used for all
masked entries in the table. Otherwise the individual
``fill_value`` defined for each table column is used.
Parameters
----------
fill_value : str
If supplied, this ``fill_value`` is used for all masked entries
in the entire table.
Returns
-------
filled_table : `~astropy.table.Table`
New table with masked values filled
"""
if self.masked or self.has_masked_columns or self.has_masked_values:
# Get new columns with masked values filled, then create Table with those
# new cols (copy=False) but deepcopy the meta.
data = [col.filled(fill_value) if hasattr(col, 'filled') else col
for col in self.itercols()]
return self.__class__(data, meta=deepcopy(self.meta), copy=False)
else:
# Return copy of the original object.
return self.copy()
@property
def indices(self):
'''
Return the indices associated with columns of the table
as a TableIndices object.
'''
lst = []
for column in self.columns.values():
for index in column.info.indices:
if sum([index is x for x in lst]) == 0: # ensure uniqueness
lst.append(index)
return TableIndices(lst)
@property
def loc(self):
'''
Return a TableLoc object that can be used for retrieving
rows by index in a given data range. Note that both loc
and iloc work only with single-column indices.
'''
return TableLoc(self)
@property
def loc_indices(self):
"""
Return a TableLocIndices object that can be used for retrieving
the row indices corresponding to given table index key value or values.
"""
return TableLocIndices(self)
@property
def iloc(self):
'''
Return a TableILoc object that can be used for retrieving
indexed rows in the order they appear in the index.
'''
return TableILoc(self)
def add_index(self, colnames, engine=None, unique=False):
'''
Insert a new index among one or more columns.
If there are no indices, make this index the
primary table index.
Parameters
----------
colnames : str or list
List of column names (or a single column name) to index
engine : type or None
Indexing engine class to use, from among SortedArray, BST,
and SCEngine. If the supplied argument is None
(by default), use SortedArray.
unique : bool
Whether the values of the index must be unique. Default is False.
'''
if isinstance(colnames, str):
colnames = (colnames,)
columns = self.columns[tuple(colnames)].values()
# make sure all columns support indexing
for col in columns:
if not getattr(col.info, '_supports_indexing', False):
raise ValueError('Cannot create an index on column "{}", of '
'type "{}"'.format(col.info.name, type(col)))
is_primary = not self.indices
index = Index(columns, engine=engine, unique=unique)
sliced_index = SlicedIndex(index, slice(0, 0, None), original=True)
if is_primary:
self.primary_key = colnames
for col in columns:
col.info.indices.append(sliced_index)
def remove_indices(self, colname):
'''
Remove all indices involving the given column.
If the primary index is removed, the new primary
index will be the most recently added remaining
index.
Parameters
----------
colname : str
Name of column
'''
col = self.columns[colname]
for index in self.indices:
try:
index.col_position(col.info.name)
except ValueError:
pass
else:
for c in index.columns:
c.info.indices.remove(index)
def index_mode(self, mode):
'''
Return a context manager for an indexing mode.
Parameters
----------
mode : str
Either 'freeze', 'copy_on_getitem', or 'discard_on_copy'.
In 'discard_on_copy' mode,
indices are not copied whenever columns or tables are copied.
In 'freeze' mode, indices are not modified whenever columns are
modified; at the exit of the context, indices refresh themselves
based on column values. This mode is intended for scenarios in
which one intends to make many additions or modifications in an
indexed column.
In 'copy_on_getitem' mode, indices are copied when taking column
slices as well as table slices, so col[i0:i1] will preserve
indices.
'''
return _IndexModeContext(self, mode)
def __array__(self, dtype=None):
"""Support converting Table to np.array via np.array(table).
Coercion to a different dtype via np.array(table, dtype) is not
supported and will raise a ValueError.
"""
if dtype is not None:
raise ValueError('Datatype coercion is not allowed')
# This limitation is because of the following unexpected result that
# should have made a table copy while changing the column names.
#
# >>> d = astropy.table.Table([[1,2],[3,4]])
# >>> np.array(d, dtype=[('a', 'i8'), ('b', 'i8')])
# array([(0, 0), (0, 0)],
# dtype=[('a', '<i8'), ('b', '<i8')])
out = self.as_array()
return out.data if isinstance(out, np.ma.MaskedArray) else out
def _check_names_dtype(self, names, dtype, n_cols):
"""Make sure that names and dtype are both iterable and have
the same length as data.
"""
for inp_list, inp_str in ((dtype, 'dtype'), (names, 'names')):
if not isiterable(inp_list):
raise ValueError(f'{inp_str} must be a list or None')
if len(names) != n_cols or len(dtype) != n_cols:
raise ValueError(
'Arguments "names" and "dtype" must match number of columns')
def _init_from_list_of_dicts(self, data, names, dtype, n_cols, copy):
"""Initialize table from a list of dictionaries representing rows."""
# Define placeholder for missing values as a unique object that cannot
# every occur in user data.
MISSING = object()
# Gather column names that exist in the input `data`.
names_from_data = set()
for row in data:
names_from_data.update(row)
if set(data[0].keys()) == names_from_data:
names_from_data = list(data[0].keys())
else:
names_from_data = sorted(names_from_data)
# Note: if set(data[0].keys()) != names_from_data, this will give an
# exception later, so NO need to catch here.
# Convert list of dict into dict of list (cols), keep track of missing
# indexes and put in MISSING placeholders in the `cols` lists.
cols = {}
missing_indexes = defaultdict(list)
for name in names_from_data:
cols[name] = []
for ii, row in enumerate(data):
try:
val = row[name]
except KeyError:
missing_indexes[name].append(ii)
val = MISSING
cols[name].append(val)
# Fill the missing entries with first values
if missing_indexes:
for name, indexes in missing_indexes.items():
col = cols[name]
first_val = next(val for val in col if val is not MISSING)
for index in indexes:
col[index] = first_val
# prepare initialization
if all(name is None for name in names):
names = names_from_data
self._init_from_dict(cols, names, dtype, n_cols, copy)
# Mask the missing values if necessary, converting columns to MaskedColumn
# as needed.
if missing_indexes:
for name, indexes in missing_indexes.items():
col = self[name]
# Ensure that any Column subclasses with MISSING values can support
# setting masked values. As of astropy 4.0 the test condition below is
# always True since _init_from_dict cannot result in mixin columns.
if isinstance(col, Column) and not isinstance(col, MaskedColumn):
self[name] = self.MaskedColumn(col, copy=False)
# Finally do the masking in a mixin-safe way.
self[name][indexes] = np.ma.masked
return
def _init_from_list(self, data, names, dtype, n_cols, copy):
"""Initialize table from a list of column data. A column can be a
Column object, np.ndarray, mixin, or any other iterable object.
"""
# Special case of initializing an empty table like `t = Table()`. No
# action required at this point.
if n_cols == 0:
return
cols = []
default_names = _auto_names(n_cols)
for col, name, default_name, dtype in zip(data, names, default_names, dtype):
col = self._convert_data_to_col(col, copy, default_name, dtype, name)
cols.append(col)
self._init_from_cols(cols)
def _convert_data_to_col(self, data, copy=True, default_name=None, dtype=None, name=None):
"""
Convert any allowed sequence data ``col`` to a column object that can be used
directly in the self.columns dict. This could be a Column, MaskedColumn,
or mixin column.
The final column name is determined by::
name or data.info.name or def_name
If ``data`` has no ``info`` then ``name = name or def_name``.
The behavior of ``copy`` for Column objects is:
- copy=True: new class instance with a copy of data and deep copy of meta
- copy=False: new class instance with same data and a key-only copy of meta
For mixin columns:
- copy=True: new class instance with copy of data and deep copy of meta
- copy=False: original instance (no copy at all)
Parameters
----------
data : object (column-like sequence)
Input column data
copy : bool
Make a copy
default_name : str
Default name
dtype : np.dtype or None
Data dtype
name : str or None
Column name
Returns
-------
col : Column, MaskedColumn, mixin-column type
Object that can be used as a column in self
"""
data_is_mixin = self._is_mixin_for_table(data)
masked_col_cls = (self.ColumnClass
if issubclass(self.ColumnClass, self.MaskedColumn)
else self.MaskedColumn)
try:
data0_is_mixin = self._is_mixin_for_table(data[0])
except Exception:
# Need broad exception, cannot predict what data[0] raises for arbitrary data
data0_is_mixin = False
# If the data is not an instance of Column or a mixin class, we can
# check the registry of mixin 'handlers' to see if the column can be
# converted to a mixin class
if (handler := get_mixin_handler(data)) is not None:
original_data = data
data = handler(data)
if not (data_is_mixin := self._is_mixin_for_table(data)):
fully_qualified_name = (original_data.__class__.__module__ + '.'
+ original_data.__class__.__name__)
raise TypeError('Mixin handler for object of type '
f'{fully_qualified_name} '
'did not return a valid mixin column')
# Structured ndarray gets viewed as a mixin unless already a valid
# mixin class
if (not isinstance(data, Column) and not data_is_mixin
and isinstance(data, np.ndarray) and len(data.dtype) > 1):
data = data.view(NdarrayMixin)
data_is_mixin = True
# Get the final column name using precedence. Some objects may not
# have an info attribute. Also avoid creating info as a side effect.
if not name:
if isinstance(data, Column):
name = data.name or default_name
elif 'info' in getattr(data, '__dict__', ()):
name = data.info.name or default_name
else:
name = default_name
if isinstance(data, Column):
# If self.ColumnClass is a subclass of col, then "upgrade" to ColumnClass,
# otherwise just use the original class. The most common case is a
# table with masked=True and ColumnClass=MaskedColumn. Then a Column
# gets upgraded to MaskedColumn, but the converse (pre-4.0) behavior
# of downgrading from MaskedColumn to Column (for non-masked table)
# does not happen.
col_cls = self._get_col_cls_for_table(data)
elif data_is_mixin:
# Copy the mixin column attributes if they exist since the copy below
# may not get this attribute.
col = col_copy(data, copy_indices=self._init_indices) if copy else data
col.info.name = name
return col
elif data0_is_mixin:
# Handle case of a sequence of a mixin, e.g. [1*u.m, 2*u.m].
try:
col = data[0].__class__(data)
col.info.name = name
return col
except Exception:
# If that didn't work for some reason, just turn it into np.array of object
data = np.array(data, dtype=object)
col_cls = self.ColumnClass
elif isinstance(data, (np.ma.MaskedArray, Masked)):
# Require that col_cls be a subclass of MaskedColumn, remembering
# that ColumnClass could be a user-defined subclass (though more-likely
# could be MaskedColumn).
col_cls = masked_col_cls
elif data is None:
# Special case for data passed as the None object (for broadcasting
# to an object column). Need to turn data into numpy `None` scalar
# object, otherwise `Column` interprets data=None as no data instead
# of a object column of `None`.
data = np.array(None)
col_cls = self.ColumnClass
elif not hasattr(data, 'dtype'):
# `data` is none of the above, convert to numpy array or MaskedArray
# assuming only that it is a scalar or sequence or N-d nested
# sequence. This function is relatively intricate and tries to
# maintain performance for common cases while handling things like
# list input with embedded np.ma.masked entries. If `data` is a
# scalar then it gets returned unchanged so the original object gets
# passed to `Column` later.
data = _convert_sequence_data_to_array(data, dtype)
copy = False # Already made a copy above
col_cls = masked_col_cls if isinstance(data, np.ma.MaskedArray) else self.ColumnClass
else:
col_cls = self.ColumnClass
try:
col = col_cls(name=name, data=data, dtype=dtype,
copy=copy, copy_indices=self._init_indices)
except Exception:
# Broad exception class since we don't know what might go wrong
raise ValueError('unable to convert data to Column for Table')
col = self._convert_col_for_table(col)
return col
def _init_from_ndarray(self, data, names, dtype, n_cols, copy):
"""Initialize table from an ndarray structured array"""
data_names = data.dtype.names or _auto_names(n_cols)
struct = data.dtype.names is not None
names = [name or data_names[i] for i, name in enumerate(names)]
cols = ([data[name] for name in data_names] if struct else
[data[:, i] for i in range(n_cols)])
self._init_from_list(cols, names, dtype, n_cols, copy)
def _init_from_dict(self, data, names, dtype, n_cols, copy):
"""Initialize table from a dictionary of columns"""
data_list = [data[name] for name in names]
self._init_from_list(data_list, names, dtype, n_cols, copy)
def _get_col_cls_for_table(self, col):
"""Get the correct column class to use for upgrading any Column-like object.
For a masked table, ensure any Column-like object is a subclass
of the table MaskedColumn.
For unmasked table, ensure any MaskedColumn-like object is a subclass
of the table MaskedColumn. If not a MaskedColumn, then ensure that any
Column-like object is a subclass of the table Column.
"""
col_cls = col.__class__
if self.masked:
if isinstance(col, Column) and not isinstance(col, self.MaskedColumn):
col_cls = self.MaskedColumn
else:
if isinstance(col, MaskedColumn):
if not isinstance(col, self.MaskedColumn):
col_cls = self.MaskedColumn
elif isinstance(col, Column) and not isinstance(col, self.Column):
col_cls = self.Column
return col_cls
def _convert_col_for_table(self, col):
"""
Make sure that all Column objects have correct base class for this type of
Table. For a base Table this most commonly means setting to
MaskedColumn if the table is masked. Table subclasses like QTable
override this method.
"""
if isinstance(col, Column) and not isinstance(col, self.ColumnClass):
col_cls = self._get_col_cls_for_table(col)
if col_cls is not col.__class__:
col = col_cls(col, copy=False)
return col
def _init_from_cols(self, cols):
"""Initialize table from a list of Column or mixin objects"""
lengths = set(len(col) for col in cols)
if len(lengths) > 1:
raise ValueError(f'Inconsistent data column lengths: {lengths}')
# Make sure that all Column-based objects have correct class. For
# plain Table this is self.ColumnClass, but for instance QTable will
# convert columns with units to a Quantity mixin.
newcols = [self._convert_col_for_table(col) for col in cols]
self._make_table_from_cols(self, newcols)
# Deduplicate indices. It may happen that after pickling or when
# initing from an existing table that column indices which had been
# references to a single index object got *copied* into an independent
# object. This results in duplicates which will cause downstream problems.
index_dict = {}
for col in self.itercols():
for i, index in enumerate(col.info.indices or []):
names = tuple(ind_col.info.name for ind_col in index.columns)
if names in index_dict:
col.info.indices[i] = index_dict[names]
else:
index_dict[names] = index
def _new_from_slice(self, slice_):
"""Create a new table as a referenced slice from self."""
table = self.__class__(masked=self.masked)
if self.meta:
table.meta = self.meta.copy() # Shallow copy for slice
table.primary_key = self.primary_key
newcols = []
for col in self.columns.values():
newcol = col[slice_]
# Note in line below, use direct attribute access to col.indices for Column
# instances instead of the generic col.info.indices. This saves about 4 usec
# per column.
if (col if isinstance(col, Column) else col.info).indices:
# TODO : as far as I can tell the only purpose of setting _copy_indices
# here is to communicate that to the initial test in `slice_indices`.
# Why isn't that just sent as an arg to the function?
col.info._copy_indices = self._copy_indices
newcol = col.info.slice_indices(newcol, slice_, len(col))
# Don't understand why this is forcing a value on the original column.
# Normally col.info does not even have a _copy_indices attribute. Tests
# still pass if this line is deleted. (Each col.info attribute access
# is expensive).
col.info._copy_indices = True
newcols.append(newcol)
self._make_table_from_cols(table, newcols, verify=False, names=self.columns.keys())
return table
@staticmethod
def _make_table_from_cols(table, cols, verify=True, names=None):
"""
Make ``table`` in-place so that it represents the given list of ``cols``.
"""
if names is None:
names = [col.info.name for col in cols]
# Note: we do not test for len(names) == len(cols) if names is not None. In that
# case the function is being called by from "trusted" source (e.g. right above here)
# that is assumed to provide valid inputs. In that case verify=False.
if verify:
if None in names:
raise TypeError('Cannot have None for column name')
if len(set(names)) != len(names):
raise ValueError('Duplicate column names')
table.columns = table.TableColumns((name, col) for name, col in zip(names, cols))
for col in cols:
table._set_col_parent_table_and_mask(col)
def _set_col_parent_table_and_mask(self, col):
"""
Set ``col.parent_table = self`` and force ``col`` to have ``mask``
attribute if the table is masked and ``col.mask`` does not exist.
"""
# For Column instances it is much faster to do direct attribute access
# instead of going through .info
col_info = col if isinstance(col, Column) else col.info
col_info.parent_table = self
# Legacy behavior for masked table
if self.masked and not hasattr(col, 'mask'):
col.mask = FalseArray(col.shape)
def itercols(self):
"""
Iterate over the columns of this table.
Examples
--------
To iterate over the columns of a table::
>>> t = Table([[1], [2]])
>>> for col in t.itercols():
... print(col)
col0
----
1
col1
----
2
Using ``itercols()`` is similar to ``for col in t.columns.values()``
but is syntactically preferred.
"""
for colname in self.columns:
yield self[colname]
def _base_repr_(self, html=False, descr_vals=None, max_width=None,
tableid=None, show_dtype=True, max_lines=None,
tableclass=None):
if descr_vals is None:
descr_vals = [self.__class__.__name__]
if self.masked:
descr_vals.append('masked=True')
descr_vals.append(f'length={len(self)}')
descr = ' '.join(descr_vals)
if html:
from astropy.utils.xml.writer import xml_escape
descr = f'<i>{xml_escape(descr)}</i>\n'
else:
descr = f'<{descr}>\n'
if tableid is None:
tableid = f'table{id(self)}'
data_lines, outs = self.formatter._pformat_table(
self, tableid=tableid, html=html, max_width=max_width,
show_name=True, show_unit=None, show_dtype=show_dtype,
max_lines=max_lines, tableclass=tableclass)
out = descr + '\n'.join(data_lines)
return out
def _repr_html_(self):
out = self._base_repr_(html=True, max_width=-1,
tableclass=conf.default_notebook_table_class)
# Wrap <table> in <div>. This follows the pattern in pandas and allows
# table to be scrollable horizontally in VS Code notebook display.
out = f'<div>{out}</div>'
return out
def __repr__(self):
return self._base_repr_(html=False, max_width=None)
def __str__(self):
return '\n'.join(self.pformat())
def __bytes__(self):
return str(self).encode('utf-8')
@property
def has_mixin_columns(self):
"""
True if table has any mixin columns (defined as columns that are not Column
subclasses).
"""
return any(has_info_class(col, MixinInfo) for col in self.columns.values())
@property
def has_masked_columns(self):
"""True if table has any ``MaskedColumn`` columns.
This does not check for mixin columns that may have masked values, use the
``has_masked_values`` property in that case.
"""
return any(isinstance(col, MaskedColumn) for col in self.itercols())
@property
def has_masked_values(self):
"""True if column in the table has values which are masked.
This may be relatively slow for large tables as it requires checking the mask
values of each column.
"""
for col in self.itercols():
if hasattr(col, 'mask') and np.any(col.mask):
return True
else:
return False
def _is_mixin_for_table(self, col):
"""
Determine if ``col`` should be added to the table directly as
a mixin column.
"""
if isinstance(col, BaseColumn):
return False
# Is it a mixin but not [Masked]Quantity (which gets converted to
# [Masked]Column with unit set).
return has_info_class(col, MixinInfo) and not has_info_class(col, QuantityInfo)
@format_doc(_pprint_docs)
def pprint(self, max_lines=None, max_width=None, show_name=True,
show_unit=None, show_dtype=False, align=None):
"""Print a formatted string representation of the table.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default is taken from the
configuration item ``astropy.conf.max_lines``. If a negative
value of ``max_lines`` is supplied then there is no line limit
applied.
The same applies for max_width except the configuration item is
``astropy.conf.max_width``.
"""
lines, outs = self.formatter._pformat_table(self, max_lines, max_width,
show_name=show_name, show_unit=show_unit,
show_dtype=show_dtype, align=align)
if outs['show_length']:
lines.append(f'Length = {len(self)} rows')
n_header = outs['n_header']
for i, line in enumerate(lines):
if i < n_header:
color_print(line, 'red')
else:
print(line)
@format_doc(_pprint_docs)
def pprint_all(self, max_lines=-1, max_width=-1, show_name=True,
show_unit=None, show_dtype=False, align=None):
"""Print a formatted string representation of the entire table.
This method is the same as `astropy.table.Table.pprint` except that
the default ``max_lines`` and ``max_width`` are both -1 so that by
default the entire table is printed instead of restricting to the size
of the screen terminal.
"""
return self.pprint(max_lines, max_width, show_name,
show_unit, show_dtype, align)
def _make_index_row_display_table(self, index_row_name):
if index_row_name not in self.columns:
idx_col = self.ColumnClass(name=index_row_name, data=np.arange(len(self)))
return self.__class__([idx_col] + list(self.columns.values()),
copy=False)
else:
return self
def show_in_notebook(self, tableid=None, css=None, display_length=50,
table_class='astropy-default', show_row_index='idx'):
"""Render the table in HTML and show it in the IPython notebook.
Parameters
----------
tableid : str or None
An html ID tag for the table. Default is ``table{id}-XXX``, where
id is the unique integer id of the table object, id(self), and XXX
is a random number to avoid conflicts when printing the same table
multiple times.
table_class : str or None
A string with a list of HTML classes used to style the table.
The special default string ('astropy-default') means that the string
will be retrieved from the configuration item
``astropy.table.default_notebook_table_class``. Note that these
table classes may make use of bootstrap, as this is loaded with the
notebook. See `this page <https://getbootstrap.com/css/#tables>`_
for the list of classes.
css : str
A valid CSS string declaring the formatting for the table. Defaults
to ``astropy.table.jsviewer.DEFAULT_CSS_NB``.
display_length : int, optional
Number or rows to show. Defaults to 50.
show_row_index : str or False
If this does not evaluate to False, a column with the given name
will be added to the version of the table that gets displayed.
This new column shows the index of the row in the table itself,
even when the displayed table is re-sorted by another column. Note
that if a column with this name already exists, this option will be
ignored. Defaults to "idx".
Notes
-----
Currently, unlike `show_in_browser` (with ``jsviewer=True``), this
method needs to access online javascript code repositories. This is due
to modern browsers' limitations on accessing local files. Hence, if you
call this method while offline (and don't have a cached version of
jquery and jquery.dataTables), you will not get the jsviewer features.
"""
from .jsviewer import JSViewer
from IPython.display import HTML
if tableid is None:
tableid = f'table{id(self)}-{np.random.randint(1, 1e6)}'
jsv = JSViewer(display_length=display_length)
if show_row_index:
display_table = self._make_index_row_display_table(show_row_index)
else:
display_table = self
if table_class == 'astropy-default':
table_class = conf.default_notebook_table_class
html = display_table._base_repr_(html=True, max_width=-1, tableid=tableid,
max_lines=-1, show_dtype=False,
tableclass=table_class)
columns = display_table.columns.values()
sortable_columns = [i for i, col in enumerate(columns)
if col.info.dtype.kind in 'iufc']
html += jsv.ipynb(tableid, css=css, sort_columns=sortable_columns)
return HTML(html)
def show_in_browser(self, max_lines=5000, jsviewer=False,
browser='default', jskwargs={'use_local_files': True},
tableid=None, table_class="display compact",
css=None, show_row_index='idx'):
"""Render the table in HTML and show it in a web browser.
Parameters
----------
max_lines : int
Maximum number of rows to export to the table (set low by default
to avoid memory issues, since the browser view requires duplicating
the table in memory). A negative value of ``max_lines`` indicates
no row limit.
jsviewer : bool
If `True`, prepends some javascript headers so that the table is
rendered as a `DataTables <https://datatables.net>`_ data table.
This allows in-browser searching & sorting.
browser : str
Any legal browser name, e.g. ``'firefox'``, ``'chrome'``,
``'safari'`` (for mac, you may need to use ``'open -a
"/Applications/Google Chrome.app" {}'`` for Chrome). If
``'default'``, will use the system default browser.
jskwargs : dict
Passed to the `astropy.table.JSViewer` init. Defaults to
``{'use_local_files': True}`` which means that the JavaScript
libraries will be served from local copies.
tableid : str or None
An html ID tag for the table. Default is ``table{id}``, where id
is the unique integer id of the table object, id(self).
table_class : str or None
A string with a list of HTML classes used to style the table.
Default is "display compact", and other possible values can be
found in https://www.datatables.net/manual/styling/classes
css : str
A valid CSS string declaring the formatting for the table. Defaults
to ``astropy.table.jsviewer.DEFAULT_CSS``.
show_row_index : str or False
If this does not evaluate to False, a column with the given name
will be added to the version of the table that gets displayed.
This new column shows the index of the row in the table itself,
even when the displayed table is re-sorted by another column. Note
that if a column with this name already exists, this option will be
ignored. Defaults to "idx".
"""
import os
import webbrowser
import tempfile
from .jsviewer import DEFAULT_CSS
from urllib.parse import urljoin
from urllib.request import pathname2url
if css is None:
css = DEFAULT_CSS
# We can't use NamedTemporaryFile here because it gets deleted as
# soon as it gets garbage collected.
tmpdir = tempfile.mkdtemp()
path = os.path.join(tmpdir, 'table.html')
with open(path, 'w') as tmp:
if jsviewer:
if show_row_index:
display_table = self._make_index_row_display_table(show_row_index)
else:
display_table = self
display_table.write(tmp, format='jsviewer', css=css,
max_lines=max_lines, jskwargs=jskwargs,
table_id=tableid, table_class=table_class)
else:
self.write(tmp, format='html')
try:
br = webbrowser.get(None if browser == 'default' else browser)
except webbrowser.Error:
log.error(f"Browser '{browser}' not found.")
else:
br.open(urljoin('file:', pathname2url(path)))
@format_doc(_pformat_docs, id="{id}")
def pformat(self, max_lines=None, max_width=None, show_name=True,
show_unit=None, show_dtype=False, html=False, tableid=None,
align=None, tableclass=None):
"""Return a list of lines for the formatted string representation of
the table.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default is taken from the
configuration item ``astropy.conf.max_lines``. If a negative
value of ``max_lines`` is supplied then there is no line limit
applied.
The same applies for ``max_width`` except the configuration item is
``astropy.conf.max_width``.
"""
lines, outs = self.formatter._pformat_table(
self, max_lines, max_width, show_name=show_name,
show_unit=show_unit, show_dtype=show_dtype, html=html,
tableid=tableid, tableclass=tableclass, align=align)
if outs['show_length']:
lines.append(f'Length = {len(self)} rows')
return lines
@format_doc(_pformat_docs, id="{id}")
def pformat_all(self, max_lines=-1, max_width=-1, show_name=True,
show_unit=None, show_dtype=False, html=False, tableid=None,
align=None, tableclass=None):
"""Return a list of lines for the formatted string representation of
the entire table.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default is taken from the
configuration item ``astropy.conf.max_lines``. If a negative
value of ``max_lines`` is supplied then there is no line limit
applied.
The same applies for ``max_width`` except the configuration item is
``astropy.conf.max_width``.
"""
return self.pformat(max_lines, max_width, show_name,
show_unit, show_dtype, html, tableid,
align, tableclass)
def more(self, max_lines=None, max_width=None, show_name=True,
show_unit=None, show_dtype=False):
"""Interactively browse table with a paging interface.
Supported keys::
f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help
Parameters
----------
max_lines : int
Maximum number of lines in table output
max_width : int or None
Maximum character width of output
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include a header row for column dtypes. Default is True.
"""
self.formatter._more_tabcol(self, max_lines, max_width, show_name=show_name,
show_unit=show_unit, show_dtype=show_dtype)
def __getitem__(self, item):
if isinstance(item, str):
return self.columns[item]
elif isinstance(item, (int, np.integer)):
return self.Row(self, item)
elif (isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == 'i'):
return self.Row(self, item.item())
elif self._is_list_or_tuple_of_str(item):
out = self.__class__([self[x] for x in item],
copy_indices=self._copy_indices)
out._groups = groups.TableGroups(out, indices=self.groups._indices,
keys=self.groups._keys)
out.meta = self.meta.copy() # Shallow copy for meta
return out
elif ((isinstance(item, np.ndarray) and item.size == 0)
or (isinstance(item, (tuple, list)) and not item)):
# If item is an empty array/list/tuple then return the table with no rows
return self._new_from_slice([])
elif (isinstance(item, slice)
or isinstance(item, np.ndarray)
or isinstance(item, list)
or isinstance(item, tuple) and all(isinstance(x, np.ndarray)
for x in item)):
# here for the many ways to give a slice; a tuple of ndarray
# is produced by np.where, as in t[np.where(t['a'] > 2)]
# For all, a new table is constructed with slice of all columns
return self._new_from_slice(item)
else:
raise ValueError(f'Illegal type {type(item)} for table item access')
def __setitem__(self, item, value):
# If the item is a string then it must be the name of a column.
# If that column doesn't already exist then create it now.
if isinstance(item, str) and item not in self.colnames:
self.add_column(value, name=item, copy=True)
else:
n_cols = len(self.columns)
if isinstance(item, str):
# Set an existing column by first trying to replace, and if
# this fails do an in-place update. See definition of mask
# property for discussion of the _setitem_inplace attribute.
if (not getattr(self, '_setitem_inplace', False)
and not conf.replace_inplace):
try:
self._replace_column_warnings(item, value)
return
except Exception:
pass
self.columns[item][:] = value
elif isinstance(item, (int, np.integer)):
self._set_row(idx=item, colnames=self.colnames, vals=value)
elif (isinstance(item, slice)
or isinstance(item, np.ndarray)
or isinstance(item, list)
or (isinstance(item, tuple) # output from np.where
and all(isinstance(x, np.ndarray) for x in item))):
if isinstance(value, Table):
vals = (col for col in value.columns.values())
elif isinstance(value, np.ndarray) and value.dtype.names:
vals = (value[name] for name in value.dtype.names)
elif np.isscalar(value):
vals = itertools.repeat(value, n_cols)
else: # Assume this is an iterable that will work
if len(value) != n_cols:
raise ValueError('Right side value needs {} elements (one for each column)'
.format(n_cols))
vals = value
for col, val in zip(self.columns.values(), vals):
col[item] = val
else:
raise ValueError(f'Illegal type {type(item)} for table item access')
def __delitem__(self, item):
if isinstance(item, str):
self.remove_column(item)
elif isinstance(item, (int, np.integer)):
self.remove_row(item)
elif (isinstance(item, (list, tuple, np.ndarray))
and all(isinstance(x, str) for x in item)):
self.remove_columns(item)
elif (isinstance(item, (list, np.ndarray))
and np.asarray(item).dtype.kind == 'i'):
self.remove_rows(item)
elif isinstance(item, slice):
self.remove_rows(item)
else:
raise IndexError('illegal key or index value')
def _ipython_key_completions_(self):
return self.colnames
def field(self, item):
"""Return column[item] for recarray compatibility."""
return self.columns[item]
@property
def masked(self):
return self._masked
@masked.setter
def masked(self, masked):
raise Exception('Masked attribute is read-only (use t = Table(t, masked=True)'
' to convert to a masked table)')
def _set_masked(self, masked):
"""
Set the table masked property.
Parameters
----------
masked : bool
State of table masking (`True` or `False`)
"""
if masked in [True, False, None]:
self._masked = masked
else:
raise ValueError("masked should be one of True, False, None")
self._column_class = self.MaskedColumn if self._masked else self.Column
@property
def ColumnClass(self):
if self._column_class is None:
return self.Column
else:
return self._column_class
@property
def dtype(self):
return np.dtype([descr(col) for col in self.columns.values()])
@property
def colnames(self):
return list(self.columns.keys())
@staticmethod
def _is_list_or_tuple_of_str(names):
"""Check that ``names`` is a tuple or list of strings"""
return (isinstance(names, (tuple, list)) and names
and all(isinstance(x, str) for x in names))
def keys(self):
return list(self.columns.keys())
def values(self):
return self.columns.values()
def items(self):
return self.columns.items()
def __len__(self):
# For performance reasons (esp. in Row) cache the first column name
# and use that subsequently for the table length. If might not be
# available yet or the column might be gone now, in which case
# try again in the except block.
try:
return len(OrderedDict.__getitem__(self.columns, self._first_colname))
except (AttributeError, KeyError):
if len(self.columns) == 0:
return 0
# Get the first column name
self._first_colname = next(iter(self.columns))
return len(self.columns[self._first_colname])
def index_column(self, name):
"""
Return the positional index of column ``name``.
Parameters
----------
name : str
column name
Returns
-------
index : int
Positional index of column ``name``.
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Get index of column 'b' of the table::
>>> t.index_column('b')
1
"""
try:
return self.colnames.index(name)
except ValueError:
raise ValueError(f"Column {name} does not exist")
def add_column(self, col, index=None, name=None, rename_duplicate=False, copy=True,
default_name=None):
"""
Add a new column to the table using ``col`` as input. If ``index``
is supplied then insert column before ``index`` position
in the list of columns, otherwise append column to the end
of the list.
The ``col`` input can be any data object which is acceptable as a
`~astropy.table.Table` column object or can be converted. This includes
mixin columns and scalar or length=1 objects which get broadcast to match
the table length.
To add several columns at once use ``add_columns()`` or simply call
``add_column()`` for each one. There is very little performance difference
in the two approaches.
Parameters
----------
col : object
Data object for the new column
index : int or None
Insert column before this position or at end (default).
name : str
Column name
rename_duplicate : bool
Uniquify column name if it already exist. Default is False.
copy : bool
Make a copy of the new column. Default is True.
default_name : str or None
Name to use if both ``name`` and ``col.info.name`` are not available.
Defaults to ``col{number_of_columns}``.
Examples
--------
Create a table with two columns 'a' and 'b', then create a third column 'c'
and append it to the end of the table::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y'])
>>> t.add_column(col_c)
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
Add column 'd' at position 1. Note that the column is inserted
before the given index::
>>> t.add_column(['a', 'b'], name='d', index=1)
>>> print(t)
a d b c
--- --- --- ---
1 a 0.1 x
2 b 0.2 y
Add second column named 'b' with rename_duplicate::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_column(1.1, name='b', rename_duplicate=True)
>>> print(t)
a b b_1
--- --- ---
1 0.1 1.1
2 0.2 1.1
Add an unnamed column or mixin object in the table using a default name
or by specifying an explicit name with ``name``. Name can also be overridden::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_column(['a', 'b'])
>>> t.add_column(col_c, name='d')
>>> print(t)
a b col2 d
--- --- ---- ---
1 0.1 a x
2 0.2 b y
"""
if default_name is None:
default_name = f'col{len(self.columns)}'
# Convert col data to acceptable object for insertion into self.columns.
# Note that along with the lines above and below, this allows broadcasting
# of scalars to the correct shape for adding to table.
col = self._convert_data_to_col(col, name=name, copy=copy,
default_name=default_name)
# Assigning a scalar column to an empty table should result in an
# exception (see #3811).
if col.shape == () and len(self) == 0:
raise TypeError('Empty table cannot have column set to scalar value')
# Make col data shape correct for scalars. The second test is to allow
# broadcasting an N-d element to a column, e.g. t['new'] = [[1, 2]].
elif (col.shape == () or col.shape[0] == 1) and len(self) > 0:
new_shape = (len(self),) + getattr(col, 'shape', ())[1:]
if isinstance(col, np.ndarray):
col = np.broadcast_to(col, shape=new_shape,
subok=True)
elif isinstance(col, ShapedLikeNDArray):
col = col._apply(np.broadcast_to, shape=new_shape,
subok=True)
# broadcast_to() results in a read-only array. Apparently it only changes
# the view to look like the broadcasted array. So copy.
col = col_copy(col)
name = col.info.name
# Ensure that new column is the right length
if len(self.columns) > 0 and len(col) != len(self):
raise ValueError('Inconsistent data column lengths')
if rename_duplicate:
orig_name = name
i = 1
while name in self.columns:
# Iterate until a unique name is found
name = orig_name + '_' + str(i)
i += 1
col.info.name = name
# Set col parent_table weakref and ensure col has mask attribute if table.masked
self._set_col_parent_table_and_mask(col)
# Add new column as last column
self.columns[name] = col
if index is not None:
# Move the other cols to the right of the new one
move_names = self.colnames[index:-1]
for move_name in move_names:
self.columns.move_to_end(move_name, last=True)
def add_columns(self, cols, indexes=None, names=None, copy=True, rename_duplicate=False):
"""
Add a list of new columns the table using ``cols`` data objects. If a
corresponding list of ``indexes`` is supplied then insert column
before each ``index`` position in the *original* list of columns,
otherwise append columns to the end of the list.
The ``cols`` input can include any data objects which are acceptable as
`~astropy.table.Table` column objects or can be converted. This includes
mixin columns and scalar or length=1 objects which get broadcast to match
the table length.
From a performance perspective there is little difference between calling
this method once or looping over the new columns and calling ``add_column()``
for each column.
Parameters
----------
cols : list of object
List of data objects for the new columns
indexes : list of int or None
Insert column before this position or at end (default).
names : list of str
Column names
copy : bool
Make a copy of the new columns. Default is True.
rename_duplicate : bool
Uniquify new column names if they duplicate the existing ones.
Default is False.
See Also
--------
astropy.table.hstack, update, replace_column
Examples
--------
Create a table with two columns 'a' and 'b', then create columns 'c' and 'd'
and append them to the end of the table::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y'])
>>> col_d = Column(name='d', data=['u', 'v'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
a b c d
--- --- --- ---
1 0.1 x u
2 0.2 y v
Add column 'c' at position 0 and column 'd' at position 1. Note that
the columns are inserted before the given position::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([['x', 'y'], ['u', 'v']], names=['c', 'd'],
... indexes=[0, 1])
>>> print(t)
c a d b
--- --- --- ---
x 1 u 0.1
y 2 v 0.2
Add second column 'b' and column 'c' with ``rename_duplicate``::
>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([[1.1, 1.2], ['x', 'y']], names=('b', 'c'),
... rename_duplicate=True)
>>> print(t)
a b b_1 c
--- --- --- ---
1 0.1 1.1 x
2 0.2 1.2 y
Add unnamed columns or mixin objects in the table using default names
or by specifying explicit names with ``names``. Names can also be overridden::
>>> t = Table()
>>> col_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([[1, 2], col_b])
>>> t.add_columns([[3, 4], col_b], names=['c', 'd'])
>>> print(t)
col0 b c d
---- --- --- ---
1 u 3 u
2 v 4 v
"""
if indexes is None:
indexes = [len(self.columns)] * len(cols)
elif len(indexes) != len(cols):
raise ValueError('Number of indexes must match number of cols')
if names is None:
names = (None,) * len(cols)
elif len(names) != len(cols):
raise ValueError('Number of names must match number of cols')
default_names = [f'col{ii + len(self.columns)}'
for ii in range(len(cols))]
for ii in reversed(np.argsort(indexes)):
self.add_column(cols[ii], index=indexes[ii], name=names[ii],
default_name=default_names[ii],
rename_duplicate=rename_duplicate, copy=copy)
def _replace_column_warnings(self, name, col):
"""
Same as replace_column but issues warnings under various circumstances.
"""
warns = conf.replace_warnings
refcount = None
old_col = None
if 'refcount' in warns and name in self.colnames:
refcount = sys.getrefcount(self[name])
if name in self.colnames:
old_col = self[name]
# This may raise an exception (e.g. t['a'] = 1) in which case none of
# the downstream code runs.
self.replace_column(name, col)
if 'always' in warns:
warnings.warn(f"replaced column '{name}'",
TableReplaceWarning, stacklevel=3)
if 'slice' in warns:
try:
# Check for ndarray-subclass slice. An unsliced instance
# has an ndarray for the base while sliced has the same class
# as parent.
if isinstance(old_col.base, old_col.__class__):
msg = ("replaced column '{}' which looks like an array slice. "
"The new column no longer shares memory with the "
"original array.".format(name))
warnings.warn(msg, TableReplaceWarning, stacklevel=3)
except AttributeError:
pass
if 'refcount' in warns:
# Did reference count change?
new_refcount = sys.getrefcount(self[name])
if refcount != new_refcount:
msg = ("replaced column '{}' and the number of references "
"to the column changed.".format(name))
warnings.warn(msg, TableReplaceWarning, stacklevel=3)
if 'attributes' in warns:
# Any of the standard column attributes changed?
changed_attrs = []
new_col = self[name]
# Check base DataInfo attributes that any column will have
for attr in DataInfo.attr_names:
if getattr(old_col.info, attr) != getattr(new_col.info, attr):
changed_attrs.append(attr)
if changed_attrs:
msg = ("replaced column '{}' and column attributes {} changed."
.format(name, changed_attrs))
warnings.warn(msg, TableReplaceWarning, stacklevel=3)
def replace_column(self, name, col, copy=True):
"""
Replace column ``name`` with the new ``col`` object.
The behavior of ``copy`` for Column objects is:
- copy=True: new class instance with a copy of data and deep copy of meta
- copy=False: new class instance with same data and a key-only copy of meta
For mixin columns:
- copy=True: new class instance with copy of data and deep copy of meta
- copy=False: original instance (no copy at all)
Parameters
----------
name : str
Name of column to replace
col : `~astropy.table.Column` or `~numpy.ndarray` or sequence
New column object to replace the existing column.
copy : bool
Make copy of the input ``col``, default=True
See Also
--------
add_columns, astropy.table.hstack, update
Examples
--------
Replace column 'a' with a float version of itself::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> float_a = t['a'].astype(float)
>>> t.replace_column('a', float_a)
"""
if name not in self.colnames:
raise ValueError(f'column name {name} is not in the table')
if self[name].info.indices:
raise ValueError('cannot replace a table index column')
col = self._convert_data_to_col(col, name=name, copy=copy)
self._set_col_parent_table_and_mask(col)
# Ensure that new column is the right length, unless it is the only column
# in which case re-sizing is allowed.
if len(self.columns) > 1 and len(col) != len(self[name]):
raise ValueError('length of new column must match table length')
self.columns.__setitem__(name, col, validated=True)
def remove_row(self, index):
"""
Remove a row from the table.
Parameters
----------
index : int
Index of row to remove
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove row 1 from the table::
>>> t.remove_row(1)
>>> print(t)
a b c
--- --- ---
1 0.1 x
3 0.3 z
To remove several rows at the same time use remove_rows.
"""
# check the index against the types that work with np.delete
if not isinstance(index, (int, np.integer)):
raise TypeError("Row index must be an integer")
self.remove_rows(index)
def remove_rows(self, row_specifier):
"""
Remove rows from the table.
Parameters
----------
row_specifier : slice or int or array of int
Specification for rows to remove
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove rows 0 and 2 from the table::
>>> t.remove_rows([0, 2])
>>> print(t)
a b c
--- --- ---
2 0.2 y
Note that there are no warnings if the slice operator extends
outside the data::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
"""
# Update indices
for index in self.indices:
index.remove_rows(row_specifier)
keep_mask = np.ones(len(self), dtype=bool)
keep_mask[row_specifier] = False
columns = self.TableColumns()
for name, col in self.columns.items():
newcol = col[keep_mask]
newcol.info.parent_table = self
columns[name] = newcol
self._replace_cols(columns)
# Revert groups to default (ungrouped) state
if hasattr(self, '_groups'):
del self._groups
def iterrows(self, *names):
"""
Iterate over rows of table returning a tuple of values for each row.
This method is especially useful when only a subset of columns are needed.
The ``iterrows`` method can be substantially faster than using the standard
Table row iteration (e.g. ``for row in tbl:``), since that returns a new
``~astropy.table.Row`` object for each row and accessing a column in that
row (e.g. ``row['col0']``) is slower than tuple access.
Parameters
----------
names : list
List of column names (default to all columns if no names provided)
Returns
-------
rows : iterable
Iterator returns tuples of row values
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table({'a': [1, 2, 3],
... 'b': [1.0, 2.5, 3.0],
... 'c': ['x', 'y', 'z']})
To iterate row-wise using column names::
>>> for a, c in t.iterrows('a', 'c'):
... print(a, c)
1 x
2 y
3 z
"""
if len(names) == 0:
names = self.colnames
else:
for name in names:
if name not in self.colnames:
raise ValueError(f'{name} is not a valid column name')
cols = (self[name] for name in names)
out = zip(*cols)
return out
def _set_of_names_in_colnames(self, names):
"""Return ``names`` as a set if valid, or raise a `KeyError`.
``names`` is valid if all elements in it are in ``self.colnames``.
If ``names`` is a string then it is interpreted as a single column
name.
"""
names = {names} if isinstance(names, str) else set(names)
invalid_names = names.difference(self.colnames)
if len(invalid_names) == 1:
raise KeyError(f'column "{invalid_names.pop()}" does not exist')
elif len(invalid_names) > 1:
raise KeyError(f'columns {invalid_names} do not exist')
return names
def remove_column(self, name):
"""
Remove a column from the table.
This can also be done with::
del table[name]
Parameters
----------
name : str
Name of column to remove
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove column 'b' from the table::
>>> t.remove_column('b')
>>> print(t)
a c
--- ---
1 x
2 y
3 z
To remove several columns at the same time use remove_columns.
"""
self.remove_columns([name])
def remove_columns(self, names):
'''
Remove several columns from the table.
Parameters
----------
names : str or iterable of str
Names of the columns to remove
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove columns 'b' and 'c' from the table::
>>> t.remove_columns(['b', 'c'])
>>> print(t)
a
---
1
2
3
Specifying only a single column also works. Remove column 'b' from the table::
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
a c
--- ---
1 x
2 y
3 z
This gives the same as using remove_column.
'''
for name in self._set_of_names_in_colnames(names):
self.columns.pop(name)
def _convert_string_dtype(self, in_kind, out_kind, encode_decode_func):
"""
Convert string-like columns to/from bytestring and unicode (internal only).
Parameters
----------
in_kind : str
Input dtype.kind
out_kind : str
Output dtype.kind
"""
for col in self.itercols():
if col.dtype.kind == in_kind:
try:
# This requires ASCII and is faster by a factor of up to ~8, so
# try that first.
newcol = col.__class__(col, dtype=out_kind)
except (UnicodeEncodeError, UnicodeDecodeError):
newcol = col.__class__(encode_decode_func(col, 'utf-8'))
# Quasi-manually copy info attributes. Unfortunately
# DataInfo.__set__ does not do the right thing in this case
# so newcol.info = col.info does not get the old info attributes.
for attr in col.info.attr_names - col.info._attrs_no_copy - set(['dtype']):
value = deepcopy(getattr(col.info, attr))
setattr(newcol.info, attr, value)
self[col.name] = newcol
def convert_bytestring_to_unicode(self):
"""
Convert bytestring columns (dtype.kind='S') to unicode (dtype.kind='U')
using UTF-8 encoding.
Internally this changes string columns to represent each character
in the string with a 4-byte UCS-4 equivalent, so it is inefficient
for memory but allows scripts to manipulate string arrays with
natural syntax.
"""
self._convert_string_dtype('S', 'U', np.char.decode)
def convert_unicode_to_bytestring(self):
"""
Convert unicode columns (dtype.kind='U') to bytestring (dtype.kind='S')
using UTF-8 encoding.
When exporting a unicode string array to a file, it may be desirable
to encode unicode columns as bytestrings.
"""
self._convert_string_dtype('U', 'S', np.char.encode)
def keep_columns(self, names):
'''
Keep only the columns specified (remove the others).
Parameters
----------
names : str or iterable of str
The columns to keep. All other columns will be removed.
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Keep only column 'a' of the table::
>>> t.keep_columns('a')
>>> print(t)
a
---
1
2
3
Keep columns 'a' and 'c' of the table::
>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
a c
--- ---
1 x
2 y
3 z
'''
names = self._set_of_names_in_colnames(names)
for colname in self.colnames:
if colname not in names:
self.columns.pop(colname)
def rename_column(self, name, new_name):
'''
Rename a column.
This can also be done directly with by setting the ``name`` attribute
for a column::
table[name].name = new_name
TODO: this won't work for mixins
Parameters
----------
name : str
The current name of the column.
new_name : str
The new name for the column
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
a b c
--- --- ---
1 3 5
2 4 6
Renaming column 'a' to 'aa'::
>>> t.rename_column('a' , 'aa')
>>> print(t)
aa b c
--- --- ---
1 3 5
2 4 6
'''
if name not in self.keys():
raise KeyError(f"Column {name} does not exist")
self.columns[name].info.name = new_name
def rename_columns(self, names, new_names):
'''
Rename multiple columns.
Parameters
----------
names : list, tuple
A list or tuple of existing column names.
new_names : list, tuple
A list or tuple of new column names.
Examples
--------
Create a table with three columns 'a', 'b', 'c'::
>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
a b c
--- --- ---
1 3 5
2 4 6
Renaming columns 'a' to 'aa' and 'b' to 'bb'::
>>> names = ('a','b')
>>> new_names = ('aa','bb')
>>> t.rename_columns(names, new_names)
>>> print(t)
aa bb c
--- --- ---
1 3 5
2 4 6
'''
if not self._is_list_or_tuple_of_str(names):
raise TypeError("input 'names' must be a tuple or a list of column names")
if not self._is_list_or_tuple_of_str(new_names):
raise TypeError("input 'new_names' must be a tuple or a list of column names")
if len(names) != len(new_names):
raise ValueError("input 'names' and 'new_names' list arguments must be the same length")
for name, new_name in zip(names, new_names):
self.rename_column(name, new_name)
def _set_row(self, idx, colnames, vals):
try:
assert len(vals) == len(colnames)
except Exception:
raise ValueError('right hand side must be a sequence of values with '
'the same length as the number of selected columns')
# Keep track of original values before setting each column so that
# setting row can be transactional.
orig_vals = []
cols = self.columns
try:
for name, val in zip(colnames, vals):
orig_vals.append(cols[name][idx])
cols[name][idx] = val
except Exception:
# If anything went wrong first revert the row update then raise
for name, val in zip(colnames, orig_vals[:-1]):
cols[name][idx] = val
raise
def add_row(self, vals=None, mask=None):
"""Add a new row to the end of the table.
The ``vals`` argument can be:
sequence (e.g. tuple or list)
Column values in the same order as table columns.
mapping (e.g. dict)
Keys corresponding to column names. Missing values will be
filled with np.zeros for the column dtype.
`None`
All values filled with np.zeros for the column dtype.
This method requires that the Table object "owns" the underlying array
data. In particular one cannot add a row to a Table that was
initialized with copy=False from an existing array.
The ``mask`` attribute should give (if desired) the mask for the
values. The type of the mask should match that of the values, i.e. if
``vals`` is an iterable, then ``mask`` should also be an iterable
with the same length, and if ``vals`` is a mapping, then ``mask``
should be a dictionary.
Parameters
----------
vals : tuple, list, dict or None
Use the specified values in the new row
mask : tuple, list, dict or None
Use the specified mask values in the new row
Examples
--------
Create a table with three columns 'a', 'b' and 'c'::
>>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> print(t)
a b c
--- --- ---
1 4 7
2 5 8
Adding a new row with entries '3' in 'a', '6' in 'b' and '9' in 'c'::
>>> t.add_row([3,6,9])
>>> print(t)
a b c
--- --- ---
1 4 7
2 5 8
3 6 9
"""
self.insert_row(len(self), vals, mask)
def insert_row(self, index, vals=None, mask=None):
"""Add a new row before the given ``index`` position in the table.
The ``vals`` argument can be:
sequence (e.g. tuple or list)
Column values in the same order as table columns.
mapping (e.g. dict)
Keys corresponding to column names. Missing values will be
filled with np.zeros for the column dtype.
`None`
All values filled with np.zeros for the column dtype.
The ``mask`` attribute should give (if desired) the mask for the
values. The type of the mask should match that of the values, i.e. if
``vals`` is an iterable, then ``mask`` should also be an iterable
with the same length, and if ``vals`` is a mapping, then ``mask``
should be a dictionary.
Parameters
----------
vals : tuple, list, dict or None
Use the specified values in the new row
mask : tuple, list, dict or None
Use the specified mask values in the new row
"""
colnames = self.colnames
N = len(self)
if index < -N or index > N:
raise IndexError("Index {} is out of bounds for table with length {}"
.format(index, N))
if index < 0:
index += N
if isinstance(vals, Mapping) or vals is None:
# From the vals and/or mask mappings create the corresponding lists
# that have entries for each table column.
if mask is not None and not isinstance(mask, Mapping):
raise TypeError("Mismatch between type of vals and mask")
# Now check that the mask is specified for the same keys as the
# values, otherwise things get really confusing.
if mask is not None and set(vals.keys()) != set(mask.keys()):
raise ValueError('keys in mask should match keys in vals')
if vals and any(name not in colnames for name in vals):
raise ValueError('Keys in vals must all be valid column names')
vals_list = []
mask_list = []
for name in colnames:
if vals and name in vals:
vals_list.append(vals[name])
mask_list.append(False if mask is None else mask[name])
else:
col = self[name]
if hasattr(col, 'dtype'):
# Make a placeholder zero element of the right type which is masked.
# This assumes the appropriate insert() method will broadcast a
# numpy scalar to the right shape.
vals_list.append(np.zeros(shape=(), dtype=col.dtype))
# For masked table any unsupplied values are masked by default.
mask_list.append(self.masked and vals is not None)
else:
raise ValueError(f"Value must be supplied for column '{name}'")
vals = vals_list
mask = mask_list
if isiterable(vals):
if mask is not None and (not isiterable(mask) or isinstance(mask, Mapping)):
raise TypeError("Mismatch between type of vals and mask")
if len(self.columns) != len(vals):
raise ValueError('Mismatch between number of vals and columns')
if mask is not None:
if len(self.columns) != len(mask):
raise ValueError('Mismatch between number of masks and columns')
else:
mask = [False] * len(self.columns)
else:
raise TypeError('Vals must be an iterable or mapping or None')
# Insert val at index for each column
columns = self.TableColumns()
for name, col, val, mask_ in zip(colnames, self.columns.values(), vals, mask):
try:
# If new val is masked and the existing column does not support masking
# then upgrade the column to a mask-enabled type: either the table-level
# default ColumnClass or else MaskedColumn.
if mask_ and isinstance(col, Column) and not isinstance(col, MaskedColumn):
col_cls = (self.ColumnClass
if issubclass(self.ColumnClass, self.MaskedColumn)
else self.MaskedColumn)
col = col_cls(col, copy=False)
newcol = col.insert(index, val, axis=0)
if len(newcol) != N + 1:
raise ValueError('Incorrect length for column {} after inserting {}'
' (expected {}, got {})'
.format(name, val, len(newcol), N + 1))
newcol.info.parent_table = self
# Set mask if needed and possible
if mask_:
if hasattr(newcol, 'mask'):
newcol[index] = np.ma.masked
else:
raise TypeError("mask was supplied for column '{}' but it does not "
"support masked values".format(col.info.name))
columns[name] = newcol
except Exception as err:
raise ValueError("Unable to insert row because of exception in column '{}':\n{}"
.format(name, err)) from err
for table_index in self.indices:
table_index.insert_row(index, vals, self.columns.values())
self._replace_cols(columns)
# Revert groups to default (ungrouped) state
if hasattr(self, '_groups'):
del self._groups
def _replace_cols(self, columns):
for col, new_col in zip(self.columns.values(), columns.values()):
new_col.info.indices = []
for index in col.info.indices:
index.columns[index.col_position(col.info.name)] = new_col
new_col.info.indices.append(index)
self.columns = columns
def update(self, other, copy=True):
"""
Perform a dictionary-style update and merge metadata.
The argument ``other`` must be a |Table|, or something that can be used
to initialize a table. Columns from (possibly converted) ``other`` are
added to this table. In case of matching column names the column from
this table is replaced with the one from ``other``.
Parameters
----------
other : table-like
Data to update this table with.
copy : bool
Whether the updated columns should be copies of or references to
the originals.
See Also
--------
add_columns, astropy.table.hstack, replace_column
Examples
--------
Update a table with another table::
>>> t1 = Table({'a': ['foo', 'bar'], 'b': [0., 0.]}, meta={'i': 0})
>>> t2 = Table({'b': [1., 2.], 'c': [7., 11.]}, meta={'n': 2})
>>> t1.update(t2)
>>> t1
<Table length=2>
a b c
str3 float64 float64
---- ------- -------
foo 1.0 7.0
bar 2.0 11.0
>>> t1.meta
{'i': 0, 'n': 2}
Update a table with a dictionary::
>>> t = Table({'a': ['foo', 'bar'], 'b': [0., 0.]})
>>> t.update({'b': [1., 2.]})
>>> t
<Table length=2>
a b
str3 float64
---- -------
foo 1.0
bar 2.0
"""
from .operations import _merge_table_meta
if not isinstance(other, Table):
other = self.__class__(other, copy=copy)
common_cols = set(self.colnames).intersection(other.colnames)
for name, col in other.items():
if name in common_cols:
self.replace_column(name, col, copy=copy)
else:
self.add_column(col, name=name, copy=copy)
_merge_table_meta(self, [self, other], metadata_conflicts='silent')
def argsort(self, keys=None, kind=None, reverse=False):
"""
Return the indices which would sort the table according to one or
more key columns. This simply calls the `numpy.argsort` function on
the table with the ``order`` parameter set to ``keys``.
Parameters
----------
keys : str or list of str
The column name(s) to order the table by
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm used by ``numpy.argsort``.
reverse : bool
Sort in reverse order (default=False)
Returns
-------
index_array : ndarray, int
Array of indices that sorts the table by the specified key
column(s).
"""
if isinstance(keys, str):
keys = [keys]
# use index sorted order if possible
if keys is not None:
index = get_index(self, names=keys)
if index is not None:
idx = np.asarray(index.sorted_data())
return idx[::-1] if reverse else idx
kwargs = {}
if keys:
# For multiple keys return a structured array which gets sorted,
# while for a single key return a single ndarray. Sorting a
# one-column structured array is slower than ndarray (e.g. a
# factor of ~6 for a 10 million long random array), and much slower
# for in principle sortable columns like Time, which get stored as
# object arrays.
if len(keys) > 1:
kwargs['order'] = keys
data = self.as_array(names=keys)
else:
data = self[keys[0]]
else:
# No keys provided so sort on all columns.
data = self.as_array()
if kind:
kwargs['kind'] = kind
# np.argsort will look for a possible .argsort method (e.g., for Time),
# and if that fails cast to an array and try sorting that way.
idx = np.argsort(data, **kwargs)
return idx[::-1] if reverse else idx
def sort(self, keys=None, *, kind=None, reverse=False):
'''
Sort the table according to one or more keys. This operates
on the existing table and does not return a new table.
Parameters
----------
keys : str or list of str
The key(s) to order the table by. If None, use the
primary index of the Table.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm used by ``numpy.argsort``.
reverse : bool
Sort in reverse order (default=False)
Examples
--------
Create a table with 3 columns::
>>> t = Table([['Max', 'Jo', 'John'], ['Miller', 'Miller', 'Jackson'],
... [12, 15, 18]], names=('firstname', 'name', 'tel'))
>>> print(t)
firstname name tel
--------- ------- ---
Max Miller 12
Jo Miller 15
John Jackson 18
Sorting according to standard sorting rules, first 'name' then 'firstname'::
>>> t.sort(['name', 'firstname'])
>>> print(t)
firstname name tel
--------- ------- ---
John Jackson 18
Jo Miller 15
Max Miller 12
Sorting according to standard sorting rules, first 'firstname' then 'tel',
in reverse order::
>>> t.sort(['firstname', 'tel'], reverse=True)
>>> print(t)
firstname name tel
--------- ------- ---
Max Miller 12
John Jackson 18
Jo Miller 15
'''
if keys is None:
if not self.indices:
raise ValueError("Table sort requires input keys or a table index")
keys = [x.info.name for x in self.indices[0].columns]
if isinstance(keys, str):
keys = [keys]
indexes = self.argsort(keys, kind=kind, reverse=reverse)
with self.index_mode('freeze'):
for name, col in self.columns.items():
# Make a new sorted column. This requires that take() also copies
# relevant info attributes for mixin columns.
new_col = col.take(indexes, axis=0)
# First statement in try: will succeed if the column supports an in-place
# update, and matches the legacy behavior of astropy Table. However,
# some mixin classes may not support this, so in that case just drop
# in the entire new column. See #9553 and #9536 for discussion.
try:
col[:] = new_col
except Exception:
# In-place update failed for some reason, exception class not
# predictable for arbitrary mixin.
self[col.info.name] = new_col
def reverse(self):
'''
Reverse the row order of table rows. The table is reversed
in place and there are no function arguments.
Examples
--------
Create a table with three columns::
>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
... [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname name tel
--------- ------- ---
Max Miller 12
Jo Miller 15
John Jackson 18
Reversing order::
>>> t.reverse()
>>> print(t)
firstname name tel
--------- ------- ---
John Jackson 18
Jo Miller 15
Max Miller 12
'''
for col in self.columns.values():
# First statement in try: will succeed if the column supports an in-place
# update, and matches the legacy behavior of astropy Table. However,
# some mixin classes may not support this, so in that case just drop
# in the entire new column. See #9836, #9553, and #9536 for discussion.
new_col = col[::-1]
try:
col[:] = new_col
except Exception:
# In-place update failed for some reason, exception class not
# predictable for arbitrary mixin.
self[col.info.name] = new_col
for index in self.indices:
index.reverse()
def round(self, decimals=0):
'''
Round numeric columns in-place to the specified number of decimals.
Non-numeric columns will be ignored.
Examples
--------
Create three columns with different types:
>>> t = Table([[1, 4, 5], [-25.55, 12.123, 85],
... ['a', 'b', 'c']], names=('a', 'b', 'c'))
>>> print(t)
a b c
--- ------ ---
1 -25.55 a
4 12.123 b
5 85.0 c
Round them all to 0:
>>> t.round(0)
>>> print(t)
a b c
--- ----- ---
1 -26.0 a
4 12.0 b
5 85.0 c
Round column 'a' to -1 decimal:
>>> t.round({'a':-1})
>>> print(t)
a b c
--- ----- ---
0 -26.0 a
0 12.0 b
0 85.0 c
Parameters
----------
decimals: int, dict
Number of decimals to round the columns to. If a dict is given,
the columns will be rounded to the number specified as the value.
If a certain column is not in the dict given, it will remain the
same.
'''
if isinstance(decimals, Mapping):
decimal_values = decimals.values()
column_names = decimals.keys()
elif isinstance(decimals, int):
decimal_values = itertools.repeat(decimals)
column_names = self.colnames
else:
raise ValueError("'decimals' argument must be an int or a dict")
for colname, decimal in zip(column_names, decimal_values):
col = self.columns[colname]
if np.issubdtype(col.info.dtype, np.number):
try:
np.around(col, decimals=decimal, out=col)
except TypeError:
# Bug in numpy see https://github.com/numpy/numpy/issues/15438
col[()] = np.around(col, decimals=decimal)
def copy(self, copy_data=True):
'''
Return a copy of the table.
Parameters
----------
copy_data : bool
If `True` (the default), copy the underlying data array.
Otherwise, use the same data array. The ``meta`` is always
deepcopied regardless of the value for ``copy_data``.
'''
out = self.__class__(self, copy=copy_data)
# If the current table is grouped then do the same in the copy
if hasattr(self, '_groups'):
out._groups = groups.TableGroups(out, indices=self._groups._indices,
keys=self._groups._keys)
return out
def __deepcopy__(self, memo=None):
return self.copy(True)
def __copy__(self):
return self.copy(False)
def __lt__(self, other):
return super().__lt__(other)
def __gt__(self, other):
return super().__gt__(other)
def __le__(self, other):
return super().__le__(other)
def __ge__(self, other):
return super().__ge__(other)
def __eq__(self, other):
return self._rows_equal(other)
def __ne__(self, other):
return ~self.__eq__(other)
def _rows_equal(self, other):
"""
Row-wise comparison of table with any other object.
This is actual implementation for __eq__.
Returns a 1-D boolean numpy array showing result of row-wise comparison.
This is the same as the ``==`` comparison for tables.
Parameters
----------
other : Table or DataFrame or ndarray
An object to compare with table
Examples
--------
Comparing one Table with other::
>>> t1 = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> t2 = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> t1._rows_equal(t2)
array([ True, True])
"""
if isinstance(other, Table):
other = other.as_array()
if self.has_masked_columns:
if isinstance(other, np.ma.MaskedArray):
result = self.as_array() == other
else:
# If mask is True, then by definition the row doesn't match
# because the other array is not masked.
false_mask = np.zeros(1, dtype=[(n, bool) for n in self.dtype.names])
result = (self.as_array().data == other) & (self.mask == false_mask)
else:
if isinstance(other, np.ma.MaskedArray):
# If mask is True, then by definition the row doesn't match
# because the other array is not masked.
false_mask = np.zeros(1, dtype=[(n, bool) for n in other.dtype.names])
result = (self.as_array() == other.data) & (other.mask == false_mask)
else:
result = self.as_array() == other
return result
def values_equal(self, other):
"""
Element-wise comparison of table with another table, list, or scalar.
Returns a ``Table`` with the same columns containing boolean values
showing result of comparison.
Parameters
----------
other : table-like object or list or scalar
Object to compare with table
Examples
--------
Compare one Table with other::
>>> t1 = Table([[1, 2], [4, 5], [-7, 8]], names=('a', 'b', 'c'))
>>> t2 = Table([[1, 2], [-4, 5], [7, 8]], names=('a', 'b', 'c'))
>>> t1.values_equal(t2)
<Table length=2>
a b c
bool bool bool
---- ----- -----
True False False
True True True
"""
if isinstance(other, Table):
names = other.colnames
else:
try:
other = Table(other, copy=False)
names = other.colnames
except Exception:
# Broadcast other into a dict, so e.g. other = 2 will turn into
# other = {'a': 2, 'b': 2} and then equality does a
# column-by-column broadcasting.
names = self.colnames
other = {name: other for name in names}
# Require column names match but do not require same column order
if set(self.colnames) != set(names):
raise ValueError('cannot compare tables with different column names')
eqs = []
for name in names:
try:
np.broadcast(self[name], other[name]) # Check if broadcast-able
# Catch the numpy FutureWarning related to equality checking,
# "elementwise comparison failed; returning scalar instead, but
# in the future will perform elementwise comparison". Turn this
# into an exception since the scalar answer is not what we want.
with warnings.catch_warnings(record=True) as warns:
warnings.simplefilter('always')
eq = self[name] == other[name]
if (warns and issubclass(warns[-1].category, FutureWarning)
and 'elementwise comparison failed' in str(warns[-1].message)):
raise FutureWarning(warns[-1].message)
except Exception as err:
raise ValueError(f'unable to compare column {name}') from err
# Be strict about the result from the comparison. E.g. SkyCoord __eq__ is just
# broken and completely ignores that it should return an array.
if not (isinstance(eq, np.ndarray)
and eq.dtype is np.dtype('bool')
and len(eq) == len(self)):
raise TypeError(f'comparison for column {name} returned {eq} '
f'instead of the expected boolean ndarray')
eqs.append(eq)
out = Table(eqs, names=names)
return out
@property
def groups(self):
if not hasattr(self, '_groups'):
self._groups = groups.TableGroups(self)
return self._groups
def group_by(self, keys):
"""
Group this table by the specified ``keys``
This effectively splits the table into groups which correspond to unique
values of the ``keys`` grouping object. The output is a new
`~astropy.table.TableGroups` which contains a copy of this table but
sorted by row according to ``keys``.
The ``keys`` input to `group_by` can be specified in different ways:
- String or list of strings corresponding to table column name(s)
- Numpy array (homogeneous or structured) with same length as this table
- `~astropy.table.Table` with same length as this table
Parameters
----------
keys : str, list of str, numpy array, or `~astropy.table.Table`
Key grouping object
Returns
-------
out : `~astropy.table.Table`
New table with groups set
"""
return groups.table_group_by(self, keys)
def to_pandas(self, index=None, use_nullable_int=True):
"""
Return a :class:`pandas.DataFrame` instance
The index of the created DataFrame is controlled by the ``index``
argument. For ``index=True`` or the default ``None``, an index will be
specified for the DataFrame if there is a primary key index on the
Table *and* if it corresponds to a single column. If ``index=False``
then no DataFrame index will be specified. If ``index`` is the name of
a column in the table then that will be the DataFrame index.
In addition to vanilla columns or masked columns, this supports Table
mixin columns like Quantity, Time, or SkyCoord. In many cases these
objects have no analog in pandas and will be converted to a "encoded"
representation using only Column or MaskedColumn. The exception is
Time or TimeDelta columns, which will be converted to the corresponding
representation in pandas using ``np.datetime64`` or ``np.timedelta64``.
See the example below.
Parameters
----------
index : None, bool, str
Specify DataFrame index mode
use_nullable_int : bool, default=True
Convert integer MaskedColumn to pandas nullable integer type.
If ``use_nullable_int=False`` or the pandas version does not support
nullable integer types (version < 0.24), then the column is converted
to float with NaN for missing elements and a warning is issued.
Returns
-------
dataframe : :class:`pandas.DataFrame`
A pandas :class:`pandas.DataFrame` instance
Raises
------
ImportError
If pandas is not installed
ValueError
If the Table has multi-dimensional columns
Examples
--------
Here we convert a table with a few mixins to a
:class:`pandas.DataFrame` instance.
>>> import pandas as pd
>>> from astropy.table import QTable
>>> import astropy.units as u
>>> from astropy.time import Time, TimeDelta
>>> from astropy.coordinates import SkyCoord
>>> q = [1, 2] * u.m
>>> tm = Time([1998, 2002], format='jyear')
>>> sc = SkyCoord([5, 6], [7, 8], unit='deg')
>>> dt = TimeDelta([3, 200] * u.s)
>>> t = QTable([q, tm, sc, dt], names=['q', 'tm', 'sc', 'dt'])
>>> df = t.to_pandas(index='tm')
>>> with pd.option_context('display.max_columns', 20):
... print(df)
q sc.ra sc.dec dt
tm
1998-01-01 1.0 5.0 7.0 0 days 00:00:03
2002-01-01 2.0 6.0 8.0 0 days 00:03:20
"""
from pandas import DataFrame, Series
if index is not False:
if index in (None, True):
# Default is to use the table primary key if available and a single column
if self.primary_key and len(self.primary_key) == 1:
index = self.primary_key[0]
else:
index = False
else:
if index not in self.colnames:
raise ValueError('index must be None, False, True or a table '
'column name')
def _encode_mixins(tbl):
"""Encode a Table ``tbl`` that may have mixin columns to a Table with only
astropy Columns + appropriate meta-data to allow subsequent decoding.
"""
from . import serialize
from astropy.time import TimeBase, TimeDelta
# Convert any Time or TimeDelta columns and pay attention to masking
time_cols = [col for col in tbl.itercols() if isinstance(col, TimeBase)]
if time_cols:
# Make a light copy of table and clear any indices
new_cols = []
for col in tbl.itercols():
new_col = col_copy(col, copy_indices=False) if col.info.indices else col
new_cols.append(new_col)
tbl = tbl.__class__(new_cols, copy=False)
# Certain subclasses (e.g. TimeSeries) may generate new indices on
# table creation, so make sure there are no indices on the table.
for col in tbl.itercols():
col.info.indices.clear()
for col in time_cols:
if isinstance(col, TimeDelta):
# Convert to nanoseconds (matches astropy datetime64 support)
new_col = (col.sec * 1e9).astype('timedelta64[ns]')
nat = np.timedelta64('NaT')
else:
new_col = col.datetime64.copy()
nat = np.datetime64('NaT')
if col.masked:
new_col[col.mask] = nat
tbl[col.info.name] = new_col
# Convert the table to one with no mixins, only Column objects.
encode_tbl = serialize.represent_mixins_as_columns(tbl)
return encode_tbl
tbl = _encode_mixins(self)
badcols = [name for name, col in self.columns.items() if len(col.shape) > 1]
if badcols:
raise ValueError(
f'Cannot convert a table with multidimensional columns to a '
f'pandas DataFrame. Offending columns are: {badcols}\n'
f'One can filter out such columns using:\n'
f'names = [name for name in tbl.colnames if len(tbl[name].shape) <= 1]\n'
f'tbl[names].to_pandas(...)')
out = OrderedDict()
for name, column in tbl.columns.items():
if getattr(column.dtype, 'isnative', True):
out[name] = column
else:
out[name] = column.data.byteswap().newbyteorder('=')
if isinstance(column, MaskedColumn) and np.any(column.mask):
if column.dtype.kind in ['i', 'u']:
pd_dtype = column.dtype.name
if use_nullable_int:
# Convert int64 to Int64, uint32 to UInt32, etc for nullable types
pd_dtype = pd_dtype.replace('i', 'I').replace('u', 'U')
out[name] = Series(out[name], dtype=pd_dtype)
# If pandas is older than 0.24 the type may have turned to float
if column.dtype.kind != out[name].dtype.kind:
warnings.warn(
f"converted column '{name}' from {column.dtype} to {out[name].dtype}",
TableReplaceWarning, stacklevel=3)
elif column.dtype.kind not in ['f', 'c']:
out[name] = column.astype(object).filled(np.nan)
kwargs = {}
if index:
idx = out.pop(index)
kwargs['index'] = idx
# We add the table index to Series inputs (MaskedColumn with int values) to override
# its default RangeIndex, see #11432
for v in out.values():
if isinstance(v, Series):
v.index = idx
df = DataFrame(out, **kwargs)
if index:
# Explicitly set the pandas DataFrame index to the original table
# index name.
df.index.name = idx.info.name
return df
@classmethod
def from_pandas(cls, dataframe, index=False, units=None):
"""
Create a `~astropy.table.Table` from a :class:`pandas.DataFrame` instance
In addition to converting generic numeric or string columns, this supports
conversion of pandas Date and Time delta columns to `~astropy.time.Time`
and `~astropy.time.TimeDelta` columns, respectively.
Parameters
----------
dataframe : :class:`pandas.DataFrame`
A pandas :class:`pandas.DataFrame` instance
index : bool
Include the index column in the returned table (default=False)
units: dict
A dict mapping column names to to a `~astropy.units.Unit`.
The columns will have the specified unit in the Table.
Returns
-------
table : `~astropy.table.Table`
A `~astropy.table.Table` (or subclass) instance
Raises
------
ImportError
If pandas is not installed
Examples
--------
Here we convert a :class:`pandas.DataFrame` instance
to a `~astropy.table.QTable`.
>>> import numpy as np
>>> import pandas as pd
>>> from astropy.table import QTable
>>> time = pd.Series(['1998-01-01', '2002-01-01'], dtype='datetime64[ns]')
>>> dt = pd.Series(np.array([1, 300], dtype='timedelta64[s]'))
>>> df = pd.DataFrame({'time': time})
>>> df['dt'] = dt
>>> df['x'] = [3., 4.]
>>> with pd.option_context('display.max_columns', 20):
... print(df)
time dt x
0 1998-01-01 0 days 00:00:01 3.0
1 2002-01-01 0 days 00:05:00 4.0
>>> QTable.from_pandas(df)
<QTable length=2>
time dt x
Time TimeDelta float64
----------------------- --------- -------
1998-01-01T00:00:00.000 1.0 3.0
2002-01-01T00:00:00.000 300.0 4.0
"""
out = OrderedDict()
names = list(dataframe.columns)
columns = [dataframe[name] for name in names]
datas = [np.array(column) for column in columns]
masks = [np.array(column.isnull()) for column in columns]
if index:
index_name = dataframe.index.name or 'index'
while index_name in names:
index_name = '_' + index_name + '_'
names.insert(0, index_name)
columns.insert(0, dataframe.index)
datas.insert(0, np.array(dataframe.index))
masks.insert(0, np.zeros(len(dataframe), dtype=bool))
if units is None:
units = [None] * len(names)
else:
if not isinstance(units, Mapping):
raise TypeError('Expected a Mapping "column-name" -> "unit"')
not_found = set(units.keys()) - set(names)
if not_found:
warnings.warn(f'`units` contains additional columns: {not_found}')
units = [units.get(name) for name in names]
for name, column, data, mask, unit in zip(names, columns, datas, masks, units):
if column.dtype.kind in ['u', 'i'] and np.any(mask):
# Special-case support for pandas nullable int
np_dtype = str(column.dtype).lower()
data = np.zeros(shape=column.shape, dtype=np_dtype)
data[~mask] = column[~mask]
out[name] = MaskedColumn(data=data, name=name, mask=mask, unit=unit, copy=False)
continue
if data.dtype.kind == 'O':
# If all elements of an object array are string-like or np.nan
# then coerce back to a native numpy str/unicode array.
string_types = (str, bytes)
nan = np.nan
if all(isinstance(x, string_types) or x is nan for x in data):
# Force any missing (null) values to b''. Numpy will
# upcast to str/unicode as needed.
data[mask] = b''
# When the numpy object array is represented as a list then
# numpy initializes to the correct string or unicode type.
data = np.array([x for x in data])
# Numpy datetime64
if data.dtype.kind == 'M':
from astropy.time import Time
out[name] = Time(data, format='datetime64')
if np.any(mask):
out[name][mask] = np.ma.masked
out[name].format = 'isot'
# Numpy timedelta64
elif data.dtype.kind == 'm':
from astropy.time import TimeDelta
data_sec = data.astype('timedelta64[ns]').astype(np.float64) / 1e9
out[name] = TimeDelta(data_sec, format='sec')
if np.any(mask):
out[name][mask] = np.ma.masked
else:
if np.any(mask):
out[name] = MaskedColumn(data=data, name=name, mask=mask, unit=unit)
else:
out[name] = Column(data=data, name=name, unit=unit)
return cls(out)
info = TableInfo()
class QTable(Table):
"""A class to represent tables of heterogeneous data.
`~astropy.table.QTable` provides a class for heterogeneous tabular data
which can be easily modified, for instance adding columns or new rows.
The `~astropy.table.QTable` class is identical to `~astropy.table.Table`
except that columns with an associated ``unit`` attribute are converted to
`~astropy.units.Quantity` objects.
See also:
- https://docs.astropy.org/en/stable/table/
- https://docs.astropy.org/en/stable/table/mixin_columns.html
Parameters
----------
data : numpy ndarray, dict, list, table-like object, optional
Data to initialize table.
masked : bool, optional
Specify whether the table is masked.
names : list, optional
Specify column names.
dtype : list, optional
Specify column data types.
meta : dict, optional
Metadata associated with the table.
copy : bool, optional
Copy the input data. Default is True.
rows : numpy ndarray, list of list, optional
Row-oriented data for table instead of ``data`` argument.
copy_indices : bool, optional
Copy any indices in the input data. Default is True.
**kwargs : dict, optional
Additional keyword args when converting table-like object.
"""
def _is_mixin_for_table(self, col):
"""
Determine if ``col`` should be added to the table directly as
a mixin column.
"""
return has_info_class(col, MixinInfo)
def _convert_col_for_table(self, col):
if isinstance(col, Column) and getattr(col, 'unit', None) is not None:
# We need to turn the column into a quantity; use subok=True to allow
# Quantity subclasses identified in the unit (such as u.mag()).
q_cls = Masked(Quantity) if isinstance(col, MaskedColumn) else Quantity
try:
qcol = q_cls(col.data, col.unit, copy=False, subok=True)
except Exception as exc:
warnings.warn(f"column {col.info.name} has a unit but is kept as "
f"a {col.__class__.__name__} as an attempt to "
f"convert it to Quantity failed with:\n{exc!r}",
AstropyUserWarning)
else:
qcol.info = col.info
qcol.info.indices = col.info.indices
col = qcol
else:
col = super()._convert_col_for_table(col)
return col
|
9f55f5b6c001ac7b387648d082eaf005e29735058b770c5ccbea1720f1fb5642 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import astropy.config as _config
from .column import Column, MaskedColumn, StringTruncateWarning, ColumnInfo
__all__ = ['BST', 'Column', 'ColumnGroups', 'ColumnInfo', 'Conf',
'JSViewer', 'MaskedColumn', 'NdarrayMixin', 'QTable', 'Row',
'SCEngine', 'SerializedColumn', 'SortedArray', 'StringTruncateWarning',
'Table', 'TableAttribute', 'TableColumns', 'TableFormatter',
'TableGroups', 'TableMergeError', 'TableReplaceWarning', 'conf',
'connect', 'hstack', 'join', 'registry', 'represent_mixins_as_columns',
'setdiff', 'unique', 'vstack', 'dstack', 'conf', 'join_skycoord',
'join_distance', 'PprintIncludeExclude']
class Conf(_config.ConfigNamespace): # noqa
"""
Configuration parameters for `astropy.table`.
"""
auto_colname = _config.ConfigItem(
'col{0}',
'The template that determines the name of a column if it cannot be '
'determined. Uses new-style (format method) string formatting.',
aliases=['astropy.table.column.auto_colname'])
default_notebook_table_class = _config.ConfigItem(
'table-striped table-bordered table-condensed',
'The table class to be used in Jupyter notebooks when displaying '
'tables (and not overridden). See <https://getbootstrap.com/css/#tables '
'for a list of useful bootstrap classes.')
replace_warnings = _config.ConfigItem(
[],
'List of conditions for issuing a warning when replacing a table '
"column using setitem, e.g. t['a'] = value. Allowed options are "
"'always', 'slice', 'refcount', 'attributes'.",
'string_list')
replace_inplace = _config.ConfigItem(
False,
'Always use in-place update of a table column when using setitem, '
"e.g. t['a'] = value. This overrides the default behavior of "
"replacing the column entirely with the new value when possible. "
"This configuration option will be deprecated and then removed in "
"subsequent major releases.")
conf = Conf() # noqa
from . import connect # noqa: E402
from .groups import TableGroups, ColumnGroups # noqa: E402
from .table import (Table, QTable, TableColumns, Row, TableFormatter,
NdarrayMixin, TableReplaceWarning, TableAttribute,
PprintIncludeExclude) # noqa: E402
from .operations import (join, setdiff, hstack, dstack, vstack, unique, # noqa: E402
TableMergeError, join_skycoord, join_distance) # noqa: E402
from .bst import BST # noqa: E402
from .sorted_array import SortedArray # noqa: E402
from .soco import SCEngine # noqa: E402
from .serialize import SerializedColumn, represent_mixins_as_columns # noqa: E402
# Finally import the formats for the read and write method but delay building
# the documentation until all are loaded. (#5275)
from astropy.io import registry # noqa: E402
with registry.delay_doc_updates(Table):
# Import routines that connect readers/writers to astropy.table
from .jsviewer import JSViewer
import astropy.io.ascii.connect
import astropy.io.fits.connect
import astropy.io.misc.connect
import astropy.io.votable.connect
import astropy.io.misc.asdf.connect
import astropy.io.misc.pandas.connect # noqa: F401
|
ef336ec0932a28b8b9b88157706988ce3b213d6c365c2299e9bf4a64a0ef5be7 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import itertools
import warnings
import weakref
from copy import deepcopy
import numpy as np
from numpy import ma
from astropy.units import Unit, Quantity
from astropy.utils.console import color_print
from astropy.utils.metadata import MetaData
from astropy.utils.data_info import BaseColumnInfo, dtype_info_name
from astropy.utils.misc import dtype_bytes_or_chars
from . import groups
from . import pprint
# These "shims" provide __getitem__ implementations for Column and MaskedColumn
from ._column_mixins import _ColumnGetitemShim, _MaskedColumnGetitemShim
# Create a generic TableFormatter object for use by bare columns with no
# parent table.
FORMATTER = pprint.TableFormatter()
class StringTruncateWarning(UserWarning):
"""
Warning class for when a string column is assigned a value
that gets truncated because the base (numpy) string length
is too short.
This does not inherit from AstropyWarning because we want to use
stacklevel=2 to show the user where the issue occurred in their code.
"""
pass
# Always emit this warning, not just the first instance
warnings.simplefilter('always', StringTruncateWarning)
def _auto_names(n_cols):
from . import conf
return [str(conf.auto_colname).format(i) for i in range(n_cols)]
# list of one and two-dimensional comparison functions, which sometimes return
# a Column class and sometimes a plain array. Used in __array_wrap__ to ensure
# they only return plain (masked) arrays (see #1446 and #1685)
_comparison_functions = set(
[np.greater, np.greater_equal, np.less, np.less_equal,
np.not_equal, np.equal,
np.isfinite, np.isinf, np.isnan, np.sign, np.signbit])
def col_copy(col, copy_indices=True):
"""
Mixin-safe version of Column.copy() (with copy_data=True).
Parameters
----------
col : Column or mixin column
Input column
copy_indices : bool
Copy the column ``indices`` attribute
Returns
-------
col : Copy of input column
"""
if isinstance(col, BaseColumn):
return col.copy()
newcol = col.copy() if hasattr(col, 'copy') else deepcopy(col)
# If the column has info defined, we copy it and adjust any indices
# to point to the copied column. By guarding with the if statement,
# we avoid side effects (of creating the default info instance).
if 'info' in col.__dict__:
newcol.info = col.info
if copy_indices and col.info.indices:
newcol.info.indices = deepcopy(col.info.indices)
for index in newcol.info.indices:
index.replace_col(col, newcol)
return newcol
class FalseArray(np.ndarray):
"""
Boolean mask array that is always False.
This is used to create a stub ``mask`` property which is a boolean array of
``False`` used by default for mixin columns and corresponding to the mixin
column data shape. The ``mask`` looks like a normal numpy array but an
exception will be raised if ``True`` is assigned to any element. The
consequences of the limitation are most obvious in the high-level table
operations.
Parameters
----------
shape : tuple
Data shape
"""
def __new__(cls, shape):
obj = np.zeros(shape, dtype=bool).view(cls)
return obj
def __setitem__(self, item, val):
val = np.asarray(val)
if np.any(val):
raise ValueError('Cannot set any element of {} class to True'
.format(self.__class__.__name__))
def _expand_string_array_for_values(arr, values):
"""
For string-dtype return a version of ``arr`` that is wide enough for ``values``.
If ``arr`` is not string-dtype or does not need expansion then return ``arr``.
Parameters
----------
arr : np.ndarray
Input array
values : scalar or array-like
Values for width comparison for string arrays
Returns
-------
arr_expanded : np.ndarray
"""
if arr.dtype.kind in ('U', 'S') and values is not np.ma.masked:
# Find the length of the longest string in the new values.
values_str_len = np.char.str_len(values).max()
# Determine character repeat count of arr.dtype. Returns a positive
# int or None (something like 'U0' is not possible in numpy). If new values
# are longer than current then make a new (wider) version of arr.
arr_str_len = dtype_bytes_or_chars(arr.dtype)
if arr_str_len and values_str_len > arr_str_len:
arr_dtype = arr.dtype.byteorder + arr.dtype.kind + str(values_str_len)
arr = arr.astype(arr_dtype)
return arr
def _convert_sequence_data_to_array(data, dtype=None):
"""Convert N-d sequence-like data to ndarray or MaskedArray.
This is the core function for converting Python lists or list of lists to a
numpy array. This handles embedded np.ma.masked constants in ``data`` along
with the special case of an homogeneous list of MaskedArray elements.
Considerations:
- np.ma.array is about 50 times slower than np.array for list input. This
function avoids using np.ma.array on list input.
- np.array emits a UserWarning for embedded np.ma.masked, but only for int
or float inputs. For those it converts to np.nan and forces float dtype.
For other types np.array is inconsistent, for instance converting
np.ma.masked to "0.0" for str types.
- Searching in pure Python for np.ma.masked in ``data`` is comparable in
speed to calling ``np.array(data)``.
- This function may end up making two additional copies of input ``data``.
Parameters
----------
data : N-d sequence
Input data, typically list or list of lists
dtype : None or dtype-like
Output datatype (None lets np.array choose)
Returns
-------
np_data : np.ndarray or np.ma.MaskedArray
"""
np_ma_masked = np.ma.masked # Avoid repeated lookups of this object
# Special case of an homogeneous list of MaskedArray elements (see #8977).
# np.ma.masked is an instance of MaskedArray, so exclude those values.
if (hasattr(data, '__len__')
and len(data) > 0
and all(isinstance(val, np.ma.MaskedArray)
and val is not np_ma_masked for val in data)):
np_data = np.ma.array(data, dtype=dtype)
return np_data
# First convert data to a plain ndarray. If there are instances of np.ma.masked
# in the data this will issue a warning for int and float.
with warnings.catch_warnings(record=True) as warns:
# Ensure this warning from numpy is always enabled and that it is not
# converted to an error (which can happen during pytest).
warnings.filterwarnings('always', category=UserWarning,
message='.*converting a masked element.*')
# FutureWarning in numpy 1.21. See https://github.com/astropy/astropy/issues/11291
# and https://github.com/numpy/numpy/issues/18425.
warnings.filterwarnings('always', category=FutureWarning,
message='.*Promotion of numbers and bools to strings.*')
try:
np_data = np.array(data, dtype=dtype)
except np.ma.MaskError:
# Catches case of dtype=int with masked values, instead let it
# convert to float
np_data = np.array(data)
except Exception:
# Conversion failed for some reason, e.g. [2, 1*u.m] gives TypeError in Quantity.
# First try to interpret the data as Quantity. If that still fails then fall
# through to object
try:
np_data = Quantity(data, dtype)
except Exception:
dtype = object
np_data = np.array(data, dtype=dtype)
if np_data.ndim == 0 or (np_data.ndim > 0 and len(np_data) == 0):
# Implies input was a scalar or an empty list (e.g. initializing an
# empty table with pre-declared names and dtypes but no data). Here we
# need to fall through to initializing with the original data=[].
return data
# If there were no warnings and the data are int or float, then we are done.
# Other dtypes like string or complex can have masked values and the
# np.array() conversion gives the wrong answer (e.g. converting np.ma.masked
# to the string "0.0").
if len(warns) == 0 and np_data.dtype.kind in ('i', 'f'):
return np_data
# Now we need to determine if there is an np.ma.masked anywhere in input data.
# Make a statement like below to look for np.ma.masked in a nested sequence.
# Because np.array(data) succeeded we know that `data` has a regular N-d
# structure. Find ma_masked:
# any(any(any(d2 is ma_masked for d2 in d1) for d1 in d0) for d0 in data)
# Using this eval avoids creating a copy of `data` in the more-usual case of
# no masked elements.
any_statement = 'd0 is ma_masked'
for ii in reversed(range(np_data.ndim)):
if ii == 0:
any_statement = f'any({any_statement} for d0 in data)'
elif ii == np_data.ndim - 1:
any_statement = f'any(d{ii} is ma_masked for d{ii} in d{ii-1})'
else:
any_statement = f'any({any_statement} for d{ii} in d{ii-1})'
context = {'ma_masked': np.ma.masked, 'data': data}
has_masked = eval(any_statement, context)
# If there are any masks then explicitly change each one to a fill value and
# set a mask boolean array. If not has_masked then we're done.
if has_masked:
mask = np.zeros(np_data.shape, dtype=bool)
data_filled = np.array(data, dtype=object)
# Make type-appropriate fill value based on initial conversion.
if np_data.dtype.kind == 'U':
fill = ''
elif np_data.dtype.kind == 'S':
fill = b''
else:
# Zero works for every numeric type.
fill = 0
ranges = [range(dim) for dim in np_data.shape]
for idxs in itertools.product(*ranges):
val = data_filled[idxs]
if val is np_ma_masked:
data_filled[idxs] = fill
mask[idxs] = True
elif isinstance(val, bool) and dtype is None:
# If we see a bool and dtype not specified then assume bool for
# the entire array. Not perfect but in most practical cases OK.
# Unfortunately numpy types [False, 0] as int, not bool (and
# [False, np.ma.masked] => array([0.0, np.nan])).
dtype = bool
# If no dtype is provided then need to convert back to list so np.array
# does type autodetection.
if dtype is None:
data_filled = data_filled.tolist()
# Use np.array first to convert `data` to ndarray (fast) and then make
# masked array from an ndarray with mask (fast) instead of from `data`.
np_data = np.ma.array(np.array(data_filled, dtype=dtype), mask=mask)
return np_data
def _make_compare(oper):
"""
Make Column comparison methods which encode the ``other`` object to utf-8
in the case of a bytestring dtype for Py3+.
Parameters
----------
oper : str
Operator name
"""
swapped_oper = {'__eq__': '__eq__',
'__ne__': '__ne__',
'__gt__': '__lt__',
'__lt__': '__gt__',
'__ge__': '__le__',
'__le__': '__ge__'}[oper]
def _compare(self, other):
op = oper # copy enclosed ref to allow swap below
# Special case to work around #6838. Other combinations work OK,
# see tests.test_column.test_unicode_sandwich_compare(). In this
# case just swap self and other.
#
# This is related to an issue in numpy that was addressed in np 1.13.
# However that fix does not make this problem go away, but maybe
# future numpy versions will do so. NUMPY_LT_1_13 to get the
# attention of future maintainers to check (by deleting or versioning
# the if block below). See #6899 discussion.
# 2019-06-21: still needed with numpy 1.16.
if (isinstance(self, MaskedColumn) and self.dtype.kind == 'U'
and isinstance(other, MaskedColumn) and other.dtype.kind == 'S'):
self, other = other, self
op = swapped_oper
if self.dtype.char == 'S':
other = self._encode_str(other)
# Now just let the regular ndarray.__eq__, etc., take over.
result = getattr(super(Column, self), op)(other)
# But we should not return Column instances for this case.
return result.data if isinstance(result, Column) else result
return _compare
class ColumnInfo(BaseColumnInfo):
"""
Container for meta information like name, description, format.
This is required when the object is used as a mixin column within a table,
but can be used as a general way to store meta information.
"""
attrs_from_parent = BaseColumnInfo.attr_names
_supports_indexing = True
def new_like(self, cols, length, metadata_conflicts='warn', name=None):
"""
Return a new Column instance which is consistent with the
input ``cols`` and has ``length`` rows.
This is intended for creating an empty column object whose elements can
be set in-place for table operations like join or vstack.
Parameters
----------
cols : list
List of input columns
length : int
Length of the output column object
metadata_conflicts : str ('warn'|'error'|'silent')
How to handle metadata conflicts
name : str
Output column name
Returns
-------
col : Column (or subclass)
New instance of this class consistent with ``cols``
"""
attrs = self.merge_cols_attributes(cols, metadata_conflicts, name,
('meta', 'unit', 'format', 'description'))
return self._parent_cls(length=length, **attrs)
def get_sortable_arrays(self):
"""
Return a list of arrays which can be lexically sorted to represent
the order of the parent column.
For Column this is just the column itself.
Returns
-------
arrays : list of ndarray
"""
return [self._parent]
class BaseColumn(_ColumnGetitemShim, np.ndarray):
meta = MetaData()
def __new__(cls, data=None, name=None,
dtype=None, shape=(), length=0,
description=None, unit=None, format=None, meta=None,
copy=False, copy_indices=True):
if data is None:
self_data = np.zeros((length,)+shape, dtype=dtype)
elif isinstance(data, BaseColumn) and hasattr(data, '_name'):
# When unpickling a MaskedColumn, ``data`` will be a bare
# BaseColumn with none of the expected attributes. In this case
# do NOT execute this block which initializes from ``data``
# attributes.
self_data = np.array(data.data, dtype=dtype, copy=copy)
if description is None:
description = data.description
if unit is None:
unit = unit or data.unit
if format is None:
format = data.format
if meta is None:
meta = data.meta
if name is None:
name = data.name
elif isinstance(data, Quantity):
if unit is None:
self_data = np.array(data, dtype=dtype, copy=copy)
unit = data.unit
else:
self_data = Quantity(data, unit, dtype=dtype, copy=copy).value
# If 'info' has been defined, copy basic properties (if needed).
if 'info' in data.__dict__:
if description is None:
description = data.info.description
if format is None:
format = data.info.format
if meta is None:
meta = data.info.meta
else:
if np.dtype(dtype).char == 'S':
data = cls._encode_str(data)
self_data = np.array(data, dtype=dtype, copy=copy)
self = self_data.view(cls)
self._name = None if name is None else str(name)
self._parent_table = None
self.unit = unit
self._format = format
self.description = description
self.meta = meta
self.indices = deepcopy(getattr(data, 'indices', [])) if copy_indices else []
for index in self.indices:
index.replace_col(data, self)
return self
@property
def data(self):
return self.view(np.ndarray)
@property
def value(self):
return self.data
@property
def parent_table(self):
# Note: It seems there are some cases where _parent_table is not set,
# such after restoring from a pickled Column. Perhaps that should be
# fixed, but this is also okay for now.
if getattr(self, '_parent_table', None) is None:
return None
else:
return self._parent_table()
@parent_table.setter
def parent_table(self, table):
if table is None:
self._parent_table = None
else:
self._parent_table = weakref.ref(table)
info = ColumnInfo()
def copy(self, order='C', data=None, copy_data=True):
"""
Return a copy of the current instance.
If ``data`` is supplied then a view (reference) of ``data`` is used,
and ``copy_data`` is ignored.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of ``a`` as closely
as possible. (Note that this function and :func:numpy.copy are very
similar, but have different default values for their order=
arguments.) Default is 'C'.
data : array, optional
If supplied then use a view of ``data`` instead of the instance
data. This allows copying the instance attributes and meta.
copy_data : bool, optional
Make a copy of the internal numpy array instead of using a
reference. Default is True.
Returns
-------
col : Column or MaskedColumn
Copy of the current column (same type as original)
"""
if data is None:
data = self.data
if copy_data:
data = data.copy(order)
out = data.view(self.__class__)
out.__array_finalize__(self)
# If there is meta on the original column then deepcopy (since "copy" of column
# implies complete independence from original). __array_finalize__ will have already
# made a light copy. I'm not sure how to avoid that initial light copy.
if self.meta is not None:
out.meta = self.meta # MetaData descriptor does a deepcopy here
# for MaskedColumn, MaskedArray.__array_finalize__ also copies mask
# from self, which is not the idea here, so undo
if isinstance(self, MaskedColumn):
out._mask = data._mask
self._copy_groups(out)
return out
def __setstate__(self, state):
"""
Restore the internal state of the Column/MaskedColumn for pickling
purposes. This requires that the last element of ``state`` is a
5-tuple that has Column-specific state values.
"""
# Get the Column attributes
names = ('_name', '_unit', '_format', 'description', 'meta', 'indices')
attrs = {name: val for name, val in zip(names, state[-1])}
state = state[:-1]
# Using super().__setstate__(state) gives
# "TypeError 'int' object is not iterable", raised in
# astropy.table._column_mixins._ColumnGetitemShim.__setstate_cython__()
# Previously, it seems to have given an infinite recursion.
# Hence, manually call the right super class to actually set up
# the array object.
super_class = ma.MaskedArray if isinstance(self, ma.MaskedArray) else np.ndarray
super_class.__setstate__(self, state)
# Set the Column attributes
for name, val in attrs.items():
setattr(self, name, val)
self._parent_table = None
def __reduce__(self):
"""
Return a 3-tuple for pickling a Column. Use the super-class
functionality but then add in a 5-tuple of Column-specific values
that get used in __setstate__.
"""
super_class = ma.MaskedArray if isinstance(self, ma.MaskedArray) else np.ndarray
reconstruct_func, reconstruct_func_args, state = super_class.__reduce__(self)
# Define Column-specific attrs and meta that gets added to state.
column_state = (self.name, self.unit, self.format, self.description,
self.meta, self.indices)
state = state + (column_state,)
return reconstruct_func, reconstruct_func_args, state
def __array_finalize__(self, obj):
# Obj will be none for direct call to Column() creator
if obj is None:
return
if callable(super().__array_finalize__):
super().__array_finalize__(obj)
# Self was created from template (e.g. obj[slice] or (obj * 2))
# or viewcast e.g. obj.view(Column). In either case we want to
# init Column attributes for self from obj if possible.
self.parent_table = None
if not hasattr(self, 'indices'): # may have been copied in __new__
self.indices = []
self._copy_attrs(obj)
if 'info' in getattr(obj, '__dict__', {}):
self.info = obj.info
def __array_wrap__(self, out_arr, context=None):
"""
__array_wrap__ is called at the end of every ufunc.
Normally, we want a Column object back and do not have to do anything
special. But there are two exceptions:
1) If the output shape is different (e.g. for reduction ufuncs
like sum() or mean()), a Column still linking to a parent_table
makes little sense, so we return the output viewed as the
column content (ndarray or MaskedArray).
For this case, we use "[()]" to select everything, and to ensure we
convert a zero rank array to a scalar. (For some reason np.sum()
returns a zero rank scalar array while np.mean() returns a scalar;
So the [()] is needed for this case.
2) When the output is created by any function that returns a boolean
we also want to consistently return an array rather than a column
(see #1446 and #1685)
"""
out_arr = super().__array_wrap__(out_arr, context)
if (self.shape != out_arr.shape
or (isinstance(out_arr, BaseColumn)
and (context is not None
and context[0] in _comparison_functions))):
return out_arr.data[()]
else:
return out_arr
@property
def name(self):
"""
The name of this column.
"""
return self._name
@name.setter
def name(self, val):
if val is not None:
val = str(val)
if self.parent_table is not None:
table = self.parent_table
table.columns._rename_column(self.name, val)
self._name = val
@property
def format(self):
"""
Format string for displaying values in this column.
"""
return self._format
@format.setter
def format(self, format_string):
prev_format = getattr(self, '_format', None)
self._format = format_string # set new format string
try:
# test whether it formats without error exemplarily
self.pformat(max_lines=1)
except Exception as err:
# revert to restore previous format if there was one
self._format = prev_format
raise ValueError(
"Invalid format for column '{}': could not display "
"values in this column using this format".format(
self.name)) from err
@property
def descr(self):
"""Array-interface compliant full description of the column.
This returns a 3-tuple (name, type, shape) that can always be
used in a structured array dtype definition.
"""
return (self.name, self.dtype.str, self.shape[1:])
def iter_str_vals(self):
"""
Return an iterator that yields the string-formatted values of this
column.
Returns
-------
str_vals : iterator
Column values formatted as strings
"""
# Iterate over formatted values with no max number of lines, no column
# name, no unit, and ignoring the returned header info in outs.
_pformat_col_iter = self._formatter._pformat_col_iter
for str_val in _pformat_col_iter(self, -1, show_name=False, show_unit=False,
show_dtype=False, outs={}):
yield str_val
def attrs_equal(self, col):
"""Compare the column attributes of ``col`` to this object.
The comparison attributes are: ``name``, ``unit``, ``dtype``,
``format``, ``description``, and ``meta``.
Parameters
----------
col : Column
Comparison column
Returns
-------
equal : bool
True if all attributes are equal
"""
if not isinstance(col, BaseColumn):
raise ValueError('Comparison `col` must be a Column or '
'MaskedColumn object')
attrs = ('name', 'unit', 'dtype', 'format', 'description', 'meta')
equal = all(getattr(self, x) == getattr(col, x) for x in attrs)
return equal
@property
def _formatter(self):
return FORMATTER if (self.parent_table is None) else self.parent_table.formatter
def pformat(self, max_lines=None, show_name=True, show_unit=False, show_dtype=False,
html=False):
"""Return a list of formatted string representation of column values.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default will be
determined using the ``astropy.conf.max_lines`` configuration
item. If a negative value of ``max_lines`` is supplied then
there is no line limit applied.
Parameters
----------
max_lines : int
Maximum lines of output (header + data rows)
show_name : bool
Include column name. Default is True.
show_unit : bool
Include a header row for unit. Default is False.
show_dtype : bool
Include column dtype. Default is False.
html : bool
Format the output as an HTML table. Default is False.
Returns
-------
lines : list
List of lines with header and formatted column values
"""
_pformat_col = self._formatter._pformat_col
lines, outs = _pformat_col(self, max_lines, show_name=show_name,
show_unit=show_unit, show_dtype=show_dtype,
html=html)
return lines
def pprint(self, max_lines=None, show_name=True, show_unit=False, show_dtype=False):
"""Print a formatted string representation of column values.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default will be
determined using the ``astropy.conf.max_lines`` configuration
item. If a negative value of ``max_lines`` is supplied then
there is no line limit applied.
Parameters
----------
max_lines : int
Maximum number of values in output
show_name : bool
Include column name. Default is True.
show_unit : bool
Include a header row for unit. Default is False.
show_dtype : bool
Include column dtype. Default is True.
"""
_pformat_col = self._formatter._pformat_col
lines, outs = _pformat_col(self, max_lines, show_name=show_name, show_unit=show_unit,
show_dtype=show_dtype)
n_header = outs['n_header']
for i, line in enumerate(lines):
if i < n_header:
color_print(line, 'red')
else:
print(line)
def more(self, max_lines=None, show_name=True, show_unit=False):
"""Interactively browse column with a paging interface.
Supported keys::
f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help
Parameters
----------
max_lines : int
Maximum number of lines in table output.
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is False.
"""
_more_tabcol = self._formatter._more_tabcol
_more_tabcol(self, max_lines=max_lines, show_name=show_name,
show_unit=show_unit)
@property
def unit(self):
"""
The unit associated with this column. May be a string or a
`astropy.units.UnitBase` instance.
Setting the ``unit`` property does not change the values of the
data. To perform a unit conversion, use ``convert_unit_to``.
"""
return self._unit
@unit.setter
def unit(self, unit):
if unit is None:
self._unit = None
else:
self._unit = Unit(unit, parse_strict='silent')
@unit.deleter
def unit(self):
self._unit = None
def searchsorted(self, v, side='left', sorter=None):
# For bytes type data, encode the `v` value as UTF-8 (if necessary) before
# calling searchsorted. This prevents a factor of 1000 slowdown in
# searchsorted in this case.
a = self.data
if a.dtype.kind == 'S' and not isinstance(v, bytes):
v = np.asarray(v)
if v.dtype.kind == 'U':
v = np.char.encode(v, 'utf-8')
return np.searchsorted(a, v, side=side, sorter=sorter)
searchsorted.__doc__ = np.ndarray.searchsorted.__doc__
def convert_unit_to(self, new_unit, equivalencies=[]):
"""
Converts the values of the column in-place from the current
unit to the given unit.
To change the unit associated with this column without
actually changing the data values, simply set the ``unit``
property.
Parameters
----------
new_unit : str or `astropy.units.UnitBase` instance
The unit to convert to.
equivalencies : list of tuple
A list of equivalence pairs to try if the unit are not
directly convertible. See :ref:`astropy:unit_equivalencies`.
Raises
------
astropy.units.UnitsError
If units are inconsistent
"""
if self.unit is None:
raise ValueError("No unit set on column")
self.data[:] = self.unit.to(
new_unit, self.data, equivalencies=equivalencies)
self.unit = new_unit
@property
def groups(self):
if not hasattr(self, '_groups'):
self._groups = groups.ColumnGroups(self)
return self._groups
def group_by(self, keys):
"""
Group this column by the specified ``keys``
This effectively splits the column into groups which correspond to
unique values of the ``keys`` grouping object. The output is a new
`Column` or `MaskedColumn` which contains a copy of this column but
sorted by row according to ``keys``.
The ``keys`` input to ``group_by`` must be a numpy array with the
same length as this column.
Parameters
----------
keys : numpy array
Key grouping object
Returns
-------
out : Column
New column with groups attribute set accordingly
"""
return groups.column_group_by(self, keys)
def _copy_groups(self, out):
"""
Copy current groups into a copy of self ``out``
"""
if self.parent_table:
if hasattr(self.parent_table, '_groups'):
out._groups = groups.ColumnGroups(out, indices=self.parent_table._groups._indices)
elif hasattr(self, '_groups'):
out._groups = groups.ColumnGroups(out, indices=self._groups._indices)
# Strip off the BaseColumn-ness for repr and str so that
# MaskedColumn.data __repr__ does not include masked_BaseColumn(data =
# [1 2], ...).
def __repr__(self):
return np.asarray(self).__repr__()
@property
def quantity(self):
"""
A view of this table column as a `~astropy.units.Quantity` object with
units given by the Column's `unit` parameter.
"""
# the Quantity initializer is used here because it correctly fails
# if the column's values are non-numeric (like strings), while .view
# will happily return a quantity with gibberish for numerical values
return Quantity(self, self.unit, copy=False, dtype=self.dtype, order='A', subok=True)
def to(self, unit, equivalencies=[], **kwargs):
"""
Converts this table column to a `~astropy.units.Quantity` object with
the requested units.
Parameters
----------
unit : unit-like
The unit to convert to (i.e., a valid argument to the
:meth:`astropy.units.Quantity.to` method).
equivalencies : list of tuple
Equivalencies to use for this conversion. See
:meth:`astropy.units.Quantity.to` for more details.
Returns
-------
quantity : `~astropy.units.Quantity`
A quantity object with the contents of this column in the units
``unit``.
"""
return self.quantity.to(unit, equivalencies)
def _copy_attrs(self, obj):
"""
Copy key column attributes from ``obj`` to self
"""
for attr in ('name', 'unit', '_format', 'description'):
val = getattr(obj, attr, None)
setattr(self, attr, val)
# Light copy of meta if it is not empty
obj_meta = getattr(obj, 'meta', None)
if obj_meta:
self.meta = obj_meta.copy()
@staticmethod
def _encode_str(value):
"""
Encode anything that is unicode-ish as utf-8. This method is only
called for Py3+.
"""
if isinstance(value, str):
value = value.encode('utf-8')
elif isinstance(value, bytes) or value is np.ma.masked:
pass
else:
arr = np.asarray(value)
if arr.dtype.char == 'U':
arr = np.char.encode(arr, encoding='utf-8')
if isinstance(value, np.ma.MaskedArray):
arr = np.ma.array(arr, mask=value.mask, copy=False)
value = arr
return value
def tolist(self):
if self.dtype.kind == 'S':
return np.chararray.decode(self, encoding='utf-8').tolist()
else:
return super().tolist()
class Column(BaseColumn):
"""Define a data column for use in a Table object.
Parameters
----------
data : list, ndarray, or None
Column data values
name : str
Column name and key for reference within Table
dtype : `~numpy.dtype`-like
Data type for column
shape : tuple or ()
Dimensions of a single row element in the column data
length : int or 0
Number of row elements in column data
description : str or None
Full description of column
unit : str or None
Physical unit
format : str, None, or callable
Format string for outputting column values. This can be an
"old-style" (``format % value``) or "new-style" (`str.format`)
format specification string or a function or any callable object that
accepts a single value and returns a string.
meta : dict-like or None
Meta-data associated with the column
Examples
--------
A Column can be created in two different ways:
- Provide a ``data`` value but not ``shape`` or ``length`` (which are
inferred from the data).
Examples::
col = Column(data=[1, 2], name='name') # shape=(2,)
col = Column(data=[[1, 2], [3, 4]], name='name') # shape=(2, 2)
col = Column(data=[1, 2], name='name', dtype=float)
col = Column(data=np.array([1, 2]), name='name')
col = Column(data=['hello', 'world'], name='name')
The ``dtype`` argument can be any value which is an acceptable
fixed-size data-type initializer for the numpy.dtype() method. See
`<https://numpy.org/doc/stable/reference/arrays.dtypes.html>`_.
Examples include:
- Python non-string type (float, int, bool)
- Numpy non-string type (e.g. np.float32, np.int64, np.bool\\_)
- Numpy.dtype array-protocol type strings (e.g. 'i4', 'f8', 'S15')
If no ``dtype`` value is provide then the type is inferred using
``np.array(data)``.
- Provide ``length`` and optionally ``shape``, but not ``data``
Examples::
col = Column(name='name', length=5)
col = Column(name='name', dtype=int, length=10, shape=(3,4))
The default ``dtype`` is ``np.float64``. The ``shape`` argument is the
array shape of a single cell in the column.
"""
def __new__(cls, data=None, name=None,
dtype=None, shape=(), length=0,
description=None, unit=None, format=None, meta=None,
copy=False, copy_indices=True):
if isinstance(data, MaskedColumn) and np.any(data.mask):
raise TypeError("Cannot convert a MaskedColumn with masked value to a Column")
self = super().__new__(
cls, data=data, name=name, dtype=dtype, shape=shape, length=length,
description=description, unit=unit, format=format, meta=meta,
copy=copy, copy_indices=copy_indices)
return self
def __setattr__(self, item, value):
if not isinstance(self, MaskedColumn) and item == "mask":
raise AttributeError("cannot set mask value to a column in non-masked Table")
super().__setattr__(item, value)
if item == 'unit' and issubclass(self.dtype.type, np.number):
try:
converted = self.parent_table._convert_col_for_table(self)
except AttributeError: # Either no parent table or parent table is None
pass
else:
if converted is not self:
self.parent_table.replace_column(self.name, converted)
def _base_repr_(self, html=False):
# If scalar then just convert to correct numpy type and use numpy repr
if self.ndim == 0:
return repr(self.item())
descr_vals = [self.__class__.__name__]
unit = None if self.unit is None else str(self.unit)
shape = None if self.ndim <= 1 else self.shape[1:]
for attr, val in (('name', self.name),
('dtype', dtype_info_name(self.dtype)),
('shape', shape),
('unit', unit),
('format', self.format),
('description', self.description),
('length', len(self))):
if val is not None:
descr_vals.append(f'{attr}={val!r}')
descr = '<' + ' '.join(descr_vals) + '>\n'
if html:
from astropy.utils.xml.writer import xml_escape
descr = xml_escape(descr)
data_lines, outs = self._formatter._pformat_col(
self, show_name=False, show_unit=False, show_length=False, html=html)
out = descr + '\n'.join(data_lines)
return out
def _repr_html_(self):
return self._base_repr_(html=True)
def __repr__(self):
return self._base_repr_(html=False)
def __str__(self):
# If scalar then just convert to correct numpy type and use numpy repr
if self.ndim == 0:
return str(self.item())
lines, outs = self._formatter._pformat_col(self)
return '\n'.join(lines)
def __bytes__(self):
return str(self).encode('utf-8')
def _check_string_truncate(self, value):
"""
Emit a warning if any elements of ``value`` will be truncated when
``value`` is assigned to self.
"""
# Convert input ``value`` to the string dtype of this column and
# find the length of the longest string in the array.
value = np.asanyarray(value, dtype=self.dtype.type)
if value.size == 0:
return
value_str_len = np.char.str_len(value).max()
# Parse the array-protocol typestring (e.g. '|U15') of self.dtype which
# has the character repeat count on the right side.
self_str_len = dtype_bytes_or_chars(self.dtype)
if value_str_len > self_str_len:
warnings.warn('truncated right side string(s) longer than {} '
'character(s) during assignment'
.format(self_str_len),
StringTruncateWarning,
stacklevel=3)
def __setitem__(self, index, value):
if self.dtype.char == 'S':
value = self._encode_str(value)
# Issue warning for string assignment that truncates ``value``
if issubclass(self.dtype.type, np.character):
self._check_string_truncate(value)
# update indices
self.info.adjust_indices(index, value, len(self))
# Set items using a view of the underlying data, as it gives an
# order-of-magnitude speed-up. [#2994]
self.data[index] = value
__eq__ = _make_compare('__eq__')
__ne__ = _make_compare('__ne__')
__gt__ = _make_compare('__gt__')
__lt__ = _make_compare('__lt__')
__ge__ = _make_compare('__ge__')
__le__ = _make_compare('__le__')
def insert(self, obj, values, axis=0):
"""
Insert values before the given indices in the column and return
a new `~astropy.table.Column` object.
Parameters
----------
obj : int, slice or sequence of int
Object that defines the index or indices before which ``values`` is
inserted.
values : array-like
Value(s) to insert. If the type of ``values`` is different from
that of the column, ``values`` is converted to the matching type.
``values`` should be shaped so that it can be broadcast appropriately.
axis : int, optional
Axis along which to insert ``values``. If ``axis`` is None then
the column array is flattened before insertion. Default is 0,
which will insert a row.
Returns
-------
out : `~astropy.table.Column`
A copy of column with ``values`` and ``mask`` inserted. Note that the
insertion does not occur in-place: a new column is returned.
"""
if self.dtype.kind == 'O':
# Even if values is array-like (e.g. [1,2,3]), insert as a single
# object. Numpy.insert instead inserts each element in an array-like
# input individually.
data = np.insert(self, obj, None, axis=axis)
data[obj] = values
else:
self_for_insert = _expand_string_array_for_values(self, values)
data = np.insert(self_for_insert, obj, values, axis=axis)
out = data.view(self.__class__)
out.__array_finalize__(self)
return out
# We do this to make the methods show up in the API docs
name = BaseColumn.name
unit = BaseColumn.unit
copy = BaseColumn.copy
more = BaseColumn.more
pprint = BaseColumn.pprint
pformat = BaseColumn.pformat
convert_unit_to = BaseColumn.convert_unit_to
quantity = BaseColumn.quantity
to = BaseColumn.to
class MaskedColumnInfo(ColumnInfo):
"""
Container for meta information like name, description, format.
This is required when the object is used as a mixin column within a table,
but can be used as a general way to store meta information. In this case
it just adds the ``mask_val`` attribute.
"""
# Add `serialize_method` attribute to the attrs that MaskedColumnInfo knows
# about. This allows customization of the way that MaskedColumn objects
# get written to file depending on format. The default is to use whatever
# the writer would normally do, which in the case of FITS or ECSV is to use
# a NULL value within the data itself. If serialize_method is 'data_mask'
# then the mask is explicitly written out as a separate column if there
# are any masked values. See also code below.
attr_names = ColumnInfo.attr_names | {'serialize_method'}
# When `serialize_method` is 'data_mask', and data and mask are being written
# as separate columns, use column names <name> and <name>.mask (instead
# of default encoding as <name>.data and <name>.mask).
_represent_as_dict_primary_data = 'data'
mask_val = np.ma.masked
def __init__(self, bound=False):
super().__init__(bound)
# If bound to a data object instance then create the dict of attributes
# which stores the info attribute values.
if bound:
# Specify how to serialize this object depending on context.
self.serialize_method = {'fits': 'null_value',
'ecsv': 'null_value',
'hdf5': 'data_mask',
'parquet': 'data_mask',
None: 'null_value'}
def _represent_as_dict(self):
out = super()._represent_as_dict()
col = self._parent
# If the serialize method for this context (e.g. 'fits' or 'ecsv') is
# 'data_mask', that means to serialize using an explicit mask column.
method = self.serialize_method[self._serialize_context]
if method == 'data_mask':
# Note: a driver here is a performance issue in #8443 where repr() of a
# np.ma.MaskedArray value is up to 10 times slower than repr of a normal array
# value. So regardless of whether there are masked elements it is useful to
# explicitly define this as a serialized column and use col.data.data (ndarray)
# instead of letting it fall through to the "standard" serialization machinery.
out['data'] = col.data.data
if np.any(col.mask):
# Only if there are actually masked elements do we add the ``mask`` column
out['mask'] = col.mask
elif method == 'null_value':
pass
else:
raise ValueError('serialize method must be either "data_mask" or "null_value"')
return out
class MaskedColumn(Column, _MaskedColumnGetitemShim, ma.MaskedArray):
"""Define a masked data column for use in a Table object.
Parameters
----------
data : list, ndarray, or None
Column data values
name : str
Column name and key for reference within Table
mask : list, ndarray or None
Boolean mask for which True indicates missing or invalid data
fill_value : float, int, str, or None
Value used when filling masked column elements
dtype : `~numpy.dtype`-like
Data type for column
shape : tuple or ()
Dimensions of a single row element in the column data
length : int or 0
Number of row elements in column data
description : str or None
Full description of column
unit : str or None
Physical unit
format : str, None, or callable
Format string for outputting column values. This can be an
"old-style" (``format % value``) or "new-style" (`str.format`)
format specification string or a function or any callable object that
accepts a single value and returns a string.
meta : dict-like or None
Meta-data associated with the column
Examples
--------
A MaskedColumn is similar to a Column except that it includes ``mask`` and
``fill_value`` attributes. It can be created in two different ways:
- Provide a ``data`` value but not ``shape`` or ``length`` (which are
inferred from the data).
Examples::
col = MaskedColumn(data=[1, 2], name='name')
col = MaskedColumn(data=[1, 2], name='name', mask=[True, False])
col = MaskedColumn(data=[1, 2], name='name', dtype=float, fill_value=99)
The ``mask`` argument will be cast as a boolean array and specifies
which elements are considered to be missing or invalid.
The ``dtype`` argument can be any value which is an acceptable
fixed-size data-type initializer for the numpy.dtype() method. See
`<https://numpy.org/doc/stable/reference/arrays.dtypes.html>`_.
Examples include:
- Python non-string type (float, int, bool)
- Numpy non-string type (e.g. np.float32, np.int64, np.bool\\_)
- Numpy.dtype array-protocol type strings (e.g. 'i4', 'f8', 'S15')
If no ``dtype`` value is provide then the type is inferred using
``np.array(data)``. When ``data`` is provided then the ``shape``
and ``length`` arguments are ignored.
- Provide ``length`` and optionally ``shape``, but not ``data``
Examples::
col = MaskedColumn(name='name', length=5)
col = MaskedColumn(name='name', dtype=int, length=10, shape=(3,4))
The default ``dtype`` is ``np.float64``. The ``shape`` argument is the
array shape of a single cell in the column.
"""
info = MaskedColumnInfo()
def __new__(cls, data=None, name=None, mask=None, fill_value=None,
dtype=None, shape=(), length=0,
description=None, unit=None, format=None, meta=None,
copy=False, copy_indices=True):
if mask is None:
# If mask is None then we need to determine the mask (if any) from the data.
# The naive method is looking for a mask attribute on data, but this can fail,
# see #8816. Instead use ``MaskedArray`` to do the work.
mask = ma.MaskedArray(data).mask
if mask is np.ma.nomask:
# Handle odd-ball issue with np.ma.nomask (numpy #13758), and see below.
mask = False
elif copy:
mask = mask.copy()
elif mask is np.ma.nomask:
# Force the creation of a full mask array as nomask is tricky to
# use and will fail in an unexpected manner when setting a value
# to the mask.
mask = False
else:
mask = deepcopy(mask)
# Create self using MaskedArray as a wrapper class, following the example of
# class MSubArray in
# https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/ma/tests/test_subclassing.py
# This pattern makes it so that __array_finalize__ is called as expected (e.g. #1471 and
# https://github.com/astropy/astropy/commit/ff6039e8)
# First just pass through all args and kwargs to BaseColumn, then wrap that object
# with MaskedArray.
self_data = BaseColumn(data, dtype=dtype, shape=shape, length=length, name=name,
unit=unit, format=format, description=description,
meta=meta, copy=copy, copy_indices=copy_indices)
self = ma.MaskedArray.__new__(cls, data=self_data, mask=mask)
# The above process preserves info relevant for Column, but this does
# not include serialize_method (and possibly other future attributes)
# relevant for MaskedColumn, so we set info explicitly.
if 'info' in getattr(data, '__dict__', {}):
self.info = data.info
# Note: do not set fill_value in the MaskedArray constructor because this does not
# go through the fill_value workarounds.
if fill_value is None and getattr(data, 'fill_value', None) is not None:
# Coerce the fill_value to the correct type since `data` may be a
# different dtype than self.
fill_value = np.array(data.fill_value, self.dtype)[()]
self.fill_value = fill_value
self.parent_table = None
# needs to be done here since self doesn't come from BaseColumn.__new__
for index in self.indices:
index.replace_col(self_data, self)
return self
@property
def fill_value(self):
return self.get_fill_value() # defer to native ma.MaskedArray method
@fill_value.setter
def fill_value(self, val):
"""Set fill value both in the masked column view and in the parent table
if it exists. Setting one or the other alone doesn't work."""
# another ma bug workaround: If the value of fill_value for a string array is
# requested but not yet set then it gets created as 'N/A'. From this point onward
# any new fill_values are truncated to 3 characters. Note that this does not
# occur if the masked array is a structured array (as in the previous block that
# deals with the parent table).
#
# >>> x = ma.array(['xxxx'])
# >>> x.fill_value # fill_value now gets represented as an 'S3' array
# 'N/A'
# >>> x.fill_value='yyyy'
# >>> x.fill_value
# 'yyy'
#
# To handle this we are forced to reset a private variable first:
self._fill_value = None
self.set_fill_value(val) # defer to native ma.MaskedArray method
@property
def data(self):
"""The plain MaskedArray data held by this column."""
out = self.view(np.ma.MaskedArray)
# By default, a MaskedArray view will set the _baseclass to be the
# same as that of our own class, i.e., BaseColumn. Since we want
# to return a plain MaskedArray, we reset the baseclass accordingly.
out._baseclass = np.ndarray
return out
def filled(self, fill_value=None):
"""Return a copy of self, with masked values filled with a given value.
Parameters
----------
fill_value : scalar; optional
The value to use for invalid entries (`None` by default). If
`None`, the ``fill_value`` attribute of the array is used
instead.
Returns
-------
filled_column : Column
A copy of ``self`` with masked entries replaced by `fill_value`
(be it the function argument or the attribute of ``self``).
"""
if fill_value is None:
fill_value = self.fill_value
data = super().filled(fill_value)
# Use parent table definition of Column if available
column_cls = self.parent_table.Column if (self.parent_table is not None) else Column
out = column_cls(name=self.name, data=data, unit=self.unit,
format=self.format, description=self.description,
meta=deepcopy(self.meta))
return out
def insert(self, obj, values, mask=None, axis=0):
"""
Insert values along the given axis before the given indices and return
a new `~astropy.table.MaskedColumn` object.
Parameters
----------
obj : int, slice or sequence of int
Object that defines the index or indices before which ``values`` is
inserted.
values : array-like
Value(s) to insert. If the type of ``values`` is different from
that of the column, ``values`` is converted to the matching type.
``values`` should be shaped so that it can be broadcast appropriately.
mask : bool or array-like
Mask value(s) to insert. If not supplied, and values does not have
a mask either, then False is used.
axis : int, optional
Axis along which to insert ``values``. If ``axis`` is None then
the column array is flattened before insertion. Default is 0,
which will insert a row.
Returns
-------
out : `~astropy.table.MaskedColumn`
A copy of column with ``values`` and ``mask`` inserted. Note that the
insertion does not occur in-place: a new masked column is returned.
"""
self_ma = self.data # self viewed as MaskedArray
if self.dtype.kind == 'O':
# Even if values is array-like (e.g. [1,2,3]), insert as a single
# object. Numpy.insert instead inserts each element in an array-like
# input individually.
new_data = np.insert(self_ma.data, obj, None, axis=axis)
new_data[obj] = values
else:
self_ma = _expand_string_array_for_values(self_ma, values)
new_data = np.insert(self_ma.data, obj, values, axis=axis)
if mask is None:
mask = getattr(values, 'mask', np.ma.nomask)
if mask is np.ma.nomask:
if self.dtype.kind == 'O':
mask = False
else:
mask = np.zeros(np.shape(values), dtype=bool)
new_mask = np.insert(self_ma.mask, obj, mask, axis=axis)
new_ma = np.ma.array(new_data, mask=new_mask, copy=False)
out = new_ma.view(self.__class__)
out.parent_table = None
out.indices = []
out._copy_attrs(self)
out.fill_value = self.fill_value
return out
def _copy_attrs_slice(self, out):
# Fixes issue #3023: when calling getitem with a MaskedArray subclass
# the original object attributes are not copied.
if out.__class__ is self.__class__:
# TODO: this part is essentially the same as what is done in
# __array_finalize__ and could probably be called directly in our
# override of __getitem__ in _columns_mixins.pyx). Refactor?
if 'info' in self.__dict__:
out.info = self.info
out.parent_table = None
# we need this because __getitem__ does a shallow copy of indices
if out.indices is self.indices:
out.indices = []
out._copy_attrs(self)
return out
def __setitem__(self, index, value):
# Issue warning for string assignment that truncates ``value``
if self.dtype.char == 'S':
value = self._encode_str(value)
if issubclass(self.dtype.type, np.character):
# Account for a bug in np.ma.MaskedArray setitem.
# https://github.com/numpy/numpy/issues/8624
value = np.ma.asanyarray(value, dtype=self.dtype.type)
# Check for string truncation after filling masked items with
# empty (zero-length) string. Note that filled() does not make
# a copy if there are no masked items.
self._check_string_truncate(value.filled(''))
# update indices
self.info.adjust_indices(index, value, len(self))
ma.MaskedArray.__setitem__(self, index, value)
# We do this to make the methods show up in the API docs
name = BaseColumn.name
copy = BaseColumn.copy
more = BaseColumn.more
pprint = BaseColumn.pprint
pformat = BaseColumn.pformat
convert_unit_to = BaseColumn.convert_unit_to
|
a7dc76058f28227b71b261fbb2ed79e521f40d68fe418394cacafc67740247d8 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import operator
__all__ = ['BST']
class MaxValue:
'''
Represents an infinite value for purposes
of tuple comparison.
'''
def __gt__(self, other):
return True
def __ge__(self, other):
return True
def __lt__(self, other):
return False
def __le__(self, other):
return False
def __repr__(self):
return "MAX"
__str__ = __repr__
class MinValue:
'''
The opposite of MaxValue, i.e. a representation of
negative infinity.
'''
def __lt__(self, other):
return True
def __le__(self, other):
return True
def __gt__(self, other):
return False
def __ge__(self, other):
return False
def __repr__(self):
return "MIN"
__str__ = __repr__
class Epsilon:
'''
Represents the "next largest" version of a given value,
so that for all valid comparisons we have
x < y < Epsilon(y) < z whenever x < y < z and x, z are
not Epsilon objects.
Parameters
----------
val : object
Original value
'''
__slots__ = ('val',)
def __init__(self, val):
self.val = val
def __lt__(self, other):
if self.val == other:
return False
return self.val < other
def __gt__(self, other):
if self.val == other:
return True
return self.val > other
def __eq__(self, other):
return False
def __repr__(self):
return repr(self.val) + " + epsilon"
class Node:
'''
An element in a binary search tree, containing
a key, data, and references to children nodes and
a parent node.
Parameters
----------
key : tuple
Node key
data : list or int
Node data
'''
__lt__ = lambda x, y: x.key < y.key
__le__ = lambda x, y: x.key <= y.key
__eq__ = lambda x, y: x.key == y.key
__ge__ = lambda x, y: x.key >= y.key
__gt__ = lambda x, y: x.key > y.key
__ne__ = lambda x, y: x.key != y.key
__slots__ = ('key', 'data', 'left', 'right')
# each node has a key and data list
def __init__(self, key, data):
self.key = key
self.data = data if isinstance(data, list) else [data]
self.left = None
self.right = None
def replace(self, child, new_child):
'''
Replace this node's child with a new child.
'''
if self.left is not None and self.left == child:
self.left = new_child
elif self.right is not None and self.right == child:
self.right = new_child
else:
raise ValueError("Cannot call replace() on non-child")
def remove(self, child):
'''
Remove the given child.
'''
self.replace(child, None)
def set(self, other):
'''
Copy the given node.
'''
self.key = other.key
self.data = other.data[:]
def __str__(self):
return str((self.key, self.data))
def __repr__(self):
return str(self)
class BST:
'''
A basic binary search tree in pure Python, used
as an engine for indexing.
Parameters
----------
data : Table
Sorted columns of the original table
row_index : Column object
Row numbers corresponding to data columns
unique : bool
Whether the values of the index must be unique.
Defaults to False.
'''
NodeClass = Node
def __init__(self, data, row_index, unique=False):
self.root = None
self.size = 0
self.unique = unique
for key, row in zip(data, row_index):
self.add(tuple(key), row)
def add(self, key, data=None):
'''
Add a key, data pair.
'''
if data is None:
data = key
self.size += 1
node = self.NodeClass(key, data)
curr_node = self.root
if curr_node is None:
self.root = node
return
while True:
if node < curr_node:
if curr_node.left is None:
curr_node.left = node
break
curr_node = curr_node.left
elif node > curr_node:
if curr_node.right is None:
curr_node.right = node
break
curr_node = curr_node.right
elif self.unique:
raise ValueError("Cannot insert non-unique value")
else: # add data to node
curr_node.data.extend(node.data)
curr_node.data = sorted(curr_node.data)
return
def find(self, key):
'''
Return all data values corresponding to a given key.
Parameters
----------
key : tuple
Input key
Returns
-------
data_vals : list
List of rows corresponding to the input key
'''
node, parent = self.find_node(key)
return node.data if node is not None else []
def find_node(self, key):
'''
Find the node associated with the given key.
'''
if self.root is None:
return (None, None)
return self._find_recursive(key, self.root, None)
def shift_left(self, row):
'''
Decrement all rows larger than the given row.
'''
for node in self.traverse():
node.data = [x - 1 if x > row else x for x in node.data]
def shift_right(self, row):
'''
Increment all rows greater than or equal to the given row.
'''
for node in self.traverse():
node.data = [x + 1 if x >= row else x for x in node.data]
def _find_recursive(self, key, node, parent):
try:
if key == node.key:
return (node, parent)
elif key > node.key:
if node.right is None:
return (None, None)
return self._find_recursive(key, node.right, node)
else:
if node.left is None:
return (None, None)
return self._find_recursive(key, node.left, node)
except TypeError: # wrong key type
return (None, None)
def traverse(self, order='inorder'):
'''
Return nodes of the BST in the given order.
Parameters
----------
order : str
The order in which to recursively search the BST.
Possible values are:
"preorder": current node, left subtree, right subtree
"inorder": left subtree, current node, right subtree
"postorder": left subtree, right subtree, current node
'''
if order == 'preorder':
return self._preorder(self.root, [])
elif order == 'inorder':
return self._inorder(self.root, [])
elif order == 'postorder':
return self._postorder(self.root, [])
raise ValueError(f"Invalid traversal method: \"{order}\"")
def items(self):
'''
Return BST items in order as (key, data) pairs.
'''
return [(x.key, x.data) for x in self.traverse()]
def sort(self):
'''
Make row order align with key order.
'''
i = 0
for node in self.traverse():
num_rows = len(node.data)
node.data = [x for x in range(i, i + num_rows)]
i += num_rows
def sorted_data(self):
'''
Return BST rows sorted by key values.
'''
return [x for node in self.traverse() for x in node.data]
def _preorder(self, node, lst):
if node is None:
return lst
lst.append(node)
self._preorder(node.left, lst)
self._preorder(node.right, lst)
return lst
def _inorder(self, node, lst):
if node is None:
return lst
self._inorder(node.left, lst)
lst.append(node)
self._inorder(node.right, lst)
return lst
def _postorder(self, node, lst):
if node is None:
return lst
self._postorder(node.left, lst)
self._postorder(node.right, lst)
lst.append(node)
return lst
def _substitute(self, node, parent, new_node):
if node is self.root:
self.root = new_node
else:
parent.replace(node, new_node)
def remove(self, key, data=None):
'''
Remove data corresponding to the given key.
Parameters
----------
key : tuple
The key to remove
data : int or None
If None, remove the node corresponding to the given key.
If not None, remove only the given data value from the node.
Returns
-------
successful : bool
True if removal was successful, false otherwise
'''
node, parent = self.find_node(key)
if node is None:
return False
if data is not None:
if data not in node.data:
raise ValueError("Data does not belong to correct node")
elif len(node.data) > 1:
node.data.remove(data)
return True
if node.left is None and node.right is None:
self._substitute(node, parent, None)
elif node.left is None and node.right is not None:
self._substitute(node, parent, node.right)
elif node.right is None and node.left is not None:
self._substitute(node, parent, node.left)
else:
# find largest element of left subtree
curr_node = node.left
parent = node
while curr_node.right is not None:
parent = curr_node
curr_node = curr_node.right
self._substitute(curr_node, parent, curr_node.left)
node.set(curr_node)
self.size -= 1
return True
def is_valid(self):
'''
Returns whether this is a valid BST.
'''
return self._is_valid(self.root)
def _is_valid(self, node):
if node is None:
return True
return (node.left is None or node.left <= node) and \
(node.right is None or node.right >= node) and \
self._is_valid(node.left) and self._is_valid(node.right)
def range(self, lower, upper, bounds=(True, True)):
'''
Return all nodes with keys in the given range.
Parameters
----------
lower : tuple
Lower bound
upper : tuple
Upper bound
bounds : (2,) tuple of bool
Indicates whether the search should be inclusive or
exclusive with respect to the endpoints. The first
argument corresponds to an inclusive lower bound,
and the second argument to an inclusive upper bound.
'''
nodes = self.range_nodes(lower, upper, bounds)
return [x for node in nodes for x in node.data]
def range_nodes(self, lower, upper, bounds=(True, True)):
'''
Return nodes in the given range.
'''
if self.root is None:
return []
# op1 is <= or <, op2 is >= or >
op1 = operator.le if bounds[0] else operator.lt
op2 = operator.ge if bounds[1] else operator.gt
return self._range(lower, upper, op1, op2, self.root, [])
def same_prefix(self, val):
'''
Assuming the given value has smaller length than keys, return
nodes whose keys have this value as a prefix.
'''
if self.root is None:
return []
nodes = self._same_prefix(val, self.root, [])
return [x for node in nodes for x in node.data]
def _range(self, lower, upper, op1, op2, node, lst):
if op1(lower, node.key) and op2(upper, node.key):
lst.append(node)
if upper > node.key and node.right is not None:
self._range(lower, upper, op1, op2, node.right, lst)
if lower < node.key and node.left is not None:
self._range(lower, upper, op1, op2, node.left, lst)
return lst
def _same_prefix(self, val, node, lst):
prefix = node.key[:len(val)]
if prefix == val:
lst.append(node)
if prefix <= val and node.right is not None:
self._same_prefix(val, node.right, lst)
if prefix >= val and node.left is not None:
self._same_prefix(val, node.left, lst)
return lst
def __repr__(self):
return f'<{self.__class__.__name__}>'
def _print(self, node, level):
line = '\t' * level + str(node) + '\n'
if node.left is not None:
line += self._print(node.left, level + 1)
if node.right is not None:
line += self._print(node.right, level + 1)
return line
@property
def height(self):
'''
Return the BST height.
'''
return self._height(self.root)
def _height(self, node):
if node is None:
return -1
return max(self._height(node.left),
self._height(node.right)) + 1
def replace_rows(self, row_map):
'''
Replace all rows with the values they map to in the
given dictionary. Any rows not present as keys in
the dictionary will have their nodes deleted.
Parameters
----------
row_map : dict
Mapping of row numbers to new row numbers
'''
for key, data in self.items():
data[:] = [row_map[x] for x in data if x in row_map]
|
01500c44fbe1b840b5994b9590d7d938abcd8d814e5cddacd971e7b2d1a1f3db | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
import sys
import re
import fnmatch
import numpy as np
from astropy import log
from astropy.utils.console import Getch, color_print, terminal_size, conf
from astropy.utils.data_info import dtype_info_name
__all__ = []
def default_format_func(format_, val):
if isinstance(val, bytes):
return val.decode('utf-8', errors='replace')
else:
return str(val)
# The first three functions are helpers for _auto_format_func
def _use_str_for_masked_values(format_func):
"""Wrap format function to trap masked values.
String format functions and most user functions will not be able to deal
with masked values, so we wrap them to ensure they are passed to str().
"""
return lambda format_, val: (str(val) if val is np.ma.masked
else format_func(format_, val))
def _possible_string_format_functions(format_):
"""Iterate through possible string-derived format functions.
A string can either be a format specifier for the format built-in,
a new-style format string, or an old-style format string.
"""
yield lambda format_, val: format(val, format_)
yield lambda format_, val: format_.format(val)
yield lambda format_, val: format_ % val
def get_auto_format_func(
col=None,
possible_string_format_functions=_possible_string_format_functions):
"""
Return a wrapped ``auto_format_func`` function which is used in
formatting table columns. This is primarily an internal function but
gets used directly in other parts of astropy, e.g. `astropy.io.ascii`.
Parameters
----------
col_name : object, optional
Hashable object to identify column like id or name. Default is None.
possible_string_format_functions : func, optional
Function that yields possible string formatting functions
(defaults to internal function to do this).
Returns
-------
Wrapped ``auto_format_func`` function
"""
def _auto_format_func(format_, val):
"""Format ``val`` according to ``format_`` for a plain format specifier,
old- or new-style format strings, or using a user supplied function.
More importantly, determine and cache (in _format_funcs) a function
that will do this subsequently. In this way this complicated logic is
only done for the first value.
Returns the formatted value.
"""
if format_ is None:
return default_format_func(format_, val)
if format_ in col.info._format_funcs:
return col.info._format_funcs[format_](format_, val)
if callable(format_):
format_func = lambda format_, val: format_(val) # noqa
try:
out = format_func(format_, val)
if not isinstance(out, str):
raise ValueError('Format function for value {} returned {} '
'instead of string type'
.format(val, type(val)))
except Exception as err:
# For a masked element, the format function call likely failed
# to handle it. Just return the string representation for now,
# and retry when a non-masked value comes along.
if val is np.ma.masked:
return str(val)
raise ValueError(f'Format function for value {val} failed.') from err
# If the user-supplied function handles formatting masked elements, use
# it directly. Otherwise, wrap it in a function that traps them.
try:
format_func(format_, np.ma.masked)
except Exception:
format_func = _use_str_for_masked_values(format_func)
else:
# For a masked element, we cannot set string-based format functions yet,
# as all tests below will fail. Just return the string representation
# of masked for now, and retry when a non-masked value comes along.
if val is np.ma.masked:
return str(val)
for format_func in possible_string_format_functions(format_):
try:
# Does this string format method work?
out = format_func(format_, val)
# Require that the format statement actually did something.
if out == format_:
raise ValueError('the format passed in did nothing.')
except Exception:
continue
else:
break
else:
# None of the possible string functions passed muster.
raise ValueError('unable to parse format string {} for its '
'column.'.format(format_))
# String-based format functions will fail on masked elements;
# wrap them in a function that traps them.
format_func = _use_str_for_masked_values(format_func)
col.info._format_funcs[format_] = format_func
return out
return _auto_format_func
def _get_pprint_include_names(table):
"""Get the set of names to show in pprint from the table pprint_include_names
and pprint_exclude_names attributes.
These may be fnmatch unix-style globs.
"""
def get_matches(name_globs, default):
match_names = set()
if name_globs: # For None or () use the default
for name in table.colnames:
for name_glob in name_globs:
if fnmatch.fnmatch(name, name_glob):
match_names.add(name)
break
else:
match_names.update(default)
return match_names
include_names = get_matches(table.pprint_include_names(), table.colnames)
exclude_names = get_matches(table.pprint_exclude_names(), [])
return include_names - exclude_names
class TableFormatter:
@staticmethod
def _get_pprint_size(max_lines=None, max_width=None):
"""Get the output size (number of lines and character width) for Column and
Table pformat/pprint methods.
If no value of ``max_lines`` is supplied then the height of the
screen terminal is used to set ``max_lines``. If the terminal
height cannot be determined then the default will be determined
using the ``astropy.table.conf.max_lines`` configuration item. If a
negative value of ``max_lines`` is supplied then there is no line
limit applied.
The same applies for max_width except the configuration item is
``astropy.table.conf.max_width``.
Parameters
----------
max_lines : int or None
Maximum lines of output (header + data rows)
max_width : int or None
Maximum width (characters) output
Returns
-------
max_lines, max_width : int
"""
# Declare to keep static type checker happy.
lines = None
width = None
if max_lines is None:
max_lines = conf.max_lines
if max_width is None:
max_width = conf.max_width
if max_lines is None or max_width is None:
lines, width = terminal_size()
if max_lines is None:
max_lines = lines
elif max_lines < 0:
max_lines = sys.maxsize
if max_lines < 8:
max_lines = 8
if max_width is None:
max_width = width
elif max_width < 0:
max_width = sys.maxsize
if max_width < 10:
max_width = 10
return max_lines, max_width
def _pformat_col(self, col, max_lines=None, show_name=True, show_unit=None,
show_dtype=False, show_length=None, html=False, align=None):
"""Return a list of formatted string representation of column values.
Parameters
----------
max_lines : int
Maximum lines of output (header + data rows)
show_name : bool
Include column name. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include column dtype. Default is False.
show_length : bool
Include column length at end. Default is to show this only
if the column is not shown completely.
html : bool
Output column as HTML
align : str
Left/right alignment of columns. Default is '>' (right) for all
columns. Other allowed values are '<', '^', and '0=' for left,
centered, and 0-padded, respectively.
Returns
-------
lines : list
List of lines with formatted column values
outs : dict
Dict which is used to pass back additional values
defined within the iterator.
"""
if show_unit is None:
show_unit = col.info.unit is not None
outs = {} # Some values from _pformat_col_iter iterator that are needed here
col_strs_iter = self._pformat_col_iter(col, max_lines, show_name=show_name,
show_unit=show_unit,
show_dtype=show_dtype,
show_length=show_length,
outs=outs)
# Replace tab and newline with text representations so they display nicely.
# Newline in particular is a problem in a multicolumn table.
col_strs = [val.replace('\t', '\\t').replace('\n', '\\n') for val in col_strs_iter]
if len(col_strs) > 0:
col_width = max(len(x) for x in col_strs)
if html:
from astropy.utils.xml.writer import xml_escape
n_header = outs['n_header']
for i, col_str in enumerate(col_strs):
# _pformat_col output has a header line '----' which is not needed here
if i == n_header - 1:
continue
td = 'th' if i < n_header else 'td'
val = f'<{td}>{xml_escape(col_str.strip())}</{td}>'
row = ('<tr>' + val + '</tr>')
if i < n_header:
row = ('<thead>' + row + '</thead>')
col_strs[i] = row
if n_header > 0:
# Get rid of '---' header line
col_strs.pop(n_header - 1)
col_strs.insert(0, '<table>')
col_strs.append('</table>')
# Now bring all the column string values to the same fixed width
else:
col_width = max(len(x) for x in col_strs) if col_strs else 1
# Center line header content and generate dashed headerline
for i in outs['i_centers']:
col_strs[i] = col_strs[i].center(col_width)
if outs['i_dashes'] is not None:
col_strs[outs['i_dashes']] = '-' * col_width
# Format columns according to alignment. `align` arg has precedent, otherwise
# use `col.format` if it starts as a legal alignment string. If neither applies
# then right justify.
re_fill_align = re.compile(r'(?P<fill>.?)(?P<align>[<^>=])')
match = None
if align:
# If there is an align specified then it must match
match = re_fill_align.match(align)
if not match:
raise ValueError("column align must be one of '<', '^', '>', or '='")
elif isinstance(col.info.format, str):
# col.info.format need not match, in which case rjust gets used
match = re_fill_align.match(col.info.format)
if match:
fill_char = match.group('fill')
align_char = match.group('align')
if align_char == '=':
if fill_char != '0':
raise ValueError("fill character must be '0' for '=' align")
fill_char = '' # str.zfill gets used which does not take fill char arg
else:
fill_char = ''
align_char = '>'
justify_methods = {'<': 'ljust', '^': 'center', '>': 'rjust', '=': 'zfill'}
justify_method = justify_methods[align_char]
justify_args = (col_width, fill_char) if fill_char else (col_width,)
for i, col_str in enumerate(col_strs):
col_strs[i] = getattr(col_str, justify_method)(*justify_args)
if outs['show_length']:
col_strs.append(f'Length = {len(col)} rows')
return col_strs, outs
def _pformat_col_iter(self, col, max_lines, show_name, show_unit, outs,
show_dtype=False, show_length=None):
"""Iterator which yields formatted string representation of column values.
Parameters
----------
max_lines : int
Maximum lines of output (header + data rows)
show_name : bool
Include column name. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
outs : dict
Must be a dict which is used to pass back additional values
defined within the iterator.
show_dtype : bool
Include column dtype. Default is False.
show_length : bool
Include column length at end. Default is to show this only
if the column is not shown completely.
"""
max_lines, _ = self._get_pprint_size(max_lines, -1)
multidims = getattr(col, 'shape', [0])[1:]
if multidims:
multidim0 = tuple(0 for n in multidims)
multidim1 = tuple(n - 1 for n in multidims)
trivial_multidims = np.prod(multidims) == 1
i_dashes = None
i_centers = [] # Line indexes where content should be centered
n_header = 0
if show_name:
i_centers.append(n_header)
# Get column name (or 'None' if not set)
col_name = str(col.info.name)
if multidims:
col_name += f" [{','.join(str(n) for n in multidims)}]"
n_header += 1
yield col_name
if show_unit:
i_centers.append(n_header)
n_header += 1
yield str(col.info.unit or '')
if show_dtype:
i_centers.append(n_header)
n_header += 1
try:
dtype = dtype_info_name(col.dtype)
except AttributeError:
dtype = col.__class__.__qualname__ or 'object'
yield str(dtype)
if show_unit or show_name or show_dtype:
i_dashes = n_header
n_header += 1
yield '---'
max_lines -= n_header
n_print2 = max_lines // 2
n_rows = len(col)
# This block of code is responsible for producing the function that
# will format values for this column. The ``format_func`` function
# takes two args (col_format, val) and returns the string-formatted
# version. Some points to understand:
#
# - col_format could itself be the formatting function, so it will
# actually end up being called with itself as the first arg. In
# this case the function is expected to ignore its first arg.
#
# - auto_format_func is a function that gets called on the first
# column value that is being formatted. It then determines an
# appropriate formatting function given the actual value to be
# formatted. This might be deterministic or it might involve
# try/except. The latter allows for different string formatting
# options like %f or {:5.3f}. When auto_format_func is called it:
# 1. Caches the function in the _format_funcs dict so for subsequent
# values the right function is called right away.
# 2. Returns the formatted value.
#
# - possible_string_format_functions is a function that yields a
# succession of functions that might successfully format the
# value. There is a default, but Mixin methods can override this.
# See Quantity for an example.
#
# - get_auto_format_func() returns a wrapped version of auto_format_func
# with the column id and possible_string_format_functions as
# enclosed variables.
col_format = col.info.format or getattr(col.info, 'default_format',
None)
pssf = (getattr(col.info, 'possible_string_format_functions', None)
or _possible_string_format_functions)
auto_format_func = get_auto_format_func(col, pssf)
format_func = col.info._format_funcs.get(col_format, auto_format_func)
if len(col) > max_lines:
if show_length is None:
show_length = True
i0 = n_print2 - (1 if show_length else 0)
i1 = n_rows - n_print2 - max_lines % 2
indices = np.concatenate([np.arange(0, i0 + 1),
np.arange(i1 + 1, len(col))])
else:
i0 = -1
indices = np.arange(len(col))
def format_col_str(idx):
if multidims:
# Prevents columns like Column(data=[[(1,)],[(2,)]], name='a')
# with shape (n,1,...,1) from being printed as if there was
# more than one element in a row
if trivial_multidims:
return format_func(col_format, col[(idx,) + multidim0])
else:
left = format_func(col_format, col[(idx,) + multidim0])
right = format_func(col_format, col[(idx,) + multidim1])
return f'{left} .. {right}'
else:
return format_func(col_format, col[idx])
# Add formatted values if within bounds allowed by max_lines
for idx in indices:
if idx == i0:
yield '...'
else:
try:
yield format_col_str(idx)
except ValueError:
raise ValueError(
'Unable to parse format string "{}" for entry "{}" '
'in column "{}"'.format(col_format, col[idx],
col.info.name))
outs['show_length'] = show_length
outs['n_header'] = n_header
outs['i_centers'] = i_centers
outs['i_dashes'] = i_dashes
def _pformat_table(self, table, max_lines=None, max_width=None,
show_name=True, show_unit=None, show_dtype=False,
html=False, tableid=None, tableclass=None, align=None):
"""Return a list of lines for the formatted string representation of
the table.
Parameters
----------
max_lines : int or None
Maximum number of rows to output
max_width : int or None
Maximum character width of output
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include a header row for column dtypes. Default is False.
html : bool
Format the output as an HTML table. Default is False.
tableid : str or None
An ID tag for the table; only used if html is set. Default is
"table{id}", where id is the unique integer id of the table object,
id(table)
tableclass : str or list of str or None
CSS classes for the table; only used if html is set. Default is
none
align : str or list or tuple
Left/right alignment of columns. Default is '>' (right) for all
columns. Other allowed values are '<', '^', and '0=' for left,
centered, and 0-padded, respectively. A list of strings can be
provided for alignment of tables with multiple columns.
Returns
-------
rows : list
Formatted table as a list of strings
outs : dict
Dict which is used to pass back additional values
defined within the iterator.
"""
# "Print" all the values into temporary lists by column for subsequent
# use and to determine the width
max_lines, max_width = self._get_pprint_size(max_lines, max_width)
if show_unit is None:
show_unit = any(col.info.unit for col in table.columns.values())
# Coerce align into a correctly-sized list of alignments (if possible)
n_cols = len(table.columns)
if align is None or isinstance(align, str):
align = [align] * n_cols
elif isinstance(align, (list, tuple)):
if len(align) != n_cols:
raise ValueError('got {} alignment values instead of '
'the number of columns ({})'
.format(len(align), n_cols))
else:
raise TypeError('align keyword must be str or list or tuple (got {})'
.format(type(align)))
# Process column visibility from table pprint_include_names and
# pprint_exclude_names attributes and get the set of columns to show.
pprint_include_names = _get_pprint_include_names(table)
cols = []
outs = None # Initialize so static type checker is happy
for align_, col in zip(align, table.columns.values()):
if col.info.name not in pprint_include_names:
continue
lines, outs = self._pformat_col(col, max_lines, show_name=show_name,
show_unit=show_unit, show_dtype=show_dtype,
align=align_)
if outs['show_length']:
lines = lines[:-1]
cols.append(lines)
if not cols:
return ['<No columns>'], {'show_length': False}
# Use the values for the last column since they are all the same
n_header = outs['n_header']
n_rows = len(cols[0])
def outwidth(cols):
return sum(len(c[0]) for c in cols) + len(cols) - 1
dots_col = ['...'] * n_rows
middle = len(cols) // 2
while outwidth(cols) > max_width:
if len(cols) == 1:
break
if len(cols) == 2:
cols[1] = dots_col
break
if cols[middle] is dots_col:
cols.pop(middle)
middle = len(cols) // 2
cols[middle] = dots_col
# Now "print" the (already-stringified) column values into a
# row-oriented list.
rows = []
if html:
from astropy.utils.xml.writer import xml_escape
if tableid is None:
tableid = f'table{id(table)}'
if tableclass is not None:
if isinstance(tableclass, list):
tableclass = ' '.join(tableclass)
rows.append(f'<table id="{tableid}" class="{tableclass}">')
else:
rows.append(f'<table id="{tableid}">')
for i in range(n_rows):
# _pformat_col output has a header line '----' which is not needed here
if i == n_header - 1:
continue
td = 'th' if i < n_header else 'td'
vals = (f'<{td}>{xml_escape(col[i].strip())}</{td}>'
for col in cols)
row = ('<tr>' + ''.join(vals) + '</tr>')
if i < n_header:
row = ('<thead>' + row + '</thead>')
rows.append(row)
rows.append('</table>')
else:
for i in range(n_rows):
row = ' '.join(col[i] for col in cols)
rows.append(row)
return rows, outs
def _more_tabcol(self, tabcol, max_lines=None, max_width=None,
show_name=True, show_unit=None, show_dtype=False):
"""Interactive "more" of a table or column.
Parameters
----------
max_lines : int or None
Maximum number of rows to output
max_width : int or None
Maximum character width of output
show_name : bool
Include a header row for column names. Default is True.
show_unit : bool
Include a header row for unit. Default is to show a row
for units only if one or more columns has a defined value
for the unit.
show_dtype : bool
Include a header row for column dtypes. Default is False.
"""
allowed_keys = 'f br<>qhpn'
# Count the header lines
n_header = 0
if show_name:
n_header += 1
if show_unit:
n_header += 1
if show_dtype:
n_header += 1
if show_name or show_unit or show_dtype:
n_header += 1
# Set up kwargs for pformat call. Only Table gets max_width.
kwargs = dict(max_lines=-1, show_name=show_name, show_unit=show_unit,
show_dtype=show_dtype)
if hasattr(tabcol, 'columns'): # tabcol is a table
kwargs['max_width'] = max_width
# If max_lines is None (=> query screen size) then increase by 2.
# This is because get_pprint_size leaves 6 extra lines so that in
# ipython you normally see the last input line.
max_lines1, max_width = self._get_pprint_size(max_lines, max_width)
if max_lines is None:
max_lines1 += 2
delta_lines = max_lines1 - n_header
# Set up a function to get a single character on any platform
inkey = Getch()
i0 = 0 # First table/column row to show
showlines = True
while True:
i1 = i0 + delta_lines # Last table/col row to show
if showlines: # Don't always show the table (e.g. after help)
try:
os.system('cls' if os.name == 'nt' else 'clear')
except Exception:
pass # No worries if clear screen call fails
lines = tabcol[i0:i1].pformat(**kwargs)
colors = ('red' if i < n_header else 'default'
for i in range(len(lines)))
for color, line in zip(colors, lines):
color_print(line, color)
showlines = True
print()
print("-- f, <space>, b, r, p, n, <, >, q h (help) --", end=' ')
# Get a valid key
while True:
try:
key = inkey().lower()
except Exception:
print("\n")
log.error('Console does not support getting a character'
' as required by more(). Use pprint() instead.')
return
if key in allowed_keys:
break
print(key)
if key.lower() == 'q':
break
elif key == ' ' or key == 'f':
i0 += delta_lines
elif key == 'b':
i0 = i0 - delta_lines
elif key == 'r':
pass
elif key == '<':
i0 = 0
elif key == '>':
i0 = len(tabcol)
elif key == 'p':
i0 -= 1
elif key == 'n':
i0 += 1
elif key == 'h':
showlines = False
print("""
Browsing keys:
f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help""", end=' ')
if i0 < 0:
i0 = 0
if i0 >= len(tabcol) - delta_lines:
i0 = len(tabcol) - delta_lines
print("\n")
|
ab5e01a4fb8d8a279059c81db4a7b0a1576ef1e408f5507c38e6f8f05008a664 | """
High-level table operations:
- join()
- setdiff()
- hstack()
- vstack()
- dstack()
"""
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from copy import deepcopy
import collections
import itertools
from collections import OrderedDict, Counter
from collections.abc import Mapping, Sequence
import numpy as np
from astropy.utils import metadata
from astropy.utils.masked import Masked
from .table import Table, QTable, Row, Column, MaskedColumn
from astropy.units import Quantity
from . import _np_utils
from .np_utils import TableMergeError
__all__ = ['join', 'setdiff', 'hstack', 'vstack', 'unique',
'join_skycoord', 'join_distance']
__doctest_requires__ = {'join_skycoord': ['scipy'], 'join_distance': ['scipy']}
def _merge_table_meta(out, tables, metadata_conflicts='warn'):
out_meta = deepcopy(tables[0].meta)
for table in tables[1:]:
out_meta = metadata.merge(out_meta, table.meta, metadata_conflicts=metadata_conflicts)
out.meta.update(out_meta)
def _get_list_of_tables(tables):
"""
Check that tables is a Table or sequence of Tables. Returns the
corresponding list of Tables.
"""
# Make sure we have a list of things
if not isinstance(tables, Sequence):
tables = [tables]
# Make sure there is something to stack
if len(tables) == 0:
raise ValueError('no values provided to stack.')
# Convert inputs (Table, Row, or anything column-like) to Tables.
# Special case that Quantity converts to a QTable.
for ii, val in enumerate(tables):
if isinstance(val, Table):
pass
elif isinstance(val, Row):
tables[ii] = Table(val)
elif isinstance(val, Quantity):
tables[ii] = QTable([val])
else:
try:
tables[ii] = Table([val])
except (ValueError, TypeError) as err:
raise TypeError(f'Cannot convert {val} to table column.') from err
return tables
def _get_out_class(objs):
"""
From a list of input objects ``objs`` get merged output object class.
This is just taken as the deepest subclass. This doesn't handle complicated
inheritance schemes, but as a special case, classes which share ``info``
are taken to be compatible.
"""
out_class = objs[0].__class__
for obj in objs[1:]:
if issubclass(obj.__class__, out_class):
out_class = obj.__class__
if any(not (issubclass(out_class, obj.__class__)
or out_class.info is obj.__class__.info) for obj in objs):
raise ValueError('unmergeable object classes {}'
.format([obj.__class__.__name__ for obj in objs]))
return out_class
def join_skycoord(distance, distance_func='search_around_sky'):
"""Helper function to join on SkyCoord columns using distance matching.
This function is intended for use in ``table.join()`` to allow performing a
table join where the key columns are both ``SkyCoord`` objects, matched by
computing the distance between points and accepting values below
``distance``.
The distance cross-matching is done using either
`~astropy.coordinates.search_around_sky` or
`~astropy.coordinates.search_around_3d`, depending on the value of
``distance_func``. The default is ``'search_around_sky'``.
One can also provide a function object for ``distance_func``, in which case
it must be a function that follows the same input and output API as
`~astropy.coordinates.search_around_sky`. In this case the function will
be called with ``(skycoord1, skycoord2, distance)`` as arguments.
Parameters
----------
distance : `~astropy.units.Quantity` ['angle', 'length']
Maximum distance between points to be considered a join match.
Must have angular or distance units.
distance_func : str or function
Specifies the function for performing the cross-match based on
``distance``. If supplied as a string this specifies the name of a
function in `astropy.coordinates`. If supplied as a function then that
function is called directly.
Returns
-------
join_func : function
Function that accepts two ``SkyCoord`` columns (col1, col2) and returns
the tuple (ids1, ids2) of pair-matched unique identifiers.
Examples
--------
This example shows an inner join of two ``SkyCoord`` columns, taking any
sources within 0.2 deg to be a match. Note the new ``sc_id`` column which
is added and provides a unique source identifier for the matches.
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> from astropy.table import Table, join_skycoord
>>> from astropy import table
>>> sc1 = SkyCoord([0, 1, 1.1, 2], [0, 0, 0, 0], unit='deg')
>>> sc2 = SkyCoord([0.5, 1.05, 2.1], [0, 0, 0], unit='deg')
>>> join_func = join_skycoord(0.2 * u.deg)
>>> join_func(sc1, sc2) # Associate each coordinate with unique source ID
(array([3, 1, 1, 2]), array([4, 1, 2]))
>>> t1 = Table([sc1], names=['sc'])
>>> t2 = Table([sc2], names=['sc'])
>>> t12 = table.join(t1, t2, join_funcs={'sc': join_skycoord(0.2 * u.deg)})
>>> print(t12) # Note new `sc_id` column with the IDs from join_func()
sc_id sc_1 sc_2
deg,deg deg,deg
----- ------- --------
1 1.0,0.0 1.05,0.0
1 1.1,0.0 1.05,0.0
2 2.0,0.0 2.1,0.0
"""
if isinstance(distance_func, str):
import astropy.coordinates as coords
try:
distance_func = getattr(coords, distance_func)
except AttributeError as err:
raise ValueError('distance_func must be a function in astropy.coordinates') from err
else:
from inspect import isfunction
if not isfunction(distance_func):
raise ValueError('distance_func must be a str or function')
def join_func(sc1, sc2):
# Call the appropriate SkyCoord method to find pairs within distance
idxs1, idxs2, d2d, d3d = distance_func(sc1, sc2, distance)
# Now convert that into unique identifiers for each near-pair. This is
# taken to be transitive, so that if points 1 and 2 are "near" and points
# 1 and 3 are "near", then 1, 2, and 3 are all given the same identifier.
# This identifier will then be used in the table join matching.
# Identifiers for each column, initialized to all zero.
ids1 = np.zeros(len(sc1), dtype=int)
ids2 = np.zeros(len(sc2), dtype=int)
# Start the identifier count at 1
id_ = 1
for idx1, idx2 in zip(idxs1, idxs2):
# If this col1 point is previously identified then set corresponding
# col2 point to same identifier. Likewise for col2 and col1.
if ids1[idx1] > 0:
ids2[idx2] = ids1[idx1]
elif ids2[idx2] > 0:
ids1[idx1] = ids2[idx2]
else:
# Not yet seen so set identifier for col1 and col2
ids1[idx1] = id_
ids2[idx2] = id_
id_ += 1
# Fill in unique identifiers for points with no near neighbor
for ids in (ids1, ids2):
for idx in np.flatnonzero(ids == 0):
ids[idx] = id_
id_ += 1
# End of enclosure join_func()
return ids1, ids2
return join_func
def join_distance(distance, kdtree_args=None, query_args=None):
"""Helper function to join table columns using distance matching.
This function is intended for use in ``table.join()`` to allow performing
a table join where the key columns are matched by computing the distance
between points and accepting values below ``distance``. This numerical
"fuzzy" match can apply to 1-D or 2-D columns, where in the latter case
the distance is a vector distance.
The distance cross-matching is done using `scipy.spatial.cKDTree`. If
necessary you can tweak the default behavior by providing ``dict`` values
for the ``kdtree_args`` or ``query_args``.
Parameters
----------
distance : float or `~astropy.units.Quantity` ['length']
Maximum distance between points to be considered a join match
kdtree_args : dict, None
Optional extra args for `~scipy.spatial.cKDTree`
query_args : dict, None
Optional extra args for `~scipy.spatial.cKDTree.query_ball_tree`
Returns
-------
join_func : function
Function that accepts (skycoord1, skycoord2) and returns the tuple
(ids1, ids2) of pair-matched unique identifiers.
Examples
--------
>>> from astropy.table import Table, join_distance
>>> from astropy import table
>>> c1 = [0, 1, 1.1, 2]
>>> c2 = [0.5, 1.05, 2.1]
>>> t1 = Table([c1], names=['col'])
>>> t2 = Table([c2], names=['col'])
>>> t12 = table.join(t1, t2, join_type='outer', join_funcs={'col': join_distance(0.2)})
>>> print(t12)
col_id col_1 col_2
------ ----- -----
1 1.0 1.05
1 1.1 1.05
2 2.0 2.1
3 0.0 --
4 -- 0.5
"""
try:
from scipy.spatial import cKDTree
except ImportError as exc:
raise ImportError('scipy is required to use join_distance()') from exc
if kdtree_args is None:
kdtree_args = {}
if query_args is None:
query_args = {}
def join_func(col1, col2):
if col1.ndim > 2 or col2.ndim > 2:
raise ValueError('columns for isclose_join must be 1- or 2-dimensional')
if isinstance(distance, Quantity):
# Convert to np.array with common unit
col1 = col1.to_value(distance.unit)
col2 = col2.to_value(distance.unit)
dist = distance.value
else:
# Convert to np.array to allow later in-place shape changing
col1 = np.asarray(col1)
col2 = np.asarray(col2)
dist = distance
# Ensure columns are pure np.array and are 2-D for use with KDTree
if col1.ndim == 1:
col1.shape = col1.shape + (1,)
if col2.ndim == 1:
col2.shape = col2.shape + (1,)
# Cross-match col1 and col2 within dist using KDTree
kd1 = cKDTree(col1, **kdtree_args)
kd2 = cKDTree(col2, **kdtree_args)
nears = kd1.query_ball_tree(kd2, r=dist, **query_args)
# Output of above is nears which is a list of lists, where the outer
# list corresponds to each item in col1, and where the inner lists are
# indexes into col2 of elements within the distance tolerance. This
# identifies col1 / col2 near pairs.
# Now convert that into unique identifiers for each near-pair. This is
# taken to be transitive, so that if points 1 and 2 are "near" and points
# 1 and 3 are "near", then 1, 2, and 3 are all given the same identifier.
# This identifier will then be used in the table join matching.
# Identifiers for each column, initialized to all zero.
ids1 = np.zeros(len(col1), dtype=int)
ids2 = np.zeros(len(col2), dtype=int)
# Start the identifier count at 1
id_ = 1
for idx1, idxs2 in enumerate(nears):
for idx2 in idxs2:
# If this col1 point is previously identified then set corresponding
# col2 point to same identifier. Likewise for col2 and col1.
if ids1[idx1] > 0:
ids2[idx2] = ids1[idx1]
elif ids2[idx2] > 0:
ids1[idx1] = ids2[idx2]
else:
# Not yet seen so set identifier for col1 and col2
ids1[idx1] = id_
ids2[idx2] = id_
id_ += 1
# Fill in unique identifiers for points with no near neighbor
for ids in (ids1, ids2):
for idx in np.flatnonzero(ids == 0):
ids[idx] = id_
id_ += 1
# End of enclosure join_func()
return ids1, ids2
return join_func
def join(left, right, keys=None, join_type='inner', *,
keys_left=None, keys_right=None,
uniq_col_name='{col_name}_{table_name}',
table_names=['1', '2'], metadata_conflicts='warn',
join_funcs=None):
"""
Perform a join of the left table with the right table on specified keys.
Parameters
----------
left : `~astropy.table.Table`-like object
Left side table in the join. If not a Table, will call ``Table(left)``
right : `~astropy.table.Table`-like object
Right side table in the join. If not a Table, will call ``Table(right)``
keys : str or list of str
Name(s) of column(s) used to match rows of left and right tables.
Default is to use all columns which are common to both tables.
join_type : str
Join type ('inner' | 'outer' | 'left' | 'right' | 'cartesian'), default is 'inner'
keys_left : str or list of str or list of column-like, optional
Left column(s) used to match rows instead of ``keys`` arg. This can be
be a single left table column name or list of column names, or a list of
column-like values with the same lengths as the left table.
keys_right : str or list of str or list of column-like, optional
Same as ``keys_left``, but for the right side of the join.
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2'].
metadata_conflicts : str
How to proceed with metadata conflicts. This should be one of:
* ``'silent'``: silently pick the last conflicting meta-data value
* ``'warn'``: pick the last conflicting meta-data value, but emit a warning (default)
* ``'error'``: raise an exception.
join_funcs : dict, None
Dict of functions to use for matching the corresponding key column(s).
See `~astropy.table.join_skycoord` for an example and details.
Returns
-------
joined_table : `~astropy.table.Table` object
New table containing the result of the join operation.
"""
# Try converting inputs to Table as needed
if not isinstance(left, Table):
left = Table(left)
if not isinstance(right, Table):
right = Table(right)
col_name_map = OrderedDict()
out = _join(left, right, keys, join_type,
uniq_col_name, table_names, col_name_map, metadata_conflicts,
join_funcs,
keys_left=keys_left, keys_right=keys_right)
# Merge the column and table meta data. Table subclasses might override
# these methods for custom merge behavior.
_merge_table_meta(out, [left, right], metadata_conflicts=metadata_conflicts)
return out
def setdiff(table1, table2, keys=None):
"""
Take a set difference of table rows.
The row set difference will contain all rows in ``table1`` that are not
present in ``table2``. If the keys parameter is not defined, all columns in
``table1`` will be included in the output table.
Parameters
----------
table1 : `~astropy.table.Table`
``table1`` is on the left side of the set difference.
table2 : `~astropy.table.Table`
``table2`` is on the right side of the set difference.
keys : str or list of str
Name(s) of column(s) used to match rows of left and right tables.
Default is to use all columns in ``table1``.
Returns
-------
diff_table : `~astropy.table.Table`
New table containing the set difference between tables. If the set
difference is none, an empty table will be returned.
Examples
--------
To get a set difference between two tables::
>>> from astropy.table import setdiff, Table
>>> t1 = Table({'a': [1, 4, 9], 'b': ['c', 'd', 'f']}, names=('a', 'b'))
>>> t2 = Table({'a': [1, 5, 9], 'b': ['c', 'b', 'f']}, names=('a', 'b'))
>>> print(t1)
a b
--- ---
1 c
4 d
9 f
>>> print(t2)
a b
--- ---
1 c
5 b
9 f
>>> print(setdiff(t1, t2))
a b
--- ---
4 d
>>> print(setdiff(t2, t1))
a b
--- ---
5 b
"""
if keys is None:
keys = table1.colnames
# Check that all keys are in table1 and table2
for tbl, tbl_str in ((table1, 'table1'), (table2, 'table2')):
diff_keys = np.setdiff1d(keys, tbl.colnames)
if len(diff_keys) != 0:
raise ValueError("The {} columns are missing from {}, cannot take "
"a set difference.".format(diff_keys, tbl_str))
# Make a light internal copy of both tables
t1 = table1.copy(copy_data=False)
t1.meta = {}
t1.keep_columns(keys)
t1['__index1__'] = np.arange(len(table1)) # Keep track of rows indices
# Make a light internal copy to avoid touching table2
t2 = table2.copy(copy_data=False)
t2.meta = {}
t2.keep_columns(keys)
# Dummy column to recover rows after join
t2['__index2__'] = np.zeros(len(t2), dtype=np.uint8) # dummy column
t12 = _join(t1, t2, join_type='left', keys=keys,
metadata_conflicts='silent')
# If t12 index2 is masked then that means some rows were in table1 but not table2.
if hasattr(t12['__index2__'], 'mask'):
# Define bool mask of table1 rows not in table2
diff = t12['__index2__'].mask
# Get the row indices of table1 for those rows
idx = t12['__index1__'][diff]
# Select corresponding table1 rows straight from table1 to ensure
# correct table and column types.
t12_diff = table1[idx]
else:
t12_diff = table1[[]]
return t12_diff
def dstack(tables, join_type='outer', metadata_conflicts='warn'):
"""
Stack columns within tables depth-wise
A ``join_type`` of 'exact' means that the tables must all have exactly
the same column names (though the order can vary). If ``join_type``
is 'inner' then the intersection of common columns will be the output.
A value of 'outer' (default) means the output will have the union of
all columns, with table values being masked where no common values are
available.
Parameters
----------
tables : `~astropy.table.Table` or `~astropy.table.Row` or list thereof
Table(s) to stack along depth-wise with the current table
Table columns should have same shape and name for depth-wise stacking
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'outer'
metadata_conflicts : str
How to proceed with metadata conflicts. This should be one of:
* ``'silent'``: silently pick the last conflicting meta-data value
* ``'warn'``: pick the last conflicting meta-data value, but emit a warning (default)
* ``'error'``: raise an exception.
Returns
-------
stacked_table : `~astropy.table.Table` object
New table containing the stacked data from the input tables.
Examples
--------
To stack two tables along rows do::
>>> from astropy.table import vstack, Table
>>> t1 = Table({'a': [1, 2], 'b': [3, 4]}, names=('a', 'b'))
>>> t2 = Table({'a': [5, 6], 'b': [7, 8]}, names=('a', 'b'))
>>> print(t1)
a b
--- ---
1 3
2 4
>>> print(t2)
a b
--- ---
5 7
6 8
>>> print(dstack([t1, t2]))
a [2] b [2]
------ ------
1 .. 5 3 .. 7
2 .. 6 4 .. 8
"""
_check_join_type(join_type, 'dstack')
tables = _get_list_of_tables(tables)
if len(tables) == 1:
return tables[0] # no point in stacking a single table
n_rows = set(len(table) for table in tables)
if len(n_rows) != 1:
raise ValueError('Table lengths must all match for dstack')
n_row = n_rows.pop()
out = vstack(tables, join_type, metadata_conflicts)
for name, col in out.columns.items():
col = out[name]
# Reshape to so each original column is now in a row.
# If entries are not 0-dim then those additional shape dims
# are just carried along.
# [x x x y y y] => [[x x x],
# [y y y]]
new_shape = (len(tables), n_row) + col.shape[1:]
try:
col.shape = (len(tables), n_row) + col.shape[1:]
except AttributeError:
col = col.reshape(new_shape)
# Transpose the table and row axes to get to
# [[x, y],
# [x, y]
# [x, y]]
axes = np.arange(len(col.shape))
axes[:2] = [1, 0]
# This temporarily makes `out` be corrupted (columns of different
# length) but it all works out in the end.
out.columns.__setitem__(name, col.transpose(axes), validated=True)
return out
def vstack(tables, join_type='outer', metadata_conflicts='warn'):
"""
Stack tables vertically (along rows)
A ``join_type`` of 'exact' means that the tables must all have exactly
the same column names (though the order can vary). If ``join_type``
is 'inner' then the intersection of common columns will be the output.
A value of 'outer' (default) means the output will have the union of
all columns, with table values being masked where no common values are
available.
Parameters
----------
tables : `~astropy.table.Table` or `~astropy.table.Row` or list thereof
Table(s) to stack along rows (vertically) with the current table
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'outer'
metadata_conflicts : str
How to proceed with metadata conflicts. This should be one of:
* ``'silent'``: silently pick the last conflicting meta-data value
* ``'warn'``: pick the last conflicting meta-data value, but emit a warning (default)
* ``'error'``: raise an exception.
Returns
-------
stacked_table : `~astropy.table.Table` object
New table containing the stacked data from the input tables.
Examples
--------
To stack two tables along rows do::
>>> from astropy.table import vstack, Table
>>> t1 = Table({'a': [1, 2], 'b': [3, 4]}, names=('a', 'b'))
>>> t2 = Table({'a': [5, 6], 'b': [7, 8]}, names=('a', 'b'))
>>> print(t1)
a b
--- ---
1 3
2 4
>>> print(t2)
a b
--- ---
5 7
6 8
>>> print(vstack([t1, t2]))
a b
--- ---
1 3
2 4
5 7
6 8
"""
_check_join_type(join_type, 'vstack')
tables = _get_list_of_tables(tables) # validates input
if len(tables) == 1:
return tables[0] # no point in stacking a single table
col_name_map = OrderedDict()
out = _vstack(tables, join_type, col_name_map, metadata_conflicts)
# Merge table metadata
_merge_table_meta(out, tables, metadata_conflicts=metadata_conflicts)
return out
def hstack(tables, join_type='outer',
uniq_col_name='{col_name}_{table_name}', table_names=None,
metadata_conflicts='warn'):
"""
Stack tables along columns (horizontally)
A ``join_type`` of 'exact' means that the tables must all
have exactly the same number of rows. If ``join_type`` is 'inner' then
the intersection of rows will be the output. A value of 'outer' (default)
means the output will have the union of all rows, with table values being
masked where no common values are available.
Parameters
----------
tables : `~astropy.table.Table` or `~astropy.table.Row` or list thereof
Tables to stack along columns (horizontally) with the current table
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'outer'
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2', ..].
metadata_conflicts : str
How to proceed with metadata conflicts. This should be one of:
* ``'silent'``: silently pick the last conflicting meta-data value
* ``'warn'``: pick the last conflicting meta-data value,
but emit a warning (default)
* ``'error'``: raise an exception.
Returns
-------
stacked_table : `~astropy.table.Table` object
New table containing the stacked data from the input tables.
See Also
--------
Table.add_columns, Table.replace_column, Table.update
Examples
--------
To stack two tables horizontally (along columns) do::
>>> from astropy.table import Table, hstack
>>> t1 = Table({'a': [1, 2], 'b': [3, 4]}, names=('a', 'b'))
>>> t2 = Table({'c': [5, 6], 'd': [7, 8]}, names=('c', 'd'))
>>> print(t1)
a b
--- ---
1 3
2 4
>>> print(t2)
c d
--- ---
5 7
6 8
>>> print(hstack([t1, t2]))
a b c d
--- --- --- ---
1 3 5 7
2 4 6 8
"""
_check_join_type(join_type, 'hstack')
tables = _get_list_of_tables(tables) # validates input
if len(tables) == 1:
return tables[0] # no point in stacking a single table
col_name_map = OrderedDict()
out = _hstack(tables, join_type, uniq_col_name, table_names,
col_name_map)
_merge_table_meta(out, tables, metadata_conflicts=metadata_conflicts)
return out
def unique(input_table, keys=None, silent=False, keep='first'):
"""
Returns the unique rows of a table.
Parameters
----------
input_table : table-like
keys : str or list of str
Name(s) of column(s) used to create unique rows.
Default is to use all columns.
keep : {'first', 'last', 'none'}
Whether to keep the first or last row for each set of
duplicates. If 'none', all rows that are duplicate are
removed, leaving only rows that are already unique in
the input.
Default is 'first'.
silent : bool
If `True`, masked value column(s) are silently removed from
``keys``. If `False`, an exception is raised when ``keys``
contains masked value column(s).
Default is `False`.
Returns
-------
unique_table : `~astropy.table.Table` object
New table containing only the unique rows of ``input_table``.
Examples
--------
>>> from astropy.table import unique, Table
>>> import numpy as np
>>> table = Table(data=[[1,2,3,2,3,3],
... [2,3,4,5,4,6],
... [3,4,5,6,7,8]],
... names=['col1', 'col2', 'col3'],
... dtype=[np.int32, np.int32, np.int32])
>>> table
<Table length=6>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
2 3 4
3 4 5
2 5 6
3 4 7
3 6 8
>>> unique(table, keys='col1')
<Table length=3>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
2 3 4
3 4 5
>>> unique(table, keys=['col1'], keep='last')
<Table length=3>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
2 5 6
3 6 8
>>> unique(table, keys=['col1', 'col2'])
<Table length=5>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
2 3 4
2 5 6
3 4 5
3 6 8
>>> unique(table, keys=['col1', 'col2'], keep='none')
<Table length=4>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
2 3 4
2 5 6
3 6 8
>>> unique(table, keys=['col1'], keep='none')
<Table length=1>
col1 col2 col3
int32 int32 int32
----- ----- -----
1 2 3
"""
if keep not in ('first', 'last', 'none'):
raise ValueError("'keep' should be one of 'first', 'last', 'none'")
if isinstance(keys, str):
keys = [keys]
if keys is None:
keys = input_table.colnames
else:
if len(set(keys)) != len(keys):
raise ValueError("duplicate key names")
# Check for columns with masked values
for key in keys[:]:
col = input_table[key]
if hasattr(col, 'mask') and np.any(col.mask):
if not silent:
raise ValueError(
"cannot use columns with masked values as keys; "
"remove column '{}' from keys and rerun "
"unique()".format(key))
del keys[keys.index(key)]
if len(keys) == 0:
raise ValueError("no column remained in ``keys``; "
"unique() cannot work with masked value "
"key columns")
grouped_table = input_table.group_by(keys)
indices = grouped_table.groups.indices
if keep == 'first':
indices = indices[:-1]
elif keep == 'last':
indices = indices[1:] - 1
else:
indices = indices[:-1][np.diff(indices) == 1]
return grouped_table[indices]
def get_col_name_map(arrays, common_names, uniq_col_name='{col_name}_{table_name}',
table_names=None):
"""
Find the column names mapping when merging the list of tables
``arrays``. It is assumed that col names in ``common_names`` are to be
merged into a single column while the rest will be uniquely represented
in the output. The args ``uniq_col_name`` and ``table_names`` specify
how to rename columns in case of conflicts.
Returns a dict mapping each output column name to the input(s). This takes the form
{outname : (col_name_0, col_name_1, ...), ... }. For key columns all of input names
will be present, while for the other non-key columns the value will be (col_name_0,
None, ..) or (None, col_name_1, ..) etc.
"""
col_name_map = collections.defaultdict(lambda: [None] * len(arrays))
col_name_list = []
if table_names is None:
table_names = [str(ii + 1) for ii in range(len(arrays))]
for idx, array in enumerate(arrays):
table_name = table_names[idx]
for name in array.colnames:
out_name = name
if name in common_names:
# If name is in the list of common_names then insert into
# the column name list, but just once.
if name not in col_name_list:
col_name_list.append(name)
else:
# If name is not one of the common column outputs, and it collides
# with the names in one of the other arrays, then rename
others = list(arrays)
others.pop(idx)
if any(name in other.colnames for other in others):
out_name = uniq_col_name.format(table_name=table_name, col_name=name)
col_name_list.append(out_name)
col_name_map[out_name][idx] = name
# Check for duplicate output column names
col_name_count = Counter(col_name_list)
repeated_names = [name for name, count in col_name_count.items() if count > 1]
if repeated_names:
raise TableMergeError('Merging column names resulted in duplicates: {}. '
'Change uniq_col_name or table_names args to fix this.'
.format(repeated_names))
# Convert col_name_map to a regular dict with tuple (immutable) values
col_name_map = OrderedDict((name, col_name_map[name]) for name in col_name_list)
return col_name_map
def get_descrs(arrays, col_name_map):
"""
Find the dtypes descrs resulting from merging the list of arrays' dtypes,
using the column name mapping ``col_name_map``.
Return a list of descrs for the output.
"""
out_descrs = []
for out_name, in_names in col_name_map.items():
# List of input arrays that contribute to this output column
in_cols = [arr[name] for arr, name in zip(arrays, in_names) if name is not None]
# List of names of the columns that contribute to this output column.
names = [name for name in in_names if name is not None]
# Output dtype is the superset of all dtypes in in_arrays
try:
dtype = common_dtype(in_cols)
except TableMergeError as tme:
# Beautify the error message when we are trying to merge columns with incompatible
# types by including the name of the columns that originated the error.
raise TableMergeError("The '{}' columns have incompatible types: {}"
.format(names[0], tme._incompat_types)) from tme
# Make sure all input shapes are the same
uniq_shapes = set(col.shape[1:] for col in in_cols)
if len(uniq_shapes) != 1:
raise TableMergeError(f'Key columns {names!r} have different shape')
shape = uniq_shapes.pop()
if out_name is not None:
out_name = str(out_name)
out_descrs.append((out_name, dtype, shape))
return out_descrs
def common_dtype(cols):
"""
Use numpy to find the common dtype for a list of columns.
Only allow columns within the following fundamental numpy data types:
np.bool_, np.object_, np.number, np.character, np.void
"""
try:
return metadata.common_dtype(cols)
except metadata.MergeConflictError as err:
tme = TableMergeError(f'Columns have incompatible types {err._incompat_types}')
tme._incompat_types = err._incompat_types
raise tme from err
def _get_join_sort_idxs(keys, left, right):
# Go through each of the key columns in order and make columns for
# a new structured array that represents the lexical ordering of those
# key columns. This structured array is then argsort'ed. The trick here
# is that some columns (e.g. Time) may need to be expanded into multiple
# columns for ordering here.
ii = 0 # Index for uniquely naming the sort columns
sort_keys_dtypes = [] # sortable_table dtypes as list of (name, dtype_str, shape) tuples
sort_keys = [] # sortable_table (structured ndarray) column names
sort_left = {} # sortable ndarrays from left table
sort_right = {} # sortable ndarray from right table
for key in keys:
# get_sortable_arrays() returns a list of ndarrays that can be lexically
# sorted to represent the order of the column. In most cases this is just
# a single element of the column itself.
left_sort_cols = left[key].info.get_sortable_arrays()
right_sort_cols = right[key].info.get_sortable_arrays()
if len(left_sort_cols) != len(right_sort_cols):
# Should never happen because cols are screened beforehand for compatibility
raise RuntimeError('mismatch in sort cols lengths')
for left_sort_col, right_sort_col in zip(left_sort_cols, right_sort_cols):
# Check for consistency of shapes. Mismatch should never happen.
shape = left_sort_col.shape[1:]
if shape != right_sort_col.shape[1:]:
raise RuntimeError('mismatch in shape of left vs. right sort array')
if shape != ():
raise ValueError(f'sort key column {key!r} must be 1-d')
sort_key = str(ii)
sort_keys.append(sort_key)
sort_left[sort_key] = left_sort_col
sort_right[sort_key] = right_sort_col
# Build up dtypes for the structured array that gets sorted.
dtype_str = common_dtype([left_sort_col, right_sort_col])
sort_keys_dtypes.append((sort_key, dtype_str))
ii += 1
# Make the empty sortable table and fill it
len_left = len(left)
sortable_table = np.empty(len_left + len(right), dtype=sort_keys_dtypes)
for key in sort_keys:
sortable_table[key][:len_left] = sort_left[key]
sortable_table[key][len_left:] = sort_right[key]
# Finally do the (lexical) argsort and make a new sorted version
idx_sort = sortable_table.argsort(order=sort_keys)
sorted_table = sortable_table[idx_sort]
# Get indexes of unique elements (i.e. the group boundaries)
diffs = np.concatenate(([True], sorted_table[1:] != sorted_table[:-1], [True]))
idxs = np.flatnonzero(diffs)
return idxs, idx_sort
def _apply_join_funcs(left, right, keys, join_funcs):
"""Apply join_funcs
"""
# Make light copies of left and right, then add new index columns.
left = left.copy(copy_data=False)
right = right.copy(copy_data=False)
for key, join_func in join_funcs.items():
ids1, ids2 = join_func(left[key], right[key])
# Define a unique id_key name, and keep adding underscores until we have
# a name not yet present.
id_key = key + '_id'
while id_key in left.columns or id_key in right.columns:
id_key = id_key[:-2] + '_id'
keys = tuple(id_key if orig_key == key else orig_key for orig_key in keys)
left.add_column(ids1, index=0, name=id_key) # [id_key] = ids1
right.add_column(ids2, index=0, name=id_key) # [id_key] = ids2
return left, right, keys
def _join(left, right, keys=None, join_type='inner',
uniq_col_name='{col_name}_{table_name}',
table_names=['1', '2'],
col_name_map=None, metadata_conflicts='warn',
join_funcs=None,
keys_left=None, keys_right=None):
"""
Perform a join of the left and right Tables on specified keys.
Parameters
----------
left : Table
Left side table in the join
right : Table
Right side table in the join
keys : str or list of str
Name(s) of column(s) used to match rows of left and right tables.
Default is to use all columns which are common to both tables.
join_type : str
Join type ('inner' | 'outer' | 'left' | 'right' | 'cartesian'), default is 'inner'
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2'].
col_name_map : empty dict or None
If passed as a dict then it will be updated in-place with the
mapping of output to input column names.
metadata_conflicts : str
How to proceed with metadata conflicts. This should be one of:
* ``'silent'``: silently pick the last conflicting meta-data value
* ``'warn'``: pick the last conflicting meta-data value, but emit a warning (default)
* ``'error'``: raise an exception.
join_funcs : dict, None
Dict of functions to use for matching the corresponding key column(s).
See `~astropy.table.join_skycoord` for an example and details.
Returns
-------
joined_table : `~astropy.table.Table` object
New table containing the result of the join operation.
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
# Special column name for cartesian join, should never collide with real column
cartesian_index_name = '__table_cartesian_join_temp_index__'
if join_type not in ('inner', 'outer', 'left', 'right', 'cartesian'):
raise ValueError("The 'join_type' argument should be in 'inner', "
"'outer', 'left', 'right', or 'cartesian' "
"(got '{}' instead)".
format(join_type))
if join_type == 'cartesian':
if keys:
raise ValueError('cannot supply keys for a cartesian join')
if join_funcs:
raise ValueError('cannot supply join_funcs for a cartesian join')
# Make light copies of left and right, then add temporary index columns
# with all the same value so later an outer join turns into a cartesian join.
left = left.copy(copy_data=False)
right = right.copy(copy_data=False)
left[cartesian_index_name] = np.uint8(0)
right[cartesian_index_name] = np.uint8(0)
keys = (cartesian_index_name, )
# Handle the case of join key columns that are different between left and
# right via keys_left/keys_right args. This is done by saving the original
# input tables and making new left and right tables that contain only the
# key cols but with common column names ['0', '1', etc]. This sets `keys` to
# those fake key names in the left and right tables
if keys_left is not None or keys_right is not None:
left_orig = left
right_orig = right
left, right, keys = _join_keys_left_right(
left, right, keys, keys_left, keys_right, join_funcs)
if keys is None:
keys = tuple(name for name in left.colnames if name in right.colnames)
if len(keys) == 0:
raise TableMergeError('No keys in common between left and right tables')
elif isinstance(keys, str):
# If we have a single key, put it in a tuple
keys = (keys,)
# Check the key columns
for arr, arr_label in ((left, 'Left'), (right, 'Right')):
for name in keys:
if name not in arr.colnames:
raise TableMergeError('{} table does not have key column {!r}'
.format(arr_label, name))
if hasattr(arr[name], 'mask') and np.any(arr[name].mask):
raise TableMergeError('{} key column {!r} has missing values'
.format(arr_label, name))
if join_funcs is not None:
if not all(key in keys for key in join_funcs):
raise ValueError(f'join_funcs keys {join_funcs.keys()} must be a '
f'subset of join keys {keys}')
left, right, keys = _apply_join_funcs(left, right, keys, join_funcs)
len_left, len_right = len(left), len(right)
if len_left == 0 or len_right == 0:
raise ValueError('input tables for join must both have at least one row')
try:
idxs, idx_sort = _get_join_sort_idxs(keys, left, right)
except NotImplementedError:
raise TypeError('one or more key columns are not sortable')
# Now that we have idxs and idx_sort, revert to the original table args to
# carry on with making the output joined table. `keys` is set to to an empty
# list so that all original left and right columns are included in the
# output table.
if keys_left is not None or keys_right is not None:
keys = []
left = left_orig
right = right_orig
# Joined array dtype as a list of descr (name, type_str, shape) tuples
col_name_map = get_col_name_map([left, right], keys, uniq_col_name, table_names)
out_descrs = get_descrs([left, right], col_name_map)
# Main inner loop in Cython to compute the cartesian product
# indices for the given join type
int_join_type = {'inner': 0, 'outer': 1, 'left': 2, 'right': 3,
'cartesian': 1}[join_type]
masked, n_out, left_out, left_mask, right_out, right_mask = \
_np_utils.join_inner(idxs, idx_sort, len_left, int_join_type)
out = _get_out_class([left, right])()
for out_name, dtype, shape in out_descrs:
if out_name == cartesian_index_name:
continue
left_name, right_name = col_name_map[out_name]
if left_name and right_name: # this is a key which comes from left and right
cols = [left[left_name], right[right_name]]
col_cls = _get_out_class(cols)
if not hasattr(col_cls.info, 'new_like'):
raise NotImplementedError('join unavailable for mixin column type(s): {}'
.format(col_cls.__name__))
out[out_name] = col_cls.info.new_like(cols, n_out, metadata_conflicts, out_name)
out[out_name][:] = np.where(right_mask,
left[left_name].take(left_out),
right[right_name].take(right_out))
continue
elif left_name: # out_name came from the left table
name, array, array_out, array_mask = left_name, left, left_out, left_mask
elif right_name:
name, array, array_out, array_mask = right_name, right, right_out, right_mask
else:
raise TableMergeError('Unexpected column names (maybe one is ""?)')
# Select the correct elements from the original table
col = array[name][array_out]
# If the output column is masked then set the output column masking
# accordingly. Check for columns that don't support a mask attribute.
if masked and np.any(array_mask):
# If col is a Column but not MaskedColumn then upgrade at this point
# because masking is required.
if isinstance(col, Column) and not isinstance(col, MaskedColumn):
col = out.MaskedColumn(col, copy=False)
if isinstance(col, Quantity) and not isinstance(col, Masked):
col = Masked(col, copy=False)
# array_mask is 1-d corresponding to length of output column. We need
# make it have the correct shape for broadcasting, i.e. (length, 1, 1, ..).
# Mixin columns might not have ndim attribute so use len(col.shape).
array_mask.shape = (col.shape[0],) + (1,) * (len(col.shape) - 1)
# Now broadcast to the correct final shape
array_mask = np.broadcast_to(array_mask, col.shape)
try:
col[array_mask] = col.info.mask_val
except Exception as err: # Not clear how different classes will fail here
raise NotImplementedError(
"join requires masking column '{}' but column"
" type {} does not support masking"
.format(out_name, col.__class__.__name__)) from err
# Set the output table column to the new joined column
out[out_name] = col
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, Mapping):
_col_name_map.update(col_name_map)
return out
def _join_keys_left_right(left, right, keys, keys_left, keys_right, join_funcs):
"""Do processing to handle keys_left / keys_right args for join.
This takes the keys_left/right inputs and turns them into a list of left/right
columns corresponding to those inputs (which can be column names or column
data values). It also generates the list of fake key column names (strings
of "1", "2", etc.) that correspond to the input keys.
"""
def _keys_to_cols(keys, table, label):
# Process input `keys`, which is a str or list of str column names in
# `table` or a list of column-like objects. The `label` is just for
# error reporting.
if isinstance(keys, str):
keys = [keys]
cols = []
for key in keys:
if isinstance(key, str):
try:
cols.append(table[key])
except KeyError:
raise ValueError(f'{label} table does not have key column {key!r}')
else:
if len(key) != len(table):
raise ValueError(f'{label} table has different length from key {key}')
cols.append(key)
return cols
if join_funcs is not None:
raise ValueError('cannot supply join_funcs arg and keys_left / keys_right')
if keys_left is None or keys_right is None:
raise ValueError('keys_left and keys_right must both be provided')
if keys is not None:
raise ValueError('keys arg must be None if keys_left and keys_right are supplied')
cols_left = _keys_to_cols(keys_left, left, 'left')
cols_right = _keys_to_cols(keys_right, right, 'right')
if len(cols_left) != len(cols_right):
raise ValueError('keys_left and keys_right args must have same length')
# Make two new temp tables for the join with only the join columns and
# key columns in common.
keys = [f'{ii}' for ii in range(len(cols_left))]
left = left.__class__(cols_left, names=keys, copy=False)
right = right.__class__(cols_right, names=keys, copy=False)
return left, right, keys
def _check_join_type(join_type, func_name):
"""Check join_type arg in hstack and vstack.
This specifically checks for the common mistake of call vstack(t1, t2)
instead of vstack([t1, t2]). The subsequent check of
``join_type in ('inner', ..)`` does not raise in this case.
"""
if not isinstance(join_type, str):
msg = '`join_type` arg must be a string'
if isinstance(join_type, Table):
msg += ('. Did you accidentally '
f'call {func_name}(t1, t2, ..) instead of '
f'{func_name}([t1, t2], ..)?')
raise TypeError(msg)
if join_type not in ('inner', 'exact', 'outer'):
raise ValueError("`join_type` arg must be one of 'inner', 'exact' or 'outer'")
def _vstack(arrays, join_type='outer', col_name_map=None, metadata_conflicts='warn'):
"""
Stack Tables vertically (by rows)
A ``join_type`` of 'exact' (default) means that the arrays must all
have exactly the same column names (though the order can vary). If
``join_type`` is 'inner' then the intersection of common columns will
be the output. A value of 'outer' means the output will have the union of
all columns, with array values being masked where no common values are
available.
Parameters
----------
arrays : list of Tables
Tables to stack by rows (vertically)
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'outer'
col_name_map : empty dict or None
If passed as a dict then it will be updated in-place with the
mapping of output to input column names.
Returns
-------
stacked_table : `~astropy.table.Table` object
New table containing the stacked data from the input tables.
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
# Trivial case of one input array
if len(arrays) == 1:
return arrays[0]
# Start by assuming an outer match where all names go to output
names = set(itertools.chain(*[arr.colnames for arr in arrays]))
col_name_map = get_col_name_map(arrays, names)
# If require_match is True then the output must have exactly the same
# number of columns as each input array
if join_type == 'exact':
for names in col_name_map.values():
if any(x is None for x in names):
raise TableMergeError('Inconsistent columns in input arrays '
"(use 'inner' or 'outer' join_type to "
"allow non-matching columns)")
join_type = 'outer'
# For an inner join, keep only columns where all input arrays have that column
if join_type == 'inner':
col_name_map = OrderedDict((name, in_names) for name, in_names in col_name_map.items()
if all(x is not None for x in in_names))
if len(col_name_map) == 0:
raise TableMergeError('Input arrays have no columns in common')
lens = [len(arr) for arr in arrays]
n_rows = sum(lens)
out = _get_out_class(arrays)()
for out_name, in_names in col_name_map.items():
# List of input arrays that contribute to this output column
cols = [arr[name] for arr, name in zip(arrays, in_names) if name is not None]
col_cls = _get_out_class(cols)
if not hasattr(col_cls.info, 'new_like'):
raise NotImplementedError('vstack unavailable for mixin column type(s): {}'
.format(col_cls.__name__))
try:
col = col_cls.info.new_like(cols, n_rows, metadata_conflicts, out_name)
except metadata.MergeConflictError as err:
# Beautify the error message when we are trying to merge columns with incompatible
# types by including the name of the columns that originated the error.
raise TableMergeError("The '{}' columns have incompatible types: {}"
.format(out_name, err._incompat_types)) from err
idx0 = 0
for name, array in zip(in_names, arrays):
idx1 = idx0 + len(array)
if name in array.colnames:
col[idx0:idx1] = array[name]
else:
# If col is a Column but not MaskedColumn then upgrade at this point
# because masking is required.
if isinstance(col, Column) and not isinstance(col, MaskedColumn):
col = out.MaskedColumn(col, copy=False)
if isinstance(col, Quantity) and not isinstance(col, Masked):
col = Masked(col, copy=False)
try:
col[idx0:idx1] = col.info.mask_val
except Exception as err:
raise NotImplementedError(
"vstack requires masking column '{}' but column"
" type {} does not support masking"
.format(out_name, col.__class__.__name__)) from err
idx0 = idx1
out[out_name] = col
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, Mapping):
_col_name_map.update(col_name_map)
return out
def _hstack(arrays, join_type='outer', uniq_col_name='{col_name}_{table_name}',
table_names=None, col_name_map=None):
"""
Stack tables horizontally (by columns)
A ``join_type`` of 'exact' (default) means that the arrays must all
have exactly the same number of rows. If ``join_type`` is 'inner' then
the intersection of rows will be the output. A value of 'outer' means
the output will have the union of all rows, with array values being
masked where no common values are available.
Parameters
----------
arrays : List of tables
Tables to stack by columns (horizontally)
join_type : str
Join type ('inner' | 'exact' | 'outer'), default is 'outer'
uniq_col_name : str or None
String generate a unique output column name in case of a conflict.
The default is '{col_name}_{table_name}'.
table_names : list of str or None
Two-element list of table names used when generating unique output
column names. The default is ['1', '2', ..].
Returns
-------
stacked_table : `~astropy.table.Table` object
New table containing the stacked data from the input tables.
"""
# Store user-provided col_name_map until the end
_col_name_map = col_name_map
if table_names is None:
table_names = [f'{ii + 1}' for ii in range(len(arrays))]
if len(arrays) != len(table_names):
raise ValueError('Number of arrays must match number of table_names')
# Trivial case of one input arrays
if len(arrays) == 1:
return arrays[0]
col_name_map = get_col_name_map(arrays, [], uniq_col_name, table_names)
# If require_match is True then all input arrays must have the same length
arr_lens = [len(arr) for arr in arrays]
if join_type == 'exact':
if len(set(arr_lens)) > 1:
raise TableMergeError("Inconsistent number of rows in input arrays "
"(use 'inner' or 'outer' join_type to allow "
"non-matching rows)")
join_type = 'outer'
# For an inner join, keep only the common rows
if join_type == 'inner':
min_arr_len = min(arr_lens)
if len(set(arr_lens)) > 1:
arrays = [arr[:min_arr_len] for arr in arrays]
arr_lens = [min_arr_len for arr in arrays]
# If there are any output rows where one or more input arrays are missing
# then the output must be masked. If any input arrays are masked then
# output is masked.
n_rows = max(arr_lens)
out = _get_out_class(arrays)()
for out_name, in_names in col_name_map.items():
for name, array, arr_len in zip(in_names, arrays, arr_lens):
if name is None:
continue
if n_rows > arr_len:
indices = np.arange(n_rows)
indices[arr_len:] = 0
col = array[name][indices]
# If col is a Column but not MaskedColumn then upgrade at this point
# because masking is required.
if isinstance(col, Column) and not isinstance(col, MaskedColumn):
col = out.MaskedColumn(col, copy=False)
if isinstance(col, Quantity) and not isinstance(col, Masked):
col = Masked(col, copy=False)
try:
col[arr_len:] = col.info.mask_val
except Exception as err:
raise NotImplementedError(
"hstack requires masking column '{}' but column"
" type {} does not support masking"
.format(out_name, col.__class__.__name__)) from err
else:
col = array[name][:n_rows]
out[out_name] = col
# If col_name_map supplied as a dict input, then update.
if isinstance(_col_name_map, Mapping):
_col_name_map.update(col_name_map)
return out
|
f1ee16e162b20815876d2bc1e600a2b4c85cb8b2b7dc527b91caf1979ac11af1 | """
High-level operations for numpy structured arrays.
Some code and inspiration taken from numpy.lib.recfunctions.join_by().
Redistribution license restrictions apply.
"""
import collections
from collections import OrderedDict, Counter
from collections.abc import Sequence
import numpy as np
__all__ = ['TableMergeError']
class TableMergeError(ValueError):
pass
def get_col_name_map(arrays, common_names, uniq_col_name='{col_name}_{table_name}',
table_names=None):
"""
Find the column names mapping when merging the list of structured ndarrays
``arrays``. It is assumed that col names in ``common_names`` are to be
merged into a single column while the rest will be uniquely represented
in the output. The args ``uniq_col_name`` and ``table_names`` specify
how to rename columns in case of conflicts.
Returns a dict mapping each output column name to the input(s). This takes the form
{outname : (col_name_0, col_name_1, ...), ... }. For key columns all of input names
will be present, while for the other non-key columns the value will be (col_name_0,
None, ..) or (None, col_name_1, ..) etc.
"""
col_name_map = collections.defaultdict(lambda: [None] * len(arrays))
col_name_list = []
if table_names is None:
table_names = [str(ii + 1) for ii in range(len(arrays))]
for idx, array in enumerate(arrays):
table_name = table_names[idx]
for name in array.dtype.names:
out_name = name
if name in common_names:
# If name is in the list of common_names then insert into
# the column name list, but just once.
if name not in col_name_list:
col_name_list.append(name)
else:
# If name is not one of the common column outputs, and it collides
# with the names in one of the other arrays, then rename
others = list(arrays)
others.pop(idx)
if any(name in other.dtype.names for other in others):
out_name = uniq_col_name.format(table_name=table_name, col_name=name)
col_name_list.append(out_name)
col_name_map[out_name][idx] = name
# Check for duplicate output column names
col_name_count = Counter(col_name_list)
repeated_names = [name for name, count in col_name_count.items() if count > 1]
if repeated_names:
raise TableMergeError('Merging column names resulted in duplicates: {}. '
'Change uniq_col_name or table_names args to fix this.'
.format(repeated_names))
# Convert col_name_map to a regular dict with tuple (immutable) values
col_name_map = OrderedDict((name, col_name_map[name]) for name in col_name_list)
return col_name_map
def get_descrs(arrays, col_name_map):
"""
Find the dtypes descrs resulting from merging the list of arrays' dtypes,
using the column name mapping ``col_name_map``.
Return a list of descrs for the output.
"""
out_descrs = []
for out_name, in_names in col_name_map.items():
# List of input arrays that contribute to this output column
in_cols = [arr[name] for arr, name in zip(arrays, in_names) if name is not None]
# List of names of the columns that contribute to this output column.
names = [name for name in in_names if name is not None]
# Output dtype is the superset of all dtypes in in_arrays
try:
dtype = common_dtype(in_cols)
except TableMergeError as tme:
# Beautify the error message when we are trying to merge columns with incompatible
# types by including the name of the columns that originated the error.
raise TableMergeError("The '{}' columns have incompatible types: {}"
.format(names[0], tme._incompat_types)) from tme
# Make sure all input shapes are the same
uniq_shapes = set(col.shape[1:] for col in in_cols)
if len(uniq_shapes) != 1:
raise TableMergeError('Key columns have different shape')
shape = uniq_shapes.pop()
if out_name is not None:
out_name = str(out_name)
out_descrs.append((out_name, dtype, shape))
return out_descrs
def common_dtype(cols):
"""
Use numpy to find the common dtype for a list of structured ndarray columns.
Only allow columns within the following fundamental numpy data types:
np.bool_, np.object_, np.number, np.character, np.void
"""
np_types = (np.bool_, np.object_, np.number, np.character, np.void)
uniq_types = set(tuple(issubclass(col.dtype.type, np_type) for np_type in np_types)
for col in cols)
if len(uniq_types) > 1:
# Embed into the exception the actual list of incompatible types.
incompat_types = [col.dtype.name for col in cols]
tme = TableMergeError(f'Columns have incompatible types {incompat_types}')
tme._incompat_types = incompat_types
raise tme
arrs = [np.empty(1, dtype=col.dtype) for col in cols]
# For string-type arrays need to explicitly fill in non-zero
# values or the final arr_common = .. step is unpredictable.
for arr in arrs:
if arr.dtype.kind in ('S', 'U'):
arr[0] = '0' * arr.itemsize
arr_common = np.array([arr[0] for arr in arrs])
return arr_common.dtype.str
def _check_for_sequence_of_structured_arrays(arrays):
err = '`arrays` arg must be a sequence (e.g. list) of structured arrays'
if not isinstance(arrays, Sequence):
raise TypeError(err)
for array in arrays:
# Must be structured array
if not isinstance(array, np.ndarray) or array.dtype.names is None:
raise TypeError(err)
if len(arrays) == 0:
raise ValueError('`arrays` arg must include at least one array')
|
db6cde48d019a7516a096527dfad4880ddfce737eed00ed463371a967d1e3290 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
from setuptools import Extension
import numpy
ROOT = os.path.relpath(os.path.dirname(__file__))
def get_extensions():
sources = ["_np_utils.pyx", "_column_mixins.pyx"]
include_dirs = [numpy.get_include()]
exts = [
Extension(name='astropy.table.' + os.path.splitext(source)[0],
sources=[os.path.join(ROOT, source)],
include_dirs=include_dirs)
for source in sources
]
return exts
|
0edf849fcdd5a25cd472ccc4709c3def8dce4f5d16be9e1c20e54ec0c3fd805a | import json
import textwrap
import copy
from collections import OrderedDict
import numpy as np
import yaml
__all__ = ['get_header_from_yaml', 'get_yaml_from_header', 'get_yaml_from_table']
class ColumnOrderList(list):
"""
List of tuples that sorts in a specific order that makes sense for
astropy table column attributes.
"""
def sort(self, *args, **kwargs):
super().sort()
column_keys = ['name', 'unit', 'datatype', 'format', 'description', 'meta']
in_dict = dict(self)
out_list = []
for key in column_keys:
if key in in_dict:
out_list.append((key, in_dict[key]))
for key, val in self:
if key not in column_keys:
out_list.append((key, val))
# Clear list in-place
del self[:]
self.extend(out_list)
class ColumnDict(dict):
"""
Specialized dict subclass to represent attributes of a Column
and return items() in a preferred order. This is only for use
in generating a YAML map representation that has a fixed order.
"""
def items(self):
"""
Return items as a ColumnOrderList, which sorts in the preferred
way for column attributes.
"""
return ColumnOrderList(super().items())
def _construct_odict(load, node):
"""
Construct OrderedDict from !!omap in yaml safe load.
Source: https://gist.github.com/weaver/317164
License: Unspecified
This is the same as SafeConstructor.construct_yaml_omap(),
except the data type is changed to OrderedDict() and setitem is
used instead of append in the loop
Examples
--------
::
>>> yaml.load(''' # doctest: +SKIP
... !!omap
... - foo: bar
... - mumble: quux
... - baz: gorp
... ''')
OrderedDict([('foo', 'bar'), ('mumble', 'quux'), ('baz', 'gorp')])
>>> yaml.load('''!!omap [ foo: bar, mumble: quux, baz : gorp ]''') # doctest: +SKIP
OrderedDict([('foo', 'bar'), ('mumble', 'quux'), ('baz', 'gorp')])
"""
omap = OrderedDict()
yield omap
if not isinstance(node, yaml.SequenceNode):
raise yaml.constructor.ConstructorError(
"while constructing an ordered map", node.start_mark,
f"expected a sequence, but found {node.id}", node.start_mark)
for subnode in node.value:
if not isinstance(subnode, yaml.MappingNode):
raise yaml.constructor.ConstructorError(
"while constructing an ordered map", node.start_mark,
f"expected a mapping of length 1, but found {subnode.id}",
subnode.start_mark)
if len(subnode.value) != 1:
raise yaml.constructor.ConstructorError(
"while constructing an ordered map", node.start_mark,
f"expected a single mapping item, but found {len(subnode.value)} items",
subnode.start_mark)
key_node, value_node = subnode.value[0]
key = load.construct_object(key_node)
value = load.construct_object(value_node)
omap[key] = value
def _repr_pairs(dump, tag, sequence, flow_style=None):
"""
This is the same code as BaseRepresenter.represent_sequence(),
but the value passed to dump.represent_data() in the loop is a
dictionary instead of a tuple.
Source: https://gist.github.com/weaver/317164
License: Unspecified
"""
value = []
node = yaml.SequenceNode(tag, value, flow_style=flow_style)
if dump.alias_key is not None:
dump.represented_objects[dump.alias_key] = node
best_style = True
for (key, val) in sequence:
item = dump.represent_data({key: val})
if not (isinstance(item, yaml.ScalarNode) and not item.style):
best_style = False
value.append(item)
if flow_style is None:
if dump.default_flow_style is not None:
node.flow_style = dump.default_flow_style
else:
node.flow_style = best_style
return node
def _repr_odict(dumper, data):
"""
Represent OrderedDict in yaml dump.
Source: https://gist.github.com/weaver/317164
License: Unspecified
>>> data = OrderedDict([('foo', 'bar'), ('mumble', 'quux'), ('baz', 'gorp')])
>>> yaml.dump(data, default_flow_style=False) # doctest: +SKIP
'!!omap\\n- foo: bar\\n- mumble: quux\\n- baz: gorp\\n'
>>> yaml.dump(data, default_flow_style=True) # doctest: +SKIP
'!!omap [foo: bar, mumble: quux, baz: gorp]\\n'
"""
return _repr_pairs(dumper, 'tag:yaml.org,2002:omap', data.items())
def _repr_column_dict(dumper, data):
"""
Represent ColumnDict in yaml dump.
This is the same as an ordinary mapping except that the keys
are written in a fixed order that makes sense for astropy table
columns.
"""
return dumper.represent_mapping('tag:yaml.org,2002:map', data)
def _get_variable_length_array_shape(col):
"""Check if object-type ``col`` is really a variable length list.
That is true if the object consists purely of list of nested lists, where
the shape of every item can be represented as (m, n, ..., *) where the (m,
n, ...) are constant and only the lists in the last axis have variable
shape. If so the returned value of shape will be a tuple in the form (m, n,
..., None).
If ``col`` is a variable length array then the return ``dtype`` corresponds
to the type found by numpy for all the individual values. Otherwise it will
be ``np.dtype(object)``.
Parameters
==========
col : column-like
Input table column, assumed to be object-type
Returns
=======
shape : tuple
Inferred variable length shape or None
dtype : np.dtype
Numpy dtype that applies to col
"""
class ConvertError(ValueError):
"""Local conversion error used below"""
# Numpy types supported as variable-length arrays
np_classes = (np.floating, np.integer, np.bool_, np.unicode_)
try:
if len(col) == 0 or not all(isinstance(val, np.ndarray) for val in col):
raise ConvertError
dtype = col[0].dtype
shape = col[0].shape[:-1]
for val in col:
if not issubclass(val.dtype.type, np_classes) or val.shape[:-1] != shape:
raise ConvertError
dtype = np.promote_types(dtype, val.dtype)
shape = shape + (None,)
except ConvertError:
# `col` is not a variable length array, return shape and dtype to
# the original. Note that this function is only called if
# col.shape[1:] was () and col.info.dtype is object.
dtype = col.info.dtype
shape = ()
return shape, dtype
def _get_datatype_from_dtype(dtype):
"""Return string version of ``dtype`` for writing to ECSV ``datatype``"""
datatype = dtype.name
if datatype.startswith(('bytes', 'str')):
datatype = 'string'
if datatype.endswith('_'):
datatype = datatype[:-1] # string_ and bool_ lose the final _ for ECSV
return datatype
def _get_col_attributes(col):
"""
Extract information from a column (apart from the values) that is required
to fully serialize the column.
Parameters
----------
col : column-like
Input Table column
Returns
-------
attrs : dict
Dict of ECSV attributes for ``col``
"""
dtype = col.info.dtype # Type of column values that get written
subtype = None # Type of data for object columns serialized with JSON
shape = col.shape[1:] # Shape of multidim / variable length columns
if dtype.name == 'object':
if shape == ():
# 1-d object type column might be a variable length array
dtype = np.dtype(str)
shape, subtype = _get_variable_length_array_shape(col)
else:
# N-d object column is subtype object but serialized as JSON string
dtype = np.dtype(str)
subtype = np.dtype(object)
elif shape:
# N-d column which is not object is serialized as JSON string
dtype = np.dtype(str)
subtype = col.info.dtype
datatype = _get_datatype_from_dtype(dtype)
# Set the output attributes
attrs = ColumnDict()
attrs['name'] = col.info.name
attrs['datatype'] = datatype
for attr, nontrivial, xform in (('unit', lambda x: x is not None, str),
('format', lambda x: x is not None, None),
('description', lambda x: x is not None, None),
('meta', lambda x: x, None)):
col_attr = getattr(col.info, attr)
if nontrivial(col_attr):
attrs[attr] = xform(col_attr) if xform else col_attr
if subtype:
attrs['subtype'] = _get_datatype_from_dtype(subtype)
# Numpy 'object' maps to 'subtype' of 'json' in ECSV
if attrs['subtype'] == 'object':
attrs['subtype'] = 'json'
if shape:
attrs['subtype'] += json.dumps(list(shape), separators=(',', ':'))
return attrs
def get_yaml_from_table(table):
"""
Return lines with a YAML representation of header content from the ``table``.
Parameters
----------
table : `~astropy.table.Table` object
Table for which header content is output
Returns
-------
lines : list
List of text lines with YAML header content
"""
header = {'cols': list(table.columns.values())}
if table.meta:
header['meta'] = table.meta
return get_yaml_from_header(header)
def get_yaml_from_header(header):
"""
Return lines with a YAML representation of header content from a Table.
The ``header`` dict must contain these keys:
- 'cols' : list of table column objects (required)
- 'meta' : table 'meta' attribute (optional)
Other keys included in ``header`` will be serialized in the output YAML
representation.
Parameters
----------
header : dict
Table header content
Returns
-------
lines : list
List of text lines with YAML header content
"""
from astropy.io.misc.yaml import AstropyDumper
class TableDumper(AstropyDumper):
"""
Custom Dumper that represents OrderedDict as an !!omap object.
"""
def represent_mapping(self, tag, mapping, flow_style=None):
"""
This is a combination of the Python 2 and 3 versions of this method
in the PyYAML library to allow the required key ordering via the
ColumnOrderList object. The Python 3 version insists on turning the
items() mapping into a list object and sorting, which results in
alphabetical order for the column keys.
"""
value = []
node = yaml.MappingNode(tag, value, flow_style=flow_style)
if self.alias_key is not None:
self.represented_objects[self.alias_key] = node
best_style = True
if hasattr(mapping, 'items'):
mapping = mapping.items()
if hasattr(mapping, 'sort'):
mapping.sort()
else:
mapping = list(mapping)
try:
mapping = sorted(mapping)
except TypeError:
pass
for item_key, item_value in mapping:
node_key = self.represent_data(item_key)
node_value = self.represent_data(item_value)
if not (isinstance(node_key, yaml.ScalarNode) and not node_key.style):
best_style = False
if not (isinstance(node_value, yaml.ScalarNode) and not node_value.style):
best_style = False
value.append((node_key, node_value))
if flow_style is None:
if self.default_flow_style is not None:
node.flow_style = self.default_flow_style
else:
node.flow_style = best_style
return node
TableDumper.add_representer(OrderedDict, _repr_odict)
TableDumper.add_representer(ColumnDict, _repr_column_dict)
header = copy.copy(header) # Don't overwrite original
header['datatype'] = [_get_col_attributes(col) for col in header['cols']]
del header['cols']
lines = yaml.dump(header, default_flow_style=None,
Dumper=TableDumper, width=130).splitlines()
return lines
class YamlParseError(Exception):
pass
def get_header_from_yaml(lines):
"""
Get a header dict from input ``lines`` which should be valid YAML. This
input will typically be created by get_yaml_from_header. The output is a
dictionary which describes all the table and column meta.
The get_cols() method in the io/ascii/ecsv.py file should be used as a
guide to using the information when constructing a table using this
header dict information.
Parameters
----------
lines : list
List of text lines with YAML header content
Returns
-------
header : dict
Dictionary describing table and column meta
"""
from astropy.io.misc.yaml import AstropyLoader
class TableLoader(AstropyLoader):
"""
Custom Loader that constructs OrderedDict from an !!omap object.
This does nothing but provide a namespace for adding the
custom odict constructor.
"""
TableLoader.add_constructor('tag:yaml.org,2002:omap', _construct_odict)
# Now actually load the YAML data structure into `meta`
header_yaml = textwrap.dedent('\n'.join(lines))
try:
header = yaml.load(header_yaml, Loader=TableLoader)
except Exception as err:
raise YamlParseError() from err
return header
|
2a7110f80c98f5adf82ba5a6956cf2039621dea290b84866126f7edc9f35c40e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
def _searchsorted(array, val, side='left'):
'''
Call np.searchsorted or use a custom binary
search if necessary.
'''
if hasattr(array, 'searchsorted'):
return array.searchsorted(val, side=side)
# Python binary search
begin = 0
end = len(array)
while begin < end:
mid = (begin + end) // 2
if val > array[mid]:
begin = mid + 1
elif val < array[mid]:
end = mid
elif side == 'right':
begin = mid + 1
else:
end = mid
return begin
class SortedArray:
'''
Implements a sorted array container using
a list of numpy arrays.
Parameters
----------
data : Table
Sorted columns of the original table
row_index : Column object
Row numbers corresponding to data columns
unique : bool
Whether the values of the index must be unique.
Defaults to False.
'''
def __init__(self, data, row_index, unique=False):
self.data = data
self.row_index = row_index
self.num_cols = len(getattr(data, 'colnames', []))
self.unique = unique
@property
def cols(self):
return list(self.data.columns.values())
def add(self, key, row):
'''
Add a new entry to the sorted array.
Parameters
----------
key : tuple
Column values at the given row
row : int
Row number
'''
pos = self.find_pos(key, row) # first >= key
if self.unique and 0 <= pos < len(self.row_index) and \
all(self.data[pos][i] == key[i] for i in range(len(key))):
# already exists
raise ValueError(f'Cannot add duplicate value "{key}" in a unique index')
self.data.insert_row(pos, key)
self.row_index = self.row_index.insert(pos, row)
def _get_key_slice(self, i, begin, end):
'''
Retrieve the ith slice of the sorted array
from begin to end.
'''
if i < self.num_cols:
return self.cols[i][begin:end]
else:
return self.row_index[begin:end]
def find_pos(self, key, data, exact=False):
'''
Return the index of the largest key in data greater than or
equal to the given key, data pair.
Parameters
----------
key : tuple
Column key
data : int
Row number
exact : bool
If True, return the index of the given key in data
or -1 if the key is not present.
'''
begin = 0
end = len(self.row_index)
num_cols = self.num_cols
if not self.unique:
# consider the row value as well
key = key + (data,)
num_cols += 1
# search through keys in lexicographic order
for i in range(num_cols):
key_slice = self._get_key_slice(i, begin, end)
t = _searchsorted(key_slice, key[i])
# t is the smallest index >= key[i]
if exact and (t == len(key_slice) or key_slice[t] != key[i]):
# no match
return -1
elif t == len(key_slice) or (t == 0 and len(key_slice) > 0
and key[i] < key_slice[0]):
# too small or too large
return begin + t
end = begin + _searchsorted(key_slice, key[i], side='right')
begin += t
if begin >= len(self.row_index): # greater than all keys
return begin
return begin
def find(self, key):
'''
Find all rows matching the given key.
Parameters
----------
key : tuple
Column values
Returns
-------
matching_rows : list
List of rows matching the input key
'''
begin = 0
end = len(self.row_index)
# search through keys in lexicographic order
for i in range(self.num_cols):
key_slice = self._get_key_slice(i, begin, end)
t = _searchsorted(key_slice, key[i])
# t is the smallest index >= key[i]
if t == len(key_slice) or key_slice[t] != key[i]:
# no match
return []
elif t == 0 and len(key_slice) > 0 and key[i] < key_slice[0]:
# too small or too large
return []
end = begin + _searchsorted(key_slice, key[i], side='right')
begin += t
if begin >= len(self.row_index): # greater than all keys
return []
return self.row_index[begin:end]
def range(self, lower, upper, bounds):
'''
Find values in the given range.
Parameters
----------
lower : tuple
Lower search bound
upper : tuple
Upper search bound
bounds : (2,) tuple of bool
Indicates whether the search should be inclusive or
exclusive with respect to the endpoints. The first
argument corresponds to an inclusive lower bound,
and the second argument to an inclusive upper bound.
'''
lower_pos = self.find_pos(lower, 0)
upper_pos = self.find_pos(upper, 0)
if lower_pos == len(self.row_index):
return []
lower_bound = tuple([col[lower_pos] for col in self.cols])
if not bounds[0] and lower_bound == lower:
lower_pos += 1 # data[lower_pos] > lower
# data[lower_pos] >= lower
# data[upper_pos] >= upper
if upper_pos < len(self.row_index):
upper_bound = tuple([col[upper_pos] for col in self.cols])
if not bounds[1] and upper_bound == upper:
upper_pos -= 1 # data[upper_pos] < upper
elif upper_bound > upper:
upper_pos -= 1 # data[upper_pos] <= upper
return self.row_index[lower_pos:upper_pos + 1]
def remove(self, key, data):
'''
Remove the given entry from the sorted array.
Parameters
----------
key : tuple
Column values
data : int
Row number
Returns
-------
successful : bool
Whether the entry was successfully removed
'''
pos = self.find_pos(key, data, exact=True)
if pos == -1: # key not found
return False
self.data.remove_row(pos)
keep_mask = np.ones(len(self.row_index), dtype=bool)
keep_mask[pos] = False
self.row_index = self.row_index[keep_mask]
return True
def shift_left(self, row):
'''
Decrement all row numbers greater than the input row.
Parameters
----------
row : int
Input row number
'''
self.row_index[self.row_index > row] -= 1
def shift_right(self, row):
'''
Increment all row numbers greater than or equal to the input row.
Parameters
----------
row : int
Input row number
'''
self.row_index[self.row_index >= row] += 1
def replace_rows(self, row_map):
'''
Replace all rows with the values they map to in the
given dictionary. Any rows not present as keys in
the dictionary will have their entries deleted.
Parameters
----------
row_map : dict
Mapping of row numbers to new row numbers
'''
num_rows = len(row_map)
keep_rows = np.zeros(len(self.row_index), dtype=bool)
tagged = 0
for i, row in enumerate(self.row_index):
if row in row_map:
keep_rows[i] = True
tagged += 1
if tagged == num_rows:
break
self.data = self.data[keep_rows]
self.row_index = np.array(
[row_map[x] for x in self.row_index[keep_rows]])
def items(self):
'''
Retrieve all array items as a list of pairs of the form
[(key, [row 1, row 2, ...]), ...]
'''
array = []
last_key = None
for i, key in enumerate(zip(*self.data.columns.values())):
row = self.row_index[i]
if key == last_key:
array[-1][1].append(row)
else:
last_key = key
array.append((key, [row]))
return array
def sort(self):
'''
Make row order align with key order.
'''
self.row_index = np.arange(len(self.row_index))
def sorted_data(self):
'''
Return rows in sorted order.
'''
return self.row_index
def __getitem__(self, item):
'''
Return a sliced reference to this sorted array.
Parameters
----------
item : slice
Slice to use for referencing
'''
return SortedArray(self.data[item], self.row_index[item])
def __repr__(self):
t = self.data.copy()
t['rows'] = self.row_index
return f'<{self.__class__.__name__} length={len(t)}>\n{t}'
|
96256403fa381e0dcf27947de86da85a4bd9ba0b09218f5e935f8f308e8c00bd | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from importlib import import_module
import re
from copy import deepcopy
from collections import OrderedDict
import numpy as np
from astropy.utils.data_info import MixinInfo
from .column import Column, MaskedColumn
from .table import Table, QTable, has_info_class
from astropy.units.quantity import QuantityInfo
# TODO: some of this might be better done programmatically, through
# code like
# __construct_mixin_classes += tuple(
# f'astropy.coordinates.representation.{cls.__name__}'
# for cls in (list(coorep.REPRESENTATION_CLASSES.values())
# + list(coorep.DIFFERENTIAL_CLASSES.values()))
# if cls.__name__ in coorep.__all__)
# However, to avoid very hard to track import issues, the definition
# should then be done at the point where it is actually needed,
# using local imports. See also
# https://github.com/astropy/astropy/pull/10210#discussion_r419087286
__construct_mixin_classes = (
'astropy.time.core.Time',
'astropy.time.core.TimeDelta',
'astropy.units.quantity.Quantity',
'astropy.units.function.logarithmic.Magnitude',
'astropy.units.function.logarithmic.Decibel',
'astropy.units.function.logarithmic.Dex',
'astropy.coordinates.angles.Latitude',
'astropy.coordinates.angles.Longitude',
'astropy.coordinates.angles.Angle',
'astropy.coordinates.distances.Distance',
'astropy.coordinates.earth.EarthLocation',
'astropy.coordinates.sky_coordinate.SkyCoord',
'astropy.table.ndarray_mixin.NdarrayMixin',
'astropy.table.table_helpers.ArrayWrapper',
'astropy.table.column.MaskedColumn',
'astropy.coordinates.representation.CartesianRepresentation',
'astropy.coordinates.representation.UnitSphericalRepresentation',
'astropy.coordinates.representation.RadialRepresentation',
'astropy.coordinates.representation.SphericalRepresentation',
'astropy.coordinates.representation.PhysicsSphericalRepresentation',
'astropy.coordinates.representation.CylindricalRepresentation',
'astropy.coordinates.representation.CartesianDifferential',
'astropy.coordinates.representation.UnitSphericalDifferential',
'astropy.coordinates.representation.SphericalDifferential',
'astropy.coordinates.representation.UnitSphericalCosLatDifferential',
'astropy.coordinates.representation.SphericalCosLatDifferential',
'astropy.coordinates.representation.RadialDifferential',
'astropy.coordinates.representation.PhysicsSphericalDifferential',
'astropy.coordinates.representation.CylindricalDifferential',
'astropy.utils.masked.core.MaskedNDArray',
)
class SerializedColumn(dict):
"""
Subclass of dict that is a used in the representation to contain the name
(and possible other info) for a mixin attribute (either primary data or an
array-like attribute) that is serialized as a column in the table.
Normally contains the single key ``name`` with the name of the column in the
table.
"""
pass
def _represent_mixin_as_column(col, name, new_cols, mixin_cols,
exclude_classes=()):
"""Carry out processing needed to serialize ``col`` in an output table
consisting purely of plain ``Column`` or ``MaskedColumn`` columns. This
relies on the object determine if any transformation is required and may
depend on the ``serialize_method`` and ``serialize_context`` context
variables. For instance a ``MaskedColumn`` may be stored directly to
FITS, but can also be serialized as separate data and mask columns.
This function builds up a list of plain columns in the ``new_cols`` arg (which
is passed as a persistent list). This includes both plain columns from the
original table and plain columns that represent data from serialized columns
(e.g. ``jd1`` and ``jd2`` arrays from a ``Time`` column).
For serialized columns the ``mixin_cols`` dict is updated with required
attributes and information to subsequently reconstruct the table.
Table mixin columns are always serialized and get represented by one
or more data columns. In earlier versions of the code *only* mixin
columns were serialized, hence the use within this code of "mixin"
to imply serialization. Starting with version 3.1, the non-mixin
``MaskedColumn`` can also be serialized.
"""
obj_attrs = col.info._represent_as_dict()
# If serialization is not required (see function docstring above)
# or explicitly specified as excluded, then treat as a normal column.
if not obj_attrs or col.__class__ in exclude_classes:
new_cols.append(col)
return
# Subtlety here is handling mixin info attributes. The basic list of such
# attributes is: 'name', 'unit', 'dtype', 'format', 'description', 'meta'.
# - name: handled directly [DON'T store]
# - unit: DON'T store if this is a parent attribute
# - dtype: captured in plain Column if relevant [DON'T store]
# - format: possibly irrelevant but settable post-object creation [DO store]
# - description: DO store
# - meta: DO store
info = {}
for attr, nontrivial in (('unit', lambda x: x is not None and x != ''),
('format', lambda x: x is not None),
('description', lambda x: x is not None),
('meta', lambda x: x)):
col_attr = getattr(col.info, attr)
if nontrivial(col_attr):
info[attr] = col_attr
# Find column attributes that have the same length as the column itself.
# These will be stored in the table as new columns (aka "data attributes").
# Examples include SkyCoord.ra (what is typically considered the data and is
# always an array) and Skycoord.obs_time (which can be a scalar or an
# array).
data_attrs = [key for key, value in obj_attrs.items() if
getattr(value, 'shape', ())[:1] == col.shape[:1]]
for data_attr in data_attrs:
data = obj_attrs[data_attr]
# New column name combines the old name and attribute
# (e.g. skycoord.ra, skycoord.dec).unless it is the primary data
# attribute for the column (e.g. value for Quantity or data for
# MaskedColumn). For primary data, we attempt to store any info on
# the format, etc., on the column, but not for ancillary data (e.g.,
# no sense to use a float format for a mask).
is_primary = data_attr == col.info._represent_as_dict_primary_data
if is_primary:
new_name = name
new_info = info
else:
new_name = name + '.' + data_attr
new_info = {}
if not has_info_class(data, MixinInfo):
col_cls = MaskedColumn if (hasattr(data, 'mask')
and np.any(data.mask)) else Column
new_cols.append(col_cls(data, name=new_name, **new_info))
obj_attrs[data_attr] = SerializedColumn({'name': new_name})
if is_primary:
# Don't store info in the __serialized_columns__ dict for this column
# since this is redundant with info stored on the new column.
info = {}
else:
# recurse. This will define obj_attrs[new_name].
_represent_mixin_as_column(data, new_name, new_cols, obj_attrs)
obj_attrs[data_attr] = SerializedColumn(obj_attrs.pop(new_name))
# Strip out from info any attributes defined by the parent,
# and store whatever remains.
for attr in col.info.attrs_from_parent:
if attr in info:
del info[attr]
if info:
obj_attrs['__info__'] = info
# Store the fully qualified class name
obj_attrs.setdefault('__class__',
col.__module__ + '.' + col.__class__.__name__)
mixin_cols[name] = obj_attrs
def represent_mixins_as_columns(tbl, exclude_classes=()):
"""Represent input Table ``tbl`` using only `~astropy.table.Column`
or `~astropy.table.MaskedColumn` objects.
This function represents any mixin columns like `~astropy.time.Time` in
``tbl`` to one or more plain ``~astropy.table.Column`` objects and returns
a new Table. A single mixin column may be split into multiple column
components as needed for fully representing the column. This includes the
possibility of recursive splitting, as shown in the example below. The
new column names are formed as ``<column_name>.<component>``, e.g.
``sc.ra`` for a `~astropy.coordinates.SkyCoord` column named ``sc``.
In addition to splitting columns, this function updates the table ``meta``
dictionary to include a dict named ``__serialized_columns__`` which provides
additional information needed to construct the original mixin columns from
the split columns.
This function is used by astropy I/O when writing tables to ECSV, FITS,
HDF5 formats.
Note that if the table does not include any mixin columns then the original
table is returned with no update to ``meta``.
Parameters
----------
tbl : `~astropy.table.Table` or subclass
Table to represent mixins as Columns
exclude_classes : tuple of class
Exclude any mixin columns which are instannces of any classes in the tuple
Returns
-------
tbl : `~astropy.table.Table`
New Table with updated columns, or else the original input ``tbl``
Examples
--------
>>> from astropy.table import Table, represent_mixins_as_columns
>>> from astropy.time import Time
>>> from astropy.coordinates import SkyCoord
>>> x = [100.0, 200.0]
>>> obstime = Time([1999.0, 2000.0], format='jyear')
>>> sc = SkyCoord([1, 2], [3, 4], unit='deg', obstime=obstime)
>>> tbl = Table([sc, x], names=['sc', 'x'])
>>> represent_mixins_as_columns(tbl)
<Table length=2>
sc.ra sc.dec sc.obstime.jd1 sc.obstime.jd2 x
deg deg
float64 float64 float64 float64 float64
------- ------- -------------- -------------- -------
1.0 3.0 2451180.0 -0.25 100.0
2.0 4.0 2451545.0 0.0 200.0
"""
# Dict of metadata for serializing each column, keyed by column name.
# Gets filled in place by _represent_mixin_as_column().
mixin_cols = {}
# List of columns for the output table. For plain Column objects
# this will just be the original column object.
new_cols = []
# Go through table columns and represent each column as one or more
# plain Column objects (in new_cols) + metadata (in mixin_cols).
for col in tbl.itercols():
_represent_mixin_as_column(col, col.info.name, new_cols, mixin_cols,
exclude_classes=exclude_classes)
# If no metadata was created then just return the original table.
if mixin_cols:
meta = deepcopy(tbl.meta)
meta['__serialized_columns__'] = mixin_cols
out = Table(new_cols, meta=meta, copy=False)
else:
out = tbl
for col in out.itercols():
if not isinstance(col, Column) and col.__class__ not in exclude_classes:
# This catches columns for which info has not been set up right and
# therefore were not converted. See the corresponding test in
# test_mixin.py for an example.
raise TypeError(
'failed to represent column '
f'{col.info.name!r} ({col.__class__.__name__}) as one '
'or more Column subclasses. This looks like a mixin class '
'that does not have the correct _represent_as_dict() method '
'in the class `info` attribute.')
return out
def _construct_mixin_from_obj_attrs_and_info(obj_attrs, info):
cls_full_name = obj_attrs.pop('__class__')
# If this is a supported class then import the class and run
# the _construct_from_col method. Prevent accidentally running
# untrusted code by only importing known astropy classes.
if cls_full_name not in __construct_mixin_classes:
raise ValueError(f'unsupported class for construct {cls_full_name}')
mod_name, cls_name = re.match(r'(.+)\.(\w+)', cls_full_name).groups()
module = import_module(mod_name)
cls = getattr(module, cls_name)
for attr, value in info.items():
if attr in cls.info.attrs_from_parent:
obj_attrs[attr] = value
mixin = cls.info._construct_from_dict(obj_attrs)
for attr, value in info.items():
if attr not in obj_attrs:
setattr(mixin.info, attr, value)
return mixin
class _TableLite(OrderedDict):
"""
Minimal table-like object for _construct_mixin_from_columns. This allows
manipulating the object like a Table but without the actual overhead
for a full Table.
More pressing, there is an issue with constructing MaskedColumn, where the
encoded Column components (data, mask) are turned into a MaskedColumn.
When this happens in a real table then all other columns are immediately
Masked and a warning is issued. This is not desirable.
"""
def add_column(self, col, index=0):
colnames = self.colnames
self[col.info.name] = col
for ii, name in enumerate(colnames):
if ii >= index:
self.move_to_end(name)
@property
def colnames(self):
return list(self.keys())
def itercols(self):
return self.values()
def _construct_mixin_from_columns(new_name, obj_attrs, out):
data_attrs_map = {}
for name, val in obj_attrs.items():
if isinstance(val, SerializedColumn):
if 'name' in val:
data_attrs_map[val['name']] = name
else:
out_name = f'{new_name}.{name}'
_construct_mixin_from_columns(out_name, val, out)
data_attrs_map[out_name] = name
for name in data_attrs_map.values():
del obj_attrs[name]
# Get the index where to add new column
idx = min(out.colnames.index(name) for name in data_attrs_map)
# Name is the column name in the table (e.g. "coord.ra") and
# data_attr is the object attribute name (e.g. "ra"). A different
# example would be a formatted time object that would have (e.g.)
# "time_col" and "value", respectively.
for name, data_attr in data_attrs_map.items():
obj_attrs[data_attr] = out[name]
del out[name]
info = obj_attrs.pop('__info__', {})
if len(data_attrs_map) == 1:
# col is the first and only serialized column; in that case, use info
# stored on the column. First step is to get that first column which
# has been moved from `out` to `obj_attrs` above.
data_attr = next(iter(data_attrs_map.values()))
col = obj_attrs[data_attr]
# Now copy the relevant attributes
for attr, nontrivial in (('unit', lambda x: x not in (None, '')),
('format', lambda x: x is not None),
('description', lambda x: x is not None),
('meta', lambda x: x)):
col_attr = getattr(col.info, attr)
if nontrivial(col_attr):
info[attr] = col_attr
info['name'] = new_name
col = _construct_mixin_from_obj_attrs_and_info(obj_attrs, info)
out.add_column(col, index=idx)
def _construct_mixins_from_columns(tbl):
if '__serialized_columns__' not in tbl.meta:
return tbl
meta = tbl.meta.copy()
mixin_cols = meta.pop('__serialized_columns__')
out = _TableLite(tbl.columns)
for new_name, obj_attrs in mixin_cols.items():
_construct_mixin_from_columns(new_name, obj_attrs, out)
# If no quantity subclasses are in the output then output as Table.
# For instance ascii.read(file, format='ecsv') doesn't specify an
# output class and should return the minimal table class that
# represents the table file.
has_quantities = any(isinstance(col.info, QuantityInfo)
for col in out.itercols())
out_cls = QTable if has_quantities else Table
return out_cls(list(out.values()), names=out.colnames, copy=False, meta=meta)
|
74d945b8e25af2d5a74510c2585b7ad8c67bb880a8881cf09cb305eec7e60ff8 | # -*- coding: utf-8 -*-
ascii_coded = ('Ò♙♙♙♙♙♙♙♙♌♐♐♌♙♙♙♙♙♙♌♌♙♙Ò♙♙♙♙♙♙♙♘♐♐♐♈♙♙♙♙♙♌♐♐♐♔Ò♙♙♌♈♙♙♌♐♈♈♙♙♙♙♙♙♙♙♈♐♐♙Ò♙♐♙♙♙♐♐♙♙♙'
'♙♙♙♙♙♙♙♙♙♙♙♙Ò♐♔♙♙♘♐♐♙♙♌♐♐♔♙♙♌♌♌♙♙♙♌Ò♐♐♙♙♘♐♐♌♙♈♐♈♙♙♙♈♐♐♙♙♘♔Ò♐♐♌♙♘♐♐♐♌♌♙♙♌♌♌♙♈♈♙♌♐'
'♐Ò♘♐♐♐♌♐♐♐♐♐♐♌♙♈♙♌♐♐♐♐♐♔Ò♘♐♐♐♐♐♐♐♐♐♐♐♐♈♈♐♐♐♐♐♐♙Ò♙♘♐♐♐♐♈♐♐♐♐♐♐♙♙♐♐♐♐♐♙♙Ò♙♙♙♈♈♈♙♙♐'
'♐♐♐♐♔♙♐♐♐♐♈♙♙Ò♙♙♙♙♙♙♙♙♙♈♈♐♐♐♙♈♈♈♙♙♙♙Ò')
ascii_uncoded = ''.join([chr(ord(c) - 200) for c in ascii_coded])
url = 'https://media.giphy.com/media/e24Q8FKE2mxRS/giphy.gif'
message_coded = 'ĘĩĶĬĩĻ÷ĜĩĪĴĭèıĶļĭĺĩīļıķĶ'
message_uncoded = ''.join([chr(ord(c) - 200) for c in message_coded])
try:
from IPython import display
html = display.Image(url=url)._repr_html_()
class HTMLWithBackup(display.HTML):
def __init__(self, data, backup_text):
super().__init__(data)
self.backup_text = backup_text
def __repr__(self):
if self.backup_text is None:
return super().__repr__()
else:
return self.backup_text
dhtml = HTMLWithBackup(html, ascii_uncoded)
display.display(dhtml)
except ImportError:
print(ascii_uncoded)
except (UnicodeEncodeError, SyntaxError):
pass
|
c7a43c550ca093c289825e09947505b08851e8ed59f33d5e4b4baa243433acc7 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Helper functions for table development, mostly creating useful
tables for testing.
"""
from itertools import cycle
import string
import numpy as np
from .table import Table, Column
from astropy.utils.data_info import ParentDtypeInfo
class TimingTables:
"""
Object which contains two tables and various other attributes that
are useful for timing and other API tests.
"""
def __init__(self, size=1000, masked=False):
self.masked = masked
# Initialize table
self.table = Table(masked=self.masked)
# Create column with mixed types
np.random.seed(12345)
self.table['i'] = np.arange(size)
self.table['a'] = np.random.random(size) # float
self.table['b'] = np.random.random(size) > 0.5 # bool
self.table['c'] = np.random.random((size, 10)) # 2d column
self.table['d'] = np.random.choice(np.array(list(string.ascii_letters)), size)
self.extra_row = {'a': 1.2, 'b': True, 'c': np.repeat(1, 10), 'd': 'Z'}
self.extra_column = np.random.randint(0, 100, size)
self.row_indices = np.where(self.table['a'] > 0.9)[0]
self.table_grouped = self.table.group_by('d')
# Another table for testing joining
self.other_table = Table(masked=self.masked)
self.other_table['i'] = np.arange(1, size, 3)
self.other_table['f'] = np.random.random()
self.other_table.sort('f')
# Another table for testing hstack
self.other_table_2 = Table(masked=self.masked)
self.other_table_2['g'] = np.random.random(size)
self.other_table_2['h'] = np.random.random((size, 10))
self.bool_mask = self.table['a'] > 0.6
def simple_table(size=3, cols=None, kinds='ifS', masked=False):
"""
Return a simple table for testing.
Example
--------
::
>>> from astropy.table.table_helpers import simple_table
>>> print(simple_table(3, 6, masked=True, kinds='ifOS'))
a b c d e f
--- --- -------- --- --- ---
-- 1.0 {'c': 2} -- 5 5.0
2 2.0 -- e 6 --
3 -- {'e': 4} f -- 7.0
Parameters
----------
size : int
Number of table rows
cols : int, optional
Number of table columns. Defaults to number of kinds.
kinds : str
String consisting of the column dtype.kinds. This string
will be cycled through to generate the column dtype.
The allowed values are 'i', 'f', 'S', 'O'.
Returns
-------
out : `Table`
New table with appropriate characteristics
"""
if cols is None:
cols = len(kinds)
if cols > 26:
raise ValueError("Max 26 columns in SimpleTable")
columns = []
names = [chr(ord('a') + ii) for ii in range(cols)]
letters = np.array([c for c in string.ascii_letters])
for jj, kind in zip(range(cols), cycle(kinds)):
if kind == 'i':
data = np.arange(1, size + 1, dtype=np.int64) + jj
elif kind == 'f':
data = np.arange(size, dtype=np.float64) + jj
elif kind == 'S':
indices = (np.arange(size) + jj) % len(letters)
data = letters[indices]
elif kind == 'O':
indices = (np.arange(size) + jj) % len(letters)
vals = letters[indices]
data = [{val: index} for val, index in zip(vals, indices)]
else:
raise ValueError('Unknown data kind')
columns.append(Column(data))
table = Table(columns, names=names, masked=masked)
if masked:
for ii, col in enumerate(table.columns.values()):
mask = np.array((np.arange(size) + ii) % 3, dtype=bool)
col.mask = ~mask
return table
def complex_table():
"""
Return a masked table from the io.votable test set that has a wide variety
of stressing types.
"""
from astropy.utils.data import get_pkg_data_filename
from astropy.io.votable.table import parse
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
votable = parse(get_pkg_data_filename('../io/votable/tests/data/regression.xml'),
pedantic=False)
first_table = votable.get_first_table()
table = first_table.to_table()
return table
class ArrayWrapperInfo(ParentDtypeInfo):
_represent_as_dict_primary_data = 'data'
def _represent_as_dict(self):
"""Represent Column as a dict that can be serialized."""
col = self._parent
out = {'data': col.data}
return out
def _construct_from_dict(self, map):
"""Construct Column from ``map``."""
data = map.pop('data')
out = self._parent_cls(data, **map)
return out
class ArrayWrapper:
"""
Minimal mixin using a simple wrapper around a numpy array
TODO: think about the future of this class as it is mostly for demonstration
purposes (of the mixin protocol). Consider taking it out of core and putting
it into a tutorial. One advantage of having this in core is that it is
getting tested in the mixin testing though it doesn't work for multidim
data.
"""
info = ArrayWrapperInfo()
def __init__(self, data):
self.data = np.array(data)
if 'info' in getattr(data, '__dict__', ()):
self.info = data.info
def __getitem__(self, item):
if isinstance(item, (int, np.integer)):
out = self.data[item]
else:
out = self.__class__(self.data[item])
if 'info' in self.__dict__:
out.info = self.info
return out
def __setitem__(self, item, value):
self.data[item] = value
def __len__(self):
return len(self.data)
def __eq__(self, other):
"""Minimal equality testing, mostly for mixin unit tests"""
if isinstance(other, ArrayWrapper):
return self.data == other.data
else:
return self.data == other
@property
def dtype(self):
return self.data.dtype
@property
def shape(self):
return self.data.shape
def __repr__(self):
return f"<{self.__class__.__name__} name='{self.info.name}' data={self.data}>"
|
3803d4f018c9f128ab44a84800013570dd6f86430357447cbe49546064f5766e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
The SCEngine class uses the ``sortedcontainers`` package to implement an
Index engine for Tables.
"""
from collections import OrderedDict
from itertools import starmap
from astropy.utils.compat.optional_deps import HAS_SORTEDCONTAINERS
if HAS_SORTEDCONTAINERS:
from sortedcontainers import SortedList
class Node(object):
__slots__ = ('key', 'value')
def __init__(self, key, value):
self.key = key
self.value = value
def __lt__(self, other):
if other.__class__ is Node:
return (self.key, self.value) < (other.key, other.value)
return self.key < other
def __le__(self, other):
if other.__class__ is Node:
return (self.key, self.value) <= (other.key, other.value)
return self.key <= other
def __eq__(self, other):
if other.__class__ is Node:
return (self.key, self.value) == (other.key, other.value)
return self.key == other
def __ne__(self, other):
if other.__class__ is Node:
return (self.key, self.value) != (other.key, other.value)
return self.key != other
def __gt__(self, other):
if other.__class__ is Node:
return (self.key, self.value) > (other.key, other.value)
return self.key > other
def __ge__(self, other):
if other.__class__ is Node:
return (self.key, self.value) >= (other.key, other.value)
return self.key >= other
__hash__ = None
def __repr__(self):
return f'Node({self.key!r}, {self.value!r})'
class SCEngine:
'''
Fast tree-based implementation for indexing, using the
``sortedcontainers`` package.
Parameters
----------
data : Table
Sorted columns of the original table
row_index : Column object
Row numbers corresponding to data columns
unique : bool
Whether the values of the index must be unique.
Defaults to False.
'''
def __init__(self, data, row_index, unique=False):
node_keys = map(tuple, data)
self._nodes = SortedList(starmap(Node, zip(node_keys, row_index)))
self._unique = unique
def add(self, key, value):
'''
Add a key, value pair.
'''
if self._unique and (key in self._nodes):
message = f'duplicate {key!r} in unique index'
raise ValueError(message)
self._nodes.add(Node(key, value))
def find(self, key):
'''
Find rows corresponding to the given key.
'''
return [node.value for node in self._nodes.irange(key, key)]
def remove(self, key, data=None):
'''
Remove data from the given key.
'''
if data is not None:
item = Node(key, data)
try:
self._nodes.remove(item)
except ValueError:
return False
return True
items = list(self._nodes.irange(key, key))
for item in items:
self._nodes.remove(item)
return bool(items)
def shift_left(self, row):
'''
Decrement rows larger than the given row.
'''
for node in self._nodes:
if node.value > row:
node.value -= 1
def shift_right(self, row):
'''
Increment rows greater than or equal to the given row.
'''
for node in self._nodes:
if node.value >= row:
node.value += 1
def items(self):
'''
Return a list of key, data tuples.
'''
result = OrderedDict()
for node in self._nodes:
if node.key in result:
result[node.key].append(node.value)
else:
result[node.key] = [node.value]
return result.items()
def sort(self):
'''
Make row order align with key order.
'''
for index, node in enumerate(self._nodes):
node.value = index
def sorted_data(self):
'''
Return a list of rows in order sorted by key.
'''
return [node.value for node in self._nodes]
def range(self, lower, upper, bounds=(True, True)):
'''
Return row values in the given range.
'''
iterator = self._nodes.irange(lower, upper, bounds)
return [node.value for node in iterator]
def replace_rows(self, row_map):
'''
Replace rows with the values in row_map.
'''
nodes = [node for node in self._nodes if node.value in row_map]
for node in nodes:
node.value = row_map[node.value]
self._nodes.clear()
self._nodes.update(nodes)
def __repr__(self):
if len(self._nodes) > 6:
nodes = list(self._nodes[:3]) + ['...'] + list(self._nodes[-3:])
else:
nodes = self._nodes
nodes_str = ', '.join(str(node) for node in nodes)
return f'<{self.__class__.__name__} nodes={nodes_str}>'
|
682e3d8156b7564494d9716af5da937fdb12074a6c1a7a91053209788ed2b994 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from astropy.utils.data_info import ParentDtypeInfo
class NdarrayMixinInfo(ParentDtypeInfo):
_represent_as_dict_primary_data = 'data'
def _represent_as_dict(self):
"""Represent Column as a dict that can be serialized."""
col = self._parent
out = {'data': col.view(np.ndarray)}
return out
def _construct_from_dict(self, map):
"""Construct Column from ``map``."""
data = map.pop('data')
out = self._parent_cls(data, **map)
return out
class NdarrayMixin(np.ndarray):
"""
Mixin column class to allow storage of arbitrary numpy
ndarrays within a Table. This is a subclass of numpy.ndarray
and has the same initialization options as ``np.array()``.
"""
info = NdarrayMixinInfo()
def __new__(cls, obj, *args, **kwargs):
self = np.array(obj, *args, **kwargs).view(cls)
if 'info' in getattr(obj, '__dict__', ()):
self.info = obj.info
return self
def __array_finalize__(self, obj):
if obj is None:
return
if callable(super().__array_finalize__):
super().__array_finalize__(obj)
# Self was created from template (e.g. obj[slice] or (obj * 2))
# or viewcast e.g. obj.view(Column). In either case we want to
# init Column attributes for self from obj if possible.
if 'info' in getattr(obj, '__dict__', ()):
self.info = obj.info
def __reduce__(self):
# patch to pickle NdArrayMixin objects (ndarray subclasses), see
# http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html
object_state = list(super().__reduce__())
object_state[2] = (object_state[2], self.__dict__)
return tuple(object_state)
def __setstate__(self, state):
# patch to unpickle NdarrayMixin objects (ndarray subclasses), see
# http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html
nd_state, own_state = state
super().__setstate__(nd_state)
self.__dict__.update(own_state)
|
bb3a1d5060a3ff11882fcb64bfcb2ffc30c582bf7acef0c413a695b2f26886c4 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import math
import numpy as np
from astropy.modeling import models
from astropy.modeling.core import Fittable1DModel, Fittable2DModel
from astropy.utils.decorators import deprecated
from .core import Kernel, Kernel1D, Kernel2D
from .utils import KernelSizeError, has_even_axis, raise_even_kernel_exception
__all__ = ['Gaussian1DKernel', 'Gaussian2DKernel', 'CustomKernel',
'Box1DKernel', 'Box2DKernel', 'Tophat2DKernel',
'Trapezoid1DKernel', 'RickerWavelet1DKernel', 'RickerWavelet2DKernel',
'AiryDisk2DKernel', 'Moffat2DKernel', 'Model1DKernel',
'Model2DKernel', 'TrapezoidDisk2DKernel', 'Ring2DKernel']
def _round_up_to_odd_integer(value):
i = math.ceil(value)
if i % 2 == 0:
return i + 1
else:
return i
class Gaussian1DKernel(Kernel1D):
"""
1D Gaussian filter kernel.
The Gaussian filter is a filter with great smoothing properties. It is
isotropic and does not produce artifacts.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
stddev : number
Standard deviation of the Gaussian kernel.
x_size : int, optional
Size of the kernel array. Default = ⌊8*stddev+1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin. Very slow.
factor : number, optional
Factor of oversampling. Default factor = 10. If the factor
is too large, evaluation can be very slow.
See Also
--------
Box1DKernel, Trapezoid1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Gaussian1DKernel
gauss_1D_kernel = Gaussian1DKernel(10)
plt.plot(gauss_1D_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_separable = True
_is_bool = False
def __init__(self, stddev, **kwargs):
self._model = models.Gaussian1D(1. / (np.sqrt(2 * np.pi) * stddev),
0, stddev)
self._default_size = _round_up_to_odd_integer(8 * stddev)
super().__init__(**kwargs)
self._truncation = np.abs(1. - self._array.sum())
class Gaussian2DKernel(Kernel2D):
"""
2D Gaussian filter kernel.
The Gaussian filter is a filter with great smoothing properties. It is
isotropic and does not produce artifacts.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
x_stddev : float
Standard deviation of the Gaussian in x before rotating by theta.
y_stddev : float
Standard deviation of the Gaussian in y before rotating by theta.
theta : float or `~astropy.units.Quantity` ['angle']
Rotation angle. If passed as a float, it is assumed to be in radians.
The rotation angle increases counterclockwise.
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*stddev + 1⌋.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*stddev + 1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Gaussian2DKernel
gaussian_2D_kernel = Gaussian2DKernel(10)
plt.imshow(gaussian_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_separable = True
_is_bool = False
def __init__(self, x_stddev, y_stddev=None, theta=0.0, **kwargs):
if y_stddev is None:
y_stddev = x_stddev
self._model = models.Gaussian2D(1. / (2 * np.pi * x_stddev * y_stddev),
0, 0, x_stddev=x_stddev,
y_stddev=y_stddev, theta=theta)
self._default_size = _round_up_to_odd_integer(
8 * np.max([x_stddev, y_stddev]))
super().__init__(**kwargs)
self._truncation = np.abs(1. - self._array.sum())
class Box1DKernel(Kernel1D):
"""
1D Box filter kernel.
The Box filter or running mean is a smoothing filter. It is not isotropic
and can produce artifacts when applied repeatedly to the same data.
The generated kernel is normalized so that it integrates to 1.
By default the Box kernel uses the ``linear_interp`` discretization mode,
which allows non-shifting, even-sized kernels. This is achieved by
weighting the edge pixels with 1/2. E.g a Box kernel with an effective
smoothing of 4 pixel would have the following array: [0.5, 1, 1, 1, 0.5].
Parameters
----------
width : number
Width of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center'
Discretize model by taking the value
at the center of the bin.
* 'linear_interp' (default)
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian1DKernel, Trapezoid1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response function:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Box1DKernel
box_1D_kernel = Box1DKernel(9)
plt.plot(box_1D_kernel, drawstyle='steps')
plt.xlim(-1, 9)
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_separable = True
_is_bool = True
def __init__(self, width, **kwargs):
self._model = models.Box1D(1. / width, 0, width)
self._default_size = _round_up_to_odd_integer(width)
kwargs['mode'] = 'linear_interp'
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class Box2DKernel(Kernel2D):
"""
2D Box filter kernel.
The Box filter or running mean is a smoothing filter. It is not isotropic
and can produce artifacts when applied repeatedly to the same data.
The generated kernel is normalized so that it integrates to 1.
By default the Box kernel uses the ``linear_interp`` discretization mode,
which allows non-shifting, even-sized kernels. This is achieved by
weighting the edge pixels with 1/2.
Parameters
----------
width : number
Width of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center'
Discretize model by taking the value
at the center of the bin.
* 'linear_interp' (default)
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Tophat2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Box2DKernel
box_2D_kernel = Box2DKernel(9)
plt.imshow(box_2D_kernel, interpolation='none', origin='lower',
vmin=0.0, vmax=0.015)
plt.xlim(-1, 9)
plt.ylim(-1, 9)
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_separable = True
_is_bool = True
def __init__(self, width, **kwargs):
self._model = models.Box2D(1. / width ** 2, 0, 0, width, width)
self._default_size = _round_up_to_odd_integer(width)
kwargs['mode'] = 'linear_interp'
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class Tophat2DKernel(Kernel2D):
"""
2D Tophat filter kernel.
The Tophat filter is an isotropic smoothing filter. It can produce
artifacts when applied repeatedly on the same data.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
radius : int
Radius of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, RickerWavelet2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Tophat2DKernel
tophat_2D_kernel = Tophat2DKernel(40)
plt.imshow(tophat_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
def __init__(self, radius, **kwargs):
self._model = models.Disk2D(1. / (np.pi * radius ** 2), 0, 0, radius)
self._default_size = _round_up_to_odd_integer(2 * radius)
super().__init__(**kwargs)
self._truncation = 0
class Ring2DKernel(Kernel2D):
"""
2D Ring filter kernel.
The Ring filter kernel is the difference between two Tophat kernels of
different width. This kernel is useful for, e.g., background estimation.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
radius_in : number
Inner radius of the ring kernel.
width : number
Width of the ring kernel.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Ring2DKernel
ring_2D_kernel = Ring2DKernel(9, 8)
plt.imshow(ring_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
def __init__(self, radius_in, width, **kwargs):
radius_out = radius_in + width
self._model = models.Ring2D(1. / (np.pi * (radius_out ** 2 - radius_in ** 2)),
0, 0, radius_in, width)
self._default_size = _round_up_to_odd_integer(2 * radius_out)
super().__init__(**kwargs)
self._truncation = 0
class Trapezoid1DKernel(Kernel1D):
"""
1D trapezoid kernel.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
width : number
Width of the filter kernel, defined as the width of the constant part,
before it begins to slope down.
slope : number
Slope of the filter kernel's tails
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box1DKernel, Gaussian1DKernel, RickerWavelet1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Trapezoid1DKernel
trapezoid_1D_kernel = Trapezoid1DKernel(17, slope=0.2)
plt.plot(trapezoid_1D_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('amplitude')
plt.xlim(-1, 28)
plt.show()
"""
_is_bool = False
def __init__(self, width, slope=1., **kwargs):
self._model = models.Trapezoid1D(1, 0, width, slope)
self._default_size = _round_up_to_odd_integer(width + 2. / slope)
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class TrapezoidDisk2DKernel(Kernel2D):
"""
2D trapezoid kernel.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
radius : number
Width of the filter kernel, defined as the width of the constant part,
before it begins to slope down.
slope : number
Slope of the filter kernel's tails
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import TrapezoidDisk2DKernel
trapezoid_2D_kernel = TrapezoidDisk2DKernel(20, slope=0.2)
plt.imshow(trapezoid_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, radius, slope=1., **kwargs):
self._model = models.TrapezoidDisk2D(1, 0, 0, radius, slope)
self._default_size = _round_up_to_odd_integer(2 * radius + 2. / slope)
super().__init__(**kwargs)
self._truncation = 0
self.normalize()
class RickerWavelet1DKernel(Kernel1D):
"""
1D Ricker wavelet filter kernel (sometimes known as a "Mexican Hat"
kernel).
The Ricker wavelet, or inverted Gaussian-Laplace filter, is a
bandpass filter. It smooths the data and removes slowly varying
or constant structures (e.g. Background). It is useful for peak or
multi-scale detection.
This kernel is derived from a normalized Gaussian function, by
computing the second derivative. This results in an amplitude
at the kernels center of 1. / (sqrt(2 * pi) * width ** 3). The
normalization is the same as for `scipy.ndimage.gaussian_laplace`,
except for a minus sign.
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this kernel.
Parameters
----------
width : number
Width of the filter kernel, defined as the standard deviation
of the Gaussian function from which it is derived.
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Box1DKernel, Gaussian1DKernel, Trapezoid1DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import RickerWavelet1DKernel
ricker_1d_kernel = RickerWavelet1DKernel(10)
plt.plot(ricker_1d_kernel, drawstyle='steps')
plt.xlabel('x [pixels]')
plt.ylabel('value')
plt.show()
"""
_is_bool = True
def __init__(self, width, **kwargs):
amplitude = 1.0 / (np.sqrt(2 * np.pi) * width ** 3)
self._model = models.RickerWavelet1D(amplitude, 0, width)
self._default_size = _round_up_to_odd_integer(8 * width)
super().__init__(**kwargs)
self._truncation = np.abs(self._array.sum() / self._array.size)
class RickerWavelet2DKernel(Kernel2D):
"""
2D Ricker wavelet filter kernel (sometimes known as a "Mexican Hat"
kernel).
The Ricker wavelet, or inverted Gaussian-Laplace filter, is a
bandpass filter. It smooths the data and removes slowly varying
or constant structures (e.g. Background). It is useful for peak or
multi-scale detection.
This kernel is derived from a normalized Gaussian function, by
computing the second derivative. This results in an amplitude
at the kernels center of 1. / (pi * width ** 4). The normalization
is the same as for `scipy.ndimage.gaussian_laplace`, except
for a minus sign.
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this kernel.
Parameters
----------
width : number
Width of the filter kernel, defined as the standard deviation
of the Gaussian function from which it is derived.
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*width +1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, Ring2DKernel,
TrapezoidDisk2DKernel, AiryDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import RickerWavelet2DKernel
ricker_2d_kernel = RickerWavelet2DKernel(10)
plt.imshow(ricker_2d_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, width, **kwargs):
amplitude = 1.0 / (np.pi * width ** 4)
self._model = models.RickerWavelet2D(amplitude, 0, 0, width)
self._default_size = _round_up_to_odd_integer(8 * width)
super().__init__(**kwargs)
self._truncation = np.abs(self._array.sum() / self._array.size)
class AiryDisk2DKernel(Kernel2D):
"""
2D Airy disk kernel.
This kernel models the diffraction pattern of a circular aperture.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
radius : float
The radius of the Airy disk kernel (radius of the first zero).
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*radius + 1⌋.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*radius + 1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, TrapezoidDisk2DKernel, Moffat2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import AiryDisk2DKernel
airydisk_2D_kernel = AiryDisk2DKernel(10)
plt.imshow(airydisk_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, radius, **kwargs):
self._model = models.AiryDisk2D(1, 0, 0, radius)
self._default_size = _round_up_to_odd_integer(8 * radius)
super().__init__(**kwargs)
self.normalize()
self._truncation = None
class Moffat2DKernel(Kernel2D):
"""
2D Moffat kernel.
This kernel is a typical model for a seeing limited PSF.
The generated kernel is normalized so that it integrates to 1.
Parameters
----------
gamma : float
Core width of the Moffat model.
alpha : float
Power index of the Moffat model.
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*radius + 1⌋.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*radius + 1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
See Also
--------
Gaussian2DKernel, Box2DKernel, Tophat2DKernel, RickerWavelet2DKernel,
Ring2DKernel, TrapezoidDisk2DKernel, AiryDisk2DKernel
Examples
--------
Kernel response:
.. plot::
:include-source:
import matplotlib.pyplot as plt
from astropy.convolution import Moffat2DKernel
moffat_2D_kernel = Moffat2DKernel(3, 2)
plt.imshow(moffat_2D_kernel, interpolation='none', origin='lower')
plt.xlabel('x [pixels]')
plt.ylabel('y [pixels]')
plt.colorbar()
plt.show()
"""
_is_bool = False
def __init__(self, gamma, alpha, **kwargs):
# Compute amplitude, from
# https://en.wikipedia.org/wiki/Moffat_distribution
amplitude = (alpha - 1.0) / (np.pi * gamma * gamma)
self._model = models.Moffat2D(amplitude, 0, 0, gamma, alpha)
self._default_size = _round_up_to_odd_integer(4.0 * self._model.fwhm)
super().__init__(**kwargs)
self.normalize()
self._truncation = None
class Model1DKernel(Kernel1D):
"""
Create kernel from 1D model.
The model has to be centered on x = 0.
Parameters
----------
model : `~astropy.modeling.Fittable1DModel`
Kernel response function model
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
Must be odd.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
Raises
------
TypeError
If model is not an instance of `~astropy.modeling.Fittable1DModel`
See also
--------
Model2DKernel : Create kernel from `~astropy.modeling.Fittable2DModel`
CustomKernel : Create kernel from list or array
Examples
--------
Define a Gaussian1D model:
>>> from astropy.modeling.models import Gaussian1D
>>> from astropy.convolution.kernels import Model1DKernel
>>> gauss = Gaussian1D(1, 0, 2)
And create a custom one dimensional kernel from it:
>>> gauss_kernel = Model1DKernel(gauss, x_size=9)
This kernel can now be used like a usual Astropy kernel.
"""
_separable = False
_is_bool = False
def __init__(self, model, **kwargs):
if isinstance(model, Fittable1DModel):
self._model = model
else:
raise TypeError("Must be Fittable1DModel")
super().__init__(**kwargs)
class Model2DKernel(Kernel2D):
"""
Create kernel from 2D model.
The model has to be centered on x = 0 and y = 0.
Parameters
----------
model : `~astropy.modeling.Fittable2DModel`
Kernel response function model
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*width +1⌋.
Must be odd.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*width +1⌋.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
Raises
------
TypeError
If model is not an instance of `~astropy.modeling.Fittable2DModel`
See also
--------
Model1DKernel : Create kernel from `~astropy.modeling.Fittable1DModel`
CustomKernel : Create kernel from list or array
Examples
--------
Define a Gaussian2D model:
>>> from astropy.modeling.models import Gaussian2D
>>> from astropy.convolution.kernels import Model2DKernel
>>> gauss = Gaussian2D(1, 0, 0, 2, 2)
And create a custom two dimensional kernel from it:
>>> gauss_kernel = Model2DKernel(gauss, x_size=9)
This kernel can now be used like a usual astropy kernel.
"""
_is_bool = False
_separable = False
def __init__(self, model, **kwargs):
self._separable = False
if isinstance(model, Fittable2DModel):
self._model = model
else:
raise TypeError("Must be Fittable2DModel")
super().__init__(**kwargs)
class PSFKernel(Kernel2D):
"""
Initialize filter kernel from astropy PSF instance.
"""
_separable = False
def __init__(self):
raise NotImplementedError('Not yet implemented')
class CustomKernel(Kernel):
"""
Create filter kernel from list or array.
Parameters
----------
array : list or array
Filter kernel array. Size must be odd.
Raises
------
TypeError
If array is not a list or array.
`~astropy.convolution.KernelSizeError`
If array size is even.
See also
--------
Model2DKernel, Model1DKernel
Examples
--------
Define one dimensional array:
>>> from astropy.convolution.kernels import CustomKernel
>>> import numpy as np
>>> array = np.array([1, 2, 3, 2, 1])
>>> kernel = CustomKernel(array)
>>> kernel.dimension
1
Define two dimensional array:
>>> array = np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
>>> kernel = CustomKernel(array)
>>> kernel.dimension
2
"""
def __init__(self, array):
self.array = array
super().__init__(self._array)
@property
def array(self):
"""
Filter kernel array.
"""
return self._array
@array.setter
def array(self, array):
"""
Filter kernel array setter
"""
if isinstance(array, np.ndarray):
self._array = array.astype(np.float64)
elif isinstance(array, list):
self._array = np.array(array, dtype=np.float64)
else:
raise TypeError("Must be list or array.")
# Check if array is odd in all axes
if has_even_axis(self):
raise_even_kernel_exception()
# Check if array is bool
ones = self._array == 1.
zeros = self._array == 0
self._is_bool = bool(np.all(np.logical_or(ones, zeros)))
self._truncation = 0.0
@deprecated('4.0', alternative='RickerWavelet1DKernel')
class MexicanHat1DKernel(RickerWavelet1DKernel):
pass
@deprecated('4.0', alternative='RickerWavelet2DKernel')
class MexicanHat2DKernel(RickerWavelet2DKernel):
pass
|
c169b389cf567fdfb734c0d9e7996b62eef67e6e6afc2671a8bcbe4d7f1c5134 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains the convolution and filter functionalities of astropy.
A few conceptual notes:
A filter kernel is mainly characterized by its response function. In the 1D
case we speak of "impulse response function", in the 2D case we call it "point
spread function". This response function is given for every kernel by an
astropy `FittableModel`, which is evaluated on a grid to obtain a filter array,
which can then be applied to binned data.
The model is centered on the array and should have an amplitude such that the array
integrates to one per default.
Currently only symmetric 2D kernels are supported.
"""
import copy
import warnings
import numpy as np
from astropy.utils.exceptions import AstropyUserWarning
from .utils import add_kernel_arrays_1D, add_kernel_arrays_2D, discretize_model
MAX_NORMALIZATION = 100
__all__ = ['Kernel', 'Kernel1D', 'Kernel2D', 'kernel_arithmetics']
class Kernel:
"""
Convolution kernel base class.
Parameters
----------
array : ndarray
Kernel array.
"""
_separable = False
_is_bool = True
_model = None
def __init__(self, array):
self._array = np.asanyarray(array)
@property
def truncation(self):
"""
Deviation from the normalization to one.
"""
return self._truncation
@property
def is_bool(self):
"""
Indicates if kernel is bool.
If the kernel is bool the multiplication in the convolution could
be omitted, to increase the performance.
"""
return self._is_bool
@property
def model(self):
"""
Kernel response model.
"""
return self._model
@property
def dimension(self):
"""
Kernel dimension.
"""
return self.array.ndim
@property
def center(self):
"""
Index of the kernel center.
"""
return [axes_size // 2 for axes_size in self._array.shape]
def normalize(self, mode='integral'):
"""
Normalize the filter kernel.
Parameters
----------
mode : {'integral', 'peak'}
One of the following modes:
* 'integral' (default)
Kernel is normalized such that its integral = 1.
* 'peak'
Kernel is normalized such that its peak = 1.
"""
if mode == 'integral':
normalization = self._array.sum()
elif mode == 'peak':
normalization = self._array.max()
else:
raise ValueError("invalid mode, must be 'integral' or 'peak'")
# Warn the user for kernels that sum to zero
if normalization == 0:
warnings.warn('The kernel cannot be normalized because it '
'sums to zero.', AstropyUserWarning)
else:
np.divide(self._array, normalization, self._array)
self._kernel_sum = self._array.sum()
@property
def shape(self):
"""
Shape of the kernel array.
"""
return self._array.shape
@property
def separable(self):
"""
Indicates if the filter kernel is separable.
A 2D filter is separable, when its filter array can be written as the
outer product of two 1D arrays.
If a filter kernel is separable, higher dimension convolutions will be
performed by applying the 1D filter array consecutively on every dimension.
This is significantly faster, than using a filter array with the same
dimension.
"""
return self._separable
@property
def array(self):
"""
Filter kernel array.
"""
return self._array
def __add__(self, kernel):
"""
Add two filter kernels.
"""
return kernel_arithmetics(self, kernel, 'add')
def __sub__(self, kernel):
"""
Subtract two filter kernels.
"""
return kernel_arithmetics(self, kernel, 'sub')
def __mul__(self, value):
"""
Multiply kernel with number or convolve two kernels.
"""
return kernel_arithmetics(self, value, "mul")
def __rmul__(self, value):
"""
Multiply kernel with number or convolve two kernels.
"""
return kernel_arithmetics(self, value, "mul")
def __array__(self):
"""
Array representation of the kernel.
"""
return self._array
def __array_wrap__(self, array, context=None):
"""
Wrapper for multiplication with numpy arrays.
"""
if type(context[0]) == np.ufunc:
return NotImplemented
else:
return array
class Kernel1D(Kernel):
"""
Base class for 1D filter kernels.
Parameters
----------
model : `~astropy.modeling.FittableModel`
Model to be evaluated.
x_size : int or None, optional
Size of the kernel array. Default = ⌊8*width+1⌋.
Only used if ``array`` is None.
array : ndarray or None, optional
Kernel array.
width : number
Width of the filter kernel.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by linearly interpolating
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
factor : number, optional
Factor of oversampling. Default factor = 10.
"""
def __init__(self, model=None, x_size=None, array=None, **kwargs):
# Initialize from model
if self._model:
if array is not None:
# Reject "array" keyword for kernel models, to avoid them not being
# populated as expected.
raise TypeError("Array argument not allowed for kernel models.")
if x_size is None:
x_size = self._default_size
elif x_size != int(x_size):
raise TypeError("x_size should be an integer")
# Set ranges where to evaluate the model
if x_size % 2 == 0: # even kernel
x_range = (-(int(x_size)) // 2 + 0.5, (int(x_size)) // 2 + 0.5)
else: # odd kernel
x_range = (-(int(x_size) - 1) // 2, (int(x_size) - 1) // 2 + 1)
array = discretize_model(self._model, x_range, **kwargs)
# Initialize from array
elif array is None:
raise TypeError("Must specify either array or model.")
super().__init__(array)
class Kernel2D(Kernel):
"""
Base class for 2D filter kernels.
Parameters
----------
model : `~astropy.modeling.FittableModel`
Model to be evaluated.
x_size : int, optional
Size in x direction of the kernel array. Default = ⌊8*width + 1⌋.
Only used if ``array`` is None.
y_size : int, optional
Size in y direction of the kernel array. Default = ⌊8*width + 1⌋.
Only used if ``array`` is None,
array : ndarray or None, optional
Kernel array. Default is None.
mode : str, optional
One of the following discretization modes:
* 'center' (default)
Discretize model by taking the value
at the center of the bin.
* 'linear_interp'
Discretize model by performing a bilinear interpolation
between the values at the corners of the bin.
* 'oversample'
Discretize model by taking the average
on an oversampled grid.
* 'integrate'
Discretize model by integrating the
model over the bin.
width : number
Width of the filter kernel.
factor : number, optional
Factor of oversampling. Default factor = 10.
"""
def __init__(self, model=None, x_size=None, y_size=None, array=None, **kwargs):
# Initialize from model
if self._model:
if array is not None:
# Reject "array" keyword for kernel models, to avoid them not being
# populated as expected.
raise TypeError("Array argument not allowed for kernel models.")
if x_size is None:
x_size = self._default_size
elif x_size != int(x_size):
raise TypeError("x_size should be an integer")
if y_size is None:
y_size = x_size
elif y_size != int(y_size):
raise TypeError("y_size should be an integer")
# Set ranges where to evaluate the model
if x_size % 2 == 0: # even kernel
x_range = (-(int(x_size)) // 2 + 0.5, (int(x_size)) // 2 + 0.5)
else: # odd kernel
x_range = (-(int(x_size) - 1) // 2, (int(x_size) - 1) // 2 + 1)
if y_size % 2 == 0: # even kernel
y_range = (-(int(y_size)) // 2 + 0.5, (int(y_size)) // 2 + 0.5)
else: # odd kernel
y_range = (-(int(y_size) - 1) // 2, (int(y_size) - 1) // 2 + 1)
array = discretize_model(self._model, x_range, y_range, **kwargs)
# Initialize from array
elif array is None:
raise TypeError("Must specify either array or model.")
super().__init__(array)
def kernel_arithmetics(kernel, value, operation):
"""
Add, subtract or multiply two kernels.
Parameters
----------
kernel : `astropy.convolution.Kernel`
Kernel instance.
value : `astropy.convolution.Kernel`, float, or int
Value to operate with.
operation : {'add', 'sub', 'mul'}
One of the following operations:
* 'add'
Add two kernels
* 'sub'
Subtract two kernels
* 'mul'
Multiply kernel with number or convolve two kernels.
"""
# 1D kernels
if isinstance(kernel, Kernel1D) and isinstance(value, Kernel1D):
if operation == "add":
new_array = add_kernel_arrays_1D(kernel.array, value.array)
if operation == "sub":
new_array = add_kernel_arrays_1D(kernel.array, -value.array)
if operation == "mul":
raise Exception("Kernel operation not supported. Maybe you want "
"to use convolve(kernel1, kernel2) instead.")
new_kernel = Kernel1D(array=new_array)
new_kernel._separable = kernel._separable and value._separable
new_kernel._is_bool = kernel._is_bool or value._is_bool
# 2D kernels
elif isinstance(kernel, Kernel2D) and isinstance(value, Kernel2D):
if operation == "add":
new_array = add_kernel_arrays_2D(kernel.array, value.array)
if operation == "sub":
new_array = add_kernel_arrays_2D(kernel.array, -value.array)
if operation == "mul":
raise Exception("Kernel operation not supported. Maybe you want "
"to use convolve(kernel1, kernel2) instead.")
new_kernel = Kernel2D(array=new_array)
new_kernel._separable = kernel._separable and value._separable
new_kernel._is_bool = kernel._is_bool or value._is_bool
# kernel and number
elif ((isinstance(kernel, Kernel1D) or isinstance(kernel, Kernel2D))
and np.isscalar(value)):
if operation == "mul":
new_kernel = copy.copy(kernel)
new_kernel._array *= value
else:
raise Exception("Kernel operation not supported.")
else:
raise Exception("Kernel operation not supported.")
return new_kernel
|
ecda11a531f97c021445f7caedb217964c658e8037c2f2315026505046833d26 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from .convolve import (convolve, convolve_fft, convolve_models,
convolve_models_fft, interpolate_replace_nans)
from .core import *
from .kernels import *
from .kernels import MexicanHat1DKernel, MexicanHat2DKernel # Deprecated kernels
from .utils import *
|
4f343f23058dc3e840031492f54e1a195e40f2107527243a03304b4175ec108e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
import sys
from setuptools import Extension
import numpy
C_CONVOLVE_PKGDIR = os.path.relpath(os.path.dirname(__file__))
SRC_FILES = [os.path.join(C_CONVOLVE_PKGDIR, filename)
for filename in ['src/convolve.c']]
extra_compile_args = ['-UNDEBUG']
if not sys.platform.startswith('win'):
extra_compile_args.append('-fPIC')
def get_extensions():
# Add '-Rpass-missed=.*' to ``extra_compile_args`` when compiling with clang
# to report missed optimizations
_convolve_ext = Extension(name='astropy.convolution._convolve', sources=SRC_FILES,
extra_compile_args=extra_compile_args,
include_dirs=[numpy.get_include()],
language='c')
return [_convolve_ext]
|
17726359557846a9bc285294a7b63453a7a92b7213ff381ea2c89fe9b000744e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import ctypes
import numpy as np
from astropy.modeling.core import FittableModel, custom_model
__all__ = ['discretize_model', 'KernelSizeError']
class DiscretizationError(Exception):
"""
Called when discretization of models goes wrong.
"""
class KernelSizeError(Exception):
"""
Called when size of kernels is even.
"""
def has_even_axis(array):
if isinstance(array, (list, tuple)):
return not len(array) % 2
else:
return any(not axes_size % 2 for axes_size in array.shape)
def raise_even_kernel_exception():
raise KernelSizeError("Kernel size must be odd in all axes.")
def add_kernel_arrays_1D(array_1, array_2):
"""
Add two 1D kernel arrays of different size.
The arrays are added with the centers lying upon each other.
"""
if array_1.size > array_2.size:
new_array = array_1.copy()
center = array_1.size // 2
slice_ = slice(center - array_2.size // 2,
center + array_2.size // 2 + 1)
new_array[slice_] += array_2
return new_array
elif array_2.size > array_1.size:
new_array = array_2.copy()
center = array_2.size // 2
slice_ = slice(center - array_1.size // 2,
center + array_1.size // 2 + 1)
new_array[slice_] += array_1
return new_array
return array_2 + array_1
def add_kernel_arrays_2D(array_1, array_2):
"""
Add two 2D kernel arrays of different size.
The arrays are added with the centers lying upon each other.
"""
if array_1.size > array_2.size:
new_array = array_1.copy()
center = [axes_size // 2 for axes_size in array_1.shape]
slice_x = slice(center[1] - array_2.shape[1] // 2,
center[1] + array_2.shape[1] // 2 + 1)
slice_y = slice(center[0] - array_2.shape[0] // 2,
center[0] + array_2.shape[0] // 2 + 1)
new_array[slice_y, slice_x] += array_2
return new_array
elif array_2.size > array_1.size:
new_array = array_2.copy()
center = [axes_size // 2 for axes_size in array_2.shape]
slice_x = slice(center[1] - array_1.shape[1] // 2,
center[1] + array_1.shape[1] // 2 + 1)
slice_y = slice(center[0] - array_1.shape[0] // 2,
center[0] + array_1.shape[0] // 2 + 1)
new_array[slice_y, slice_x] += array_1
return new_array
return array_2 + array_1
def discretize_model(model, x_range, y_range=None, mode='center', factor=10):
"""
Function to evaluate analytical model functions on a grid.
So far the function can only deal with pixel coordinates.
Parameters
----------
model : `~astropy.modeling.FittableModel` or callable.
Analytic model function to be discretized. Callables, which are not an
instances of `~astropy.modeling.FittableModel` are passed to
`~astropy.modeling.custom_model` and then evaluated.
x_range : tuple
x range in which the model is evaluated. The difference between the
upper an lower limit must be a whole number, so that the output array
size is well defined.
y_range : tuple, optional
y range in which the model is evaluated. The difference between the
upper an lower limit must be a whole number, so that the output array
size is well defined. Necessary only for 2D models.
mode : str, optional
One of the following modes:
* ``'center'`` (default)
Discretize model by taking the value
at the center of the bin.
* ``'linear_interp'``
Discretize model by linearly interpolating
between the values at the corners of the bin.
For 2D models interpolation is bilinear.
* ``'oversample'``
Discretize model by taking the average
on an oversampled grid.
* ``'integrate'``
Discretize model by integrating the model
over the bin using `scipy.integrate.quad`.
Very slow.
factor : float or int
Factor of oversampling. Default = 10.
Returns
-------
array : `numpy.array`
Model value array
Notes
-----
The ``oversample`` mode allows to conserve the integral on a subpixel
scale. Here is the example of a normalized Gaussian1D:
.. plot::
:include-source:
import matplotlib.pyplot as plt
import numpy as np
from astropy.modeling.models import Gaussian1D
from astropy.convolution.utils import discretize_model
gauss_1D = Gaussian1D(1 / (0.5 * np.sqrt(2 * np.pi)), 0, 0.5)
y_center = discretize_model(gauss_1D, (-2, 3), mode='center')
y_corner = discretize_model(gauss_1D, (-2, 3), mode='linear_interp')
y_oversample = discretize_model(gauss_1D, (-2, 3), mode='oversample')
plt.plot(y_center, label='center sum = {0:3f}'.format(y_center.sum()))
plt.plot(y_corner, label='linear_interp sum = {0:3f}'.format(y_corner.sum()))
plt.plot(y_oversample, label='oversample sum = {0:3f}'.format(y_oversample.sum()))
plt.xlabel('pixels')
plt.ylabel('value')
plt.legend()
plt.show()
"""
if not callable(model):
raise TypeError('Model must be callable.')
if not isinstance(model, FittableModel):
model = custom_model(model)()
ndim = model.n_inputs
if ndim > 2:
raise ValueError('discretize_model only supports 1-d and 2-d models.')
if not float(np.diff(x_range)).is_integer():
raise ValueError("The difference between the upper and lower limit of"
" 'x_range' must be a whole number.")
if y_range:
if not float(np.diff(y_range)).is_integer():
raise ValueError("The difference between the upper and lower limit of"
" 'y_range' must be a whole number.")
if ndim == 2 and y_range is None:
raise ValueError("y range not specified, but model is 2-d")
if ndim == 1 and y_range is not None:
raise ValueError("y range specified, but model is only 1-d.")
if mode == "center":
if ndim == 1:
return discretize_center_1D(model, x_range)
elif ndim == 2:
return discretize_center_2D(model, x_range, y_range)
elif mode == "linear_interp":
if ndim == 1:
return discretize_linear_1D(model, x_range)
if ndim == 2:
return discretize_bilinear_2D(model, x_range, y_range)
elif mode == "oversample":
if ndim == 1:
return discretize_oversample_1D(model, x_range, factor)
if ndim == 2:
return discretize_oversample_2D(model, x_range, y_range, factor)
elif mode == "integrate":
if ndim == 1:
return discretize_integrate_1D(model, x_range)
if ndim == 2:
return discretize_integrate_2D(model, x_range, y_range)
else:
raise DiscretizationError('Invalid mode.')
def discretize_center_1D(model, x_range):
"""
Discretize model by taking the value at the center of the bin.
"""
x = np.arange(*x_range)
return model(x)
def discretize_center_2D(model, x_range, y_range):
"""
Discretize model by taking the value at the center of the pixel.
"""
x = np.arange(*x_range)
y = np.arange(*y_range)
x, y = np.meshgrid(x, y)
return model(x, y)
def discretize_linear_1D(model, x_range):
"""
Discretize model by performing a linear interpolation.
"""
# Evaluate model 0.5 pixel outside the boundaries
x = np.arange(x_range[0] - 0.5, x_range[1] + 0.5)
values_intermediate_grid = model(x)
return 0.5 * (values_intermediate_grid[1:] + values_intermediate_grid[:-1])
def discretize_bilinear_2D(model, x_range, y_range):
"""
Discretize model by performing a bilinear interpolation.
"""
# Evaluate model 0.5 pixel outside the boundaries
x = np.arange(x_range[0] - 0.5, x_range[1] + 0.5)
y = np.arange(y_range[0] - 0.5, y_range[1] + 0.5)
x, y = np.meshgrid(x, y)
values_intermediate_grid = model(x, y)
# Mean in y direction
values = 0.5 * (values_intermediate_grid[1:, :]
+ values_intermediate_grid[:-1, :])
# Mean in x direction
values = 0.5 * (values[:, 1:]
+ values[:, :-1])
return values
def discretize_oversample_1D(model, x_range, factor=10):
"""
Discretize model by taking the average on an oversampled grid.
"""
# Evaluate model on oversampled grid
x = np.linspace(x_range[0] - 0.5 * (1 - 1 / factor),
x_range[1] - 0.5 * (1 + 1 / factor),
num=int((x_range[1] - x_range[0]) * factor))
values = model(x)
# Reshape and compute mean
values = np.reshape(values, (x.size // factor, factor))
return values.mean(axis=1)
def discretize_oversample_2D(model, x_range, y_range, factor=10):
"""
Discretize model by taking the average on an oversampled grid.
"""
# Evaluate model on oversampled grid
x = np.linspace(x_range[0] - 0.5 * (1 - 1 / factor),
x_range[1] - 0.5 * (1 + 1 / factor),
num=int((x_range[1] - x_range[0]) * factor))
y = np.linspace(y_range[0] - 0.5 * (1 - 1 / factor),
y_range[1] - 0.5 * (1 + 1 / factor),
num=int((y_range[1] - y_range[0]) * factor))
x_grid, y_grid = np.meshgrid(x, y)
values = model(x_grid, y_grid)
# Reshape and compute mean
shape = (y.size // factor, factor, x.size // factor, factor)
values = np.reshape(values, shape)
return values.mean(axis=3).mean(axis=1)
def discretize_integrate_1D(model, x_range):
"""
Discretize model by integrating numerically the model over the bin.
"""
from scipy.integrate import quad
# Set up grid
x = np.arange(x_range[0] - 0.5, x_range[1] + 0.5)
values = np.array([])
# Integrate over all bins
for i in range(x.size - 1):
values = np.append(values, quad(model, x[i], x[i + 1])[0])
return values
def discretize_integrate_2D(model, x_range, y_range):
"""
Discretize model by integrating the model over the pixel.
"""
from scipy.integrate import dblquad
# Set up grid
x = np.arange(x_range[0] - 0.5, x_range[1] + 0.5)
y = np.arange(y_range[0] - 0.5, y_range[1] + 0.5)
values = np.empty((y.size - 1, x.size - 1))
# Integrate over all pixels
for i in range(x.size - 1):
for j in range(y.size - 1):
values[j, i] = dblquad(lambda y, x: model(x, y), x[i], x[i + 1],
lambda x: y[j], lambda x: y[j + 1])[0]
return values
|
ad77159859fcbbd1117e643d270cc9a4d805e88c60bc461208a00c7620d33e63 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import ctypes
import os
import warnings
from functools import partial
import numpy as np
from numpy.ctypeslib import load_library, ndpointer
from astropy import units as u
from astropy.modeling.convolution import Convolution
from astropy.modeling.core import SPECIAL_OPERATORS, CompoundModel
from astropy.nddata import support_nddata
from astropy.utils.console import human_file_size
from astropy.utils.exceptions import AstropyUserWarning
from .core import MAX_NORMALIZATION, Kernel, Kernel1D, Kernel2D
from .utils import KernelSizeError, has_even_axis, raise_even_kernel_exception
LIBRARY_PATH = os.path.dirname(__file__)
try:
with warnings.catch_warnings():
# numpy.distutils is deprecated since numpy 1.23
# see https://github.com/astropy/astropy/issues/12865
warnings.simplefilter('ignore', DeprecationWarning)
_convolve = load_library("_convolve", LIBRARY_PATH)
except Exception:
raise ImportError("Convolution C extension is missing. Try re-building astropy.")
# The GIL is automatically released by default when calling functions imported
# from libraries loaded by ctypes.cdll.LoadLibrary(<path>)
# Declare prototypes
# Boundary None
_convolveNd_c = _convolve.convolveNd_c
_convolveNd_c.restype = None
_convolveNd_c.argtypes = [ndpointer(ctypes.c_double, flags={"C_CONTIGUOUS", "WRITEABLE"}), # return array
ndpointer(ctypes.c_double, flags="C_CONTIGUOUS"), # input array
ctypes.c_uint, # N dim
# size array for input and result unless
# embed_result_within_padded_region is False,
# in which case the result array is assumed to be
# input.shape - 2*(kernel.shape//2). Note: integer division.
ndpointer(ctypes.c_size_t, flags="C_CONTIGUOUS"),
ndpointer(ctypes.c_double, flags="C_CONTIGUOUS"), # kernel array
ndpointer(ctypes.c_size_t, flags="C_CONTIGUOUS"), # size array for kernel
ctypes.c_bool, # nan_interpolate
ctypes.c_bool, # embed_result_within_padded_region
ctypes.c_uint] # n_threads
# np.unique([scipy.fft.next_fast_len(i, real=True) for i in range(10000)])
_good_sizes = np.array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, # noqa E201
15, 16, 18, 20, 24, 25, 27, 30, 32, 36, 40,
45, 48, 50, 54, 60, 64, 72, 75, 80, 81, 90,
96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162,
180, 192, 200, 216, 225, 240, 243, 250, 256, 270, 288,
300, 320, 324, 360, 375, 384, 400, 405, 432, 450, 480,
486, 500, 512, 540, 576, 600, 625, 640, 648, 675, 720,
729, 750, 768, 800, 810, 864, 900, 960, 972, 1000, 1024,
1080, 1125, 1152, 1200, 1215, 1250, 1280, 1296, 1350, 1440, 1458,
1500, 1536, 1600, 1620, 1728, 1800, 1875, 1920, 1944, 2000, 2025,
2048, 2160, 2187, 2250, 2304, 2400, 2430, 2500, 2560, 2592, 2700,
2880, 2916, 3000, 3072, 3125, 3200, 3240, 3375, 3456, 3600, 3645,
3750, 3840, 3888, 4000, 4050, 4096, 4320, 4374, 4500, 4608, 4800,
4860, 5000, 5120, 5184, 5400, 5625, 5760, 5832, 6000, 6075, 6144,
6250, 6400, 6480, 6561, 6750, 6912, 7200, 7290, 7500, 7680, 7776,
8000, 8100, 8192, 8640, 8748, 9000, 9216, 9375, 9600, 9720, 10000])
_good_range = int(np.log10(_good_sizes[-1]))
# Disabling doctests when scipy isn't present.
__doctest_requires__ = {('convolve_fft',): ['scipy.fft']}
BOUNDARY_OPTIONS = [None, 'fill', 'wrap', 'extend']
def _next_fast_lengths(shape):
"""
Find optimal or good sizes to pad an array of ``shape`` to for better
performance with `numpy.fft.*fft` and `scipy.fft.*fft`.
Calculated directly with `scipy.fft.next_fast_len`, if available; otherwise
looked up from list and scaled by powers of 10, if necessary.
"""
try:
import scipy.fft
return np.array([scipy.fft.next_fast_len(j) for j in shape])
except ImportError:
pass
newshape = np.empty(len(np.atleast_1d(shape)), dtype=int)
for i, j in enumerate(shape):
scale = 10 ** max(int(np.ceil(np.log10(j))) - _good_range, 0)
for n in _good_sizes:
if n * scale >= j:
newshape[i] = n * scale
break
else:
raise ValueError(f'No next fast length for {j} found in list of _good_sizes '
f'<= {_good_sizes[-1] * scale}.')
return newshape
def _copy_input_if_needed(input, dtype=float, order='C', nan_treatment=None,
mask=None, fill_value=None):
# Alias input
input = input.array if isinstance(input, Kernel) else input
# strip quantity attributes
if hasattr(input, 'unit'):
input = input.value
output = input
# Copy input
try:
# Anything that's masked must be turned into NaNs for the interpolation.
# This requires copying. A copy is also needed for nan_treatment == 'fill'
# A copy prevents possible function side-effects of the input array.
if nan_treatment == 'fill' or np.ma.is_masked(input) or mask is not None:
if np.ma.is_masked(input):
# ``np.ma.maskedarray.filled()`` returns a copy, however there
# is no way to specify the return type or order etc. In addition
# ``np.nan`` is a ``float`` and there is no conversion to an
# ``int`` type. Therefore, a pre-fill copy is needed for non
# ``float`` masked arrays. ``subok=True`` is needed to retain
# ``np.ma.maskedarray.filled()``. ``copy=False`` allows the fill
# to act as the copy if type and order are already correct.
output = np.array(input, dtype=dtype, copy=False, order=order, subok=True)
output = output.filled(fill_value)
else:
# Since we're making a copy, we might as well use `subok=False` to save,
# what is probably, a negligible amount of memory.
output = np.array(input, dtype=dtype, copy=True, order=order, subok=False)
if mask is not None:
# mask != 0 yields a bool mask for all ints/floats/bool
output[mask != 0] = fill_value
else:
# The call below is synonymous with np.asanyarray(array, ftype=float, order='C')
# The advantage of `subok=True` is that it won't copy when array is an ndarray subclass. If it
# is and `subok=False` (default), then it will copy even if `copy=False`. This uses less memory
# when ndarray subclasses are passed in.
output = np.array(input, dtype=dtype, copy=False, order=order, subok=True)
except (TypeError, ValueError) as e:
raise TypeError('input should be a Numpy array or something '
'convertible into a float array', e)
return output
@support_nddata(data='array')
def convolve(array, kernel, boundary='fill', fill_value=0.,
nan_treatment='interpolate', normalize_kernel=True, mask=None,
preserve_nan=False, normalization_zero_tol=1e-8):
"""
Convolve an array with a kernel.
This routine differs from `scipy.ndimage.convolve` because
it includes a special treatment for ``NaN`` values. Rather than
including ``NaN`` values in the array in the convolution calculation, which
causes large ``NaN`` holes in the convolved array, ``NaN`` values are
replaced with interpolated values using the kernel as an interpolation
function.
Parameters
----------
array : `~astropy.nddata.NDData` or array-like
The array to convolve. This should be a 1, 2, or 3-dimensional array
or a list or a set of nested lists representing a 1, 2, or
3-dimensional array. If an `~astropy.nddata.NDData`, the ``mask`` of
the `~astropy.nddata.NDData` will be used as the ``mask`` argument.
kernel : `numpy.ndarray` or `~astropy.convolution.Kernel`
The convolution kernel. The number of dimensions should match those for
the array, and the dimensions should be odd in all directions. If a
masked array, the masked values will be replaced by ``fill_value``.
boundary : str, optional
A flag indicating how to handle boundaries:
* `None`
Set the ``result`` values to zero where the kernel
extends beyond the edge of the array.
* 'fill'
Set values outside the array boundary to ``fill_value`` (default).
* 'wrap'
Periodic boundary that wrap to the other side of ``array``.
* 'extend'
Set values outside the array to the nearest ``array``
value.
fill_value : float, optional
The value to use outside the array when using ``boundary='fill'``.
normalize_kernel : bool, optional
Whether to normalize the kernel to have a sum of one.
nan_treatment : {'interpolate', 'fill'}, optional
The method used to handle NaNs in the input ``array``:
* ``'interpolate'``: ``NaN`` values are replaced with
interpolated values using the kernel as an interpolation
function. Note that if the kernel has a sum equal to
zero, NaN interpolation is not possible and will raise an
exception.
* ``'fill'``: ``NaN`` values are replaced by ``fill_value``
prior to convolution.
preserve_nan : bool, optional
After performing convolution, should pixels that were originally NaN
again become NaN?
mask : None or ndarray, optional
A "mask" array. Shape must match ``array``, and anything that is masked
(i.e., not 0/`False`) will be set to NaN for the convolution. If
`None`, no masking will be performed unless ``array`` is a masked array.
If ``mask`` is not `None` *and* ``array`` is a masked array, a pixel is
masked of it is masked in either ``mask`` *or* ``array.mask``.
normalization_zero_tol : float, optional
The absolute tolerance on whether the kernel is different than zero.
If the kernel sums to zero to within this precision, it cannot be
normalized. Default is "1e-8".
Returns
-------
result : `numpy.ndarray`
An array with the same dimensions and as the input array,
convolved with kernel. The data type depends on the input
array type. If array is a floating point type, then the
return array keeps the same data type, otherwise the type
is ``numpy.float``.
Notes
-----
For masked arrays, masked values are treated as NaNs. The convolution
is always done at ``numpy.float`` precision.
"""
if boundary not in BOUNDARY_OPTIONS:
raise ValueError(f"Invalid boundary option: must be one of {BOUNDARY_OPTIONS}")
if nan_treatment not in ('interpolate', 'fill'):
raise ValueError("nan_treatment must be one of 'interpolate','fill'")
# OpenMP support is disabled at the C src code level, changing this will have
# no effect.
n_threads = 1
# Keep refs to originals
passed_kernel = kernel
passed_array = array
# The C routines all need float type inputs (so, a particular
# bit size, endianness, etc.). So we have to convert, which also
# has the effect of making copies so we don't modify the inputs.
# After this, the variables we work with will be array_internal, and
# kernel_internal. However -- we do want to keep track of what type
# the input array was so we can cast the result to that at the end
# if it's a floating point type. Don't bother with this for lists --
# just always push those as float.
# It is always necessary to make a copy of kernel (since it is modified),
# but, if we just so happen to be lucky enough to have the input array
# have exactly the desired type, we just alias to array_internal
# Convert kernel to ndarray if not already
# Copy or alias array to array_internal
array_internal = _copy_input_if_needed(passed_array, dtype=float, order='C',
nan_treatment=nan_treatment, mask=mask,
fill_value=np.nan)
array_dtype = getattr(passed_array, 'dtype', array_internal.dtype)
# Copy or alias kernel to kernel_internal
kernel_internal = _copy_input_if_needed(passed_kernel, dtype=float, order='C',
nan_treatment=None, mask=None,
fill_value=fill_value)
# Make sure kernel has all odd axes
if has_even_axis(kernel_internal):
raise_even_kernel_exception()
# If both image array and kernel are Kernel instances
# constrain convolution method
# This must occur before the main alias/copy of ``passed_kernel`` to
# ``kernel_internal`` as it is used for filling masked kernels.
if isinstance(passed_array, Kernel) and isinstance(passed_kernel, Kernel):
warnings.warn("Both array and kernel are Kernel instances, hardwiring "
"the following parameters: boundary='fill', fill_value=0,"
" normalize_Kernel=True, nan_treatment='interpolate'",
AstropyUserWarning)
boundary = 'fill'
fill_value = 0
normalize_kernel = True
nan_treatment = 'interpolate'
# -----------------------------------------------------------------------
# From this point onwards refer only to ``array_internal`` and
# ``kernel_internal``.
# Assume both are base np.ndarrays and NOT subclasses e.g. NOT
# ``Kernel`` nor ``np.ma.maskedarray`` classes.
# -----------------------------------------------------------------------
# Check dimensionality
if array_internal.ndim == 0:
raise Exception("cannot convolve 0-dimensional arrays")
elif array_internal.ndim > 3:
raise NotImplementedError('convolve only supports 1, 2, and 3-dimensional '
'arrays at this time')
elif array_internal.ndim != kernel_internal.ndim:
raise Exception('array and kernel have differing number of '
'dimensions.')
array_shape = np.array(array_internal.shape)
kernel_shape = np.array(kernel_internal.shape)
pad_width = kernel_shape//2
# For boundary=None only the center space is convolved. All array indices within a
# distance kernel.shape//2 from the edge are completely ignored (zeroed).
# E.g. (1D list) only the indices len(kernel)//2 : len(array)-len(kernel)//2
# are convolved. It is therefore not possible to use this method to convolve an
# array by a kernel that is larger (see note below) than the array - as ALL pixels would be ignored
# leaving an array of only zeros.
# Note: For even kernels the correctness condition is array_shape > kernel_shape.
# For odd kernels it is:
# array_shape >= kernel_shape OR array_shape > kernel_shape-1 OR array_shape > 2*(kernel_shape//2).
# Since the latter is equal to the former two for even lengths, the latter condition is complete.
if boundary is None and not np.all(array_shape > 2*pad_width):
raise KernelSizeError("for boundary=None all kernel axes must be smaller than array's - "
"use boundary in ['fill', 'extend', 'wrap'] instead.")
# NaN interpolation significantly slows down the C convolution
# computation. Since nan_treatment = 'interpolate', is the default
# check whether it is even needed, if not, don't interpolate.
# NB: np.isnan(array_internal.sum()) is faster than np.isnan(array_internal).any()
nan_interpolate = (nan_treatment == 'interpolate') and np.isnan(array_internal.sum())
# Check if kernel is normalizable
if normalize_kernel or nan_interpolate:
kernel_sum = kernel_internal.sum()
kernel_sums_to_zero = np.isclose(kernel_sum, 0,
atol=normalization_zero_tol)
if kernel_sum < 1. / MAX_NORMALIZATION or kernel_sums_to_zero:
if nan_interpolate:
raise ValueError("Setting nan_treatment='interpolate' "
"requires the kernel to be normalized, "
"but the input kernel has a sum close "
"to zero. For a zero-sum kernel and "
"data with NaNs, set nan_treatment='fill'.")
else:
raise ValueError("The kernel can't be normalized, because "
"its sum is close to zero. The sum of the "
"given kernel is < {}"
.format(1. / MAX_NORMALIZATION))
# Mark the NaN values so we can replace them later if interpolate_nan is
# not set
if preserve_nan or nan_treatment == 'fill':
initially_nan = np.isnan(array_internal)
if nan_treatment == 'fill':
array_internal[initially_nan] = fill_value
# Avoid any memory allocation within the C code. Allocate output array
# here and pass through instead.
result = np.zeros(array_internal.shape, dtype=float, order='C')
embed_result_within_padded_region = True
array_to_convolve = array_internal
if boundary in ('fill', 'extend', 'wrap'):
embed_result_within_padded_region = False
if boundary == 'fill':
# This method is faster than using numpy.pad(..., mode='constant')
array_to_convolve = np.full(array_shape + 2*pad_width, fill_value=fill_value, dtype=float, order='C')
# Use bounds [pad_width[0]:array_shape[0]+pad_width[0]] instead of [pad_width[0]:-pad_width[0]]
# to account for when the kernel has size of 1 making pad_width = 0.
if array_internal.ndim == 1:
array_to_convolve[pad_width[0]:array_shape[0]+pad_width[0]] = array_internal
elif array_internal.ndim == 2:
array_to_convolve[pad_width[0]:array_shape[0]+pad_width[0],
pad_width[1]:array_shape[1]+pad_width[1]] = array_internal
else:
array_to_convolve[pad_width[0]:array_shape[0]+pad_width[0],
pad_width[1]:array_shape[1]+pad_width[1],
pad_width[2]:array_shape[2]+pad_width[2]] = array_internal
else:
np_pad_mode_dict = {'fill': 'constant', 'extend': 'edge', 'wrap': 'wrap'}
np_pad_mode = np_pad_mode_dict[boundary]
pad_width = kernel_shape // 2
if array_internal.ndim == 1:
np_pad_width = (pad_width[0],)
elif array_internal.ndim == 2:
np_pad_width = ((pad_width[0],), (pad_width[1],))
else:
np_pad_width = ((pad_width[0],), (pad_width[1],), (pad_width[2],))
array_to_convolve = np.pad(array_internal, pad_width=np_pad_width,
mode=np_pad_mode)
_convolveNd_c(result, array_to_convolve,
array_to_convolve.ndim,
np.array(array_to_convolve.shape, dtype=ctypes.c_size_t, order='C'),
kernel_internal,
np.array(kernel_shape, dtype=ctypes.c_size_t, order='C'),
nan_interpolate, embed_result_within_padded_region,
n_threads)
# So far, normalization has only occurred for nan_treatment == 'interpolate'
# because this had to happen within the C extension so as to ignore
# any NaNs
if normalize_kernel:
if not nan_interpolate:
result /= kernel_sum
elif nan_interpolate:
result *= kernel_sum
if nan_interpolate and not preserve_nan and np.isnan(result.sum()):
warnings.warn("nan_treatment='interpolate', however, NaN values detected "
"post convolution. A contiguous region of NaN values, larger "
"than the kernel size, are present in the input array. "
"Increase the kernel size to avoid this.", AstropyUserWarning)
if preserve_nan:
result[initially_nan] = np.nan
# Convert result to original data type
array_unit = getattr(passed_array, "unit", None)
if array_unit is not None:
result <<= array_unit
if isinstance(passed_array, Kernel):
if isinstance(passed_array, Kernel1D):
new_result = Kernel1D(array=result)
elif isinstance(passed_array, Kernel2D):
new_result = Kernel2D(array=result)
else:
raise TypeError("Only 1D and 2D Kernels are supported.")
new_result._is_bool = False
new_result._separable = passed_array._separable
if isinstance(passed_kernel, Kernel):
new_result._separable = new_result._separable and passed_kernel._separable
return new_result
elif array_dtype.kind == 'f':
# Try to preserve the input type if it's a floating point type
# Avoid making another copy if possible
try:
return result.astype(array_dtype, copy=False)
except TypeError:
return result.astype(array_dtype)
else:
return result
@support_nddata(data='array')
def convolve_fft(array, kernel, boundary='fill', fill_value=0.,
nan_treatment='interpolate', normalize_kernel=True,
normalization_zero_tol=1e-8,
preserve_nan=False, mask=None, crop=True, return_fft=False,
fft_pad=None, psf_pad=None, min_wt=0.0, allow_huge=False,
fftn=np.fft.fftn, ifftn=np.fft.ifftn,
complex_dtype=complex, dealias=False):
"""
Convolve an ndarray with an nd-kernel. Returns a convolved image with
``shape = array.shape``. Assumes kernel is centered.
`convolve_fft` is very similar to `convolve` in that it replaces ``NaN``
values in the original image with interpolated values using the kernel as
an interpolation function. However, it also includes many additional
options specific to the implementation.
`convolve_fft` differs from `scipy.signal.fftconvolve` in a few ways:
* It can treat ``NaN`` values as zeros or interpolate over them.
* ``inf`` values are treated as ``NaN``
* It optionally pads to the nearest faster sizes to improve FFT speed.
These sizes are optimized for the numpy and scipy implementations, and
``fftconvolve`` uses them by default as well; when using other external
functions (see below), results may vary.
* Its only valid ``mode`` is 'same' (i.e., the same shape array is returned)
* It lets you use your own fft, e.g.,
`pyFFTW <https://pypi.org/project/pyFFTW/>`_ or
`pyFFTW3 <https://pypi.org/project/PyFFTW3/0.2.1/>`_ , which can lead to
performance improvements, depending on your system configuration. pyFFTW3
is threaded, and therefore may yield significant performance benefits on
multi-core machines at the cost of greater memory requirements. Specify
the ``fftn`` and ``ifftn`` keywords to override the default, which is
`numpy.fft.fftn` and `numpy.fft.ifftn`. The `scipy.fft` functions also
offer somewhat better performance and a multi-threaded option.
Parameters
----------
array : `numpy.ndarray`
Array to be convolved with ``kernel``. It can be of any
dimensionality, though only 1, 2, and 3d arrays have been tested.
kernel : `numpy.ndarray` or `astropy.convolution.Kernel`
The convolution kernel. The number of dimensions should match those
for the array. The dimensions *do not* have to be odd in all directions,
unlike in the non-fft `convolve` function. The kernel will be
normalized if ``normalize_kernel`` is set. It is assumed to be centered
(i.e., shifts may result if your kernel is asymmetric)
boundary : {'fill', 'wrap'}, optional
A flag indicating how to handle boundaries:
* 'fill': set values outside the array boundary to fill_value
(default)
* 'wrap': periodic boundary
The `None` and 'extend' parameters are not supported for FFT-based
convolution.
fill_value : float, optional
The value to use outside the array when using boundary='fill'.
nan_treatment : {'interpolate', 'fill'}, optional
The method used to handle NaNs in the input ``array``:
* ``'interpolate'``: ``NaN`` values are replaced with
interpolated values using the kernel as an interpolation
function. Note that if the kernel has a sum equal to
zero, NaN interpolation is not possible and will raise an
exception.
* ``'fill'``: ``NaN`` values are replaced by ``fill_value``
prior to convolution.
normalize_kernel : callable or boolean, optional
If specified, this is the function to divide kernel by to normalize it.
e.g., ``normalize_kernel=np.sum`` means that kernel will be modified to be:
``kernel = kernel / np.sum(kernel)``. If True, defaults to
``normalize_kernel = np.sum``.
normalization_zero_tol : float, optional
The absolute tolerance on whether the kernel is different than zero.
If the kernel sums to zero to within this precision, it cannot be
normalized. Default is "1e-8".
preserve_nan : bool, optional
After performing convolution, should pixels that were originally NaN
again become NaN?
mask : None or ndarray, optional
A "mask" array. Shape must match ``array``, and anything that is masked
(i.e., not 0/`False`) will be set to NaN for the convolution. If
`None`, no masking will be performed unless ``array`` is a masked array.
If ``mask`` is not `None` *and* ``array`` is a masked array, a pixel is
masked of it is masked in either ``mask`` *or* ``array.mask``.
crop : bool, optional
Default on. Return an image of the size of the larger of the input
image and the kernel.
If the image and kernel are asymmetric in opposite directions, will
return the largest image in both directions.
For example, if an input image has shape [100,3] but a kernel with shape
[6,6] is used, the output will be [100,6].
return_fft : bool, optional
Return the ``fft(image)*fft(kernel)`` instead of the convolution (which is
``ifft(fft(image)*fft(kernel))``). Useful for making PSDs.
fft_pad : bool, optional
Default on. Zero-pad image to the nearest size supporting more efficient
execution of the FFT, generally values factorizable into the first 3-5
prime numbers. With ``boundary='wrap'``, this will be disabled.
psf_pad : bool, optional
Zero-pad image to be at least the sum of the image sizes to avoid
edge-wrapping when smoothing. This is enabled by default with
``boundary='fill'``, but it can be overridden with a boolean option.
``boundary='wrap'`` and ``psf_pad=True`` are not compatible.
min_wt : float, optional
If ignoring ``NaN`` / zeros, force all grid points with a weight less than
this value to ``NaN`` (the weight of a grid point with *no* ignored
neighbors is 1.0).
If ``min_wt`` is zero, then all zero-weight points will be set to zero
instead of ``NaN`` (which they would be otherwise, because 1/0 = nan).
See the examples below.
allow_huge : bool, optional
Allow huge arrays in the FFT? If False, will raise an exception if the
array or kernel size is >1 GB.
fftn : callable, optional
The fft function. Can be overridden to use your own ffts,
e.g. an fftw3 wrapper or scipy's fftn, ``fft=scipy.fftpack.fftn``.
ifftn : callable, optional
The inverse fft function. Can be overridden the same way ``fttn``.
complex_dtype : complex type, optional
Which complex dtype to use. `numpy` has a range of options, from 64 to
256.
dealias: bool, optional
Default off. Zero-pad image to enable explicit dealiasing
of convolution. With ``boundary='wrap'``, this will be disabled.
Note that for an input of nd dimensions this will increase
the size of the temporary arrays by at least ``1.5**nd``.
This may result in significantly more memory usage.
Returns
-------
default : ndarray
``array`` convolved with ``kernel``. If ``return_fft`` is set, returns
``fft(array) * fft(kernel)``. If crop is not set, returns the
image, but with the fft-padded size instead of the input size.
Raises
------
`ValueError`
If the array is bigger than 1 GB after padding, will raise this
exception unless ``allow_huge`` is True.
See Also
--------
convolve:
Convolve is a non-fft version of this code. It is more memory
efficient and for small kernels can be faster.
Notes
-----
With ``psf_pad=True`` and a large PSF, the resulting data
can become large and consume a lot of memory. See Issue
https://github.com/astropy/astropy/pull/4366 and the update in
https://github.com/astropy/astropy/pull/11533 for further details.
Dealiasing of pseudospectral convolutions is necessary for
numerical stability of the underlying algorithms. A common
method for handling this is to zero pad the image by at least
1/2 to eliminate the wavenumbers which have been aliased
by convolution. This is so that the aliased 1/3 of the
results of the convolution computation can be thrown out. See
https://doi.org/10.1175/1520-0469(1971)028%3C1074:OTEOAI%3E2.0.CO;2
https://iopscience.iop.org/article/10.1088/1742-6596/318/7/072037
Note that if dealiasing is necessary to your application, but your
process is memory constrained, you may want to consider using
FFTW++: https://github.com/dealias/fftwpp. It includes python
wrappers for a pseudospectral convolution which will implicitly
dealias your convolution without the need for additional padding.
Note that one cannot use FFTW++'s convlution directly in this
method as in handles the entire convolution process internally.
Additionally, FFTW++ includes other useful pseudospectral methods to
consider.
Examples
--------
>>> convolve_fft([1, 0, 3], [1, 1, 1])
array([0.33333333, 1.33333333, 1. ])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1])
array([0.5, 2. , 1.5])
>>> convolve_fft([1, 0, 3], [0, 1, 0]) # doctest: +FLOAT_CMP
array([ 1.00000000e+00, -3.70074342e-17, 3.00000000e+00])
>>> convolve_fft([1, 2, 3], [1])
array([1., 2., 3.])
>>> convolve_fft([1, np.nan, 3], [0, 1, 0], nan_treatment='interpolate')
array([1., 0., 3.])
>>> convolve_fft([1, np.nan, 3], [0, 1, 0], nan_treatment='interpolate',
... min_wt=1e-8)
array([ 1., nan, 3.])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate')
array([0.5, 2. , 1.5])
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate',
... normalize_kernel=True)
array([0.5, 2. , 1.5])
>>> import scipy.fft # optional - requires scipy
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate',
... normalize_kernel=True,
... fftn=scipy.fft.fftn, ifftn=scipy.fft.ifftn)
array([0.5, 2. , 1.5])
>>> fft_mp = lambda a: scipy.fft.fftn(a, workers=-1) # use all available cores
>>> ifft_mp = lambda a: scipy.fft.ifftn(a, workers=-1)
>>> convolve_fft([1, np.nan, 3], [1, 1, 1], nan_treatment='interpolate',
... normalize_kernel=True, fftn=fft_mp, ifftn=ifft_mp)
array([0.5, 2. , 1.5])
"""
# Checking copied from convolve.py - however, since FFTs have real &
# complex components, we change the types. Only the real part will be
# returned! Note that this always makes a copy.
# Check kernel is kernel instance
if isinstance(kernel, Kernel):
kernel = kernel.array
if isinstance(array, Kernel):
raise TypeError("Can't convolve two kernels with convolve_fft. Use convolve instead.")
if nan_treatment not in ('interpolate', 'fill'):
raise ValueError("nan_treatment must be one of 'interpolate','fill'")
# Get array quantity if it exists
array_unit = getattr(array, "unit", None)
# Convert array dtype to complex
# and ensure that list inputs become arrays
array = _copy_input_if_needed(array, dtype=complex, order='C',
nan_treatment=nan_treatment, mask=mask,
fill_value=np.nan)
kernel = _copy_input_if_needed(kernel, dtype=complex, order='C',
nan_treatment=None, mask=None,
fill_value=0)
# Check that the number of dimensions is compatible
if array.ndim != kernel.ndim:
raise ValueError("Image and kernel must have same number of dimensions")
arrayshape = array.shape
kernshape = kernel.shape
array_size_B = (np.product(arrayshape, dtype=np.int64) *
np.dtype(complex_dtype).itemsize) * u.byte
if array_size_B > 1 * u.GB and not allow_huge:
raise ValueError(f"Size Error: Arrays will be {human_file_size(array_size_B)}. "
f"Use allow_huge=True to override this exception.")
# NaN and inf catching
nanmaskarray = np.isnan(array) | np.isinf(array)
if nan_treatment == 'fill':
array[nanmaskarray] = fill_value
else:
array[nanmaskarray] = 0
nanmaskkernel = np.isnan(kernel) | np.isinf(kernel)
kernel[nanmaskkernel] = 0
if normalize_kernel is True:
if kernel.sum() < 1. / MAX_NORMALIZATION:
raise Exception("The kernel can't be normalized, because its sum is "
"close to zero. The sum of the given kernel is < {}"
.format(1. / MAX_NORMALIZATION))
kernel_scale = kernel.sum()
normalized_kernel = kernel / kernel_scale
kernel_scale = 1 # if we want to normalize it, leave it normed!
elif normalize_kernel:
# try this. If a function is not passed, the code will just crash... I
# think type checking would be better but PEPs say otherwise...
kernel_scale = normalize_kernel(kernel)
normalized_kernel = kernel / kernel_scale
else:
kernel_scale = kernel.sum()
if np.abs(kernel_scale) < normalization_zero_tol:
if nan_treatment == 'interpolate':
raise ValueError('Cannot interpolate NaNs with an unnormalizable kernel')
else:
# the kernel's sum is near-zero, so it can't be scaled
kernel_scale = 1
normalized_kernel = kernel
else:
# the kernel is normalizable; we'll temporarily normalize it
# now and undo the normalization later.
normalized_kernel = kernel / kernel_scale
if boundary is None:
warnings.warn("The convolve_fft version of boundary=None is "
"equivalent to the convolve boundary='fill'. There is "
"no FFT equivalent to convolve's "
"zero-if-kernel-leaves-boundary", AstropyUserWarning)
if psf_pad is None:
psf_pad = True
if fft_pad is None:
fft_pad = True
elif boundary == 'fill':
# create a boundary region at least as large as the kernel
if psf_pad is False:
warnings.warn(f"psf_pad was set to {psf_pad}, which overrides the "
f"boundary='fill' setting.", AstropyUserWarning)
else:
psf_pad = True
if fft_pad is None:
# default is 'True' according to the docstring
fft_pad = True
elif boundary == 'wrap':
if psf_pad:
raise ValueError("With boundary='wrap', psf_pad cannot be enabled.")
psf_pad = False
if fft_pad:
raise ValueError("With boundary='wrap', fft_pad cannot be enabled.")
fft_pad = False
if dealias:
raise ValueError("With boundary='wrap', dealias cannot be enabled.")
fill_value = 0 # force zero; it should not be used
elif boundary == 'extend':
raise NotImplementedError("The 'extend' option is not implemented "
"for fft-based convolution")
# Add shapes elementwise for psf_pad.
if psf_pad: # default=False
# add the sizes along each dimension (bigger)
newshape = np.array(arrayshape) + np.array(kernshape)
else:
# take the larger shape in each dimension (smaller)
newshape = np.maximum(arrayshape, kernshape)
if dealias:
# Extend shape by 1/2 for dealiasing
newshape += np.ceil(newshape / 2).astype(int)
# Find ideal size for fft (was power of 2, now any powers of prime factors 2, 3, 5).
if fft_pad: # default=True
# Get optimized sizes from scipy.
newshape = _next_fast_lengths(newshape)
# perform a second check after padding
array_size_C = (np.product(newshape, dtype=np.int64) *
np.dtype(complex_dtype).itemsize) * u.byte
if array_size_C > 1 * u.GB and not allow_huge:
raise ValueError(f"Size Error: Arrays will be {human_file_size(array_size_C)}. "
f"Use allow_huge=True to override this exception.")
# For future reference, this can be used to predict "almost exactly"
# how much *additional* memory will be used.
# size * (array + kernel + kernelfft + arrayfft +
# (kernel*array)fft +
# optional(weight image + weight_fft + weight_ifft) +
# optional(returned_fft))
# total_memory_used_GB = (np.product(newshape)*np.dtype(complex_dtype).itemsize
# * (5 + 3*((interpolate_nan or ) and kernel_is_normalized))
# + (1 + (not return_fft)) *
# np.product(arrayshape)*np.dtype(complex_dtype).itemsize
# + np.product(arrayshape)*np.dtype(bool).itemsize
# + np.product(kernshape)*np.dtype(bool).itemsize)
# ) / 1024.**3
# separate each dimension by the padding size... this is to determine the
# appropriate slice size to get back to the input dimensions
arrayslices = []
kernslices = []
for ii, (newdimsize, arraydimsize, kerndimsize) in enumerate(zip(newshape, arrayshape, kernshape)):
center = newdimsize - (newdimsize + 1) // 2
arrayslices += [slice(center - arraydimsize // 2,
center + (arraydimsize + 1) // 2)]
kernslices += [slice(center - kerndimsize // 2,
center + (kerndimsize + 1) // 2)]
arrayslices = tuple(arrayslices)
kernslices = tuple(kernslices)
if not np.all(newshape == arrayshape):
if np.isfinite(fill_value):
bigarray = np.ones(newshape, dtype=complex_dtype) * fill_value
else:
bigarray = np.zeros(newshape, dtype=complex_dtype)
bigarray[arrayslices] = array
else:
bigarray = array
if not np.all(newshape == kernshape):
bigkernel = np.zeros(newshape, dtype=complex_dtype)
bigkernel[kernslices] = normalized_kernel
else:
bigkernel = normalized_kernel
arrayfft = fftn(bigarray)
# need to shift the kernel so that, e.g., [0,0,1,0] -> [1,0,0,0] = unity
kernfft = fftn(np.fft.ifftshift(bigkernel))
fftmult = arrayfft * kernfft
interpolate_nan = (nan_treatment == 'interpolate')
if interpolate_nan:
if not np.isfinite(fill_value):
bigimwt = np.zeros(newshape, dtype=complex_dtype)
else:
bigimwt = np.ones(newshape, dtype=complex_dtype)
bigimwt[arrayslices] = 1.0 - nanmaskarray * interpolate_nan
wtfft = fftn(bigimwt)
# You can only get to this point if kernel_is_normalized
wtfftmult = wtfft * kernfft
wtsm = ifftn(wtfftmult)
# need to re-zero weights outside of the image (if it is padded, we
# still don't weight those regions)
bigimwt[arrayslices] = wtsm.real[arrayslices]
else:
bigimwt = 1
if np.isnan(fftmult).any():
# this check should be unnecessary; call it an insanity check
raise ValueError("Encountered NaNs in convolve. This is disallowed.")
fftmult *= kernel_scale
if array_unit is not None:
fftmult <<= array_unit
if return_fft:
return fftmult
if interpolate_nan:
with np.errstate(divide='ignore', invalid='ignore'):
# divide by zeros are expected here; if the weight is zero, we want
# the output to be nan or inf
rifft = (ifftn(fftmult)) / bigimwt
if not np.isscalar(bigimwt):
if min_wt > 0.:
rifft[bigimwt < min_wt] = np.nan
else:
# Set anything with no weight to zero (taking into account
# slight offsets due to floating-point errors).
rifft[bigimwt < 10 * np.finfo(bigimwt.dtype).eps] = 0.0
else:
rifft = ifftn(fftmult)
if preserve_nan:
rifft[arrayslices][nanmaskarray] = np.nan
if crop:
result = rifft[arrayslices].real
return result
else:
return rifft.real
def interpolate_replace_nans(array, kernel, convolve=convolve, **kwargs):
"""
Given a data set containing NaNs, replace the NaNs by interpolating from
neighboring data points with a given kernel.
Parameters
----------
array : `numpy.ndarray`
Array to be convolved with ``kernel``. It can be of any
dimensionality, though only 1, 2, and 3d arrays have been tested.
kernel : `numpy.ndarray` or `astropy.convolution.Kernel`
The convolution kernel. The number of dimensions should match those
for the array. The dimensions *do not* have to be odd in all directions,
unlike in the non-fft `convolve` function. The kernel will be
normalized if ``normalize_kernel`` is set. It is assumed to be centered
(i.e., shifts may result if your kernel is asymmetric). The kernel
*must be normalizable* (i.e., its sum cannot be zero).
convolve : `convolve` or `convolve_fft`
One of the two convolution functions defined in this package.
Returns
-------
newarray : `numpy.ndarray`
A copy of the original array with NaN pixels replaced with their
interpolated counterparts
"""
if not np.any(np.isnan(array)):
return array.copy()
newarray = array.copy()
convolved = convolve(array, kernel, nan_treatment='interpolate',
normalize_kernel=True, preserve_nan=False, **kwargs)
isnan = np.isnan(array)
newarray[isnan] = convolved[isnan]
return newarray
def convolve_models(model, kernel, mode='convolve_fft', **kwargs):
"""
Convolve two models using `~astropy.convolution.convolve_fft`.
Parameters
----------
model : `~astropy.modeling.core.Model`
Functional model
kernel : `~astropy.modeling.core.Model`
Convolution kernel
mode : str
Keyword representing which function to use for convolution.
* 'convolve_fft' : use `~astropy.convolution.convolve_fft` function.
* 'convolve' : use `~astropy.convolution.convolve`.
**kwargs : dict
Keyword arguments to me passed either to `~astropy.convolution.convolve`
or `~astropy.convolution.convolve_fft` depending on ``mode``.
Returns
-------
default : `~astropy.modeling.core.CompoundModel`
Convolved model
"""
if mode == 'convolve_fft':
operator = SPECIAL_OPERATORS.add('convolve_fft', partial(convolve_fft, **kwargs))
elif mode == 'convolve':
operator = SPECIAL_OPERATORS.add('convolve', partial(convolve, **kwargs))
else:
raise ValueError(f'Mode {mode} is not supported.')
return CompoundModel(operator, model, kernel)
def convolve_models_fft(model, kernel, bounding_box, resolution, cache=True, **kwargs):
"""
Convolve two models using `~astropy.convolution.convolve_fft`.
Parameters
----------
model : `~astropy.modeling.core.Model`
Functional model
kernel : `~astropy.modeling.core.Model`
Convolution kernel
bounding_box : tuple
The bounding box which encompasses enough of the support of both
the ``model`` and ``kernel`` so that an accurate convolution can be
computed.
resolution : float
The resolution that one wishes to approximate the convolution
integral at.
cache : optional, bool
Default value True. Allow for the storage of the convolution
computation for later reuse.
**kwargs : dict
Keyword arguments to be passed either to `~astropy.convolution.convolve`
or `~astropy.convolution.convolve_fft` depending on ``mode``.
Returns
-------
default : `~astropy.modeling.core.CompoundModel`
Convolved model
"""
operator = SPECIAL_OPERATORS.add('convolve_fft', partial(convolve_fft, **kwargs))
return Convolution(operator, model, kernel, bounding_box, resolution, cache)
|
20b60272a89c86924e46de801ee814347866d77118b3895b57e692ff92f284c4 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains configuration and setup utilities for the
Astropy project. This includes all functionality related to the
affiliated package index.
"""
from .paths import *
from .configuration import *
from .affiliated import *
|
8d985199515e02e6e2f400c162a9db1a28a38141101a08cebd7c16fa5ff6bfe0 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""This module contains classes and functions to standardize access to
configuration files for Astropy and affiliated packages.
.. note::
The configuration system makes use of the 'configobj' package, which stores
configuration in a text format like that used in the standard library
`ConfigParser`. More information and documentation for configobj can be
found at https://configobj.readthedocs.io .
"""
import io
import pkgutil
import warnings
import importlib
import contextlib
import os
from os import path
from textwrap import TextWrapper
from warnings import warn
from contextlib import contextmanager, nullcontext
from astropy.extern.configobj import configobj, validate
from astropy.utils import find_current_module, silence
from astropy.utils.decorators import deprecated
from astropy.utils.exceptions import AstropyDeprecationWarning, AstropyWarning
from astropy.utils.introspection import resolve_name
from .paths import get_config_dir
__all__ = ('InvalidConfigurationItemWarning', 'ConfigurationMissingWarning',
'get_config', 'reload_config', 'ConfigNamespace', 'ConfigItem',
'generate_config', 'create_config_file')
class InvalidConfigurationItemWarning(AstropyWarning):
""" A Warning that is issued when the configuration value specified in the
astropy configuration file does not match the type expected for that
configuration value.
"""
# This was raised with Astropy < 4.3 when the configuration file was not found.
# It is kept for compatibility and should be removed at some point.
@deprecated('5.0')
class ConfigurationMissingWarning(AstropyWarning):
""" A Warning that is issued when the configuration directory cannot be
accessed (usually due to a permissions problem). If this warning appears,
configuration items will be set to their defaults rather than read from the
configuration file, and no configuration will persist across sessions.
"""
# these are not in __all__ because it's not intended that a user ever see them
class ConfigurationDefaultMissingError(ValueError):
""" An exception that is raised when the configuration defaults (which
should be generated at build-time) are missing.
"""
# this is used in astropy/__init__.py
class ConfigurationDefaultMissingWarning(AstropyWarning):
""" A warning that is issued when the configuration defaults (which
should be generated at build-time) are missing.
"""
class ConfigurationChangedWarning(AstropyWarning):
"""
A warning that the configuration options have changed.
"""
class _ConfigNamespaceMeta(type):
def __init__(cls, name, bases, dict):
if cls.__bases__[0] is object:
return
for key, val in dict.items():
if isinstance(val, ConfigItem):
val.name = key
class ConfigNamespace(metaclass=_ConfigNamespaceMeta):
"""
A namespace of configuration items. Each subpackage with
configuration items should define a subclass of this class,
containing `ConfigItem` instances as members.
For example::
class Conf(_config.ConfigNamespace):
unicode_output = _config.ConfigItem(
False,
'Use Unicode characters when outputting values, ...')
use_color = _config.ConfigItem(
sys.platform != 'win32',
'When True, use ANSI color escape sequences when ...',
aliases=['astropy.utils.console.USE_COLOR'])
conf = Conf()
"""
def __iter__(self):
for key, val in self.__class__.__dict__.items():
if isinstance(val, ConfigItem):
yield key
keys = __iter__
"""Iterate over configuration item names."""
def values(self):
"""Iterate over configuration item values."""
for val in self.__class__.__dict__.values():
if isinstance(val, ConfigItem):
yield val
def items(self):
"""Iterate over configuration item ``(name, value)`` pairs."""
for key, val in self.__class__.__dict__.items():
if isinstance(val, ConfigItem):
yield key, val
def set_temp(self, attr, value):
"""
Temporarily set a configuration value.
Parameters
----------
attr : str
Configuration item name
value : object
The value to set temporarily.
Examples
--------
>>> import astropy
>>> with astropy.conf.set_temp('use_color', False):
... pass
... # console output will not contain color
>>> # console output contains color again...
"""
if hasattr(self, attr):
return self.__class__.__dict__[attr].set_temp(value)
raise AttributeError(f"No configuration parameter '{attr}'")
def reload(self, attr=None):
"""
Reload a configuration item from the configuration file.
Parameters
----------
attr : str, optional
The name of the configuration parameter to reload. If not
provided, reload all configuration parameters.
"""
if attr is not None:
if hasattr(self, attr):
return self.__class__.__dict__[attr].reload()
raise AttributeError(f"No configuration parameter '{attr}'")
for item in self.values():
item.reload()
def reset(self, attr=None):
"""
Reset a configuration item to its default.
Parameters
----------
attr : str, optional
The name of the configuration parameter to reload. If not
provided, reset all configuration parameters.
"""
if attr is not None:
if hasattr(self, attr):
prop = self.__class__.__dict__[attr]
prop.set(prop.defaultvalue)
return
raise AttributeError(f"No configuration parameter '{attr}'")
for item in self.values():
item.set(item.defaultvalue)
class ConfigItem:
"""
A setting and associated value stored in a configuration file.
These objects should be created as members of
`ConfigNamespace` subclasses, for example::
class _Conf(config.ConfigNamespace):
unicode_output = config.ConfigItem(
False,
'Use Unicode characters when outputting values, and writing widgets '
'to the console.')
conf = _Conf()
Parameters
----------
defaultvalue : object, optional
The default value for this item. If this is a list of strings, this
item will be interpreted as an 'options' value - this item must be one
of those values, and the first in the list will be taken as the default
value.
description : str or None, optional
A description of this item (will be shown as a comment in the
configuration file)
cfgtype : str or None, optional
A type specifier like those used as the *values* of a particular key
in a ``configspec`` file of ``configobj``. If None, the type will be
inferred from the default value.
module : str or None, optional
The full module name that this item is associated with. The first
element (e.g. 'astropy' if this is 'astropy.config.configuration')
will be used to determine the name of the configuration file, while
the remaining items determine the section. If None, the package will be
inferred from the package within which this object's initializer is
called.
aliases : str, or list of str, optional
The deprecated location(s) of this configuration item. If the
config item is not found at the new location, it will be
searched for at all of the old locations.
Raises
------
RuntimeError
If ``module`` is `None`, but the module this item is created from
cannot be determined.
"""
# this is used to make validation faster so a Validator object doesn't
# have to be created every time
_validator = validate.Validator()
cfgtype = None
"""
A type specifier like those used as the *values* of a particular key in a
``configspec`` file of ``configobj``.
"""
rootname = 'astropy'
"""
Rootname sets the base path for all config files.
"""
def __init__(self, defaultvalue='', description=None, cfgtype=None,
module=None, aliases=None):
from astropy.utils import isiterable
if module is None:
module = find_current_module(2)
if module is None:
msg1 = 'Cannot automatically determine get_config module, '
msg2 = 'because it is not called from inside a valid module'
raise RuntimeError(msg1 + msg2)
else:
module = module.__name__
self.module = module
self.description = description
self.__doc__ = description
# now determine cfgtype if it is not given
if cfgtype is None:
if (isiterable(defaultvalue) and not
isinstance(defaultvalue, str)):
# it is an options list
dvstr = [str(v) for v in defaultvalue]
cfgtype = 'option(' + ', '.join(dvstr) + ')'
defaultvalue = dvstr[0]
elif isinstance(defaultvalue, bool):
cfgtype = 'boolean'
elif isinstance(defaultvalue, int):
cfgtype = 'integer'
elif isinstance(defaultvalue, float):
cfgtype = 'float'
elif isinstance(defaultvalue, str):
cfgtype = 'string'
defaultvalue = str(defaultvalue)
self.cfgtype = cfgtype
self._validate_val(defaultvalue)
self.defaultvalue = defaultvalue
if aliases is None:
self.aliases = []
elif isinstance(aliases, str):
self.aliases = [aliases]
else:
self.aliases = aliases
def __set__(self, obj, value):
return self.set(value)
def __get__(self, obj, objtype=None):
if obj is None:
return self
return self()
def set(self, value):
"""
Sets the current value of this ``ConfigItem``.
This also updates the comments that give the description and type
information.
Parameters
----------
value
The value this item should be set to.
Raises
------
TypeError
If the provided ``value`` is not valid for this ``ConfigItem``.
"""
try:
value = self._validate_val(value)
except validate.ValidateError as e:
msg = 'Provided value for configuration item {0} not valid: {1}'
raise TypeError(msg.format(self.name, e.args[0]))
sec = get_config(self.module, rootname=self.rootname)
sec[self.name] = value
@contextmanager
def set_temp(self, value):
"""
Sets this item to a specified value only inside a with block.
Use as::
ITEM = ConfigItem('ITEM', 'default', 'description')
with ITEM.set_temp('newval'):
#... do something that wants ITEM's value to be 'newval' ...
print(ITEM)
# ITEM is now 'default' after the with block
Parameters
----------
value
The value to set this item to inside the with block.
"""
initval = self()
self.set(value)
try:
yield
finally:
self.set(initval)
def reload(self):
""" Reloads the value of this ``ConfigItem`` from the relevant
configuration file.
Returns
-------
val : object
The new value loaded from the configuration file.
"""
self.set(self.defaultvalue)
baseobj = get_config(self.module, True, rootname=self.rootname)
secname = baseobj.name
cobj = baseobj
# a ConfigObj's parent is itself, so we look for the parent with that
while cobj.parent is not cobj:
cobj = cobj.parent
newobj = configobj.ConfigObj(cobj.filename, interpolation=False)
if secname is not None:
if secname not in newobj:
return baseobj.get(self.name)
newobj = newobj[secname]
if self.name in newobj:
baseobj[self.name] = newobj[self.name]
return baseobj.get(self.name)
def __repr__(self):
out = '<{}: name={!r} value={!r} at 0x{:x}>'.format(
self.__class__.__name__, self.name, self(), id(self))
return out
def __str__(self):
out = '\n'.join(('{0}: {1}',
' cfgtype={2!r}',
' defaultvalue={3!r}',
' description={4!r}',
' module={5}',
' value={6!r}'))
out = out.format(self.__class__.__name__, self.name, self.cfgtype,
self.defaultvalue, self.description, self.module,
self())
return out
def __call__(self):
""" Returns the value of this ``ConfigItem``
Returns
-------
val : object
This item's value, with a type determined by the ``cfgtype``
attribute.
Raises
------
TypeError
If the configuration value as stored is not this item's type.
"""
def section_name(section):
if section == '':
return 'at the top-level'
else:
return f'in section [{section}]'
options = []
sec = get_config(self.module, rootname=self.rootname)
if self.name in sec:
options.append((sec[self.name], self.module, self.name))
for alias in self.aliases:
module, name = alias.rsplit('.', 1)
sec = get_config(module, rootname=self.rootname)
if '.' in module:
filename, module = module.split('.', 1)
else:
filename = module
module = ''
if name in sec:
if '.' in self.module:
new_module = self.module.split('.', 1)[1]
else:
new_module = ''
warn(
"Config parameter '{}' {} of the file '{}' "
"is deprecated. Use '{}' {} instead.".format(
name, section_name(module), get_config_filename(filename,
rootname=self.rootname),
self.name, section_name(new_module)),
AstropyDeprecationWarning)
options.append((sec[name], module, name))
if len(options) == 0:
self.set(self.defaultvalue)
options.append((self.defaultvalue, None, None))
if len(options) > 1:
filename, sec = self.module.split('.', 1)
warn(
"Config parameter '{}' {} of the file '{}' is "
"given by more than one alias ({}). Using the first.".format(
self.name, section_name(sec), get_config_filename(filename,
rootname=self.rootname),
', '.join([
'.'.join(x[1:3]) for x in options if x[1] is not None])),
AstropyDeprecationWarning)
val = options[0][0]
try:
return self._validate_val(val)
except validate.ValidateError as e:
raise TypeError('Configuration value not valid:' + e.args[0])
def _validate_val(self, val):
""" Validates the provided value based on cfgtype and returns the
type-cast value
throws the underlying configobj exception if it fails
"""
# note that this will normally use the *class* attribute `_validator`,
# but if some arcane reason is needed for making a special one for an
# instance or sub-class, it will be used
return self._validator.check(self.cfgtype, val)
# this dictionary stores the primary copy of the ConfigObj's for each
# root package
_cfgobjs = {}
def get_config_filename(packageormod=None, rootname=None):
"""
Get the filename of the config file associated with the given
package or module.
"""
cfg = get_config(packageormod, rootname=rootname)
while cfg.parent is not cfg:
cfg = cfg.parent
return cfg.filename
# This is used by testing to override the config file, so we can test
# with various config files that exercise different features of the
# config system.
_override_config_file = None
def get_config(packageormod=None, reload=False, rootname=None):
""" Gets the configuration object or section associated with a particular
package or module.
Parameters
----------
packageormod : str or None
The package for which to retrieve the configuration object. If a
string, it must be a valid package name, or if ``None``, the package from
which this function is called will be used.
reload : bool, optional
Reload the file, even if we have it cached.
rootname : str or None
Name of the root configuration directory. If ``None`` and
``packageormod`` is ``None``, this defaults to be the name of
the package from which this function is called. If ``None`` and
``packageormod`` is not ``None``, this defaults to ``astropy``.
Returns
-------
cfgobj : ``configobj.ConfigObj`` or ``configobj.Section``
If the requested package is a base package, this will be the
``configobj.ConfigObj`` for that package, or if it is a subpackage or
module, it will return the relevant ``configobj.Section`` object.
Raises
------
RuntimeError
If ``packageormod`` is `None`, but the package this item is created
from cannot be determined.
"""
if packageormod is None:
packageormod = find_current_module(2)
if packageormod is None:
msg1 = 'Cannot automatically determine get_config module, '
msg2 = 'because it is not called from inside a valid module'
raise RuntimeError(msg1 + msg2)
else:
packageormod = packageormod.__name__
_autopkg = True
else:
_autopkg = False
packageormodspl = packageormod.split('.')
pkgname = packageormodspl[0]
secname = '.'.join(packageormodspl[1:])
if rootname is None:
if _autopkg:
rootname = pkgname
else:
rootname = 'astropy' # so we don't break affiliated packages
cobj = _cfgobjs.get(pkgname, None)
if cobj is None or reload:
cfgfn = None
try:
# This feature is intended only for use by the unit tests
if _override_config_file is not None:
cfgfn = _override_config_file
else:
cfgfn = path.join(get_config_dir(rootname=rootname), pkgname + '.cfg')
cobj = configobj.ConfigObj(cfgfn, interpolation=False)
except OSError:
# This can happen when HOME is not set
cobj = configobj.ConfigObj(interpolation=False)
# This caches the object, so if the file becomes accessible, this
# function won't see it unless the module is reloaded
_cfgobjs[pkgname] = cobj
if secname: # not the root package
if secname not in cobj:
cobj[secname] = {}
return cobj[secname]
else:
return cobj
def generate_config(pkgname='astropy', filename=None, verbose=False):
"""Generates a configuration file, from the list of `ConfigItem`
objects for each subpackage.
.. versionadded:: 4.1
Parameters
----------
pkgname : str or None
The package for which to retrieve the configuration object.
filename : str or file-like or None
If None, the default configuration path is taken from `get_config`.
"""
if verbose:
verbosity = nullcontext
filter_warnings = AstropyDeprecationWarning
else:
verbosity = silence
filter_warnings = Warning
package = importlib.import_module(pkgname)
with verbosity(), warnings.catch_warnings():
warnings.simplefilter('ignore', category=filter_warnings)
for mod in pkgutil.walk_packages(path=package.__path__,
prefix=package.__name__ + '.'):
if (mod.module_finder.path.endswith(('test', 'tests')) or
mod.name.endswith('setup_package')):
# Skip test and setup_package modules
continue
if mod.name.split('.')[-1].startswith('_'):
# Skip private modules
continue
with contextlib.suppress(ImportError):
importlib.import_module(mod.name)
wrapper = TextWrapper(initial_indent="## ", subsequent_indent='## ',
width=78)
if filename is None:
filename = get_config_filename(pkgname)
with contextlib.ExitStack() as stack:
if isinstance(filename, (str, os.PathLike)):
fp = stack.enter_context(open(filename, 'w'))
else:
# assume it's a file object, or io.StringIO
fp = filename
# Parse the subclasses, ordered by their module name
subclasses = ConfigNamespace.__subclasses__()
processed = set()
for conf in sorted(subclasses, key=lambda x: x.__module__):
mod = conf.__module__
# Skip modules for other packages, e.g. astropy modules that
# would be imported when running the function for astroquery.
if mod.split('.')[0] != pkgname:
continue
# Check that modules are not processed twice, which can happen
# when they are imported in another module.
if mod in processed:
continue
else:
processed.add(mod)
print_module = True
for item in conf().values():
if print_module:
# If this is the first item of the module, we print the
# module name, but not if this is the root package...
if item.module != pkgname:
modname = item.module.replace(f'{pkgname}.', '')
fp.write(f"[{modname}]\n\n")
print_module = False
fp.write(wrapper.fill(item.description) + '\n')
if isinstance(item.defaultvalue, (tuple, list)):
if len(item.defaultvalue) == 0:
fp.write(f'# {item.name} = ,\n\n')
elif len(item.defaultvalue) == 1:
fp.write(f'# {item.name} = {item.defaultvalue[0]},\n\n')
else:
fp.write(f'# {item.name} = {",".join(map(str, item.defaultvalue))}\n\n')
else:
fp.write(f'# {item.name} = {item.defaultvalue}\n\n')
def reload_config(packageormod=None, rootname=None):
""" Reloads configuration settings from a configuration file for the root
package of the requested package/module.
This overwrites any changes that may have been made in `ConfigItem`
objects. This applies for any items that are based on this file, which
is determined by the *root* package of ``packageormod``
(e.g. ``'astropy.cfg'`` for the ``'astropy.config.configuration'``
module).
Parameters
----------
packageormod : str or None
The package or module name - see `get_config` for details.
rootname : str or None
Name of the root configuration directory - see `get_config`
for details.
"""
sec = get_config(packageormod, True, rootname=rootname)
# look for the section that is its own parent - that's the base object
while sec.parent is not sec:
sec = sec.parent
sec.reload()
def is_unedited_config_file(content, template_content=None):
"""
Determines if a config file can be safely replaced because it doesn't
actually contain any meaningful content, i.e. if it contains only comments
or is completely empty.
"""
buffer = io.StringIO(content)
raw_cfg = configobj.ConfigObj(buffer, interpolation=True)
# If any of the items is set, return False
return not any(len(v) > 0 for v in raw_cfg.values())
# This function is no more used by astropy but it is kept for the other
# packages that may use it (e.g. astroquery). It should be removed at some
# point.
# this is not in __all__ because it's not intended that a user uses it
@deprecated('5.0')
def update_default_config(pkg, default_cfg_dir_or_fn, version=None, rootname='astropy'):
"""
Checks if the configuration file for the specified package exists,
and if not, copy over the default configuration. If the
configuration file looks like it has already been edited, we do
not write over it, but instead write a file alongside it named
``pkg.version.cfg`` as a "template" for the user.
Parameters
----------
pkg : str
The package to be updated.
default_cfg_dir_or_fn : str
The filename or directory name where the default configuration file is.
If a directory name, ``'pkg.cfg'`` will be used in that directory.
version : str, optional
The current version of the given package. If not provided, it will
be obtained from ``pkg.__version__``.
rootname : str
Name of the root configuration directory.
Returns
-------
updated : bool
If the profile was updated, `True`, otherwise `False`.
Raises
------
AttributeError
If the version number of the package could not determined.
"""
if path.isdir(default_cfg_dir_or_fn):
default_cfgfn = path.join(default_cfg_dir_or_fn, pkg + '.cfg')
else:
default_cfgfn = default_cfg_dir_or_fn
if not path.isfile(default_cfgfn):
# There is no template configuration file, which basically
# means the affiliated package is not using the configuration
# system, so just return.
return False
cfgfn = get_config(pkg, rootname=rootname).filename
with open(default_cfgfn, 'rt', encoding='latin-1') as fr:
template_content = fr.read()
doupdate = False
if cfgfn is not None:
if path.exists(cfgfn):
with open(cfgfn, 'rt', encoding='latin-1') as fd:
content = fd.read()
identical = (content == template_content)
if not identical:
doupdate = is_unedited_config_file(
content, template_content)
elif path.exists(path.dirname(cfgfn)):
doupdate = True
identical = False
if version is None:
version = resolve_name(pkg, '__version__')
# Don't install template files for dev versions, or we'll end up
# spamming `~/.astropy/config`.
if version and 'dev' not in version and cfgfn is not None:
template_path = path.join(
get_config_dir(rootname=rootname), f'{pkg}.{version}.cfg')
needs_template = not path.exists(template_path)
else:
needs_template = False
if doupdate or needs_template:
if needs_template:
with open(template_path, 'wt', encoding='latin-1') as fw:
fw.write(template_content)
# If we just installed a new template file and we can't
# update the main configuration file because it has user
# changes, display a warning.
if not identical and not doupdate:
warn(
"The configuration options in {} {} may have changed, "
"your configuration file was not updated in order to "
"preserve local changes. A new configuration template "
"has been saved to '{}'.".format(
pkg, version, template_path),
ConfigurationChangedWarning)
if doupdate and not identical:
with open(cfgfn, 'wt', encoding='latin-1') as fw:
fw.write(template_content)
return True
return False
def create_config_file(pkg, rootname='astropy', overwrite=False):
"""
Create the default configuration file for the specified package.
If the file already exists, it is updated only if it has not been
modified. Otherwise the ``overwrite`` flag is needed to overwrite it.
Parameters
----------
pkg : str
The package to be updated.
rootname : str
Name of the root configuration directory.
overwrite : bool
Force updating the file if it already exists.
Returns
-------
updated : bool
If the profile was updated, `True`, otherwise `False`.
"""
# local import to prevent using the logger before it is configured
from astropy.logger import log
cfgfn = get_config_filename(pkg, rootname=rootname)
# generate the default config template
template_content = io.StringIO()
generate_config(pkg, template_content)
template_content.seek(0)
template_content = template_content.read()
doupdate = True
# if the file already exists, check that it has not been modified
if cfgfn is not None and path.exists(cfgfn):
with open(cfgfn, 'rt', encoding='latin-1') as fd:
content = fd.read()
doupdate = is_unedited_config_file(content, template_content)
if doupdate or overwrite:
with open(cfgfn, 'wt', encoding='latin-1') as fw:
fw.write(template_content)
log.info('The configuration file has been successfully written '
f'to {cfgfn}')
return True
elif not doupdate:
log.warning('The configuration file already exists and seems to '
'have been customized, so it has not been updated. '
'Use overwrite=True if you really want to update it.')
return False
|
8bf4abe46121188d939158adccea14afa50aab22c5370d1d97fde6081caea281 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
""" This module contains functions to determine where configuration and
data/cache files used by Astropy should be placed.
"""
from functools import wraps
import os
import shutil
import sys
__all__ = ['get_config_dir', 'get_cache_dir', 'set_temp_config',
'set_temp_cache']
def _find_home():
"""Locates and return the home directory (or best approximation) on this
system.
Raises
------
OSError
If the home directory cannot be located - usually means you are running
Astropy on some obscure platform that doesn't have standard home
directories.
"""
try:
homedir = os.path.expanduser('~')
except Exception:
# Linux, Unix, AIX, OS X
if os.name == 'posix':
if 'HOME' in os.environ:
homedir = os.environ['HOME']
else:
raise OSError('Could not find unix home directory to search for '
'astropy config dir')
elif os.name == 'nt': # This is for all modern Windows (NT or after)
if 'MSYSTEM' in os.environ and os.environ.get('HOME'):
# Likely using an msys shell; use whatever it is using for its
# $HOME directory
homedir = os.environ['HOME']
# See if there's a local home
elif 'HOMEDRIVE' in os.environ and 'HOMEPATH' in os.environ:
homedir = os.path.join(os.environ['HOMEDRIVE'],
os.environ['HOMEPATH'])
# Maybe a user profile?
elif 'USERPROFILE' in os.environ:
homedir = os.path.join(os.environ['USERPROFILE'])
else:
try:
import winreg as wreg
shell_folders = r'Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders' # noqa: E501
key = wreg.OpenKey(wreg.HKEY_CURRENT_USER, shell_folders)
homedir = wreg.QueryValueEx(key, 'Personal')[0]
key.Close()
except Exception:
# As a final possible resort, see if HOME is present
if 'HOME' in os.environ:
homedir = os.environ['HOME']
else:
raise OSError('Could not find windows home directory to '
'search for astropy config dir')
else:
# for other platforms, try HOME, although it probably isn't there
if 'HOME' in os.environ:
homedir = os.environ['HOME']
else:
raise OSError('Could not find a home directory to search for '
'astropy config dir - are you on an unsupported '
'platform?')
return homedir
def get_config_dir(rootname='astropy'):
"""
Determines the package configuration directory name and creates the
directory if it doesn't exist.
This directory is typically ``$HOME/.astropy/config``, but if the
XDG_CONFIG_HOME environment variable is set and the
``$XDG_CONFIG_HOME/astropy`` directory exists, it will be that directory.
If neither exists, the former will be created and symlinked to the latter.
Parameters
----------
rootname : str
Name of the root configuration directory. For example, if ``rootname =
'pkgname'``, the configuration directory would be ``<home>/.pkgname/``
rather than ``<home>/.astropy`` (depending on platform).
Returns
-------
configdir : str
The absolute path to the configuration directory.
"""
# symlink will be set to this if the directory is created
linkto = None
# If using set_temp_config, that overrides all
if set_temp_config._temp_path is not None:
xch = set_temp_config._temp_path
config_path = os.path.join(xch, rootname)
if not os.path.exists(config_path):
os.mkdir(config_path)
return os.path.abspath(config_path)
# first look for XDG_CONFIG_HOME
xch = os.environ.get('XDG_CONFIG_HOME')
if xch is not None and os.path.exists(xch):
xchpth = os.path.join(xch, rootname)
if not os.path.islink(xchpth):
if os.path.exists(xchpth):
return os.path.abspath(xchpth)
else:
linkto = xchpth
return os.path.abspath(_find_or_create_root_dir('config', linkto, rootname))
def get_cache_dir(rootname="astropy"):
"""
Determines the Astropy cache directory name and creates the directory if it
doesn't exist.
This directory is typically ``$HOME/.astropy/cache``, but if the
XDG_CACHE_HOME environment variable is set and the
``$XDG_CACHE_HOME/astropy`` directory exists, it will be that directory.
If neither exists, the former will be created and symlinked to the latter.
Parameters
----------
rootname : str
Name of the root cache directory. For example, if
``rootname = 'pkgname'``, the cache directory will be
``<cache>/.pkgname/``.
Returns
-------
cachedir : str
The absolute path to the cache directory.
"""
# symlink will be set to this if the directory is created
linkto = None
# If using set_temp_cache, that overrides all
if set_temp_cache._temp_path is not None:
xch = set_temp_cache._temp_path
cache_path = os.path.join(xch, rootname)
if not os.path.exists(cache_path):
os.mkdir(cache_path)
return os.path.abspath(cache_path)
# first look for XDG_CACHE_HOME
xch = os.environ.get('XDG_CACHE_HOME')
if xch is not None and os.path.exists(xch):
xchpth = os.path.join(xch, rootname)
if not os.path.islink(xchpth):
if os.path.exists(xchpth):
return os.path.abspath(xchpth)
else:
linkto = xchpth
return os.path.abspath(_find_or_create_root_dir('cache', linkto, rootname))
class _SetTempPath:
_temp_path = None
_default_path_getter = None
def __init__(self, path=None, delete=False):
if path is not None:
path = os.path.abspath(path)
self._path = path
self._delete = delete
self._prev_path = self.__class__._temp_path
def __enter__(self):
self.__class__._temp_path = self._path
try:
return self._default_path_getter('astropy')
except Exception:
self.__class__._temp_path = self._prev_path
raise
def __exit__(self, *args):
self.__class__._temp_path = self._prev_path
if self._delete and self._path is not None:
shutil.rmtree(self._path)
def __call__(self, func):
"""Implements use as a decorator."""
@wraps(func)
def wrapper(*args, **kwargs):
with self:
func(*args, **kwargs)
return wrapper
class set_temp_config(_SetTempPath):
"""
Context manager to set a temporary path for the Astropy config, primarily
for use with testing.
If the path set by this context manager does not already exist it will be
created, if possible.
This may also be used as a decorator on a function to set the config path
just within that function.
Parameters
----------
path : str, optional
The directory (which must exist) in which to find the Astropy config
files, or create them if they do not already exist. If None, this
restores the config path to the user's default config path as returned
by `get_config_dir` as though this context manager were not in effect
(this is useful for testing). In this case the ``delete`` argument is
always ignored.
delete : bool, optional
If True, cleans up the temporary directory after exiting the temp
context (default: False).
"""
_default_path_getter = staticmethod(get_config_dir)
def __enter__(self):
# Special case for the config case, where we need to reset all the
# cached config objects. We do keep the cache, since some of it
# may have been set programmatically rather than be stored in the
# config file (e.g., iers.conf.auto_download=False for our tests).
from .configuration import _cfgobjs
self._cfgobjs_copy = _cfgobjs.copy()
_cfgobjs.clear()
return super().__enter__()
def __exit__(self, *args):
from .configuration import _cfgobjs
_cfgobjs.clear()
_cfgobjs.update(self._cfgobjs_copy)
del self._cfgobjs_copy
super().__exit__(*args)
class set_temp_cache(_SetTempPath):
"""
Context manager to set a temporary path for the Astropy download cache,
primarily for use with testing (though there may be other applications
for setting a different cache directory, for example to switch to a cache
dedicated to large files).
If the path set by this context manager does not already exist it will be
created, if possible.
This may also be used as a decorator on a function to set the cache path
just within that function.
Parameters
----------
path : str
The directory (which must exist) in which to find the Astropy cache
files, or create them if they do not already exist. If None, this
restores the cache path to the user's default cache path as returned
by `get_cache_dir` as though this context manager were not in effect
(this is useful for testing). In this case the ``delete`` argument is
always ignored.
delete : bool, optional
If True, cleans up the temporary directory after exiting the temp
context (default: False).
"""
_default_path_getter = staticmethod(get_cache_dir)
def _find_or_create_root_dir(dirnm, linkto, pkgname='astropy'):
innerdir = os.path.join(_find_home(), f'.{pkgname}')
maindir = os.path.join(_find_home(), f'.{pkgname}', dirnm)
if not os.path.exists(maindir):
# first create .astropy dir if needed
if not os.path.exists(innerdir):
try:
os.mkdir(innerdir)
except OSError:
if not os.path.isdir(innerdir):
raise
elif not os.path.isdir(innerdir):
msg = 'Intended {0} {1} directory {1} is actually a file.'
raise OSError(msg.format(pkgname, dirnm, maindir))
try:
os.mkdir(maindir)
except OSError:
if not os.path.isdir(maindir):
raise
if (not sys.platform.startswith('win') and
linkto is not None and
not os.path.exists(linkto)):
os.symlink(maindir, linkto)
elif not os.path.isdir(maindir):
msg = 'Intended {0} {1} directory {1} is actually a file.'
raise OSError(msg.format(pkgname, dirnm, maindir))
return os.path.abspath(maindir)
|
61c457aac7921f6bc626391214e3718a070d8bd5742e8d0f6b0160a2b5a11f7e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""This module contains functions and classes for finding information about
affiliated packages and installing them.
"""
__all__ = []
|
0002370a62a344c79c835fbaa5f03fdb563af7d571be9a74a35c87370b3ae238 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Distribution class and associated machinery.
"""
import builtins
import numpy as np
from astropy import units as u
from astropy import stats
__all__ = ['Distribution']
# we set this by hand because the symbolic expression (below) requires scipy
# SMAD_SCALE_FACTOR = 1 / scipy.stats.norm.ppf(0.75)
SMAD_SCALE_FACTOR = 1.48260221850560203193936104071326553821563720703125
class Distribution:
"""
A scalar value or array values with associated uncertainty distribution.
This object will take its exact type from whatever the ``samples`` argument
is. In general this is expected to be an `~astropy.units.Quantity` or
`numpy.ndarray`, although anything compatible with `numpy.asanyarray` is
possible.
See also: https://docs.astropy.org/en/stable/uncertainty/
Parameters
----------
samples : array-like
The distribution, with sampling along the *leading* axis. If 1D, the
sole dimension is used as the sampling axis (i.e., it is a scalar
distribution).
"""
_generated_subclasses = {}
def __new__(cls, samples):
if isinstance(samples, Distribution):
samples = samples.distribution
else:
samples = np.asanyarray(samples, order='C')
if samples.shape == ():
raise TypeError('Attempted to initialize a Distribution with a scalar')
new_dtype = np.dtype({'names': ['samples'],
'formats': [(samples.dtype, (samples.shape[-1],))]})
samples_cls = type(samples)
new_cls = cls._generated_subclasses.get(samples_cls)
if new_cls is None:
# Make a new class with the combined name, inserting Distribution
# itself below the samples class since that way Quantity methods
# like ".to" just work (as .view() gets intercepted). However,
# repr and str are problems, so we put those on top.
# TODO: try to deal with this at the lower level. The problem is
# that array2string does not allow one to override how structured
# arrays are typeset, leading to all samples to be shown. It may
# be possible to hack oneself out by temporarily becoming a void.
new_name = samples_cls.__name__ + cls.__name__
new_cls = type(
new_name,
(_DistributionRepr, samples_cls, ArrayDistribution),
{'_samples_cls': samples_cls})
cls._generated_subclasses[samples_cls] = new_cls
self = samples.view(dtype=new_dtype, type=new_cls)
# Get rid of trailing dimension of 1.
self.shape = samples.shape[:-1]
return self
@property
def distribution(self):
return self['samples']
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
converted = []
outputs = kwargs.pop('out', None)
if outputs:
kwargs['out'] = tuple((output.distribution if
isinstance(output, Distribution)
else output) for output in outputs)
if method in {'reduce', 'accumulate', 'reduceat'}:
axis = kwargs.get('axis', None)
if axis is None:
assert isinstance(inputs[0], Distribution)
kwargs['axis'] = tuple(range(inputs[0].ndim))
for input_ in inputs:
if isinstance(input_, Distribution):
converted.append(input_.distribution)
else:
shape = getattr(input_, 'shape', ())
if shape:
converted.append(input_[..., np.newaxis])
else:
converted.append(input_)
results = getattr(ufunc, method)(*converted, **kwargs)
if not isinstance(results, tuple):
results = (results,)
if outputs is None:
outputs = (None,) * len(results)
finals = []
for result, output in zip(results, outputs):
if output is not None:
finals.append(output)
else:
if getattr(result, 'shape', False):
finals.append(Distribution(result))
else:
finals.append(result)
return finals if len(finals) > 1 else finals[0]
@property
def n_samples(self):
"""
The number of samples of this distribution. A single `int`.
"""
return self.dtype['samples'].shape[0]
def pdf_mean(self, dtype=None, out=None):
"""
The mean of this distribution.
Arguments are as for `numpy.mean`.
"""
return self.distribution.mean(axis=-1, dtype=dtype, out=out)
def pdf_std(self, dtype=None, out=None, ddof=0):
"""
The standard deviation of this distribution.
Arguments are as for `numpy.std`.
"""
return self.distribution.std(axis=-1, dtype=dtype, out=out, ddof=ddof)
def pdf_var(self, dtype=None, out=None, ddof=0):
"""
The variance of this distribution.
Arguments are as for `numpy.var`.
"""
return self.distribution.var(axis=-1, dtype=dtype, out=out, ddof=ddof)
def pdf_median(self, out=None):
"""
The median of this distribution.
Parameters
----------
out : array, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
"""
return np.median(self.distribution, axis=-1, out=out)
def pdf_mad(self, out=None):
"""
The median absolute deviation of this distribution.
Parameters
----------
out : array, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
"""
median = self.pdf_median(out=out)
absdiff = np.abs(self - median)
return np.median(absdiff.distribution, axis=-1, out=median,
overwrite_input=True)
def pdf_smad(self, out=None):
"""
The median absolute deviation of this distribution rescaled to match the
standard deviation for a normal distribution.
Parameters
----------
out : array, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
"""
result = self.pdf_mad(out=out)
result *= SMAD_SCALE_FACTOR
return result
def pdf_percentiles(self, percentile, **kwargs):
"""
Compute percentiles of this Distribution.
Parameters
----------
percentile : float or array of float or `~astropy.units.Quantity`
The desired percentiles of the distribution (i.e., on [0,100]).
`~astropy.units.Quantity` will be converted to percent, meaning
that a ``dimensionless_unscaled`` `~astropy.units.Quantity` will
be interpreted as a quantile.
Additional keywords are passed into `numpy.percentile`.
Returns
-------
percentiles : `~astropy.units.Quantity` ['dimensionless']
The ``fracs`` percentiles of this distribution.
"""
percentile = u.Quantity(percentile, u.percent).value
percs = np.percentile(self.distribution, percentile, axis=-1, **kwargs)
# numpy.percentile strips units for unclear reasons, so we have to make
# a new object with units
if hasattr(self.distribution, '_new_view'):
return self.distribution._new_view(percs)
else:
return percs
def pdf_histogram(self, **kwargs):
"""
Compute histogram over the samples in the distribution.
Parameters
----------
All keyword arguments are passed into `astropy.stats.histogram`. Note
That some of these options may not be valid for some multidimensional
distributions.
Returns
-------
hist : array
The values of the histogram. Trailing dimension is the histogram
dimension.
bin_edges : array of dtype float
Return the bin edges ``(length(hist)+1)``. Trailing dimension is the
bin histogram dimension.
"""
distr = self.distribution
raveled_distr = distr.reshape(distr.size//distr.shape[-1], distr.shape[-1])
nhists = []
bin_edges = []
for d in raveled_distr:
nhist, bin_edge = stats.histogram(d, **kwargs)
nhists.append(nhist)
bin_edges.append(bin_edge)
nhists = np.array(nhists)
nh_shape = self.shape + (nhists.size//self.size,)
bin_edges = np.array(bin_edges)
be_shape = self.shape + (bin_edges.size//self.size,)
return nhists.reshape(nh_shape), bin_edges.reshape(be_shape)
class ScalarDistribution(Distribution, np.void):
"""Scalar distribution.
This class mostly exists to make `~numpy.array2print` possible for
all subclasses. It is a scalar element, still with n_samples samples.
"""
pass
class ArrayDistribution(Distribution, np.ndarray):
# This includes the important override of view and __getitem__
# which are needed for all ndarray subclass Distributions, but not
# for the scalar one.
_samples_cls = np.ndarray
# Override view so that we stay a Distribution version of the new type.
def view(self, dtype=None, type=None):
"""New view of array with the same data.
Like `~numpy.ndarray.view` except that the result will always be a new
`~astropy.uncertainty.Distribution` instance. If the requested
``type`` is a `~astropy.uncertainty.Distribution`, then no change in
``dtype`` is allowed.
"""
if type is None and (isinstance(dtype, builtins.type)
and issubclass(dtype, np.ndarray)):
type = dtype
dtype = None
view_args = [item for item in (dtype, type) if item is not None]
if type is None or (isinstance(type, builtins.type)
and issubclass(type, Distribution)):
if dtype is not None and dtype != self.dtype:
raise ValueError('cannot view as Distribution subclass with a new dtype.')
return super().view(*view_args)
# View as the new non-Distribution class, but turn into a Distribution again.
result = self.distribution.view(*view_args)
return Distribution(result)
# Override __getitem__ so that 'samples' is returned as the sample class.
def __getitem__(self, item):
result = super().__getitem__(item)
if item == 'samples':
# Here, we need to avoid our own redefinition of view.
return super(ArrayDistribution, result).view(self._samples_cls)
elif isinstance(result, np.void):
return result.view((ScalarDistribution, result.dtype))
else:
return result
class _DistributionRepr:
def __repr__(self):
reprarr = repr(self.distribution)
if reprarr.endswith('>'):
firstspace = reprarr.find(' ')
reprarr = reprarr[firstspace+1:-1] # :-1] removes the ending '>'
return '<{} {} with n_samples={}>'.format(self.__class__.__name__,
reprarr, self.n_samples)
else: # numpy array-like
firstparen = reprarr.find('(')
reprarr = reprarr[firstparen:]
return f'{self.__class__.__name__}{reprarr} with n_samples={self.n_samples}'
return reprarr
def __str__(self):
distrstr = str(self.distribution)
toadd = f' with n_samples={self.n_samples}'
return distrstr + toadd
def _repr_latex_(self):
if hasattr(self.distribution, '_repr_latex_'):
superlatex = self.distribution._repr_latex_()
toadd = fr', \; n_{{\rm samp}}={self.n_samples}'
return superlatex[:-1] + toadd + superlatex[-1]
else:
return None
class NdarrayDistribution(_DistributionRepr, ArrayDistribution):
pass
# Ensure our base NdarrayDistribution is known.
Distribution._generated_subclasses[np.ndarray] = NdarrayDistribution
|
c42dd216e1a0c69e490d61fb1b09076b19404c9c809f8f2594a05375328d6013 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Built-in distribution-creation functions.
"""
from warnings import warn
import numpy as np
from astropy import units as u
from .core import Distribution
__all__ = ['normal', 'poisson', 'uniform']
def normal(center, *, std=None, var=None, ivar=None, n_samples,
cls=Distribution, **kwargs):
"""
Create a Gaussian/normal distribution.
Parameters
----------
center : `~astropy.units.Quantity`
The center of this distribution
std : `~astropy.units.Quantity` or None
The standard deviation/σ of this distribution. Shape must match and unit
must be compatible with ``center``, or be `None` (if ``var`` or ``ivar``
are set).
var : `~astropy.units.Quantity` or None
The variance of this distribution. Shape must match and unit must be
compatible with ``center``, or be `None` (if ``std`` or ``ivar`` are set).
ivar : `~astropy.units.Quantity` or None
The inverse variance of this distribution. Shape must match and unit
must be compatible with ``center``, or be `None` (if ``std`` or ``var``
are set).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution. Typically a
`Distribution` subclass.
Remaining keywords are passed into the constructor of the ``cls``
Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled Gaussian distribution.
The type will be the same as the parameter ``cls``.
"""
center = np.asanyarray(center)
if var is not None:
if std is None:
std = np.asanyarray(var)**0.5
else:
raise ValueError('normal cannot take both std and var')
if ivar is not None:
if std is None:
std = np.asanyarray(ivar)**-0.5
else:
raise ValueError('normal cannot take both ivar and '
'and std or var')
if std is None:
raise ValueError('normal requires one of std, var, or ivar')
else:
std = np.asanyarray(std)
randshape = np.broadcast(std, center).shape + (n_samples,)
samples = center[..., np.newaxis] + np.random.randn(*randshape) * std[..., np.newaxis]
return cls(samples, **kwargs)
COUNT_UNITS = (u.count, u.electron, u.dimensionless_unscaled, u.chan, u.bin, u.vox, u.bit, u.byte)
def poisson(center, n_samples, cls=Distribution, **kwargs):
"""
Create a Poisson distribution.
Parameters
----------
center : `~astropy.units.Quantity`
The center value of this distribution (i.e., λ).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution. Typically a
`Distribution` subclass.
Remaining keywords are passed into the constructor of the ``cls``
Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled Poisson distribution.
The type will be the same as the parameter ``cls``.
"""
# we convert to arrays because np.random.poisson has trouble with quantities
has_unit = False
if hasattr(center, 'unit'):
has_unit = True
poissonarr = np.asanyarray(center.value)
else:
poissonarr = np.asanyarray(center)
randshape = poissonarr.shape + (n_samples,)
samples = np.random.poisson(poissonarr[..., np.newaxis], randshape)
if has_unit:
if center.unit == u.adu:
warn('ADUs were provided to poisson. ADUs are not strictly count'
'units because they need the gain to be applied. It is '
'recommended you apply the gain to convert to e.g. electrons.')
elif center.unit not in COUNT_UNITS:
warn('Unit {} was provided to poisson, which is not one of {}, '
'and therefore suspect as a "counting" unit. Ensure you mean '
'to use Poisson statistics.'.format(center.unit, COUNT_UNITS))
# re-attach the unit
samples = samples * center.unit
return cls(samples, **kwargs)
def uniform(*, lower=None, upper=None, center=None, width=None, n_samples,
cls=Distribution, **kwargs):
"""
Create a Uniform distriution from the lower and upper bounds.
Note that this function requires keywords to be explicit, and requires
either ``lower``/``upper`` or ``center``/``width``.
Parameters
----------
lower : array-like
The lower edge of this distribution. If a `~astropy.units.Quantity`, the
distribution will have the same units as ``lower``.
upper : `~astropy.units.Quantity`
The upper edge of this distribution. Must match shape and if a
`~astropy.units.Quantity` must have compatible units with ``lower``.
center : array-like
The center value of the distribution. Cannot be provided at the same
time as ``lower``/``upper``.
width : array-like
The width of the distribution. Must have the same shape and compatible
units with ``center`` (if any).
n_samples : int
The number of Monte Carlo samples to use with this distribution
cls : class
The class to use to create this distribution. Typically a
`Distribution` subclass.
Remaining keywords are passed into the constructor of the ``cls``
Returns
-------
distr : `~astropy.uncertainty.Distribution` or object
The sampled uniform distribution.
The type will be the same as the parameter ``cls``.
"""
if center is None and width is None:
lower = np.asanyarray(lower)
upper = np.asanyarray(upper)
if lower.shape != upper.shape:
raise ValueError('lower and upper must have consistent shapes')
elif upper is None and lower is None:
center = np.asanyarray(center)
width = np.asanyarray(width)
lower = center - width/2
upper = center + width/2
else:
raise ValueError('either upper/lower or center/width must be given '
'to uniform - other combinations are not valid')
newshape = lower.shape + (n_samples,)
if lower.shape == tuple() and upper.shape == tuple():
width = upper - lower # scalar
else:
width = (upper - lower)[:, np.newaxis]
lower = lower[:, np.newaxis]
samples = lower + width * np.random.uniform(size=newshape)
return cls(samples, **kwargs)
|
326a2c41c6da21e1d0352cef4a889f332c24215e9bcc8355d84fc5234b9733e1 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This sub-package contains classes and functions for creating distributions that
work similar to `~astropy.units.Quantity` or array objects, but can propagate
uncertainties.
"""
from .core import *
from .distributions import *
|
888d3afe6e2163ca0358471c4f37d2c3a445eb2336a61ba3440d12ed34d6e4d1 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module implements classes (called Fitters) which combine optimization
algorithms (typically from `scipy.optimize`) with statistic functions to perform
fitting. Fitters are implemented as callable classes. In addition to the data
to fit, the ``__call__`` method takes an instance of
`~astropy.modeling.core.FittableModel` as input, and returns a copy of the
model with its parameters determined by the optimizer.
Optimization algorithms, called "optimizers" are implemented in
`~astropy.modeling.optimizers` and statistic functions are in
`~astropy.modeling.statistic`. The goal is to provide an easy to extend
framework and allow users to easily create new fitters by combining statistics
with optimizers.
There are two exceptions to the above scheme.
`~astropy.modeling.fitting.LinearLSQFitter` uses Numpy's `~numpy.linalg.lstsq`
function. `~astropy.modeling.fitting.LevMarLSQFitter` uses
`~scipy.optimize.leastsq` which combines optimization and statistic in one
implementation.
"""
# pylint: disable=invalid-name
import abc
import inspect
import operator
import warnings
from importlib.metadata import entry_points
from functools import reduce, wraps
import numpy as np
from astropy.units import Quantity
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils.decorators import deprecated
from .utils import poly_map_domain, _combine_equivalency_dict
from .optimizers import (SLSQP, Simplex)
from .statistic import (leastsquare)
from .optimizers import (DEFAULT_MAXITER, DEFAULT_EPS, DEFAULT_ACC)
from .spline import (SplineInterpolateFitter, SplineSmoothingFitter,
SplineExactKnotsFitter, SplineSplrepFitter)
__all__ = ['LinearLSQFitter', 'LevMarLSQFitter', 'FittingWithOutlierRemoval',
'SLSQPLSQFitter', 'SimplexLSQFitter', 'JointFitter', 'Fitter',
"ModelLinearityError", "ModelsError"]
# Statistic functions implemented in `astropy.modeling.statistic.py
STATISTICS = [leastsquare]
# Optimizers implemented in `astropy.modeling.optimizers.py
OPTIMIZERS = [Simplex, SLSQP]
class Covariance():
"""Class for covariance matrix calculated by fitter. """
def __init__(self, cov_matrix, param_names):
self.cov_matrix = cov_matrix
self.param_names = param_names
def pprint(self, max_lines, round_val):
# Print and label lower triangle of covariance matrix
# Print rows for params up to `max_lines`, round floats to 'round_val'
longest_name = max([len(x) for x in self.param_names])
ret_str = 'parameter variances / covariances \n'
fstring = f'{"": <{longest_name}}| {{0}}\n'
for i, row in enumerate(self.cov_matrix):
if i <= max_lines-1:
param = self.param_names[i]
ret_str += fstring.replace(' '*len(param), param, 1).\
format(repr(np.round(row[:i+1], round_val))[7:-2])
else:
ret_str += '...'
return(ret_str.rstrip())
def __repr__(self):
return(self.pprint(max_lines=10, round_val=3))
def __getitem__(self, params):
# index covariance matrix by parameter names or indices
if len(params) != 2:
raise ValueError('Covariance must be indexed by two values.')
if all(isinstance(item, str) for item in params):
i1, i2 = self.param_names.index(params[0]), self.param_names.index(params[1])
elif all(isinstance(item, int) for item in params):
i1, i2 = params
else:
raise TypeError('Covariance can be indexed by two parameter names or integer indices.')
return(self.cov_matrix[i1][i2])
class StandardDeviations():
""" Class for fitting uncertainties."""
def __init__(self, cov_matrix, param_names):
self.param_names = param_names
self.stds = self._calc_stds(cov_matrix)
def _calc_stds(self, cov_matrix):
# sometimes scipy lstsq returns a non-sensical negative vals in the
# diagonals of the cov_x it computes.
stds = [np.sqrt(x) if x > 0 else None for x in np.diag(cov_matrix)]
return stds
def pprint(self, max_lines, round_val):
longest_name = max([len(x) for x in self.param_names])
ret_str = 'standard deviations\n'
fstring = '{0}{1}| {2}\n'
for i, std in enumerate(self.stds):
if i <= max_lines-1:
param = self.param_names[i]
ret_str += fstring.format(param,
' ' * (longest_name - len(param)),
str(np.round(std, round_val)))
else:
ret_str += '...'
return(ret_str.rstrip())
def __repr__(self):
return(self.pprint(max_lines=10, round_val=3))
def __getitem__(self, param):
if isinstance(param, str):
i = self.param_names.index(param)
elif isinstance(param, int):
i = param
else:
raise TypeError('Standard deviation can be indexed by parameter name or integer.')
return(self.stds[i])
class ModelsError(Exception):
"""Base class for model exceptions"""
class ModelLinearityError(ModelsError):
""" Raised when a non-linear model is passed to a linear fitter."""
class UnsupportedConstraintError(ModelsError, ValueError):
"""
Raised when a fitter does not support a type of constraint.
"""
class _FitterMeta(abc.ABCMeta):
"""
Currently just provides a registry for all Fitter classes.
"""
registry = set()
def __new__(mcls, name, bases, members):
cls = super().__new__(mcls, name, bases, members)
if not inspect.isabstract(cls) and not name.startswith('_'):
mcls.registry.add(cls)
return cls
def fitter_unit_support(func):
"""
This is a decorator that can be used to add support for dealing with
quantities to any __call__ method on a fitter which may not support
quantities itself. This is done by temporarily removing units from all
parameters then adding them back once the fitting has completed.
"""
@wraps(func)
def wrapper(self, model, x, y, z=None, **kwargs):
equivalencies = kwargs.pop('equivalencies', None)
data_has_units = (isinstance(x, Quantity) or
isinstance(y, Quantity) or
isinstance(z, Quantity))
model_has_units = model._has_units
if data_has_units or model_has_units:
if model._supports_unit_fitting:
# We now combine any instance-level input equivalencies with user
# specified ones at call-time.
input_units_equivalencies = _combine_equivalency_dict(
model.inputs, equivalencies, model.input_units_equivalencies)
# If input_units is defined, we transform the input data into those
# expected by the model. We hard-code the input names 'x', and 'y'
# here since FittableModel instances have input names ('x',) or
# ('x', 'y')
if model.input_units is not None:
if isinstance(x, Quantity):
x = x.to(model.input_units[model.inputs[0]],
equivalencies=input_units_equivalencies[model.inputs[0]])
if isinstance(y, Quantity) and z is not None:
y = y.to(model.input_units[model.inputs[1]],
equivalencies=input_units_equivalencies[model.inputs[1]])
# Create a dictionary mapping the real model inputs and outputs
# names to the data. This remapping of names must be done here, after
# the input data is converted to the correct units.
rename_data = {model.inputs[0]: x}
if z is not None:
rename_data[model.outputs[0]] = z
rename_data[model.inputs[1]] = y
else:
rename_data[model.outputs[0]] = y
rename_data['z'] = None
# We now strip away the units from the parameters, taking care to
# first convert any parameters to the units that correspond to the
# input units (to make sure that initial guesses on the parameters)
# are in the right unit system
model = model.without_units_for_data(**rename_data)
if isinstance(model, tuple):
rename_data['_left_kwargs'] = model[1]
rename_data['_right_kwargs'] = model[2]
model = model[0]
# We strip away the units from the input itself
add_back_units = False
if isinstance(x, Quantity):
add_back_units = True
xdata = x.value
else:
xdata = np.asarray(x)
if isinstance(y, Quantity):
add_back_units = True
ydata = y.value
else:
ydata = np.asarray(y)
if z is not None:
if isinstance(z, Quantity):
add_back_units = True
zdata = z.value
else:
zdata = np.asarray(z)
# We run the fitting
if z is None:
model_new = func(self, model, xdata, ydata, **kwargs)
else:
model_new = func(self, model, xdata, ydata, zdata, **kwargs)
# And finally we add back units to the parameters
if add_back_units:
model_new = model_new.with_units_from_data(**rename_data)
return model_new
else:
raise NotImplementedError("This model does not support being "
"fit to data with units.")
else:
return func(self, model, x, y, z=z, **kwargs)
return wrapper
class Fitter(metaclass=_FitterMeta):
"""
Base class for all fitters.
Parameters
----------
optimizer : callable
A callable implementing an optimization algorithm
statistic : callable
Statistic function
"""
supported_constraints = []
def __init__(self, optimizer, statistic):
if optimizer is None:
raise ValueError("Expected an optimizer.")
if statistic is None:
raise ValueError("Expected a statistic function.")
if inspect.isclass(optimizer):
# a callable class
self._opt_method = optimizer()
elif inspect.isfunction(optimizer):
self._opt_method = optimizer
else:
raise ValueError("Expected optimizer to be a callable class or a function.")
if inspect.isclass(statistic):
self._stat_method = statistic()
else:
self._stat_method = statistic
def objective_function(self, fps, *args):
"""
Function to minimize.
Parameters
----------
fps : list
parameters returned by the fitter
args : list
[model, [other_args], [input coordinates]]
other_args may include weights or any other quantities specific for
a statistic
Notes
-----
The list of arguments (args) is set in the `__call__` method.
Fitters may overwrite this method, e.g. when statistic functions
require other arguments.
"""
model = args[0]
meas = args[-1]
fitter_to_model_params(model, fps)
res = self._stat_method(meas, model, *args[1:-1])
return res
@staticmethod
def _add_fitting_uncertainties(*args):
"""
When available, calculate and sets the parameter covariance matrix
(model.cov_matrix) and standard deviations (model.stds).
"""
return None
@abc.abstractmethod
def __call__(self):
"""
This method performs the actual fitting and modifies the parameter list
of a model.
Fitter subclasses should implement this method.
"""
raise NotImplementedError("Subclasses should implement this method.")
# TODO: I have ongoing branch elsewhere that's refactoring this module so that
# all the fitter classes in here are Fitter subclasses. In the meantime we
# need to specify that _FitterMeta is its metaclass.
class LinearLSQFitter(metaclass=_FitterMeta):
"""
A class performing a linear least square fitting.
Uses `numpy.linalg.lstsq` to do the fitting.
Given a model and data, fits the model to the data and changes the
model's parameters. Keeps a dictionary of auxiliary fitting information.
Notes
-----
Note that currently LinearLSQFitter does not support compound models.
"""
supported_constraints = ['fixed']
supports_masked_input = True
def __init__(self, calc_uncertainties=False):
self.fit_info = {'residuals': None,
'rank': None,
'singular_values': None,
'params': None
}
self._calc_uncertainties=calc_uncertainties
@staticmethod
def _is_invertible(m):
"""Check if inverse of matrix can be obtained."""
if m.shape[0] != m.shape[1]:
return False
if np.linalg.matrix_rank(m) < m.shape[0]:
return False
return True
def _add_fitting_uncertainties(self, model, a, n_coeff, x, y, z=None,
resids=None):
"""
Calculate and parameter covariance matrix and standard deviations
and set `cov_matrix` and `stds` attributes.
"""
x_dot_x_prime = np.dot(a.T, a)
masked = False or hasattr(y, 'mask')
# check if invertible. if not, can't calc covariance.
if not self._is_invertible(x_dot_x_prime):
return(model)
inv_x_dot_x_prime = np.linalg.inv(x_dot_x_prime)
if z is None: # 1D models
if len(model) == 1: # single model
mask = None
if masked:
mask = y.mask
xx = np.ma.array(x, mask=mask)
RSS = [(1/(xx.count()-n_coeff)) * resids]
if len(model) > 1: # model sets
RSS = [] # collect sum residuals squared for each model in set
for j in range(len(model)):
mask = None
if masked:
mask = y.mask[..., j].flatten()
xx = np.ma.array(x, mask=mask)
eval_y = model(xx, model_set_axis=False)
eval_y = np.rollaxis(eval_y, model.model_set_axis)[j]
RSS.append((1/(xx.count()-n_coeff)) * np.sum((y[..., j] - eval_y)**2))
else: # 2D model
if len(model) == 1:
mask = None
if masked:
warnings.warn('Calculation of fitting uncertainties '
'for 2D models with masked values not '
'currently supported.\n',
AstropyUserWarning)
return
xx, yy = np.ma.array(x, mask=mask), np.ma.array(y, mask=mask)
# len(xx) instead of xx.count. this will break if values are masked?
RSS = [(1/(len(xx)-n_coeff)) * resids]
else:
RSS = []
for j in range(len(model)):
eval_z = model(x, y, model_set_axis=False)
mask = None # need to figure out how to deal w/ masking here.
if model.model_set_axis == 1:
# model_set_axis passed when evaluating only refers to input shapes
# so output must be reshaped for model_set_axis=1.
eval_z = np.rollaxis(eval_z, 1)
eval_z = eval_z[j]
RSS.append([(1/(len(x)-n_coeff)) * np.sum((z[j] - eval_z)**2)])
covs = [inv_x_dot_x_prime * r for r in RSS]
free_param_names = [x for x in model.fixed if (model.fixed[x] is False)
and (model.tied[x] is False)]
if len(covs) == 1:
model.cov_matrix = Covariance(covs[0], model.param_names)
model.stds = StandardDeviations(covs[0], free_param_names)
else:
model.cov_matrix = [Covariance(cov, model.param_names) for cov in covs]
model.stds = [StandardDeviations(cov, free_param_names) for cov in covs]
@staticmethod
def _deriv_with_constraints(model, param_indices, x=None, y=None):
if y is None:
d = np.array(model.fit_deriv(x, *model.parameters))
else:
d = np.array(model.fit_deriv(x, y, *model.parameters))
if model.col_fit_deriv:
return d[param_indices]
else:
return d[..., param_indices]
def _map_domain_window(self, model, x, y=None):
"""
Maps domain into window for a polynomial model which has these
attributes.
"""
if y is None:
if hasattr(model, 'domain') and model.domain is None:
model.domain = [x.min(), x.max()]
if hasattr(model, 'window') and model.window is None:
model.window = [-1, 1]
return poly_map_domain(x, model.domain, model.window)
else:
if hasattr(model, 'x_domain') and model.x_domain is None:
model.x_domain = [x.min(), x.max()]
if hasattr(model, 'y_domain') and model.y_domain is None:
model.y_domain = [y.min(), y.max()]
if hasattr(model, 'x_window') and model.x_window is None:
model.x_window = [-1., 1.]
if hasattr(model, 'y_window') and model.y_window is None:
model.y_window = [-1., 1.]
xnew = poly_map_domain(x, model.x_domain, model.x_window)
ynew = poly_map_domain(y, model.y_domain, model.y_window)
return xnew, ynew
@fitter_unit_support
def __call__(self, model, x, y, z=None, weights=None, rcond=None):
"""
Fit data to this model.
Parameters
----------
model : `~astropy.modeling.FittableModel`
model to fit to x, y, z
x : array
Input coordinates
y : array-like
Input coordinates
z : array-like, optional
Input coordinates.
If the dependent (``y`` or ``z``) coordinate values are provided
as a `numpy.ma.MaskedArray`, any masked points are ignored when
fitting. Note that model set fitting is significantly slower when
there are masked points (not just an empty mask), as the matrix
equation has to be solved for each model separately when their
coordinate grids differ.
weights : array, optional
Weights for fitting.
For data with Gaussian uncertainties, the weights should be
1/sigma.
rcond : float, optional
Cut-off ratio for small singular values of ``a``.
Singular values are set to zero if they are smaller than ``rcond``
times the largest singular value of ``a``.
equivalencies : list or None, optional, keyword-only
List of *additional* equivalencies that are should be applied in
case x, y and/or z have units. Default is None.
Returns
-------
model_copy : `~astropy.modeling.FittableModel`
a copy of the input model with parameters set by the fitter
"""
if not model.fittable:
raise ValueError("Model must be a subclass of FittableModel")
if not model.linear:
raise ModelLinearityError('Model is not linear in parameters, '
'linear fit methods should not be used.')
if hasattr(model, "submodel_names"):
raise ValueError("Model must be simple, not compound")
_validate_constraints(self.supported_constraints, model)
model_copy = model.copy()
model_copy.sync_constraints = False
_, fitparam_indices = model_to_fit_params(model_copy)
if model_copy.n_inputs == 2 and z is None:
raise ValueError("Expected x, y and z for a 2 dimensional model.")
farg = _convert_input(x, y, z, n_models=len(model_copy),
model_set_axis=model_copy.model_set_axis)
has_fixed = any(model_copy.fixed.values())
# This is also done by _convert_inputs, but we need it here to allow
# checking the array dimensionality before that gets called:
if weights is not None:
weights = np.asarray(weights, dtype=float)
if has_fixed:
# The list of fixed params is the complement of those being fitted:
fixparam_indices = [idx for idx in
range(len(model_copy.param_names))
if idx not in fitparam_indices]
# Construct matrix of user-fixed parameters that can be dotted with
# the corresponding fit_deriv() terms, to evaluate corrections to
# the dependent variable in order to fit only the remaining terms:
fixparams = np.asarray([getattr(model_copy,
model_copy.param_names[idx]).value
for idx in fixparam_indices])
if len(farg) == 2:
x, y = farg
if weights is not None:
# If we have separate weights for each model, apply the same
# conversion as for the data, otherwise check common weights
# as if for a single model:
_, weights = _convert_input(
x, weights,
n_models=len(model_copy) if weights.ndim == y.ndim else 1,
model_set_axis=model_copy.model_set_axis
)
# map domain into window
if hasattr(model_copy, 'domain'):
x = self._map_domain_window(model_copy, x)
if has_fixed:
lhs = np.asarray(self._deriv_with_constraints(model_copy,
fitparam_indices,
x=x))
fixderivs = self._deriv_with_constraints(model_copy, fixparam_indices, x=x)
else:
lhs = np.asarray(model_copy.fit_deriv(x, *model_copy.parameters))
sum_of_implicit_terms = model_copy.sum_of_implicit_terms(x)
rhs = y
else:
x, y, z = farg
if weights is not None:
# If we have separate weights for each model, apply the same
# conversion as for the data, otherwise check common weights
# as if for a single model:
_, _, weights = _convert_input(
x, y, weights,
n_models=len(model_copy) if weights.ndim == z.ndim else 1,
model_set_axis=model_copy.model_set_axis
)
# map domain into window
if hasattr(model_copy, 'x_domain'):
x, y = self._map_domain_window(model_copy, x, y)
if has_fixed:
lhs = np.asarray(self._deriv_with_constraints(model_copy,
fitparam_indices, x=x, y=y))
fixderivs = self._deriv_with_constraints(model_copy,
fixparam_indices,
x=x, y=y)
else:
lhs = np.asanyarray(model_copy.fit_deriv(x, y, *model_copy.parameters))
sum_of_implicit_terms = model_copy.sum_of_implicit_terms(x, y)
if len(model_copy) > 1:
# Just to be explicit (rather than baking in False == 0):
model_axis = model_copy.model_set_axis or 0
if z.ndim > 2:
# For higher-dimensional z, flatten all the axes except the
# dimension along which models are stacked and transpose so
# the model axis is *last* (I think this resolves Erik's
# pending generalization from 80a6f25a):
rhs = np.rollaxis(z, model_axis, z.ndim)
rhs = rhs.reshape(-1, rhs.shape[-1])
else:
# This "else" seems to handle the corner case where the
# user has already flattened x/y before attempting a 2D fit
# but z has a second axis for the model set. NB. This is
# ~5-10x faster than using rollaxis.
rhs = z.T if model_axis == 0 else z
if weights is not None:
# Same for weights
if weights.ndim > 2:
# Separate 2D weights for each model:
weights = np.rollaxis(weights, model_axis, weights.ndim)
weights = weights.reshape(-1, weights.shape[-1])
elif weights.ndim == z.ndim:
# Separate, flattened weights for each model:
weights = weights.T if model_axis == 0 else weights
else:
# Common weights for all the models:
weights = weights.flatten()
else:
rhs = z.flatten()
if weights is not None:
weights = weights.flatten()
# If the derivative is defined along rows (as with non-linear models)
if model_copy.col_fit_deriv:
lhs = np.asarray(lhs).T
# Some models (eg. Polynomial1D) don't flatten multi-dimensional inputs
# when constructing their Vandermonde matrix, which can lead to obscure
# failures below. Ultimately, np.linalg.lstsq can't handle >2D matrices,
# so just raise a slightly more informative error when this happens:
if np.asanyarray(lhs).ndim > 2:
raise ValueError('{} gives unsupported >2D derivative matrix for '
'this x/y'.format(type(model_copy).__name__))
# Subtract any terms fixed by the user from (a copy of) the RHS, in
# order to fit the remaining terms correctly:
if has_fixed:
if model_copy.col_fit_deriv:
fixderivs = np.asarray(fixderivs).T # as for lhs above
rhs = rhs - fixderivs.dot(fixparams) # evaluate user-fixed terms
# Subtract any terms implicit in the model from the RHS, which, like
# user-fixed terms, affect the dependent variable but are not fitted:
if sum_of_implicit_terms is not None:
# If we have a model set, the extra axis must be added to
# sum_of_implicit_terms as its innermost dimension, to match the
# dimensionality of rhs after _convert_input "rolls" it as needed
# by np.linalg.lstsq. The vector then gets broadcast to the right
# number of sets (columns). This assumes all the models share the
# same input coordinates, as is currently the case.
if len(model_copy) > 1:
sum_of_implicit_terms = sum_of_implicit_terms[..., np.newaxis]
rhs = rhs - sum_of_implicit_terms
if weights is not None:
if rhs.ndim == 2:
if weights.shape == rhs.shape:
# separate weights for multiple models case: broadcast
# lhs to have more dimension (for each model)
lhs = lhs[..., np.newaxis] * weights[:, np.newaxis]
rhs = rhs * weights
else:
lhs *= weights[:, np.newaxis]
# Don't modify in-place in case rhs was the original
# dependent variable array
rhs = rhs * weights[:, np.newaxis]
else:
lhs *= weights[:, np.newaxis]
rhs = rhs * weights
scl = (lhs * lhs).sum(0)
lhs /= scl
masked = np.any(np.ma.getmask(rhs))
if weights is not None and not masked and np.any(np.isnan(lhs)):
raise ValueError('Found NaNs in the coefficient matrix, which '
'should not happen and would crash the lapack '
'routine. Maybe check that weights are not null.')
a = None # need for calculating covarience
if ((masked and len(model_copy) > 1) or
(weights is not None and weights.ndim > 1)):
# Separate masks or weights for multiple models case: Numpy's
# lstsq supports multiple dimensions only for rhs, so we need to
# loop manually on the models. This may be fixed in the future
# with https://github.com/numpy/numpy/pull/15777.
# Initialize empty array of coefficients and populate it one model
# at a time. The shape matches the number of coefficients from the
# Vandermonde matrix and the number of models from the RHS:
lacoef = np.zeros(lhs.shape[1:2] + rhs.shape[-1:], dtype=rhs.dtype)
# Arrange the lhs as a stack of 2D matrices that we can iterate
# over to get the correctly-orientated lhs for each model:
if lhs.ndim > 2:
lhs_stack = np.rollaxis(lhs, -1, 0)
else:
lhs_stack = np.broadcast_to(lhs, rhs.shape[-1:] + lhs.shape)
# Loop over the models and solve for each one. By this point, the
# model set axis is the second of two. Transpose rather than using,
# say, np.moveaxis(array, -1, 0), since it's slightly faster and
# lstsq can't handle >2D arrays anyway. This could perhaps be
# optimized by collecting together models with identical masks
# (eg. those with no rejected points) into one operation, though it
# will still be relatively slow when calling lstsq repeatedly.
for model_lhs, model_rhs, model_lacoef in zip(lhs_stack, rhs.T, lacoef.T):
# Cull masked points on both sides of the matrix equation:
good = ~model_rhs.mask if masked else slice(None)
model_lhs = model_lhs[good]
model_rhs = model_rhs[good][..., np.newaxis]
a = model_lhs
# Solve for this model:
t_coef, resids, rank, sval = np.linalg.lstsq(model_lhs,
model_rhs, rcond)
model_lacoef[:] = t_coef.T
else:
# If we're fitting one or more models over a common set of points,
# we only have to solve a single matrix equation, which is an order
# of magnitude faster than calling lstsq() once per model below:
good = ~rhs.mask if masked else slice(None) # latter is a no-op
a = lhs[good]
# Solve for one or more models:
lacoef, resids, rank, sval = np.linalg.lstsq(lhs[good],
rhs[good], rcond)
self.fit_info['residuals'] = resids
self.fit_info['rank'] = rank
self.fit_info['singular_values'] = sval
lacoef /= scl[:, np.newaxis] if scl.ndim < rhs.ndim else scl
self.fit_info['params'] = lacoef
fitter_to_model_params(model_copy, lacoef.flatten())
# TODO: Only Polynomial models currently have an _order attribute;
# maybe change this to read isinstance(model, PolynomialBase)
if hasattr(model_copy, '_order') and len(model_copy) == 1 \
and not has_fixed and rank != model_copy._order:
warnings.warn("The fit may be poorly conditioned\n",
AstropyUserWarning)
# calculate and set covariance matrix and standard devs. on model
if self._calc_uncertainties:
if len(y) > len(lacoef):
self._add_fitting_uncertainties(model_copy, a*scl,
len(lacoef), x, y, z, resids)
model_copy.sync_constraints = True
return model_copy
class FittingWithOutlierRemoval:
"""
This class combines an outlier removal technique with a fitting procedure.
Basically, given a maximum number of iterations ``niter``, outliers are
removed and fitting is performed for each iteration, until no new outliers
are found or ``niter`` is reached.
Parameters
----------
fitter : `Fitter`
An instance of any Astropy fitter, i.e., LinearLSQFitter,
LevMarLSQFitter, SLSQPLSQFitter, SimplexLSQFitter, JointFitter. For
model set fitting, this must understand masked input data (as
indicated by the fitter class attribute ``supports_masked_input``).
outlier_func : callable
A function for outlier removal.
If this accepts an ``axis`` parameter like the `numpy` functions, the
appropriate value will be supplied automatically when fitting model
sets (unless overridden in ``outlier_kwargs``), to find outliers for
each model separately; otherwise, the same filtering must be performed
in a loop over models, which is almost an order of magnitude slower.
niter : int, optional
Maximum number of iterations.
outlier_kwargs : dict, optional
Keyword arguments for outlier_func.
Attributes
----------
fit_info : dict
The ``fit_info`` (if any) from the last iteration of the wrapped
``fitter`` during the most recent fit. An entry is also added with the
keyword ``niter`` that records the actual number of fitting iterations
performed (as opposed to the user-specified maximum).
"""
def __init__(self, fitter, outlier_func, niter=3, **outlier_kwargs):
self.fitter = fitter
self.outlier_func = outlier_func
self.niter = niter
self.outlier_kwargs = outlier_kwargs
self.fit_info = {'niter': None}
def __str__(self):
return ("Fitter: {0}\nOutlier function: {1}\nNum. of iterations: {2}" +
("\nOutlier func. args.: {3}"))\
.format(self.fitter.__class__.__name__,
self.outlier_func.__name__, self.niter,
self.outlier_kwargs)
def __repr__(self):
return ("{0}(fitter: {1}, outlier_func: {2}," +
" niter: {3}, outlier_kwargs: {4})")\
.format(self.__class__.__name__,
self.fitter.__class__.__name__,
self.outlier_func.__name__, self.niter,
self.outlier_kwargs)
def __call__(self, model, x, y, z=None, weights=None, **kwargs):
"""
Parameters
----------
model : `~astropy.modeling.FittableModel`
An analytic model which will be fit to the provided data.
This also contains the initial guess for an optimization
algorithm.
x : array-like
Input coordinates.
y : array-like
Data measurements (1D case) or input coordinates (2D case).
z : array-like, optional
Data measurements (2D case).
weights : array-like, optional
Weights to be passed to the fitter.
kwargs : dict, optional
Keyword arguments to be passed to the fitter.
Returns
-------
fitted_model : `~astropy.modeling.FittableModel`
Fitted model after outlier removal.
mask : `numpy.ndarray`
Boolean mask array, identifying which points were used in the final
fitting iteration (False) and which were found to be outliers or
were masked in the input (True).
"""
# For single models, the data get filtered here at each iteration and
# then passed to the fitter, which is the historical behavior and
# works even for fitters that don't understand masked arrays. For model
# sets, the fitter must be able to filter masked data internally,
# because fitters require a single set of x/y coordinates whereas the
# eliminated points can vary between models. To avoid this limitation,
# we could fall back to looping over individual model fits, but it
# would likely be fiddly and involve even more overhead (and the
# non-linear fitters don't work with model sets anyway, as of writing).
if len(model) == 1:
model_set_axis = None
else:
if not hasattr(self.fitter, 'supports_masked_input') or \
self.fitter.supports_masked_input is not True:
raise ValueError("{} cannot fit model sets with masked "
"values".format(type(self.fitter).__name__))
# Fitters use their input model's model_set_axis to determine how
# their input data are stacked:
model_set_axis = model.model_set_axis
# Construct input coordinate tuples for fitters & models that are
# appropriate for the dimensionality being fitted:
if z is None:
coords = (x, )
data = y
else:
coords = x, y
data = z
# For model sets, construct a numpy-standard "axis" tuple for the
# outlier function, to treat each model separately (if supported):
if model_set_axis is not None:
if model_set_axis < 0:
model_set_axis += data.ndim
if 'axis' not in self.outlier_kwargs: # allow user override
# This also works for False (like model instantiation):
self.outlier_kwargs['axis'] = tuple(
n for n in range(data.ndim) if n != model_set_axis
)
loop = False
# Starting fit, prior to any iteration and masking:
fitted_model = self.fitter(model, x, y, z, weights=weights, **kwargs)
filtered_data = np.ma.masked_array(data)
if filtered_data.mask is np.ma.nomask:
filtered_data.mask = False
filtered_weights = weights
last_n_masked = filtered_data.mask.sum()
n = 0 # (allow recording no. of iterations when 0)
# Perform the iterative fitting:
for n in range(1, self.niter + 1):
# (Re-)evaluate the last model:
model_vals = fitted_model(*coords, model_set_axis=False)
# Determine the outliers:
if not loop:
# Pass axis parameter if outlier_func accepts it, otherwise
# prepare for looping over models:
try:
filtered_data = self.outlier_func(
filtered_data - model_vals, **self.outlier_kwargs
)
# If this happens to catch an error with a parameter other
# than axis, the next attempt will fail accordingly:
except TypeError:
if model_set_axis is None:
raise
else:
self.outlier_kwargs.pop('axis', None)
loop = True
# Construct MaskedArray to hold filtered values:
filtered_data = np.ma.masked_array(
filtered_data,
dtype=np.result_type(filtered_data, model_vals),
copy=True
)
# Make sure the mask is an array, not just nomask:
if filtered_data.mask is np.ma.nomask:
filtered_data.mask = False
# Get views transposed appropriately for iteration
# over the set (handling data & mask separately due to
# NumPy issue #8506):
data_T = np.rollaxis(filtered_data, model_set_axis, 0)
mask_T = np.rollaxis(filtered_data.mask,
model_set_axis, 0)
if loop:
model_vals_T = np.rollaxis(model_vals, model_set_axis, 0)
for row_data, row_mask, row_mod_vals in zip(data_T, mask_T,
model_vals_T):
masked_residuals = self.outlier_func(
row_data - row_mod_vals, **self.outlier_kwargs
)
row_data.data[:] = masked_residuals.data
row_mask[:] = masked_residuals.mask
# Issue speed warning after the fact, so it only shows up when
# the TypeError is genuinely due to the axis argument.
warnings.warn('outlier_func did not accept axis argument; '
'reverted to slow loop over models.',
AstropyUserWarning)
# Recombine newly-masked residuals with model to get masked values:
filtered_data += model_vals
# Re-fit the data after filtering, passing masked/unmasked values
# for single models / sets, respectively:
if model_set_axis is None:
good = ~filtered_data.mask
if weights is not None:
filtered_weights = weights[good]
fitted_model = self.fitter(fitted_model,
*(c[good] for c in coords),
filtered_data.data[good],
weights=filtered_weights, **kwargs)
else:
fitted_model = self.fitter(fitted_model, *coords,
filtered_data,
weights=filtered_weights, **kwargs)
# Stop iteration if the masked points are no longer changing (with
# cumulative rejection we only need to compare how many there are):
this_n_masked = filtered_data.mask.sum() # (minimal overhead)
if this_n_masked == last_n_masked:
break
last_n_masked = this_n_masked
self.fit_info = {'niter': n}
self.fit_info.update(getattr(self.fitter, 'fit_info', {}))
return fitted_model, filtered_data.mask
class LevMarLSQFitter(metaclass=_FitterMeta):
"""
Levenberg-Marquardt algorithm and least squares statistic.
Attributes
----------
fit_info : dict
The `scipy.optimize.leastsq` result for the most recent fit (see
notes).
Notes
-----
The ``fit_info`` dictionary contains the values returned by
`scipy.optimize.leastsq` for the most recent fit, including the values from
the ``infodict`` dictionary it returns. See the `scipy.optimize.leastsq`
documentation for details on the meaning of these values. Note that the
``x`` return value is *not* included (as it is instead the parameter values
of the returned model).
Additionally, one additional element of ``fit_info`` is computed whenever a
model is fit, with the key 'param_cov'. The corresponding value is the
covariance matrix of the parameters as a 2D numpy array. The order of the
matrix elements matches the order of the parameters in the fitted model
(i.e., the same order as ``model.param_names``).
"""
supported_constraints = ['fixed', 'tied', 'bounds']
"""
The constraint types supported by this fitter type.
"""
def __init__(self, calc_uncertainties=False):
self.fit_info = {'nfev': None,
'fvec': None,
'fjac': None,
'ipvt': None,
'qtf': None,
'message': None,
'ierr': None,
'param_jac': None,
'param_cov': None}
self._calc_uncertainties=calc_uncertainties
super().__init__()
def objective_function(self, fps, *args):
"""
Function to minimize.
Parameters
----------
fps : list
parameters returned by the fitter
args : list
[model, [weights], [input coordinates]]
"""
model = args[0]
weights = args[1]
fitter_to_model_params(model, fps)
meas = args[-1]
if weights is None:
return np.ravel(model(*args[2: -1]) - meas)
else:
return np.ravel(weights * (model(*args[2: -1]) - meas))
@staticmethod
def _add_fitting_uncertainties(model, cov_matrix):
"""
Set ``cov_matrix`` and ``stds`` attributes on model with parameter
covariance matrix returned by ``optimize.leastsq``.
"""
free_param_names = [x for x in model.fixed if (model.fixed[x] is False)
and (model.tied[x] is False)]
model.cov_matrix = Covariance(cov_matrix, free_param_names)
model.stds = StandardDeviations(cov_matrix, free_param_names)
@fitter_unit_support
def __call__(self, model, x, y, z=None, weights=None,
maxiter=DEFAULT_MAXITER, acc=DEFAULT_ACC,
epsilon=DEFAULT_EPS, estimate_jacobian=False):
"""
Fit data to this model.
Parameters
----------
model : `~astropy.modeling.FittableModel`
model to fit to x, y, z
x : array
input coordinates
y : array
input coordinates
z : array, optional
input coordinates
weights : array, optional
Weights for fitting.
For data with Gaussian uncertainties, the weights should be
1/sigma.
maxiter : int
maximum number of iterations
acc : float
Relative error desired in the approximate solution
epsilon : float
A suitable step length for the forward-difference
approximation of the Jacobian (if model.fjac=None). If
epsfcn is less than the machine precision, it is
assumed that the relative errors in the functions are
of the order of the machine precision.
estimate_jacobian : bool
If False (default) and if the model has a fit_deriv method,
it will be used. Otherwise the Jacobian will be estimated.
If True, the Jacobian will be estimated in any case.
equivalencies : list or None, optional, keyword-only
List of *additional* equivalencies that are should be applied in
case x, y and/or z have units. Default is None.
Returns
-------
model_copy : `~astropy.modeling.FittableModel`
a copy of the input model with parameters set by the fitter
"""
from scipy import optimize
model_copy = _validate_model(model, self.supported_constraints)
model_copy.sync_constraints = False
farg = (model_copy, weights, ) + _convert_input(x, y, z)
if model_copy.fit_deriv is None or estimate_jacobian:
dfunc = None
else:
dfunc = self._wrap_deriv
init_values, _ = model_to_fit_params(model_copy)
fitparams, cov_x, dinfo, mess, ierr = optimize.leastsq(
self.objective_function, init_values, args=farg, Dfun=dfunc,
col_deriv=model_copy.col_fit_deriv, maxfev=maxiter, epsfcn=epsilon,
xtol=acc, full_output=True)
fitter_to_model_params(model_copy, fitparams)
self.fit_info.update(dinfo)
self.fit_info['cov_x'] = cov_x
self.fit_info['message'] = mess
self.fit_info['ierr'] = ierr
if ierr not in [1, 2, 3, 4]:
warnings.warn("The fit may be unsuccessful; check "
"fit_info['message'] for more information.",
AstropyUserWarning)
# now try to compute the true covariance matrix
if (len(y) > len(init_values)) and cov_x is not None:
sum_sqrs = np.sum(self.objective_function(fitparams, *farg)**2)
dof = len(y) - len(init_values)
self.fit_info['param_cov'] = cov_x * sum_sqrs / dof
else:
self.fit_info['param_cov'] = None
if self._calc_uncertainties is True:
if self.fit_info['param_cov'] is not None:
self._add_fitting_uncertainties(model_copy,
self.fit_info['param_cov'])
model_copy.sync_constraints = True
return model_copy
@staticmethod
def _wrap_deriv(params, model, weights, x, y, z=None):
"""
Wraps the method calculating the Jacobian of the function to account
for model constraints.
`scipy.optimize.leastsq` expects the function derivative to have the
above signature (parlist, (argtuple)). In order to accommodate model
constraints, instead of using p directly, we set the parameter list in
this function.
"""
if weights is None:
weights = 1.0
if any(model.fixed.values()) or any(model.tied.values()):
# update the parameters with the current values from the fitter
fitter_to_model_params(model, params)
if z is None:
full = np.array(model.fit_deriv(x, *model.parameters))
if not model.col_fit_deriv:
full_deriv = np.ravel(weights) * full.T
else:
full_deriv = np.ravel(weights) * full
else:
full = np.array([np.ravel(_) for _ in model.fit_deriv(x, y, *model.parameters)])
if not model.col_fit_deriv:
full_deriv = np.ravel(weights) * full.T
else:
full_deriv = np.ravel(weights) * full
pars = [getattr(model, name) for name in model.param_names]
fixed = [par.fixed for par in pars]
tied = [par.tied for par in pars]
tied = list(np.where([par.tied is not False for par in pars],
True, tied))
fix_and_tie = np.logical_or(fixed, tied)
ind = np.logical_not(fix_and_tie)
if not model.col_fit_deriv:
residues = np.asarray(full_deriv[np.nonzero(ind)]).T
else:
residues = full_deriv[np.nonzero(ind)]
return [np.ravel(_) for _ in residues]
else:
if z is None:
try:
return np.array([np.ravel(_) for _ in np.array(weights) *
np.array(model.fit_deriv(x, *params))])
except ValueError:
return np.array([np.ravel(_) for _ in np.array(weights) *
np.moveaxis(
np.array(model.fit_deriv(x, *params)),
-1, 0)]).transpose()
else:
if not model.col_fit_deriv:
return [np.ravel(_) for _ in
(np.ravel(weights) * np.array(model.fit_deriv(x, y, *params)).T).T]
return [np.ravel(_) for _ in weights * np.array(model.fit_deriv(x, y, *params))]
class SLSQPLSQFitter(Fitter):
"""
Sequential Least Squares Programming (SLSQP) optimization algorithm and
least squares statistic.
Raises
------
ModelLinearityError
A linear model is passed to a nonlinear fitter
Notes
-----
See also the `~astropy.modeling.optimizers.SLSQP` optimizer.
"""
supported_constraints = SLSQP.supported_constraints
def __init__(self):
super().__init__(optimizer=SLSQP, statistic=leastsquare)
self.fit_info = {}
@fitter_unit_support
def __call__(self, model, x, y, z=None, weights=None, **kwargs):
"""
Fit data to this model.
Parameters
----------
model : `~astropy.modeling.FittableModel`
model to fit to x, y, z
x : array
input coordinates
y : array
input coordinates
z : array, optional
input coordinates
weights : array, optional
Weights for fitting.
For data with Gaussian uncertainties, the weights should be
1/sigma.
kwargs : dict
optional keyword arguments to be passed to the optimizer or the statistic
verblevel : int
0-silent
1-print summary upon completion,
2-print summary after each iteration
maxiter : int
maximum number of iterations
epsilon : float
the step size for finite-difference derivative estimates
acc : float
Requested accuracy
equivalencies : list or None, optional, keyword-only
List of *additional* equivalencies that are should be applied in
case x, y and/or z have units. Default is None.
Returns
-------
model_copy : `~astropy.modeling.FittableModel`
a copy of the input model with parameters set by the fitter
"""
model_copy = _validate_model(model, self._opt_method.supported_constraints)
model_copy.sync_constraints = False
farg = _convert_input(x, y, z)
farg = (model_copy, weights, ) + farg
init_values, _ = model_to_fit_params(model_copy)
fitparams, self.fit_info = self._opt_method(
self.objective_function, init_values, farg, **kwargs)
fitter_to_model_params(model_copy, fitparams)
model_copy.sync_constraints = True
return model_copy
class SimplexLSQFitter(Fitter):
"""
Simplex algorithm and least squares statistic.
Raises
------
`ModelLinearityError`
A linear model is passed to a nonlinear fitter
"""
supported_constraints = Simplex.supported_constraints
def __init__(self):
super().__init__(optimizer=Simplex, statistic=leastsquare)
self.fit_info = {}
@fitter_unit_support
def __call__(self, model, x, y, z=None, weights=None, **kwargs):
"""
Fit data to this model.
Parameters
----------
model : `~astropy.modeling.FittableModel`
model to fit to x, y, z
x : array
input coordinates
y : array
input coordinates
z : array, optional
input coordinates
weights : array, optional
Weights for fitting.
For data with Gaussian uncertainties, the weights should be
1/sigma.
kwargs : dict
optional keyword arguments to be passed to the optimizer or the statistic
maxiter : int
maximum number of iterations
acc : float
Relative error in approximate solution
equivalencies : list or None, optional, keyword-only
List of *additional* equivalencies that are should be applied in
case x, y and/or z have units. Default is None.
Returns
-------
model_copy : `~astropy.modeling.FittableModel`
a copy of the input model with parameters set by the fitter
"""
model_copy = _validate_model(model,
self._opt_method.supported_constraints)
model_copy.sync_constraints = False
farg = _convert_input(x, y, z)
farg = (model_copy, weights, ) + farg
init_values, _ = model_to_fit_params(model_copy)
fitparams, self.fit_info = self._opt_method(
self.objective_function, init_values, farg, **kwargs)
fitter_to_model_params(model_copy, fitparams)
model_copy.sync_constraints = True
return model_copy
class JointFitter(metaclass=_FitterMeta):
"""
Fit models which share a parameter.
For example, fit two gaussians to two data sets but keep
the FWHM the same.
Parameters
----------
models : list
a list of model instances
jointparameters : list
a list of joint parameters
initvals : list
a list of initial values
"""
def __init__(self, models, jointparameters, initvals):
self.models = list(models)
self.initvals = list(initvals)
self.jointparams = jointparameters
self._verify_input()
self.fitparams = self.model_to_fit_params()
# a list of model.n_inputs
self.modeldims = [m.n_inputs for m in self.models]
# sum all model dimensions
self.ndim = np.sum(self.modeldims)
def model_to_fit_params(self):
fparams = []
fparams.extend(self.initvals)
for model in self.models:
params = model.parameters.tolist()
joint_params = self.jointparams[model]
param_metrics = model._param_metrics
for param_name in joint_params:
slice_ = param_metrics[param_name]['slice']
del params[slice_]
fparams.extend(params)
return fparams
def objective_function(self, fps, *args):
"""
Function to minimize.
Parameters
----------
fps : list
the fitted parameters - result of an one iteration of the
fitting algorithm
args : dict
tuple of measured and input coordinates
args is always passed as a tuple from optimize.leastsq
"""
lstsqargs = list(args)
fitted = []
fitparams = list(fps)
numjp = len(self.initvals)
# make a separate list of the joint fitted parameters
jointfitparams = fitparams[:numjp]
del fitparams[:numjp]
for model in self.models:
joint_params = self.jointparams[model]
margs = lstsqargs[:model.n_inputs + 1]
del lstsqargs[:model.n_inputs + 1]
# separate each model separately fitted parameters
numfp = len(model._parameters) - len(joint_params)
mfparams = fitparams[:numfp]
del fitparams[:numfp]
# recreate the model parameters
mparams = []
param_metrics = model._param_metrics
for param_name in model.param_names:
if param_name in joint_params:
index = joint_params.index(param_name)
# should do this with slices in case the
# parameter is not a number
mparams.extend([jointfitparams[index]])
else:
slice_ = param_metrics[param_name]['slice']
plen = slice_.stop - slice_.start
mparams.extend(mfparams[:plen])
del mfparams[:plen]
modelfit = model.evaluate(margs[:-1], *mparams)
fitted.extend(modelfit - margs[-1])
return np.ravel(fitted)
def _verify_input(self):
if len(self.models) <= 1:
raise TypeError(f"Expected >1 models, {len(self.models)} is given")
if len(self.jointparams.keys()) < 2:
raise TypeError("At least two parameters are expected, "
"{} is given".format(len(self.jointparams.keys())))
for j in self.jointparams.keys():
if len(self.jointparams[j]) != len(self.initvals):
raise TypeError("{} parameter(s) provided but {} expected".format(
len(self.jointparams[j]), len(self.initvals)))
def __call__(self, *args):
"""
Fit data to these models keeping some of the parameters common to the
two models.
"""
from scipy import optimize
if len(args) != reduce(lambda x, y: x + 1 + y + 1, self.modeldims):
raise ValueError("Expected {} coordinates in args but {} provided"
.format(reduce(lambda x, y: x + 1 + y + 1,
self.modeldims), len(args)))
self.fitparams[:], _ = optimize.leastsq(self.objective_function,
self.fitparams, args=args)
fparams = self.fitparams[:]
numjp = len(self.initvals)
# make a separate list of the joint fitted parameters
jointfitparams = fparams[:numjp]
del fparams[:numjp]
for model in self.models:
# extract each model's fitted parameters
joint_params = self.jointparams[model]
numfp = len(model._parameters) - len(joint_params)
mfparams = fparams[:numfp]
del fparams[:numfp]
# recreate the model parameters
mparams = []
param_metrics = model._param_metrics
for param_name in model.param_names:
if param_name in joint_params:
index = joint_params.index(param_name)
# should do this with slices in case the parameter
# is not a number
mparams.extend([jointfitparams[index]])
else:
slice_ = param_metrics[param_name]['slice']
plen = slice_.stop - slice_.start
mparams.extend(mfparams[:plen])
del mfparams[:plen]
model.parameters = np.array(mparams)
def _convert_input(x, y, z=None, n_models=1, model_set_axis=0):
"""Convert inputs to float arrays."""
x = np.asanyarray(x, dtype=float)
y = np.asanyarray(y, dtype=float)
if z is not None:
z = np.asanyarray(z, dtype=float)
data_ndim, data_shape = z.ndim, z.shape
else:
data_ndim, data_shape = y.ndim, y.shape
# For compatibility with how the linear fitter code currently expects to
# work, shift the dependent variable's axes to the expected locations
if n_models > 1 or data_ndim > x.ndim:
if (model_set_axis or 0) >= data_ndim:
raise ValueError("model_set_axis out of range")
if data_shape[model_set_axis] != n_models:
raise ValueError(
"Number of data sets (y or z array) is expected to equal "
"the number of parameter sets"
)
if z is None:
# For a 1-D model the y coordinate's model-set-axis is expected to
# be last, so that its first dimension is the same length as the x
# coordinates. This is in line with the expectations of
# numpy.linalg.lstsq:
# https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html
# That is, each model should be represented by a column. TODO:
# Obviously this is a detail of np.linalg.lstsq and should be
# handled specifically by any fitters that use it...
y = np.rollaxis(y, model_set_axis, y.ndim)
data_shape = y.shape[:-1]
else:
# Shape of z excluding model_set_axis
data_shape = (z.shape[:model_set_axis] +
z.shape[model_set_axis + 1:])
if z is None:
if data_shape != x.shape:
raise ValueError("x and y should have the same shape")
farg = (x, y)
else:
if not (x.shape == y.shape == data_shape):
raise ValueError("x, y and z should have the same shape")
farg = (x, y, z)
return farg
# TODO: These utility functions are really particular to handling
# bounds/tied/fixed constraints for scipy.optimize optimizers that do not
# support them inherently; this needs to be reworked to be clear about this
# distinction (and the fact that these are not necessarily applicable to any
# arbitrary fitter--as evidenced for example by the fact that JointFitter has
# its own versions of these)
# TODO: Most of this code should be entirely rewritten; it should not be as
# inefficient as it is.
def fitter_to_model_params(model, fps):
"""
Constructs the full list of model parameters from the fitted and
constrained parameters.
"""
_, fit_param_indices = model_to_fit_params(model)
has_tied = any(model.tied.values())
has_fixed = any(model.fixed.values())
has_bound = any(b != (None, None) for b in model.bounds.values())
parameters = model.parameters
if not (has_tied or has_fixed or has_bound):
# We can just assign directly
model.parameters = fps
return
fit_param_indices = set(fit_param_indices)
offset = 0
param_metrics = model._param_metrics
for idx, name in enumerate(model.param_names):
if idx not in fit_param_indices:
continue
slice_ = param_metrics[name]['slice']
shape = param_metrics[name]['shape']
# This is determining which range of fps (the fitted parameters) maps
# to parameters of the model
size = reduce(operator.mul, shape, 1)
values = fps[offset:offset + size]
# Check bounds constraints
if model.bounds[name] != (None, None):
_min, _max = model.bounds[name]
if _min is not None:
values = np.fmax(values, _min)
if _max is not None:
values = np.fmin(values, _max)
parameters[slice_] = values
offset += size
# Update model parameters before calling ``tied`` constraints.
model._array_to_parameters()
# This has to be done in a separate loop due to how tied parameters are
# currently evaluated (the fitted parameters need to actually be *set* on
# the model first, for use in evaluating the "tied" expression--it might be
# better to change this at some point
if has_tied:
for idx, name in enumerate(model.param_names):
if model.tied[name]:
value = model.tied[name](model)
slice_ = param_metrics[name]['slice']
# To handle multiple tied constraints, model parameters
# need to be updated after each iteration.
parameters[slice_] = value
model._array_to_parameters()
@deprecated('5.1', 'private method: _fitter_to_model_params has been made public now')
def _fitter_to_model_params(model, fps):
return fitter_to_model_params(model, fps)
def model_to_fit_params(model):
"""
Convert a model instance's parameter array to an array that can be used
with a fitter that doesn't natively support fixed or tied parameters.
In particular, it removes fixed/tied parameters from the parameter
array.
These may be a subset of the model parameters, if some of them are held
constant or tied.
"""
fitparam_indices = list(range(len(model.param_names)))
if any(model.fixed.values()) or any(model.tied.values()):
params = list(model.parameters)
param_metrics = model._param_metrics
for idx, name in list(enumerate(model.param_names))[::-1]:
if model.fixed[name] or model.tied[name]:
slice_ = param_metrics[name]['slice']
del params[slice_]
del fitparam_indices[idx]
return (np.array(params), fitparam_indices)
return (model.parameters, fitparam_indices)
@deprecated('5.1', 'private method: _model_to_fit_params has been made public now')
def _model_to_fit_params(model):
return model_to_fit_params(model)
def _validate_constraints(supported_constraints, model):
"""Make sure model constraints are supported by the current fitter."""
message = 'Optimizer cannot handle {0} constraints.'
if (any(model.fixed.values()) and
'fixed' not in supported_constraints):
raise UnsupportedConstraintError(
message.format('fixed parameter'))
if any(model.tied.values()) and 'tied' not in supported_constraints:
raise UnsupportedConstraintError(
message.format('tied parameter'))
if (any(tuple(b) != (None, None) for b in model.bounds.values()) and
'bounds' not in supported_constraints):
raise UnsupportedConstraintError(
message.format('bound parameter'))
if model.eqcons and 'eqcons' not in supported_constraints:
raise UnsupportedConstraintError(message.format('equality'))
if model.ineqcons and 'ineqcons' not in supported_constraints:
raise UnsupportedConstraintError(message.format('inequality'))
def _validate_model(model, supported_constraints):
"""
Check that model and fitter are compatible and return a copy of the model.
"""
if not model.fittable:
raise ValueError("Model does not appear to be fittable.")
if model.linear:
warnings.warn('Model is linear in parameters; '
'consider using linear fitting methods.',
AstropyUserWarning)
elif len(model) != 1:
# for now only single data sets ca be fitted
raise ValueError("Non-linear fitters can only fit "
"one data set at a time.")
_validate_constraints(supported_constraints, model)
model_copy = model.copy()
return model_copy
def populate_entry_points(entry_points):
"""
This injects entry points into the `astropy.modeling.fitting` namespace.
This provides a means of inserting a fitting routine without requirement
of it being merged into astropy's core.
Parameters
----------
entry_points : list of `~importlib.metadata.EntryPoint`
entry_points are objects which encapsulate importable objects and
are defined on the installation of a package.
Notes
-----
An explanation of entry points can be found `here <http://setuptools.readthedocs.io/en/latest/setuptools.html#dynamic-discovery-of-services-and-plugins>`
"""
for entry_point in entry_points:
name = entry_point.name
try:
entry_point = entry_point.load()
except Exception as e:
# This stops the fitting from choking if an entry_point produces an error.
warnings.warn(AstropyUserWarning(
f'{type(e).__name__} error occurred in entry point {name}.'))
else:
if not inspect.isclass(entry_point):
warnings.warn(AstropyUserWarning(
f'Modeling entry point {name} expected to be a Class.'))
else:
if issubclass(entry_point, Fitter):
name = entry_point.__name__
globals()[name] = entry_point
__all__.append(name)
else:
warnings.warn(AstropyUserWarning(
'Modeling entry point {} expected to extend '
'astropy.modeling.Fitter' .format(name)))
def _populate_ep():
# TODO: Exclusively use select when Python minversion is 3.10
ep = entry_points()
if hasattr(ep, 'select'):
populate_entry_points(ep.select(group='astropy.modeling'))
else:
populate_entry_points(ep.get('astropy.modeling', []))
_populate_ep()
|
e66c5c40664fb019d58b6bb243b4abf6008929498af2300c8ea6f353835cd61c | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# pylint: disable=invalid-name
"""
This module defines classes that deal with parameters.
It is unlikely users will need to work with these classes directly,
unless they define their own models.
"""
import functools
import numbers
import operator
import numpy as np
from astropy.units import Quantity
from astropy.utils import isiterable
from .utils import array_repr_oneline
from .utils import get_inputs_and_params
__all__ = ['Parameter', 'InputParameterError', 'ParameterError']
class ParameterError(Exception):
"""Generic exception class for all exceptions pertaining to Parameters."""
class InputParameterError(ValueError, ParameterError):
"""Used for incorrect input parameter values and definitions."""
class ParameterDefinitionError(ParameterError):
"""Exception in declaration of class-level Parameters."""
def _tofloat(value):
"""Convert a parameter to float or float array"""
if isiterable(value):
try:
value = np.asanyarray(value, dtype=float)
except (TypeError, ValueError):
# catch arrays with strings or user errors like different
# types of parameters in a parameter set
raise InputParameterError(
f"Parameter of {type(value)} could not be converted to float")
elif isinstance(value, Quantity):
# Quantities are fine as is
pass
elif isinstance(value, np.ndarray):
# A scalar/dimensionless array
value = float(value.item())
elif isinstance(value, (numbers.Number, np.number)) and not isinstance(value, bool):
value = float(value)
elif isinstance(value, bool):
raise InputParameterError(
"Expected parameter to be of numerical type, not boolean")
else:
raise InputParameterError(
f"Don't know how to convert parameter of {type(value)} to float")
return value
# Helpers for implementing operator overloading on Parameter
def _binary_arithmetic_operation(op, reflected=False):
@functools.wraps(op)
def wrapper(self, val):
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
if reflected:
return op(val, self_value)
else:
return op(self_value, val)
return wrapper
def _binary_comparison_operation(op):
@functools.wraps(op)
def wrapper(self, val):
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
return op(self_value, val)
return wrapper
def _unary_arithmetic_operation(op):
@functools.wraps(op)
def wrapper(self):
if self.unit is not None:
self_value = Quantity(self.value, self.unit)
else:
self_value = self.value
return op(self_value)
return wrapper
class Parameter:
"""
Wraps individual parameters.
Since 4.0 Parameters are no longer descriptors and are based on a new
implementation of the Parameter class. Parameters now (as of 4.0) store
values locally (as instead previously in the associated model)
This class represents a model's parameter (in a somewhat broad sense). It
serves a number of purposes:
1) A type to be recognized by models and treated specially at class
initialization (i.e., if it is found that there is a class definition
of a Parameter, the model initializer makes a copy at the instance level).
2) Managing the handling of allowable parameter values and once defined,
ensuring updates are consistent with the Parameter definition. This
includes the optional use of units and quantities as well as transforming
values to an internally consistent representation (e.g., from degrees to
radians through the use of getters and setters).
3) Holding attributes of parameters relevant to fitting, such as whether
the parameter may be varied in fitting, or whether there are constraints
that must be satisfied.
See :ref:`astropy:modeling-parameters` for more details.
Parameters
----------
name : str
parameter name
.. warning::
The fact that `Parameter` accepts ``name`` as an argument is an
implementation detail, and should not be used directly. When
defining a new `Model` class, parameter names are always
automatically defined by the class attribute they're assigned to.
description : str
parameter description
default : float or array
default value to use for this parameter
unit : `~astropy.units.Unit`
if specified, the parameter will be in these units, and when the
parameter is updated in future, it should be set to a
:class:`~astropy.units.Quantity` that has equivalent units.
getter : callable
a function that wraps the raw (internal) value of the parameter
when returning the value through the parameter proxy (eg. a
parameter may be stored internally as radians but returned to the
user as degrees)
setter : callable
a function that wraps any values assigned to this parameter; should
be the inverse of getter
fixed : bool
if True the parameter is not varied during fitting
tied : callable or False
if callable is supplied it provides a way to link the value of this
parameter to another parameter (or some other arbitrary function)
min : float
the lower bound of a parameter
max : float
the upper bound of a parameter
bounds : tuple
specify min and max as a single tuple--bounds may not be specified
simultaneously with min or max
"""
constraints = ('fixed', 'tied', 'bounds')
"""
Types of constraints a parameter can have. Excludes 'min' and 'max'
which are just aliases for the first and second elements of the 'bounds'
constraint (which is represented as a 2-tuple). 'prior' and 'posterior'
are available for use by user fitters but are not used by any built-in
fitters as of this writing.
"""
def __init__(self, name='', description='', default=None, unit=None,
getter=None, setter=None, fixed=False, tied=False, min=None,
max=None, bounds=None, prior=None, posterior=None):
super().__init__()
self._model = None
self._model_required = False
self._setter = self._create_value_wrapper(setter, None)
self._getter = self._create_value_wrapper(getter, None)
self._name = name
self.__doc__ = self._description = description.strip()
# We only need to perform this check on unbound parameters
if isinstance(default, Quantity):
if unit is not None and not unit.is_equivalent(default.unit):
raise ParameterDefinitionError(
"parameter default {0} does not have units equivalent to "
"the required unit {1}".format(default, unit))
unit = default.unit
default = default.value
self._default = default
self._unit = unit
# Internal units correspond to raw_units held by the model in the
# previous implementation. The private _getter and _setter methods
# use this to convert to and from the public unit defined for the
# parameter.
self._internal_unit = None
if not self._model_required:
if self._default is not None:
self.value = self._default
else:
self._value = None
# NOTE: These are *default* constraints--on model instances constraints
# are taken from the model if set, otherwise the defaults set here are
# used
if bounds is not None:
if min is not None or max is not None:
raise ValueError(
'bounds may not be specified simultaneously with min or '
'max when instantiating Parameter {}'.format(name))
else:
bounds = (min, max)
self._fixed = fixed
self._tied = tied
self._bounds = bounds
self._order = None
self._validator = None
self._prior = prior
self._posterior = posterior
self._std = None
def __set_name__(self, owner, name):
self._name = name
def __len__(self):
val = self.value
if val.shape == ():
return 1
else:
return val.shape[0]
def __getitem__(self, key):
value = self.value
if len(value.shape) == 0:
# Wrap the value in a list so that getitem can work for sensible
# indices like [0] and [-1]
value = [value]
return value[key]
def __setitem__(self, key, value):
# Get the existing value and check whether it even makes sense to
# apply this index
oldvalue = self.value
if isinstance(key, slice):
if len(oldvalue[key]) == 0:
raise InputParameterError(
"Slice assignment outside the parameter dimensions for "
"'{}'".format(self.name))
for idx, val in zip(range(*key.indices(len(self))), value):
self.__setitem__(idx, val)
else:
try:
oldvalue[key] = value
except IndexError:
raise InputParameterError(
"Input dimension {} invalid for {!r} parameter with "
"dimension {}".format(key, self.name, value.shape[0])) # likely wrong
def __repr__(self):
args = f"'{self._name}'"
args += f', value={self.value}'
if self.unit is not None:
args += f', unit={self.unit}'
for cons in self.constraints:
val = getattr(self, cons)
if val not in (None, False, (None, None)):
# Maybe non-obvious, but False is the default for the fixed and
# tied constraints
args += f', {cons}={val}'
return f"{self.__class__.__name__}({args})"
@property
def name(self):
"""Parameter name"""
return self._name
@property
def default(self):
"""Parameter default value"""
return self._default
@property
def value(self):
"""The unadorned value proxied by this parameter."""
if self._getter is None and self._setter is None:
return np.float64(self._value)
else:
# This new implementation uses the names of internal_unit
# in place of raw_unit used previously. The contrast between
# internal values and units is that between the public
# units that the parameter advertises to what it actually
# uses internally.
if self.internal_unit:
return np.float64(self._getter(self._internal_value,
self.internal_unit,
self.unit).value)
elif self._getter:
return np.float64(self._getter(self._internal_value))
elif self._setter:
return np.float64(self._internal_value)
@value.setter
def value(self, value):
if isinstance(value, Quantity):
raise TypeError("The .value property on parameters should be set"
" to unitless values, not Quantity objects. To set"
"a parameter to a quantity simply set the "
"parameter directly without using .value")
if self._setter is None:
self._value = np.array(value, dtype=np.float64)
else:
self._internal_value = np.array(self._setter(value),
dtype=np.float64)
@property
def unit(self):
"""
The unit attached to this parameter, if any.
On unbound parameters (i.e. parameters accessed through the
model class, rather than a model instance) this is the required/
default unit for the parameter.
"""
return self._unit
@unit.setter
def unit(self, unit):
if self.unit is None:
raise ValueError('Cannot attach units to parameters that were '
'not initially specified with units')
else:
raise ValueError('Cannot change the unit attribute directly, '
'instead change the parameter to a new quantity')
def _set_unit(self, unit, force=False):
if force:
self._unit = unit
else:
self.unit = unit
@property
def internal_unit(self):
"""
Return the internal unit the parameter uses for the internal value stored
"""
return self._internal_unit
@internal_unit.setter
def internal_unit(self, internal_unit):
"""
Set the unit the parameter will convert the supplied value to the
representation used internally.
"""
self._internal_unit = internal_unit
@property
def quantity(self):
"""
This parameter, as a :class:`~astropy.units.Quantity` instance.
"""
if self.unit is None:
return None
return self.value * self.unit
@quantity.setter
def quantity(self, quantity):
if not isinstance(quantity, Quantity):
raise TypeError("The .quantity attribute should be set "
"to a Quantity object")
self.value = quantity.value
self._unit = quantity.unit
@property
def shape(self):
"""The shape of this parameter's value array."""
if self._setter is None:
return self._value.shape
return self._internal_value.shape
@shape.setter
def shape(self, value):
if isinstance(self.value, np.generic):
if value not in ((), (1,)):
raise ValueError("Cannot assign this shape to a scalar quantity")
else:
self.value.shape = value
@property
def size(self):
"""The size of this parameter's value array."""
return np.size(self.value)
@property
def std(self):
"""Standard deviation, if available from fit."""
return self._std
@std.setter
def std(self, value):
self._std = value
@property
def prior(self):
return self._prior
@prior.setter
def prior(self, val):
self._prior = val
@property
def posterior(self):
return self._posterior
@posterior.setter
def posterior(self, val):
self._posterior = val
@property
def fixed(self):
"""
Boolean indicating if the parameter is kept fixed during fitting.
"""
return self._fixed
@fixed.setter
def fixed(self, value):
""" Fix a parameter. """
if not isinstance(value, bool):
raise ValueError("Value must be boolean")
self._fixed = value
@property
def tied(self):
"""
Indicates that this parameter is linked to another one.
A callable which provides the relationship of the two parameters.
"""
return self._tied
@tied.setter
def tied(self, value):
"""Tie a parameter"""
if not callable(value) and value not in (False, None):
raise TypeError("Tied must be a callable or set to False or None")
self._tied = value
@property
def bounds(self):
"""The minimum and maximum values of a parameter as a tuple"""
return self._bounds
@bounds.setter
def bounds(self, value):
"""Set the minimum and maximum values of a parameter from a tuple"""
_min, _max = value
if _min is not None:
if not isinstance(_min, (numbers.Number, Quantity)):
raise TypeError("Min value must be a number or a Quantity")
if isinstance(_min, Quantity):
_min = float(_min.value)
else:
_min = float(_min)
if _max is not None:
if not isinstance(_max, (numbers.Number, Quantity)):
raise TypeError("Max value must be a number or a Quantity")
if isinstance(_max, Quantity):
_max = float(_max.value)
else:
_max = float(_max)
self._bounds = (_min, _max)
@property
def min(self):
"""A value used as a lower bound when fitting a parameter"""
return self.bounds[0]
@min.setter
def min(self, value):
"""Set a minimum value of a parameter"""
self.bounds = (value, self.max)
@property
def max(self):
"""A value used as an upper bound when fitting a parameter"""
return self.bounds[1]
@max.setter
def max(self, value):
"""Set a maximum value of a parameter."""
self.bounds = (self.min, value)
@property
def validator(self):
"""
Used as a decorator to set the validator method for a `Parameter`.
The validator method validates any value set for that parameter.
It takes two arguments--``self``, which refers to the `Model`
instance (remember, this is a method defined on a `Model`), and
the value being set for this parameter. The validator method's
return value is ignored, but it may raise an exception if the value
set on the parameter is invalid (typically an `InputParameterError`
should be raised, though this is not currently a requirement).
"""
def validator(func, self=self):
if callable(func):
self._validator = func
return self
else:
raise ValueError("This decorator method expects a callable.\n"
"The use of this method as a direct validator is\n"
"deprecated; use the new validate method instead\n")
return validator
def validate(self, value):
""" Run the validator on this parameter"""
if self._validator is not None and self._model is not None:
self._validator(self._model, value)
def copy(self, name=None, description=None, default=None, unit=None,
getter=None, setter=None, fixed=False, tied=False, min=None,
max=None, bounds=None, prior=None, posterior=None):
"""
Make a copy of this `Parameter`, overriding any of its core attributes
in the process (or an exact copy).
The arguments to this method are the same as those for the `Parameter`
initializer. This simply returns a new `Parameter` instance with any
or all of the attributes overridden, and so returns the equivalent of:
.. code:: python
Parameter(self.name, self.description, ...)
"""
kwargs = locals().copy()
del kwargs['self']
for key, value in kwargs.items():
if value is None:
# Annoying special cases for min/max where are just aliases for
# the components of bounds
if key in ('min', 'max'):
continue
else:
if hasattr(self, key):
value = getattr(self, key)
elif hasattr(self, '_' + key):
value = getattr(self, '_' + key)
kwargs[key] = value
return self.__class__(**kwargs)
@property
def model(self):
""" Return the model this parameter is associated with."""
return self._model
@model.setter
def model(self, value):
self._model = value
self._setter = self._create_value_wrapper(self._setter, value)
self._getter = self._create_value_wrapper(self._getter, value)
if self._model_required:
if self._default is not None:
self.value = self._default
else:
self._value = None
@property
def _raw_value(self):
"""
Currently for internal use only.
Like Parameter.value but does not pass the result through
Parameter.getter. By design this should only be used from bound
parameters.
This will probably be removed are retweaked at some point in the
process of rethinking how parameter values are stored/updated.
"""
if self._setter:
return self._internal_value
return self.value
def _create_value_wrapper(self, wrapper, model):
"""Wraps a getter/setter function to support optionally passing in
a reference to the model object as the second argument.
If a model is tied to this parameter and its getter/setter supports
a second argument then this creates a partial function using the model
instance as the second argument.
"""
if isinstance(wrapper, np.ufunc):
if wrapper.nin != 1:
raise TypeError("A numpy.ufunc used for Parameter "
"getter/setter may only take one input "
"argument")
elif wrapper is None:
# Just allow non-wrappers to fall through silently, for convenience
return None
else:
inputs, _ = get_inputs_and_params(wrapper)
nargs = len(inputs)
if nargs == 1:
pass
elif nargs == 2:
self._model_required = True
if model is not None:
# Don't make a partial function unless we're tied to a
# specific model instance
model_arg = inputs[1].name
wrapper = functools.partial(wrapper, **{model_arg: model})
else:
raise TypeError("Parameter getter/setter must be a function "
"of either one or two arguments")
return wrapper
def __array__(self, dtype=None):
# Make np.asarray(self) work a little more straightforwardly
arr = np.asarray(self.value, dtype=dtype)
if self.unit is not None:
arr = Quantity(arr, self.unit, copy=False)
return arr
def __bool__(self):
return bool(np.all(self.value))
__add__ = _binary_arithmetic_operation(operator.add)
__radd__ = _binary_arithmetic_operation(operator.add, reflected=True)
__sub__ = _binary_arithmetic_operation(operator.sub)
__rsub__ = _binary_arithmetic_operation(operator.sub, reflected=True)
__mul__ = _binary_arithmetic_operation(operator.mul)
__rmul__ = _binary_arithmetic_operation(operator.mul, reflected=True)
__pow__ = _binary_arithmetic_operation(operator.pow)
__rpow__ = _binary_arithmetic_operation(operator.pow, reflected=True)
__truediv__ = _binary_arithmetic_operation(operator.truediv)
__rtruediv__ = _binary_arithmetic_operation(operator.truediv,
reflected=True)
__eq__ = _binary_comparison_operation(operator.eq)
__ne__ = _binary_comparison_operation(operator.ne)
__lt__ = _binary_comparison_operation(operator.lt)
__gt__ = _binary_comparison_operation(operator.gt)
__le__ = _binary_comparison_operation(operator.le)
__ge__ = _binary_comparison_operation(operator.ge)
__neg__ = _unary_arithmetic_operation(operator.neg)
__abs__ = _unary_arithmetic_operation(operator.abs)
def param_repr_oneline(param):
"""
Like array_repr_oneline but works on `Parameter` objects and supports
rendering parameters with units like quantities.
"""
out = array_repr_oneline(param.value)
if param.unit is not None:
out = f'{out} {param.unit!s}'
return out
|
13615f0d5a58344aa0054b03669f5badc519443b3578ecd78868f3fef9f7cb1e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module defines base classes for all models. The base class of all
models is `~astropy.modeling.Model`. `~astropy.modeling.FittableModel` is
the base class for all fittable models. Fittable models can be linear or
nonlinear in a regression analysis sense.
All models provide a `__call__` method which performs the transformation in
a purely mathematical way, i.e. the models are unitless. Model instances can
represent either a single model, or a "model set" representing multiple copies
of the same type of model, but with potentially different values of the
parameters in each model making up the set.
"""
# pylint: disable=invalid-name, protected-access, redefined-outer-name
import abc
import copy
import inspect
import itertools
import functools
import operator
import types
from collections import defaultdict, deque
from inspect import signature
from itertools import chain
import numpy as np
from astropy.utils import indent, metadata
from astropy.table import Table
from astropy.units import Quantity, UnitsError, dimensionless_unscaled
from astropy.units.utils import quantity_asanyarray
from astropy.utils import (sharedmethod, find_current_module,
check_broadcast, IncompatibleShapeError, isiterable)
from astropy.utils.codegen import make_function_with_signature
from astropy.nddata.utils import add_array, extract_array
from .utils import (combine_labels, make_binary_operator_eval,
get_inputs_and_params, _combine_equivalency_dict,
_ConstraintsDict, _SpecialOperatorsDict)
from .bounding_box import ModelBoundingBox, CompoundBoundingBox
from .parameters import (Parameter, InputParameterError,
param_repr_oneline, _tofloat)
__all__ = ['Model', 'FittableModel', 'Fittable1DModel', 'Fittable2DModel',
'CompoundModel', 'fix_inputs', 'custom_model', 'ModelDefinitionError',
'bind_bounding_box', 'bind_compound_bounding_box']
def _model_oper(oper, **kwargs):
"""
Returns a function that evaluates a given Python arithmetic operator
between two models. The operator should be given as a string, like ``'+'``
or ``'**'``.
"""
return lambda left, right: CompoundModel(oper, left, right, **kwargs)
class ModelDefinitionError(TypeError):
"""Used for incorrect models definitions."""
class _ModelMeta(abc.ABCMeta):
"""
Metaclass for Model.
Currently just handles auto-generating the param_names list based on
Parameter descriptors declared at the class-level of Model subclasses.
"""
_is_dynamic = False
"""
This flag signifies whether this class was created in the "normal" way,
with a class statement in the body of a module, as opposed to a call to
`type` or some other metaclass constructor, such that the resulting class
does not belong to a specific module. This is important for pickling of
dynamic classes.
This flag is always forced to False for new classes, so code that creates
dynamic classes should manually set it to True on those classes when
creating them.
"""
# Default empty dict for _parameters_, which will be empty on model
# classes that don't have any Parameters
def __new__(mcls, name, bases, members, **kwds):
# See the docstring for _is_dynamic above
if '_is_dynamic' not in members:
members['_is_dynamic'] = mcls._is_dynamic
opermethods = [
('__add__', _model_oper('+')),
('__sub__', _model_oper('-')),
('__mul__', _model_oper('*')),
('__truediv__', _model_oper('/')),
('__pow__', _model_oper('**')),
('__or__', _model_oper('|')),
('__and__', _model_oper('&')),
('_fix_inputs', _model_oper('fix_inputs'))
]
members['_parameters_'] = {k: v for k, v in members.items()
if isinstance(v, Parameter)}
for opermethod, opercall in opermethods:
members[opermethod] = opercall
cls = super().__new__(mcls, name, bases, members, **kwds)
param_names = list(members['_parameters_'])
# Need to walk each base MRO to collect all parameter names
for base in bases:
for tbase in base.__mro__:
if issubclass(tbase, Model):
# Preserve order of definitions
param_names = list(tbase._parameters_) + param_names
# Remove duplicates (arising from redefinition in subclass).
param_names = list(dict.fromkeys(param_names))
if cls._parameters_:
if hasattr(cls, '_param_names'):
# Slight kludge to support compound models, where
# cls.param_names is a property; could be improved with a
# little refactoring but fine for now
cls._param_names = tuple(param_names)
else:
cls.param_names = tuple(param_names)
return cls
def __init__(cls, name, bases, members, **kwds):
super(_ModelMeta, cls).__init__(name, bases, members, **kwds)
cls._create_inverse_property(members)
cls._create_bounding_box_property(members)
pdict = {}
for base in bases:
for tbase in base.__mro__:
if issubclass(tbase, Model):
for parname, val in cls._parameters_.items():
pdict[parname] = val
cls._handle_special_methods(members, pdict)
def __repr__(cls):
"""
Custom repr for Model subclasses.
"""
return cls._format_cls_repr()
def _repr_pretty_(cls, p, cycle):
"""
Repr for IPython's pretty printer.
By default IPython "pretty prints" classes, so we need to implement
this so that IPython displays the custom repr for Models.
"""
p.text(repr(cls))
def __reduce__(cls):
if not cls._is_dynamic:
# Just return a string specifying where the class can be imported
# from
return cls.__name__
members = dict(cls.__dict__)
# Delete any ABC-related attributes--these will be restored when
# the class is reconstructed:
for key in list(members):
if key.startswith('_abc_'):
del members[key]
# Delete custom __init__ and __call__ if they exist:
for key in ('__init__', '__call__'):
if key in members:
del members[key]
return (type(cls), (cls.__name__, cls.__bases__, members))
@property
def name(cls):
"""
The name of this model class--equivalent to ``cls.__name__``.
This attribute is provided for symmetry with the `Model.name` attribute
of model instances.
"""
return cls.__name__
@property
def _is_concrete(cls):
"""
A class-level property that determines whether the class is a concrete
implementation of a Model--i.e. it is not some abstract base class or
internal implementation detail (i.e. begins with '_').
"""
return not (cls.__name__.startswith('_') or inspect.isabstract(cls))
def rename(cls, name=None, inputs=None, outputs=None):
"""
Creates a copy of this model class with a new name, inputs or outputs.
The new class is technically a subclass of the original class, so that
instance and type checks will still work. For example::
>>> from astropy.modeling.models import Rotation2D
>>> SkyRotation = Rotation2D.rename('SkyRotation')
>>> SkyRotation
<class 'astropy.modeling.core.SkyRotation'>
Name: SkyRotation (Rotation2D)
N_inputs: 2
N_outputs: 2
Fittable parameters: ('angle',)
>>> issubclass(SkyRotation, Rotation2D)
True
>>> r = SkyRotation(90)
>>> isinstance(r, Rotation2D)
True
"""
mod = find_current_module(2)
if mod:
modname = mod.__name__
else:
modname = '__main__'
if name is None:
name = cls.name
if inputs is None:
inputs = cls.inputs
else:
if not isinstance(inputs, tuple):
raise TypeError("Expected 'inputs' to be a tuple of strings.")
elif len(inputs) != len(cls.inputs):
raise ValueError(f'{cls.name} expects {len(cls.inputs)} inputs')
if outputs is None:
outputs = cls.outputs
else:
if not isinstance(outputs, tuple):
raise TypeError("Expected 'outputs' to be a tuple of strings.")
elif len(outputs) != len(cls.outputs):
raise ValueError(f'{cls.name} expects {len(cls.outputs)} outputs')
new_cls = type(name, (cls,), {"inputs": inputs, "outputs": outputs})
new_cls.__module__ = modname
new_cls.__qualname__ = name
return new_cls
def _create_inverse_property(cls, members):
inverse = members.get('inverse')
if inverse is None or cls.__bases__[0] is object:
# The latter clause is the prevent the below code from running on
# the Model base class, which implements the default getter and
# setter for .inverse
return
if isinstance(inverse, property):
# We allow the @property decorator to be omitted entirely from
# the class definition, though its use should be encouraged for
# clarity
inverse = inverse.fget
# Store the inverse getter internally, then delete the given .inverse
# attribute so that cls.inverse resolves to Model.inverse instead
cls._inverse = inverse
del cls.inverse
def _create_bounding_box_property(cls, members):
"""
Takes any bounding_box defined on a concrete Model subclass (either
as a fixed tuple or a property or method) and wraps it in the generic
getter/setter interface for the bounding_box attribute.
"""
# TODO: Much of this is verbatim from _create_inverse_property--I feel
# like there could be a way to generify properties that work this way,
# but for the time being that would probably only confuse things more.
bounding_box = members.get('bounding_box')
if bounding_box is None or cls.__bases__[0] is object:
return
if isinstance(bounding_box, property):
bounding_box = bounding_box.fget
if not callable(bounding_box):
# See if it's a hard-coded bounding_box (as a sequence) and
# normalize it
try:
bounding_box = ModelBoundingBox.validate(cls, bounding_box, _preserve_ignore=True)
except ValueError as exc:
raise ModelDefinitionError(exc.args[0])
else:
sig = signature(bounding_box)
# May be a method that only takes 'self' as an argument (like a
# property, but the @property decorator was forgotten)
#
# However, if the method takes additional arguments then this is a
# parameterized bounding box and should be callable
if len(sig.parameters) > 1:
bounding_box = \
cls._create_bounding_box_subclass(bounding_box, sig)
# See the Model.bounding_box getter definition for how this attribute
# is used
cls._bounding_box = bounding_box
del cls.bounding_box
def _create_bounding_box_subclass(cls, func, sig):
"""
For Models that take optional arguments for defining their bounding
box, we create a subclass of ModelBoundingBox with a ``__call__`` method
that supports those additional arguments.
Takes the function's Signature as an argument since that is already
computed in _create_bounding_box_property, so no need to duplicate that
effort.
"""
# TODO: Might be convenient if calling the bounding box also
# automatically sets the _user_bounding_box. So that
#
# >>> model.bounding_box(arg=1)
#
# in addition to returning the computed bbox, also sets it, so that
# it's a shortcut for
#
# >>> model.bounding_box = model.bounding_box(arg=1)
#
# Not sure if that would be non-obvious / confusing though...
def __call__(self, **kwargs):
return func(self._model, **kwargs)
kwargs = []
for idx, param in enumerate(sig.parameters.values()):
if idx == 0:
# Presumed to be a 'self' argument
continue
if param.default is param.empty:
raise ModelDefinitionError(
'The bounding_box method for {0} is not correctly '
'defined: If defined as a method all arguments to that '
'method (besides self) must be keyword arguments with '
'default values that can be used to compute a default '
'bounding box.'.format(cls.name))
kwargs.append((param.name, param.default))
__call__.__signature__ = sig
return type(f'{cls.name}ModelBoundingBox', (ModelBoundingBox,),
{'__call__': __call__})
def _handle_special_methods(cls, members, pdict):
# Handle init creation from inputs
def update_wrapper(wrapper, cls):
# Set up the new __call__'s metadata attributes as though it were
# manually defined in the class definition
# A bit like functools.update_wrapper but uses the class instead of
# the wrapped function
wrapper.__module__ = cls.__module__
wrapper.__doc__ = getattr(cls, wrapper.__name__).__doc__
if hasattr(cls, '__qualname__'):
wrapper.__qualname__ = f'{cls.__qualname__}.{wrapper.__name__}'
if ('__call__' not in members and 'n_inputs' in members and
isinstance(members['n_inputs'], int) and members['n_inputs'] > 0):
# Don't create a custom __call__ for classes that already have one
# explicitly defined (this includes the Model base class, and any
# other classes that manually override __call__
def __call__(self, *inputs, **kwargs):
"""Evaluate this model on the supplied inputs."""
return super(cls, self).__call__(*inputs, **kwargs)
# When called, models can take two optional keyword arguments:
#
# * model_set_axis, which indicates (for multi-dimensional input)
# which axis is used to indicate different models
#
# * equivalencies, a dictionary of equivalencies to be applied to
# the input values, where each key should correspond to one of
# the inputs.
#
# The following code creates the __call__ function with these
# two keyword arguments.
args = ('self',)
kwargs = dict([('model_set_axis', None),
('with_bounding_box', False),
('fill_value', np.nan),
('equivalencies', None),
('inputs_map', None)])
new_call = make_function_with_signature(
__call__, args, kwargs, varargs='inputs', varkwargs='new_inputs')
# The following makes it look like __call__
# was defined in the class
update_wrapper(new_call, cls)
cls.__call__ = new_call
if ('__init__' not in members and not inspect.isabstract(cls) and
cls._parameters_):
# Build list of all parameters including inherited ones
# If *all* the parameters have default values we can make them
# keyword arguments; otherwise they must all be positional
# arguments
if all(p.default is not None for p in pdict.values()):
args = ('self',)
kwargs = []
for param_name, param_val in pdict.items():
default = param_val.default
unit = param_val.unit
# If the unit was specified in the parameter but the
# default is not a Quantity, attach the unit to the
# default.
if unit is not None:
default = Quantity(default, unit, copy=False)
kwargs.append((param_name, default))
else:
args = ('self',) + tuple(pdict.keys())
kwargs = {}
def __init__(self, *params, **kwargs):
return super(cls, self).__init__(*params, **kwargs)
new_init = make_function_with_signature(
__init__, args, kwargs, varkwargs='kwargs')
update_wrapper(new_init, cls)
cls.__init__ = new_init
# *** Arithmetic operators for creating compound models ***
__add__ = _model_oper('+')
__sub__ = _model_oper('-')
__mul__ = _model_oper('*')
__truediv__ = _model_oper('/')
__pow__ = _model_oper('**')
__or__ = _model_oper('|')
__and__ = _model_oper('&')
_fix_inputs = _model_oper('fix_inputs')
# *** Other utilities ***
def _format_cls_repr(cls, keywords=[]):
"""
Internal implementation of ``__repr__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__repr__`` while keeping the same basic
formatting.
"""
# For the sake of familiarity start the output with the standard class
# __repr__
parts = [super().__repr__()]
if not cls._is_concrete:
return parts[0]
def format_inheritance(cls):
bases = []
for base in cls.mro()[1:]:
if not issubclass(base, Model):
continue
elif (inspect.isabstract(base) or
base.__name__.startswith('_')):
break
bases.append(base.name)
if bases:
return f"{cls.name} ({' -> '.join(bases)})"
return cls.name
try:
default_keywords = [
('Name', format_inheritance(cls)),
('N_inputs', cls.n_inputs),
('N_outputs', cls.n_outputs),
]
if cls.param_names:
default_keywords.append(('Fittable parameters',
cls.param_names))
for keyword, value in default_keywords + keywords:
if value is not None:
parts.append(f'{keyword}: {value}')
return '\n'.join(parts)
except Exception:
# If any of the above formatting fails fall back on the basic repr
# (this is particularly useful in debugging)
return parts[0]
class Model(metaclass=_ModelMeta):
"""
Base class for all models.
This is an abstract class and should not be instantiated directly.
The following initialization arguments apply to the majority of Model
subclasses by default (exceptions include specialized utility models
like `~astropy.modeling.mappings.Mapping`). Parametric models take all
their parameters as arguments, followed by any of the following optional
keyword arguments:
Parameters
----------
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict, optional
An optional dict of user-defined metadata to attach to this model.
How this is used and interpreted is up to the user or individual use
case.
n_models : int, optional
If given an integer greater than 1, a *model set* is instantiated
instead of a single model. This affects how the parameter arguments
are interpreted. In this case each parameter must be given as a list
or array--elements of this array are taken along the first axis (or
``model_set_axis`` if specified), such that the Nth element is the
value of that parameter for the Nth model in the set.
See the section on model sets in the documentation for more details.
model_set_axis : int, optional
This argument only applies when creating a model set (i.e. ``n_models >
1``). It changes how parameter values are interpreted. Normally the
first axis of each input parameter array (properly the 0th axis) is
taken as the axis corresponding to the model sets. However, any axis
of an input array may be taken as this "model set axis". This accepts
negative integers as well--for example use ``model_set_axis=-1`` if the
last (most rapidly changing) axis should be associated with the model
sets. Also, ``model_set_axis=False`` can be used to tell that a given
input should be used to evaluate all the models in the model set.
fixed : dict, optional
Dictionary ``{parameter_name: bool}`` setting the fixed constraint
for one or more parameters. `True` means the parameter is held fixed
during fitting and is prevented from updates once an instance of the
model has been created.
Alternatively the `~astropy.modeling.Parameter.fixed` property of a
parameter may be used to lock or unlock individual parameters.
tied : dict, optional
Dictionary ``{parameter_name: callable}`` of parameters which are
linked to some other parameter. The dictionary values are callables
providing the linking relationship.
Alternatively the `~astropy.modeling.Parameter.tied` property of a
parameter may be used to set the ``tied`` constraint on individual
parameters.
bounds : dict, optional
A dictionary ``{parameter_name: value}`` of lower and upper bounds of
parameters. Keys are parameter names. Values are a list or a tuple
of length 2 giving the desired range for the parameter.
Alternatively the `~astropy.modeling.Parameter.min` and
`~astropy.modeling.Parameter.max` or
~astropy.modeling.Parameter.bounds` properties of a parameter may be
used to set bounds on individual parameters.
eqcons : list, optional
List of functions of length n such that ``eqcons[j](x0, *args) == 0.0``
in a successfully optimized problem.
ineqcons : list, optional
List of functions of length n such that ``ieqcons[j](x0, *args) >=
0.0`` is a successfully optimized problem.
Examples
--------
>>> from astropy.modeling import models
>>> def tie_center(model):
... mean = 50 * model.stddev
... return mean
>>> tied_parameters = {'mean': tie_center}
Specify that ``'mean'`` is a tied parameter in one of two ways:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... tied=tied_parameters)
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.mean.tied
False
>>> g1.mean.tied = tie_center
>>> g1.mean.tied
<function tie_center at 0x...>
Fixed parameters:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... fixed={'stddev': True})
>>> g1.stddev.fixed
True
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.stddev.fixed
False
>>> g1.stddev.fixed = True
>>> g1.stddev.fixed
True
"""
parameter_constraints = Parameter.constraints
"""
Primarily for informational purposes, these are the types of constraints
that can be set on a model's parameters.
"""
model_constraints = ('eqcons', 'ineqcons')
"""
Primarily for informational purposes, these are the types of constraints
that constrain model evaluation.
"""
param_names = ()
"""
Names of the parameters that describe models of this type.
The parameters in this tuple are in the same order they should be passed in
when initializing a model of a specific type. Some types of models, such
as polynomial models, have a different number of parameters depending on
some other property of the model, such as the degree.
When defining a custom model class the value of this attribute is
automatically set by the `~astropy.modeling.Parameter` attributes defined
in the class body.
"""
n_inputs = 0
"""The number of inputs."""
n_outputs = 0
""" The number of outputs."""
standard_broadcasting = True
fittable = False
linear = True
_separable = None
""" A boolean flag to indicate whether a model is separable."""
meta = metadata.MetaData()
"""A dict-like object to store optional information."""
# By default models either use their own inverse property or have no
# inverse at all, but users may also assign a custom inverse to a model,
# optionally; in that case it is of course up to the user to determine
# whether their inverse is *actually* an inverse to the model they assign
# it to.
_inverse = None
_user_inverse = None
_bounding_box = None
_user_bounding_box = None
_has_inverse_bounding_box = False
# Default n_models attribute, so that __len__ is still defined even when a
# model hasn't completed initialization yet
_n_models = 1
# New classes can set this as a boolean value.
# It is converted to a dictionary mapping input name to a boolean value.
_input_units_strict = False
# Allow dimensionless input (and corresponding output). If this is True,
# input values to evaluate will gain the units specified in input_units. If
# this is a dictionary then it should map input name to a bool to allow
# dimensionless numbers for that input.
# Only has an effect if input_units is defined.
_input_units_allow_dimensionless = False
# Default equivalencies to apply to input values. If set, this should be a
# dictionary where each key is a string that corresponds to one of the
# model inputs. Only has an effect if input_units is defined.
input_units_equivalencies = None
# Covariance matrix can be set by fitter if available.
# If cov_matrix is available, then std will set as well
_cov_matrix = None
_stds = None
def __init_subclass__(cls, **kwargs):
super().__init_subclass__()
def __init__(self, *args, meta=None, name=None, **kwargs):
super().__init__()
self._default_inputs_outputs()
if meta is not None:
self.meta = meta
self._name = name
# add parameters to instance level by walking MRO list
mro = self.__class__.__mro__
for cls in mro:
if issubclass(cls, Model):
for parname, val in cls._parameters_.items():
newpar = copy.deepcopy(val)
newpar.model = self
if parname not in self.__dict__:
self.__dict__[parname] = newpar
self._initialize_constraints(kwargs)
kwargs = self._initialize_setters(kwargs)
# Remaining keyword args are either parameter values or invalid
# Parameter values must be passed in as keyword arguments in order to
# distinguish them
self._initialize_parameters(args, kwargs)
self._initialize_slices()
self._initialize_unit_support()
def _default_inputs_outputs(self):
if self.n_inputs == 1 and self.n_outputs == 1:
self._inputs = ("x",)
self._outputs = ("y",)
elif self.n_inputs == 2 and self.n_outputs == 1:
self._inputs = ("x", "y")
self._outputs = ("z",)
else:
try:
self._inputs = tuple("x" + str(idx) for idx in range(self.n_inputs))
self._outputs = tuple("x" + str(idx) for idx in range(self.n_outputs))
except TypeError:
# self.n_inputs and self.n_outputs are properties
# This is the case when subclasses of Model do not define
# ``n_inputs``, ``n_outputs``, ``inputs`` or ``outputs``.
self._inputs = ()
self._outputs = ()
def _initialize_setters(self, kwargs):
"""
This exists to inject defaults for settable properties for models
originating from `custom_model`.
"""
if hasattr(self, '_settable_properties'):
setters = {name: kwargs.pop(name, default)
for name, default in self._settable_properties.items()}
for name, value in setters.items():
setattr(self, name, value)
return kwargs
@property
def inputs(self):
return self._inputs
@inputs.setter
def inputs(self, val):
if len(val) != self.n_inputs:
raise ValueError(f"Expected {self.n_inputs} number of inputs, got {len(val)}.")
self._inputs = val
self._initialize_unit_support()
@property
def outputs(self):
return self._outputs
@outputs.setter
def outputs(self, val):
if len(val) != self.n_outputs:
raise ValueError(f"Expected {self.n_outputs} number of outputs, got {len(val)}.")
self._outputs = val
@property
def n_inputs(self):
# TODO: remove the code in the ``if`` block when support
# for models with ``inputs`` as class variables is removed.
if hasattr(self.__class__, 'n_inputs') and isinstance(self.__class__.n_inputs, property):
try:
return len(self.__class__.inputs)
except TypeError:
try:
return len(self.inputs)
except AttributeError:
return 0
return self.__class__.n_inputs
@property
def n_outputs(self):
# TODO: remove the code in the ``if`` block when support
# for models with ``outputs`` as class variables is removed.
if hasattr(self.__class__, 'n_outputs') and isinstance(self.__class__.n_outputs, property):
try:
return len(self.__class__.outputs)
except TypeError:
try:
return len(self.outputs)
except AttributeError:
return 0
return self.__class__.n_outputs
def _calculate_separability_matrix(self):
"""
This is a hook which customises the behavior of modeling.separable.
This allows complex subclasses to customise the separability matrix.
If it returns `NotImplemented` the default behavior is used.
"""
return NotImplemented
def _initialize_unit_support(self):
"""
Convert self._input_units_strict and
self.input_units_allow_dimensionless to dictionaries
mapping input name to a boolean value.
"""
if isinstance(self._input_units_strict, bool):
self._input_units_strict = {key: self._input_units_strict for
key in self.inputs}
if isinstance(self._input_units_allow_dimensionless, bool):
self._input_units_allow_dimensionless = {key: self._input_units_allow_dimensionless
for key in self.inputs}
@property
def input_units_strict(self):
"""
Enforce strict units on inputs to evaluate. If this is set to True,
input values to evaluate will be in the exact units specified by
input_units. If the input quantities are convertible to input_units,
they are converted. If this is a dictionary then it should map input
name to a bool to set strict input units for that parameter.
"""
val = self._input_units_strict
if isinstance(val, bool):
return {key: val for key in self.inputs}
return dict(zip(self.inputs, val.values()))
@property
def input_units_allow_dimensionless(self):
"""
Allow dimensionless input (and corresponding output). If this is True,
input values to evaluate will gain the units specified in input_units. If
this is a dictionary then it should map input name to a bool to allow
dimensionless numbers for that input.
Only has an effect if input_units is defined.
"""
val = self._input_units_allow_dimensionless
if isinstance(val, bool):
return {key: val for key in self.inputs}
return dict(zip(self.inputs, val.values()))
@property
def uses_quantity(self):
"""
True if this model has been created with `~astropy.units.Quantity`
objects or if there are no parameters.
This can be used to determine if this model should be evaluated with
`~astropy.units.Quantity` or regular floats.
"""
pisq = [isinstance(p, Quantity) for p in self._param_sets(units=True)]
return (len(pisq) == 0) or any(pisq)
def __repr__(self):
return self._format_repr()
def __str__(self):
return self._format_str()
def __len__(self):
return self._n_models
@staticmethod
def _strip_ones(intup):
return tuple(item for item in intup if item != 1)
def __setattr__(self, attr, value):
if isinstance(self, CompoundModel):
param_names = self._param_names
param_names = self.param_names
if param_names is not None and attr in self.param_names:
param = self.__dict__[attr]
value = _tofloat(value)
if param._validator is not None:
param._validator(self, value)
# check consistency with previous shape and size
eshape = self._param_metrics[attr]['shape']
if eshape == ():
eshape = (1,)
vshape = np.array(value).shape
if vshape == ():
vshape = (1,)
esize = self._param_metrics[attr]['size']
if (np.size(value) != esize or
self._strip_ones(vshape) != self._strip_ones(eshape)):
raise InputParameterError(
"Value for parameter {0} does not match shape or size\n"
"expected by model ({1}, {2}) vs ({3}, {4})".format(
attr, vshape, np.size(value), eshape, esize))
if param.unit is None:
if isinstance(value, Quantity):
param._unit = value.unit
param.value = value.value
else:
param.value = value
else:
if not isinstance(value, Quantity):
raise UnitsError(f"The '{param.name}' parameter should be given as a"
" Quantity because it was originally "
"initialized as a Quantity")
param._unit = value.unit
param.value = value.value
else:
if attr in ['fittable', 'linear']:
self.__dict__[attr] = value
else:
super().__setattr__(attr, value)
def _pre_evaluate(self, *args, **kwargs):
"""
Model specific input setup that needs to occur prior to model evaluation
"""
# Broadcast inputs into common size
inputs, broadcasted_shapes = self.prepare_inputs(*args, **kwargs)
# Setup actual model evaluation method
parameters = self._param_sets(raw=True, units=True)
def evaluate(_inputs):
return self.evaluate(*chain(_inputs, parameters))
return evaluate, inputs, broadcasted_shapes, kwargs
def get_bounding_box(self, with_bbox=True):
"""
Return the ``bounding_box`` of a model if it exists or ``None``
otherwise.
Parameters
----------
with_bbox :
The value of the ``with_bounding_box`` keyword argument
when calling the model. Default is `True` for usage when
looking up the model's ``bounding_box`` without risk of error.
"""
bbox = None
if not isinstance(with_bbox, bool) or with_bbox:
try:
bbox = self.bounding_box
except NotImplementedError:
pass
if isinstance(bbox, CompoundBoundingBox) and not isinstance(with_bbox, bool):
bbox = bbox[with_bbox]
return bbox
@property
def _argnames(self):
"""The inputs used to determine input_shape for bounding_box evaluation"""
return self.inputs
def _validate_input_shape(self, _input, idx, argnames, model_set_axis, check_model_set_axis):
"""
Perform basic validation of a single model input's shape
-- it has the minimum dimensions for the given model_set_axis
Returns the shape of the input if validation succeeds.
"""
input_shape = np.shape(_input)
# Ensure that the input's model_set_axis matches the model's
# n_models
if input_shape and check_model_set_axis:
# Note: Scalar inputs *only* get a pass on this
if len(input_shape) < model_set_axis + 1:
raise ValueError(
f"For model_set_axis={model_set_axis}, all inputs must be at "
f"least {model_set_axis + 1}-dimensional.")
if input_shape[model_set_axis] != self._n_models:
try:
argname = argnames[idx]
except IndexError:
# the case of model.inputs = ()
argname = str(idx)
raise ValueError(
f"Input argument '{argname}' does not have the correct "
f"dimensions in model_set_axis={model_set_axis} for a model set with "
f"n_models={self._n_models}.")
return input_shape
def _validate_input_shapes(self, inputs, argnames, model_set_axis):
"""
Perform basic validation of model inputs
--that they are mutually broadcastable and that they have
the minimum dimensions for the given model_set_axis.
If validation succeeds, returns the total shape that will result from
broadcasting the input arrays with each other.
"""
check_model_set_axis = self._n_models > 1 and model_set_axis is not False
all_shapes = []
for idx, _input in enumerate(inputs):
all_shapes.append(self._validate_input_shape(_input, idx, argnames,
model_set_axis, check_model_set_axis))
input_shape = check_broadcast(*all_shapes)
if input_shape is None:
raise ValueError(
"All inputs must have identical shapes or must be scalars.")
return input_shape
def input_shape(self, inputs):
"""Get input shape for bounding_box evaluation"""
return self._validate_input_shapes(inputs, self._argnames, self.model_set_axis)
def _generic_evaluate(self, evaluate, _inputs, fill_value, with_bbox):
"""
Generic model evaluation routine
Selects and evaluates model with or without bounding_box enforcement
"""
# Evaluate the model using the prepared evaluation method either
# enforcing the bounding_box or not.
bbox = self.get_bounding_box(with_bbox)
if (not isinstance(with_bbox, bool) or with_bbox) and bbox is not None:
outputs = bbox.evaluate(evaluate, _inputs, fill_value)
else:
outputs = evaluate(_inputs)
return outputs
def _post_evaluate(self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs):
"""
Model specific post evaluation processing of outputs
"""
if self.get_bounding_box(with_bbox) is None and self.n_outputs == 1:
outputs = (outputs,)
outputs = self.prepare_outputs(broadcasted_shapes, *outputs, **kwargs)
outputs = self._process_output_units(inputs, outputs)
if self.n_outputs == 1:
return outputs[0]
return outputs
@property
def bbox_with_units(self):
return (not isinstance(self, CompoundModel))
def __call__(self, *args, **kwargs):
"""
Evaluate this model using the given input(s) and the parameter values
that were specified when the model was instantiated.
"""
# Turn any keyword arguments into positional arguments.
args, kwargs = self._get_renamed_inputs_as_positional(*args, **kwargs)
# Read model evaluation related parameters
with_bbox = kwargs.pop('with_bounding_box', False)
fill_value = kwargs.pop('fill_value', np.nan)
# prepare for model evaluation (overridden in CompoundModel)
evaluate, inputs, broadcasted_shapes, kwargs = self._pre_evaluate(*args, **kwargs)
outputs = self._generic_evaluate(evaluate, inputs,
fill_value, with_bbox)
# post-process evaluation results (overridden in CompoundModel)
return self._post_evaluate(inputs, outputs, broadcasted_shapes, with_bbox, **kwargs)
def _get_renamed_inputs_as_positional(self, *args, **kwargs):
def _keyword2positional(kwargs):
# Inputs were passed as keyword (not positional) arguments.
# Because the signature of the ``__call__`` is defined at
# the class level, the name of the inputs cannot be changed at
# the instance level and the old names are always present in the
# signature of the method. In order to use the new names of the
# inputs, the old names are taken out of ``kwargs``, the input
# values are sorted in the order of self.inputs and passed as
# positional arguments to ``__call__``.
# These are the keys that are always present as keyword arguments.
keys = ['model_set_axis', 'with_bounding_box', 'fill_value',
'equivalencies', 'inputs_map']
new_inputs = {}
# kwargs contain the names of the new inputs + ``keys``
allkeys = list(kwargs.keys())
# Remove the names of the new inputs from kwargs and save them
# to a dict ``new_inputs``.
for key in allkeys:
if key not in keys:
new_inputs[key] = kwargs[key]
del kwargs[key]
return new_inputs, kwargs
n_args = len(args)
new_inputs, kwargs = _keyword2positional(kwargs)
n_all_args = n_args + len(new_inputs)
if n_all_args < self.n_inputs:
raise ValueError(f"Missing input arguments - expected {self.n_inputs}, got {n_all_args}")
elif n_all_args > self.n_inputs:
raise ValueError(f"Too many input arguments - expected {self.n_inputs}, got {n_all_args}")
if n_args == 0:
# Create positional arguments from the keyword arguments in ``new_inputs``.
new_args = []
for k in self.inputs:
new_args.append(new_inputs[k])
elif n_args != self.n_inputs:
# Some inputs are passed as positional, others as keyword arguments.
args = list(args)
# Create positional arguments from the keyword arguments in ``new_inputs``.
new_args = []
for k in self.inputs:
if k in new_inputs:
new_args.append(new_inputs[k])
else:
new_args.append(args[0])
del args[0]
else:
new_args = args
return new_args, kwargs
# *** Properties ***
@property
def name(self):
"""User-provided name for this model instance."""
return self._name
@name.setter
def name(self, val):
"""Assign a (new) name to this model."""
self._name = val
@property
def model_set_axis(self):
"""
The index of the model set axis--that is the axis of a parameter array
that pertains to which model a parameter value pertains to--as
specified when the model was initialized.
See the documentation on :ref:`astropy:modeling-model-sets`
for more details.
"""
return self._model_set_axis
@property
def param_sets(self):
"""
Return parameters as a pset.
This is a list with one item per parameter set, which is an array of
that parameter's values across all parameter sets, with the last axis
associated with the parameter set.
"""
return self._param_sets()
@property
def parameters(self):
"""
A flattened array of all parameter values in all parameter sets.
Fittable parameters maintain this list and fitters modify it.
"""
# Currently the sequence of a model's parameters must be contiguous
# within the _parameters array (which may be a view of a larger array,
# for example when taking a sub-expression of a compound model), so
# the assumption here is reliable:
if not self.param_names:
# Trivial, but not unheard of
return self._parameters
self._parameters_to_array()
start = self._param_metrics[self.param_names[0]]['slice'].start
stop = self._param_metrics[self.param_names[-1]]['slice'].stop
return self._parameters[start:stop]
@parameters.setter
def parameters(self, value):
"""
Assigning to this attribute updates the parameters array rather than
replacing it.
"""
if not self.param_names:
return
start = self._param_metrics[self.param_names[0]]['slice'].start
stop = self._param_metrics[self.param_names[-1]]['slice'].stop
try:
value = np.array(value).flatten()
self._parameters[start:stop] = value
except ValueError as e:
raise InputParameterError(
"Input parameter values not compatible with the model "
"parameters array: {0}".format(e))
self._array_to_parameters()
@property
def sync_constraints(self):
'''
This is a boolean property that indicates whether or not accessing constraints
automatically check the constituent models current values. It defaults to True
on creation of a model, but for fitting purposes it should be set to False
for performance reasons.
'''
if not hasattr(self, '_sync_constraints'):
self._sync_constraints = True
return self._sync_constraints
@sync_constraints.setter
def sync_constraints(self, value):
if not isinstance(value, bool):
raise ValueError('sync_constraints only accepts True or False as values')
self._sync_constraints = value
@property
def fixed(self):
"""
A ``dict`` mapping parameter names to their fixed constraint.
"""
if not hasattr(self, '_fixed') or self.sync_constraints:
self._fixed = _ConstraintsDict(self, 'fixed')
return self._fixed
@property
def bounds(self):
"""
A ``dict`` mapping parameter names to their upper and lower bounds as
``(min, max)`` tuples or ``[min, max]`` lists.
"""
if not hasattr(self, '_bounds') or self.sync_constraints:
self._bounds = _ConstraintsDict(self, 'bounds')
return self._bounds
@property
def tied(self):
"""
A ``dict`` mapping parameter names to their tied constraint.
"""
if not hasattr(self, '_tied') or self.sync_constraints:
self._tied = _ConstraintsDict(self, 'tied')
return self._tied
@property
def eqcons(self):
"""List of parameter equality constraints."""
return self._mconstraints['eqcons']
@property
def ineqcons(self):
"""List of parameter inequality constraints."""
return self._mconstraints['ineqcons']
def has_inverse(self):
"""
Returns True if the model has an analytic or user
inverse defined.
"""
try:
self.inverse
except NotImplementedError:
return False
return True
@property
def inverse(self):
"""
Returns a new `~astropy.modeling.Model` instance which performs the
inverse transform, if an analytic inverse is defined for this model.
Even on models that don't have an inverse defined, this property can be
set with a manually-defined inverse, such a pre-computed or
experimentally determined inverse (often given as a
`~astropy.modeling.polynomial.PolynomialModel`, but not by
requirement).
A custom inverse can be deleted with ``del model.inverse``. In this
case the model's inverse is reset to its default, if a default exists
(otherwise the default is to raise `NotImplementedError`).
Note to authors of `~astropy.modeling.Model` subclasses: To define an
inverse for a model simply override this property to return the
appropriate model representing the inverse. The machinery that will
make the inverse manually-overridable is added automatically by the
base class.
"""
if self._user_inverse is not None:
return self._user_inverse
elif self._inverse is not None:
result = self._inverse()
if result is not NotImplemented:
if not self._has_inverse_bounding_box:
result.bounding_box = None
return result
raise NotImplementedError("No analytical or user-supplied inverse transform "
"has been implemented for this model.")
@inverse.setter
def inverse(self, value):
if not isinstance(value, (Model, type(None))):
raise ValueError(
"The ``inverse`` attribute may be assigned a `Model` "
"instance or `None` (where `None` explicitly forces the "
"model to have no inverse.")
self._user_inverse = value
@inverse.deleter
def inverse(self):
"""
Resets the model's inverse to its default (if one exists, otherwise
the model will have no inverse).
"""
try:
del self._user_inverse
except AttributeError:
pass
@property
def has_user_inverse(self):
"""
A flag indicating whether or not a custom inverse model has been
assigned to this model by a user, via assignment to ``model.inverse``.
"""
return self._user_inverse is not None
@property
def bounding_box(self):
r"""
A `tuple` of length `n_inputs` defining the bounding box limits, or
raise `NotImplementedError` for no bounding_box.
The default limits are given by a ``bounding_box`` property or method
defined in the class body of a specific model. If not defined then
this property just raises `NotImplementedError` by default (but may be
assigned a custom value by a user). ``bounding_box`` can be set
manually to an array-like object of shape ``(model.n_inputs, 2)``. For
further usage, see :ref:`astropy:bounding-boxes`
The limits are ordered according to the `numpy` ``'C'`` indexing
convention, and are the reverse of the model input order,
e.g. for inputs ``('x', 'y', 'z')``, ``bounding_box`` is defined:
* for 1D: ``(x_low, x_high)``
* for 2D: ``((y_low, y_high), (x_low, x_high))``
* for 3D: ``((z_low, z_high), (y_low, y_high), (x_low, x_high))``
Examples
--------
Setting the ``bounding_box`` limits for a 1D and 2D model:
>>> from astropy.modeling.models import Gaussian1D, Gaussian2D
>>> model_1d = Gaussian1D()
>>> model_2d = Gaussian2D(x_stddev=1, y_stddev=1)
>>> model_1d.bounding_box = (-5, 5)
>>> model_2d.bounding_box = ((-6, 6), (-5, 5))
Setting the bounding_box limits for a user-defined 3D `custom_model`:
>>> from astropy.modeling.models import custom_model
>>> def const3d(x, y, z, amp=1):
... return amp
...
>>> Const3D = custom_model(const3d)
>>> model_3d = Const3D()
>>> model_3d.bounding_box = ((-6, 6), (-5, 5), (-4, 4))
To reset ``bounding_box`` to its default limits just delete the
user-defined value--this will reset it back to the default defined
on the class:
>>> del model_1d.bounding_box
To disable the bounding box entirely (including the default),
set ``bounding_box`` to `None`:
>>> model_1d.bounding_box = None
>>> model_1d.bounding_box # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
NotImplementedError: No bounding box is defined for this model
(note: the bounding box was explicitly disabled for this model;
use `del model.bounding_box` to restore the default bounding box,
if one is defined for this model).
"""
if self._user_bounding_box is not None:
if self._user_bounding_box is NotImplemented:
raise NotImplementedError(
"No bounding box is defined for this model (note: the "
"bounding box was explicitly disabled for this model; "
"use `del model.bounding_box` to restore the default "
"bounding box, if one is defined for this model).")
return self._user_bounding_box
elif self._bounding_box is None:
raise NotImplementedError(
"No bounding box is defined for this model.")
elif isinstance(self._bounding_box, ModelBoundingBox):
# This typically implies a hard-coded bounding box. This will
# probably be rare, but it is an option
return self._bounding_box
elif isinstance(self._bounding_box, types.MethodType):
return ModelBoundingBox.validate(self, self._bounding_box())
else:
# The only other allowed possibility is that it's a ModelBoundingBox
# subclass, so we call it with its default arguments and return an
# instance of it (that can be called to recompute the bounding box
# with any optional parameters)
# (In other words, in this case self._bounding_box is a *class*)
bounding_box = self._bounding_box((), model=self)()
return self._bounding_box(bounding_box, model=self)
@bounding_box.setter
def bounding_box(self, bounding_box):
"""
Assigns the bounding box limits.
"""
if bounding_box is None:
cls = None
# We use this to explicitly set an unimplemented bounding box (as
# opposed to no user bounding box defined)
bounding_box = NotImplemented
elif (isinstance(bounding_box, CompoundBoundingBox) or
isinstance(bounding_box, dict)):
cls = CompoundBoundingBox
elif (isinstance(self._bounding_box, type) and
issubclass(self._bounding_box, ModelBoundingBox)):
cls = self._bounding_box
else:
cls = ModelBoundingBox
if cls is not None:
try:
bounding_box = cls.validate(self, bounding_box, _preserve_ignore=True)
except ValueError as exc:
raise ValueError(exc.args[0])
self._user_bounding_box = bounding_box
def set_slice_args(self, *args):
if isinstance(self._user_bounding_box, CompoundBoundingBox):
self._user_bounding_box.slice_args = args
else:
raise RuntimeError('The bounding_box for this model is not compound')
@bounding_box.deleter
def bounding_box(self):
self._user_bounding_box = None
@property
def has_user_bounding_box(self):
"""
A flag indicating whether or not a custom bounding_box has been
assigned to this model by a user, via assignment to
``model.bounding_box``.
"""
return self._user_bounding_box is not None
@property
def cov_matrix(self):
"""
Fitter should set covariance matrix, if available.
"""
return self._cov_matrix
@cov_matrix.setter
def cov_matrix(self, cov):
self._cov_matrix = cov
unfix_untied_params = [p for p in self.param_names if (self.fixed[p] is False)
and (self.tied[p] is False)]
if type(cov) == list: # model set
param_stds = []
for c in cov:
param_stds.append([np.sqrt(x) if x > 0 else None for x in np.diag(c.cov_matrix)])
for p, param_name in enumerate(unfix_untied_params):
par = getattr(self, param_name)
par.std = [item[p] for item in param_stds]
setattr(self, param_name, par)
else:
param_stds = [np.sqrt(x) if x > 0 else None for x in np.diag(cov.cov_matrix)]
for param_name in unfix_untied_params:
par = getattr(self, param_name)
par.std = param_stds.pop(0)
setattr(self, param_name, par)
@property
def stds(self):
"""
Standard deviation of parameters, if covariance matrix is available.
"""
return self._stds
@stds.setter
def stds(self, stds):
self._stds = stds
@property
def separable(self):
""" A flag indicating whether a model is separable."""
if self._separable is not None:
return self._separable
raise NotImplementedError(
'The "separable" property is not defined for '
'model {}'.format(self.__class__.__name__))
# *** Public methods ***
def without_units_for_data(self, **kwargs):
"""
Return an instance of the model for which the parameter values have
been converted to the right units for the data, then the units have
been stripped away.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters should be converted to are not
necessarily the units of the input data, but are derived from them.
Model subclasses that want fitting to work in the presence of
quantities need to define a ``_parameter_units_for_data_units`` method
that takes the input and output units (as two dictionaries) and
returns a dictionary giving the target units for each parameter.
"""
model = self.copy()
inputs_unit = {inp: getattr(kwargs[inp], 'unit', dimensionless_unscaled)
for inp in self.inputs if kwargs[inp] is not None}
outputs_unit = {out: getattr(kwargs[out], 'unit', dimensionless_unscaled)
for out in self.outputs if kwargs[out] is not None}
parameter_units = self._parameter_units_for_data_units(inputs_unit,
outputs_unit)
for name, unit in parameter_units.items():
parameter = getattr(model, name)
if parameter.unit is not None:
parameter.value = parameter.quantity.to(unit).value
parameter._set_unit(None, force=True)
if isinstance(model, CompoundModel):
model.strip_units_from_tree()
return model
def output_units(self, **kwargs):
"""
Return a dictionary of output units for this model given a dictionary
of fitting inputs and outputs
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
This method will force extra model evaluations, which maybe computationally
expensive. To avoid this, one can add a return_units property to the model,
see :ref:`astropy:models_return_units`.
"""
units = self.return_units
if units is None or units == {}:
inputs = {inp: kwargs[inp] for inp in self.inputs}
values = self(**inputs)
if self.n_outputs == 1:
values = (values,)
units = {out: getattr(values[index], 'unit', dimensionless_unscaled)
for index, out in enumerate(self.outputs)}
return units
def strip_units_from_tree(self):
for item in self._leaflist:
for parname in item.param_names:
par = getattr(item, parname)
par._set_unit(None, force=True)
def with_units_from_data(self, **kwargs):
"""
Return an instance of the model which has units for which the parameter
values are compatible with the data units specified.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters will gain are not necessarily the units
of the input data, but are derived from them. Model subclasses that
want fitting to work in the presence of quantities need to define a
``_parameter_units_for_data_units`` method that takes the input and output
units (as two dictionaries) and returns a dictionary giving the target
units for each parameter.
"""
model = self.copy()
inputs_unit = {inp: getattr(kwargs[inp], 'unit', dimensionless_unscaled)
for inp in self.inputs if kwargs[inp] is not None}
outputs_unit = {out: getattr(kwargs[out], 'unit', dimensionless_unscaled)
for out in self.outputs if kwargs[out] is not None}
parameter_units = self._parameter_units_for_data_units(inputs_unit,
outputs_unit)
# We are adding units to parameters that already have a value, but we
# don't want to convert the parameter, just add the unit directly,
# hence the call to ``_set_unit``.
for name, unit in parameter_units.items():
parameter = getattr(model, name)
parameter._set_unit(unit, force=True)
return model
@property
def _has_units(self):
# Returns True if any of the parameters have units
for param in self.param_names:
if getattr(self, param).unit is not None:
return True
else:
return False
@property
def _supports_unit_fitting(self):
# If the model has a ``_parameter_units_for_data_units`` method, this
# indicates that we have enough information to strip the units away
# and add them back after fitting, when fitting quantities
return hasattr(self, '_parameter_units_for_data_units')
@abc.abstractmethod
def evaluate(self, *args, **kwargs):
"""Evaluate the model on some input variables."""
def sum_of_implicit_terms(self, *args, **kwargs):
"""
Evaluate the sum of any implicit model terms on some input variables.
This includes any fixed terms used in evaluating a linear model that
do not have corresponding parameters exposed to the user. The
prototypical case is `astropy.modeling.functional_models.Shift`, which
corresponds to a function y = a + bx, where b=1 is intrinsically fixed
by the type of model, such that sum_of_implicit_terms(x) == x. This
method is needed by linear fitters to correct the dependent variable
for the implicit term(s) when solving for the remaining terms
(ie. a = y - bx).
"""
def render(self, out=None, coords=None):
"""
Evaluate a model at fixed positions, respecting the ``bounding_box``.
The key difference relative to evaluating the model directly is that
this method is limited to a bounding box if the `Model.bounding_box`
attribute is set.
Parameters
----------
out : `numpy.ndarray`, optional
An array that the evaluated model will be added to. If this is not
given (or given as ``None``), a new array will be created.
coords : array-like, optional
An array to be used to translate from the model's input coordinates
to the ``out`` array. It should have the property that
``self(coords)`` yields the same shape as ``out``. If ``out`` is
not specified, ``coords`` will be used to determine the shape of
the returned array. If this is not provided (or None), the model
will be evaluated on a grid determined by `Model.bounding_box`.
Returns
-------
out : `numpy.ndarray`
The model added to ``out`` if ``out`` is not ``None``, or else a
new array from evaluating the model over ``coords``.
If ``out`` and ``coords`` are both `None`, the returned array is
limited to the `Model.bounding_box` limits. If
`Model.bounding_box` is `None`, ``arr`` or ``coords`` must be
passed.
Raises
------
ValueError
If ``coords`` are not given and the the `Model.bounding_box` of
this model is not set.
Examples
--------
:ref:`astropy:bounding-boxes`
"""
try:
bbox = self.bounding_box
except NotImplementedError:
bbox = None
if isinstance(bbox, ModelBoundingBox):
bbox = bbox.bounding_box()
ndim = self.n_inputs
if (coords is None) and (out is None) and (bbox is None):
raise ValueError('If no bounding_box is set, '
'coords or out must be input.')
# for consistent indexing
if ndim == 1:
if coords is not None:
coords = [coords]
if bbox is not None:
bbox = [bbox]
if coords is not None:
coords = np.asanyarray(coords, dtype=float)
# Check dimensions match out and model
assert len(coords) == ndim
if out is not None:
if coords[0].shape != out.shape:
raise ValueError('inconsistent shape of the output.')
else:
out = np.zeros(coords[0].shape)
if out is not None:
out = np.asanyarray(out)
if out.ndim != ndim:
raise ValueError('the array and model must have the same '
'number of dimensions.')
if bbox is not None:
# Assures position is at center pixel,
# important when using add_array.
pd = np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2))
for bb in bbox]).astype(int).T
pos, delta = pd
if coords is not None:
sub_shape = tuple(delta * 2 + 1)
sub_coords = np.array([extract_array(c, sub_shape, pos)
for c in coords])
else:
limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
sub_coords = np.mgrid[limits]
sub_coords = sub_coords[::-1]
if out is None:
out = self(*sub_coords)
else:
try:
out = add_array(out, self(*sub_coords), pos)
except ValueError:
raise ValueError(
'The `bounding_box` is larger than the input out in '
'one or more dimensions. Set '
'`model.bounding_box = None`.')
else:
if coords is None:
im_shape = out.shape
limits = [slice(i) for i in im_shape]
coords = np.mgrid[limits]
coords = coords[::-1]
out += self(*coords)
return out
@property
def input_units(self):
"""
This property is used to indicate what units or sets of units the
evaluate method expects, and returns a dictionary mapping inputs to
units (or `None` if any units are accepted).
Model sub-classes can also use function annotations in evaluate to
indicate valid input units, in which case this property should
not be overridden since it will return the input units based on the
annotations.
"""
if hasattr(self, '_input_units'):
return self._input_units
elif hasattr(self.evaluate, '__annotations__'):
annotations = self.evaluate.__annotations__.copy()
annotations.pop('return', None)
if annotations:
# If there are not annotations for all inputs this will error.
return dict((name, annotations[name]) for name in self.inputs)
else:
# None means any unit is accepted
return None
@property
def return_units(self):
"""
This property is used to indicate what units or sets of units the
output of evaluate should be in, and returns a dictionary mapping
outputs to units (or `None` if any units are accepted).
Model sub-classes can also use function annotations in evaluate to
indicate valid output units, in which case this property should not be
overridden since it will return the return units based on the
annotations.
"""
if hasattr(self, '_return_units'):
return self._return_units
elif hasattr(self.evaluate, '__annotations__'):
return self.evaluate.__annotations__.get('return', None)
else:
# None means any unit is accepted
return None
def _prepare_inputs_single_model(self, params, inputs, **kwargs):
broadcasts = []
for idx, _input in enumerate(inputs):
input_shape = _input.shape
# Ensure that array scalars are always upgrade to 1-D arrays for the
# sake of consistency with how parameters work. They will be cast back
# to scalars at the end
if not input_shape:
inputs[idx] = _input.reshape((1,))
if not params:
max_broadcast = input_shape
else:
max_broadcast = ()
for param in params:
try:
if self.standard_broadcasting:
broadcast = check_broadcast(input_shape, param.shape)
else:
broadcast = input_shape
except IncompatibleShapeError:
raise ValueError(
"self input argument {0!r} of shape {1!r} cannot be "
"broadcast with parameter {2!r} of shape "
"{3!r}.".format(self.inputs[idx], input_shape,
param.name, param.shape))
if len(broadcast) > len(max_broadcast):
max_broadcast = broadcast
elif len(broadcast) == len(max_broadcast):
max_broadcast = max(max_broadcast, broadcast)
broadcasts.append(max_broadcast)
if self.n_outputs > self.n_inputs:
extra_outputs = self.n_outputs - self.n_inputs
if not broadcasts:
# If there were no inputs then the broadcasts list is empty
# just add a None since there is no broadcasting of outputs and
# inputs necessary (see _prepare_outputs_single_self)
broadcasts.append(None)
broadcasts.extend([broadcasts[0]] * extra_outputs)
return inputs, (broadcasts,)
@staticmethod
def _remove_axes_from_shape(shape, axis):
"""
Given a shape tuple as the first input, construct a new one by removing
that particular axis from the shape and all preceeding axes. Negative axis
numbers are permittted, where the axis is relative to the last axis.
"""
if len(shape) == 0:
return shape
if axis < 0:
axis = len(shape) + axis
return shape[:axis] + shape[axis+1:]
if axis >= len(shape):
axis = len(shape)-1
shape = shape[axis+1:]
return shape
def _prepare_inputs_model_set(self, params, inputs, model_set_axis_input,
**kwargs):
reshaped = []
pivots = []
model_set_axis_param = self.model_set_axis # needed to reshape param
for idx, _input in enumerate(inputs):
max_param_shape = ()
if self._n_models > 1 and model_set_axis_input is not False:
# Use the shape of the input *excluding* the model axis
input_shape = (_input.shape[:model_set_axis_input] +
_input.shape[model_set_axis_input + 1:])
else:
input_shape = _input.shape
for param in params:
try:
check_broadcast(input_shape,
self._remove_axes_from_shape(param.shape,
model_set_axis_param))
except IncompatibleShapeError:
raise ValueError(
"Model input argument {0!r} of shape {1!r} cannot be "
"broadcast with parameter {2!r} of shape "
"{3!r}.".format(self.inputs[idx], input_shape,
param.name,
self._remove_axes_from_shape(param.shape,
model_set_axis_param)))
if len(param.shape) - 1 > len(max_param_shape):
max_param_shape = self._remove_axes_from_shape(param.shape,
model_set_axis_param)
# We've now determined that, excluding the model_set_axis, the
# input can broadcast with all the parameters
input_ndim = len(input_shape)
if model_set_axis_input is False:
if len(max_param_shape) > input_ndim:
# Just needs to prepend new axes to the input
n_new_axes = 1 + len(max_param_shape) - input_ndim
new_axes = (1,) * n_new_axes
new_shape = new_axes + _input.shape
pivot = model_set_axis_param
else:
pivot = input_ndim - len(max_param_shape)
new_shape = (_input.shape[:pivot] + (1,) +
_input.shape[pivot:])
new_input = _input.reshape(new_shape)
else:
if len(max_param_shape) >= input_ndim:
n_new_axes = len(max_param_shape) - input_ndim
pivot = self.model_set_axis
new_axes = (1,) * n_new_axes
new_shape = (_input.shape[:pivot + 1] + new_axes +
_input.shape[pivot + 1:])
new_input = _input.reshape(new_shape)
else:
pivot = _input.ndim - len(max_param_shape) - 1
new_input = np.rollaxis(_input, model_set_axis_input,
pivot + 1)
pivots.append(pivot)
reshaped.append(new_input)
if self.n_inputs < self.n_outputs:
pivots.extend([model_set_axis_input] * (self.n_outputs - self.n_inputs))
return reshaped, (pivots,)
def prepare_inputs(self, *inputs, model_set_axis=None, equivalencies=None,
**kwargs):
"""
This method is used in `~astropy.modeling.Model.__call__` to ensure
that all the inputs to the model can be broadcast into compatible
shapes (if one or both of them are input as arrays), particularly if
there are more than one parameter sets. This also makes sure that (if
applicable) the units of the input will be compatible with the evaluate
method.
"""
# When we instantiate the model class, we make sure that __call__ can
# take the following two keyword arguments: model_set_axis and
# equivalencies.
if model_set_axis is None:
# By default the model_set_axis for the input is assumed to be the
# same as that for the parameters the model was defined with
# TODO: Ensure that negative model_set_axis arguments are respected
model_set_axis = self.model_set_axis
params = [getattr(self, name) for name in self.param_names]
inputs = [np.asanyarray(_input, dtype=float) for _input in inputs]
self._validate_input_shapes(inputs, self.inputs, model_set_axis)
inputs_map = kwargs.get('inputs_map', None)
inputs = self._validate_input_units(inputs, equivalencies, inputs_map)
# The input formatting required for single models versus a multiple
# model set are different enough that they've been split into separate
# subroutines
if self._n_models == 1:
return self._prepare_inputs_single_model(params, inputs, **kwargs)
else:
return self._prepare_inputs_model_set(params, inputs,
model_set_axis, **kwargs)
def _validate_input_units(self, inputs, equivalencies=None, inputs_map=None):
inputs = list(inputs)
name = self.name or self.__class__.__name__
# Check that the units are correct, if applicable
if self.input_units is not None:
# If a leaflist is provided that means this is in the context of
# a compound model and it is necessary to create the appropriate
# alias for the input coordinate name for the equivalencies dict
if inputs_map:
edict = {}
for mod, mapping in inputs_map:
if self is mod:
edict[mapping[0]] = equivalencies[mapping[1]]
else:
edict = equivalencies
# We combine any instance-level input equivalencies with user
# specified ones at call-time.
input_units_equivalencies = _combine_equivalency_dict(self.inputs,
edict,
self.input_units_equivalencies)
# We now iterate over the different inputs and make sure that their
# units are consistent with those specified in input_units.
for i in range(len(inputs)):
input_name = self.inputs[i]
input_unit = self.input_units.get(input_name, None)
if input_unit is None:
continue
if isinstance(inputs[i], Quantity):
# We check for consistency of the units with input_units,
# taking into account any equivalencies
if inputs[i].unit.is_equivalent(
input_unit,
equivalencies=input_units_equivalencies[input_name]):
# If equivalencies have been specified, we need to
# convert the input to the input units - this is
# because some equivalencies are non-linear, and
# we need to be sure that we evaluate the model in
# its own frame of reference. If input_units_strict
# is set, we also need to convert to the input units.
if len(input_units_equivalencies) > 0 or self.input_units_strict[input_name]:
inputs[i] = inputs[i].to(input_unit,
equivalencies=input_units_equivalencies[input_name])
else:
# We consider the following two cases separately so as
# to be able to raise more appropriate/nicer exceptions
if input_unit is dimensionless_unscaled:
raise UnitsError("{0}: Units of input '{1}', {2} ({3}),"
"could not be converted to "
"required dimensionless "
"input".format(name,
self.inputs[i],
inputs[i].unit,
inputs[i].unit.physical_type))
else:
raise UnitsError("{0}: Units of input '{1}', {2} ({3}),"
" could not be "
"converted to required input"
" units of {4} ({5})".format(
name,
self.inputs[i],
inputs[i].unit,
inputs[i].unit.physical_type,
input_unit,
input_unit.physical_type))
else:
# If we allow dimensionless input, we add the units to the
# input values without conversion, otherwise we raise an
# exception.
if (not self.input_units_allow_dimensionless[input_name] and
input_unit is not dimensionless_unscaled and
input_unit is not None):
if np.any(inputs[i] != 0):
raise UnitsError("{0}: Units of input '{1}', (dimensionless), could not be "
"converted to required input units of "
"{2} ({3})".format(name, self.inputs[i], input_unit,
input_unit.physical_type))
return inputs
def _process_output_units(self, inputs, outputs):
inputs_are_quantity = any([isinstance(i, Quantity) for i in inputs])
if self.return_units and inputs_are_quantity:
# We allow a non-iterable unit only if there is one output
if self.n_outputs == 1 and not isiterable(self.return_units):
return_units = {self.outputs[0]: self.return_units}
else:
return_units = self.return_units
outputs = tuple([Quantity(out, return_units.get(out_name, None), subok=True)
for out, out_name in zip(outputs, self.outputs)])
return outputs
@staticmethod
def _prepare_output_single_model(output, broadcast_shape):
if broadcast_shape is not None:
if not broadcast_shape:
return output.item()
else:
try:
return output.reshape(broadcast_shape)
except ValueError:
try:
return output.item()
except ValueError:
return output
return output
def _prepare_outputs_single_model(self, outputs, broadcasted_shapes):
outputs = list(outputs)
for idx, output in enumerate(outputs):
try:
broadcast_shape = check_broadcast(*broadcasted_shapes[0])
except (IndexError, TypeError):
broadcast_shape = broadcasted_shapes[0][idx]
outputs[idx] = self._prepare_output_single_model(output, broadcast_shape)
return tuple(outputs)
def _prepare_outputs_model_set(self, outputs, broadcasted_shapes, model_set_axis):
pivots = broadcasted_shapes[0]
# If model_set_axis = False was passed then use
# self._model_set_axis to format the output.
if model_set_axis is None or model_set_axis is False:
model_set_axis = self.model_set_axis
outputs = list(outputs)
for idx, output in enumerate(outputs):
pivot = pivots[idx]
if pivot < output.ndim and pivot != model_set_axis:
outputs[idx] = np.rollaxis(output, pivot,
model_set_axis)
return tuple(outputs)
def prepare_outputs(self, broadcasted_shapes, *outputs, **kwargs):
model_set_axis = kwargs.get('model_set_axis', None)
if len(self) == 1:
return self._prepare_outputs_single_model(outputs, broadcasted_shapes)
else:
return self._prepare_outputs_model_set(outputs, broadcasted_shapes, model_set_axis)
def copy(self):
"""
Return a copy of this model.
Uses a deep copy so that all model attributes, including parameter
values, are copied as well.
"""
return copy.deepcopy(self)
def deepcopy(self):
"""
Return a deep copy of this model.
"""
return self.copy()
@sharedmethod
def rename(self, name):
"""
Return a copy of this model with a new name.
"""
new_model = self.copy()
new_model._name = name
return new_model
def coerce_units(
self,
input_units=None,
return_units=None,
input_units_equivalencies=None,
input_units_allow_dimensionless=False
):
"""
Attach units to this (unitless) model.
Parameters
----------
input_units : dict or tuple, optional
Input units to attach. If dict, each key is the name of a model input,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.inputs`.
return_units : dict or tuple, optional
Output units to attach. If dict, each key is the name of a model output,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.outputs`.
input_units_equivalencies : dict, optional
Default equivalencies to apply to input values. If set, this should be a
dictionary where each key is a string that corresponds to one of the
model inputs.
input_units_allow_dimensionless : bool or dict, optional
Allow dimensionless input. If this is True, input values to evaluate will
gain the units specified in input_units. If this is a dictionary then it
should map input name to a bool to allow dimensionless numbers for that
input.
Returns
-------
`CompoundModel`
A `CompoundModel` composed of the current model plus
`~astropy.modeling.mappings.UnitsMapping` model(s) that attach the units.
Raises
------
ValueError
If the current model already has units.
Examples
--------
Wrapping a unitless model to require and convert units:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,))
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(1000, u.cm)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(10, u.cm)) # doctest: +FLOAT_CMP
<Quantity 1.2 s>
Wrapping a unitless model but still permitting unitless input:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,), input_units_allow_dimensionless=True)
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(10) # doctest: +FLOAT_CMP
<Quantity 21. s>
"""
from .mappings import UnitsMapping
result = self
if input_units is not None:
if self.input_units is not None:
model_units = self.input_units
else:
model_units = {}
for unit in [model_units.get(i) for i in self.inputs]:
if unit is not None and unit != dimensionless_unscaled:
raise ValueError("Cannot specify input_units for model with existing input units")
if isinstance(input_units, dict):
if input_units.keys() != set(self.inputs):
message = (
f"""input_units keys ({", ".join(input_units.keys())}) """
f"""do not match model inputs ({", ".join(self.inputs)})"""
)
raise ValueError(message)
input_units = [input_units[i] for i in self.inputs]
if len(input_units) != self.n_inputs:
message = (
"input_units length does not match n_inputs: "
f"expected {self.n_inputs}, received {len(input_units)}"
)
raise ValueError(message)
mapping = tuple((unit, model_units.get(i)) for i, unit in zip(self.inputs, input_units))
input_mapping = UnitsMapping(
mapping,
input_units_equivalencies=input_units_equivalencies,
input_units_allow_dimensionless=input_units_allow_dimensionless
)
input_mapping.inputs = self.inputs
input_mapping.outputs = self.inputs
result = input_mapping | result
if return_units is not None:
if self.return_units is not None:
model_units = self.return_units
else:
model_units = {}
for unit in [model_units.get(i) for i in self.outputs]:
if unit is not None and unit != dimensionless_unscaled:
raise ValueError("Cannot specify return_units for model with existing output units")
if isinstance(return_units, dict):
if return_units.keys() != set(self.outputs):
message = (
f"""return_units keys ({", ".join(return_units.keys())}) """
f"""do not match model outputs ({", ".join(self.outputs)})"""
)
raise ValueError(message)
return_units = [return_units[i] for i in self.outputs]
if len(return_units) != self.n_outputs:
message = (
"return_units length does not match n_outputs: "
f"expected {self.n_outputs}, received {len(return_units)}"
)
raise ValueError(message)
mapping = tuple((model_units.get(i), unit) for i, unit in zip(self.outputs, return_units))
return_mapping = UnitsMapping(mapping)
return_mapping.inputs = self.outputs
return_mapping.outputs = self.outputs
result = result | return_mapping
return result
@property
def n_submodels(self):
"""
Return the number of components in a single model, which is
obviously 1.
"""
return 1
def _initialize_constraints(self, kwargs):
"""
Pop parameter constraint values off the keyword arguments passed to
`Model.__init__` and store them in private instance attributes.
"""
# Pop any constraints off the keyword arguments
for constraint in self.parameter_constraints:
values = kwargs.pop(constraint, {})
for ckey, cvalue in values.items():
param = getattr(self, ckey)
setattr(param, constraint, cvalue)
self._mconstraints = {}
for constraint in self.model_constraints:
values = kwargs.pop(constraint, [])
self._mconstraints[constraint] = values
def _initialize_parameters(self, args, kwargs):
"""
Initialize the _parameters array that stores raw parameter values for
all parameter sets for use with vectorized fitting algorithms; on
FittableModels the _param_name attributes actually just reference
slices of this array.
"""
n_models = kwargs.pop('n_models', None)
if not (n_models is None or
(isinstance(n_models, (int, np.integer)) and n_models >= 1)):
raise ValueError(
"n_models must be either None (in which case it is "
"determined from the model_set_axis of the parameter initial "
"values) or it must be a positive integer "
"(got {0!r})".format(n_models))
model_set_axis = kwargs.pop('model_set_axis', None)
if model_set_axis is None:
if n_models is not None and n_models > 1:
# Default to zero
model_set_axis = 0
else:
# Otherwise disable
model_set_axis = False
else:
if not (model_set_axis is False or
np.issubdtype(type(model_set_axis), np.integer)):
raise ValueError(
"model_set_axis must be either False or an integer "
"specifying the parameter array axis to map to each "
"model in a set of models (got {0!r}).".format(
model_set_axis))
# Process positional arguments by matching them up with the
# corresponding parameters in self.param_names--if any also appear as
# keyword arguments this presents a conflict
params = set()
if len(args) > len(self.param_names):
raise TypeError(
"{0}.__init__() takes at most {1} positional arguments ({2} "
"given)".format(self.__class__.__name__, len(self.param_names),
len(args)))
self._model_set_axis = model_set_axis
self._param_metrics = defaultdict(dict)
for idx, arg in enumerate(args):
if arg is None:
# A value of None implies using the default value, if exists
continue
# We use quantity_asanyarray here instead of np.asanyarray because
# if any of the arguments are quantities, we need to return a
# Quantity object not a plain Numpy array.
param_name = self.param_names[idx]
params.add(param_name)
if not isinstance(arg, Parameter):
value = quantity_asanyarray(arg, dtype=float)
else:
value = arg
self._initialize_parameter_value(param_name, value)
# At this point the only remaining keyword arguments should be
# parameter names; any others are in error.
for param_name in self.param_names:
if param_name in kwargs:
if param_name in params:
raise TypeError(
"{0}.__init__() got multiple values for parameter "
"{1!r}".format(self.__class__.__name__, param_name))
value = kwargs.pop(param_name)
if value is None:
continue
# We use quantity_asanyarray here instead of np.asanyarray
# because if any of the arguments are quantities, we need
# to return a Quantity object not a plain Numpy array.
value = quantity_asanyarray(value, dtype=float)
params.add(param_name)
self._initialize_parameter_value(param_name, value)
# Now deal with case where param_name is not supplied by args or kwargs
for param_name in self.param_names:
if param_name not in params:
self._initialize_parameter_value(param_name, None)
if kwargs:
# If any keyword arguments were left over at this point they are
# invalid--the base class should only be passed the parameter
# values, constraints, and param_dim
for kwarg in kwargs:
# Just raise an error on the first unrecognized argument
raise TypeError(
'{0}.__init__() got an unrecognized parameter '
'{1!r}'.format(self.__class__.__name__, kwarg))
# Determine the number of model sets: If the model_set_axis is
# None then there is just one parameter set; otherwise it is determined
# by the size of that axis on the first parameter--if the other
# parameters don't have the right number of axes or the sizes of their
# model_set_axis don't match an error is raised
if model_set_axis is not False and n_models != 1 and params:
max_ndim = 0
if model_set_axis < 0:
min_ndim = abs(model_set_axis)
else:
min_ndim = model_set_axis + 1
for name in self.param_names:
value = getattr(self, name)
param_ndim = np.ndim(value)
if param_ndim < min_ndim:
raise InputParameterError(
"All parameter values must be arrays of dimension "
"at least {0} for model_set_axis={1} (the value "
"given for {2!r} is only {3}-dimensional)".format(
min_ndim, model_set_axis, name, param_ndim))
max_ndim = max(max_ndim, param_ndim)
if n_models is None:
# Use the dimensions of the first parameter to determine
# the number of model sets
n_models = value.shape[model_set_axis]
elif value.shape[model_set_axis] != n_models:
raise InputParameterError(
"Inconsistent dimensions for parameter {0!r} for "
"{1} model sets. The length of axis {2} must be the "
"same for all input parameter values".format(
name, n_models, model_set_axis))
self._check_param_broadcast(max_ndim)
else:
if n_models is None:
n_models = 1
self._check_param_broadcast(None)
self._n_models = n_models
# now validate parameters
for name in params:
param = getattr(self, name)
if param._validator is not None:
param._validator(self, param.value)
def _initialize_parameter_value(self, param_name, value):
"""Mostly deals with consistency checks and determining unit issues."""
if isinstance(value, Parameter):
self.__dict__[param_name] = value
return
param = getattr(self, param_name)
# Use default if value is not provided
if value is None:
default = param.default
if default is None:
# No value was supplied for the parameter and the
# parameter does not have a default, therefore the model
# is underspecified
raise TypeError("{0}.__init__() requires a value for parameter "
"{1!r}".format(self.__class__.__name__, param_name))
value = default
unit = param.unit
else:
if isinstance(value, Quantity):
unit = value.unit
value = value.value
else:
unit = None
if unit is None and param.unit is not None:
raise InputParameterError(
"{0}.__init__() requires a Quantity for parameter "
"{1!r}".format(self.__class__.__name__, param_name))
param._unit = unit
param.internal_unit = None
if param._setter is not None:
if unit is not None:
_val = param._setter(value * unit)
else:
_val = param._setter(value)
if isinstance(_val, Quantity):
param.internal_unit = _val.unit
param._internal_value = np.array(_val.value)
else:
param.internal_unit = None
param._internal_value = np.array(_val)
else:
param._value = np.array(value)
def _initialize_slices(self):
param_metrics = self._param_metrics
total_size = 0
for name in self.param_names:
param = getattr(self, name)
value = param.value
param_size = np.size(value)
param_shape = np.shape(value)
param_slice = slice(total_size, total_size + param_size)
param_metrics[name]['slice'] = param_slice
param_metrics[name]['shape'] = param_shape
param_metrics[name]['size'] = param_size
total_size += param_size
self._parameters = np.empty(total_size, dtype=np.float64)
def _parameters_to_array(self):
# Now set the parameter values (this will also fill
# self._parameters)
param_metrics = self._param_metrics
for name in self.param_names:
param = getattr(self, name)
value = param.value
if not isinstance(value, np.ndarray):
value = np.array([value])
self._parameters[param_metrics[name]['slice']] = value.ravel()
# Finally validate all the parameters; we do this last so that
# validators that depend on one of the other parameters' values will
# work
def _array_to_parameters(self):
param_metrics = self._param_metrics
for name in self.param_names:
param = getattr(self, name)
value = self._parameters[param_metrics[name]['slice']]
value.shape = param_metrics[name]['shape']
param.value = value
def _check_param_broadcast(self, max_ndim):
"""
This subroutine checks that all parameter arrays can be broadcast
against each other, and determines the shapes parameters must have in
order to broadcast correctly.
If model_set_axis is None this merely checks that the parameters
broadcast and returns an empty dict if so. This mode is only used for
single model sets.
"""
all_shapes = []
model_set_axis = self._model_set_axis
for name in self.param_names:
param = getattr(self, name)
value = param.value
param_shape = np.shape(value)
param_ndim = len(param_shape)
if max_ndim is not None and param_ndim < max_ndim:
# All arrays have the same number of dimensions up to the
# model_set_axis dimension, but after that they may have a
# different number of trailing axes. The number of trailing
# axes must be extended for mutual compatibility. For example
# if max_ndim = 3 and model_set_axis = 0, an array with the
# shape (2, 2) must be extended to (2, 1, 2). However, an
# array with shape (2,) is extended to (2, 1).
new_axes = (1,) * (max_ndim - param_ndim)
if model_set_axis < 0:
# Just need to prepend axes to make up the difference
broadcast_shape = new_axes + param_shape
else:
broadcast_shape = (param_shape[:model_set_axis + 1] +
new_axes +
param_shape[model_set_axis + 1:])
self._param_metrics[name]['broadcast_shape'] = broadcast_shape
all_shapes.append(broadcast_shape)
else:
all_shapes.append(param_shape)
# Now check mutual broadcastability of all shapes
try:
check_broadcast(*all_shapes)
except IncompatibleShapeError as exc:
shape_a, shape_a_idx, shape_b, shape_b_idx = exc.args
param_a = self.param_names[shape_a_idx]
param_b = self.param_names[shape_b_idx]
raise InputParameterError(
"Parameter {0!r} of shape {1!r} cannot be broadcast with "
"parameter {2!r} of shape {3!r}. All parameter arrays "
"must have shapes that are mutually compatible according "
"to the broadcasting rules.".format(param_a, shape_a,
param_b, shape_b))
def _param_sets(self, raw=False, units=False):
"""
Implementation of the Model.param_sets property.
This internal implementation has a ``raw`` argument which controls
whether or not to return the raw parameter values (i.e. the values that
are actually stored in the ._parameters array, as opposed to the values
displayed to users. In most cases these are one in the same but there
are currently a few exceptions.
Note: This is notably an overcomplicated device and may be removed
entirely in the near future.
"""
values = []
shapes = []
for name in self.param_names:
param = getattr(self, name)
if raw and param._setter:
value = param._internal_value
else:
value = param.value
broadcast_shape = self._param_metrics[name].get('broadcast_shape')
if broadcast_shape is not None:
value = value.reshape(broadcast_shape)
shapes.append(np.shape(value))
if len(self) == 1:
# Add a single param set axis to the parameter's value (thus
# converting scalars to shape (1,) array values) for
# consistency
value = np.array([value])
if units:
if raw and param.internal_unit is not None:
unit = param.internal_unit
else:
unit = param.unit
if unit is not None:
value = Quantity(value, unit)
values.append(value)
if len(set(shapes)) != 1 or units:
# If the parameters are not all the same shape, converting to an
# array is going to produce an object array
# However the way Numpy creates object arrays is tricky in that it
# will recurse into array objects in the list and break them up
# into separate objects. Doing things this way ensures a 1-D
# object array the elements of which are the individual parameter
# arrays. There's not much reason to do this over returning a list
# except for consistency
psets = np.empty(len(values), dtype=object)
psets[:] = values
return psets
return np.array(values)
def _format_repr(self, args=[], kwargs={}, defaults={}):
"""
Internal implementation of ``__repr__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__repr__`` while keeping the same basic
formatting.
"""
parts = [repr(a) for a in args]
parts.extend(
f"{name}={param_repr_oneline(getattr(self, name))}"
for name in self.param_names)
if self.name is not None:
parts.append(f'name={self.name!r}')
for kwarg, value in kwargs.items():
if kwarg in defaults and defaults[kwarg] == value:
continue
parts.append(f'{kwarg}={value!r}')
if len(self) > 1:
parts.append(f"n_models={len(self)}")
return f"<{self.__class__.__name__}({', '.join(parts)})>"
def _format_str(self, keywords=[], defaults={}):
"""
Internal implementation of ``__str__``.
This is separated out for ease of use by subclasses that wish to
override the default ``__str__`` while keeping the same basic
formatting.
"""
default_keywords = [
('Model', self.__class__.__name__),
('Name', self.name),
('Inputs', self.inputs),
('Outputs', self.outputs),
('Model set size', len(self))
]
parts = [f'{keyword}: {value}'
for keyword, value in default_keywords
if value is not None]
for keyword, value in keywords:
if keyword.lower() in defaults and defaults[keyword.lower()] == value:
continue
parts.append(f'{keyword}: {value}')
parts.append('Parameters:')
if len(self) == 1:
columns = [[getattr(self, name).value]
for name in self.param_names]
else:
columns = [getattr(self, name).value
for name in self.param_names]
if columns:
param_table = Table(columns, names=self.param_names)
# Set units on the columns
for name in self.param_names:
param_table[name].unit = getattr(self, name).unit
parts.append(indent(str(param_table), width=4))
return '\n'.join(parts)
class FittableModel(Model):
"""
Base class for models that can be fitted using the built-in fitting
algorithms.
"""
linear = False
# derivative with respect to parameters
fit_deriv = None
"""
Function (similar to the model's `~Model.evaluate`) to compute the
derivatives of the model with respect to its parameters, for use by fitting
algorithms. In other words, this computes the Jacobian matrix with respect
to the model's parameters.
"""
# Flag that indicates if the model derivatives with respect to parameters
# are given in columns or rows
col_fit_deriv = True
fittable = True
class Fittable1DModel(FittableModel):
"""
Base class for one-dimensional fittable models.
This class provides an easier interface to defining new models.
Examples can be found in `astropy.modeling.functional_models`.
"""
n_inputs = 1
n_outputs = 1
_separable = True
class Fittable2DModel(FittableModel):
"""
Base class for two-dimensional fittable models.
This class provides an easier interface to defining new models.
Examples can be found in `astropy.modeling.functional_models`.
"""
n_inputs = 2
n_outputs = 1
def _make_arithmetic_operator(oper):
# We don't bother with tuple unpacking here for efficiency's sake, but for
# documentation purposes:
#
# f_eval, f_n_inputs, f_n_outputs = f
#
# and similarly for g
def op(f, g):
return (make_binary_operator_eval(oper, f[0], g[0]), f[1], f[2])
return op
def _composition_operator(f, g):
# We don't bother with tuple unpacking here for efficiency's sake, but for
# documentation purposes:
#
# f_eval, f_n_inputs, f_n_outputs = f
#
# and similarly for g
return (lambda inputs, params: g[0](f[0](inputs, params), params),
f[1], g[2])
def _join_operator(f, g):
# We don't bother with tuple unpacking here for efficiency's sake, but for
# documentation purposes:
#
# f_eval, f_n_inputs, f_n_outputs = f
#
# and similarly for g
return (lambda inputs, params: (f[0](inputs[:f[1]], params) +
g[0](inputs[f[1]:], params)),
f[1] + g[1], f[2] + g[2])
BINARY_OPERATORS = {
'+': _make_arithmetic_operator(operator.add),
'-': _make_arithmetic_operator(operator.sub),
'*': _make_arithmetic_operator(operator.mul),
'/': _make_arithmetic_operator(operator.truediv),
'**': _make_arithmetic_operator(operator.pow),
'|': _composition_operator,
'&': _join_operator
}
SPECIAL_OPERATORS = _SpecialOperatorsDict()
def _add_special_operator(sop_name, sop):
return SPECIAL_OPERATORS.add(sop_name, sop)
class CompoundModel(Model):
'''
Base class for compound models.
While it can be used directly, the recommended way
to combine models is through the model operators.
'''
def __init__(self, op, left, right, name=None):
self.__dict__['_param_names'] = None
self._n_submodels = None
self.op = op
self.left = left
self.right = right
self._bounding_box = None
self._user_bounding_box = None
self._leaflist = None
self._tdict = None
self._parameters = None
self._parameters_ = None
self._param_metrics = None
if op != 'fix_inputs' and len(left) != len(right):
raise ValueError(
'Both operands must have equal values for n_models')
self._n_models = len(left)
if op != 'fix_inputs' and ((left.model_set_axis != right.model_set_axis)
or left.model_set_axis): # not False and not 0
raise ValueError("model_set_axis must be False or 0 and consistent for operands")
self._model_set_axis = left.model_set_axis
if op in ['+', '-', '*', '/', '**'] or op in SPECIAL_OPERATORS:
if (left.n_inputs != right.n_inputs) or \
(left.n_outputs != right.n_outputs):
raise ModelDefinitionError(
'Both operands must match numbers of inputs and outputs')
self.n_inputs = left.n_inputs
self.n_outputs = left.n_outputs
self.inputs = left.inputs
self.outputs = left.outputs
elif op == '&':
self.n_inputs = left.n_inputs + right.n_inputs
self.n_outputs = left.n_outputs + right.n_outputs
self.inputs = combine_labels(left.inputs, right.inputs)
self.outputs = combine_labels(left.outputs, right.outputs)
elif op == '|':
if left.n_outputs != right.n_inputs:
raise ModelDefinitionError(
"Unsupported operands for |: {0} (n_inputs={1}, "
"n_outputs={2}) and {3} (n_inputs={4}, n_outputs={5}); "
"n_outputs for the left-hand model must match n_inputs "
"for the right-hand model.".format(
left.name, left.n_inputs, left.n_outputs, right.name,
right.n_inputs, right.n_outputs))
self.n_inputs = left.n_inputs
self.n_outputs = right.n_outputs
self.inputs = left.inputs
self.outputs = right.outputs
elif op == 'fix_inputs':
if not isinstance(left, Model):
raise ValueError('First argument to "fix_inputs" must be an instance of an astropy Model.')
if not isinstance(right, dict):
raise ValueError('Expected a dictionary for second argument of "fix_inputs".')
# Dict keys must match either possible indices
# for model on left side, or names for inputs.
self.n_inputs = left.n_inputs - len(right)
# Assign directly to the private attribute (instead of using the setter)
# to avoid asserting the new number of outputs matches the old one.
self._outputs = left.outputs
self.n_outputs = left.n_outputs
newinputs = list(left.inputs)
keys = right.keys()
input_ind = []
for key in keys:
if np.issubdtype(type(key), np.integer):
if key >= left.n_inputs or key < 0:
raise ValueError(
'Substitution key integer value '
'not among possible input choices.')
if key in input_ind:
raise ValueError("Duplicate specification of "
"same input (index/name).")
input_ind.append(key)
elif isinstance(key, str):
if key not in left.inputs:
raise ValueError(
'Substitution key string not among possible '
'input choices.')
# Check to see it doesn't match positional
# specification.
ind = left.inputs.index(key)
if ind in input_ind:
raise ValueError("Duplicate specification of "
"same input (index/name).")
input_ind.append(ind)
# Remove substituted inputs
input_ind.sort()
input_ind.reverse()
for ind in input_ind:
del newinputs[ind]
self.inputs = tuple(newinputs)
# Now check to see if the input model has bounding_box defined.
# If so, remove the appropriate dimensions and set it for this
# instance.
try:
self.bounding_box = \
self.left.bounding_box.fix_inputs(self, right)
except NotImplementedError:
pass
else:
raise ModelDefinitionError('Illegal operator: ', self.op)
self.name = name
self._fittable = None
self.fit_deriv = None
self.col_fit_deriv = None
if op in ('|', '+', '-'):
self.linear = left.linear and right.linear
else:
self.linear = False
self.eqcons = []
self.ineqcons = []
self.n_left_params = len(self.left.parameters)
self._map_parameters()
def _get_left_inputs_from_args(self, args):
return args[:self.left.n_inputs]
def _get_right_inputs_from_args(self, args):
op = self.op
if op == '&':
# Args expected to look like (*left inputs, *right inputs, *left params, *right params)
return args[self.left.n_inputs: self.left.n_inputs + self.right.n_inputs]
elif op == '|' or op == 'fix_inputs':
return None
else:
return args[:self.left.n_inputs]
def _get_left_params_from_args(self, args):
op = self.op
if op == '&':
# Args expected to look like (*left inputs, *right inputs, *left params, *right params)
n_inputs = self.left.n_inputs + self.right.n_inputs
return args[n_inputs: n_inputs + self.n_left_params]
else:
return args[self.left.n_inputs: self.left.n_inputs + self.n_left_params]
def _get_right_params_from_args(self, args):
op = self.op
if op == 'fix_inputs':
return None
if op == '&':
# Args expected to look like (*left inputs, *right inputs, *left params, *right params)
return args[self.left.n_inputs + self.right.n_inputs + self.n_left_params:]
else:
return args[self.left.n_inputs + self.n_left_params:]
def _get_kwarg_model_parameters_as_positional(self, args, kwargs):
# could do it with inserts but rebuilding seems like simpilist way
#TODO: Check if any param names are in kwargs maybe as an intersection of sets?
if self.op == "&":
new_args = list(args[:self.left.n_inputs + self.right.n_inputs])
args_pos = self.left.n_inputs + self.right.n_inputs
else:
new_args = list(args[:self.left.n_inputs])
args_pos = self.left.n_inputs
for param_name in self.param_names:
kw_value = kwargs.pop(param_name, None)
if kw_value is not None:
value = kw_value
else:
try:
value = args[args_pos]
except IndexError:
raise IndexError("Missing parameter or input")
args_pos += 1
new_args.append(value)
return new_args, kwargs
def _apply_operators_to_value_lists(self, leftval, rightval, **kw):
op = self.op
if op == '+':
return binary_operation(operator.add, leftval, rightval)
elif op == '-':
return binary_operation(operator.sub, leftval, rightval)
elif op == '*':
return binary_operation(operator.mul, leftval, rightval)
elif op == '/':
return binary_operation(operator.truediv, leftval, rightval)
elif op == '**':
return binary_operation(operator.pow, leftval, rightval)
elif op == '&':
if not isinstance(leftval, tuple):
leftval = (leftval,)
if not isinstance(rightval, tuple):
rightval = (rightval,)
return leftval + rightval
elif op in SPECIAL_OPERATORS:
return binary_operation(SPECIAL_OPERATORS[op], leftval, rightval)
else:
raise ModelDefinitionError('Unrecognized operator {op}')
def evaluate(self, *args, **kw):
op = self.op
args, kw = self._get_kwarg_model_parameters_as_positional(args, kw)
left_inputs = self._get_left_inputs_from_args(args)
left_params = self._get_left_params_from_args(args)
if op == 'fix_inputs':
pos_index = dict(zip(self.left.inputs, range(self.left.n_inputs)))
fixed_inputs = {
key if np.issubdtype(type(key), np.integer) else pos_index[key]: value
for key, value in self.right.items()
}
left_inputs = [
fixed_inputs[ind] if ind in fixed_inputs.keys() else inp
for ind, inp in enumerate(left_inputs)
]
leftval = self.left.evaluate(*itertools.chain(left_inputs, left_params))
if op == 'fix_inputs':
return leftval
right_inputs = self._get_right_inputs_from_args(args)
right_params = self._get_right_params_from_args(args)
if op == "|":
if isinstance(leftval, tuple):
return self.right.evaluate(*itertools.chain(leftval, right_params))
else:
return self.right.evaluate(leftval, *right_params)
else:
rightval = self.right.evaluate(*itertools.chain(right_inputs, right_params))
return self._apply_operators_to_value_lists(leftval, rightval, **kw)
@property
def n_submodels(self):
if self._leaflist is None:
self._make_leaflist()
return len(self._leaflist)
@property
def submodel_names(self):
""" Return the names of submodels in a ``CompoundModel``."""
if self._leaflist is None:
self._make_leaflist()
names = [item.name for item in self._leaflist]
nonecount = 0
newnames = []
for item in names:
if item is None:
newnames.append(f'None_{nonecount}')
nonecount += 1
else:
newnames.append(item)
return tuple(newnames)
def both_inverses_exist(self):
'''
if both members of this compound model have inverses return True
'''
warnings.warn(
"CompoundModel.both_inverses_exist is deprecated. "
"Use has_inverse instead.",
AstropyDeprecationWarning
)
try:
linv = self.left.inverse
rinv = self.right.inverse
except NotImplementedError:
return False
return True
def _pre_evaluate(self, *args, **kwargs):
"""
CompoundModel specific input setup that needs to occur prior to
model evaluation.
Note
----
All of the _pre_evaluate for each component model will be
performed at the time that the individual model is evaluated.
"""
# If equivalencies are provided, necessary to map parameters and pass
# the leaflist as a keyword input for use by model evaluation so that
# the compound model input names can be matched to the model input
# names.
if 'equivalencies' in kwargs:
# Restructure to be useful for the individual model lookup
kwargs['inputs_map'] = [(value[0], (value[1], key)) for
key, value in self.inputs_map().items()]
# Setup actual model evaluation method
def evaluate(_inputs):
return self._evaluate(*_inputs, **kwargs)
return evaluate, args, None, kwargs
@property
def _argnames(self):
"""No inputs should be used to determine input_shape when handling compound models"""
return ()
def _post_evaluate(self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs):
"""
CompoundModel specific post evaluation processing of outputs
Note
----
All of the _post_evaluate for each component model will be
performed at the time that the individual model is evaluated.
"""
if self.get_bounding_box(with_bbox) is not None and self.n_outputs == 1:
return outputs[0]
return outputs
def _evaluate(self, *args, **kw):
op = self.op
if op != 'fix_inputs':
if op != '&':
leftval = self.left(*args, **kw)
if op != '|':
rightval = self.right(*args, **kw)
else:
rightval = None
else:
leftval = self.left(*(args[:self.left.n_inputs]), **kw)
rightval = self.right(*(args[self.left.n_inputs:]), **kw)
if op != "|":
return self._apply_operators_to_value_lists(leftval, rightval, **kw)
elif op == '|':
if isinstance(leftval, tuple):
return self.right(*leftval, **kw)
else:
return self.right(leftval, **kw)
else:
subs = self.right
newargs = list(args)
subinds = []
subvals = []
for key in subs.keys():
if np.issubdtype(type(key), np.integer):
subinds.append(key)
elif isinstance(key, str):
ind = self.left.inputs.index(key)
subinds.append(ind)
subvals.append(subs[key])
# Turn inputs specified in kw into positional indices.
# Names for compound inputs do not propagate to sub models.
kwind = []
kwval = []
for kwkey in list(kw.keys()):
if kwkey in self.inputs:
ind = self.inputs.index(kwkey)
if ind < len(args):
raise ValueError("Keyword argument duplicates "
"positional value supplied.")
kwind.append(ind)
kwval.append(kw[kwkey])
del kw[kwkey]
# Build new argument list
# Append keyword specified args first
if kwind:
kwargs = list(zip(kwind, kwval))
kwargs.sort()
kwindsorted, kwvalsorted = list(zip(*kwargs))
newargs = newargs + list(kwvalsorted)
if subinds:
subargs = list(zip(subinds, subvals))
subargs.sort()
# subindsorted, subvalsorted = list(zip(*subargs))
# The substitutions must be inserted in order
for ind, val in subargs:
newargs.insert(ind, val)
return self.left(*newargs, **kw)
@property
def param_names(self):
""" An ordered list of parameter names."""
return self._param_names
def _make_leaflist(self):
tdict = {}
leaflist = []
make_subtree_dict(self, '', tdict, leaflist)
self._leaflist = leaflist
self._tdict = tdict
def __getattr__(self, name):
"""
If someone accesses an attribute not already defined, map the
parameters, and then see if the requested attribute is one of
the parameters
"""
# The following test is needed to avoid infinite recursion
# caused by deepcopy. There may be other such cases discovered.
if name == '__setstate__':
raise AttributeError
if name in self._param_names:
return self.__dict__[name]
else:
raise AttributeError(f'Attribute "{name}" not found')
def __getitem__(self, index):
if self._leaflist is None:
self._make_leaflist()
leaflist = self._leaflist
tdict = self._tdict
if isinstance(index, slice):
if index.step:
raise ValueError('Steps in slices not supported '
'for compound models')
if index.start is not None:
if isinstance(index.start, str):
start = self._str_index_to_int(index.start)
else:
start = index.start
else:
start = 0
if index.stop is not None:
if isinstance(index.stop, str):
stop = self._str_index_to_int(index.stop)
else:
stop = index.stop - 1
else:
stop = len(leaflist) - 1
if index.stop == 0:
raise ValueError("Slice endpoint cannot be 0")
if start < 0:
start = len(leaflist) + start
if stop < 0:
stop = len(leaflist) + stop
# now search for matching node:
if stop == start: # only single value, get leaf instead in code below
index = start
else:
for key in tdict:
node, leftind, rightind = tdict[key]
if leftind == start and rightind == stop:
return node
raise IndexError("No appropriate subtree matches slice")
if isinstance(index, type(0)):
return leaflist[index]
elif isinstance(index, type('')):
return leaflist[self._str_index_to_int(index)]
else:
raise TypeError('index must be integer, slice, or model name string')
def _str_index_to_int(self, str_index):
# Search through leaflist for item with that name
found = []
for nleaf, leaf in enumerate(self._leaflist):
if getattr(leaf, 'name', None) == str_index:
found.append(nleaf)
if len(found) == 0:
raise IndexError(f"No component with name '{str_index}' found")
if len(found) > 1:
raise IndexError("Multiple components found using '{}' as name\n"
"at indices {}".format(str_index, found))
return found[0]
@property
def n_inputs(self):
""" The number of inputs of a model."""
return self._n_inputs
@n_inputs.setter
def n_inputs(self, value):
self._n_inputs = value
@property
def n_outputs(self):
""" The number of outputs of a model."""
return self._n_outputs
@n_outputs.setter
def n_outputs(self, value):
self._n_outputs = value
@property
def eqcons(self):
return self._eqcons
@eqcons.setter
def eqcons(self, value):
self._eqcons = value
@property
def ineqcons(self):
return self._eqcons
@ineqcons.setter
def ineqcons(self, value):
self._eqcons = value
def traverse_postorder(self, include_operator=False):
""" Postorder traversal of the CompoundModel tree."""
res = []
if isinstance(self.left, CompoundModel):
res = res + self.left.traverse_postorder(include_operator)
else:
res = res + [self.left]
if isinstance(self.right, CompoundModel):
res = res + self.right.traverse_postorder(include_operator)
else:
res = res + [self.right]
if include_operator:
res.append(self.op)
else:
res.append(self)
return res
def _format_expression(self, format_leaf=None):
leaf_idx = 0
operands = deque()
if format_leaf is None:
format_leaf = lambda i, l: f'[{i}]'
for node in self.traverse_postorder():
if not isinstance(node, CompoundModel):
operands.append(format_leaf(leaf_idx, node))
leaf_idx += 1
continue
right = operands.pop()
left = operands.pop()
if node.op in OPERATOR_PRECEDENCE:
oper_order = OPERATOR_PRECEDENCE[node.op]
if isinstance(node, CompoundModel):
if (isinstance(node.left, CompoundModel) and
OPERATOR_PRECEDENCE[node.left.op] < oper_order):
left = f'({left})'
if (isinstance(node.right, CompoundModel) and
OPERATOR_PRECEDENCE[node.right.op] < oper_order):
right = f'({right})'
operands.append(' '.join((left, node.op, right)))
else:
left = f'(({left}),'
right = f'({right}))'
operands.append(' '.join((node.op[0], left, right)))
return ''.join(operands)
def _format_components(self):
if self._parameters_ is None:
self._map_parameters()
return '\n\n'.join('[{0}]: {1!r}'.format(idx, m)
for idx, m in enumerate(self._leaflist))
def __str__(self):
expression = self._format_expression()
components = self._format_components()
keywords = [
('Expression', expression),
('Components', '\n' + indent(components))
]
return super()._format_str(keywords=keywords)
def rename(self, name):
self.name = name
return self
@property
def isleaf(self):
return False
@property
def inverse(self):
if self.op == '|':
return self.right.inverse | self.left.inverse
elif self.op == '&':
return self.left.inverse & self.right.inverse
else:
return NotImplemented
@property
def fittable(self):
""" Set the fittable attribute on a compound model."""
if self._fittable is None:
if self._leaflist is None:
self._map_parameters()
self._fittable = all(m.fittable for m in self._leaflist)
return self._fittable
__add__ = _model_oper('+')
__sub__ = _model_oper('-')
__mul__ = _model_oper('*')
__truediv__ = _model_oper('/')
__pow__ = _model_oper('**')
__or__ = _model_oper('|')
__and__ = _model_oper('&')
def _map_parameters(self):
"""
Map all the constituent model parameters to the compound object,
renaming as necessary by appending a suffix number.
This can be an expensive operation, particularly for a complex
expression tree.
All the corresponding parameter attributes are created that one
expects for the Model class.
The parameter objects that the attributes point to are the same
objects as in the constiutent models. Changes made to parameter
values to either are seen by both.
Prior to calling this, none of the associated attributes will
exist. This method must be called to make the model usable by
fitting engines.
If oldnames=True, then parameters are named as in the original
implementation of compound models.
"""
if self._parameters is not None:
# do nothing
return
if self._leaflist is None:
self._make_leaflist()
self._parameters_ = {}
param_map = {}
self._param_names = []
for lindex, leaf in enumerate(self._leaflist):
if not isinstance(leaf, dict):
for param_name in leaf.param_names:
param = getattr(leaf, param_name)
new_param_name = f"{param_name}_{lindex}"
self.__dict__[new_param_name] = param
self._parameters_[new_param_name] = param
self._param_names.append(new_param_name)
param_map[new_param_name] = (lindex, param_name)
self._param_metrics = {}
self._param_map = param_map
self._param_map_inverse = dict((v, k) for k, v in param_map.items())
self._initialize_slices()
self._param_names = tuple(self._param_names)
def _initialize_slices(self):
param_metrics = self._param_metrics
total_size = 0
for name in self.param_names:
param = getattr(self, name)
value = param.value
param_size = np.size(value)
param_shape = np.shape(value)
param_slice = slice(total_size, total_size + param_size)
param_metrics[name] = {}
param_metrics[name]['slice'] = param_slice
param_metrics[name]['shape'] = param_shape
param_metrics[name]['size'] = param_size
total_size += param_size
self._parameters = np.empty(total_size, dtype=np.float64)
@staticmethod
def _recursive_lookup(branch, adict, key):
if isinstance(branch, CompoundModel):
return adict[key]
return branch, key
def inputs_map(self):
"""
Map the names of the inputs to this ExpressionTree to the inputs to the leaf models.
"""
inputs_map = {}
if not isinstance(self.op, str): # If we don't have an operator the mapping is trivial
return {inp: (self, inp) for inp in self.inputs}
elif self.op == '|':
if isinstance(self.left, CompoundModel):
l_inputs_map = self.left.inputs_map()
for inp in self.inputs:
if isinstance(self.left, CompoundModel):
inputs_map[inp] = l_inputs_map[inp]
else:
inputs_map[inp] = self.left, inp
elif self.op == '&':
if isinstance(self.left, CompoundModel):
l_inputs_map = self.left.inputs_map()
if isinstance(self.right, CompoundModel):
r_inputs_map = self.right.inputs_map()
for i, inp in enumerate(self.inputs):
if i < len(self.left.inputs): # Get from left
if isinstance(self.left, CompoundModel):
inputs_map[inp] = l_inputs_map[self.left.inputs[i]]
else:
inputs_map[inp] = self.left, self.left.inputs[i]
else: # Get from right
if isinstance(self.right, CompoundModel):
inputs_map[inp] = r_inputs_map[self.right.inputs[i - len(self.left.inputs)]]
else:
inputs_map[inp] = self.right, self.right.inputs[i - len(self.left.inputs)]
elif self.op == 'fix_inputs':
fixed_ind = list(self.right.keys())
ind = [list(self.left.inputs).index(i) if isinstance(i, str) else i for i in fixed_ind]
inp_ind = list(range(self.left.n_inputs))
for i in ind:
inp_ind.remove(i)
for i in inp_ind:
inputs_map[self.left.inputs[i]] = self.left, self.left.inputs[i]
else:
if isinstance(self.left, CompoundModel):
l_inputs_map = self.left.inputs_map()
for inp in self.left.inputs:
if isinstance(self.left, CompoundModel):
inputs_map[inp] = l_inputs_map[inp]
else:
inputs_map[inp] = self.left, inp
return inputs_map
def _parameter_units_for_data_units(self, input_units, output_units):
if self._leaflist is None:
self._map_parameters()
units_for_data = {}
for imodel, model in enumerate(self._leaflist):
units_for_data_leaf = model._parameter_units_for_data_units(input_units, output_units)
for param_leaf in units_for_data_leaf:
param = self._param_map_inverse[(imodel, param_leaf)]
units_for_data[param] = units_for_data_leaf[param_leaf]
return units_for_data
@property
def input_units(self):
inputs_map = self.inputs_map()
input_units_dict = {key: inputs_map[key][0].input_units[orig_key]
for key, (mod, orig_key) in inputs_map.items()
if inputs_map[key][0].input_units is not None}
if input_units_dict:
return input_units_dict
return None
@property
def input_units_equivalencies(self):
inputs_map = self.inputs_map()
input_units_equivalencies_dict = {
key: inputs_map[key][0].input_units_equivalencies[orig_key]
for key, (mod, orig_key) in inputs_map.items()
if inputs_map[key][0].input_units_equivalencies is not None
}
if not input_units_equivalencies_dict:
return None
return input_units_equivalencies_dict
@property
def input_units_allow_dimensionless(self):
inputs_map = self.inputs_map()
return {key: inputs_map[key][0].input_units_allow_dimensionless[orig_key]
for key, (mod, orig_key) in inputs_map.items()}
@property
def input_units_strict(self):
inputs_map = self.inputs_map()
return {key: inputs_map[key][0].input_units_strict[orig_key]
for key, (mod, orig_key) in inputs_map.items()}
@property
def return_units(self):
outputs_map = self.outputs_map()
return {key: outputs_map[key][0].return_units[orig_key]
for key, (mod, orig_key) in outputs_map.items()
if outputs_map[key][0].return_units is not None}
def outputs_map(self):
"""
Map the names of the outputs to this ExpressionTree to the outputs to the leaf models.
"""
outputs_map = {}
if not isinstance(self.op, str): # If we don't have an operator the mapping is trivial
return {out: (self, out) for out in self.outputs}
elif self.op == '|':
if isinstance(self.right, CompoundModel):
r_outputs_map = self.right.outputs_map()
for out in self.outputs:
if isinstance(self.right, CompoundModel):
outputs_map[out] = r_outputs_map[out]
else:
outputs_map[out] = self.right, out
elif self.op == '&':
if isinstance(self.left, CompoundModel):
l_outputs_map = self.left.outputs_map()
if isinstance(self.right, CompoundModel):
r_outputs_map = self.right.outputs_map()
for i, out in enumerate(self.outputs):
if i < len(self.left.outputs): # Get from left
if isinstance(self.left, CompoundModel):
outputs_map[out] = l_outputs_map[self.left.outputs[i]]
else:
outputs_map[out] = self.left, self.left.outputs[i]
else: # Get from right
if isinstance(self.right, CompoundModel):
outputs_map[out] = r_outputs_map[self.right.outputs[i - len(self.left.outputs)]]
else:
outputs_map[out] = self.right, self.right.outputs[i - len(self.left.outputs)]
elif self.op == 'fix_inputs':
return self.left.outputs_map()
else:
if isinstance(self.left, CompoundModel):
l_outputs_map = self.left.outputs_map()
for out in self.left.outputs:
if isinstance(self.left, CompoundModel):
outputs_map[out] = l_outputs_map()[out]
else:
outputs_map[out] = self.left, out
return outputs_map
@property
def has_user_bounding_box(self):
"""
A flag indicating whether or not a custom bounding_box has been
assigned to this model by a user, via assignment to
``model.bounding_box``.
"""
return self._user_bounding_box is not None
def render(self, out=None, coords=None):
"""
Evaluate a model at fixed positions, respecting the ``bounding_box``.
The key difference relative to evaluating the model directly is that
this method is limited to a bounding box if the `Model.bounding_box`
attribute is set.
Parameters
----------
out : `numpy.ndarray`, optional
An array that the evaluated model will be added to. If this is not
given (or given as ``None``), a new array will be created.
coords : array-like, optional
An array to be used to translate from the model's input coordinates
to the ``out`` array. It should have the property that
``self(coords)`` yields the same shape as ``out``. If ``out`` is
not specified, ``coords`` will be used to determine the shape of
the returned array. If this is not provided (or None), the model
will be evaluated on a grid determined by `Model.bounding_box`.
Returns
-------
out : `numpy.ndarray`
The model added to ``out`` if ``out`` is not ``None``, or else a
new array from evaluating the model over ``coords``.
If ``out`` and ``coords`` are both `None`, the returned array is
limited to the `Model.bounding_box` limits. If
`Model.bounding_box` is `None`, ``arr`` or ``coords`` must be
passed.
Raises
------
ValueError
If ``coords`` are not given and the the `Model.bounding_box` of
this model is not set.
Examples
--------
:ref:`astropy:bounding-boxes`
"""
bbox = self.get_bounding_box()
ndim = self.n_inputs
if (coords is None) and (out is None) and (bbox is None):
raise ValueError('If no bounding_box is set, '
'coords or out must be input.')
# for consistent indexing
if ndim == 1:
if coords is not None:
coords = [coords]
if bbox is not None:
bbox = [bbox]
if coords is not None:
coords = np.asanyarray(coords, dtype=float)
# Check dimensions match out and model
assert len(coords) == ndim
if out is not None:
if coords[0].shape != out.shape:
raise ValueError('inconsistent shape of the output.')
else:
out = np.zeros(coords[0].shape)
if out is not None:
out = np.asanyarray(out)
if out.ndim != ndim:
raise ValueError('the array and model must have the same '
'number of dimensions.')
if bbox is not None:
# Assures position is at center pixel, important when using
# add_array.
pd = np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2))
for bb in bbox]).astype(int).T
pos, delta = pd
if coords is not None:
sub_shape = tuple(delta * 2 + 1)
sub_coords = np.array([extract_array(c, sub_shape, pos)
for c in coords])
else:
limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
sub_coords = np.mgrid[limits]
sub_coords = sub_coords[::-1]
if out is None:
out = self(*sub_coords)
else:
try:
out = add_array(out, self(*sub_coords), pos)
except ValueError:
raise ValueError(
'The `bounding_box` is larger than the input out in '
'one or more dimensions. Set '
'`model.bounding_box = None`.')
else:
if coords is None:
im_shape = out.shape
limits = [slice(i) for i in im_shape]
coords = np.mgrid[limits]
coords = coords[::-1]
out += self(*coords)
return out
def replace_submodel(self, name, model):
"""
Construct a new `~astropy.modeling.CompoundModel` instance from an
existing CompoundModel, replacing the named submodel with a new model.
In order to ensure that inverses and names are kept/reconstructed, it's
necessary to rebuild the CompoundModel from the replaced node all the
way back to the base. The original CompoundModel is left untouched.
Parameters
----------
name : str
name of submodel to be replaced
model : `~astropy.modeling.Model`
replacement model
"""
submodels = [m for m in self.traverse_postorder()
if getattr(m, 'name', None) == name]
if submodels:
if len(submodels) > 1:
raise ValueError(f"More than one submodel named {name}")
old_model = submodels.pop()
if len(old_model) != len(model):
raise ValueError("New and old models must have equal values "
"for n_models")
# Do this check first in order to raise a more helpful Exception,
# although it would fail trying to construct the new CompoundModel
if (old_model.n_inputs != model.n_inputs or
old_model.n_outputs != model.n_outputs):
raise ValueError("New model must match numbers of inputs and "
"outputs of existing model")
tree = _get_submodel_path(self, name)
while tree:
branch = self.copy()
for node in tree[:-1]:
branch = getattr(branch, node)
setattr(branch, tree[-1], model)
model = CompoundModel(branch.op, branch.left, branch.right,
name=branch.name)
tree = tree[:-1]
return model
else:
raise ValueError(f"No submodels found named {name}")
def _set_sub_models_and_parameter_units(self, left, right):
"""
Provides a work-around to properly set the sub models and respective
parameters's units/values when using ``without_units_for_data``
or ``without_units_for_data`` methods.
"""
model = CompoundModel(self.op, left, right)
self.left = left
self.right = right
for name in model.param_names:
model_parameter = getattr(model, name)
parameter = getattr(self, name)
parameter.value = model_parameter.value
parameter._set_unit(model_parameter.unit, force=True)
def without_units_for_data(self, **kwargs):
"""
See `~astropy.modeling.Model.without_units_for_data` for overview
of this method.
Notes
-----
This modifies the behavior of the base method to account for the
case where the sub-models of a compound model have different output
units. This is only valid for compound * and / compound models as
in that case it is reasonable to mix the output units. It does this
by modifying the output units of each sub model by using the output
units of the other sub model so that we can apply the original function
and get the desired result.
Additional data has to be output in the mixed output unit case
so that the units can be properly rebuilt by
`~astropy.modeling.CompoundModel.with_units_from_data`.
Outside the mixed output units, this method is identical to the
base method.
"""
if self.op in ['*', '/']:
model = self.copy()
inputs = {inp: kwargs[inp] for inp in self.inputs}
left_units = self.left.output_units(**kwargs)
right_units = self.right.output_units(**kwargs)
if self.op == '*':
left_kwargs = {out: kwargs[out] / right_units[out]
for out in self.left.outputs if kwargs[out] is not None}
right_kwargs = {out: kwargs[out] / left_units[out]
for out in self.right.outputs if kwargs[out] is not None}
else:
left_kwargs = {out: kwargs[out] * right_units[out]
for out in self.left.outputs if kwargs[out] is not None}
right_kwargs = {out: 1 / kwargs[out] * left_units[out]
for out in self.right.outputs if kwargs[out] is not None}
left_kwargs.update(inputs.copy())
right_kwargs.update(inputs.copy())
left = self.left.without_units_for_data(**left_kwargs)
if isinstance(left, tuple):
left_kwargs['_left_kwargs'] = left[1]
left_kwargs['_right_kwargs'] = left[2]
left = left[0]
right = self.right.without_units_for_data(**right_kwargs)
if isinstance(right, tuple):
right_kwargs['_left_kwargs'] = right[1]
right_kwargs['_right_kwargs'] = right[2]
right = right[0]
model._set_sub_models_and_parameter_units(left, right)
return model, left_kwargs, right_kwargs
else:
return super().without_units_for_data(**kwargs)
def with_units_from_data(self, **kwargs):
"""
See `~astropy.modeling.Model.with_units_from_data` for overview
of this method.
Notes
-----
This modifies the behavior of the base method to account for the
case where the sub-models of a compound model have different output
units. This is only valid for compound * and / compound models as
in that case it is reasonable to mix the output units. In order to
do this it requires some additional information output by
`~astropy.modeling.CompoundModel.without_units_for_data` passed as
keyword arguments under the keywords ``_left_kwargs`` and ``_right_kwargs``.
Outside the mixed output units, this method is identical to the
base method.
"""
if self.op in ['*', '/']:
left_kwargs = kwargs.pop('_left_kwargs')
right_kwargs = kwargs.pop('_right_kwargs')
left = self.left.with_units_from_data(**left_kwargs)
right = self.right.with_units_from_data(**right_kwargs)
model = self.copy()
model._set_sub_models_and_parameter_units(left, right)
return model
else:
return super().with_units_from_data(**kwargs)
def _get_submodel_path(model, name):
"""Find the route down a CompoundModel's tree to the model with the
specified name (whether it's a leaf or not)"""
if getattr(model, 'name', None) == name:
return []
try:
return ['left'] + _get_submodel_path(model.left, name)
except (AttributeError, TypeError):
pass
try:
return ['right'] + _get_submodel_path(model.right, name)
except (AttributeError, TypeError):
pass
def binary_operation(binoperator, left, right):
'''
Perform binary operation. Operands may be matching tuples of operands.
'''
if isinstance(left, tuple) and isinstance(right, tuple):
return tuple([binoperator(item[0], item[1])
for item in zip(left, right)])
return binoperator(left, right)
def get_ops(tree, opset):
"""
Recursive function to collect operators used.
"""
if isinstance(tree, CompoundModel):
opset.add(tree.op)
get_ops(tree.left, opset)
get_ops(tree.right, opset)
else:
return
def make_subtree_dict(tree, nodepath, tdict, leaflist):
'''
Traverse a tree noting each node by a key that indicates all the
left/right choices necessary to reach that node. Each key will
reference a tuple that contains:
- reference to the compound model for that node.
- left most index contained within that subtree
(relative to all indices for the whole tree)
- right most index contained within that subtree
'''
# if this is a leaf, just append it to the leaflist
if not hasattr(tree, 'isleaf'):
leaflist.append(tree)
else:
leftmostind = len(leaflist)
make_subtree_dict(tree.left, nodepath+'l', tdict, leaflist)
make_subtree_dict(tree.right, nodepath+'r', tdict, leaflist)
rightmostind = len(leaflist)-1
tdict[nodepath] = (tree, leftmostind, rightmostind)
_ORDER_OF_OPERATORS = [('fix_inputs',), ('|',), ('&',), ('+', '-'), ('*', '/'), ('**',)]
OPERATOR_PRECEDENCE = {}
for idx, ops in enumerate(_ORDER_OF_OPERATORS):
for op in ops:
OPERATOR_PRECEDENCE[op] = idx
del idx, op, ops
def fix_inputs(modelinstance, values, bounding_boxes=None, selector_args=None):
"""
This function creates a compound model with one or more of the input
values of the input model assigned fixed values (scalar or array).
Parameters
----------
modelinstance : `~astropy.modeling.Model` instance
This is the model that one or more of the
model input values will be fixed to some constant value.
values : dict
A dictionary where the key identifies which input to fix
and its value is the value to fix it at. The key may either be the
name of the input or a number reflecting its order in the inputs.
Examples
--------
>>> from astropy.modeling.models import Gaussian2D
>>> g = Gaussian2D(1, 2, 3, 4, 5)
>>> gv = fix_inputs(g, {0: 2.5})
Results in a 1D function equivalent to Gaussian2D(1, 2, 3, 4, 5)(x=2.5, y)
"""
model = CompoundModel('fix_inputs', modelinstance, values)
if bounding_boxes is not None:
if selector_args is None:
selector_args = tuple([(key, True) for key in values.keys()])
bbox = CompoundBoundingBox.validate(modelinstance, bounding_boxes, selector_args)
_selector = bbox.selector_args.get_fixed_values(modelinstance, values)
new_bbox = bbox[_selector]
new_bbox = new_bbox.__class__.validate(model, new_bbox)
model.bounding_box = new_bbox
return model
def bind_bounding_box(modelinstance, bounding_box, ignored=None, order='C'):
"""
Set a validated bounding box to a model instance.
Parameters
----------
modelinstance : `~astropy.modeling.Model` instance
This is the model that the validated bounding box will be set on.
bounding_box : tuple
A bounding box tuple, see :ref:`astropy:bounding-boxes` for details
ignored : list
List of the inputs to be ignored by the bounding box.
order : str, optional
The ordering of the bounding box tuple, can be either ``'C'`` or
``'F'``.
"""
modelinstance.bounding_box = ModelBoundingBox.validate(modelinstance,
bounding_box,
ignored=ignored,
order=order)
def bind_compound_bounding_box(modelinstance, bounding_boxes, selector_args,
create_selector=None, ignored=None, order='C'):
"""
Add a validated compound bounding box to a model instance.
Parameters
----------
modelinstance : `~astropy.modeling.Model` instance
This is the model that the validated compound bounding box will be set on.
bounding_boxes : dict
A dictionary of bounding box tuples, see :ref:`astropy:bounding-boxes`
for details.
selector_args : list
List of selector argument tuples to define selection for compound
bounding box, see :ref:`astropy:bounding-boxes` for details.
create_selector : callable, optional
An optional callable with interface (selector_value, model) which
can generate a bounding box based on a selector value and model if
there is no bounding box in the compound bounding box listed under
that selector value. Default is ``None``, meaning new bounding
box entries will not be automatically generated.
ignored : list
List of the inputs to be ignored by the bounding box.
order : str, optional
The ordering of the bounding box tuple, can be either ``'C'`` or
``'F'``.
"""
modelinstance.bounding_box = CompoundBoundingBox.validate(modelinstance,
bounding_boxes, selector_args,
create_selector=create_selector,
ignored=ignored,
order=order)
def custom_model(*args, fit_deriv=None):
"""
Create a model from a user defined function. The inputs and parameters of
the model will be inferred from the arguments of the function.
This can be used either as a function or as a decorator. See below for
examples of both usages.
The model is separable only if there is a single input.
.. note::
All model parameters have to be defined as keyword arguments with
default values in the model function. Use `None` as a default argument
value if you do not want to have a default value for that parameter.
The standard settable model properties can be configured by default
using keyword arguments matching the name of the property; however,
these values are not set as model "parameters". Moreover, users
cannot use keyword arguments matching non-settable model properties,
with the exception of ``n_outputs`` which should be set to the number of
outputs of your function.
Parameters
----------
func : function
Function which defines the model. It should take N positional
arguments where ``N`` is dimensions of the model (the number of
independent variable in the model), and any number of keyword arguments
(the parameters). It must return the value of the model (typically as
an array, but can also be a scalar for scalar inputs). This
corresponds to the `~astropy.modeling.Model.evaluate` method.
fit_deriv : function, optional
Function which defines the Jacobian derivative of the model. I.e., the
derivative with respect to the *parameters* of the model. It should
have the same argument signature as ``func``, but should return a
sequence where each element of the sequence is the derivative
with respect to the corresponding argument. This corresponds to the
:meth:`~astropy.modeling.FittableModel.fit_deriv` method.
Examples
--------
Define a sinusoidal model function as a custom 1D model::
>>> from astropy.modeling.models import custom_model
>>> import numpy as np
>>> def sine_model(x, amplitude=1., frequency=1.):
... return amplitude * np.sin(2 * np.pi * frequency * x)
>>> def sine_deriv(x, amplitude=1., frequency=1.):
... return 2 * np.pi * amplitude * np.cos(2 * np.pi * frequency * x)
>>> SineModel = custom_model(sine_model, fit_deriv=sine_deriv)
Create an instance of the custom model and evaluate it::
>>> model = SineModel()
>>> model(0.25)
1.0
This model instance can now be used like a usual astropy model.
The next example demonstrates a 2D Moffat function model, and also
demonstrates the support for docstrings (this example could also include
a derivative, but it has been omitted for simplicity)::
>>> @custom_model
... def Moffat2D(x, y, amplitude=1.0, x_0=0.0, y_0=0.0, gamma=1.0,
... alpha=1.0):
... \"\"\"Two dimensional Moffat function.\"\"\"
... rr_gg = ((x - x_0) ** 2 + (y - y_0) ** 2) / gamma ** 2
... return amplitude * (1 + rr_gg) ** (-alpha)
...
>>> print(Moffat2D.__doc__)
Two dimensional Moffat function.
>>> model = Moffat2D()
>>> model(1, 1) # doctest: +FLOAT_CMP
0.3333333333333333
"""
if len(args) == 1 and callable(args[0]):
return _custom_model_wrapper(args[0], fit_deriv=fit_deriv)
elif not args:
return functools.partial(_custom_model_wrapper, fit_deriv=fit_deriv)
else:
raise TypeError(
"{0} takes at most one positional argument (the callable/"
"function to be turned into a model. When used as a decorator "
"it should be passed keyword arguments only (if "
"any).".format(__name__))
def _custom_model_inputs(func):
"""
Processes the inputs to the `custom_model`'s function into the appropriate
categories.
Parameters
----------
func : callable
Returns
-------
inputs : list
list of evaluation inputs
special_params : dict
dictionary of model properties which require special treatment
settable_params : dict
dictionary of defaults for settable model properties
params : dict
dictionary of model parameters set by `custom_model`'s function
"""
inputs, parameters = get_inputs_and_params(func)
special = ['n_outputs']
settable = [attr for attr, value in vars(Model).items()
if isinstance(value, property) and value.fset is not None]
properties = [attr for attr, value in vars(Model).items()
if isinstance(value, property) and value.fset is None and attr not in special]
special_params = {}
settable_params = {}
params = {}
for param in parameters:
if param.name in special:
special_params[param.name] = param.default
elif param.name in settable:
settable_params[param.name] = param.default
elif param.name in properties:
raise ValueError(f"Parameter '{param.name}' cannot be a model property: {properties}.")
else:
params[param.name] = param.default
return inputs, special_params, settable_params, params
def _custom_model_wrapper(func, fit_deriv=None):
"""
Internal implementation `custom_model`.
When `custom_model` is called as a function its arguments are passed to
this function, and the result of this function is returned.
When `custom_model` is used as a decorator a partial evaluation of this
function is returned by `custom_model`.
"""
if not callable(func):
raise ModelDefinitionError(
"func is not callable; it must be a function or other callable "
"object")
if fit_deriv is not None and not callable(fit_deriv):
raise ModelDefinitionError(
"fit_deriv not callable; it must be a function or other "
"callable object")
model_name = func.__name__
inputs, special_params, settable_params, params = _custom_model_inputs(func)
if (fit_deriv is not None and
len(fit_deriv.__defaults__) != len(params)):
raise ModelDefinitionError("derivative function should accept "
"same number of parameters as func.")
params = {param: Parameter(param, default=default)
for param, default in params.items()}
mod = find_current_module(2)
if mod:
modname = mod.__name__
else:
modname = '__main__'
members = {
'__module__': str(modname),
'__doc__': func.__doc__,
'n_inputs': len(inputs),
'n_outputs': special_params.pop('n_outputs', 1),
'evaluate': staticmethod(func),
'_settable_properties': settable_params
}
if fit_deriv is not None:
members['fit_deriv'] = staticmethod(fit_deriv)
members.update(params)
cls = type(model_name, (FittableModel,), members)
cls._separable = True if (len(inputs) == 1) else False
return cls
def render_model(model, arr=None, coords=None):
"""
Evaluates a model on an input array. Evaluation is limited to
a bounding box if the `Model.bounding_box` attribute is set.
Parameters
----------
model : `Model`
Model to be evaluated.
arr : `numpy.ndarray`, optional
Array on which the model is evaluated.
coords : array-like, optional
Coordinate arrays mapping to ``arr``, such that
``arr[coords] == arr``.
Returns
-------
array : `numpy.ndarray`
The model evaluated on the input ``arr`` or a new array from
``coords``.
If ``arr`` and ``coords`` are both `None`, the returned array is
limited to the `Model.bounding_box` limits. If
`Model.bounding_box` is `None`, ``arr`` or ``coords`` must be passed.
Examples
--------
:ref:`astropy:bounding-boxes`
"""
bbox = model.bounding_box
if (coords is None) & (arr is None) & (bbox is None):
raise ValueError('If no bounding_box is set,'
'coords or arr must be input.')
# for consistent indexing
if model.n_inputs == 1:
if coords is not None:
coords = [coords]
if bbox is not None:
bbox = [bbox]
if arr is not None:
arr = arr.copy()
# Check dimensions match model
if arr.ndim != model.n_inputs:
raise ValueError('number of array dimensions inconsistent with '
'number of model inputs.')
if coords is not None:
# Check dimensions match arr and model
coords = np.array(coords)
if len(coords) != model.n_inputs:
raise ValueError('coordinate length inconsistent with the number '
'of model inputs.')
if arr is not None:
if coords[0].shape != arr.shape:
raise ValueError('coordinate shape inconsistent with the '
'array shape.')
else:
arr = np.zeros(coords[0].shape)
if bbox is not None:
# assures position is at center pixel, important when using add_array
pd = pos, delta = np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2))
for bb in bbox]).astype(int).T
if coords is not None:
sub_shape = tuple(delta * 2 + 1)
sub_coords = np.array([extract_array(c, sub_shape, pos)
for c in coords])
else:
limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
sub_coords = np.mgrid[limits]
sub_coords = sub_coords[::-1]
if arr is None:
arr = model(*sub_coords)
else:
try:
arr = add_array(arr, model(*sub_coords), pos)
except ValueError:
raise ValueError('The `bounding_box` is larger than the input'
' arr in one or more dimensions. Set '
'`model.bounding_box = None`.')
else:
if coords is None:
im_shape = arr.shape
limits = [slice(i) for i in im_shape]
coords = np.mgrid[limits]
arr += model(*coords[::-1])
return arr
def hide_inverse(model):
"""
This is a convenience function intended to disable automatic generation
of the inverse in compound models by disabling one of the constituent
model's inverse. This is to handle cases where user provided inverse
functions are not compatible within an expression.
Example:
compound_model.inverse = hide_inverse(m1) + m2 + m3
This will insure that the defined inverse itself won't attempt to
build its own inverse, which would otherwise fail in this example
(e.g., m = m1 + m2 + m3 happens to raises an exception for this
reason.)
Note that this permanently disables it. To prevent that either copy
the model or restore the inverse later.
"""
del model.inverse
return model
|
3c033ea1ea18f10932c5afe71b5443b0e33743a29b69dfd3198f1cf45fb740d8 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Convolution Model"""
# pylint: disable=line-too-long, too-many-lines, too-many-arguments, invalid-name
import numpy as np
from .core import CompoundModel, SPECIAL_OPERATORS
class Convolution(CompoundModel):
"""
Wrapper class for a convolution model.
Parameters
----------
operator: tuple
The SPECIAL_OPERATORS entry for the convolution being used.
model : Model
The model for the convolution.
kernel: Model
The kernel model for the convolution.
bounding_box : tuple
A bounding box to define the limits of the integration
approximation for the convolution.
resolution : float
The resolution for the approximation of the convolution.
cache : bool, optional
Allow convolution computation to be cached for reuse. This is
enabled by default.
Notes
-----
This is wrapper is necessary to handle the limitations of the
pseudospectral convolution binary operator implemented in
astropy.convolution under `~astropy.convolution.convolve_fft`. In this `~astropy.convolution.convolve_fft` it
is assumed that the inputs ``array`` and ``kernel`` span a sufficient
portion of the support of the functions of the convolution.
Consequently, the ``Compound`` created by the `~astropy.convolution.convolve_models` function
makes the assumption that one should pass an input array that
sufficiently spans this space. This means that slightly different
input arrays to this model will result in different outputs, even
on points of intersection between these arrays.
This issue is solved by requiring a ``bounding_box`` together with a
resolution so that one can pre-calculate the entire domain and then
(by default) cache the convolution values. The function then just
interpolates the results from this cache.
"""
def __init__(self, operator, model, kernel, bounding_box, resolution, cache=True):
super().__init__(operator, model, kernel)
self.bounding_box = bounding_box
self._resolution = resolution
self._cache_convolution = cache
self._kwargs = None
self._convolution = None
def clear_cache(self):
"""
Clears the cached convolution
"""
self._kwargs = None
self._convolution = None
def _get_convolution(self, **kwargs):
if (self._convolution is None) or (self._kwargs != kwargs):
domain = self.bounding_box.domain(self._resolution)
mesh = np.meshgrid(*domain)
data = super().__call__(*mesh, **kwargs)
from scipy.interpolate import RegularGridInterpolator
convolution = RegularGridInterpolator(domain, data)
if self._cache_convolution:
self._kwargs = kwargs
self._convolution = convolution
else:
convolution = self._convolution
return convolution
@staticmethod
def _convolution_inputs(*args):
not_scalar = np.where([not np.isscalar(arg) for arg in args])[0]
if len(not_scalar) == 0:
return np.array(args), (1,)
else:
output_shape = args[not_scalar[0]].shape
if not all(args[index].shape == output_shape for index in not_scalar):
raise ValueError('Values have differing shapes')
inputs = []
for arg in args:
if np.isscalar(arg):
inputs.append(np.full(output_shape, arg))
else:
inputs.append(arg)
return np.reshape(inputs, (len(inputs), -1)).T, output_shape
@staticmethod
def _convolution_outputs(outputs, output_shape):
return outputs.reshape(output_shape)
def __call__(self, *args, **kw):
inputs, output_shape = self._convolution_inputs(*args)
convolution = self._get_convolution(**kw)
outputs = convolution(inputs)
return self._convolution_outputs(outputs, output_shape)
|
b315ab814da85e99017acc9a5594a57cb806c1d89881ce08225151779bef328e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This subpackage provides a framework for representing models and
performing model evaluation and fitting. It supports 1D and 2D models
and fitting with parameter constraints. It has some predefined models
and fitting routines.
"""
from . import fitting
from . import models
from .core import *
from .parameters import *
from .separable import *
|
c86242b0670391cc346aa3926dc7a517a37762ac732bf2b99d306dfd8a2d77b0 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Creates a common namespace for all pre-defined models.
"""
# pylint: disable=unused-wildcard-import, unused-import, wildcard-import
from .core import custom_model, hide_inverse, fix_inputs # pylint: disable=W0611
from .mappings import *
from .projections import *
from .rotations import *
from .polynomial import *
from .functional_models import *
from .physical_models import *
from .powerlaws import *
from .spline import *
from .tabular import *
from . import math_functions as math
# Attach a docstring explaining constraints to all models which support them.
# Note: add new models to this list
CONSTRAINTS_DOC = """
Other Parameters
----------------
fixed : a dict, optional
A dictionary ``{parameter_name: boolean}`` of parameters to not be
varied during fitting. True means the parameter is held fixed.
Alternatively the `~astropy.modeling.Parameter.fixed`
property of a parameter may be used.
tied : dict, optional
A dictionary ``{parameter_name: callable}`` of parameters which are
linked to some other parameter. The dictionary values are callables
providing the linking relationship. Alternatively the
`~astropy.modeling.Parameter.tied` property of a parameter
may be used.
bounds : dict, optional
A dictionary ``{parameter_name: value}`` of lower and upper bounds of
parameters. Keys are parameter names. Values are a list or a tuple
of length 2 giving the desired range for the parameter.
Alternatively, the
`~astropy.modeling.Parameter.min` and
`~astropy.modeling.Parameter.max` properties of a parameter
may be used.
eqcons : list, optional
A list of functions of length ``n`` such that ``eqcons[j](x0,*args) ==
0.0`` in a successfully optimized problem.
ineqcons : list, optional
A list of functions of length ``n`` such that ``ieqcons[j](x0,*args) >=
0.0`` is a successfully optimized problem.
"""
MODELS_WITH_CONSTRAINTS = [
AiryDisk2D, Moffat1D, Moffat2D, Box1D, Box2D,
Const1D, Const2D, Ellipse2D, Disk2D,
Gaussian1D, Gaussian2D,
Linear1D, Lorentz1D, RickerWavelet1D, RickerWavelet2D,
PowerLaw1D, Sersic1D, Sersic2D,
Sine1D, Cosine1D, Tangent1D, ArcSine1D, ArcCosine1D, ArcTangent1D,
Trapezoid1D, TrapezoidDisk2D,
Chebyshev1D, Chebyshev2D, Hermite1D, Hermite2D, Legendre2D, Legendre1D,
Polynomial1D, Polynomial2D, Voigt1D, KingProjectedAnalytic1D,
NFW
]
for item in MODELS_WITH_CONSTRAINTS:
if isinstance(item.__doc__, str):
item.__doc__ += CONSTRAINTS_DOC
|
dc4b0e635a696e8da1982942deec9dc1e477a036a86f81dc922c4593e4c9953f | """
Special models useful for complex compound models where control is needed over
which outputs from a source model are mapped to which inputs of a target model.
"""
# pylint: disable=invalid-name
from .core import FittableModel, Model
from astropy.units import Quantity
__all__ = ['Mapping', 'Identity', 'UnitsMapping']
class Mapping(FittableModel):
"""
Allows inputs to be reordered, duplicated or dropped.
Parameters
----------
mapping : tuple
A tuple of integers representing indices of the inputs to this model
to return and in what order to return them. See
:ref:`astropy:compound-model-mappings` for more details.
n_inputs : int
Number of inputs; if `None` (default) then ``max(mapping) + 1`` is
used (i.e. the highest input index used in the mapping).
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict-like
Free-form metadata to associate with this model.
Raises
------
TypeError
Raised when number of inputs is less that ``max(mapping)``.
Examples
--------
>>> from astropy.modeling.models import Polynomial2D, Shift, Mapping
>>> poly1 = Polynomial2D(1, c0_0=1, c1_0=2, c0_1=3)
>>> poly2 = Polynomial2D(1, c0_0=1, c1_0=2.4, c0_1=2.1)
>>> model = (Shift(1) & Shift(2)) | Mapping((0, 1, 0, 1)) | (poly1 & poly2)
>>> model(1, 2) # doctest: +FLOAT_CMP
(17.0, 14.2)
"""
linear = True # FittableModel is non-linear by default
def __init__(self, mapping, n_inputs=None, name=None, meta=None):
self._inputs = ()
self._outputs = ()
if n_inputs is None:
self._n_inputs = max(mapping) + 1
else:
self._n_inputs = n_inputs
self._n_outputs = len(mapping)
super().__init__(name=name, meta=meta)
self.inputs = tuple('x' + str(idx) for idx in range(self._n_inputs))
self.outputs = tuple('x' + str(idx) for idx in range(self._n_outputs))
self._mapping = mapping
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
@property
def n_inputs(self):
return self._n_inputs
@property
def n_outputs(self):
return self._n_outputs
@property
def mapping(self):
"""Integers representing indices of the inputs."""
return self._mapping
def __repr__(self):
if self.name is None:
return f'<Mapping({self.mapping})>'
return f'<Mapping({self.mapping}, name={self.name!r})>'
def evaluate(self, *args):
if len(args) != self.n_inputs:
name = self.name if self.name is not None else "Mapping"
raise TypeError(f'{name} expects {self.n_inputs} inputs; got {len(args)}')
result = tuple(args[idx] for idx in self._mapping)
if self.n_outputs == 1:
return result[0]
return result
@property
def inverse(self):
"""
A `Mapping` representing the inverse of the current mapping.
Raises
------
`NotImplementedError`
An inverse does no exist on mappings that drop some of its inputs
(there is then no way to reconstruct the inputs that were dropped).
"""
try:
mapping = tuple(self.mapping.index(idx)
for idx in range(self.n_inputs))
except ValueError:
raise NotImplementedError(
"Mappings such as {} that drop one or more of their inputs "
"are not invertible at this time.".format(self.mapping))
inv = self.__class__(mapping)
inv._inputs = self._outputs
inv._outputs = self._inputs
inv._n_inputs = len(inv._inputs)
inv._n_outputs = len(inv._outputs)
return inv
class Identity(Mapping):
"""
Returns inputs unchanged.
This class is useful in compound models when some of the inputs must be
passed unchanged to the next model.
Parameters
----------
n_inputs : int
Specifies the number of inputs this identity model accepts.
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict-like
Free-form metadata to associate with this model.
Examples
--------
Transform ``(x, y)`` by a shift in x, followed by scaling the two inputs::
>>> from astropy.modeling.models import (Polynomial1D, Shift, Scale,
... Identity)
>>> model = (Shift(1) & Identity(1)) | Scale(1.2) & Scale(2)
>>> model(1,1) # doctest: +FLOAT_CMP
(2.4, 2.0)
>>> model.inverse(2.4, 2) # doctest: +FLOAT_CMP
(1.0, 1.0)
"""
linear = True # FittableModel is non-linear by default
def __init__(self, n_inputs, name=None, meta=None):
mapping = tuple(range(n_inputs))
super().__init__(mapping, name=name, meta=meta)
def __repr__(self):
if self.name is None:
return f'<Identity({self.n_inputs})>'
return f'<Identity({self.n_inputs}, name={self.name!r})>'
@property
def inverse(self):
"""
The inverse transformation.
In this case of `Identity`, ``self.inverse is self``.
"""
return self
class UnitsMapping(Model):
"""
Mapper that operates on the units of the input, first converting to
canonical units, then assigning new units without further conversion.
Used by Model.coerce_units to support units on otherwise unitless models
such as Polynomial1D.
Parameters
----------
mapping : tuple
A tuple of (input_unit, output_unit) pairs, one per input, matched to the
inputs by position. The first element of the each pair is the unit that
the model will accept (specify ``dimensionless_unscaled``
to accept dimensionless input). The second element is the unit that the
model will return. Specify ``dimensionless_unscaled``
to return dimensionless Quantity, and `None` to return raw values without
Quantity.
input_units_equivalencies : dict, optional
Default equivalencies to apply to input values. If set, this should be a
dictionary where each key is a string that corresponds to one of the
model inputs.
input_units_allow_dimensionless : dict or bool, optional
Allow dimensionless input. If this is True, input values to evaluate will
gain the units specified in input_units. If this is a dictionary then it
should map input name to a bool to allow dimensionless numbers for that
input.
name : str, optional
A human-friendly name associated with this model instance
(particularly useful for identifying the individual components of a
compound model).
meta : dict-like, optional
Free-form metadata to associate with this model.
Examples
--------
Wrapping a unitless model to require and convert units:
>>> from astropy.modeling.models import Polynomial1D, UnitsMapping
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = UnitsMapping(((u.m, None),)) | poly
>>> model = model | UnitsMapping(((None, u.s),))
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(1000, u.cm)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(10, u.cm)) # doctest: +FLOAT_CMP
<Quantity 1.2 s>
Wrapping a unitless model but still permitting unitless input:
>>> from astropy.modeling.models import Polynomial1D, UnitsMapping
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = UnitsMapping(((u.m, None),), input_units_allow_dimensionless=True) | poly
>>> model = model | UnitsMapping(((None, u.s),))
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(10) # doctest: +FLOAT_CMP
<Quantity 21. s>
"""
def __init__(
self,
mapping,
input_units_equivalencies=None,
input_units_allow_dimensionless=False,
name=None,
meta=None
):
self._mapping = mapping
none_mapping_count = len([m for m in mapping if m[-1] is None])
if none_mapping_count > 0 and none_mapping_count != len(mapping):
raise ValueError("If one return unit is None, then all must be None")
# These attributes are read and handled by Model
self._input_units_strict = True
self.input_units_equivalencies = input_units_equivalencies
self._input_units_allow_dimensionless = input_units_allow_dimensionless
super().__init__(name=name, meta=meta)
# Can't invoke this until after super().__init__, since
# we need self.inputs and self.outputs to be populated.
self._rebuild_units()
def _rebuild_units(self):
self._input_units = {input_name: input_unit for input_name, (input_unit, _) in zip(self.inputs, self.mapping)}
@property
def n_inputs(self):
return len(self._mapping)
@property
def n_outputs(self):
return len(self._mapping)
@property
def inputs(self):
return super().inputs
@inputs.setter
def inputs(self, value):
super(UnitsMapping, self.__class__).inputs.fset(self, value)
self._rebuild_units()
@property
def outputs(self):
return super().outputs
@outputs.setter
def outputs(self, value):
super(UnitsMapping, self.__class__).outputs.fset(self, value)
self._rebuild_units()
@property
def input_units(self):
return self._input_units
@property
def mapping(self):
return self._mapping
def evaluate(self, *args):
result = []
for arg, (_, return_unit) in zip(args, self.mapping):
if isinstance(arg, Quantity):
value = arg.value
else:
value = arg
if return_unit is None:
result.append(value)
else:
result.append(Quantity(value, return_unit, subok=True))
if self.n_outputs == 1:
return result[0]
else:
return tuple(result)
def __repr__(self):
if self.name is None:
return f"<UnitsMapping({self.mapping})>"
else:
return f"<UnitsMapping({self.mapping}, name={self.name!r})>"
|
5743b99d4ac948817a5bb2262e7aff94a57e10110e870e644dd21879a9c191db | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Spline models and fitters."""
# pylint: disable=line-too-long, too-many-lines, too-many-arguments, invalid-name
import warnings
import abc
import functools
import numpy as np
from astropy.utils.exceptions import (AstropyUserWarning,)
from astropy.utils import isiterable
from .core import (FittableModel, ModelDefinitionError)
from .parameters import Parameter
__all__ = ['Spline1D', 'SplineInterpolateFitter', 'SplineSmoothingFitter',
'SplineExactKnotsFitter', 'SplineSplrepFitter']
__doctest_requires__ = {('Spline1D'): ['scipy']}
class _Spline(FittableModel):
"""Base class for spline models"""
_knot_names = ()
_coeff_names = ()
optional_inputs = {}
def __init__(self, knots=None, coeffs=None, degree=None, bounds=None,
n_models=None, model_set_axis=None, name=None, meta=None):
super().__init__(
n_models=n_models, model_set_axis=model_set_axis, name=name,
meta=meta)
self._user_knots = False
self._init_tck(degree)
# Hack to allow an optional model argument
self._create_optional_inputs()
if knots is not None:
self._init_spline(knots, coeffs, bounds)
elif coeffs is not None:
raise ValueError("If one passes a coeffs vector one needs to also pass knots!")
@property
def param_names(self):
"""
Coefficient names generated based on the spline's degree and
number of knots.
"""
return tuple(list(self._knot_names) + list(self._coeff_names))
@staticmethod
def _optional_arg(arg):
return f'_{arg}'
def _create_optional_inputs(self):
for arg in self.optional_inputs:
attribute = self._optional_arg(arg)
if hasattr(self, attribute):
raise ValueError(f'Optional argument {arg} already exists in this class!')
else:
setattr(self, attribute, None)
def _intercept_optional_inputs(self, **kwargs):
new_kwargs = kwargs
for arg in self.optional_inputs:
if (arg in kwargs):
attribute = self._optional_arg(arg)
if getattr(self, attribute) is None:
setattr(self, attribute, kwargs[arg])
del new_kwargs[arg]
else:
raise RuntimeError(f'{arg} has already been set, something has gone wrong!')
return new_kwargs
def evaluate(self, *args, **kwargs):
""" Extract the optional kwargs passed to call """
optional_inputs = kwargs
for arg in self.optional_inputs:
attribute = self._optional_arg(arg)
if arg in kwargs:
# Options passed in
optional_inputs[arg] = kwargs[arg]
elif getattr(self, attribute) is not None:
# No options passed in and Options set
optional_inputs[arg] = getattr(self, attribute)
setattr(self, attribute, None)
else:
# No options passed in and No options set
optional_inputs[arg] = self.optional_inputs[arg]
return optional_inputs
def __call__(self, *args, **kwargs):
"""
Make model callable to model evaluation
"""
# Hack to allow an optional model argument
kwargs = self._intercept_optional_inputs(**kwargs)
return super().__call__(*args, **kwargs)
def _create_parameter(self, name: str, index: int, attr: str, fixed=False):
"""
Create a spline parameter linked to an attribute array.
Parameters
----------
name : str
Name for the parameter
index : int
The index of the parameter in the array
attr : str
The name for the attribute array
fixed : optional, bool
If the parameter should be fixed or not
"""
# Hack to allow parameters and attribute array to freely exchange values
# _getter forces reading value from attribute array
# _setter forces setting value to attribute array
def _getter(value, model: "_Spline", index: int, attr: str):
return getattr(model, attr)[index]
def _setter(value, model: "_Spline", index: int, attr: str):
getattr(model, attr)[index] = value
return value
getter = functools.partial(_getter, index=index, attr=attr)
setter = functools.partial(_setter, index=index, attr=attr)
default = getattr(self, attr)
param = Parameter(name=name, default=default[index], fixed=fixed,
getter=getter, setter=setter)
# setter/getter wrapper for parameters in this case require the
# parameter to have a reference back to its parent model
param.model = self
param.value = default[index]
# Add parameter to model
self.__dict__[name] = param
def _create_parameters(self, base_name: str, attr: str, fixed=False):
"""
Create a spline parameters linked to an attribute array for all
elements in that array
Parameters
----------
base_name : str
Base name for the parameters
attr : str
The name for the attribute array
fixed : optional, bool
If the parameters should be fixed or not
"""
names = []
for index in range(len(getattr(self, attr))):
name = f"{base_name}{index}"
names.append(name)
self._create_parameter(name, index, attr, fixed)
return tuple(names)
@abc.abstractmethod
def _init_parameters(self):
raise NotImplementedError("This needs to be implemented")
@abc.abstractmethod
def _init_data(self, knots, coeffs, bounds=None):
raise NotImplementedError("This needs to be implemented")
def _init_spline(self, knots, coeffs, bounds=None):
self._init_data(knots, coeffs, bounds)
self._init_parameters()
# fill _parameters and related attributes
self._initialize_parameters((), {})
self._initialize_slices()
# Calling this will properly fill the _parameter vector, which is
# used directly sometimes without being properly filled.
_ = self.parameters
def _init_tck(self, degree):
self._c = None
self._t = None
self._degree = degree
class Spline1D(_Spline):
"""
One dimensional Spline Model
Parameters
----------
knots : optional
Define the knots for the spline. Can be 1) the number of interior
knots for the spline, 2) the array of all knots for the spline, or
3) If both bounds are defined, the interior knots for the spline
coeffs : optional
The array of knot coefficients for the spline
degree : optional
The degree of the spline. It must be 1 <= degree <= 5, default is 3.
bounds : optional
The upper and lower bounds of the spline.
Notes
-----
Much of the functionality of this model is provided by
`scipy.interpolate.BSpline` which can be directly accessed via the
bspline property.
Fitting for this model is provided by wrappers for:
`scipy.interpolate.UnivariateSpline`,
`scipy.interpolate.InterpolatedUnivariateSpline`,
and `scipy.interpolate.LSQUnivariateSpline`.
If one fails to define any knots/coefficients, no parameters will
be added to this model until a fitter is called. This is because
some of the fitters for splines vary the number of parameters and so
we cannot define the parameter set until after fitting in these cases.
Since parameters are not necessarily known at model initialization,
setting model parameters directly via the model interface has been
disabled.
Direct constructors are provided for this model which incorporate the
fitting to data directly into model construction.
Knot parameters are declared as "fixed" parameters by default to
enable the use of other `astropy.modeling` fitters to be used to
fit this model.
Examples
--------
>>> import numpy as np
>>> from astropy.modeling.models import Spline1D
>>> from astropy.modeling import fitting
>>> np.random.seed(42)
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> xs = np.linspace(-3, 3, 1000)
A 1D interpolating spline can be fit to data:
>>> fitter = fitting.SplineInterpolateFitter()
>>> spl = fitter(Spline1D(), x, y)
Similarly, a smoothing spline can be fit to data:
>>> fitter = fitting.SplineSmoothingFitter()
>>> spl = fitter(Spline1D(), x, y, s=0.5)
Similarly, a spline can be fit to data using an exact set of interior knots:
>>> t = [-1, 0, 1]
>>> fitter = fitting.SplineExactKnotsFitter()
>>> spl = fitter(Spline1D(), x, y, t=t)
"""
n_inputs = 1
n_outputs = 1
_separable = True
optional_inputs = {'nu': 0}
def __init__(self, knots=None, coeffs=None, degree=3, bounds=None,
n_models=None, model_set_axis=None, name=None, meta=None):
super().__init__(
knots=knots, coeffs=coeffs, degree=degree, bounds=bounds,
n_models=n_models, model_set_axis=model_set_axis, name=name, meta=meta
)
@property
def t(self):
"""
The knots vector
"""
if self._t is None:
return np.concatenate((np.zeros(self._degree + 1), np.ones(self._degree + 1)))
else:
return self._t
@t.setter
def t(self, value):
if self._t is None:
raise ValueError("The model parameters must be initialized before setting knots.")
elif len(value) == len(self._t):
self._t = value
else:
raise ValueError("There must be exactly as many knots as previously defined.")
@property
def t_interior(self):
"""
The interior knots
"""
return self.t[self.degree + 1: -(self.degree + 1)]
@property
def c(self):
"""
The coefficients vector
"""
if self._c is None:
return np.zeros(len(self.t))
else:
return self._c
@c.setter
def c(self, value):
if self._c is None:
raise ValueError("The model parameters must be initialized before setting coeffs.")
elif len(value) == len(self._c):
self._c = value
else:
raise ValueError("There must be exactly as many coeffs as previously defined.")
@property
def degree(self):
"""
The degree of the spline polynomials
"""
return self._degree
@property
def _initialized(self):
return self._t is not None and self._c is not None
@property
def tck(self):
"""
Scipy 'tck' tuple representation
"""
return (self.t, self.c, self.degree)
@tck.setter
def tck(self, value):
if self._initialized:
if value[2] != self.degree:
raise ValueError("tck has incompatible degree!")
self.t = value[0]
self.c = value[1]
else:
self._init_spline(value[0], value[1])
# Calling this will properly fill the _parameter vector, which is
# used directly sometimes without being properly filled.
_ = self.parameters
@property
def bspline(self):
"""
Scipy bspline object representation
"""
from scipy.interpolate import BSpline
return BSpline(*self.tck)
@bspline.setter
def bspline(self, value):
from scipy.interpolate import BSpline
if isinstance(value, BSpline):
self.tck = value.tck
else:
self.tck = value
@property
def knots(self):
"""
Dictionary of knot parameters
"""
return [getattr(self, knot) for knot in self._knot_names]
@property
def user_knots(self):
"""If the knots have been supplied by the user"""
return self._user_knots
@user_knots.setter
def user_knots(self, value):
self._user_knots = value
@property
def coeffs(self):
"""
Dictionary of coefficient parameters
"""
return [getattr(self, coeff) for coeff in self._coeff_names]
def _init_parameters(self):
self._knot_names = self._create_parameters("knot", "t", fixed=True)
self._coeff_names = self._create_parameters("coeff", "c")
def _init_bounds(self, bounds=None):
if bounds is None:
bounds = [None, None]
if bounds[0] is None:
lower = np.zeros(self._degree + 1)
else:
lower = np.array([bounds[0]] * (self._degree + 1))
if bounds[1] is None:
upper = np.ones(self._degree + 1)
else:
upper = np.array([bounds[1]] * (self._degree + 1))
if bounds[0] is not None and bounds[1] is not None:
self.bounding_box = bounds
has_bounds = True
else:
has_bounds = False
return has_bounds, lower, upper
def _init_knots(self, knots, has_bounds, lower, upper):
if np.issubdtype(type(knots), np.integer):
self._t = np.concatenate(
(lower, np.zeros(knots), upper)
)
elif isiterable(knots):
self._user_knots = True
if has_bounds:
self._t = np.concatenate(
(lower, np.array(knots), upper)
)
else:
if len(knots) < 2*(self._degree + 1):
raise ValueError(f"Must have at least {2*(self._degree + 1)} knots.")
self._t = np.array(knots)
else:
raise ValueError(f"Knots: {knots} must be iterable or value")
# check that knots form a viable spline
self.bspline
def _init_coeffs(self, coeffs=None):
if coeffs is None:
self._c = np.zeros(len(self._t))
else:
self._c = np.array(coeffs)
# check that coeffs form a viable spline
self.bspline
def _init_data(self, knots, coeffs, bounds=None):
self._init_knots(knots, *self._init_bounds(bounds))
self._init_coeffs(coeffs)
def evaluate(self, *args, **kwargs):
"""
Evaluate the spline.
Parameters
----------
x :
(positional) The points where the model is evaluating the spline at
nu : optional
(kwarg) The derivative of the spline for evaluation, 0 <= nu <= degree + 1.
Default: 0.
"""
kwargs = super().evaluate(*args, **kwargs)
x = args[0]
if 'nu' in kwargs:
if kwargs['nu'] > self.degree + 1:
raise RuntimeError("Cannot evaluate a derivative of "
f"order higher than {self.degree + 1}")
return self.bspline(x, **kwargs)
def derivative(self, nu=1):
"""
Create a spline that is the derivative of this one
Parameters
----------
nu : int, optional
Derivative order, default is 1.
"""
if nu <= self.degree:
bspline = self.bspline.derivative(nu=nu)
derivative = Spline1D(degree=bspline.k)
derivative.bspline = bspline
return derivative
else:
raise ValueError(f'Must have nu <= {self.degree}')
def antiderivative(self, nu=1):
"""
Create a spline that is an antiderivative of this one
Parameters
----------
nu : int, optional
Antiderivative order, default is 1.
Notes
-----
Assumes constant of integration is 0
"""
if (nu + self.degree) <= 5:
bspline = self.bspline.antiderivative(nu=nu)
antiderivative = Spline1D(degree=bspline.k)
antiderivative.bspline = bspline
return antiderivative
else:
raise ValueError("Supported splines can have max degree 5, "
f"antiderivative degree will be {nu + self.degree}")
class _SplineFitter(abc.ABC):
"""
Base Spline Fitter
"""
def __init__(self):
self.fit_info = {
'resid': None,
'spline': None
}
def _set_fit_info(self, spline):
self.fit_info['resid'] = spline.get_residual()
self.fit_info['spline'] = spline
@abc.abstractmethod
def _fit_method(self, model, x, y, **kwargs):
raise NotImplementedError("This has not been implemented for _SplineFitter.")
def __call__(self, model, x, y, z=None, **kwargs):
model_copy = model.copy()
if isinstance(model_copy, Spline1D):
if z is not None:
raise ValueError("1D model can only have 2 data points.")
spline = self._fit_method(model_copy, x, y, **kwargs)
else:
raise ModelDefinitionError("Only spline models are compatible with this fitter.")
self._set_fit_info(spline)
return model_copy
class SplineInterpolateFitter(_SplineFitter):
"""
Fit an interpolating spline
"""
def _fit_method(self, model, x, y, **kwargs):
weights = kwargs.pop('weights', None)
bbox = kwargs.pop('bbox', [None, None])
if model.user_knots:
warnings.warn("The current user specified knots maybe ignored for interpolating data",
AstropyUserWarning)
model.user_knots = False
if bbox != [None, None]:
model.bounding_box = bbox
from scipy.interpolate import InterpolatedUnivariateSpline
spline = InterpolatedUnivariateSpline(x, y, w=weights, bbox=bbox, k=model.degree)
model.tck = spline._eval_args
return spline
class SplineSmoothingFitter(_SplineFitter):
"""
Fit a smoothing spline
"""
def _fit_method(self, model, x, y, **kwargs):
s = kwargs.pop('s', None)
weights = kwargs.pop('weights', None)
bbox = kwargs.pop('bbox', [None, None])
if model.user_knots:
warnings.warn("The current user specified knots maybe ignored for smoothing data",
AstropyUserWarning)
model.user_knots = False
if bbox != [None, None]:
model.bounding_box = bbox
from scipy.interpolate import UnivariateSpline
spline = UnivariateSpline(x, y, w=weights, bbox=bbox, k=model.degree, s=s)
model.tck = spline._eval_args
return spline
class SplineExactKnotsFitter(_SplineFitter):
"""
Fit a spline using least-squares regression.
"""
def _fit_method(self, model, x, y, **kwargs):
t = kwargs.pop('t', None)
weights = kwargs.pop('weights', None)
bbox = kwargs.pop('bbox', [None, None])
if t is not None:
if model.user_knots:
warnings.warn("The current user specified knots will be "
"overwritten for by knots passed into this function",
AstropyUserWarning)
else:
if model.user_knots:
t = model.t_interior
else:
raise RuntimeError("No knots have been provided")
if bbox != [None, None]:
model.bounding_box = bbox
from scipy.interpolate import LSQUnivariateSpline
spline = LSQUnivariateSpline(x, y, t, w=weights, bbox=bbox, k=model.degree)
model.tck = spline._eval_args
return spline
class SplineSplrepFitter(_SplineFitter):
"""
Fit a spline using the `scipy.interpolate.splrep` function interface.
"""
def __init__(self):
super().__init__()
self.fit_info = {
'fp': None,
'ier': None,
'msg': None
}
def _fit_method(self, model, x, y, **kwargs):
t = kwargs.pop('t', None)
s = kwargs.pop('s', None)
task = kwargs.pop('task', 0)
weights = kwargs.pop('weights', None)
bbox = kwargs.pop('bbox', [None, None])
if t is not None:
if model.user_knots:
warnings.warn("The current user specified knots will be "
"overwritten for by knots passed into this function",
AstropyUserWarning)
else:
if model.user_knots:
t = model.t_interior
if bbox != [None, None]:
model.bounding_box = bbox
from scipy.interpolate import splrep
tck, fp, ier, msg = splrep(x, y, w=weights, xb=bbox[0], xe=bbox[1], k=model.degree,
s=s, t=t, task=task, full_output=1)
model.tck = tck
return fp, ier, msg
def _set_fit_info(self, spline):
self.fit_info['fp'] = spline[0]
self.fit_info['ier'] = spline[1]
self.fit_info['msg'] = spline[2]
|
7e97bc9396f770b29eda8d721de127bf2375b382b72cb6e20106270bffbba0a3 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# pylint: disable=invalid-name
"""
Optimization algorithms used in `~astropy.modeling.fitting`.
"""
import warnings
import abc
import numpy as np
from astropy.utils.exceptions import AstropyUserWarning
__all__ = ["Optimization", "SLSQP", "Simplex"]
# Maximum number of iterations
DEFAULT_MAXITER = 100
# Step for the forward difference approximation of the Jacobian
DEFAULT_EPS = np.sqrt(np.finfo(float).eps)
# Default requested accuracy
DEFAULT_ACC = 1e-07
DEFAULT_BOUNDS = (-10 ** 12, 10 ** 12)
class Optimization(metaclass=abc.ABCMeta):
"""
Base class for optimizers.
Parameters
----------
opt_method : callable
Implements optimization method
Notes
-----
The base Optimizer does not support any constraints by default; individual
optimizers should explicitly set this list to the specific constraints
it supports.
"""
supported_constraints = []
def __init__(self, opt_method):
self._opt_method = opt_method
self._maxiter = DEFAULT_MAXITER
self._eps = DEFAULT_EPS
self._acc = DEFAULT_ACC
@property
def maxiter(self):
"""Maximum number of iterations"""
return self._maxiter
@maxiter.setter
def maxiter(self, val):
"""Set maxiter"""
self._maxiter = val
@property
def eps(self):
"""Step for the forward difference approximation of the Jacobian"""
return self._eps
@eps.setter
def eps(self, val):
"""Set eps value"""
self._eps = val
@property
def acc(self):
"""Requested accuracy"""
return self._acc
@acc.setter
def acc(self, val):
"""Set accuracy"""
self._acc = val
def __repr__(self):
fmt = f"{self.__class__.__name__}()"
return fmt
@property
def opt_method(self):
""" Return the optimization method."""
return self._opt_method
@abc.abstractmethod
def __call__(self):
raise NotImplementedError("Subclasses should implement this method")
class SLSQP(Optimization):
"""
Sequential Least Squares Programming optimization algorithm.
The algorithm is described in [1]_. It supports tied and fixed
parameters, as well as bounded constraints. Uses
`scipy.optimize.fmin_slsqp`.
References
----------
.. [1] http://www.netlib.org/toms/733
"""
supported_constraints = ['bounds', 'eqcons', 'ineqcons', 'fixed', 'tied']
def __init__(self):
from scipy.optimize import fmin_slsqp
super().__init__(fmin_slsqp)
self.fit_info = {
'final_func_val': None,
'numiter': None,
'exit_mode': None,
'message': None
}
def __call__(self, objfunc, initval, fargs, **kwargs):
"""
Run the solver.
Parameters
----------
objfunc : callable
objection function
initval : iterable
initial guess for the parameter values
fargs : tuple
other arguments to be passed to the statistic function
kwargs : dict
other keyword arguments to be passed to the solver
"""
kwargs['iter'] = kwargs.pop('maxiter', self._maxiter)
if 'epsilon' not in kwargs:
kwargs['epsilon'] = self._eps
if 'acc' not in kwargs:
kwargs['acc'] = self._acc
# Get the verbosity level
disp = kwargs.pop('verblevel', None)
# set the values of constraints to match the requirements of fmin_slsqp
model = fargs[0]
pars = [getattr(model, name) for name in model.param_names]
bounds = [par.bounds for par in pars if not (par.fixed or par.tied)]
bounds = np.asarray(bounds)
for i in bounds:
if i[0] is None:
i[0] = DEFAULT_BOUNDS[0]
if i[1] is None:
i[1] = DEFAULT_BOUNDS[1]
# older versions of scipy require this array to be float
bounds = np.asarray(bounds, dtype=float)
eqcons = np.array(model.eqcons)
ineqcons = np.array(model.ineqcons)
fitparams, final_func_val, numiter, exit_mode, mess = self.opt_method(
objfunc, initval, args=fargs, full_output=True, disp=disp,
bounds=bounds, eqcons=eqcons, ieqcons=ineqcons,
**kwargs)
self.fit_info['final_func_val'] = final_func_val
self.fit_info['numiter'] = numiter
self.fit_info['exit_mode'] = exit_mode
self.fit_info['message'] = mess
if exit_mode != 0:
warnings.warn("The fit may be unsuccessful; check "
"fit_info['message'] for more information.",
AstropyUserWarning)
return fitparams, self.fit_info
class Simplex(Optimization):
"""
Neald-Mead (downhill simplex) algorithm.
This algorithm [1]_ only uses function values, not derivatives.
Uses `scipy.optimize.fmin`.
References
----------
.. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function
minimization", The Computer Journal, 7, pp. 308-313
"""
supported_constraints = ['bounds', 'fixed', 'tied']
def __init__(self):
from scipy.optimize import fmin as simplex
super().__init__(simplex)
self.fit_info = {
'final_func_val': None,
'numiter': None,
'exit_mode': None,
'num_function_calls': None
}
def __call__(self, objfunc, initval, fargs, **kwargs):
"""
Run the solver.
Parameters
----------
objfunc : callable
objection function
initval : iterable
initial guess for the parameter values
fargs : tuple
other arguments to be passed to the statistic function
kwargs : dict
other keyword arguments to be passed to the solver
"""
if 'maxiter' not in kwargs:
kwargs['maxiter'] = self._maxiter
if 'acc' in kwargs:
self._acc = kwargs['acc']
kwargs.pop('acc')
if 'xtol' in kwargs:
self._acc = kwargs['xtol']
kwargs.pop('xtol')
# Get the verbosity level
disp = kwargs.pop('verblevel', None)
fitparams, final_func_val, numiter, funcalls, exit_mode = self.opt_method(
objfunc, initval, args=fargs, xtol=self._acc, disp=disp,
full_output=True, **kwargs)
self.fit_info['final_func_val'] = final_func_val
self.fit_info['numiter'] = numiter
self.fit_info['exit_mode'] = exit_mode
self.fit_info['num_function_calls'] = funcalls
if self.fit_info['exit_mode'] == 1:
warnings.warn("The fit may be unsuccessful; "
"Maximum number of function evaluations reached.",
AstropyUserWarning)
if self.fit_info['exit_mode'] == 2:
warnings.warn("The fit may be unsuccessful; "
"Maximum number of iterations reached.",
AstropyUserWarning)
return fitparams, self.fit_info
|
715e8ec2a6f31b9cfeb3ae29a2ff518c5dd13dcab3cc2f51413609de430ee682 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Implements rotations, including spherical rotations as defined in WCS Paper II
[1]_
`RotateNative2Celestial` and `RotateCelestial2Native` follow the convention in
WCS Paper II to rotate to/from a native sphere and the celestial sphere.
The implementation uses `EulerAngleRotation`. The model parameters are
three angles: the longitude (``lon``) and latitude (``lat``) of the fiducial point
in the celestial system (``CRVAL`` keywords in FITS), and the longitude of the celestial
pole in the native system (``lon_pole``). The Euler angles are ``lon+90``, ``90-lat``
and ``-(lon_pole-90)``.
References
----------
.. [1] Calabretta, M.R., Greisen, E.W., 2002, A&A, 395, 1077 (Paper II)
"""
# pylint: disable=invalid-name, too-many-arguments, no-member
import math
import numpy as np
from astropy.coordinates.matrix_utilities import rotation_matrix, matrix_product
from astropy import units as u
from .core import Model
from .parameters import Parameter
from .utils import _to_radian, _to_orig_unit
__all__ = ['RotateCelestial2Native', 'RotateNative2Celestial', 'Rotation2D',
'EulerAngleRotation', 'RotationSequence3D', 'SphericalRotationSequence']
def _create_matrix(angles, axes_order):
matrices = []
for angle, axis in zip(angles, axes_order):
if isinstance(angle, u.Quantity):
angle = angle.value
angle = angle.item()
matrices.append(rotation_matrix(angle, axis, unit=u.rad))
result = matrix_product(*matrices[::-1])
return result
def spherical2cartesian(alpha, delta):
alpha = np.deg2rad(alpha)
delta = np.deg2rad(delta)
x = np.cos(alpha) * np.cos(delta)
y = np.cos(delta) * np.sin(alpha)
z = np.sin(delta)
return np.array([x, y, z])
def cartesian2spherical(x, y, z):
h = np.hypot(x, y)
alpha = np.rad2deg(np.arctan2(y, x))
delta = np.rad2deg(np.arctan2(z, h))
return alpha, delta
class RotationSequence3D(Model):
"""
Perform a series of rotations about different axis in 3D space.
Positive angles represent a counter-clockwise rotation.
Parameters
----------
angles : array-like
Angles of rotation in deg in the order of axes_order.
axes_order : str
A sequence of 'x', 'y', 'z' corresponding to axis of rotation.
Examples
--------
>>> model = RotationSequence3D([1.1, 2.1, 3.1, 4.1], axes_order='xyzx')
"""
standard_broadcasting = False
_separable = False
n_inputs = 3
n_outputs = 3
angles = Parameter(default=[], getter=_to_orig_unit, setter=_to_radian, description="Angles of rotation in deg in the order of axes_order")
def __init__(self, angles, axes_order, name=None):
self.axes = ['x', 'y', 'z']
unrecognized = set(axes_order).difference(self.axes)
if unrecognized:
raise ValueError("Unrecognized axis label {0}; "
"should be one of {1} ".format(unrecognized,
self.axes))
self.axes_order = axes_order
if len(angles) != len(axes_order):
raise ValueError("The number of angles {0} should match the number of axes {1}."
.format(len(angles), len(axes_order)))
super().__init__(angles, name=name)
self._inputs = ('x', 'y', 'z')
self._outputs = ('x', 'y', 'z')
@property
def inverse(self):
"""Inverse rotation."""
angles = self.angles.value[::-1] * -1
return self.__class__(angles, axes_order=self.axes_order[::-1])
def evaluate(self, x, y, z, angles):
"""
Apply the rotation to a set of 3D Cartesian coordinates.
"""
if x.shape != y.shape or x.shape != z.shape:
raise ValueError("Expected input arrays to have the same shape")
# Note: If the original shape was () (an array scalar) convert to a
# 1-element 1-D array on output for consistency with most other models
orig_shape = x.shape or (1,)
inarr = np.array([x.flatten(), y.flatten(), z.flatten()])
result = np.dot(_create_matrix(angles[0], self.axes_order), inarr)
x, y, z = result[0], result[1], result[2]
x.shape = y.shape = z.shape = orig_shape
return x, y, z
class SphericalRotationSequence(RotationSequence3D):
"""
Perform a sequence of rotations about arbitrary number of axes
in spherical coordinates.
Parameters
----------
angles : list
A sequence of angles (in deg).
axes_order : str
A sequence of characters ('x', 'y', or 'z') corresponding to the
axis of rotation and matching the order in ``angles``.
"""
def __init__(self, angles, axes_order, name=None, **kwargs):
self._n_inputs = 2
self._n_outputs = 2
super().__init__(angles, axes_order=axes_order, name=name, **kwargs)
self._inputs = ("lon", "lat")
self._outputs = ("lon", "lat")
@property
def n_inputs(self):
return self._n_inputs
@property
def n_outputs(self):
return self._n_outputs
def evaluate(self, lon, lat, angles):
x, y, z = spherical2cartesian(lon, lat)
x1, y1, z1 = super().evaluate(x, y, z, angles)
lon, lat = cartesian2spherical(x1, y1, z1)
return lon, lat
class _EulerRotation:
"""
Base class which does the actual computation.
"""
_separable = False
def evaluate(self, alpha, delta, phi, theta, psi, axes_order):
shape = None
if isinstance(alpha, np.ndarray):
alpha = alpha.flatten()
delta = delta.flatten()
shape = alpha.shape
inp = spherical2cartesian(alpha, delta)
matrix = _create_matrix([phi, theta, psi], axes_order)
result = np.dot(matrix, inp)
a, b = cartesian2spherical(*result)
if shape is not None:
a.shape = shape
b.shape = shape
return a, b
_input_units_strict = True
_input_units_allow_dimensionless = True
@property
def input_units(self):
""" Input units. """
return {self.inputs[0]: u.deg,
self.inputs[1]: u.deg}
@property
def return_units(self):
""" Output units. """
return {self.outputs[0]: u.deg,
self.outputs[1]: u.deg}
class EulerAngleRotation(_EulerRotation, Model):
"""
Implements Euler angle intrinsic rotations.
Rotates one coordinate system into another (fixed) coordinate system.
All coordinate systems are right-handed. The sign of the angles is
determined by the right-hand rule..
Parameters
----------
phi, theta, psi : float or `~astropy.units.Quantity` ['angle']
"proper" Euler angles in deg.
If floats, they should be in deg.
axes_order : str
A 3 character string, a combination of 'x', 'y' and 'z',
where each character denotes an axis in 3D space.
"""
n_inputs = 2
n_outputs = 2
phi = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian,
description="1st Euler angle (Quantity or value in deg)")
theta = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian,
description="2nd Euler angle (Quantity or value in deg)")
psi = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian,
description="3rd Euler angle (Quantity or value in deg)")
def __init__(self, phi, theta, psi, axes_order, **kwargs):
self.axes = ['x', 'y', 'z']
if len(axes_order) != 3:
raise TypeError(
"Expected axes_order to be a character sequence of length 3, "
"got {}".format(axes_order))
unrecognized = set(axes_order).difference(self.axes)
if unrecognized:
raise ValueError("Unrecognized axis label {}; "
"should be one of {} ".format(unrecognized, self.axes))
self.axes_order = axes_order
qs = [isinstance(par, u.Quantity) for par in [phi, theta, psi]]
if any(qs) and not all(qs):
raise TypeError("All parameters should be of the same type - float or Quantity.")
super().__init__(phi=phi, theta=theta, psi=psi, **kwargs)
self._inputs = ('alpha', 'delta')
self._outputs = ('alpha', 'delta')
@property
def inverse(self):
return self.__class__(phi=-self.psi,
theta=-self.theta,
psi=-self.phi,
axes_order=self.axes_order[::-1])
def evaluate(self, alpha, delta, phi, theta, psi):
a, b = super().evaluate(alpha, delta, phi, theta, psi, self.axes_order)
return a, b
class _SkyRotation(_EulerRotation, Model):
"""
Base class for RotateNative2Celestial and RotateCelestial2Native.
"""
lon = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian, description="Latitude")
lat = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian, description="Longtitude")
lon_pole = Parameter(default=0, getter=_to_orig_unit, setter=_to_radian, description="Longitude of a pole")
def __init__(self, lon, lat, lon_pole, **kwargs):
qs = [isinstance(par, u.Quantity) for par in [lon, lat, lon_pole]]
if any(qs) and not all(qs):
raise TypeError("All parameters should be of the same type - float or Quantity.")
super().__init__(lon, lat, lon_pole, **kwargs)
self.axes_order = 'zxz'
def _evaluate(self, phi, theta, lon, lat, lon_pole):
alpha, delta = super().evaluate(phi, theta, lon, lat, lon_pole,
self.axes_order)
mask = alpha < 0
if isinstance(mask, np.ndarray):
alpha[mask] += 360
else:
alpha += 360
return alpha, delta
class RotateNative2Celestial(_SkyRotation):
"""
Transform from Native to Celestial Spherical Coordinates.
Parameters
----------
lon : float or `~astropy.units.Quantity` ['angle']
Celestial longitude of the fiducial point.
lat : float or `~astropy.units.Quantity` ['angle']
Celestial latitude of the fiducial point.
lon_pole : float or `~astropy.units.Quantity` ['angle']
Longitude of the celestial pole in the native system.
Notes
-----
If ``lon``, ``lat`` and ``lon_pole`` are numerical values they
should be in units of deg. Inputs are angles on the native sphere.
Outputs are angles on the celestial sphere.
"""
n_inputs = 2
n_outputs = 2
@property
def input_units(self):
""" Input units. """
return {self.inputs[0]: u.deg,
self.inputs[1]: u.deg}
@property
def return_units(self):
""" Output units. """
return {self.outputs[0]: u.deg, self.outputs[1]: u.deg}
def __init__(self, lon, lat, lon_pole, **kwargs):
super().__init__(lon, lat, lon_pole, **kwargs)
self.inputs = ('phi_N', 'theta_N')
self.outputs = ('alpha_C', 'delta_C')
def evaluate(self, phi_N, theta_N, lon, lat, lon_pole):
"""
Parameters
----------
phi_N, theta_N : float or `~astropy.units.Quantity` ['angle']
Angles in the Native coordinate system.
it is assumed that numerical only inputs are in degrees.
If float, assumed in degrees.
lon, lat, lon_pole : float or `~astropy.units.Quantity` ['angle']
Parameter values when the model was initialized.
If float, assumed in degrees.
Returns
-------
alpha_C, delta_C : float or `~astropy.units.Quantity` ['angle']
Angles on the Celestial sphere.
If float, in degrees.
"""
# The values are in radians since they have already been through the setter.
if isinstance(lon, u.Quantity):
lon = lon.value
lat = lat.value
lon_pole = lon_pole.value
# Convert to Euler angles
phi = lon_pole - np.pi / 2
theta = - (np.pi / 2 - lat)
psi = -(np.pi / 2 + lon)
alpha_C, delta_C = super()._evaluate(phi_N, theta_N, phi, theta, psi)
return alpha_C, delta_C
@property
def inverse(self):
# convert to angles on the celestial sphere
return RotateCelestial2Native(self.lon, self.lat, self.lon_pole)
class RotateCelestial2Native(_SkyRotation):
"""
Transform from Celestial to Native Spherical Coordinates.
Parameters
----------
lon : float or `~astropy.units.Quantity` ['angle']
Celestial longitude of the fiducial point.
lat : float or `~astropy.units.Quantity` ['angle']
Celestial latitude of the fiducial point.
lon_pole : float or `~astropy.units.Quantity` ['angle']
Longitude of the celestial pole in the native system.
Notes
-----
If ``lon``, ``lat`` and ``lon_pole`` are numerical values they should be
in units of deg. Inputs are angles on the celestial sphere.
Outputs are angles on the native sphere.
"""
n_inputs = 2
n_outputs = 2
@property
def input_units(self):
""" Input units. """
return {self.inputs[0]: u.deg,
self.inputs[1]: u.deg}
@property
def return_units(self):
""" Output units. """
return {self.outputs[0]: u.deg,
self.outputs[1]: u.deg}
def __init__(self, lon, lat, lon_pole, **kwargs):
super().__init__(lon, lat, lon_pole, **kwargs)
# Inputs are angles on the celestial sphere
self.inputs = ('alpha_C', 'delta_C')
# Outputs are angles on the native sphere
self.outputs = ('phi_N', 'theta_N')
def evaluate(self, alpha_C, delta_C, lon, lat, lon_pole):
"""
Parameters
----------
alpha_C, delta_C : float or `~astropy.units.Quantity` ['angle']
Angles in the Celestial coordinate frame.
If float, assumed in degrees.
lon, lat, lon_pole : float or `~astropy.units.Quantity` ['angle']
Parameter values when the model was initialized.
If float, assumed in degrees.
Returns
-------
phi_N, theta_N : float or `~astropy.units.Quantity` ['angle']
Angles on the Native sphere.
If float, in degrees.
"""
if isinstance(lon, u.Quantity):
lon = lon.value
lat = lat.value
lon_pole = lon_pole.value
# Convert to Euler angles
phi = (np.pi / 2 + lon)
theta = (np.pi / 2 - lat)
psi = -(lon_pole - np.pi / 2)
phi_N, theta_N = super()._evaluate(alpha_C, delta_C, phi, theta, psi)
return phi_N, theta_N
@property
def inverse(self):
return RotateNative2Celestial(self.lon, self.lat, self.lon_pole)
class Rotation2D(Model):
"""
Perform a 2D rotation given an angle.
Positive angles represent a counter-clockwise rotation and vice-versa.
Parameters
----------
angle : float or `~astropy.units.Quantity` ['angle']
Angle of rotation (if float it should be in deg).
"""
n_inputs = 2
n_outputs = 2
_separable = False
angle = Parameter(default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="Angle of rotation (Quantity or value in deg)")
def __init__(self, angle=angle, **kwargs):
super().__init__(angle=angle, **kwargs)
self._inputs = ("x", "y")
self._outputs = ("x", "y")
@property
def inverse(self):
"""Inverse rotation."""
return self.__class__(angle=-self.angle)
@classmethod
def evaluate(cls, x, y, angle):
"""
Rotate (x, y) about ``angle``.
Parameters
----------
x, y : array-like
Input quantities
angle : float or `~astropy.units.Quantity` ['angle']
Angle of rotations.
If float, assumed in degrees.
"""
if x.shape != y.shape:
raise ValueError("Expected input arrays to have the same shape")
# If one argument has units, enforce they both have units and they are compatible.
x_unit = getattr(x, 'unit', None)
y_unit = getattr(y, 'unit', None)
has_units = x_unit is not None and y_unit is not None
if x_unit != y_unit:
if has_units and y_unit.is_equivalent(x_unit):
y = y.to(x_unit)
y_unit = x_unit
else:
raise u.UnitsError("x and y must have compatible units")
# Note: If the original shape was () (an array scalar) convert to a
# 1-element 1-D array on output for consistency with most other models
orig_shape = x.shape or (1,)
inarr = np.array([x.flatten(), y.flatten()])
if isinstance(angle, u.Quantity):
angle = angle.to_value(u.rad)
result = np.dot(cls._compute_matrix(angle), inarr)
x, y = result[0], result[1]
x.shape = y.shape = orig_shape
if has_units:
return u.Quantity(x, unit=x_unit), u.Quantity(y, unit=y_unit)
return x, y
@staticmethod
def _compute_matrix(angle):
return np.array([[math.cos(angle), -math.sin(angle)],
[math.sin(angle), math.cos(angle)]],
dtype=np.float64)
|
86dac592dc34e05fce53e0c958c7c1249c4d4204d65993cb4a9f8ad716cb3bfc | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import os
from os.path import join
from collections import defaultdict
from setuptools import Extension
from extension_helpers import import_file
# This defines the set of projection functions that we want to wrap.
# The key is the projection name, and the value is the number of
# parameters.
# (These are in the order that the appear in the WCS coordinate
# systems paper).
projections = {
'azp': 2,
'szp': 3,
'tan': 0,
'stg': 0,
'sin': 2,
'arc': 0,
'zea': 0,
'air': 1,
'cyp': 2,
'cea': 1,
'mer': 0,
'sfl': 0,
'par': 0,
'mol': 0,
'ait': 0,
'cop': 2,
'coe': 2,
'cod': 2,
'coo': 2,
'bon': 1,
'pco': 0,
'tsc': 0,
'csc': 0,
'qsc': 0,
'hpx': 2,
'xph': 0,
}
|
3c8495e56cdfd0aafb1dad58213fac5e4bcccf1db1006846263b7767842c267a | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Define Numpy Ufuncs as Models.
"""
import numpy as np
from astropy.modeling.core import Model
from astropy.utils.exceptions import AstropyUserWarning
trig_ufuncs = ["sin", "cos", "tan", "arcsin", "arccos", "arctan", "arctan2",
"hypot", "sinh", "cosh", "tanh", "arcsinh", "arccosh",
"arctanh", "deg2rad", "rad2deg"]
math_ops = ["add", "subtract", "multiply", "logaddexp", "logaddexp2",
"true_divide", "floor_divide", "negative", "positive", "power",
"remainder", "fmod", "divmod", "absolute", "fabs", "rint",
"exp", "exp2", "log", "log2", "log10", "expm1", "log1p", "sqrt",
"square", "cbrt", "reciprocal", "divide", "mod"]
supported_ufuncs = trig_ufuncs + math_ops
# These names are just aliases for other ufunc objects
# in the numpy API. The alias name must occur later
# in the lists above.
alias_ufuncs = {
"divide": "true_divide",
"mod": "remainder",
}
class _NPUfuncModel(Model):
_is_dynamic = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
def _make_class_name(name):
""" Make a ufunc model class name from the name of the ufunc. """
return name[0].upper() + name[1:] + 'Ufunc'
def ufunc_model(name):
""" Define a Model from a Numpy ufunc name."""
ufunc = getattr(np, name)
nin = ufunc.nin
nout = ufunc.nout
if nin == 1:
separable = True
def evaluate(self, x):
return self.func(x)
else:
separable = False
def evaluate(self, x, y):
return self.func(x, y)
klass_name = _make_class_name(name)
members = {'n_inputs': nin, 'n_outputs': nout, 'func': ufunc,
'linear': False, 'fittable': False, '_separable': separable,
'_is_dynamic': True, 'evaluate': evaluate}
klass = type(str(klass_name), (_NPUfuncModel,), members)
klass.__module__ = 'astropy.modeling.math_functions'
return klass
__all__ = []
for name in supported_ufuncs:
if name in alias_ufuncs:
klass_name = _make_class_name(name)
alias_klass_name = _make_class_name(alias_ufuncs[name])
globals()[klass_name] = globals()[alias_klass_name]
__all__.append(klass_name)
else:
m = ufunc_model(name)
klass_name = m.__name__
globals()[klass_name] = m
__all__.append(klass_name)
|
cac38fe9744203c720d50d6c88dcdcb0d93a7b2326fe89911e83640ad5676b0f | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Functions to determine if a model is separable, i.e.
if the model outputs are independent.
It analyzes ``n_inputs``, ``n_outputs`` and the operators
in a compound model by stepping through the transforms
and creating a ``coord_matrix`` of shape (``n_outputs``, ``n_inputs``).
Each modeling operator is represented by a function which
takes two simple models (or two ``coord_matrix`` arrays) and
returns an array of shape (``n_outputs``, ``n_inputs``).
"""
import numpy as np
from .core import Model, ModelDefinitionError, CompoundModel
from .mappings import Mapping
__all__ = ["is_separable", "separability_matrix"]
def is_separable(transform):
"""
A separability test for the outputs of a transform.
Parameters
----------
transform : `~astropy.modeling.core.Model`
A (compound) model.
Returns
-------
is_separable : ndarray
A boolean array with size ``transform.n_outputs`` where
each element indicates whether the output is independent
and the result of a separable transform.
Examples
--------
>>> from astropy.modeling.models import Shift, Scale, Rotation2D, Polynomial2D
>>> is_separable(Shift(1) & Shift(2) | Scale(1) & Scale(2))
array([ True, True]...)
>>> is_separable(Shift(1) & Shift(2) | Rotation2D(2))
array([False, False]...)
>>> is_separable(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]) | \
Polynomial2D(1) & Polynomial2D(2))
array([False, False]...)
>>> is_separable(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]))
array([ True, True, True, True]...)
"""
if transform.n_inputs == 1 and transform.n_outputs > 1:
is_separable = np.array([False] * transform.n_outputs).T
return is_separable
separable_matrix = _separable(transform)
is_separable = separable_matrix.sum(1)
is_separable = np.where(is_separable != 1, False, True)
return is_separable
def separability_matrix(transform):
"""
Compute the correlation between outputs and inputs.
Parameters
----------
transform : `~astropy.modeling.core.Model`
A (compound) model.
Returns
-------
separable_matrix : ndarray
A boolean correlation matrix of shape (n_outputs, n_inputs).
Indicates the dependence of outputs on inputs. For completely
independent outputs, the diagonal elements are True and
off-diagonal elements are False.
Examples
--------
>>> from astropy.modeling.models import Shift, Scale, Rotation2D, Polynomial2D
>>> separability_matrix(Shift(1) & Shift(2) | Scale(1) & Scale(2))
array([[ True, False], [False, True]]...)
>>> separability_matrix(Shift(1) & Shift(2) | Rotation2D(2))
array([[ True, True], [ True, True]]...)
>>> separability_matrix(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]) | \
Polynomial2D(1) & Polynomial2D(2))
array([[ True, True], [ True, True]]...)
>>> separability_matrix(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]))
array([[ True, False], [False, True], [ True, False], [False, True]]...)
"""
if transform.n_inputs == 1 and transform.n_outputs > 1:
return np.ones((transform.n_outputs, transform.n_inputs),
dtype=np.bool_)
separable_matrix = _separable(transform)
separable_matrix = np.where(separable_matrix != 0, True, False)
return separable_matrix
def _compute_n_outputs(left, right):
"""
Compute the number of outputs of two models.
The two models are the left and right model to an operation in
the expression tree of a compound model.
Parameters
----------
left, right : `astropy.modeling.Model` or ndarray
If input is of an array, it is the output of `coord_matrix`.
"""
if isinstance(left, Model):
lnout = left.n_outputs
else:
lnout = left.shape[0]
if isinstance(right, Model):
rnout = right.n_outputs
else:
rnout = right.shape[0]
noutp = lnout + rnout
return noutp
def _arith_oper(left, right):
"""
Function corresponding to one of the arithmetic operators
['+', '-'. '*', '/', '**'].
This always returns a nonseparable output.
Parameters
----------
left, right : `astropy.modeling.Model` or ndarray
If input is of an array, it is the output of `coord_matrix`.
Returns
-------
result : ndarray
Result from this operation.
"""
# models have the same number of inputs and outputs
def _n_inputs_outputs(input):
if isinstance(input, Model):
n_outputs, n_inputs = input.n_outputs, input.n_inputs
else:
n_outputs, n_inputs = input.shape
return n_inputs, n_outputs
left_inputs, left_outputs = _n_inputs_outputs(left)
right_inputs, right_outputs = _n_inputs_outputs(right)
if left_inputs != right_inputs or left_outputs != right_outputs:
raise ModelDefinitionError(
"Unsupported operands for arithmetic operator: left (n_inputs={}, "
"n_outputs={}) and right (n_inputs={}, n_outputs={}); "
"models must have the same n_inputs and the same "
"n_outputs for this operator.".format(
left_inputs, left_outputs, right_inputs, right_outputs))
result = np.ones((left_outputs, left_inputs))
return result
def _coord_matrix(model, pos, noutp):
"""
Create an array representing inputs and outputs of a simple model.
The array has a shape (noutp, model.n_inputs).
Parameters
----------
model : `astropy.modeling.Model`
model
pos : str
Position of this model in the expression tree.
One of ['left', 'right'].
noutp : int
Number of outputs of the compound model of which the input model
is a left or right child.
"""
if isinstance(model, Mapping):
axes = []
for i in model.mapping:
axis = np.zeros((model.n_inputs,))
axis[i] = 1
axes.append(axis)
m = np.vstack(axes)
mat = np.zeros((noutp, model.n_inputs))
if pos == 'left':
mat[: model.n_outputs, :model.n_inputs] = m
else:
mat[-model.n_outputs:, -model.n_inputs:] = m
return mat
if not model.separable:
# this does not work for more than 2 coordinates
mat = np.zeros((noutp, model.n_inputs))
if pos == 'left':
mat[:model.n_outputs, : model.n_inputs] = 1
else:
mat[-model.n_outputs:, -model.n_inputs:] = 1
else:
mat = np.zeros((noutp, model.n_inputs))
for i in range(model.n_inputs):
mat[i, i] = 1
if pos == 'right':
mat = np.roll(mat, (noutp - model.n_outputs))
return mat
def _cstack(left, right):
"""
Function corresponding to '&' operation.
Parameters
----------
left, right : `astropy.modeling.Model` or ndarray
If input is of an array, it is the output of `coord_matrix`.
Returns
-------
result : ndarray
Result from this operation.
"""
noutp = _compute_n_outputs(left, right)
if isinstance(left, Model):
cleft = _coord_matrix(left, 'left', noutp)
else:
cleft = np.zeros((noutp, left.shape[1]))
cleft[: left.shape[0], : left.shape[1]] = left
if isinstance(right, Model):
cright = _coord_matrix(right, 'right', noutp)
else:
cright = np.zeros((noutp, right.shape[1]))
cright[-right.shape[0]:, -right.shape[1]:] = 1
return np.hstack([cleft, cright])
def _cdot(left, right):
"""
Function corresponding to "|" operation.
Parameters
----------
left, right : `astropy.modeling.Model` or ndarray
If input is of an array, it is the output of `coord_matrix`.
Returns
-------
result : ndarray
Result from this operation.
"""
left, right = right, left
def _n_inputs_outputs(input, position):
"""
Return ``n_inputs``, ``n_outputs`` for a model or coord_matrix.
"""
if isinstance(input, Model):
coords = _coord_matrix(input, position, input.n_outputs)
else:
coords = input
return coords
cleft = _n_inputs_outputs(left, 'left')
cright = _n_inputs_outputs(right, 'right')
try:
result = np.dot(cleft, cright)
except ValueError:
raise ModelDefinitionError(
'Models cannot be combined with the "|" operator; '
'left coord_matrix is {}, right coord_matrix is {}'.format(
cright, cleft))
return result
def _separable(transform):
"""
Calculate the separability of outputs.
Parameters
----------
transform : `astropy.modeling.Model`
A transform (usually a compound model).
Returns :
is_separable : ndarray of dtype np.bool
An array of shape (transform.n_outputs,) of boolean type
Each element represents the separablity of the corresponding output.
"""
if (transform_matrix := transform._calculate_separability_matrix()) is not NotImplemented:
return transform_matrix
elif isinstance(transform, CompoundModel):
sepleft = _separable(transform.left)
sepright = _separable(transform.right)
return _operators[transform.op](sepleft, sepright)
elif isinstance(transform, Model):
return _coord_matrix(transform, 'left', transform.n_outputs)
# Maps modeling operators to a function computing and represents the
# relationship of axes as an array of 0-es and 1-s
_operators = {'&': _cstack, '|': _cdot, '+': _arith_oper, '-': _arith_oper,
'*': _arith_oper, '/': _arith_oper, '**': _arith_oper}
|
0077f3fde0992659533832b99495fcec65076faaec6d720662421d4f259031b4 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module provides utility functions for the models package.
"""
# pylint: disable=invalid-name
from collections import UserDict
from collections.abc import MutableMapping
from inspect import signature
import numpy as np
import warnings
from astropy import units as u
from astropy.utils.decorators import deprecated
__doctest_skip__ = ['AliasDict']
__all__ = ['AliasDict', 'poly_map_domain', 'comb', 'ellipse_extent']
deprecation_msg = """
AliasDict is deprecated because it no longer serves a function anywhere
inside astropy.
"""
@deprecated('5.0', deprecation_msg)
class AliasDict(MutableMapping):
"""
Creates a `dict` like object that wraps an existing `dict` or other
`MutableMapping`, along with a `dict` of *key aliases* that translate
between specific keys in this dict to different keys in the underlying
dict.
In other words, keys that do not have an associated alias are accessed and
stored like a normal `dict`. However, a key that has an alias is accessed
and stored to the "parent" dict via the alias.
Parameters
----------
parent : dict-like
The parent `dict` that aliased keys and accessed from and stored to.
aliases : dict-like
Maps keys in this dict to their associated keys in the parent dict.
Examples
--------
>>> parent = {'a': 1, 'b': 2, 'c': 3}
>>> aliases = {'foo': 'a', 'bar': 'c'}
>>> alias_dict = AliasDict(parent, aliases)
>>> alias_dict['foo']
1
>>> alias_dict['bar']
3
Keys in the original parent dict are not visible if they were not
aliased:
>>> alias_dict['b']
Traceback (most recent call last):
...
KeyError: 'b'
Likewise, updates to aliased keys are reflected back in the parent dict:
>>> alias_dict['foo'] = 42
>>> alias_dict['foo']
42
>>> parent['a']
42
However, updates/insertions to keys that are *not* aliased are not
reflected in the parent dict:
>>> alias_dict['qux'] = 99
>>> alias_dict['qux']
99
>>> 'qux' in parent
False
In particular, updates on the `AliasDict` to a key that is equal to
one of the aliased keys in the parent dict does *not* update the parent
dict. For example, ``alias_dict`` aliases ``'foo'`` to ``'a'``. But
assigning to a key ``'a'`` on the `AliasDict` does not impact the
parent:
>>> alias_dict['a'] = 'nope'
>>> alias_dict['a']
'nope'
>>> parent['a']
42
"""
_store_type = dict
"""
Subclasses may override this to use other mapping types as the underlying
storage, for example an `OrderedDict`. However, even in this case
additional work may be needed to get things like the ordering right.
"""
def __init__(self, parent, aliases):
self._parent = parent
self._store = self._store_type()
self._aliases = dict(aliases)
def __getitem__(self, key):
if key in self._aliases:
try:
return self._parent[self._aliases[key]]
except KeyError:
raise KeyError(key)
return self._store[key]
def __setitem__(self, key, value):
if key in self._aliases:
self._parent[self._aliases[key]] = value
else:
self._store[key] = value
def __delitem__(self, key):
if key in self._aliases:
try:
del self._parent[self._aliases[key]]
except KeyError:
raise KeyError(key)
else:
del self._store[key]
def __iter__(self):
"""
First iterates over keys from the parent dict (if the aliased keys are
present in the parent), followed by any keys in the local store.
"""
for key, alias in self._aliases.items():
if alias in self._parent:
yield key
for key in self._store:
yield key
def __len__(self):
return len(list(iter(self)))
def __repr__(self):
# repr() just like any other dict--this should look transparent
store_copy = self._store_type()
for key, alias in self._aliases.items():
if alias in self._parent:
store_copy[key] = self._parent[alias]
store_copy.update(self._store)
return repr(store_copy)
def make_binary_operator_eval(oper, f, g):
"""
Given a binary operator (as a callable of two arguments) ``oper`` and
two callables ``f`` and ``g`` which accept the same arguments,
returns a *new* function that takes the same arguments as ``f`` and ``g``,
but passes the outputs of ``f`` and ``g`` in the given ``oper``.
``f`` and ``g`` are assumed to return tuples (which may be 1-tuples). The
given operator is applied element-wise to tuple outputs).
Example
-------
>>> from operator import add
>>> def prod(x, y):
... return (x * y,)
...
>>> sum_of_prod = make_binary_operator_eval(add, prod, prod)
>>> sum_of_prod(3, 5)
(30,)
"""
return lambda inputs, params: \
tuple(oper(x, y) for x, y in zip(f(inputs, params),
g(inputs, params)))
def poly_map_domain(oldx, domain, window):
"""
Map domain into window by shifting and scaling.
Parameters
----------
oldx : array
original coordinates
domain : list or tuple of length 2
function domain
window : list or tuple of length 2
range into which to map the domain
"""
domain = np.array(domain, dtype=np.float64)
window = np.array(window, dtype=np.float64)
if domain.shape != (2,) or window.shape != (2,):
raise ValueError('Expected "domain" and "window" to be a tuple of size 2.')
scl = (window[1] - window[0]) / (domain[1] - domain[0])
off = (window[0] * domain[1] - window[1] * domain[0]) / (domain[1] - domain[0])
return off + scl * oldx
def _validate_domain_window(value):
if value is not None:
if np.asanyarray(value).shape != (2, ):
raise ValueError('domain and window should be tuples of size 2.')
return tuple(value)
return value
def comb(N, k):
"""
The number of combinations of N things taken k at a time.
Parameters
----------
N : int, array
Number of things.
k : int, array
Number of elements taken.
"""
if (k > N) or (N < 0) or (k < 0):
return 0
val = 1
for j in range(min(k, N - k)):
val = (val * (N - j)) / (j + 1)
return val
def array_repr_oneline(array):
"""
Represents a multi-dimensional Numpy array flattened onto a single line.
"""
r = np.array2string(array, separator=', ', suppress_small=True)
return ' '.join(l.strip() for l in r.splitlines())
def combine_labels(left, right):
"""
For use with the join operator &: Combine left input/output labels with
right input/output labels.
If none of the labels conflict then this just returns a sum of tuples.
However if *any* of the labels conflict, this appends '0' to the left-hand
labels and '1' to the right-hand labels so there is no ambiguity).
"""
if set(left).intersection(right):
left = tuple(l + '0' for l in left)
right = tuple(r + '1' for r in right)
return left + right
def ellipse_extent(a, b, theta):
"""
Calculates the extent of a box encapsulating a rotated 2D ellipse.
Parameters
----------
a : float or `~astropy.units.Quantity`
Major axis.
b : float or `~astropy.units.Quantity`
Minor axis.
theta : float or `~astropy.units.Quantity` ['angle']
Rotation angle. If given as a floating-point value, it is assumed to be
in radians.
Returns
-------
offsets : tuple
The absolute value of the offset distances from the ellipse center that
define its bounding box region, ``(dx, dy)``.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Ellipse2D
from astropy.modeling.utils import ellipse_extent, render_model
amplitude = 1
x0 = 50
y0 = 50
a = 30
b = 10
theta = np.pi/4
model = Ellipse2D(amplitude, x0, y0, a, b, theta)
dx, dy = ellipse_extent(a, b, theta)
limits = [x0 - dx, x0 + dx, y0 - dy, y0 + dy]
model.bounding_box = limits
image = render_model(model)
plt.imshow(image, cmap='binary', interpolation='nearest', alpha=.5,
extent = limits)
plt.show()
"""
t = np.arctan2(-b * np.tan(theta), a)
dx = a * np.cos(t) * np.cos(theta) - b * np.sin(t) * np.sin(theta)
t = np.arctan2(b, a * np.tan(theta))
dy = b * np.sin(t) * np.cos(theta) + a * np.cos(t) * np.sin(theta)
if isinstance(dx, u.Quantity) or isinstance(dy, u.Quantity):
return np.abs(u.Quantity([dx, dy]))
return np.abs([dx, dy])
def get_inputs_and_params(func):
"""
Given a callable, determine the input variables and the
parameters.
Parameters
----------
func : callable
Returns
-------
inputs, params : tuple
Each entry is a list of inspect.Parameter objects
"""
sig = signature(func)
inputs = []
params = []
for param in sig.parameters.values():
if param.kind in (param.VAR_POSITIONAL, param.VAR_KEYWORD):
raise ValueError("Signature must not have *args or **kwargs")
if param.default == param.empty:
inputs.append(param)
else:
params.append(param)
return inputs, params
def _combine_equivalency_dict(keys, eq1=None, eq2=None):
# Given two dictionaries that give equivalencies for a set of keys, for
# example input value names, return a dictionary that includes all the
# equivalencies
eq = {}
for key in keys:
eq[key] = []
if eq1 is not None and key in eq1:
eq[key].extend(eq1[key])
if eq2 is not None and key in eq2:
eq[key].extend(eq2[key])
return eq
def _to_radian(value):
""" Convert ``value`` to radian. """
if isinstance(value, u.Quantity):
return value.to(u.rad)
return np.deg2rad(value)
def _to_orig_unit(value, raw_unit=None, orig_unit=None):
""" Convert value with ``raw_unit`` to ``orig_unit``. """
if raw_unit is not None:
return (value * raw_unit).to(orig_unit)
return np.rad2deg(value)
class _ConstraintsDict(UserDict):
"""
Wrapper around UserDict to allow updating the constraints
on a Parameter when the dictionary is updated.
"""
def __init__(self, model, constraint_type):
self._model = model
self.constraint_type = constraint_type
c = {}
for name in model.param_names:
param = getattr(model, name)
c[name] = getattr(param, constraint_type)
super().__init__(c)
def __setitem__(self, key, val):
super().__setitem__(key, val)
param = getattr(self._model, key)
setattr(param, self.constraint_type, val)
class _SpecialOperatorsDict(UserDict):
"""
Wrapper around UserDict to allow for better tracking of the Special
Operators for CompoundModels. This dictionary is structured so that
one cannot inadvertently overwrite an existing special operator.
Parameters
----------
unique_id: int
the last used unique_id for a SPECIAL OPERATOR
special_operators: dict
a dictionary containing the special_operators
Notes
-----
Direct setting of operators (`dict[key] = value`) into the
dictionary has been deprecated in favor of the `.add(name, value)`
method, so that unique dictionary keys can be generated and tracked
consistently.
"""
def __init__(self, unique_id=0, special_operators={}):
super().__init__(special_operators)
self._unique_id = unique_id
def _set_value(self, key, val):
if key in self:
raise ValueError(f'Special operator "{key}" already exists')
else:
super().__setitem__(key, val)
def __setitem__(self, key, val):
self._set_value(key, val)
warnings.warn(DeprecationWarning(
"""
Special operator dictionary assignment has been deprecated.
Please use `.add` instead, so that you can capture a unique
key for your operator.
"""
))
def _get_unique_id(self):
self._unique_id += 1
return self._unique_id
def add(self, operator_name, operator):
"""
Adds a special operator to the dictionary, and then returns the
unique key that the operator is stored under for later reference.
Parameters
----------
operator_name: str
the name for the operator
operator: function
the actual operator function which will be used
Returns
-------
the unique operator key for the dictionary
`(operator_name, unique_id)`
"""
key = (operator_name, self._get_unique_id())
self._set_value(key, operator)
return key
|
7a1ea1d875c4c64951d1e16e7c47167f8c6f3c5c253c83e249bd95b389dcd9d3 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module is to contain an improved bounding box
"""
import abc
import copy
from collections import namedtuple
from typing import Dict, List, Tuple, Callable, Any
from astropy.utils import isiterable
from astropy.units import Quantity
import warnings
import numpy as np
__all__ = ['ModelBoundingBox', 'CompoundBoundingBox']
_BaseInterval = namedtuple('_BaseInterval', "lower upper")
class _Interval(_BaseInterval):
"""
A single input's bounding box interval.
Parameters
----------
lower : float
The lower bound of the interval
upper : float
The upper bound of the interval
Methods
-------
validate :
Contructs a valid interval
outside :
Determine which parts of an input array are outside the interval.
domain :
Contructs a discretization of the points inside the interval.
"""
def __repr__(self):
return f"Interval(lower={self.lower}, upper={self.upper})"
def copy(self):
return copy.deepcopy(self)
@staticmethod
def _validate_shape(interval):
"""Validate the shape of an interval representation"""
MESSAGE = """An interval must be some sort of sequence of length 2"""
try:
shape = np.shape(interval)
except TypeError:
try:
# np.shape does not work with lists of Quantities
if len(interval) == 1:
interval = interval[0]
shape = np.shape([b.to_value() for b in interval])
except (ValueError, TypeError, AttributeError):
raise ValueError(MESSAGE)
valid_shape = shape in ((2,), (1, 2), (2, 0))
if not valid_shape:
valid_shape = (len(shape) > 0) and (shape[0] == 2) and \
all(isinstance(b, np.ndarray) for b in interval)
if not isiterable(interval) or not valid_shape:
raise ValueError(MESSAGE)
@classmethod
def _validate_bounds(cls, lower, upper):
"""Validate the bounds are reasonable and construct an interval from them."""
if (np.asanyarray(lower) > np.asanyarray(upper)).all():
warnings.warn(f"Invalid interval: upper bound {upper} "
f"is strictly less than lower bound {lower}.", RuntimeWarning)
return cls(lower, upper)
@classmethod
def validate(cls, interval):
"""
Construct and validate an interval
Parameters
----------
interval : iterable
A representation of the interval.
Returns
-------
A validated interval.
"""
cls._validate_shape(interval)
if len(interval) == 1:
interval = tuple(interval[0])
else:
interval = tuple(interval)
return cls._validate_bounds(interval[0], interval[1])
def outside(self, _input: np.ndarray):
"""
Parameters
----------
_input : np.ndarray
The evaluation input in the form of an array.
Returns
-------
Boolean array indicating which parts of _input are outside the interval:
True -> position outside interval
False -> position inside interval
"""
return np.logical_or(_input < self.lower, _input > self.upper)
def domain(self, resolution):
return np.arange(self.lower, self.upper + resolution, resolution)
# The interval where all ignored inputs can be found.
_ignored_interval = _Interval.validate((-np.inf, np.inf))
def get_index(model, key) -> int:
"""
Get the input index corresponding to the given key.
Can pass in either:
the string name of the input or
the input index itself.
"""
if isinstance(key, str):
if key in model.inputs:
index = model.inputs.index(key)
else:
raise ValueError(f"'{key}' is not one of the inputs: {model.inputs}.")
elif np.issubdtype(type(key), np.integer):
if 0 <= key < len(model.inputs):
index = key
else:
raise IndexError(f"Integer key: {key} must be non-negative and < {len(model.inputs)}.")
else:
raise ValueError(f"Key value: {key} must be string or integer.")
return index
def get_name(model, index: int):
"""Get the input name corresponding to the input index"""
return model.inputs[index]
class _BoundingDomain(abc.ABC):
"""
Base class for ModelBoundingBox and CompoundBoundingBox.
This is where all the `~astropy.modeling.core.Model` evaluation
code for evaluating with a bounding box is because it is common
to both types of bounding box.
Parameters
----------
model : `~astropy.modeling.Model`
The Model this bounding domain is for.
prepare_inputs :
Generates the necessary input information so that model can
be evaluated only for input points entirely inside bounding_box.
This needs to be implemented by a subclass. Note that most of
the implementation is in ModelBoundingBox.
prepare_outputs :
Fills the output values in for any input points outside the
bounding_box.
evaluate :
Performs a complete model evaluation while enforcing the bounds
on the inputs and returns a complete output.
"""
def __init__(self, model, ignored: List[int] = None, order: str = 'C'):
self._model = model
self._ignored = self._validate_ignored(ignored)
self._order = self._get_order(order)
@property
def model(self):
return self._model
@property
def order(self) -> str:
return self._order
@property
def ignored(self) -> List[int]:
return self._ignored
def _get_order(self, order: str = None) -> str:
"""
Get if bounding_box is C/python ordered or Fortran/mathematically
ordered
"""
if order is None:
order = self._order
if order not in ('C', 'F'):
raise ValueError("order must be either 'C' (C/python order) or "
f"'F' (Fortran/mathematical order), got: {order}.")
return order
def _get_index(self, key) -> int:
"""
Get the input index corresponding to the given key.
Can pass in either:
the string name of the input or
the input index itself.
"""
return get_index(self._model, key)
def _get_name(self, index: int):
"""Get the input name corresponding to the input index"""
return get_name(self._model, index)
@property
def ignored_inputs(self) -> List[str]:
return [self._get_name(index) for index in self._ignored]
def _validate_ignored(self, ignored: list) -> List[int]:
if ignored is None:
return []
else:
return [self._get_index(key) for key in ignored]
def __call__(self, *args, **kwargs):
raise NotImplementedError(
"This bounding box is fixed by the model and does not have "
"adjustable parameters.")
@abc.abstractmethod
def fix_inputs(self, model, fixed_inputs: dict):
"""
Fix the bounding_box for a `fix_inputs` compound model.
Parameters
----------
model : `~astropy.modeling.Model`
The new model for which this will be a bounding_box
fixed_inputs : dict
Dictionary of inputs which have been fixed by this bounding box.
"""
raise NotImplementedError("This should be implemented by a child class.")
@abc.abstractmethod
def prepare_inputs(self, input_shape, inputs) -> Tuple[Any, Any, Any]:
"""
Get prepare the inputs with respect to the bounding box.
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
inputs : list
List of all the model inputs
Returns
-------
valid_inputs : list
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : array_like
array of all indices inside the bounding box
all_out: bool
if all of the inputs are outside the bounding_box
"""
raise NotImplementedError("This has not been implemented for BoundingDomain.")
@staticmethod
def _base_output(input_shape, fill_value):
"""
Create a baseline output, assuming that the entire input is outside
the bounding box
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
Returns
-------
An array of the correct shape containing all fill_value
"""
return np.zeros(input_shape) + fill_value
def _all_out_output(self, input_shape, fill_value):
"""
Create output if all inputs are outside the domain
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
Returns
-------
A full set of outputs for case that all inputs are outside domain.
"""
return [self._base_output(input_shape, fill_value)
for _ in range(self._model.n_outputs)], None
def _modify_output(self, valid_output, valid_index, input_shape, fill_value):
"""
For a single output fill in all the parts corresponding to inputs
outside the bounding box.
Parameters
----------
valid_output : numpy array
The output from the model corresponding to inputs inside the
bounding box
valid_index : numpy array
array of all indices of inputs inside the bounding box
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
Returns
-------
An output array with all the indices corresponding to inputs
outside the bounding box filled in by fill_value
"""
output = self._base_output(input_shape, fill_value)
if not output.shape:
output = np.array(valid_output)
else:
output[valid_index] = valid_output
if np.isscalar(valid_output):
output = output.item(0)
return output
def _prepare_outputs(self, valid_outputs, valid_index, input_shape, fill_value):
"""
Fill in all the outputs of the model corresponding to inputs
outside the bounding_box.
Parameters
----------
valid_outputs : list of numpy array
The list of outputs from the model corresponding to inputs
inside the bounding box
valid_index : numpy array
array of all indices of inputs inside the bounding box
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
Returns
-------
List of filled in output arrays.
"""
outputs = []
for valid_output in valid_outputs:
outputs.append(self._modify_output(valid_output, valid_index, input_shape, fill_value))
return outputs
def prepare_outputs(self, valid_outputs, valid_index, input_shape, fill_value):
"""
Fill in all the outputs of the model corresponding to inputs
outside the bounding_box, adjusting any single output model so that
its output becomes a list of containing that output.
Parameters
----------
valid_outputs : list
The list of outputs from the model corresponding to inputs
inside the bounding box
valid_index : array_like
array of all indices of inputs inside the bounding box
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
"""
if self._model.n_outputs == 1:
valid_outputs = [valid_outputs]
return self._prepare_outputs(valid_outputs, valid_index, input_shape, fill_value)
@staticmethod
def _get_valid_outputs_unit(valid_outputs, with_units: bool):
"""
Get the unit for outputs if one is required.
Parameters
----------
valid_outputs : list of numpy array
The list of outputs from the model corresponding to inputs
inside the bounding box
with_units : bool
whether or not a unit is required
"""
if with_units:
return getattr(valid_outputs, 'unit', None)
def _evaluate_model(self, evaluate: Callable, valid_inputs, valid_index,
input_shape, fill_value, with_units: bool):
"""
Evaluate the model using the given evaluate routine
Parameters
----------
evaluate : Callable
callable which takes in the valid inputs to evaluate model
valid_inputs : list of numpy arrays
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : numpy array
array of all indices inside the bounding box
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
with_units : bool
whether or not a unit is required
Returns
-------
outputs :
list containing filled in output values
valid_outputs_unit :
the unit that will be attached to the outputs
"""
valid_outputs = evaluate(valid_inputs)
valid_outputs_unit = self._get_valid_outputs_unit(valid_outputs, with_units)
return self.prepare_outputs(valid_outputs, valid_index,
input_shape, fill_value), valid_outputs_unit
def _evaluate(self, evaluate: Callable, inputs, input_shape,
fill_value, with_units: bool):
"""
Perform model evaluation steps:
prepare_inputs -> evaluate -> prepare_outputs
Parameters
----------
evaluate : Callable
callable which takes in the valid inputs to evaluate model
valid_inputs : list of numpy arrays
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : numpy array
array of all indices inside the bounding box
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
with_units : bool
whether or not a unit is required
Returns
-------
outputs :
list containing filled in output values
valid_outputs_unit :
the unit that will be attached to the outputs
"""
valid_inputs, valid_index, all_out = self.prepare_inputs(input_shape, inputs)
if all_out:
return self._all_out_output(input_shape, fill_value)
else:
return self._evaluate_model(evaluate, valid_inputs, valid_index,
input_shape, fill_value, with_units)
@staticmethod
def _set_outputs_unit(outputs, valid_outputs_unit):
"""
Set the units on the outputs
prepare_inputs -> evaluate -> prepare_outputs -> set output units
Parameters
----------
outputs :
list containing filled in output values
valid_outputs_unit :
the unit that will be attached to the outputs
Returns
-------
List containing filled in output values and units
"""
if valid_outputs_unit is not None:
return Quantity(outputs, valid_outputs_unit, copy=False)
return outputs
def evaluate(self, evaluate: Callable, inputs, fill_value):
"""
Perform full model evaluation steps:
prepare_inputs -> evaluate -> prepare_outputs -> set output units
Parameters
----------
evaluate : callable
callable which takes in the valid inputs to evaluate model
valid_inputs : list
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : array_like
array of all indices inside the bounding box
fill_value : float
The value which will be assigned to inputs which are outside
the bounding box
"""
input_shape = self._model.input_shape(inputs)
# NOTE: CompoundModel does not currently support units during
# evaluation for bounding_box so this feature is turned off
# for CompoundModel(s).
outputs, valid_outputs_unit = self._evaluate(evaluate, inputs, input_shape,
fill_value, self._model.bbox_with_units)
return tuple(self._set_outputs_unit(outputs, valid_outputs_unit))
class ModelBoundingBox(_BoundingDomain):
"""
A model's bounding box
Parameters
----------
intervals : dict
A dictionary containing all the intervals for each model input
keys -> input index
values -> interval for that index
model : `~astropy.modeling.Model`
The Model this bounding_box is for.
ignored : list
A list containing all the inputs (index) which will not be
checked for whether or not their elements are in/out of an interval.
order : optional, str
The ordering that is assumed for the tuple representation of this
bounding_box. Options: 'C': C/Python order, e.g. z, y, x.
(default), 'F': Fortran/mathematical notation order, e.g. x, y, z.
"""
def __init__(self, intervals: Dict[int, _Interval], model,
ignored: List[int] = None, order: str = 'C'):
super().__init__(model, ignored, order)
self._intervals = {}
if intervals != () and intervals != {}:
self._validate(intervals, order=order)
def copy(self, ignored=None):
intervals = {index: interval.copy()
for index, interval in self._intervals.items()}
if ignored is None:
ignored = self._ignored.copy()
return ModelBoundingBox(intervals, self._model,
ignored=ignored,
order=self._order)
@property
def intervals(self) -> Dict[int, _Interval]:
"""Return bounding_box labeled using input positions"""
return self._intervals
@property
def named_intervals(self) -> Dict[str, _Interval]:
"""Return bounding_box labeled using input names"""
return {self._get_name(index): bbox for index, bbox in self._intervals.items()}
def __repr__(self):
parts = [
'ModelBoundingBox(',
' intervals={'
]
for name, interval in self.named_intervals.items():
parts.append(f" {name}: {interval}")
parts.append(' }')
if len(self._ignored) > 0:
parts.append(f" ignored={self.ignored_inputs}")
parts.append(f' model={self._model.__class__.__name__}(inputs={self._model.inputs})')
parts.append(f" order='{self._order}'")
parts.append(')')
return '\n'.join(parts)
def __len__(self):
return len(self._intervals)
def __contains__(self, key):
try:
return self._get_index(key) in self._intervals or self._ignored
except (IndexError, ValueError):
return False
def has_interval(self, key):
return self._get_index(key) in self._intervals
def __getitem__(self, key):
"""Get bounding_box entries by either input name or input index"""
index = self._get_index(key)
if index in self._ignored:
return _ignored_interval
else:
return self._intervals[self._get_index(key)]
def bounding_box(self, order: str = None):
"""
Return the old tuple of tuples representation of the bounding_box
order='C' corresponds to the old bounding_box ordering
order='F' corresponds to the gwcs bounding_box ordering.
"""
if len(self._intervals) == 1:
return tuple(list(self._intervals.values())[0])
else:
order = self._get_order(order)
inputs = self._model.inputs
if order == 'C':
inputs = inputs[::-1]
bbox = tuple([tuple(self[input_name]) for input_name in inputs])
if len(bbox) == 1:
bbox = bbox[0]
return bbox
def __eq__(self, value):
"""Note equality can be either with old representation or new one."""
if isinstance(value, tuple):
return self.bounding_box() == value
elif isinstance(value, ModelBoundingBox):
return (self.intervals == value.intervals) and (self.ignored == value.ignored)
else:
return False
def __setitem__(self, key, value):
"""Validate and store interval under key (input index or input name)."""
index = self._get_index(key)
if index in self._ignored:
self._ignored.remove(index)
self._intervals[index] = _Interval.validate(value)
def __delitem__(self, key):
"""Delete stored interval"""
index = self._get_index(key)
if index in self._ignored:
raise RuntimeError(f"Cannot delete ignored input: {key}!")
del self._intervals[index]
self._ignored.append(index)
def _validate_dict(self, bounding_box: dict):
"""Validate passing dictionary of intervals and setting them."""
for key, value in bounding_box.items():
self[key] = value
def _validate_sequence(self, bounding_box, order: str = None):
"""Validate passing tuple of tuples representation (or related) and setting them."""
order = self._get_order(order)
if order == 'C':
# If bounding_box is C/python ordered, it needs to be reversed
# to be in Fortran/mathematical/input order.
bounding_box = bounding_box[::-1]
for index, value in enumerate(bounding_box):
self[index] = value
@property
def _n_inputs(self) -> int:
n_inputs = self._model.n_inputs - len(self._ignored)
if n_inputs > 0:
return n_inputs
else:
return 0
def _validate_iterable(self, bounding_box, order: str = None):
"""Validate and set any iterable representation"""
if len(bounding_box) != self._n_inputs:
raise ValueError(f"Found {len(bounding_box)} intervals, "
f"but must have exactly {self._n_inputs}.")
if isinstance(bounding_box, dict):
self._validate_dict(bounding_box)
else:
self._validate_sequence(bounding_box, order)
def _validate(self, bounding_box, order: str = None):
"""Validate and set any representation"""
if self._n_inputs == 1 and not isinstance(bounding_box, dict):
self[0] = bounding_box
else:
self._validate_iterable(bounding_box, order)
@classmethod
def validate(cls, model, bounding_box,
ignored: list = None, order: str = 'C', _preserve_ignore: bool = False, **kwargs):
"""
Construct a valid bounding box for a model.
Parameters
----------
model : `~astropy.modeling.Model`
The model for which this will be a bounding_box
bounding_box : dict, tuple
A possible representation of the bounding box
order : optional, str
The order that a tuple representation will be assumed to be
Default: 'C'
"""
if isinstance(bounding_box, ModelBoundingBox):
order = bounding_box.order
if _preserve_ignore:
ignored = bounding_box.ignored
bounding_box = bounding_box.intervals
new = cls({}, model, ignored=ignored, order=order)
new._validate(bounding_box)
return new
def fix_inputs(self, model, fixed_inputs: dict, _keep_ignored=False):
"""
Fix the bounding_box for a `fix_inputs` compound model.
Parameters
----------
model : `~astropy.modeling.Model`
The new model for which this will be a bounding_box
fixed_inputs : dict
Dictionary of inputs which have been fixed by this bounding box.
keep_ignored : bool
Keep the ignored inputs of the bounding box (internal argument only)
"""
new = self.copy()
for _input in fixed_inputs.keys():
del new[_input]
if _keep_ignored:
ignored = new.ignored
else:
ignored = None
return ModelBoundingBox.validate(model, new.named_intervals,
ignored=ignored, order=new._order)
@property
def dimension(self):
return len(self)
def domain(self, resolution, order: str = None):
inputs = self._model.inputs
order = self._get_order(order)
if order == 'C':
inputs = inputs[::-1]
return [self[input_name].domain(resolution) for input_name in inputs]
def _outside(self, input_shape, inputs):
"""
Get all the input positions which are outside the bounding_box,
so that the corresponding outputs can be filled with the fill
value (default NaN).
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
inputs : list
List of all the model inputs
Returns
-------
outside_index : bool-numpy array
True -> position outside bounding_box
False -> position inside bounding_box
all_out : bool
if all of the inputs are outside the bounding_box
"""
all_out = False
outside_index = np.zeros(input_shape, dtype=bool)
for index, _input in enumerate(inputs):
_input = np.asanyarray(_input)
outside = np.broadcast_to(self[index].outside(_input), input_shape)
outside_index[outside] = True
if outside_index.all():
all_out = True
break
return outside_index, all_out
def _valid_index(self, input_shape, inputs):
"""
Get the indices of all the inputs inside the bounding_box.
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
inputs : list
List of all the model inputs
Returns
-------
valid_index : numpy array
array of all indices inside the bounding box
all_out : bool
if all of the inputs are outside the bounding_box
"""
outside_index, all_out = self._outside(input_shape, inputs)
valid_index = np.atleast_1d(np.logical_not(outside_index)).nonzero()
if len(valid_index[0]) == 0:
all_out = True
return valid_index, all_out
def prepare_inputs(self, input_shape, inputs) -> Tuple[Any, Any, Any]:
"""
Get prepare the inputs with respect to the bounding box.
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
inputs : list
List of all the model inputs
Returns
-------
valid_inputs : list
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : array_like
array of all indices inside the bounding box
all_out: bool
if all of the inputs are outside the bounding_box
"""
valid_index, all_out = self._valid_index(input_shape, inputs)
valid_inputs = []
if not all_out:
for _input in inputs:
if input_shape:
valid_input = np.broadcast_to(np.atleast_1d(_input), input_shape)[valid_index]
if np.isscalar(_input):
valid_input = valid_input.item(0)
valid_inputs.append(valid_input)
else:
valid_inputs.append(_input)
return tuple(valid_inputs), valid_index, all_out
_BaseSelectorArgument = namedtuple('_BaseSelectorArgument', "index ignore")
class _SelectorArgument(_BaseSelectorArgument):
"""
Contains a single CompoundBoundingBox slicing input.
Parameters
----------
index : int
The index of the input in the input list
ignore : bool
Whether or not this input will be ignored by the bounding box.
Methods
-------
validate :
Returns a valid SelectorArgument for a given model.
get_selector :
Returns the value of the input for use in finding the correct
bounding_box.
get_fixed_value :
Gets the slicing value from a fix_inputs set of values.
"""
def __new__(cls, index, ignore):
self = super().__new__(cls, index, ignore)
return self
@classmethod
def validate(cls, model, argument, ignored: bool = True):
"""
Construct a valid selector argument for a CompoundBoundingBox.
Parameters
----------
model : `~astropy.modeling.Model`
The model for which this will be an argument for.
argument : int or str
A representation of which evaluation input to use
ignored : optional, bool
Whether or not to ignore this argument in the ModelBoundingBox.
Returns
-------
Validated selector_argument
"""
return cls(get_index(model, argument), ignored)
def get_selector(self, *inputs):
"""
Get the selector value corresponding to this argument
Parameters
----------
*inputs :
All the processed model evaluation inputs.
"""
_selector = inputs[self.index]
if isiterable(_selector):
if len(_selector) == 1:
return _selector[0]
else:
return tuple(_selector)
return _selector
def name(self, model) -> str:
"""
Get the name of the input described by this selector argument
Parameters
----------
model : `~astropy.modeling.Model`
The Model this selector argument is for.
"""
return get_name(model, self.index)
def pretty_repr(self, model):
"""
Get a pretty-print representation of this object
Parameters
----------
model : `~astropy.modeling.Model`
The Model this selector argument is for.
"""
return f"Argument(name='{self.name(model)}', ignore={self.ignore})"
def get_fixed_value(self, model, values: dict):
"""
Gets the value fixed input corresponding to this argument
Parameters
----------
model : `~astropy.modeling.Model`
The Model this selector argument is for.
values : dict
Dictionary of fixed inputs.
"""
if self.index in values:
return values[self.index]
else:
if self.name(model) in values:
return values[self.name(model)]
else:
raise RuntimeError(f"{self.pretty_repr(model)} was not found in {values}")
def is_argument(self, model, argument) -> bool:
"""
Determine if passed argument is described by this selector argument
Parameters
----------
model : `~astropy.modeling.Model`
The Model this selector argument is for.
argument : int or str
A representation of which evaluation input is being used
"""
return self.index == get_index(model, argument)
def named_tuple(self, model):
"""
Get a tuple representation of this argument using the input
name from the model.
Parameters
----------
model : `~astropy.modeling.Model`
The Model this selector argument is for.
"""
return (self.name(model), self.ignore)
class _SelectorArguments(tuple):
"""
Contains the CompoundBoundingBox slicing description
Parameters
----------
input_ :
The SelectorArgument values
Methods
-------
validate :
Returns a valid SelectorArguments for its model.
get_selector :
Returns the selector a set of inputs corresponds to.
is_selector :
Determines if a selector is correctly formatted for this CompoundBoundingBox.
get_fixed_value :
Gets the selector from a fix_inputs set of values.
"""
_kept_ignore = None
def __new__(cls, input_: Tuple[_SelectorArgument], kept_ignore: List = None):
self = super().__new__(cls, input_)
if kept_ignore is None:
self._kept_ignore = []
else:
self._kept_ignore = kept_ignore
return self
def pretty_repr(self, model):
"""
Get a pretty-print representation of this object
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
"""
parts = ['SelectorArguments(']
for argument in self:
parts.append(
f" {argument.pretty_repr(model)}"
)
parts.append(')')
return '\n'.join(parts)
@property
def ignore(self):
"""Get the list of ignored inputs"""
ignore = [argument.index for argument in self if argument.ignore]
ignore.extend(self._kept_ignore)
return ignore
@property
def kept_ignore(self):
"""The arguments to persist in ignoring"""
return self._kept_ignore
@classmethod
def validate(cls, model, arguments, kept_ignore: List=None):
"""
Construct a valid Selector description for a CompoundBoundingBox.
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
arguments :
The individual argument informations
kept_ignore :
Arguments to persist as ignored
"""
inputs = []
for argument in arguments:
_input = _SelectorArgument.validate(model, *argument)
if _input.index in [this.index for this in inputs]:
raise ValueError(f"Input: '{get_name(model, _input.index)}' has been repeated.")
inputs.append(_input)
if len(inputs) == 0:
raise ValueError("There must be at least one selector argument.")
if isinstance(arguments, _SelectorArguments):
if kept_ignore is None:
kept_ignore = []
kept_ignore.extend(arguments.kept_ignore)
return cls(tuple(inputs), kept_ignore)
def get_selector(self, *inputs):
"""
Get the selector corresponding to these inputs
Parameters
----------
*inputs :
All the processed model evaluation inputs.
"""
return tuple([argument.get_selector(*inputs) for argument in self])
def is_selector(self, _selector):
"""
Determine if this is a reasonable selector
Parameters
----------
_selector : tuple
The selector to check
"""
return isinstance(_selector, tuple) and len(_selector) == len(self)
def get_fixed_values(self, model, values: dict):
"""
Gets the value fixed input corresponding to this argument
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
values : dict
Dictionary of fixed inputs.
"""
return tuple([argument.get_fixed_value(model, values) for argument in self])
def is_argument(self, model, argument) -> bool:
"""
Determine if passed argument is one of the selector arguments
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
argument : int or str
A representation of which evaluation input is being used
"""
for selector_arg in self:
if selector_arg.is_argument(model, argument):
return True
else:
return False
def selector_index(self, model, argument):
"""
Get the index of the argument passed in the selector tuples
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
argument : int or str
A representation of which argument is being used
"""
for index, selector_arg in enumerate(self):
if selector_arg.is_argument(model, argument):
return index
else:
raise ValueError(f"{argument} does not correspond to any selector argument.")
def reduce(self, model, argument):
"""
Reduce the selector arguments by the argument given
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
argument : int or str
A representation of which argument is being used
"""
arguments = list(self)
kept_ignore = [arguments.pop(self.selector_index(model, argument)).index]
kept_ignore.extend(self._kept_ignore)
return _SelectorArguments.validate(model, tuple(arguments), kept_ignore)
def add_ignore(self, model, argument):
"""
Add argument to the kept_ignore list
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
argument : int or str
A representation of which argument is being used
"""
if self.is_argument(model, argument):
raise ValueError(f"{argument}: is a selector argument and cannot be ignored.")
kept_ignore = [get_index(model, argument)]
return _SelectorArguments.validate(model, self, kept_ignore)
def named_tuple(self, model):
"""
Get a tuple of selector argument tuples using input names
Parameters
----------
model : `~astropy.modeling.Model`
The Model these selector arguments are for.
"""
return tuple([selector_arg.named_tuple(model) for selector_arg in self])
class CompoundBoundingBox(_BoundingDomain):
"""
A model's compound bounding box
Parameters
----------
bounding_boxes : dict
A dictionary containing all the ModelBoundingBoxes that are possible
keys -> _selector (extracted from model inputs)
values -> ModelBoundingBox
model : `~astropy.modeling.Model`
The Model this compound bounding_box is for.
selector_args : _SelectorArguments
A description of how to extract the selectors from model inputs.
create_selector : optional
A method which takes in the selector and the model to return a
valid bounding corresponding to that selector. This can be used
to construct new bounding_boxes for previously undefined selectors.
These new boxes are then stored for future lookups.
order : optional, str
The ordering that is assumed for the tuple representation of the
bounding_boxes.
"""
def __init__(self, bounding_boxes: Dict[Any, ModelBoundingBox], model,
selector_args: _SelectorArguments, create_selector: Callable = None,
ignored: List[int] = None, order: str = 'C'):
super().__init__(model, ignored, order)
self._create_selector = create_selector
self._selector_args = _SelectorArguments.validate(model, selector_args)
self._bounding_boxes = {}
self._validate(bounding_boxes)
def copy(self):
bounding_boxes = {selector: bbox.copy(self.selector_args.ignore)
for selector, bbox in self._bounding_boxes.items()}
return CompoundBoundingBox(bounding_boxes, self._model,
selector_args=self._selector_args,
create_selector=copy.deepcopy(self._create_selector),
order=self._order)
def __repr__(self):
parts = ['CompoundBoundingBox(',
' bounding_boxes={']
# bounding_boxes
for _selector, bbox in self._bounding_boxes.items():
bbox_repr = bbox.__repr__().split('\n')
parts.append(f" {_selector} = {bbox_repr.pop(0)}")
for part in bbox_repr:
parts.append(f" {part}")
parts.append(' }')
# selector_args
selector_args_repr = self.selector_args.pretty_repr(self._model).split('\n')
parts.append(f" selector_args = {selector_args_repr.pop(0)}")
for part in selector_args_repr:
parts.append(f" {part}")
parts.append(')')
return '\n'.join(parts)
@property
def bounding_boxes(self) -> Dict[Any, ModelBoundingBox]:
return self._bounding_boxes
@property
def selector_args(self) -> _SelectorArguments:
return self._selector_args
@selector_args.setter
def selector_args(self, value):
self._selector_args = _SelectorArguments.validate(self._model, value)
warnings.warn("Overriding selector_args may cause problems you should re-validate "
"the compound bounding box before use!", RuntimeWarning)
@property
def named_selector_tuple(self) -> tuple:
return self._selector_args.named_tuple(self._model)
@property
def create_selector(self):
return self._create_selector
@staticmethod
def _get_selector_key(key):
if isiterable(key):
return tuple(key)
else:
return (key,)
def __setitem__(self, key, value):
_selector = self._get_selector_key(key)
if not self.selector_args.is_selector(_selector):
raise ValueError(f"{_selector} is not a selector!")
ignored = self.selector_args.ignore + self.ignored
self._bounding_boxes[_selector] = ModelBoundingBox.validate(self._model, value,
ignored,
order=self._order)
def _validate(self, bounding_boxes: dict):
for _selector, bounding_box in bounding_boxes.items():
self[_selector] = bounding_box
def __eq__(self, value):
if isinstance(value, CompoundBoundingBox):
return (self.bounding_boxes == value.bounding_boxes) and \
(self.selector_args == value.selector_args) and \
(self.create_selector == value.create_selector)
else:
return False
@classmethod
def validate(cls, model, bounding_box: dict, selector_args=None, create_selector=None,
ignored: list = None, order: str = 'C', _preserve_ignore: bool = False, **kwarg):
"""
Construct a valid compound bounding box for a model.
Parameters
----------
model : `~astropy.modeling.Model`
The model for which this will be a bounding_box
bounding_box : dict
Dictionary of possible bounding_box respresentations
selector_args : optional
Description of the selector arguments
create_selector : optional, callable
Method for generating new selectors
order : optional, str
The order that a tuple representation will be assumed to be
Default: 'C'
"""
if isinstance(bounding_box, CompoundBoundingBox):
if selector_args is None:
selector_args = bounding_box.selector_args
if create_selector is None:
create_selector = bounding_box.create_selector
order = bounding_box.order
if _preserve_ignore:
ignored = bounding_box.ignored
bounding_box = bounding_box.bounding_boxes
if selector_args is None:
raise ValueError("Selector arguments must be provided (can be passed as part of bounding_box argument)!")
return cls(bounding_box, model, selector_args,
create_selector=create_selector, ignored=ignored, order=order)
def __contains__(self, key):
return key in self._bounding_boxes
def _create_bounding_box(self, _selector):
self[_selector] = self._create_selector(_selector, model=self._model)
return self[_selector]
def __getitem__(self, key):
_selector = self._get_selector_key(key)
if _selector in self:
return self._bounding_boxes[_selector]
elif self._create_selector is not None:
return self._create_bounding_box(_selector)
else:
raise RuntimeError(f"No bounding box is defined for selector: {_selector}.")
def _select_bounding_box(self, inputs) -> ModelBoundingBox:
_selector = self.selector_args.get_selector(*inputs)
return self[_selector]
def prepare_inputs(self, input_shape, inputs) -> Tuple[Any, Any, Any]:
"""
Get prepare the inputs with respect to the bounding box.
Parameters
----------
input_shape : tuple
The shape that all inputs have be reshaped/broadcasted into
inputs : list
List of all the model inputs
Returns
-------
valid_inputs : list
The inputs reduced to just those inputs which are all inside
their respective bounding box intervals
valid_index : array_like
array of all indices inside the bounding box
all_out: bool
if all of the inputs are outside the bounding_box
"""
bounding_box = self._select_bounding_box(inputs)
return bounding_box.prepare_inputs(input_shape, inputs)
def _matching_bounding_boxes(self, argument, value) -> Dict[Any, ModelBoundingBox]:
selector_index = self.selector_args.selector_index(self._model, argument)
matching = {}
for selector_key, bbox in self._bounding_boxes.items():
if selector_key[selector_index] == value:
new_selector_key = list(selector_key)
new_selector_key.pop(selector_index)
if bbox.has_interval(argument):
new_bbox = bbox.fix_inputs(self._model, {argument: value},
_keep_ignored=True)
else:
new_bbox = bbox.copy()
matching[tuple(new_selector_key)] = new_bbox
if len(matching) == 0:
raise ValueError(f"Attempting to fix input {argument}, but there are no "
f"bounding boxes for argument value {value}.")
return matching
def _fix_input_selector_arg(self, argument, value):
matching_bounding_boxes = self._matching_bounding_boxes(argument, value)
if len(self.selector_args) == 1:
return matching_bounding_boxes[()]
else:
return CompoundBoundingBox(matching_bounding_boxes, self._model,
self.selector_args.reduce(self._model, argument))
def _fix_input_bbox_arg(self, argument, value):
bounding_boxes = {}
for selector_key, bbox in self._bounding_boxes.items():
bounding_boxes[selector_key] = bbox.fix_inputs(self._model, {argument: value},
_keep_ignored=True)
return CompoundBoundingBox(bounding_boxes, self._model,
self.selector_args.add_ignore(self._model, argument))
def fix_inputs(self, model, fixed_inputs: dict):
"""
Fix the bounding_box for a `fix_inputs` compound model.
Parameters
----------
model : `~astropy.modeling.Model`
The new model for which this will be a bounding_box
fixed_inputs : dict
Dictionary of inputs which have been fixed by this bounding box.
"""
fixed_input_keys = list(fixed_inputs.keys())
argument = fixed_input_keys.pop()
value = fixed_inputs[argument]
if self.selector_args.is_argument(self._model, argument):
bbox = self._fix_input_selector_arg(argument, value)
else:
bbox = self._fix_input_bbox_arg(argument, value)
if len(fixed_input_keys) > 0:
new_fixed_inputs = fixed_inputs.copy()
del new_fixed_inputs[argument]
bbox = bbox.fix_inputs(model, new_fixed_inputs)
if isinstance(bbox, CompoundBoundingBox):
selector_args = bbox.named_selector_tuple
bbox_dict = bbox
elif isinstance(bbox, ModelBoundingBox):
selector_args = None
bbox_dict = bbox.named_intervals
return bbox.__class__.validate(model, bbox_dict,
order=bbox.order, selector_args=selector_args)
|
58c5581842a29d765307c0976dd589195ffc47b6af4532797a4357d2fe77b0d6 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Mathematical models."""
# pylint: disable=line-too-long, too-many-lines, too-many-arguments, invalid-name
import numpy as np
from astropy import units as u
from astropy.units import Quantity, UnitsError
from .core import (Fittable1DModel, Fittable2DModel)
from .parameters import Parameter, InputParameterError
from .utils import ellipse_extent
__all__ = ['AiryDisk2D', 'Moffat1D', 'Moffat2D', 'Box1D', 'Box2D', 'Const1D',
'Const2D', 'Ellipse2D', 'Disk2D', 'Gaussian1D', 'Gaussian2D',
'Linear1D', 'Lorentz1D', 'RickerWavelet1D', 'RickerWavelet2D',
'RedshiftScaleFactor', 'Multiply', 'Planar2D', 'Scale',
'Sersic1D', 'Sersic2D', 'Shift',
'Sine1D', 'Cosine1D', 'Tangent1D',
'ArcSine1D', 'ArcCosine1D', 'ArcTangent1D',
'Trapezoid1D', 'TrapezoidDisk2D', 'Ring2D', 'Voigt1D',
'KingProjectedAnalytic1D', 'Exponential1D', 'Logarithmic1D']
TWOPI = 2 * np.pi
FLOAT_EPSILON = float(np.finfo(np.float32).tiny)
# Note that we define this here rather than using the value defined in
# astropy.stats to avoid importing astropy.stats every time astropy.modeling
# is loaded.
GAUSSIAN_SIGMA_TO_FWHM = 2.0 * np.sqrt(2.0 * np.log(2.0))
class Gaussian1D(Fittable1DModel):
"""
One dimensional Gaussian model.
Parameters
----------
amplitude : float or `~astropy.units.Quantity`.
Amplitude (peak value) of the Gaussian - for a normalized profile
(integrating to 1), set amplitude = 1 / (stddev * np.sqrt(2 * np.pi))
mean : float or `~astropy.units.Quantity`.
Mean of the Gaussian.
stddev : float or `~astropy.units.Quantity`.
Standard deviation of the Gaussian with FWHM = 2 * stddev * np.sqrt(2 * np.log(2)).
Notes
-----
Either all or none of input ``x``, ``mean`` and ``stddev`` must be provided
consistently with compatible units or as unitless numbers.
Model formula:
.. math:: f(x) = A e^{- \\frac{\\left(x - x_{0}\\right)^{2}}{2 \\sigma^{2}}}
Examples
--------
>>> from astropy.modeling import models
>>> def tie_center(model):
... mean = 50 * model.stddev
... return mean
>>> tied_parameters = {'mean': tie_center}
Specify that 'mean' is a tied parameter in one of two ways:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... tied=tied_parameters)
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.mean.tied
False
>>> g1.mean.tied = tie_center
>>> g1.mean.tied
<function tie_center at 0x...>
Fixed parameters:
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
... fixed={'stddev': True})
>>> g1.stddev.fixed
True
or
>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.stddev.fixed
False
>>> g1.stddev.fixed = True
>>> g1.stddev.fixed
True
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Gaussian1D
plt.figure()
s1 = Gaussian1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
See Also
--------
Gaussian2D, Box1D, Moffat1D, Lorentz1D
"""
amplitude = Parameter(default=1, description="Amplitude (peak value) of the Gaussian")
mean = Parameter(default=0, description="Position of peak (Gaussian)")
# Ensure stddev makes sense if its bounds are not explicitly set.
# stddev must be non-zero and positive.
stddev = Parameter(default=1, bounds=(FLOAT_EPSILON, None), description="Standard deviation of the Gaussian")
def bounding_box(self, factor=5.5):
"""
Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``
Parameters
----------
factor : float
The multiple of `stddev` used to define the limits.
The default is 5.5, corresponding to a relative error < 1e-7.
Examples
--------
>>> from astropy.modeling.models import Gaussian1D
>>> model = Gaussian1D(mean=0, stddev=2)
>>> model.bounding_box
(-11.0, 11.0)
This range can be set directly (see: `Model.bounding_box
<astropy.modeling.Model.bounding_box>`) or by using a different factor,
like:
>>> model.bounding_box = model.bounding_box(factor=2)
>>> model.bounding_box
(-4.0, 4.0)
"""
x0 = self.mean
dx = factor * self.stddev
return (x0 - dx, x0 + dx)
@property
def fwhm(self):
"""Gaussian full width at half maximum."""
return self.stddev * GAUSSIAN_SIGMA_TO_FWHM
@staticmethod
def evaluate(x, amplitude, mean, stddev):
"""
Gaussian1D model function.
"""
return amplitude * np.exp(- 0.5 * (x - mean) ** 2 / stddev ** 2)
@staticmethod
def fit_deriv(x, amplitude, mean, stddev):
"""
Gaussian1D model function derivatives.
"""
d_amplitude = np.exp(-0.5 / stddev ** 2 * (x - mean) ** 2)
d_mean = amplitude * d_amplitude * (x - mean) / stddev ** 2
d_stddev = amplitude * d_amplitude * (x - mean) ** 2 / stddev ** 3
return [d_amplitude, d_mean, d_stddev]
@property
def input_units(self):
if self.mean.unit is None:
return None
return {self.inputs[0]: self.mean.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'mean': inputs_unit[self.inputs[0]],
'stddev': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Gaussian2D(Fittable2DModel):
r"""
Two dimensional Gaussian model.
Parameters
----------
amplitude : float or `~astropy.units.Quantity`.
Amplitude (peak value) of the Gaussian.
x_mean : float or `~astropy.units.Quantity`.
Mean of the Gaussian in x.
y_mean : float or `~astropy.units.Quantity`.
Mean of the Gaussian in y.
x_stddev : float or `~astropy.units.Quantity` or None.
Standard deviation of the Gaussian in x before rotating by theta. Must
be None if a covariance matrix (``cov_matrix``) is provided. If no
``cov_matrix`` is given, ``None`` means the default value (1).
y_stddev : float or `~astropy.units.Quantity` or None.
Standard deviation of the Gaussian in y before rotating by theta. Must
be None if a covariance matrix (``cov_matrix``) is provided. If no
``cov_matrix`` is given, ``None`` means the default value (1).
theta : float or `~astropy.units.Quantity`, optional.
Rotation angle (value in radians). The rotation angle increases
counterclockwise. Must be None if a covariance matrix (``cov_matrix``)
is provided. If no ``cov_matrix`` is given, ``None`` means the default
value (0).
cov_matrix : ndarray, optional
A 2x2 covariance matrix. If specified, overrides the ``x_stddev``,
``y_stddev``, and ``theta`` defaults.
Notes
-----
Either all or none of input ``x, y``, ``[x,y]_mean`` and ``[x,y]_stddev``
must be provided consistently with compatible units or as unitless numbers.
Model formula:
.. math::
f(x, y) = A e^{-a\left(x - x_{0}\right)^{2} -b\left(x - x_{0}\right)
\left(y - y_{0}\right) -c\left(y - y_{0}\right)^{2}}
Using the following definitions:
.. math::
a = \left(\frac{\cos^{2}{\left (\theta \right )}}{2 \sigma_{x}^{2}} +
\frac{\sin^{2}{\left (\theta \right )}}{2 \sigma_{y}^{2}}\right)
b = \left(\frac{\sin{\left (2 \theta \right )}}{2 \sigma_{x}^{2}} -
\frac{\sin{\left (2 \theta \right )}}{2 \sigma_{y}^{2}}\right)
c = \left(\frac{\sin^{2}{\left (\theta \right )}}{2 \sigma_{x}^{2}} +
\frac{\cos^{2}{\left (\theta \right )}}{2 \sigma_{y}^{2}}\right)
If using a ``cov_matrix``, the model is of the form:
.. math::
f(x, y) = A e^{-0.5 \left(\vec{x} - \vec{x}_{0}\right)^{T} \Sigma^{-1} \left(\vec{x} - \vec{x}_{0}\right)}
where :math:`\vec{x} = [x, y]`, :math:`\vec{x}_{0} = [x_{0}, y_{0}]`,
and :math:`\Sigma` is the covariance matrix:
.. math::
\Sigma = \left(\begin{array}{ccc}
\sigma_x^2 & \rho \sigma_x \sigma_y \\
\rho \sigma_x \sigma_y & \sigma_y^2
\end{array}\right)
:math:`\rho` is the correlation between ``x`` and ``y``, which should
be between -1 and +1. Positive correlation corresponds to a
``theta`` in the range 0 to 90 degrees. Negative correlation
corresponds to a ``theta`` in the range of 0 to -90 degrees.
See [1]_ for more details about the 2D Gaussian function.
See Also
--------
Gaussian1D, Box2D, Moffat2D
References
----------
.. [1] https://en.wikipedia.org/wiki/Gaussian_function
"""
amplitude = Parameter(default=1, description="Amplitude of the Gaussian")
x_mean = Parameter(default=0, description="Peak position (along x axis) of Gaussian")
y_mean = Parameter(default=0, description="Peak position (along y axis) of Gaussian")
x_stddev = Parameter(default=1, description="Standard deviation of the Gaussian (along x axis)")
y_stddev = Parameter(default=1, description="Standard deviation of the Gaussian (along y axis)")
theta = Parameter(default=0.0, description="Rotation angle [in radians] (Optional parameter)")
def __init__(self, amplitude=amplitude.default, x_mean=x_mean.default,
y_mean=y_mean.default, x_stddev=None, y_stddev=None,
theta=None, cov_matrix=None, **kwargs):
if cov_matrix is None:
if x_stddev is None:
x_stddev = self.__class__.x_stddev.default
if y_stddev is None:
y_stddev = self.__class__.y_stddev.default
if theta is None:
theta = self.__class__.theta.default
else:
if x_stddev is not None or y_stddev is not None or theta is not None:
raise InputParameterError("Cannot specify both cov_matrix and "
"x/y_stddev/theta")
# Compute principle coordinate system transformation
cov_matrix = np.array(cov_matrix)
if cov_matrix.shape != (2, 2):
raise ValueError("Covariance matrix must be 2x2")
eig_vals, eig_vecs = np.linalg.eig(cov_matrix)
x_stddev, y_stddev = np.sqrt(eig_vals)
y_vec = eig_vecs[:, 0]
theta = np.arctan2(y_vec[1], y_vec[0])
# Ensure stddev makes sense if its bounds are not explicitly set.
# stddev must be non-zero and positive.
# TODO: Investigate why setting this in Parameter above causes
# convolution tests to hang.
kwargs.setdefault('bounds', {})
kwargs['bounds'].setdefault('x_stddev', (FLOAT_EPSILON, None))
kwargs['bounds'].setdefault('y_stddev', (FLOAT_EPSILON, None))
super().__init__(
amplitude=amplitude, x_mean=x_mean, y_mean=y_mean,
x_stddev=x_stddev, y_stddev=y_stddev, theta=theta, **kwargs)
@property
def x_fwhm(self):
"""Gaussian full width at half maximum in X."""
return self.x_stddev * GAUSSIAN_SIGMA_TO_FWHM
@property
def y_fwhm(self):
"""Gaussian full width at half maximum in Y."""
return self.y_stddev * GAUSSIAN_SIGMA_TO_FWHM
def bounding_box(self, factor=5.5):
"""
Tuple defining the default ``bounding_box`` limits in each dimension,
``((y_low, y_high), (x_low, x_high))``
The default offset from the mean is 5.5-sigma, corresponding
to a relative error < 1e-7. The limits are adjusted for rotation.
Parameters
----------
factor : float, optional
The multiple of `x_stddev` and `y_stddev` used to define the limits.
The default is 5.5.
Examples
--------
>>> from astropy.modeling.models import Gaussian2D
>>> model = Gaussian2D(x_mean=0, y_mean=0, x_stddev=1, y_stddev=2)
>>> model.bounding_box
((-11.0, 11.0), (-5.5, 5.5))
This range can be set directly (see: `Model.bounding_box
<astropy.modeling.Model.bounding_box>`) or by using a different factor
like:
>>> model.bounding_box = model.bounding_box(factor=2)
>>> model.bounding_box
((-4.0, 4.0), (-2.0, 2.0))
"""
a = factor * self.x_stddev
b = factor * self.y_stddev
theta = self.theta.value
dx, dy = ellipse_extent(a, b, theta)
return ((self.y_mean - dy, self.y_mean + dy),
(self.x_mean - dx, self.x_mean + dx))
@staticmethod
def evaluate(x, y, amplitude, x_mean, y_mean, x_stddev, y_stddev, theta):
"""Two dimensional Gaussian function"""
cost2 = np.cos(theta) ** 2
sint2 = np.sin(theta) ** 2
sin2t = np.sin(2. * theta)
xstd2 = x_stddev ** 2
ystd2 = y_stddev ** 2
xdiff = x - x_mean
ydiff = y - y_mean
a = 0.5 * ((cost2 / xstd2) + (sint2 / ystd2))
b = 0.5 * ((sin2t / xstd2) - (sin2t / ystd2))
c = 0.5 * ((sint2 / xstd2) + (cost2 / ystd2))
return amplitude * np.exp(-((a * xdiff ** 2) + (b * xdiff * ydiff) +
(c * ydiff ** 2)))
@staticmethod
def fit_deriv(x, y, amplitude, x_mean, y_mean, x_stddev, y_stddev, theta):
"""Two dimensional Gaussian function derivative with respect to parameters"""
cost = np.cos(theta)
sint = np.sin(theta)
cost2 = np.cos(theta) ** 2
sint2 = np.sin(theta) ** 2
cos2t = np.cos(2. * theta)
sin2t = np.sin(2. * theta)
xstd2 = x_stddev ** 2
ystd2 = y_stddev ** 2
xstd3 = x_stddev ** 3
ystd3 = y_stddev ** 3
xdiff = x - x_mean
ydiff = y - y_mean
xdiff2 = xdiff ** 2
ydiff2 = ydiff ** 2
a = 0.5 * ((cost2 / xstd2) + (sint2 / ystd2))
b = 0.5 * ((sin2t / xstd2) - (sin2t / ystd2))
c = 0.5 * ((sint2 / xstd2) + (cost2 / ystd2))
g = amplitude * np.exp(-((a * xdiff2) + (b * xdiff * ydiff) +
(c * ydiff2)))
da_dtheta = (sint * cost * ((1. / ystd2) - (1. / xstd2)))
da_dx_stddev = -cost2 / xstd3
da_dy_stddev = -sint2 / ystd3
db_dtheta = (cos2t / xstd2) - (cos2t / ystd2)
db_dx_stddev = -sin2t / xstd3
db_dy_stddev = sin2t / ystd3
dc_dtheta = -da_dtheta
dc_dx_stddev = -sint2 / xstd3
dc_dy_stddev = -cost2 / ystd3
dg_dA = g / amplitude
dg_dx_mean = g * ((2. * a * xdiff) + (b * ydiff))
dg_dy_mean = g * ((b * xdiff) + (2. * c * ydiff))
dg_dx_stddev = g * (-(da_dx_stddev * xdiff2 +
db_dx_stddev * xdiff * ydiff +
dc_dx_stddev * ydiff2))
dg_dy_stddev = g * (-(da_dy_stddev * xdiff2 +
db_dy_stddev * xdiff * ydiff +
dc_dy_stddev * ydiff2))
dg_dtheta = g * (-(da_dtheta * xdiff2 +
db_dtheta * xdiff * ydiff +
dc_dtheta * ydiff2))
return [dg_dA, dg_dx_mean, dg_dy_mean, dg_dx_stddev, dg_dy_stddev,
dg_dtheta]
@property
def input_units(self):
if self.x_mean.unit is None and self.y_mean.unit is None:
return None
return {self.inputs[0]: self.x_mean.unit,
self.inputs[1]: self.y_mean.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_mean': inputs_unit[self.inputs[0]],
'y_mean': inputs_unit[self.inputs[0]],
'x_stddev': inputs_unit[self.inputs[0]],
'y_stddev': inputs_unit[self.inputs[0]],
'theta': u.rad,
'amplitude': outputs_unit[self.outputs[0]]}
class Shift(Fittable1DModel):
"""
Shift a coordinate.
Parameters
----------
offset : float
Offset to add to a coordinate.
"""
offset = Parameter(default=0, description="Offset to add to a model")
linear = True
_has_inverse_bounding_box = True
@property
def input_units(self):
if self.offset.unit is None:
return None
return {self.inputs[0]: self.offset.unit}
@property
def inverse(self):
"""One dimensional inverse Shift model function"""
inv = self.copy()
inv.offset *= -1
try:
self.bounding_box
except NotImplementedError:
pass
else:
inv.bounding_box = tuple(self.evaluate(x, self.offset) for x in self.bounding_box)
return inv
@staticmethod
def evaluate(x, offset):
"""One dimensional Shift model function"""
return x + offset
@staticmethod
def sum_of_implicit_terms(x):
"""Evaluate the implicit term (x) of one dimensional Shift model"""
return x
@staticmethod
def fit_deriv(x, *params):
"""One dimensional Shift model derivative with respect to parameter"""
d_offset = np.ones_like(x)
return [d_offset]
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'offset': outputs_unit[self.outputs[0]]}
class Scale(Fittable1DModel):
"""
Multiply a model by a dimensionless factor.
Parameters
----------
factor : float
Factor by which to scale a coordinate.
Notes
-----
If ``factor`` is a `~astropy.units.Quantity` then the units will be
stripped before the scaling operation.
"""
factor = Parameter(default=1, description="Factor by which to scale a model")
linear = True
fittable = True
_input_units_strict = True
_input_units_allow_dimensionless = True
_has_inverse_bounding_box = True
@property
def input_units(self):
if self.factor.unit is None:
return None
return {self.inputs[0]: self.factor.unit}
@property
def inverse(self):
"""One dimensional inverse Scale model function"""
inv = self.copy()
inv.factor = 1 / self.factor
try:
self.bounding_box
except NotImplementedError:
pass
else:
inv.bounding_box = tuple(self.evaluate(x, self.factor) for x in self.bounding_box.bounding_box())
return inv
@staticmethod
def evaluate(x, factor):
"""One dimensional Scale model function"""
if isinstance(factor, u.Quantity):
factor = factor.value
return factor * x
@staticmethod
def fit_deriv(x, *params):
"""One dimensional Scale model derivative with respect to parameter"""
d_factor = x
return [d_factor]
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'factor': outputs_unit[self.outputs[0]]}
class Multiply(Fittable1DModel):
"""
Multiply a model by a quantity or number.
Parameters
----------
factor : float
Factor by which to multiply a coordinate.
"""
factor = Parameter(default=1, description="Factor by which to multiply a model")
linear = True
fittable = True
_has_inverse_bounding_box = True
@property
def inverse(self):
"""One dimensional inverse multiply model function"""
inv = self.copy()
inv.factor = 1 / self.factor
try:
self.bounding_box
except NotImplementedError:
pass
else:
inv.bounding_box = tuple(self.evaluate(x, self.factor) for x in self.bounding_box.bounding_box())
return inv
@staticmethod
def evaluate(x, factor):
"""One dimensional multiply model function"""
return factor * x
@staticmethod
def fit_deriv(x, *params):
"""One dimensional multiply model derivative with respect to parameter"""
d_factor = x
return [d_factor]
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'factor': outputs_unit[self.outputs[0]]}
class RedshiftScaleFactor(Fittable1DModel):
"""
One dimensional redshift scale factor model.
Parameters
----------
z : float
Redshift value.
Notes
-----
Model formula:
.. math:: f(x) = x (1 + z)
"""
z = Parameter(description='Redshift', default=0)
_has_inverse_bounding_box = True
@staticmethod
def evaluate(x, z):
"""One dimensional RedshiftScaleFactor model function"""
return (1 + z) * x
@staticmethod
def fit_deriv(x, z):
"""One dimensional RedshiftScaleFactor model derivative"""
d_z = x
return [d_z]
@property
def inverse(self):
"""Inverse RedshiftScaleFactor model"""
inv = self.copy()
inv.z = 1.0 / (1.0 + self.z) - 1.0
try:
self.bounding_box
except NotImplementedError:
pass
else:
inv.bounding_box = tuple(self.evaluate(x, self.z) for x in self.bounding_box.bounding_box())
return inv
class Sersic1D(Fittable1DModel):
r"""
One dimensional Sersic surface brightness profile.
Parameters
----------
amplitude : float
Surface brightness at r_eff.
r_eff : float
Effective (half-light) radius
n : float
Sersic Index.
See Also
--------
Gaussian1D, Moffat1D, Lorentz1D
Notes
-----
Model formula:
.. math::
I(r)=I_e\exp\left\{-b_n\left[\left(\frac{r}{r_{e}}\right)^{(1/n)}-1\right]\right\}
The constant :math:`b_n` is defined such that :math:`r_e` contains half the total
luminosity, and can be solved for numerically.
.. math::
\Gamma(2n) = 2\gamma (b_n,2n)
Examples
--------
.. plot::
:include-source:
import numpy as np
from astropy.modeling.models import Sersic1D
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(111, xscale='log', yscale='log')
s1 = Sersic1D(amplitude=1, r_eff=5)
r=np.arange(0, 100, .01)
for n in range(1, 10):
s1.n = n
plt.plot(r, s1(r), color=str(float(n) / 15))
plt.axis([1e-1, 30, 1e-2, 1e3])
plt.xlabel('log Radius')
plt.ylabel('log Surface Brightness')
plt.text(.25, 1.5, 'n=1')
plt.text(.25, 300, 'n=10')
plt.xticks([])
plt.yticks([])
plt.show()
References
----------
.. [1] http://ned.ipac.caltech.edu/level5/March05/Graham/Graham2.html
"""
amplitude = Parameter(default=1, description="Surface brightness at r_eff")
r_eff = Parameter(default=1, description="Effective (half-light) radius")
n = Parameter(default=4, description="Sersic Index")
_gammaincinv = None
@classmethod
def evaluate(cls, r, amplitude, r_eff, n):
"""One dimensional Sersic profile function."""
if cls._gammaincinv is None:
from scipy.special import gammaincinv
cls._gammaincinv = gammaincinv
return (amplitude * np.exp(
-cls._gammaincinv(2 * n, 0.5) * ((r / r_eff) ** (1 / n) - 1)))
@property
def input_units(self):
if self.r_eff.unit is None:
return None
return {self.inputs[0]: self.r_eff.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'r_eff': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class _Trigonometric1D(Fittable1DModel):
"""
Base class for one dimensional trigonometric and inverse trigonometric models
Parameters
----------
amplitude : float
Oscillation amplitude
frequency : float
Oscillation frequency
phase : float
Oscillation phase
"""
amplitude = Parameter(default=1, description="Oscillation amplitude")
frequency = Parameter(default=1, description="Oscillation frequency")
phase = Parameter(default=0, description="Oscillation phase")
@property
def input_units(self):
if self.frequency.unit is None:
return None
return {self.inputs[0]: 1. / self.frequency.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'frequency': inputs_unit[self.inputs[0]] ** -1,
'amplitude': outputs_unit[self.outputs[0]]}
class Sine1D(_Trigonometric1D):
"""
One dimensional Sine model.
Parameters
----------
amplitude : float
Oscillation amplitude
frequency : float
Oscillation frequency
phase : float
Oscillation phase
See Also
--------
ArcSine1D, Cosine1D, Tangent1D, Const1D, Linear1D
Notes
-----
Model formula:
.. math:: f(x) = A \\sin(2 \\pi f x + 2 \\pi p)
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Sine1D
plt.figure()
s1 = Sine1D(amplitude=1, frequency=.25)
r=np.arange(0, 10, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([0, 10, -5, 5])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional Sine model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = TWOPI * (frequency * x + phase)
if isinstance(argument, Quantity):
argument = argument.value
return amplitude * np.sin(argument)
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional Sine model derivative"""
d_amplitude = np.sin(TWOPI * frequency * x + TWOPI * phase)
d_frequency = (TWOPI * x * amplitude *
np.cos(TWOPI * frequency * x + TWOPI * phase))
d_phase = (TWOPI * amplitude *
np.cos(TWOPI * frequency * x + TWOPI * phase))
return [d_amplitude, d_frequency, d_phase]
@property
def inverse(self):
"""One dimensional inverse of Sine"""
return ArcSine1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
class Cosine1D(_Trigonometric1D):
"""
One dimensional Cosine model.
Parameters
----------
amplitude : float
Oscillation amplitude
frequency : float
Oscillation frequency
phase : float
Oscillation phase
See Also
--------
ArcCosine1D, Sine1D, Tangent1D, Const1D, Linear1D
Notes
-----
Model formula:
.. math:: f(x) = A \\cos(2 \\pi f x + 2 \\pi p)
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Cosine1D
plt.figure()
s1 = Cosine1D(amplitude=1, frequency=.25)
r=np.arange(0, 10, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([0, 10, -5, 5])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional Cosine model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = TWOPI * (frequency * x + phase)
if isinstance(argument, Quantity):
argument = argument.value
return amplitude * np.cos(argument)
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional Cosine model derivative"""
d_amplitude = np.cos(TWOPI * frequency * x + TWOPI * phase)
d_frequency = - (TWOPI * x * amplitude *
np.sin(TWOPI * frequency * x + TWOPI * phase))
d_phase = - (TWOPI * amplitude *
np.sin(TWOPI * frequency * x + TWOPI * phase))
return [d_amplitude, d_frequency, d_phase]
@property
def inverse(self):
"""One dimensional inverse of Cosine"""
return ArcCosine1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
class Tangent1D(_Trigonometric1D):
"""
One dimensional Tangent model.
Parameters
----------
amplitude : float
Oscillation amplitude
frequency : float
Oscillation frequency
phase : float
Oscillation phase
See Also
--------
Sine1D, Cosine1D, Const1D, Linear1D
Notes
-----
Model formula:
.. math:: f(x) = A \\tan(2 \\pi f x + 2 \\pi p)
Note that the tangent function is undefined for inputs of the form
pi/2 + n*pi for all integers n. Thus thus the default bounding box
has been restricted to:
.. math:: [(-1/4 - p)/f, (1/4 - p)/f]
which is the smallest interval for the tangent function to be continuous
on.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Tangent1D
plt.figure()
s1 = Tangent1D(amplitude=1, frequency=.25)
r=np.arange(0, 10, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([0, 10, -5, 5])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional Tangent model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = TWOPI * (frequency * x + phase)
if isinstance(argument, Quantity):
argument = argument.value
return amplitude * np.tan(argument)
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional Tangent model derivative"""
sec = 1 / (np.cos(TWOPI * frequency * x + TWOPI * phase))**2
d_amplitude = np.tan(TWOPI * frequency * x + TWOPI * phase)
d_frequency = TWOPI * x * amplitude * sec
d_phase = TWOPI * amplitude * sec
return [d_amplitude, d_frequency, d_phase]
@property
def inverse(self):
"""One dimensional inverse of Tangent"""
return ArcTangent1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``
"""
bbox = [(-1/4 - self.phase) / self.frequency, (1/4 - self.phase) / self.frequency]
if self.frequency.unit is not None:
bbox = bbox / self.frequency.unit
return bbox
class _InverseTrigonometric1D(_Trigonometric1D):
"""
Base class for one dimensional inverse trigonometric models
"""
@property
def input_units(self):
if self.amplitude.unit is None:
return None
return {self.inputs[0]: self.amplitude.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'frequency': outputs_unit[self.outputs[0]] ** -1,
'amplitude': inputs_unit[self.inputs[0]]}
class ArcSine1D(_InverseTrigonometric1D):
"""
One dimensional ArcSine model returning values between -pi/2 and pi/2
only.
Parameters
----------
amplitude : float
Oscillation amplitude for corresponding Sine
frequency : float
Oscillation frequency for corresponding Sine
phase : float
Oscillation phase for corresponding Sine
See Also
--------
Sine1D, ArcCosine1D, ArcTangent1D
Notes
-----
Model formula:
.. math:: f(x) = ((arcsin(x / A) / 2pi) - p) / f
The arcsin function being used for this model will only accept inputs
in [-A, A]; otherwise, a runtime warning will be thrown and the result
will be NaN. To avoid this, the bounding_box has been properly set to
accommodate this; therefore, it is recommended that this model always
be evaluated with the ``with_bounding_box=True`` option.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import ArcSine1D
plt.figure()
s1 = ArcSine1D(amplitude=1, frequency=.25)
r=np.arange(-1, 1, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([-1, 1, -np.pi/2, np.pi/2])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional ArcSine model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = x / amplitude
if isinstance(argument, Quantity):
argument = argument.value
arc_sine = np.arcsin(argument) / TWOPI
return (arc_sine - phase) / frequency
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional ArcSine model derivative"""
d_amplitude = - x / (TWOPI * frequency * amplitude**2 * np.sqrt(1 - (x / amplitude)**2))
d_frequency = (phase - (np.arcsin(x / amplitude) / TWOPI)) / frequency**2
d_phase = - 1 / frequency * np.ones(x.shape)
return [d_amplitude, d_frequency, d_phase]
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``
"""
return -1 * self.amplitude, 1 * self.amplitude
@property
def inverse(self):
"""One dimensional inverse of ArcSine"""
return Sine1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
class ArcCosine1D(_InverseTrigonometric1D):
"""
One dimensional ArcCosine returning values between 0 and pi only.
Parameters
----------
amplitude : float
Oscillation amplitude for corresponding Cosine
frequency : float
Oscillation frequency for corresponding Cosine
phase : float
Oscillation phase for corresponding Cosine
See Also
--------
Cosine1D, ArcSine1D, ArcTangent1D
Notes
-----
Model formula:
.. math:: f(x) = ((arccos(x / A) / 2pi) - p) / f
The arccos function being used for this model will only accept inputs
in [-A, A]; otherwise, a runtime warning will be thrown and the result
will be NaN. To avoid this, the bounding_box has been properly set to
accommodate this; therefore, it is recommended that this model always
be evaluated with the ``with_bounding_box=True`` option.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import ArcCosine1D
plt.figure()
s1 = ArcCosine1D(amplitude=1, frequency=.25)
r=np.arange(-1, 1, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([-1, 1, 0, np.pi])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional ArcCosine model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = x / amplitude
if isinstance(argument, Quantity):
argument = argument.value
arc_cos = np.arccos(argument) / TWOPI
return (arc_cos - phase) / frequency
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional ArcCosine model derivative"""
d_amplitude = x / (TWOPI * frequency * amplitude**2 * np.sqrt(1 - (x / amplitude)**2))
d_frequency = (phase - (np.arccos(x / amplitude) / TWOPI)) / frequency**2
d_phase = - 1 / frequency * np.ones(x.shape)
return [d_amplitude, d_frequency, d_phase]
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``
"""
return -1 * self.amplitude, 1 * self.amplitude
@property
def inverse(self):
"""One dimensional inverse of ArcCosine"""
return Cosine1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
class ArcTangent1D(_InverseTrigonometric1D):
"""
One dimensional ArcTangent model returning values between -pi/2 and
pi/2 only.
Parameters
----------
amplitude : float
Oscillation amplitude for corresponding Tangent
frequency : float
Oscillation frequency for corresponding Tangent
phase : float
Oscillation phase for corresponding Tangent
See Also
--------
Tangent1D, ArcSine1D, ArcCosine1D
Notes
-----
Model formula:
.. math:: f(x) = ((arctan(x / A) / 2pi) - p) / f
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import ArcTangent1D
plt.figure()
s1 = ArcTangent1D(amplitude=1, frequency=.25)
r=np.arange(-10, 10, .01)
for amplitude in range(1,4):
s1.amplitude = amplitude
plt.plot(r, s1(r), color=str(0.25 * amplitude), lw=2)
plt.axis([-10, 10, -np.pi/2, np.pi/2])
plt.show()
"""
@staticmethod
def evaluate(x, amplitude, frequency, phase):
"""One dimensional ArcTangent model function"""
# Note: If frequency and x are quantities, they should normally have
# inverse units, so that argument ends up being dimensionless. However,
# np.sin of a dimensionless quantity will crash, so we remove the
# quantity-ness from argument in this case (another option would be to
# multiply by * u.rad but this would be slower overall).
argument = x / amplitude
if isinstance(argument, Quantity):
argument = argument.value
arc_cos = np.arctan(argument) / TWOPI
return (arc_cos - phase) / frequency
@staticmethod
def fit_deriv(x, amplitude, frequency, phase):
"""One dimensional ArcTangent model derivative"""
d_amplitude = - x / (TWOPI * frequency * amplitude**2 * (1 + (x / amplitude)**2))
d_frequency = (phase - (np.arctan(x / amplitude) / TWOPI)) / frequency**2
d_phase = - 1 / frequency * np.ones(x.shape)
return [d_amplitude, d_frequency, d_phase]
@property
def inverse(self):
"""One dimensional inverse of ArcTangent"""
return Tangent1D(amplitude=self.amplitude, frequency=self.frequency, phase=self.phase)
class Linear1D(Fittable1DModel):
"""
One dimensional Line model.
Parameters
----------
slope : float
Slope of the straight line
intercept : float
Intercept of the straight line
See Also
--------
Const1D
Notes
-----
Model formula:
.. math:: f(x) = a x + b
"""
slope = Parameter(default=1, description="Slope of the straight line")
intercept = Parameter(default=0, description="Intercept of the straight line")
linear = True
@staticmethod
def evaluate(x, slope, intercept):
"""One dimensional Line model function"""
return slope * x + intercept
@staticmethod
def fit_deriv(x, *params):
"""One dimensional Line model derivative with respect to parameters"""
d_slope = x
d_intercept = np.ones_like(x)
return [d_slope, d_intercept]
@property
def inverse(self):
new_slope = self.slope ** -1
new_intercept = -self.intercept / self.slope
return self.__class__(slope=new_slope, intercept=new_intercept)
@property
def input_units(self):
if self.intercept.unit is None and self.slope.unit is None:
return None
return {self.inputs[0]: self.intercept.unit / self.slope.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'intercept': outputs_unit[self.outputs[0]],
'slope': outputs_unit[self.outputs[0]] / inputs_unit[self.inputs[0]]}
class Planar2D(Fittable2DModel):
"""
Two dimensional Plane model.
Parameters
----------
slope_x : float
Slope of the plane in X
slope_y : float
Slope of the plane in Y
intercept : float
Z-intercept of the plane
Notes
-----
Model formula:
.. math:: f(x, y) = a x + b y + c
"""
slope_x = Parameter(default=1, description="Slope of the plane in X")
slope_y = Parameter(default=1, description="Slope of the plane in Y")
intercept = Parameter(default=0, description="Z-intercept of the plane")
linear = True
@staticmethod
def evaluate(x, y, slope_x, slope_y, intercept):
"""Two dimensional Plane model function"""
return slope_x * x + slope_y * y + intercept
@staticmethod
def fit_deriv(x, y, *params):
"""Two dimensional Plane model derivative with respect to parameters"""
d_slope_x = x
d_slope_y = y
d_intercept = np.ones_like(x)
return [d_slope_x, d_slope_y, d_intercept]
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'intercept': outputs_unit['z'],
'slope_x': outputs_unit['z'] / inputs_unit['x'],
'slope_y': outputs_unit['z'] / inputs_unit['y']}
class Lorentz1D(Fittable1DModel):
"""
One dimensional Lorentzian model.
Parameters
----------
amplitude : float or `~astropy.units.Quantity`.
Peak value - for a normalized profile (integrating to 1),
set amplitude = 2 / (np.pi * fwhm)
x_0 : float or `~astropy.units.Quantity`.
Position of the peak
fwhm : float or `~astropy.units.Quantity`.
Full width at half maximum (FWHM)
See Also
--------
Gaussian1D, Box1D, RickerWavelet1D
Notes
-----
Either all or none of input ``x``, position ``x_0`` and ``fwhm`` must be provided
consistently with compatible units or as unitless numbers.
Model formula:
.. math::
f(x) = \\frac{A \\gamma^{2}}{\\gamma^{2} + \\left(x - x_{0}\\right)^{2}}
where :math:`\\gamma` is half of given FWHM.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Lorentz1D
plt.figure()
s1 = Lorentz1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Peak value")
x_0 = Parameter(default=0, description="Position of the peak")
fwhm = Parameter(default=1, description="Full width at half maximum")
@staticmethod
def evaluate(x, amplitude, x_0, fwhm):
"""One dimensional Lorentzian model function"""
return (amplitude * ((fwhm / 2.) ** 2) / ((x - x_0) ** 2 +
(fwhm / 2.) ** 2))
@staticmethod
def fit_deriv(x, amplitude, x_0, fwhm):
"""One dimensional Lorentzian model derivative with respect to parameters"""
d_amplitude = fwhm ** 2 / (fwhm ** 2 + (x - x_0) ** 2)
d_x_0 = (amplitude * d_amplitude * (2 * x - 2 * x_0) /
(fwhm ** 2 + (x - x_0) ** 2))
d_fwhm = 2 * amplitude * d_amplitude / fwhm * (1 - d_amplitude)
return [d_amplitude, d_x_0, d_fwhm]
def bounding_box(self, factor=25):
"""Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``.
Parameters
----------
factor : float
The multiple of FWHM used to define the limits.
Default is chosen to include most (99%) of the
area under the curve, while still showing the
central feature of interest.
"""
x0 = self.x_0
dx = factor * self.fwhm
return (x0 - dx, x0 + dx)
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'fwhm': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Voigt1D(Fittable1DModel):
"""
One dimensional model for the Voigt profile.
Parameters
----------
x_0 : float or `~astropy.units.Quantity`
Position of the peak
amplitude_L : float or `~astropy.units.Quantity`.
The Lorentzian amplitude (peak of the associated Lorentz function)
- for a normalized profile (integrating to 1), set
amplitude_L = 2 / (np.pi * fwhm_L)
fwhm_L : float or `~astropy.units.Quantity`
The Lorentzian full width at half maximum
fwhm_G : float or `~astropy.units.Quantity`.
The Gaussian full width at half maximum
method : str, optional
Algorithm for computing the complex error function; one of
'Humlicek2' (default, fast and generally more accurate than ``rtol=3.e-5``) or
'Scipy', alternatively 'wofz' (requires ``scipy``, almost as fast and
reference in accuracy).
See Also
--------
Gaussian1D, Lorentz1D
Notes
-----
Either all or none of input ``x``, position ``x_0`` and the ``fwhm_*`` must be provided
consistently with compatible units or as unitless numbers.
Voigt function is calculated as real part of the complex error function computed from either
Humlicek's rational approximations (JQSRT 21:309, 1979; 27:437, 1982) following
Schreier 2018 (MNRAS 479, 3068; and ``hum2zpf16m`` from his cpfX.py module); or
`~scipy.special.wofz` (implementing 'Faddeeva.cc').
Examples
--------
.. plot::
:include-source:
import numpy as np
from astropy.modeling.models import Voigt1D
import matplotlib.pyplot as plt
plt.figure()
x = np.arange(0, 10, 0.01)
v1 = Voigt1D(x_0=5, amplitude_L=10, fwhm_L=0.5, fwhm_G=0.9)
plt.plot(x, v1(x))
plt.show()
"""
x_0 = Parameter(default=0,
description="Position of the peak")
amplitude_L = Parameter(default=1, # noqa: N815
description="The Lorentzian amplitude")
fwhm_L = Parameter(default=2/np.pi, # noqa: N815
description="The Lorentzian full width at half maximum")
fwhm_G = Parameter(default=np.log(2), # noqa: N815
description="The Gaussian full width at half maximum")
sqrt_pi = np.sqrt(np.pi)
sqrt_ln2 = np.sqrt(np.log(2))
sqrt_ln2pi = np.sqrt(np.log(2) * np.pi)
_last_z = np.zeros(1, dtype=complex)
_last_w = np.zeros(1, dtype=float)
_faddeeva = None
def __init__(self, x_0=x_0.default, amplitude_L=amplitude_L.default, # noqa: N803
fwhm_L=fwhm_L.default, fwhm_G=fwhm_G.default, method='humlicek2', # noqa: N803
**kwargs):
if str(method).lower() in ('wofz', 'scipy'):
from scipy.special import wofz
self._faddeeva = wofz
elif str(method).lower() == 'humlicek2':
self._faddeeva = self._hum2zpf16c
else:
raise ValueError(f'Not a valid method for Voigt1D Faddeeva function: {method}.')
self.method = self._faddeeva.__name__
super().__init__(x_0=x_0, amplitude_L=amplitude_L, fwhm_L=fwhm_L, fwhm_G=fwhm_G, **kwargs)
def _wrap_wofz(self, z):
"""Call complex error (Faddeeva) function w(z) implemented by algorithm `method`;
cache results for consecutive calls from `evaluate`, `fit_deriv`."""
if (z.shape == self._last_z.shape and
np.allclose(z, self._last_z, rtol=1.e-14, atol=1.e-15)):
return self._last_w
self._last_w = self._faddeeva(z)
self._last_z = z
return self._last_w
def evaluate(self, x, x_0, amplitude_L, fwhm_L, fwhm_G): # noqa: N803
"""One dimensional Voigt function scaled to Lorentz peak amplitude."""
z = np.atleast_1d(2 * (x - x_0) + 1j * fwhm_L) * self.sqrt_ln2 / fwhm_G
# The normalised Voigt profile is w.real * self.sqrt_ln2 / (self.sqrt_pi * fwhm_G) * 2 ;
# for the legacy definition we multiply with np.pi * fwhm_L / 2 * amplitude_L
return self._wrap_wofz(z).real * self.sqrt_ln2pi / fwhm_G * fwhm_L * amplitude_L
def fit_deriv(self, x, x_0, amplitude_L, fwhm_L, fwhm_G): # noqa: N803
"""Derivative of the one dimensional Voigt function with respect to parameters."""
s = self.sqrt_ln2 / fwhm_G
z = np.atleast_1d(2 * (x - x_0) + 1j * fwhm_L) * s
# V * constant from McLean implementation (== their Voigt function)
w = self._wrap_wofz(z) * s * fwhm_L * amplitude_L * self.sqrt_pi
# Schreier (2018) Eq. 6 == (dvdx + 1j * dvdy) / (sqrt(pi) * fwhm_L * amplitude_L)
dwdz = -2 * z * w + 2j * s * fwhm_L * amplitude_L
return [-dwdz.real * 2 * s,
w.real / amplitude_L,
w.real / fwhm_L - dwdz.imag * s,
(-w.real - s * (2 * (x - x_0) * dwdz.real - fwhm_L * dwdz.imag)) / fwhm_G]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'fwhm_L': inputs_unit[self.inputs[0]],
'fwhm_G': inputs_unit[self.inputs[0]],
'amplitude_L': outputs_unit[self.outputs[0]]}
@staticmethod
def _hum2zpf16c(z, s=10.0):
"""Complex error function w(z) for z = x + iy combining Humlicek's rational approximations:
|x| + y > 10: Humlicek (JQSRT, 1982) rational approximation for region II;
else: Humlicek (JQSRT, 1979) rational approximation with n=16 and delta=y0=1.35
Version using a mask and np.place;
single complex argument version of Franz Schreier's cpfX.hum2zpf16m.
Originally licensed under a 3-clause BSD style license - see
https://atmos.eoc.dlr.de/tools/lbl4IR/cpfX.py
"""
# Optimized (single fraction) Humlicek region I rational approximation for n=16, delta=1.35
AA = np.array([+46236.3358828121, -147726.58393079657j, # noqa: N806
-206562.80451354137, 281369.1590631087j,
+183092.74968253175, -184787.96830696272j,
-66155.39578477248, 57778.05827983565j,
+11682.770904216826, -9442.402767960672j,
-1052.8438624933142, 814.0996198624186j,
+45.94499030751872, -34.59751573708725j,
-0.7616559377907136, 0.5641895835476449j]) # 1j/sqrt(pi) to the 12. digit
bb = np.array([+7918.06640624997, 0.0,
-126689.0625, 0.0,
+295607.8125, 0.0,
-236486.25, 0.0,
+84459.375, 0.0,
-15015.0, 0.0,
+1365.0, 0.0,
-60.0, 0.0,
+1.0])
sqrt_piinv = 1.0 / np.sqrt(np.pi)
zz = z * z
w = 1j * (z * (zz * sqrt_piinv - 1.410474)) / (0.75 + zz*(zz - 3.0))
if np.any(z.imag < s):
mask = abs(z.real) + z.imag < s # returns true for interior points
# returns small complex array covering only the interior region
Z = z[np.where(mask)] + 1.35j
ZZ = Z * Z
numer = (((((((((((((((AA[15]*Z + AA[14])*Z + AA[13])*Z + AA[12])*Z + AA[11])*Z +
AA[10])*Z + AA[9])*Z + AA[8])*Z + AA[7])*Z + AA[6])*Z +
AA[5])*Z + AA[4])*Z+AA[3])*Z + AA[2])*Z + AA[1])*Z + AA[0])
denom = (((((((ZZ + bb[14])*ZZ + bb[12])*ZZ + bb[10])*ZZ+bb[8])*ZZ + bb[6])*ZZ +
bb[4])*ZZ + bb[2])*ZZ + bb[0]
np.place(w, mask, numer / denom)
return w
class Const1D(Fittable1DModel):
"""
One dimensional Constant model.
Parameters
----------
amplitude : float
Value of the constant function
See Also
--------
Const2D
Notes
-----
Model formula:
.. math:: f(x) = A
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Const1D
plt.figure()
s1 = Const1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Value of the constant function")
linear = True
@staticmethod
def evaluate(x, amplitude):
"""One dimensional Constant model function"""
if amplitude.size == 1:
# This is slightly faster than using ones_like and multiplying
x = np.empty_like(x, subok=False)
x.fill(amplitude.item())
else:
# This case is less likely but could occur if the amplitude
# parameter is given an array-like value
x = amplitude * np.ones_like(x, subok=False)
if isinstance(amplitude, Quantity):
return Quantity(x, unit=amplitude.unit, copy=False)
return x
@staticmethod
def fit_deriv(x, amplitude):
"""One dimensional Constant model derivative with respect to parameters"""
d_amplitude = np.ones_like(x)
return [d_amplitude]
@property
def input_units(self):
return None
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'amplitude': outputs_unit[self.outputs[0]]}
class Const2D(Fittable2DModel):
"""
Two dimensional Constant model.
Parameters
----------
amplitude : float
Value of the constant function
See Also
--------
Const1D
Notes
-----
Model formula:
.. math:: f(x, y) = A
"""
amplitude = Parameter(default=1, description="Value of the constant function")
linear = True
@staticmethod
def evaluate(x, y, amplitude):
"""Two dimensional Constant model function"""
if amplitude.size == 1:
# This is slightly faster than using ones_like and multiplying
x = np.empty_like(x, subok=False)
x.fill(amplitude.item())
else:
# This case is less likely but could occur if the amplitude
# parameter is given an array-like value
x = amplitude * np.ones_like(x, subok=False)
if isinstance(amplitude, Quantity):
return Quantity(x, unit=amplitude.unit, copy=False)
return x
@property
def input_units(self):
return None
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'amplitude': outputs_unit[self.outputs[0]]}
class Ellipse2D(Fittable2DModel):
"""
A 2D Ellipse model.
Parameters
----------
amplitude : float
Value of the ellipse.
x_0 : float
x position of the center of the disk.
y_0 : float
y position of the center of the disk.
a : float
The length of the semimajor axis.
b : float
The length of the semiminor axis.
theta : float
The rotation angle in radians of the semimajor axis. The
rotation angle increases counterclockwise from the positive x
axis.
See Also
--------
Disk2D, Box2D
Notes
-----
Model formula:
.. math::
f(x, y) = \\left \\{
\\begin{array}{ll}
\\mathrm{amplitude} & : \\left[\\frac{(x - x_0) \\cos
\\theta + (y - y_0) \\sin \\theta}{a}\\right]^2 +
\\left[\\frac{-(x - x_0) \\sin \\theta + (y - y_0)
\\cos \\theta}{b}\\right]^2 \\leq 1 \\\\
0 & : \\mathrm{otherwise}
\\end{array}
\\right.
Examples
--------
.. plot::
:include-source:
import numpy as np
from astropy.modeling.models import Ellipse2D
from astropy.coordinates import Angle
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
x0, y0 = 25, 25
a, b = 20, 10
theta = Angle(30, 'deg')
e = Ellipse2D(amplitude=100., x_0=x0, y_0=y0, a=a, b=b,
theta=theta.radian)
y, x = np.mgrid[0:50, 0:50]
fig, ax = plt.subplots(1, 1)
ax.imshow(e(x, y), origin='lower', interpolation='none', cmap='Greys_r')
e2 = mpatches.Ellipse((x0, y0), 2*a, 2*b, theta.degree, edgecolor='red',
facecolor='none')
ax.add_patch(e2)
plt.show()
"""
amplitude = Parameter(default=1, description="Value of the ellipse")
x_0 = Parameter(default=0, description="X position of the center of the disk.")
y_0 = Parameter(default=0, description="Y position of the center of the disk.")
a = Parameter(default=1, description="The length of the semimajor axis")
b = Parameter(default=1, description="The length of the semiminor axis")
theta = Parameter(default=0, description="The rotation angle in radians of the semimajor axis (Positive - counterclockwise)")
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, a, b, theta):
"""Two dimensional Ellipse model function."""
xx = x - x_0
yy = y - y_0
cost = np.cos(theta)
sint = np.sin(theta)
numerator1 = (xx * cost) + (yy * sint)
numerator2 = -(xx * sint) + (yy * cost)
in_ellipse = (((numerator1 / a) ** 2 + (numerator2 / b) ** 2) <= 1.)
result = np.select([in_ellipse], [amplitude])
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits.
``((y_low, y_high), (x_low, x_high))``
"""
a = self.a
b = self.b
theta = self.theta.value
dx, dy = ellipse_extent(a, b, theta)
return ((self.y_0 - dy, self.y_0 + dy),
(self.x_0 - dx, self.x_0 + dx))
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'a': inputs_unit[self.inputs[0]],
'b': inputs_unit[self.inputs[0]],
'theta': u.rad,
'amplitude': outputs_unit[self.outputs[0]]}
class Disk2D(Fittable2DModel):
"""
Two dimensional radial symmetric Disk model.
Parameters
----------
amplitude : float
Value of the disk function
x_0 : float
x position center of the disk
y_0 : float
y position center of the disk
R_0 : float
Radius of the disk
See Also
--------
Box2D, TrapezoidDisk2D
Notes
-----
Model formula:
.. math::
f(r) = \\left \\{
\\begin{array}{ll}
A & : r \\leq R_0 \\\\
0 & : r > R_0
\\end{array}
\\right.
"""
amplitude = Parameter(default=1, description="Value of disk function")
x_0 = Parameter(default=0, description="X position of center of the disk")
y_0 = Parameter(default=0, description="Y position of center of the disk")
R_0 = Parameter(default=1, description="Radius of the disk")
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, R_0):
"""Two dimensional Disk model function"""
rr = (x - x_0) ** 2 + (y - y_0) ** 2
result = np.select([rr <= R_0 ** 2], [amplitude])
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits.
``((y_low, y_high), (x_low, x_high))``
"""
return ((self.y_0 - self.R_0, self.y_0 + self.R_0),
(self.x_0 - self.R_0, self.x_0 + self.R_0))
@property
def input_units(self):
if self.x_0.unit is None and self.y_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'R_0': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Ring2D(Fittable2DModel):
"""
Two dimensional radial symmetric Ring model.
Parameters
----------
amplitude : float
Value of the disk function
x_0 : float
x position center of the disk
y_0 : float
y position center of the disk
r_in : float
Inner radius of the ring
width : float
Width of the ring.
r_out : float
Outer Radius of the ring. Can be specified instead of width.
See Also
--------
Disk2D, TrapezoidDisk2D
Notes
-----
Model formula:
.. math::
f(r) = \\left \\{
\\begin{array}{ll}
A & : r_{in} \\leq r \\leq r_{out} \\\\
0 & : \\text{else}
\\end{array}
\\right.
Where :math:`r_{out} = r_{in} + r_{width}`.
"""
amplitude = Parameter(default=1, description="Value of the disk function")
x_0 = Parameter(default=0, description="X position of center of disc")
y_0 = Parameter(default=0, description="Y position of center of disc")
r_in = Parameter(default=1, description="Inner radius of the ring")
width = Parameter(default=1, description="Width of the ring")
def __init__(self, amplitude=amplitude.default, x_0=x_0.default,
y_0=y_0.default, r_in=None, width=None,
r_out=None, **kwargs):
if (r_in is None) and (r_out is None) and (width is None):
r_in = self.r_in.default
width = self.width.default
elif (r_in is not None) and (r_out is None) and (width is None):
width = self.width.default
elif (r_in is None) and (r_out is not None) and (width is None):
r_in = self.r_in.default
width = r_out - r_in
elif (r_in is None) and (r_out is None) and (width is not None):
r_in = self.r_in.default
elif (r_in is not None) and (r_out is not None) and (width is None):
width = r_out - r_in
elif (r_in is None) and (r_out is not None) and (width is not None):
r_in = r_out - width
elif (r_in is not None) and (r_out is not None) and (width is not None):
if np.any(width != (r_out - r_in)):
raise InputParameterError("Width must be r_out - r_in")
if np.any(r_in < 0) or np.any(width < 0):
raise InputParameterError(f"{r_in=} and {width=} must both be >=0")
super().__init__(
amplitude=amplitude, x_0=x_0, y_0=y_0, r_in=r_in, width=width,
**kwargs)
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, r_in, width):
"""Two dimensional Ring model function."""
rr = (x - x_0) ** 2 + (y - y_0) ** 2
r_range = np.logical_and(rr >= r_in ** 2, rr <= (r_in + width) ** 2)
result = np.select([r_range], [amplitude])
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box``.
``((y_low, y_high), (x_low, x_high))``
"""
dr = self.r_in + self.width
return ((self.y_0 - dr, self.y_0 + dr),
(self.x_0 - dr, self.x_0 + dr))
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'r_in': inputs_unit[self.inputs[0]],
'width': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Box1D(Fittable1DModel):
"""
One dimensional Box model.
Parameters
----------
amplitude : float
Amplitude A
x_0 : float
Position of the center of the box function
width : float
Width of the box
See Also
--------
Box2D, TrapezoidDisk2D
Notes
-----
Model formula:
.. math::
f(x) = \\left \\{
\\begin{array}{ll}
A & : x_0 - w/2 \\leq x \\leq x_0 + w/2 \\\\
0 & : \\text{else}
\\end{array}
\\right.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Box1D
plt.figure()
s1 = Box1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
s1.width = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Amplitude A")
x_0 = Parameter(default=0, description="Position of center of box function")
width = Parameter(default=1, description="Width of the box")
@staticmethod
def evaluate(x, amplitude, x_0, width):
"""One dimensional Box model function"""
inside = np.logical_and(x >= x_0 - width / 2., x <= x_0 + width / 2.)
return np.select([inside], [amplitude], 0)
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits.
``(x_low, x_high))``
"""
dx = self.width / 2
return (self.x_0 - dx, self.x_0 + dx)
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
@property
def return_units(self):
if self.amplitude.unit is None:
return None
return {self.outputs[0]: self.amplitude.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'width': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Box2D(Fittable2DModel):
"""
Two dimensional Box model.
Parameters
----------
amplitude : float
Amplitude
x_0 : float
x position of the center of the box function
x_width : float
Width in x direction of the box
y_0 : float
y position of the center of the box function
y_width : float
Width in y direction of the box
See Also
--------
Box1D, Gaussian2D, Moffat2D
Notes
-----
Model formula:
.. math::
f(x, y) = \\left \\{
\\begin{array}{ll}
A : & x_0 - w_x/2 \\leq x \\leq x_0 + w_x/2 \\text{ and} \\\\
& y_0 - w_y/2 \\leq y \\leq y_0 + w_y/2 \\\\
0 : & \\text{else}
\\end{array}
\\right.
"""
amplitude = Parameter(default=1, description="Amplitude")
x_0 = Parameter(default=0, description="X position of the center of the box function")
y_0 = Parameter(default=0, description="Y position of the center of the box function")
x_width = Parameter(default=1, description="Width in x direction of the box")
y_width = Parameter(default=1, description="Width in y direction of the box")
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, x_width, y_width):
"""Two dimensional Box model function"""
x_range = np.logical_and(x >= x_0 - x_width / 2.,
x <= x_0 + x_width / 2.)
y_range = np.logical_and(y >= y_0 - y_width / 2.,
y <= y_0 + y_width / 2.)
result = np.select([np.logical_and(x_range, y_range)], [amplitude], 0)
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box``.
``((y_low, y_high), (x_low, x_high))``
"""
dx = self.x_width / 2
dy = self.y_width / 2
return ((self.y_0 - dy, self.y_0 + dy),
(self.x_0 - dx, self.x_0 + dx))
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[1]],
'x_width': inputs_unit[self.inputs[0]],
'y_width': inputs_unit[self.inputs[1]],
'amplitude': outputs_unit[self.outputs[0]]}
class Trapezoid1D(Fittable1DModel):
"""
One dimensional Trapezoid model.
Parameters
----------
amplitude : float
Amplitude of the trapezoid
x_0 : float
Center position of the trapezoid
width : float
Width of the constant part of the trapezoid.
slope : float
Slope of the tails of the trapezoid
See Also
--------
Box1D, Gaussian1D, Moffat1D
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Trapezoid1D
plt.figure()
s1 = Trapezoid1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
s1.width = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Amplitude of the trapezoid")
x_0 = Parameter(default=0, description="Center position of the trapezoid")
width = Parameter(default=1, description="Width of constant part of the trapezoid")
slope = Parameter(default=1, description="Slope of the tails of trapezoid")
@staticmethod
def evaluate(x, amplitude, x_0, width, slope):
"""One dimensional Trapezoid model function"""
# Compute the four points where the trapezoid changes slope
# x1 <= x2 <= x3 <= x4
x2 = x_0 - width / 2.
x3 = x_0 + width / 2.
x1 = x2 - amplitude / slope
x4 = x3 + amplitude / slope
# Compute model values in pieces between the change points
range_a = np.logical_and(x >= x1, x < x2)
range_b = np.logical_and(x >= x2, x < x3)
range_c = np.logical_and(x >= x3, x < x4)
val_a = slope * (x - x1)
val_b = amplitude
val_c = slope * (x4 - x)
result = np.select([range_a, range_b, range_c], [val_a, val_b, val_c])
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits.
``(x_low, x_high))``
"""
dx = self.width / 2 + self.amplitude / self.slope
return (self.x_0 - dx, self.x_0 + dx)
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'width': inputs_unit[self.inputs[0]],
'slope': outputs_unit[self.outputs[0]] / inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class TrapezoidDisk2D(Fittable2DModel):
"""
Two dimensional circular Trapezoid model.
Parameters
----------
amplitude : float
Amplitude of the trapezoid
x_0 : float
x position of the center of the trapezoid
y_0 : float
y position of the center of the trapezoid
R_0 : float
Radius of the constant part of the trapezoid.
slope : float
Slope of the tails of the trapezoid in x direction.
See Also
--------
Disk2D, Box2D
"""
amplitude = Parameter(default=1, description="Amplitude of the trapezoid")
x_0 = Parameter(default=0, description="X position of the center of the trapezoid")
y_0 = Parameter(default=0, description="Y position of the center of the trapezoid")
R_0 = Parameter(default=1, description="Radius of constant part of trapezoid")
slope = Parameter(default=1, description="Slope of tails of trapezoid in x direction")
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, R_0, slope):
"""Two dimensional Trapezoid Disk model function"""
r = np.sqrt((x - x_0) ** 2 + (y - y_0) ** 2)
range_1 = r <= R_0
range_2 = np.logical_and(r > R_0, r <= R_0 + amplitude / slope)
val_1 = amplitude
val_2 = amplitude + slope * (R_0 - r)
result = np.select([range_1, range_2], [val_1, val_2])
if isinstance(amplitude, Quantity):
return Quantity(result, unit=amplitude.unit, copy=False)
return result
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box``.
``((y_low, y_high), (x_low, x_high))``
"""
dr = self.R_0 + self.amplitude / self.slope
return ((self.y_0 - dr, self.y_0 + dr),
(self.x_0 - dr, self.x_0 + dr))
@property
def input_units(self):
if self.x_0.unit is None and self.y_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit['x'] != inputs_unit['y']:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'R_0': inputs_unit[self.inputs[0]],
'slope': outputs_unit[self.outputs[0]] / inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class RickerWavelet1D(Fittable1DModel):
"""
One dimensional Ricker Wavelet model (sometimes known as a "Mexican Hat"
model).
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this model.
Parameters
----------
amplitude : float
Amplitude
x_0 : float
Position of the peak
sigma : float
Width of the Ricker wavelet
See Also
--------
RickerWavelet2D, Box1D, Gaussian1D, Trapezoid1D
Notes
-----
Model formula:
.. math::
f(x) = {A \\left(1 - \\frac{\\left(x - x_{0}\\right)^{2}}{\\sigma^{2}}\\right)
e^{- \\frac{\\left(x - x_{0}\\right)^{2}}{2 \\sigma^{2}}}}
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import RickerWavelet1D
plt.figure()
s1 = RickerWavelet1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
s1.width = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -2, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Amplitude (peak) value")
x_0 = Parameter(default=0, description="Position of the peak")
sigma = Parameter(default=1, description="Width of the Ricker wavelet")
@staticmethod
def evaluate(x, amplitude, x_0, sigma):
"""One dimensional Ricker Wavelet model function"""
xx_ww = (x - x_0) ** 2 / (2 * sigma ** 2)
return amplitude * (1 - 2 * xx_ww) * np.exp(-xx_ww)
def bounding_box(self, factor=10.0):
"""Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``.
Parameters
----------
factor : float
The multiple of sigma used to define the limits.
"""
x0 = self.x_0
dx = factor * self.sigma
return (x0 - dx, x0 + dx)
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'sigma': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class RickerWavelet2D(Fittable2DModel):
"""
Two dimensional Ricker Wavelet model (sometimes known as a "Mexican Hat"
model).
.. note::
See https://github.com/astropy/astropy/pull/9445 for discussions
related to renaming of this model.
Parameters
----------
amplitude : float
Amplitude
x_0 : float
x position of the peak
y_0 : float
y position of the peak
sigma : float
Width of the Ricker wavelet
See Also
--------
RickerWavelet1D, Gaussian2D
Notes
-----
Model formula:
.. math::
f(x, y) = A \\left(1 - \\frac{\\left(x - x_{0}\\right)^{2}
+ \\left(y - y_{0}\\right)^{2}}{\\sigma^{2}}\\right)
e^{\\frac{- \\left(x - x_{0}\\right)^{2}
- \\left(y - y_{0}\\right)^{2}}{2 \\sigma^{2}}}
"""
amplitude = Parameter(default=1, description="Amplitude (peak) value")
x_0 = Parameter(default=0, description="X position of the peak")
y_0 = Parameter(default=0, description="Y position of the peak")
sigma = Parameter(default=1, description="Width of the Ricker wavelet")
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, sigma):
"""Two dimensional Ricker Wavelet model function"""
rr_ww = ((x - x_0) ** 2 + (y - y_0) ** 2) / (2 * sigma ** 2)
return amplitude * (1 - rr_ww) * np.exp(- rr_ww)
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'sigma': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class AiryDisk2D(Fittable2DModel):
"""
Two dimensional Airy disk model.
Parameters
----------
amplitude : float
Amplitude of the Airy function.
x_0 : float
x position of the maximum of the Airy function.
y_0 : float
y position of the maximum of the Airy function.
radius : float
The radius of the Airy disk (radius of the first zero).
See Also
--------
Box2D, TrapezoidDisk2D, Gaussian2D
Notes
-----
Model formula:
.. math:: f(r) = A \\left[\\frac{2 J_1(\\frac{\\pi r}{R/R_z})}{\\frac{\\pi r}{R/R_z}}\\right]^2
Where :math:`J_1` is the first order Bessel function of the first
kind, :math:`r` is radial distance from the maximum of the Airy
function (:math:`r = \\sqrt{(x - x_0)^2 + (y - y_0)^2}`), :math:`R`
is the input ``radius`` parameter, and :math:`R_z =
1.2196698912665045`).
For an optical system, the radius of the first zero represents the
limiting angular resolution and is approximately 1.22 * lambda / D,
where lambda is the wavelength of the light and D is the diameter of
the aperture.
See [1]_ for more details about the Airy disk.
References
----------
.. [1] https://en.wikipedia.org/wiki/Airy_disk
"""
amplitude = Parameter(default=1, description="Amplitude (peak value) of the Airy function")
x_0 = Parameter(default=0, description="X position of the peak")
y_0 = Parameter(default=0, description="Y position of the peak")
radius = Parameter(default=1,
description="The radius of the Airy disk (radius of first zero crossing)")
_rz = None
_j1 = None
@classmethod
def evaluate(cls, x, y, amplitude, x_0, y_0, radius):
"""Two dimensional Airy model function"""
if cls._rz is None:
from scipy.special import j1, jn_zeros
cls._rz = jn_zeros(1, 1)[0] / np.pi
cls._j1 = j1
r = np.sqrt((x - x_0) ** 2 + (y - y_0) ** 2) / (radius / cls._rz)
if isinstance(r, Quantity):
# scipy function cannot handle Quantity, so turn into array.
r = r.to_value(u.dimensionless_unscaled)
# Since r can be zero, we have to take care to treat that case
# separately so as not to raise a numpy warning
z = np.ones(r.shape)
rt = np.pi * r[r > 0]
z[r > 0] = (2.0 * cls._j1(rt) / rt) ** 2
if isinstance(amplitude, Quantity):
# make z quantity too, otherwise in-place multiplication fails.
z = Quantity(z, u.dimensionless_unscaled, copy=False)
z *= amplitude
return z
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'radius': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Moffat1D(Fittable1DModel):
"""
One dimensional Moffat model.
Parameters
----------
amplitude : float
Amplitude of the model.
x_0 : float
x position of the maximum of the Moffat model.
gamma : float
Core width of the Moffat model.
alpha : float
Power index of the Moffat model.
See Also
--------
Gaussian1D, Box1D
Notes
-----
Model formula:
.. math::
f(x) = A \\left(1 + \\frac{\\left(x - x_{0}\\right)^{2}}{\\gamma^{2}}\\right)^{- \\alpha}
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Moffat1D
plt.figure()
s1 = Moffat1D()
r = np.arange(-5, 5, .01)
for factor in range(1, 4):
s1.amplitude = factor
s1.width = factor
plt.plot(r, s1(r), color=str(0.25 * factor), lw=2)
plt.axis([-5, 5, -1, 4])
plt.show()
"""
amplitude = Parameter(default=1, description="Amplitude of the model")
x_0 = Parameter(default=0, description="X position of maximum of Moffat model")
gamma = Parameter(default=1, description="Core width of Moffat model")
alpha = Parameter(default=1, description="Power index of the Moffat model")
@property
def fwhm(self):
"""
Moffat full width at half maximum.
Derivation of the formula is available in
`this notebook by Yoonsoo Bach <https://nbviewer.jupyter.org/github/ysbach/AO_2017/blob/master/04_Ground_Based_Concept.ipynb#1.2.-Moffat>`_.
"""
return 2.0 * np.abs(self.gamma) * np.sqrt(2.0 ** (1.0 / self.alpha) - 1.0)
@staticmethod
def evaluate(x, amplitude, x_0, gamma, alpha):
"""One dimensional Moffat model function"""
return amplitude * (1 + ((x - x_0) / gamma) ** 2) ** (-alpha)
@staticmethod
def fit_deriv(x, amplitude, x_0, gamma, alpha):
"""One dimensional Moffat model derivative with respect to parameters"""
fac = (1 + (x - x_0) ** 2 / gamma ** 2)
d_A = fac ** (-alpha)
d_x_0 = (2 * amplitude * alpha * (x - x_0) * d_A / (fac * gamma ** 2))
d_gamma = (2 * amplitude * alpha * (x - x_0) ** 2 * d_A /
(fac * gamma ** 3))
d_alpha = -amplitude * d_A * np.log(fac)
return [d_A, d_x_0, d_gamma, d_alpha]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'gamma': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Moffat2D(Fittable2DModel):
"""
Two dimensional Moffat model.
Parameters
----------
amplitude : float
Amplitude of the model.
x_0 : float
x position of the maximum of the Moffat model.
y_0 : float
y position of the maximum of the Moffat model.
gamma : float
Core width of the Moffat model.
alpha : float
Power index of the Moffat model.
See Also
--------
Gaussian2D, Box2D
Notes
-----
Model formula:
.. math::
f(x, y) = A \\left(1 + \\frac{\\left(x - x_{0}\\right)^{2} +
\\left(y - y_{0}\\right)^{2}}{\\gamma^{2}}\\right)^{- \\alpha}
"""
amplitude = Parameter(default=1, description="Amplitude (peak value) of the model")
x_0 = Parameter(default=0, description="X position of the maximum of the Moffat model")
y_0 = Parameter(default=0, description="Y position of the maximum of the Moffat model")
gamma = Parameter(default=1, description="Core width of the Moffat model")
alpha = Parameter(default=1, description="Power index of the Moffat model")
@property
def fwhm(self):
"""
Moffat full width at half maximum.
Derivation of the formula is available in
`this notebook by Yoonsoo Bach <https://nbviewer.jupyter.org/github/ysbach/AO_2017/blob/master/04_Ground_Based_Concept.ipynb#1.2.-Moffat>`_.
"""
return 2.0 * np.abs(self.gamma) * np.sqrt(2.0 ** (1.0 / self.alpha) - 1.0)
@staticmethod
def evaluate(x, y, amplitude, x_0, y_0, gamma, alpha):
"""Two dimensional Moffat model function"""
rr_gg = ((x - x_0) ** 2 + (y - y_0) ** 2) / gamma ** 2
return amplitude * (1 + rr_gg) ** (-alpha)
@staticmethod
def fit_deriv(x, y, amplitude, x_0, y_0, gamma, alpha):
"""Two dimensional Moffat model derivative with respect to parameters"""
rr_gg = ((x - x_0) ** 2 + (y - y_0) ** 2) / gamma ** 2
d_A = (1 + rr_gg) ** (-alpha)
d_x_0 = (2 * amplitude * alpha * d_A * (x - x_0) /
(gamma ** 2 * (1 + rr_gg)))
d_y_0 = (2 * amplitude * alpha * d_A * (y - y_0) /
(gamma ** 2 * (1 + rr_gg)))
d_alpha = -amplitude * d_A * np.log(1 + rr_gg)
d_gamma = (2 * amplitude * alpha * d_A * rr_gg /
(gamma * (1 + rr_gg)))
return [d_A, d_x_0, d_y_0, d_gamma, d_alpha]
@property
def input_units(self):
if self.x_0.unit is None:
return None
else:
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'gamma': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Sersic2D(Fittable2DModel):
r"""
Two dimensional Sersic surface brightness profile.
Parameters
----------
amplitude : float
Surface brightness at r_eff.
r_eff : float
Effective (half-light) radius
n : float
Sersic Index.
x_0 : float, optional
x position of the center.
y_0 : float, optional
y position of the center.
ellip : float, optional
Ellipticity.
theta : float, optional
Rotation angle in radians, counterclockwise from
the positive x-axis.
See Also
--------
Gaussian2D, Moffat2D
Notes
-----
Model formula:
.. math::
I(x,y) = I(r) = I_e\exp\left\{-b_n\left[\left(\frac{r}{r_{e}}\right)^{(1/n)}-1\right]\right\}
The constant :math:`b_n` is defined such that :math:`r_e` contains half the total
luminosity, and can be solved for numerically.
.. math::
\Gamma(2n) = 2\gamma (2n,b_n)
Examples
--------
.. plot::
:include-source:
import numpy as np
from astropy.modeling.models import Sersic2D
import matplotlib.pyplot as plt
x,y = np.meshgrid(np.arange(100), np.arange(100))
mod = Sersic2D(amplitude = 1, r_eff = 25, n=4, x_0=50, y_0=50,
ellip=.5, theta=-1)
img = mod(x, y)
log_img = np.log10(img)
plt.figure()
plt.imshow(log_img, origin='lower', interpolation='nearest',
vmin=-1, vmax=2)
plt.xlabel('x')
plt.ylabel('y')
cbar = plt.colorbar()
cbar.set_label('Log Brightness', rotation=270, labelpad=25)
cbar.set_ticks([-1, 0, 1, 2], update_ticks=True)
plt.show()
References
----------
.. [1] http://ned.ipac.caltech.edu/level5/March05/Graham/Graham2.html
"""
amplitude = Parameter(default=1, description="Surface brightness at r_eff")
r_eff = Parameter(default=1, description="Effective (half-light) radius")
n = Parameter(default=4, description="Sersic Index")
x_0 = Parameter(default=0, description="X position of the center")
y_0 = Parameter(default=0, description="Y position of the center")
ellip = Parameter(default=0, description="Ellipticity")
theta = Parameter(default=0, description="Rotation angle in radians (counterclockwise-positive)")
_gammaincinv = None
@classmethod
def evaluate(cls, x, y, amplitude, r_eff, n, x_0, y_0, ellip, theta):
"""Two dimensional Sersic profile function."""
if cls._gammaincinv is None:
from scipy.special import gammaincinv
cls._gammaincinv = gammaincinv
bn = cls._gammaincinv(2. * n, 0.5)
a, b = r_eff, (1 - ellip) * r_eff
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = np.sqrt((x_maj / a) ** 2 + (x_min / b) ** 2)
return amplitude * np.exp(-bn * (z ** (1 / n) - 1))
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit,
self.inputs[1]: self.y_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
# Note that here we need to make sure that x and y are in the same
# units otherwise this can lead to issues since rotation is not well
# defined.
if inputs_unit[self.inputs[0]] != inputs_unit[self.inputs[1]]:
raise UnitsError("Units of 'x' and 'y' inputs should match")
return {'x_0': inputs_unit[self.inputs[0]],
'y_0': inputs_unit[self.inputs[0]],
'r_eff': inputs_unit[self.inputs[0]],
'theta': u.rad,
'amplitude': outputs_unit[self.outputs[0]]}
class KingProjectedAnalytic1D(Fittable1DModel):
"""
Projected (surface density) analytic King Model.
Parameters
----------
amplitude : float
Amplitude or scaling factor.
r_core : float
Core radius (f(r_c) ~ 0.5 f_0)
r_tide : float
Tidal radius.
Notes
-----
This model approximates a King model with an analytic function. The derivation of this
equation can be found in King '62 (equation 14). This is just an approximation of the
full model and the parameters derived from this model should be taken with caution.
It usually works for models with a concentration (c = log10(r_t/r_c) parameter < 2.
Model formula:
.. math::
f(x) = A r_c^2 \\left(\\frac{1}{\\sqrt{(x^2 + r_c^2)}} -
\\frac{1}{\\sqrt{(r_t^2 + r_c^2)}}\\right)^2
Examples
--------
.. plot::
:include-source:
import numpy as np
from astropy.modeling.models import KingProjectedAnalytic1D
import matplotlib.pyplot as plt
plt.figure()
rt_list = [1, 2, 5, 10, 20]
for rt in rt_list:
r = np.linspace(0.1, rt, 100)
mod = KingProjectedAnalytic1D(amplitude = 1, r_core = 1., r_tide = rt)
sig = mod(r)
plt.loglog(r, sig/sig[0], label='c ~ {:0.2f}'.format(mod.concentration))
plt.xlabel("r")
plt.ylabel(r"$\\sigma/\\sigma_0$")
plt.legend()
plt.show()
References
----------
.. [1] https://ui.adsabs.harvard.edu/abs/1962AJ.....67..471K
"""
amplitude = Parameter(default=1, bounds=(FLOAT_EPSILON, None), description="Amplitude or scaling factor")
r_core = Parameter(default=1, bounds=(FLOAT_EPSILON, None), description="Core Radius")
r_tide = Parameter(default=2, bounds=(FLOAT_EPSILON, None), description="Tidal Radius")
@property
def concentration(self):
"""Concentration parameter of the king model"""
return np.log10(np.abs(self.r_tide/self.r_core))
@staticmethod
def evaluate(x, amplitude, r_core, r_tide):
"""
Analytic King model function.
"""
result = amplitude * r_core ** 2 * (1/np.sqrt(x ** 2 + r_core ** 2) -
1/np.sqrt(r_tide ** 2 + r_core ** 2)) ** 2
# Set invalid r values to 0
bounds = (x >= r_tide) | (x < 0)
result[bounds] = result[bounds] * 0.
return result
@staticmethod
def fit_deriv(x, amplitude, r_core, r_tide):
"""
Analytic King model function derivatives.
"""
d_amplitude = r_core ** 2 * (1/np.sqrt(x ** 2 + r_core ** 2) -
1/np.sqrt(r_tide ** 2 + r_core ** 2)) ** 2
d_r_core = 2 * amplitude * r_core ** 2 * (r_core/(r_core ** 2 + r_tide ** 2) ** (3/2) -
r_core/(r_core ** 2 + x ** 2) ** (3/2)) * \
(1./np.sqrt(r_core ** 2 + x ** 2) - 1./np.sqrt(r_core ** 2 + r_tide ** 2)) + \
2 * amplitude * r_core * (1./np.sqrt(r_core ** 2 + x ** 2) -
1./np.sqrt(r_core ** 2 + r_tide ** 2)) ** 2
d_r_tide = (2 * amplitude * r_core ** 2 * r_tide *
(1./np.sqrt(r_core ** 2 + x ** 2) -
1./np.sqrt(r_core ** 2 + r_tide ** 2)))/(r_core ** 2 + r_tide ** 2) ** (3/2)
# Set invalid r values to 0
bounds = (x >= r_tide) | (x < 0)
d_amplitude[bounds] = d_amplitude[bounds]*0
d_r_core[bounds] = d_r_core[bounds]*0
d_r_tide[bounds] = d_r_tide[bounds]*0
return [d_amplitude, d_r_core, d_r_tide]
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits.
The model is not defined for r > r_tide.
``(r_low, r_high)``
"""
return (0 * self.r_tide, 1 * self.r_tide)
@property
def input_units(self):
if self.r_core.unit is None:
return None
return {self.inputs[0]: self.r_core.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'r_core': inputs_unit[self.inputs[0]],
'r_tide': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Logarithmic1D(Fittable1DModel):
"""
One dimensional logarithmic model.
Parameters
----------
amplitude : float, optional
tau : float, optional
See Also
--------
Exponential1D, Gaussian1D
"""
amplitude = Parameter(default=1)
tau = Parameter(default=1)
@staticmethod
def evaluate(x, amplitude, tau):
return amplitude * np.log(x / tau)
@staticmethod
def fit_deriv(x, amplitude, tau):
d_amplitude = np.log(x / tau)
d_tau = np.zeros(x.shape) - (amplitude / tau)
return [d_amplitude, d_tau]
@property
def inverse(self):
new_amplitude = self.tau
new_tau = self.amplitude
return Exponential1D(amplitude=new_amplitude, tau=new_tau)
@tau.validator
def tau(self, val):
if np.all(val == 0):
raise ValueError("0 is not an allowed value for tau")
@property
def input_units(self):
if self.tau.unit is None:
return None
return {self.inputs[0]: self.tau.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'tau': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class Exponential1D(Fittable1DModel):
"""
One dimensional exponential model.
Parameters
----------
amplitude : float, optional
tau : float, optional
See Also
--------
Logarithmic1D, Gaussian1D
"""
amplitude = Parameter(default=1)
tau = Parameter(default=1)
@staticmethod
def evaluate(x, amplitude, tau):
return amplitude * np.exp(x / tau)
@staticmethod
def fit_deriv(x, amplitude, tau):
''' Derivative with respect to parameters'''
d_amplitude = np.exp(x / tau)
d_tau = -amplitude * (x / tau**2) * np.exp(x / tau)
return [d_amplitude, d_tau]
@property
def inverse(self):
new_amplitude = self.tau
new_tau = self.amplitude
return Logarithmic1D(amplitude=new_amplitude, tau=new_tau)
@tau.validator
def tau(self, val):
''' tau cannot be 0'''
if np.all(val == 0):
raise ValueError("0 is not an allowed value for tau")
@property
def input_units(self):
if self.tau.unit is None:
return None
return {self.inputs[0]: self.tau.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'tau': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
|
fa570dfec5214f7c109911af8ec37ae926a2cf3c7104a6374b3ce93ee75d3c56 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# -*- coding: utf-8 -*-
# pylint: disable=invalid-name
"""
Implements projections--particularly sky projections defined in WCS Paper II
[1]_.
All angles are set and and displayed in degrees but internally computations are
performed in radians. All functions expect inputs and outputs degrees.
References
----------
.. [1] Calabretta, M.R., Greisen, E.W., 2002, A&A, 395, 1077 (Paper II)
"""
import abc
from itertools import chain, product
import numpy as np
from astropy import units as u
from astropy import wcs
from .core import Model
from .parameters import Parameter, InputParameterError
from .utils import _to_radian, _to_orig_unit
# List of tuples of the form
# (long class name without suffix, short WCSLIB projection code):
_PROJ_NAME_CODE = [
('ZenithalPerspective', 'AZP'),
('SlantZenithalPerspective', 'SZP'),
('Gnomonic', 'TAN'),
('Stereographic', 'STG'),
('SlantOrthographic', 'SIN'),
('ZenithalEquidistant', 'ARC'),
('ZenithalEqualArea', 'ZEA'),
('Airy', 'AIR'),
('CylindricalPerspective', 'CYP'),
('CylindricalEqualArea', 'CEA'),
('PlateCarree', 'CAR'),
('Mercator', 'MER'),
('SansonFlamsteed', 'SFL'),
('Parabolic', 'PAR'),
('Molleweide', 'MOL'),
('HammerAitoff', 'AIT'),
('ConicPerspective', 'COP'),
('ConicEqualArea', 'COE'),
('ConicEquidistant', 'COD'),
('ConicOrthomorphic', 'COO'),
('BonneEqualArea', 'BON'),
('Polyconic', 'PCO'),
('TangentialSphericalCube', 'TSC'),
('COBEQuadSphericalCube', 'CSC'),
('QuadSphericalCube', 'QSC'),
('HEALPix', 'HPX'),
('HEALPixPolar', 'XPH'),
]
_NOT_SUPPORTED_PROJ_CODES = ['ZPN']
_PROJ_NAME_CODE_MAP = dict(_PROJ_NAME_CODE)
projcodes = [code for _, code in _PROJ_NAME_CODE]
__all__ = [
'Projection', 'Pix2SkyProjection', 'Sky2PixProjection', 'Zenithal',
'Cylindrical', 'PseudoCylindrical', 'Conic', 'PseudoConic', 'QuadCube',
'HEALPix', 'AffineTransformation2D', 'projcodes'
] + list(map('_'.join, product(['Pix2Sky', 'Sky2Pix'], chain(*_PROJ_NAME_CODE))))
class _ParameterDS(Parameter):
"""
Same as `Parameter` but can indicate its modified status via the ``dirty``
property. This flag also gets set automatically when a parameter is
modified.
This ability to track parameter's modified status is needed for automatic
update of WCSLIB's prjprm structure (which may be a more-time intensive
operation) *only as required*.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.dirty = True
def validate(self, value):
super().validate(value)
self.dirty = True
class Projection(Model):
"""Base class for all sky projections."""
# Radius of the generating sphere.
# This sets the circumference to 360 deg so that arc length is measured in deg.
r0 = 180 * u.deg / np.pi
_separable = False
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._prj = wcs.Prjprm()
@property
@abc.abstractmethod
def inverse(self):
"""
Inverse projection--all projection models must provide an inverse.
"""
@property
def prjprm(self):
""" WCSLIB ``prjprm`` structure. """
self._update_prj()
return self._prj
def _update_prj(self):
"""
A default updater for projection's pv.
.. warning::
This method assumes that PV0 is never modified. If a projection
that uses PV0 is ever implemented in this module, that projection
class should override this method.
.. warning::
This method assumes that the order in which PVi values (i>0)
are to be asigned is identical to the order of model parameters
in ``param_names``. That is, pv[1] = model.parameters[0], ...
"""
if not self.param_names:
return
pv = []
dirty = False
for p in self.param_names:
param = getattr(self, p)
pv.append(float(param.value))
dirty |= param.dirty
param.dirty = False
if dirty:
self._prj.pv = None, *pv
self._prj.set()
class Pix2SkyProjection(Projection):
"""Base class for all Pix2Sky projections."""
n_inputs = 2
n_outputs = 2
_input_units_strict = True
_input_units_allow_dimensionless = True
def __new__(cls, *args, **kwargs):
long_name = cls.name.split('_')[1]
cls.prj_code = _PROJ_NAME_CODE_MAP[long_name]
return super(Pix2SkyProjection, cls).__new__(cls)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._prj.code = self.prj_code
self._update_prj()
if not self.param_names:
# force initial call to Prjprm.set() for projections
# with no parameters:
self._prj.set()
self.inputs = ('x', 'y')
self.outputs = ('phi', 'theta')
@property
def input_units(self):
return {self.inputs[0]: u.deg,
self.inputs[1]: u.deg}
@property
def return_units(self):
return {self.outputs[0]: u.deg,
self.outputs[1]: u.deg}
def evaluate(self, x, y, *args, **kwargs):
self._update_prj()
return self._prj.prjx2s(x, y)
@property
def inverse(self):
pv = [getattr(self, param).value for param in self.param_names]
return self._inv_cls(*pv)
class Sky2PixProjection(Projection):
"""Base class for all Sky2Pix projections."""
n_inputs = 2
n_outputs = 2
_input_units_strict = True
_input_units_allow_dimensionless = True
def __new__(cls, *args, **kwargs):
long_name = cls.name.split('_')[1]
cls.prj_code = _PROJ_NAME_CODE_MAP[long_name]
return super(Sky2PixProjection, cls).__new__(cls)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._prj.code = self.prj_code
self._update_prj()
if not self.param_names:
# force initial call to Prjprm.set() for projections
# without parameters:
self._prj.set()
self.inputs = ('phi', 'theta')
self.outputs = ('x', 'y')
@property
def input_units(self):
return {self.inputs[0]: u.deg,
self.inputs[1]: u.deg}
@property
def return_units(self):
return {self.outputs[0]: u.deg,
self.outputs[1]: u.deg}
def evaluate(self, phi, theta, *args, **kwargs):
self._update_prj()
return self._prj.prjs2x(phi, theta)
@property
def inverse(self):
pv = [getattr(self, param).value for param in self.param_names]
return self._inv_cls(*pv)
class Zenithal(Projection):
r"""Base class for all Zenithal projections.
Zenithal (or azimuthal) projections map the sphere directly onto a
plane. All zenithal projections are specified by defining the
radius as a function of native latitude, :math:`R_\theta`.
The pixel-to-sky transformation is defined as:
.. math::
\phi &= \arg(-y, x) \\
R_\theta &= \sqrt{x^2 + y^2}
and the inverse (sky-to-pixel) is defined as:
.. math::
x &= R_\theta \sin \phi \\
y &= R_\theta \cos \phi
"""
class Pix2Sky_ZenithalPerspective(Pix2SkyProjection, Zenithal):
r"""
Zenithal perspective projection - pixel to sky.
Corresponds to the ``AZP`` projection in FITS WCS.
.. math::
\phi &= \arg(-y \cos \gamma, x) \\
\theta &= \left\{\genfrac{}{}{0pt}{}{\psi - \omega}{\psi + \omega + 180^{\circ}}\right.
where:
.. math::
\psi &= \arg(\rho, 1) \\
\omega &= \sin^{-1}\left(\frac{\rho \mu}{\sqrt{\rho^2 + 1}}\right) \\
\rho &= \frac{R}{\frac{180^{\circ}}{\pi}(\mu + 1) + y \sin \gamma} \\
R &= \sqrt{x^2 + y^2 \cos^2 \gamma}
Parameters
----------
mu : float
Distance from point of projection to center of sphere
in spherical radii, μ. Default is 0.
gamma : float
Look angle γ in degrees. Default is 0°.
"""
mu = _ParameterDS(
default=0.0, description="Distance from point of projection to center of sphere"
)
gamma = _ParameterDS(default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="Look angle γ in degrees (Default = 0°)")
@mu.validator
def mu(self, value):
if np.any(np.equal(value, -1.0)):
raise InputParameterError(
"Zenithal perspective projection is not defined for mu = -1")
class Sky2Pix_ZenithalPerspective(Sky2PixProjection, Zenithal):
r"""
Zenithal perspective projection - sky to pixel.
Corresponds to the ``AZP`` projection in FITS WCS.
.. math::
x &= R \sin \phi \\
y &= -R \sec \gamma \cos \theta
where:
.. math::
R = \frac{180^{\circ}}{\pi} \frac{(\mu + 1) \cos \theta}{(\mu + \sin \theta) + \cos \theta \cos \phi \tan \gamma}
Parameters
----------
mu : float
Distance from point of projection to center of sphere
in spherical radii, μ. Default is 0.
gamma : float
Look angle γ in degrees. Default is 0°.
"""
mu = _ParameterDS(
default=0.0,
description="Distance from point of projection to center of sphere"
)
gamma = _ParameterDS(default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="Look angle γ in degrees (Default=0°)")
@mu.validator
def mu(self, value):
if np.any(np.equal(value, -1.0)):
raise InputParameterError(
"Zenithal perspective projection is not defined for mu = -1")
class Pix2Sky_SlantZenithalPerspective(Pix2SkyProjection, Zenithal):
r"""
Slant zenithal perspective projection - pixel to sky.
Corresponds to the ``SZP`` projection in FITS WCS.
Parameters
----------
mu : float
Distance from point of projection to center of sphere
in spherical radii, μ. Default is 0.
phi0 : float
The longitude φ₀ of the reference point, in degrees. Default
is 0°.
theta0 : float
The latitude θ₀ of the reference point, in degrees. Default
is 90°.
"""
mu = _ParameterDS(
default=0.0,
description="Distance from point of projection to center of sphere"
)
phi0 = _ParameterDS(
default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="The longitude φ₀ of the reference point in degrees (Default=0°)"
)
theta0 = _ParameterDS(
default=90.0, getter=_to_orig_unit, setter=_to_radian,
description="The latitude θ₀ of the reference point, in degrees (Default=0°)"
)
@mu.validator
def mu(self, value):
if np.any(np.equal(value, -1.0)):
raise InputParameterError(
"Zenithal perspective projection is not defined for mu = -1")
class Sky2Pix_SlantZenithalPerspective(Sky2PixProjection, Zenithal):
r"""
Zenithal perspective projection - sky to pixel.
Corresponds to the ``SZP`` projection in FITS WCS.
Parameters
----------
mu : float
distance from point of projection to center of sphere
in spherical radii, μ. Default is 0.
phi0 : float
The longitude φ₀ of the reference point, in degrees. Default
is 0°.
theta0 : float
The latitude θ₀ of the reference point, in degrees. Default
is 90°.
"""
mu = _ParameterDS(
default=0.0, description="Distance from point of projection to center of sphere"
)
phi0 = _ParameterDS(
default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="The longitude φ₀ of the reference point in degrees"
)
theta0 = _ParameterDS(
default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="The latitude θ₀ of the reference point, in degrees"
)
@mu.validator
def mu(self, value):
if np.any(np.equal(value, -1.0)):
raise InputParameterError(
"Zenithal perspective projection is not defined for mu = -1")
class Pix2Sky_Gnomonic(Pix2SkyProjection, Zenithal):
r"""
Gnomonic projection - pixel to sky.
Corresponds to the ``TAN`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
\theta = \tan^{-1}\left(\frac{180^{\circ}}{\pi R_\theta}\right)
"""
class Sky2Pix_Gnomonic(Sky2PixProjection, Zenithal):
r"""
Gnomonic Projection - sky to pixel.
Corresponds to the ``TAN`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
R_\theta = \frac{180^{\circ}}{\pi}\cot \theta
"""
class Pix2Sky_Stereographic(Pix2SkyProjection, Zenithal):
r"""
Stereographic Projection - pixel to sky.
Corresponds to the ``STG`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
\theta = 90^{\circ} - 2 \tan^{-1}\left(\frac{\pi R_\theta}{360^{\circ}}\right)
"""
class Sky2Pix_Stereographic(Sky2PixProjection, Zenithal):
r"""
Stereographic Projection - sky to pixel.
Corresponds to the ``STG`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
R_\theta = \frac{180^{\circ}}{\pi}\frac{2 \cos \theta}{1 + \sin \theta}
"""
class Pix2Sky_SlantOrthographic(Pix2SkyProjection, Zenithal):
r"""
Slant orthographic projection - pixel to sky.
Corresponds to the ``SIN`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
The following transformation applies when :math:`\xi` and
:math:`\eta` are both zero.
.. math::
\theta = \cos^{-1}\left(\frac{\pi}{180^{\circ}}R_\theta\right)
The parameters :math:`\xi` and :math:`\eta` are defined from the
reference point :math:`(\phi_c, \theta_c)` as:
.. math::
\xi &= \cot \theta_c \sin \phi_c \\
\eta &= - \cot \theta_c \cos \phi_c
Parameters
----------
xi : float
Obliqueness parameter, ξ. Default is 0.0.
eta : float
Obliqueness parameter, η. Default is 0.0.
"""
xi = _ParameterDS(default=0.0, description="Obliqueness parameter")
eta = _ParameterDS(default=0.0, description="Obliqueness parameter")
class Sky2Pix_SlantOrthographic(Sky2PixProjection, Zenithal):
r"""
Slant orthographic projection - sky to pixel.
Corresponds to the ``SIN`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
The following transformation applies when :math:`\xi` and
:math:`\eta` are both zero.
.. math::
R_\theta = \frac{180^{\circ}}{\pi}\cos \theta
But more specifically are:
.. math::
x &= \frac{180^\circ}{\pi}[\cos \theta \sin \phi + \xi(1 - \sin \theta)] \\
y &= \frac{180^\circ}{\pi}[\cos \theta \cos \phi + \eta(1 - \sin \theta)]
"""
xi = _ParameterDS(default=0.0)
eta = _ParameterDS(default=0.0)
class Pix2Sky_ZenithalEquidistant(Pix2SkyProjection, Zenithal):
r"""
Zenithal equidistant projection - pixel to sky.
Corresponds to the ``ARC`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
\theta = 90^\circ - R_\theta
"""
class Sky2Pix_ZenithalEquidistant(Sky2PixProjection, Zenithal):
r"""
Zenithal equidistant projection - sky to pixel.
Corresponds to the ``ARC`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
R_\theta = 90^\circ - \theta
"""
class Pix2Sky_ZenithalEqualArea(Pix2SkyProjection, Zenithal):
r"""
Zenithal equidistant projection - pixel to sky.
Corresponds to the ``ZEA`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
\theta = 90^\circ - 2 \sin^{-1} \left(\frac{\pi R_\theta}{360^\circ}\right)
"""
class Sky2Pix_ZenithalEqualArea(Sky2PixProjection, Zenithal):
r"""
Zenithal equidistant projection - sky to pixel.
Corresponds to the ``ZEA`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
R_\theta &= \frac{180^\circ}{\pi} \sqrt{2(1 - \sin\theta)} \\
&= \frac{360^\circ}{\pi} \sin\left(\frac{90^\circ - \theta}{2}\right)
"""
class Pix2Sky_Airy(Pix2SkyProjection, Zenithal):
r"""
Airy projection - pixel to sky.
Corresponds to the ``AIR`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
Parameters
----------
theta_b : float
The latitude :math:`\theta_b` at which to minimize the error,
in degrees. Default is 90°.
"""
theta_b = _ParameterDS(default=90.0)
class Sky2Pix_Airy(Sky2PixProjection, Zenithal):
r"""
Airy - sky to pixel.
Corresponds to the ``AIR`` projection in FITS WCS.
See `Zenithal` for a definition of the full transformation.
.. math::
R_\theta = -2 \frac{180^\circ}{\pi}\left(\frac{\ln(\cos \xi)}{\tan \xi} + \frac{\ln(\cos \xi_b)}{\tan^2 \xi_b} \tan \xi \right)
where:
.. math::
\xi &= \frac{90^\circ - \theta}{2} \\
\xi_b &= \frac{90^\circ - \theta_b}{2}
Parameters
----------
theta_b : float
The latitude :math:`\theta_b` at which to minimize the error,
in degrees. Default is 90°.
"""
theta_b = _ParameterDS(default=90.0, description="The latitude at which to minimize the error,in degrees")
class Cylindrical(Projection):
r"""Base class for Cylindrical projections.
Cylindrical projections are so-named because the surface of
projection is a cylinder.
"""
_separable = True
class Pix2Sky_CylindricalPerspective(Pix2SkyProjection, Cylindrical):
r"""
Cylindrical perspective - pixel to sky.
Corresponds to the ``CYP`` projection in FITS WCS.
.. math::
\phi &= \frac{x}{\lambda} \\
\theta &= \arg(1, \eta) + \sin{-1}\left(\frac{\eta \mu}{\sqrt{\eta^2 + 1}}\right)
where:
.. math::
\eta = \frac{\pi}{180^{\circ}}\frac{y}{\mu + \lambda}
Parameters
----------
mu : float
Distance from center of sphere in the direction opposite the
projected surface, in spherical radii, μ. Default is 1.
lam : float
Radius of the cylinder in spherical radii, λ. Default is 1.
"""
mu = _ParameterDS(default=1.0)
lam = _ParameterDS(default=1.0)
@mu.validator
def mu(self, value):
if np.any(value == -self.lam):
raise InputParameterError(
"CYP projection is not defined for mu = -lambda")
@lam.validator
def lam(self, value):
if np.any(value == -self.mu):
raise InputParameterError(
"CYP projection is not defined for lambda = -mu")
class Sky2Pix_CylindricalPerspective(Sky2PixProjection, Cylindrical):
r"""
Cylindrical Perspective - sky to pixel.
Corresponds to the ``CYP`` projection in FITS WCS.
.. math::
x &= \lambda \phi \\
y &= \frac{180^{\circ}}{\pi}\left(\frac{\mu + \lambda}{\mu + \cos \theta}\right)\sin \theta
Parameters
----------
mu : float
Distance from center of sphere in the direction opposite the
projected surface, in spherical radii, μ. Default is 0.
lam : float
Radius of the cylinder in spherical radii, λ. Default is 0.
"""
mu = _ParameterDS(default=1.0, description="Distance from center of sphere in spherical radii")
lam = _ParameterDS(default=1.0, description="Radius of the cylinder in spherical radii")
@mu.validator
def mu(self, value):
if np.any(value == -self.lam):
raise InputParameterError(
"CYP projection is not defined for mu = -lambda")
@lam.validator
def lam(self, value):
if np.any(value == -self.mu):
raise InputParameterError(
"CYP projection is not defined for lambda = -mu")
class Pix2Sky_CylindricalEqualArea(Pix2SkyProjection, Cylindrical):
r"""
Cylindrical equal area projection - pixel to sky.
Corresponds to the ``CEA`` projection in FITS WCS.
.. math::
\phi &= x \\
\theta &= \sin^{-1}\left(\frac{\pi}{180^{\circ}}\lambda y\right)
Parameters
----------
lam : float
Radius of the cylinder in spherical radii, λ. Default is 1.
"""
lam = _ParameterDS(default=1)
class Sky2Pix_CylindricalEqualArea(Sky2PixProjection, Cylindrical):
r"""
Cylindrical equal area projection - sky to pixel.
Corresponds to the ``CEA`` projection in FITS WCS.
.. math::
x &= \phi \\
y &= \frac{180^{\circ}}{\pi}\frac{\sin \theta}{\lambda}
Parameters
----------
lam : float
Radius of the cylinder in spherical radii, λ. Default is 0.
"""
lam = _ParameterDS(default=1)
class Pix2Sky_PlateCarree(Pix2SkyProjection, Cylindrical):
r"""
Plate carrée projection - pixel to sky.
Corresponds to the ``CAR`` projection in FITS WCS.
.. math::
\phi &= x \\
\theta &= y
"""
@staticmethod
def evaluate(x, y):
# The intermediate variables are only used here for clarity
phi = np.array(x)
theta = np.array(y)
return phi, theta
class Sky2Pix_PlateCarree(Sky2PixProjection, Cylindrical):
r"""
Plate carrée projection - sky to pixel.
Corresponds to the ``CAR`` projection in FITS WCS.
.. math::
x &= \phi \\
y &= \theta
"""
@staticmethod
def evaluate(phi, theta):
# The intermediate variables are only used here for clarity
x = np.array(phi)
y = np.array(theta)
return x, y
class Pix2Sky_Mercator(Pix2SkyProjection, Cylindrical):
r"""
Mercator - pixel to sky.
Corresponds to the ``MER`` projection in FITS WCS.
.. math::
\phi &= x \\
\theta &= 2 \tan^{-1}\left(e^{y \pi / 180^{\circ}}\right)-90^{\circ}
"""
class Sky2Pix_Mercator(Sky2PixProjection, Cylindrical):
r"""
Mercator - sky to pixel.
Corresponds to the ``MER`` projection in FITS WCS.
.. math::
x &= \phi \\
y &= \frac{180^{\circ}}{\pi}\ln \tan \left(\frac{90^{\circ} + \theta}{2}\right)
"""
class PseudoCylindrical(Projection):
r"""Base class for pseudocylindrical projections.
Pseudocylindrical projections are like cylindrical projections
except the parallels of latitude are projected at diminishing
lengths toward the polar regions in order to reduce lateral
distortion there. Consequently, the meridians are curved.
"""
_separable = True
class Pix2Sky_SansonFlamsteed(Pix2SkyProjection, PseudoCylindrical):
r"""
Sanson-Flamsteed projection - pixel to sky.
Corresponds to the ``SFL`` projection in FITS WCS.
.. math::
\phi &= \frac{x}{\cos y} \\
\theta &= y
"""
class Sky2Pix_SansonFlamsteed(Sky2PixProjection, PseudoCylindrical):
r"""
Sanson-Flamsteed projection - sky to pixel.
Corresponds to the ``SFL`` projection in FITS WCS.
.. math::
x &= \phi \cos \theta \\
y &= \theta
"""
class Pix2Sky_Parabolic(Pix2SkyProjection, PseudoCylindrical):
r"""
Parabolic projection - pixel to sky.
Corresponds to the ``PAR`` projection in FITS WCS.
.. math::
\phi &= \frac{180^\circ}{\pi} \frac{x}{1 - 4(y / 180^\circ)^2} \\
\theta &= 3 \sin^{-1}\left(\frac{y}{180^\circ}\right)
"""
class Sky2Pix_Parabolic(Sky2PixProjection, PseudoCylindrical):
r"""
Parabolic projection - sky to pixel.
Corresponds to the ``PAR`` projection in FITS WCS.
.. math::
x &= \phi \left(2\cos\frac{2\theta}{3} - 1\right) \\
y &= 180^\circ \sin \frac{\theta}{3}
"""
class Pix2Sky_Molleweide(Pix2SkyProjection, PseudoCylindrical):
r"""
Molleweide's projection - pixel to sky.
Corresponds to the ``MOL`` projection in FITS WCS.
.. math::
\phi &= \frac{\pi x}{2 \sqrt{2 - \left(\frac{\pi}{180^\circ}y\right)^2}} \\
\theta &= \sin^{-1}\left(\frac{1}{90^\circ}\sin^{-1}\left(\frac{\pi}{180^\circ}\frac{y}{\sqrt{2}}\right) + \frac{y}{180^\circ}\sqrt{2 - \left(\frac{\pi}{180^\circ}y\right)^2}\right)
"""
class Sky2Pix_Molleweide(Sky2PixProjection, PseudoCylindrical):
r"""
Molleweide's projection - sky to pixel.
Corresponds to the ``MOL`` projection in FITS WCS.
.. math::
x &= \frac{2 \sqrt{2}}{\pi} \phi \cos \gamma \\
y &= \sqrt{2} \frac{180^\circ}{\pi} \sin \gamma
where :math:`\gamma` is defined as the solution of the
transcendental equation:
.. math::
\sin \theta = \frac{\gamma}{90^\circ} + \frac{\sin 2 \gamma}{\pi}
"""
class Pix2Sky_HammerAitoff(Pix2SkyProjection, PseudoCylindrical):
r"""
Hammer-Aitoff projection - pixel to sky.
Corresponds to the ``AIT`` projection in FITS WCS.
.. math::
\phi &= 2 \arg \left(2Z^2 - 1, \frac{\pi}{180^\circ} \frac{Z}{2}x\right) \\
\theta &= \sin^{-1}\left(\frac{\pi}{180^\circ}yZ\right)
"""
class Sky2Pix_HammerAitoff(Sky2PixProjection, PseudoCylindrical):
r"""
Hammer-Aitoff projection - sky to pixel.
Corresponds to the ``AIT`` projection in FITS WCS.
.. math::
x &= 2 \gamma \cos \theta \sin \frac{\phi}{2} \\
y &= \gamma \sin \theta
where:
.. math::
\gamma = \frac{180^\circ}{\pi} \sqrt{\frac{2}{1 + \cos \theta \cos(\phi / 2)}}
"""
class Conic(Projection):
r"""Base class for conic projections.
In conic projections, the sphere is thought to be projected onto
the surface of a cone which is then opened out.
In a general sense, the pixel-to-sky transformation is defined as:
.. math::
\phi &= \arg\left(\frac{Y_0 - y}{R_\theta}, \frac{x}{R_\theta}\right) / C \\
R_\theta &= \mathrm{sign} \theta_a \sqrt{x^2 + (Y_0 - y)^2}
and the inverse (sky-to-pixel) is defined as:
.. math::
x &= R_\theta \sin (C \phi) \\
y &= R_\theta \cos (C \phi) + Y_0
where :math:`C` is the "constant of the cone":
.. math::
C = \frac{180^\circ \cos \theta}{\pi R_\theta}
"""
sigma = _ParameterDS(default=90.0, getter=_to_orig_unit, setter=_to_radian)
delta = _ParameterDS(default=0.0, getter=_to_orig_unit, setter=_to_radian)
class Pix2Sky_ConicPerspective(Pix2SkyProjection, Conic):
r"""
Colles' conic perspective projection - pixel to sky.
Corresponds to the ``COP`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \sin \theta_a \\
R_\theta &= \frac{180^\circ}{\pi} \cos \eta [ \cot \theta_a - \tan(\theta - \theta_a)] \\
Y_0 &= \frac{180^\circ}{\pi} \cos \eta \cot \theta_a
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Sky2Pix_ConicPerspective(Sky2PixProjection, Conic):
r"""
Colles' conic perspective projection - sky to pixel.
Corresponds to the ``COP`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \sin \theta_a \\
R_\theta &= \frac{180^\circ}{\pi} \cos \eta [ \cot \theta_a - \tan(\theta - \theta_a)] \\
Y_0 &= \frac{180^\circ}{\pi} \cos \eta \cot \theta_a
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Pix2Sky_ConicEqualArea(Pix2SkyProjection, Conic):
r"""
Alber's conic equal area projection - pixel to sky.
Corresponds to the ``COE`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \gamma / 2 \\
R_\theta &= \frac{180^\circ}{\pi} \frac{2}{\gamma} \sqrt{1 + \sin \theta_1 \sin \theta_2 - \gamma \sin \theta} \\
Y_0 &= \frac{180^\circ}{\pi} \frac{2}{\gamma} \sqrt{1 + \sin \theta_1 \sin \theta_2 - \gamma \sin((\theta_1 + \theta_2)/2)}
where:
.. math::
\gamma = \sin \theta_1 + \sin \theta_2
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Sky2Pix_ConicEqualArea(Sky2PixProjection, Conic):
r"""
Alber's conic equal area projection - sky to pixel.
Corresponds to the ``COE`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \gamma / 2 \\
R_\theta &= \frac{180^\circ}{\pi} \frac{2}{\gamma} \sqrt{1 + \sin \theta_1 \sin \theta_2 - \gamma \sin \theta} \\
Y_0 &= \frac{180^\circ}{\pi} \frac{2}{\gamma} \sqrt{1 + \sin \theta_1 \sin \theta_2 - \gamma \sin((\theta_1 + \theta_2)/2)}
where:
.. math::
\gamma = \sin \theta_1 + \sin \theta_2
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Pix2Sky_ConicEquidistant(Pix2SkyProjection, Conic):
r"""
Conic equidistant projection - pixel to sky.
Corresponds to the ``COD`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \frac{180^\circ}{\pi} \frac{\sin\theta_a\sin\eta}{\eta} \\
R_\theta &= \theta_a - \theta + \eta\cot\eta\cot\theta_a \\
Y_0 = \eta\cot\eta\cot\theta_a
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Sky2Pix_ConicEquidistant(Sky2PixProjection, Conic):
r"""
Conic equidistant projection - sky to pixel.
Corresponds to the ``COD`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \frac{180^\circ}{\pi} \frac{\sin\theta_a\sin\eta}{\eta} \\
R_\theta &= \theta_a - \theta + \eta\cot\eta\cot\theta_a \\
Y_0 = \eta\cot\eta\cot\theta_a
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Pix2Sky_ConicOrthomorphic(Pix2SkyProjection, Conic):
r"""
Conic orthomorphic projection - pixel to sky.
Corresponds to the ``COO`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \frac{\ln \left( \frac{\cos\theta_2}{\cos\theta_1} \right)}
{\ln \left[ \frac{\tan\left(\frac{90^\circ-\theta_2}{2}\right)}
{\tan\left(\frac{90^\circ-\theta_1}{2}\right)} \right] } \\
R_\theta &= \psi \left[ \tan \left( \frac{90^\circ - \theta}{2} \right) \right]^C \\
Y_0 &= \psi \left[ \tan \left( \frac{90^\circ - \theta_a}{2} \right) \right]^C
where:
.. math::
\psi = \frac{180^\circ}{\pi} \frac{\cos \theta}
{C\left[\tan\left(\frac{90^\circ-\theta}{2}\right)\right]^C}
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class Sky2Pix_ConicOrthomorphic(Sky2PixProjection, Conic):
r"""
Conic orthomorphic projection - sky to pixel.
Corresponds to the ``COO`` projection in FITS WCS.
See `Conic` for a description of the entire equation.
The projection formulae are:
.. math::
C &= \frac{\ln \left( \frac{\cos\theta_2}{\cos\theta_1} \right)}
{\ln \left[ \frac{\tan\left(\frac{90^\circ-\theta_2}{2}\right)}
{\tan\left(\frac{90^\circ-\theta_1}{2}\right)} \right] } \\
R_\theta &= \psi \left[ \tan \left( \frac{90^\circ - \theta}{2} \right) \right]^C \\
Y_0 &= \psi \left[ \tan \left( \frac{90^\circ - \theta_a}{2} \right) \right]^C
where:
.. math::
\psi = \frac{180^\circ}{\pi} \frac{\cos \theta}
{C\left[\tan\left(\frac{90^\circ-\theta}{2}\right)\right]^C}
Parameters
----------
sigma : float
:math:`(\theta_1 + \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 90.
delta : float
:math:`(\theta_1 - \theta_2) / 2`, where :math:`\theta_1` and
:math:`\theta_2` are the latitudes of the standard parallels,
in degrees. Default is 0.
"""
class PseudoConic(Projection):
r"""Base class for pseudoconic projections.
Pseudoconics are a subclass of conics with concentric parallels.
"""
class Pix2Sky_BonneEqualArea(Pix2SkyProjection, PseudoConic):
r"""
Bonne's equal area pseudoconic projection - pixel to sky.
Corresponds to the ``BON`` projection in FITS WCS.
.. math::
\phi &= \frac{\pi}{180^\circ} A_\phi R_\theta / \cos \theta \\
\theta &= Y_0 - R_\theta
where:
.. math::
R_\theta &= \mathrm{sign} \theta_1 \sqrt{x^2 + (Y_0 - y)^2} \\
A_\phi &= \arg\left(\frac{Y_0 - y}{R_\theta}, \frac{x}{R_\theta}\right)
Parameters
----------
theta1 : float
Bonne conformal latitude, in degrees.
"""
_separable = True
theta1 = _ParameterDS(default=0.0, getter=_to_orig_unit, setter=_to_radian)
class Sky2Pix_BonneEqualArea(Sky2PixProjection, PseudoConic):
r"""
Bonne's equal area pseudoconic projection - sky to pixel.
Corresponds to the ``BON`` projection in FITS WCS.
.. math::
x &= R_\theta \sin A_\phi \\
y &= -R_\theta \cos A_\phi + Y_0
where:
.. math::
A_\phi &= \frac{180^\circ}{\pi R_\theta} \phi \cos \theta \\
R_\theta &= Y_0 - \theta \\
Y_0 &= \frac{180^\circ}{\pi} \cot \theta_1 + \theta_1
Parameters
----------
theta1 : float
Bonne conformal latitude, in degrees.
"""
_separable = True
theta1 = _ParameterDS(default=0.0, getter=_to_orig_unit, setter=_to_radian,
description="Bonne conformal latitude, in degrees")
class Pix2Sky_Polyconic(Pix2SkyProjection, PseudoConic):
r"""
Polyconic projection - pixel to sky.
Corresponds to the ``PCO`` projection in FITS WCS.
"""
class Sky2Pix_Polyconic(Sky2PixProjection, PseudoConic):
r"""
Polyconic projection - sky to pixel.
Corresponds to the ``PCO`` projection in FITS WCS.
"""
class QuadCube(Projection):
r"""Base class for quad cube projections.
Quadrilateralized spherical cube (quad-cube) projections belong to
the class of polyhedral projections in which the sphere is
projected onto the surface of an enclosing polyhedron.
The six faces of the quad-cube projections are numbered and laid
out as::
0
4 3 2 1 4 3 2
5
"""
class Pix2Sky_TangentialSphericalCube(Pix2SkyProjection, QuadCube):
r"""
Tangential spherical cube projection - pixel to sky.
Corresponds to the ``TSC`` projection in FITS WCS.
"""
class Sky2Pix_TangentialSphericalCube(Sky2PixProjection, QuadCube):
r"""
Tangential spherical cube projection - sky to pixel.
Corresponds to the ``TSC`` projection in FITS WCS.
"""
class Pix2Sky_COBEQuadSphericalCube(Pix2SkyProjection, QuadCube):
r"""
COBE quadrilateralized spherical cube projection - pixel to sky.
Corresponds to the ``CSC`` projection in FITS WCS.
"""
class Sky2Pix_COBEQuadSphericalCube(Sky2PixProjection, QuadCube):
r"""
COBE quadrilateralized spherical cube projection - sky to pixel.
Corresponds to the ``CSC`` projection in FITS WCS.
"""
class Pix2Sky_QuadSphericalCube(Pix2SkyProjection, QuadCube):
r"""
Quadrilateralized spherical cube projection - pixel to sky.
Corresponds to the ``QSC`` projection in FITS WCS.
"""
class Sky2Pix_QuadSphericalCube(Sky2PixProjection, QuadCube):
r"""
Quadrilateralized spherical cube projection - sky to pixel.
Corresponds to the ``QSC`` projection in FITS WCS.
"""
class HEALPix(Projection):
r"""Base class for HEALPix projections.
"""
class Pix2Sky_HEALPix(Pix2SkyProjection, HEALPix):
r"""
HEALPix - pixel to sky.
Corresponds to the ``HPX`` projection in FITS WCS.
Parameters
----------
H : float
The number of facets in longitude direction.
X : float
The number of facets in latitude direction.
"""
_separable = True
H = _ParameterDS(default=4.0, description="The number of facets in longitude direction.")
X = _ParameterDS(default=3.0, description="The number of facets in latitude direction.")
class Sky2Pix_HEALPix(Sky2PixProjection, HEALPix):
r"""
HEALPix projection - sky to pixel.
Corresponds to the ``HPX`` projection in FITS WCS.
Parameters
----------
H : float
The number of facets in longitude direction.
X : float
The number of facets in latitude direction.
"""
_separable = True
H = _ParameterDS(default=4.0, description="The number of facets in longitude direction.")
X = _ParameterDS(default=3.0, description="The number of facets in latitude direction.")
class Pix2Sky_HEALPixPolar(Pix2SkyProjection, HEALPix):
r"""
HEALPix polar, aka "butterfly" projection - pixel to sky.
Corresponds to the ``XPH`` projection in FITS WCS.
"""
class Sky2Pix_HEALPixPolar(Sky2PixProjection, HEALPix):
r"""
HEALPix polar, aka "butterfly" projection - pixel to sky.
Corresponds to the ``XPH`` projection in FITS WCS.
"""
class AffineTransformation2D(Model):
"""
Perform an affine transformation in 2 dimensions.
Parameters
----------
matrix : array
A 2x2 matrix specifying the linear transformation to apply to the
inputs
translation : array
A 2D vector (given as either a 2x1 or 1x2 array) specifying a
translation to apply to the inputs
"""
n_inputs = 2
n_outputs = 2
standard_broadcasting = False
_separable = False
matrix = Parameter(default=[[1.0, 0.0], [0.0, 1.0]])
translation = Parameter(default=[0.0, 0.0])
@matrix.validator
def matrix(self, value):
"""Validates that the input matrix is a 2x2 2D array."""
if np.shape(value) != (2, 2):
raise InputParameterError(
"Expected transformation matrix to be a 2x2 array")
@translation.validator
def translation(self, value):
"""
Validates that the translation vector is a 2D vector. This allows
either a "row" vector or a "column" vector where in the latter case the
resultant Numpy array has ``ndim=2`` but the shape is ``(1, 2)``.
"""
if not ((np.ndim(value) == 1 and np.shape(value) == (2,)) or
(np.ndim(value) == 2 and np.shape(value) == (1, 2))):
raise InputParameterError(
"Expected translation vector to be a 2 element row or column "
"vector array")
def __init__(self, matrix=matrix, translation=translation, **kwargs):
super().__init__(matrix=matrix, translation=translation, **kwargs)
self.inputs = ("x", "y")
self.outputs = ("x", "y")
@property
def inverse(self):
"""
Inverse transformation.
Raises `~astropy.modeling.InputParameterError` if the transformation cannot be inverted.
"""
det = np.linalg.det(self.matrix.value)
if det == 0:
raise InputParameterError(
"Transformation matrix is singular; {} model does not "
"have an inverse".format(self.__class__.__name__))
matrix = np.linalg.inv(self.matrix.value)
if self.matrix.unit is not None:
matrix = matrix * self.matrix.unit
# If matrix has unit then translation has unit, so no need to assign it.
translation = -np.dot(matrix, self.translation.value)
return self.__class__(matrix=matrix, translation=translation)
@classmethod
def evaluate(cls, x, y, matrix, translation):
"""
Apply the transformation to a set of 2D Cartesian coordinates given as
two lists--one for the x coordinates and one for a y coordinates--or a
single coordinate pair.
Parameters
----------
x, y : array, float
x and y coordinates
"""
if x.shape != y.shape:
raise ValueError("Expected input arrays to have the same shape")
shape = x.shape or (1,)
# Use asarray to ensure loose the units.
inarr = np.vstack([np.asarray(x).ravel(),
np.asarray(y).ravel(),
np.ones(x.size, x.dtype)])
if inarr.shape[0] != 3 or inarr.ndim != 2:
raise ValueError("Incompatible input shapes")
augmented_matrix = cls._create_augmented_matrix(matrix, translation)
result = np.dot(augmented_matrix, inarr)
x, y = result[0], result[1]
x.shape = y.shape = shape
return x, y
@staticmethod
def _create_augmented_matrix(matrix, translation):
unit = None
if any([hasattr(translation, 'unit'), hasattr(matrix, 'unit')]):
if not all([hasattr(translation, 'unit'), hasattr(matrix, 'unit')]):
raise ValueError("To use AffineTransformation with quantities, "
"both matrix and unit need to be quantities.")
unit = translation.unit
# matrix should have the same units as translation
if not (matrix.unit / translation.unit) == u.dimensionless_unscaled:
raise ValueError("matrix and translation must have the same units.")
augmented_matrix = np.empty((3, 3), dtype=float)
augmented_matrix[0:2, 0:2] = matrix
augmented_matrix[0:2, 2:].flat = translation
augmented_matrix[2] = [0, 0, 1]
if unit is not None:
return augmented_matrix * unit
return augmented_matrix
@property
def input_units(self):
if self.translation.unit is None and self.matrix.unit is None:
return None
elif self.translation.unit is not None:
return dict(zip(self.inputs, [self.translation.unit] * 2))
else:
return dict(zip(self.inputs, [self.matrix.unit] * 2))
for long_name, short_name in _PROJ_NAME_CODE:
# define short-name projection equivalent classes:
globals()['Pix2Sky_' + short_name] = globals()['Pix2Sky_' + long_name]
globals()['Sky2Pix_' + short_name] = globals()['Sky2Pix_' + long_name]
# set inverse classes:
globals()['Pix2Sky_' + long_name]._inv_cls = globals()['Sky2Pix_' + long_name]
globals()['Sky2Pix_' + long_name]._inv_cls = globals()['Pix2Sky_' + long_name]
|
332f7ac5e6eb1fff6a5d1f5dafa098593943e5579fcc6ffde5a23a62f9073ed9 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains models representing polynomials and polynomial series.
"""
# pylint: disable=invalid-name
import numpy as np
from astropy.utils import indent, check_broadcast
from .core import FittableModel, Model
from .functional_models import Shift
from .parameters import Parameter
from .utils import poly_map_domain, comb, _validate_domain_window
__all__ = [
'Chebyshev1D', 'Chebyshev2D', 'Hermite1D', 'Hermite2D',
'InverseSIP', 'Legendre1D', 'Legendre2D', 'Polynomial1D',
'Polynomial2D', 'SIP', 'OrthoPolynomialBase',
'PolynomialModel'
]
class PolynomialBase(FittableModel):
"""
Base class for all polynomial-like models with an arbitrary number of
parameters in the form of coefficients.
In this case Parameter instances are returned through the class's
``__getattr__`` rather than through class descriptors.
"""
# Default _param_names list; this will be filled in by the implementation's
# __init__
_param_names = ()
linear = True
col_fit_deriv = False
@property
def param_names(self):
"""Coefficient names generated based on the model's polynomial degree
and number of dimensions.
Subclasses should implement this to return parameter names in the
desired format.
On most `Model` classes this is a class attribute, but for polynomial
models it is an instance attribute since each polynomial model instance
can have different parameters depending on the degree of the polynomial
and the number of dimensions, for example.
"""
return self._param_names
class PolynomialModel(PolynomialBase):
"""
Base class for polynomial models.
Its main purpose is to determine how many coefficients are needed
based on the polynomial order and dimension and to provide their
default values, names and ordering.
"""
def __init__(self, degree, n_models=None, model_set_axis=None,
name=None, meta=None, **params):
self._degree = degree
self._order = self.get_num_coeff(self.n_inputs)
self._param_names = self._generate_coeff_names(self.n_inputs)
if n_models:
if model_set_axis is None:
model_set_axis = 0
minshape = (1,) * model_set_axis + (n_models,)
else:
minshape = ()
for param_name in self._param_names:
self._parameters_[param_name] = \
Parameter(param_name, default=np.zeros(minshape))
super().__init__(
n_models=n_models, model_set_axis=model_set_axis, name=name,
meta=meta, **params)
@property
def degree(self):
"""Degree of polynomial."""
return self._degree
def get_num_coeff(self, ndim):
"""
Return the number of coefficients in one parameter set
"""
if self.degree < 0:
raise ValueError("Degree of polynomial must be positive or null")
# deg+1 is used to account for the difference between iraf using
# degree and numpy using exact degree
if ndim != 1:
nmixed = comb(self.degree, ndim)
else:
nmixed = 0
numc = self.degree * ndim + nmixed + 1
return numc
def _invlex(self):
c = []
lencoeff = self.degree + 1
for i in range(lencoeff):
for j in range(lencoeff):
if i + j <= self.degree:
c.append((j, i))
return c[::-1]
def _generate_coeff_names(self, ndim):
names = []
if ndim == 1:
for n in range(self._order):
names.append(f'c{n}')
else:
for i in range(self.degree + 1):
names.append(f'c{i}_{0}')
for i in range(1, self.degree + 1):
names.append(f'c{0}_{i}')
for i in range(1, self.degree):
for j in range(1, self.degree):
if i + j < self.degree + 1:
names.append(f'c{i}_{j}')
return tuple(names)
class _PolyDomainWindow1D(PolynomialModel):
"""
This class sets ``domain`` and ``window`` of 1D polynomials.
"""
def __init__(self, degree, domain=None, window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
degree, n_models, model_set_axis, name=name, meta=meta, **params)
self._set_default_domain_window(domain, window)
@property
def window(self):
return self._window
@window.setter
def window(self, val):
self._window = _validate_domain_window(val)
@property
def domain(self):
return self._domain
@domain.setter
def domain(self, val):
self._domain = _validate_domain_window(val)
def _set_default_domain_window(self, domain, window):
"""
This method sets the ``domain`` and ``window`` attributes on 1D subclasses.
"""
self._default_domain_window = {'domain': None,
'window': (-1, 1)
}
self.window = window or (-1, 1)
self.domain = domain
def __repr__(self):
return self._format_repr([self.degree],
kwargs={'domain': self.domain, 'window': self.window},
defaults=self._default_domain_window
)
def __str__(self):
return self._format_str([('Degree', self.degree),
('Domain', self.domain),
('Window', self.window)],
self._default_domain_window)
class OrthoPolynomialBase(PolynomialBase):
"""
This is a base class for the 2D Chebyshev and Legendre models.
The polynomials implemented here require a maximum degree in x and y.
For explanation of ``x_domain``, ``y_domain``, ```x_window`` and ```y_window``
see :ref:`Notes regarding usage of domain and window <astropy:domain-window-note>`.
Parameters
----------
x_degree : int
degree in x
y_degree : int
degree in y
x_domain : tuple or None, optional
domain of the x independent variable
x_window : tuple or None, optional
range of the x independent variable
y_domain : tuple or None, optional
domain of the y independent variable
y_window : tuple or None, optional
range of the y independent variable
**params : dict
{keyword: value} pairs, representing {parameter_name: value}
"""
n_inputs = 2
n_outputs = 1
def __init__(self, x_degree, y_degree, x_domain=None, x_window=None,
y_domain=None, y_window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
self.x_degree = x_degree
self.y_degree = y_degree
self._order = self.get_num_coeff()
# Set the ``x/y_domain`` and ``x/y_wndow`` attributes in subclasses.
self._default_domain_window = {
'x_window': (-1, 1),
'y_window': (-1, 1),
'x_domain': None,
'y_domain': None
}
self.x_window = x_window or self._default_domain_window['x_window']
self.y_window = y_window or self._default_domain_window['y_window']
self.x_domain = x_domain
self.y_domain = y_domain
self._param_names = self._generate_coeff_names()
if n_models:
if model_set_axis is None:
model_set_axis = 0
minshape = (1,) * model_set_axis + (n_models,)
else:
minshape = ()
for param_name in self._param_names:
self._parameters_[param_name] = \
Parameter(param_name, default=np.zeros(minshape))
super().__init__(
n_models=n_models, model_set_axis=model_set_axis,
name=name, meta=meta, **params)
@property
def x_domain(self):
return self._x_domain
@x_domain.setter
def x_domain(self, val):
self._x_domain = _validate_domain_window(val)
@property
def y_domain(self):
return self._y_domain
@y_domain.setter
def y_domain(self, val):
self._y_domain = _validate_domain_window(val)
@property
def x_window(self):
return self._x_window
@x_window.setter
def x_window(self, val):
self._x_window = _validate_domain_window(val)
@property
def y_window(self):
return self._y_window
@y_window.setter
def y_window(self, val):
self._y_window = _validate_domain_window(val)
def __repr__(self):
return self._format_repr([self.x_degree, self.y_degree],
kwargs={'x_domain': self.x_domain,
'y_domain': self.y_domain,
'x_window': self.x_window,
'y_window': self.y_window},
defaults=self._default_domain_window)
def __str__(self):
return self._format_str(
[('X_Degree', self.x_degree),
('Y_Degree', self.y_degree),
('X_Domain', self.x_domain),
('Y_Domain', self.y_domain),
('X_Window', self.x_window),
('Y_Window', self.y_window)],
self._default_domain_window)
def get_num_coeff(self):
"""
Determine how many coefficients are needed
Returns
-------
numc : int
number of coefficients
"""
if self.x_degree < 0 or self.y_degree < 0:
raise ValueError("Degree of polynomial must be positive or null")
return (self.x_degree + 1) * (self.y_degree + 1)
def _invlex(self):
# TODO: This is a very slow way to do this; fix it and related methods
# like _alpha
c = []
xvar = np.arange(self.x_degree + 1)
yvar = np.arange(self.y_degree + 1)
for j in yvar:
for i in xvar:
c.append((i, j))
return np.array(c[::-1])
def invlex_coeff(self, coeffs):
invlex_coeffs = []
xvar = np.arange(self.x_degree + 1)
yvar = np.arange(self.y_degree + 1)
for j in yvar:
for i in xvar:
name = f'c{i}_{j}'
coeff = coeffs[self.param_names.index(name)]
invlex_coeffs.append(coeff)
return np.array(invlex_coeffs[::-1])
def _alpha(self):
invlexdeg = self._invlex()
invlexdeg[:, 1] = invlexdeg[:, 1] + self.x_degree + 1
nx = self.x_degree + 1
ny = self.y_degree + 1
alpha = np.zeros((ny * nx + 3, ny + nx))
for n in range(len(invlexdeg)):
alpha[n][invlexdeg[n]] = [1, 1]
alpha[-2, 0] = 1
alpha[-3, nx] = 1
return alpha
def imhorner(self, x, y, coeff):
_coeff = list(coeff)
_coeff.extend([0, 0, 0])
alpha = self._alpha()
r0 = _coeff[0]
nalpha = len(alpha)
karr = np.diff(alpha, axis=0)
kfunc = self._fcache(x, y)
x_terms = self.x_degree + 1
y_terms = self.y_degree + 1
nterms = x_terms + y_terms
for n in range(1, nterms + 1 + 3):
setattr(self, 'r' + str(n), 0.)
for n in range(1, nalpha):
k = karr[n - 1].nonzero()[0].max() + 1
rsum = 0
for i in range(1, k + 1):
rsum = rsum + getattr(self, 'r' + str(i))
val = kfunc[k - 1] * (r0 + rsum)
setattr(self, 'r' + str(k), val)
r0 = _coeff[n]
for i in range(1, k):
setattr(self, 'r' + str(i), 0.)
result = r0
for i in range(1, nterms + 1 + 3):
result = result + getattr(self, 'r' + str(i))
return result
def _generate_coeff_names(self):
names = []
for j in range(self.y_degree + 1):
for i in range(self.x_degree + 1):
names.append(f'c{i}_{j}')
return tuple(names)
def _fcache(self, x, y):
"""
Computation and store the individual functions.
To be implemented by subclasses"
"""
raise NotImplementedError("Subclasses should implement this")
def evaluate(self, x, y, *coeffs):
if self.x_domain is not None:
x = poly_map_domain(x, self.x_domain, self.x_window)
if self.y_domain is not None:
y = poly_map_domain(y, self.y_domain, self.y_window)
invcoeff = self.invlex_coeff(coeffs)
return self.imhorner(x, y, invcoeff)
def prepare_inputs(self, x, y, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, y, **kwargs)
x, y = inputs
if x.shape != y.shape:
raise ValueError("Expected input arrays to have the same shape")
return (x, y), broadcasted_shapes
class Chebyshev1D(_PolyDomainWindow1D):
r"""
Univariate Chebyshev series.
It is defined as:
.. math::
P(x) = \sum_{i=0}^{i=n}C_{i} * T_{i}(x)
where ``T_i(x)`` is the corresponding Chebyshev polynomial of the 1st kind.
For explanation of ```domain``, and ``window`` see
:ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
degree : int
degree of the series
domain : tuple or None, optional
window : tuple or None, optional
If None, it is set to (-1, 1)
Fitters will remap the domain to this window.
**params : dict
keyword : value pairs, representing parameter_name: value
Notes
-----
This model does not support the use of units/quantities, because each term
in the sum of Chebyshev polynomials is a polynomial in x - since the
coefficients within each Chebyshev polynomial are fixed, we can't use
quantities for x since the units would not be compatible. For example, the
third Chebyshev polynomial (T2) is 2x^2-1, but if x was specified with
units, 2x^2 and -1 would have incompatible units.
"""
n_inputs = 1
n_outputs = 1
_separable = True
def __init__(self, degree, domain=None, window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(degree, domain=domain, window=window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def fit_deriv(self, x, *params):
"""
Computes the Vandermonde matrix.
Parameters
----------
x : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
v = np.empty((self.degree + 1,) + x.shape, dtype=x.dtype)
v[0] = 1
if self.degree > 0:
x2 = 2 * x
v[1] = x
for i in range(2, self.degree + 1):
v[i] = v[i - 1] * x2 - v[i - 2]
return np.rollaxis(v, 0, v.ndim)
def prepare_inputs(self, x, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, **kwargs)
x = inputs[0]
return (x,), broadcasted_shapes
def evaluate(self, x, *coeffs):
if self.domain is not None:
x = poly_map_domain(x, self.domain, self.window)
return self.clenshaw(x, coeffs)
@staticmethod
def clenshaw(x, coeffs):
"""Evaluates the polynomial using Clenshaw's algorithm."""
if len(coeffs) == 1:
c0 = coeffs[0]
c1 = 0
elif len(coeffs) == 2:
c0 = coeffs[0]
c1 = coeffs[1]
else:
x2 = 2 * x
c0 = coeffs[-2]
c1 = coeffs[-1]
for i in range(3, len(coeffs) + 1):
tmp = c0
c0 = coeffs[-i] - c1
c1 = tmp + c1 * x2
return c0 + c1 * x
class Hermite1D(_PolyDomainWindow1D):
r"""
Univariate Hermite series.
It is defined as:
.. math::
P(x) = \sum_{i=0}^{i=n}C_{i} * H_{i}(x)
where ``H_i(x)`` is the corresponding Hermite polynomial ("Physicist's kind").
For explanation of ``domain``, and ``window`` see
:ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
degree : int
degree of the series
domain : tuple or None, optional
window : tuple or None, optional
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword : value pairs, representing parameter_name: value
Notes
-----
This model does not support the use of units/quantities, because each term
in the sum of Hermite polynomials is a polynomial in x - since the
coefficients within each Hermite polynomial are fixed, we can't use
quantities for x since the units would not be compatible. For example, the
third Hermite polynomial (H2) is 4x^2-2, but if x was specified with units,
4x^2 and -2 would have incompatible units.
"""
n_inputs = 1
n_outputs = 1
_separable = True
def __init__(self, degree, domain=None, window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
degree, domain, window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def fit_deriv(self, x, *params):
"""
Computes the Vandermonde matrix.
Parameters
----------
x : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
v = np.empty((self.degree + 1,) + x.shape, dtype=x.dtype)
v[0] = 1
if self.degree > 0:
x2 = 2 * x
v[1] = 2 * x
for i in range(2, self.degree + 1):
v[i] = x2 * v[i - 1] - 2 * (i - 1) * v[i - 2]
return np.rollaxis(v, 0, v.ndim)
def prepare_inputs(self, x, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, **kwargs)
x = inputs[0]
return (x,), broadcasted_shapes
def evaluate(self, x, *coeffs):
if self.domain is not None:
x = poly_map_domain(x, self.domain, self.window)
return self.clenshaw(x, coeffs)
@staticmethod
def clenshaw(x, coeffs):
x2 = x * 2
if len(coeffs) == 1:
c0 = coeffs[0]
c1 = 0
elif len(coeffs) == 2:
c0 = coeffs[0]
c1 = coeffs[1]
else:
nd = len(coeffs)
c0 = coeffs[-2]
c1 = coeffs[-1]
for i in range(3, len(coeffs) + 1):
temp = c0
nd = nd - 1
c0 = coeffs[-i] - c1 * (2 * (nd - 1))
c1 = temp + c1 * x2
return c0 + c1 * x2
class Hermite2D(OrthoPolynomialBase):
r"""
Bivariate Hermite series.
It is defined as
.. math:: P_{nm}(x,y) = \sum_{n,m=0}^{n=d,m=d}C_{nm} H_n(x) H_m(y)
where ``H_n(x)`` and ``H_m(y)`` are Hermite polynomials.
For explanation of ``x_domain``, ``y_domain``, ``x_window`` and ``y_window``
see :ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
x_degree : int
degree in x
y_degree : int
degree in y
x_domain : tuple or None, optional
domain of the x independent variable
y_domain : tuple or None, optional
domain of the y independent variable
x_window : tuple or None, optional
range of the x independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
y_window : tuple or None, optional
range of the y independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword: value pairs, representing parameter_name: value
Notes
-----
This model does not support the use of units/quantities, because each term
in the sum of Hermite polynomials is a polynomial in x and/or y - since the
coefficients within each Hermite polynomial are fixed, we can't use
quantities for x and/or y since the units would not be compatible. For
example, the third Hermite polynomial (H2) is 4x^2-2, but if x was
specified with units, 4x^2 and -2 would have incompatible units.
"""
_separable = False
def __init__(self, x_degree, y_degree, x_domain=None, x_window=None,
y_domain=None, y_window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
x_degree, y_degree, x_domain=x_domain, y_domain=y_domain,
x_window=x_window, y_window=y_window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def _fcache(self, x, y):
"""
Calculate the individual Hermite functions once and store them in a
dictionary to be reused.
"""
x_terms = self.x_degree + 1
y_terms = self.y_degree + 1
kfunc = {}
kfunc[0] = np.ones(x.shape)
kfunc[1] = 2 * x.copy()
kfunc[x_terms] = np.ones(y.shape)
kfunc[x_terms + 1] = 2 * y.copy()
for n in range(2, x_terms):
kfunc[n] = 2 * x * kfunc[n - 1] - 2 * (n - 1) * kfunc[n - 2]
for n in range(x_terms + 2, x_terms + y_terms):
kfunc[n] = 2 * y * kfunc[n - 1] - 2 * (n - 1) * kfunc[n - 2]
return kfunc
def fit_deriv(self, x, y, *params):
"""
Derivatives with respect to the coefficients.
This is an array with Hermite polynomials:
.. math::
H_{x_0}H_{y_0}, H_{x_1}H_{y_0}...H_{x_n}H_{y_0}...H_{x_n}H_{y_m}
Parameters
----------
x : ndarray
input
y : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
if x.shape != y.shape:
raise ValueError("x and y must have the same shape")
x = x.flatten()
y = y.flatten()
x_deriv = self._hermderiv1d(x, self.x_degree + 1).T
y_deriv = self._hermderiv1d(y, self.y_degree + 1).T
ij = []
for i in range(self.y_degree + 1):
for j in range(self.x_degree + 1):
ij.append(x_deriv[j] * y_deriv[i])
v = np.array(ij)
return v.T
def _hermderiv1d(self, x, deg):
"""
Derivative of 1D Hermite series
"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
d = np.empty((deg + 1, len(x)), dtype=x.dtype)
d[0] = x * 0 + 1
if deg > 0:
x2 = 2 * x
d[1] = x2
for i in range(2, deg + 1):
d[i] = x2 * d[i - 1] - 2 * (i - 1) * d[i - 2]
return np.rollaxis(d, 0, d.ndim)
class Legendre1D(_PolyDomainWindow1D):
r"""
Univariate Legendre series.
It is defined as:
.. math::
P(x) = \sum_{i=0}^{i=n}C_{i} * L_{i}(x)
where ``L_i(x)`` is the corresponding Legendre polynomial.
For explanation of ``domain``, and ``window`` see
:ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
degree : int
degree of the series
domain : tuple or None, optional
window : tuple or None, optional
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword: value pairs, representing parameter_name: value
Notes
-----
This model does not support the use of units/quantities, because each term
in the sum of Legendre polynomials is a polynomial in x - since the
coefficients within each Legendre polynomial are fixed, we can't use
quantities for x since the units would not be compatible. For example, the
third Legendre polynomial (P2) is 1.5x^2-0.5, but if x was specified with
units, 1.5x^2 and -0.5 would have incompatible units.
"""
n_inputs = 1
n_outputs = 1
_separable = True
def __init__(self, degree, domain=None, window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
degree, domain, window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def prepare_inputs(self, x, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, **kwargs)
x = inputs[0]
return (x,), broadcasted_shapes
def evaluate(self, x, *coeffs):
if self.domain is not None:
x = poly_map_domain(x, self.domain, self.window)
return self.clenshaw(x, coeffs)
def fit_deriv(self, x, *params):
"""
Computes the Vandermonde matrix.
Parameters
----------
x : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
v = np.empty((self.degree + 1,) + x.shape, dtype=x.dtype)
v[0] = 1
if self.degree > 0:
v[1] = x
for i in range(2, self.degree + 1):
v[i] = (v[i - 1] * x * (2 * i - 1) - v[i - 2] * (i - 1)) / i
return np.rollaxis(v, 0, v.ndim)
@staticmethod
def clenshaw(x, coeffs):
if len(coeffs) == 1:
c0 = coeffs[0]
c1 = 0
elif len(coeffs) == 2:
c0 = coeffs[0]
c1 = coeffs[1]
else:
nd = len(coeffs)
c0 = coeffs[-2]
c1 = coeffs[-1]
for i in range(3, len(coeffs) + 1):
tmp = c0
nd = nd - 1
c0 = coeffs[-i] - (c1 * (nd - 1)) / nd
c1 = tmp + (c1 * x * (2 * nd - 1)) / nd
return c0 + c1 * x
class Polynomial1D(_PolyDomainWindow1D):
r"""
1D Polynomial model.
It is defined as:
.. math::
P = \sum_{i=0}^{i=n}C_{i} * x^{i}
For explanation of ``domain``, and ``window`` see
:ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
degree : int
degree of the series
domain : tuple or None, optional
If None, it is set to (-1, 1)
window : tuple or None, optional
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword: value pairs, representing parameter_name: value
"""
n_inputs = 1
n_outputs = 1
_separable = True
def __init__(self, degree, domain=None, window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
degree, domain, window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
# Set domain separately because it's different from
# the orthogonal polynomials.
self._default_domain_window = {'domain': (-1, 1),
'window': (-1, 1),
}
self.domain = domain or self._default_domain_window['domain']
self.window = window or self._default_domain_window['window']
def prepare_inputs(self, x, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, **kwargs)
x = inputs[0]
return (x,), broadcasted_shapes
def evaluate(self, x, *coeffs):
if self.domain is not None:
x = poly_map_domain(x, self.domain, self.window)
return self.horner(x, coeffs)
def fit_deriv(self, x, *params):
"""
Computes the Vandermonde matrix.
Parameters
----------
x : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
v = np.empty((self.degree + 1,) + x.shape, dtype=float)
v[0] = 1
if self.degree > 0:
v[1] = x
for i in range(2, self.degree + 1):
v[i] = v[i - 1] * x
return np.rollaxis(v, 0, v.ndim)
@staticmethod
def horner(x, coeffs):
if len(coeffs) == 1:
c0 = coeffs[-1] * np.ones_like(x, subok=False)
else:
c0 = coeffs[-1]
for i in range(2, len(coeffs) + 1):
c0 = coeffs[-i] + c0 * x
return c0
@property
def input_units(self):
if self.degree == 0 or self.c1.unit is None:
return None
else:
return {self.inputs[0]: self.c0.unit / self.c1.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
mapping = {}
for i in range(self.degree + 1):
par = getattr(self, f'c{i}')
mapping[par.name] = outputs_unit[self.outputs[0]] / inputs_unit[self.inputs[0]] ** i
return mapping
class Polynomial2D(PolynomialModel):
r"""
2D Polynomial model.
Represents a general polynomial of degree n:
.. math::
P(x,y) = c_{00} + c_{10}x + ...+ c_{n0}x^n + c_{01}y + ...+ c_{0n}y^n
+ c_{11}xy + c_{12}xy^2 + ... + c_{1(n-1)}xy^{n-1}+ ... + c_{(n-1)1}x^{n-1}y
For explanation of ``x_domain``, ``y_domain``, ``x_window`` and ``y_window``
see :ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
degree : int
Polynomial degree: largest sum of exponents (:math:`i + j`) of
variables in each monomial term of the form :math:`x^i y^j`. The
number of terms in a 2D polynomial of degree ``n`` is given by binomial
coefficient :math:`C(n + 2, 2) = (n + 2)! / (2!\,n!) = (n + 1)(n + 2) / 2`.
x_domain : tuple or None, optional
domain of the x independent variable
If None, it is set to (-1, 1)
y_domain : tuple or None, optional
domain of the y independent variable
If None, it is set to (-1, 1)
x_window : tuple or None, optional
range of the x independent variable
If None, it is set to (-1, 1)
Fitters will remap the x_domain to x_window
y_window : tuple or None, optional
range of the y independent variable
If None, it is set to (-1, 1)
Fitters will remap the y_domain to y_window
**params : dict
keyword: value pairs, representing parameter_name: value
"""
n_inputs = 2
n_outputs = 1
_separable = False
def __init__(self, degree, x_domain=None, y_domain=None,
x_window=None, y_window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
degree, n_models=n_models, model_set_axis=model_set_axis,
name=name, meta=meta, **params)
self._default_domain_window = {
'x_domain': (-1, 1),
'y_domain': (-1, 1),
'x_window': (-1, 1),
'y_window': (-1, 1)
}
self.x_domain = x_domain or self._default_domain_window['x_domain']
self.y_domain = y_domain or self._default_domain_window['y_domain']
self.x_window = x_window or self._default_domain_window['x_window']
self.y_window = y_window or self._default_domain_window['y_window']
def prepare_inputs(self, x, y, **kwargs):
inputs, broadcasted_shapes = super().prepare_inputs(x, y, **kwargs)
x, y = inputs
return (x, y), broadcasted_shapes
def evaluate(self, x, y, *coeffs):
if self.x_domain is not None:
x = poly_map_domain(x, self.x_domain, self.x_window)
if self.y_domain is not None:
y = poly_map_domain(y, self.y_domain, self.y_window)
invcoeff = self.invlex_coeff(coeffs)
result = self.multivariate_horner(x, y, invcoeff)
# Special case for degree==0 to ensure that the shape of the output is
# still as expected by the broadcasting rules, even though the x and y
# inputs are not used in the evaluation
if self.degree == 0:
output_shape = check_broadcast(np.shape(coeffs[0]), x.shape)
if output_shape:
new_result = np.empty(output_shape)
new_result[:] = result
result = new_result
return result
def __repr__(self):
return self._format_repr([self.degree],
kwargs={'x_domain': self.x_domain,
'y_domain': self.y_domain,
'x_window': self.x_window,
'y_window': self.y_window},
defaults=self._default_domain_window)
def __str__(self):
return self._format_str([('Degree', self.degree),
('X_Domain', self.x_domain),
('Y_Domain', self.y_domain),
('X_Window', self.x_window),
('Y_Window', self.y_window)],
self._default_domain_window)
def fit_deriv(self, x, y, *params):
"""
Computes the Vandermonde matrix.
Parameters
----------
x : ndarray
input
y : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
if x.ndim == 2:
x = x.flatten()
if y.ndim == 2:
y = y.flatten()
if x.size != y.size:
raise ValueError('Expected x and y to be of equal size')
designx = x[:, None] ** np.arange(self.degree + 1)
designy = y[:, None] ** np.arange(1, self.degree + 1)
designmixed = []
for i in range(1, self.degree):
for j in range(1, self.degree):
if i + j <= self.degree:
designmixed.append((x ** i) * (y ** j))
designmixed = np.array(designmixed).T
if designmixed.any():
v = np.hstack([designx, designy, designmixed])
else:
v = np.hstack([designx, designy])
return v
def invlex_coeff(self, coeffs):
invlex_coeffs = []
lencoeff = range(self.degree + 1)
for i in lencoeff:
for j in lencoeff:
if i + j <= self.degree:
name = f'c{j}_{i}'
coeff = coeffs[self.param_names.index(name)]
invlex_coeffs.append(coeff)
return invlex_coeffs[::-1]
def multivariate_horner(self, x, y, coeffs):
"""
Multivariate Horner's scheme
Parameters
----------
x, y : array
coeffs : array
Coefficients in inverse lexical order.
"""
alpha = self._invlex()
r0 = coeffs[0]
r1 = r0 * 0.0
r2 = r0 * 0.0
karr = np.diff(alpha, axis=0)
for n in range(len(karr)):
if karr[n, 1] != 0:
r2 = y * (r0 + r1 + r2)
r1 = np.zeros_like(coeffs[0], subok=False)
else:
r1 = x * (r0 + r1)
r0 = coeffs[n + 1]
return r0 + r1 + r2
@property
def input_units(self):
if self.degree == 0 or (self.c1_0.unit is None and self.c0_1.unit is None):
return None
return {self.inputs[0]: self.c0_0.unit / self.c1_0.unit,
self.inputs[1]: self.c0_0.unit / self.c0_1.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
mapping = {}
for i in range(self.degree + 1):
for j in range(self.degree + 1):
if i + j > 2:
continue
par = getattr(self, f'c{i}_{j}')
mapping[par.name] = outputs_unit[self.outputs[0]] / inputs_unit[self.inputs[0]] ** i / inputs_unit[self.inputs[1]] ** j # noqa
return mapping
@property
def x_domain(self):
return self._x_domain
@x_domain.setter
def x_domain(self, val):
self._x_domain = _validate_domain_window(val)
@property
def y_domain(self):
return self._y_domain
@y_domain.setter
def y_domain(self, val):
self._y_domain = _validate_domain_window(val)
@property
def x_window(self):
return self._x_window
@x_window.setter
def x_window(self, val):
self._x_window = _validate_domain_window(val)
@property
def y_window(self):
return self._y_window
@y_window.setter
def y_window(self, val):
self._y_window = _validate_domain_window(val)
class Chebyshev2D(OrthoPolynomialBase):
r"""
Bivariate Chebyshev series..
It is defined as
.. math:: P_{nm}(x,y) = \sum_{n,m=0}^{n=d,m=d}C_{nm} T_n(x ) T_m(y)
where ``T_n(x)`` and ``T_m(y)`` are Chebyshev polynomials of the first kind.
For explanation of ``x_domain``, ``y_domain``, ``x_window`` and ``y_window``
see :ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
x_degree : int
degree in x
y_degree : int
degree in y
x_domain : tuple or None, optional
domain of the x independent variable
y_domain : tuple or None, optional
domain of the y independent variable
x_window : tuple or None, optional
range of the x independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
y_window : tuple or None, optional
range of the y independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword: value pairs, representing parameter_name: value
Notes
-----
This model does not support the use of units/quantities, because each term
in the sum of Chebyshev polynomials is a polynomial in x and/or y - since
the coefficients within each Chebyshev polynomial are fixed, we can't use
quantities for x and/or y since the units would not be compatible. For
example, the third Chebyshev polynomial (T2) is 2x^2-1, but if x was
specified with units, 2x^2 and -1 would have incompatible units.
"""
_separable = False
def __init__(self, x_degree, y_degree, x_domain=None, x_window=None,
y_domain=None, y_window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
x_degree, y_degree, x_domain=x_domain, y_domain=y_domain,
x_window=x_window, y_window=y_window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def _fcache(self, x, y):
"""
Calculate the individual Chebyshev functions once and store them in a
dictionary to be reused.
"""
x_terms = self.x_degree + 1
y_terms = self.y_degree + 1
kfunc = {}
kfunc[0] = np.ones(x.shape)
kfunc[1] = x.copy()
kfunc[x_terms] = np.ones(y.shape)
kfunc[x_terms + 1] = y.copy()
for n in range(2, x_terms):
kfunc[n] = 2 * x * kfunc[n - 1] - kfunc[n - 2]
for n in range(x_terms + 2, x_terms + y_terms):
kfunc[n] = 2 * y * kfunc[n - 1] - kfunc[n - 2]
return kfunc
def fit_deriv(self, x, y, *params):
"""
Derivatives with respect to the coefficients.
This is an array with Chebyshev polynomials:
.. math::
T_{x_0}T_{y_0}, T_{x_1}T_{y_0}...T_{x_n}T_{y_0}...T_{x_n}T_{y_m}
Parameters
----------
x : ndarray
input
y : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
if x.shape != y.shape:
raise ValueError("x and y must have the same shape")
x = x.flatten()
y = y.flatten()
x_deriv = self._chebderiv1d(x, self.x_degree + 1).T
y_deriv = self._chebderiv1d(y, self.y_degree + 1).T
ij = []
for i in range(self.y_degree + 1):
for j in range(self.x_degree + 1):
ij.append(x_deriv[j] * y_deriv[i])
v = np.array(ij)
return v.T
def _chebderiv1d(self, x, deg):
"""
Derivative of 1D Chebyshev series
"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
d = np.empty((deg + 1, len(x)), dtype=x.dtype)
d[0] = x * 0 + 1
if deg > 0:
x2 = 2 * x
d[1] = x
for i in range(2, deg + 1):
d[i] = d[i - 1] * x2 - d[i - 2]
return np.rollaxis(d, 0, d.ndim)
class Legendre2D(OrthoPolynomialBase):
r"""
Bivariate Legendre series.
Defined as:
.. math:: P_{n_m}(x,y) = \sum_{n,m=0}^{n=d,m=d}C_{nm} L_n(x ) L_m(y)
where ``L_n(x)`` and ``L_m(y)`` are Legendre polynomials.
For explanation of ``x_domain``, ``y_domain``, ``x_window`` and ``y_window``
see :ref:`Notes regarding usage of domain and window <domain-window-note>`.
Parameters
----------
x_degree : int
degree in x
y_degree : int
degree in y
x_domain : tuple or None, optional
domain of the x independent variable
y_domain : tuple or None, optional
domain of the y independent variable
x_window : tuple or None, optional
range of the x independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
y_window : tuple or None, optional
range of the y independent variable
If None, it is set to (-1, 1)
Fitters will remap the domain to this window
**params : dict
keyword: value pairs, representing parameter_name: value
Notes
-----
Model formula:
.. math::
P(x) = \sum_{i=0}^{i=n}C_{i} * L_{i}(x)
where ``L_{i}`` is the corresponding Legendre polynomial.
This model does not support the use of units/quantities, because each term
in the sum of Legendre polynomials is a polynomial in x - since the
coefficients within each Legendre polynomial are fixed, we can't use
quantities for x since the units would not be compatible. For example, the
third Legendre polynomial (P2) is 1.5x^2-0.5, but if x was specified with
units, 1.5x^2 and -0.5 would have incompatible units.
"""
_separable = False
def __init__(self, x_degree, y_degree, x_domain=None, x_window=None,
y_domain=None, y_window=None, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
super().__init__(
x_degree, y_degree, x_domain=x_domain, y_domain=y_domain,
x_window=x_window, y_window=y_window, n_models=n_models,
model_set_axis=model_set_axis, name=name, meta=meta, **params)
def _fcache(self, x, y):
"""
Calculate the individual Legendre functions once and store them in a
dictionary to be reused.
"""
x_terms = self.x_degree + 1
y_terms = self.y_degree + 1
kfunc = {}
kfunc[0] = np.ones(x.shape)
kfunc[1] = x.copy()
kfunc[x_terms] = np.ones(y.shape)
kfunc[x_terms + 1] = y.copy()
for n in range(2, x_terms):
kfunc[n] = (((2 * (n - 1) + 1) * x * kfunc[n - 1] -
(n - 1) * kfunc[n - 2]) / n)
for n in range(2, y_terms):
kfunc[n + x_terms] = ((2 * (n - 1) + 1) * y * kfunc[n + x_terms - 1] -
(n - 1) * kfunc[n + x_terms - 2]) / (n)
return kfunc
def fit_deriv(self, x, y, *params):
"""
Derivatives with respect to the coefficients.
This is an array with Legendre polynomials:
Lx0Ly0 Lx1Ly0...LxnLy0...LxnLym
Parameters
----------
x : ndarray
input
y : ndarray
input
*params
throw-away parameter list returned by non-linear fitters
Returns
-------
result : ndarray
The Vandermonde matrix
"""
if x.shape != y.shape:
raise ValueError("x and y must have the same shape")
x = x.flatten()
y = y.flatten()
x_deriv = self._legendderiv1d(x, self.x_degree + 1).T
y_deriv = self._legendderiv1d(y, self.y_degree + 1).T
ij = []
for i in range(self.y_degree + 1):
for j in range(self.x_degree + 1):
ij.append(x_deriv[j] * y_deriv[i])
v = np.array(ij)
return v.T
def _legendderiv1d(self, x, deg):
"""Derivative of 1D Legendre polynomial"""
x = np.array(x, dtype=float, copy=False, ndmin=1)
d = np.empty((deg + 1,) + x.shape, dtype=x.dtype)
d[0] = x * 0 + 1
if deg > 0:
d[1] = x
for i in range(2, deg + 1):
d[i] = (d[i - 1] * x * (2 * i - 1) - d[i - 2] * (i - 1)) / i
return np.rollaxis(d, 0, d.ndim)
class _SIP1D(PolynomialBase):
"""
This implements the Simple Imaging Polynomial Model (SIP) in 1D.
It's unlikely it will be used in 1D so this class is private
and SIP should be used instead.
"""
n_inputs = 2
n_outputs = 1
_separable = False
def __init__(self, order, coeff_prefix, n_models=None,
model_set_axis=None, name=None, meta=None, **params):
self.order = order
self.coeff_prefix = coeff_prefix
self._param_names = self._generate_coeff_names(coeff_prefix)
if n_models:
if model_set_axis is None:
model_set_axis = 0
minshape = (1,) * model_set_axis + (n_models,)
else:
minshape = ()
for param_name in self._param_names:
self._parameters_[param_name] = \
Parameter(param_name, default=np.zeros(minshape))
super().__init__(n_models=n_models, model_set_axis=model_set_axis,
name=name, meta=meta, **params)
def __repr__(self):
return self._format_repr(args=[self.order, self.coeff_prefix])
def __str__(self):
return self._format_str(
[('Order', self.order),
('Coeff. Prefix', self.coeff_prefix)])
def evaluate(self, x, y, *coeffs):
# TODO: Rewrite this so that it uses a simpler method of determining
# the matrix based on the number of given coefficients.
mcoef = self._coeff_matrix(self.coeff_prefix, coeffs)
return self._eval_sip(x, y, mcoef)
def get_num_coeff(self, ndim):
"""
Return the number of coefficients in one param set
"""
if self.order < 2 or self.order > 9:
raise ValueError("Degree of polynomial must be 2< deg < 9")
nmixed = comb(self.order, ndim)
# remove 3 terms because SIP deg >= 2
numc = self.order * ndim + nmixed - 2
return numc
def _generate_coeff_names(self, coeff_prefix):
names = []
for i in range(2, self.order + 1):
names.append(f'{coeff_prefix}_{i}_{0}')
for i in range(2, self.order + 1):
names.append(f'{coeff_prefix}_{0}_{i}')
for i in range(1, self.order):
for j in range(1, self.order):
if i + j < self.order + 1:
names.append(f'{coeff_prefix}_{i}_{j}')
return tuple(names)
def _coeff_matrix(self, coeff_prefix, coeffs):
mat = np.zeros((self.order + 1, self.order + 1))
for i in range(2, self.order + 1):
attr = f'{coeff_prefix}_{i}_{0}'
mat[i, 0] = coeffs[self.param_names.index(attr)]
for i in range(2, self.order + 1):
attr = f'{coeff_prefix}_{0}_{i}'
mat[0, i] = coeffs[self.param_names.index(attr)]
for i in range(1, self.order):
for j in range(1, self.order):
if i + j < self.order + 1:
attr = f'{coeff_prefix}_{i}_{j}'
mat[i, j] = coeffs[self.param_names.index(attr)]
return mat
def _eval_sip(self, x, y, coef):
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
if self.coeff_prefix == 'A':
result = np.zeros(x.shape)
else:
result = np.zeros(y.shape)
for i in range(coef.shape[0]):
for j in range(coef.shape[1]):
if 1 < i + j < self.order + 1:
result = result + coef[i, j] * x ** i * y ** j
return result
class SIP(Model):
"""
Simple Imaging Polynomial (SIP) model.
The SIP convention is used to represent distortions in FITS image headers.
See [1]_ for a description of the SIP convention.
Parameters
----------
crpix : list or (2,) ndarray
CRPIX values
a_order : int
SIP polynomial order for first axis
b_order : int
SIP order for second axis
a_coeff : dict
SIP coefficients for first axis
b_coeff : dict
SIP coefficients for the second axis
ap_order : int
order for the inverse transformation (AP coefficients)
bp_order : int
order for the inverse transformation (BP coefficients)
ap_coeff : dict
coefficients for the inverse transform
bp_coeff : dict
coefficients for the inverse transform
References
----------
.. [1] `David Shupe, et al, ADASS, ASP Conference Series, Vol. 347, 2005 <https://ui.adsabs.harvard.edu/abs/2005ASPC..347..491S>`_
"""
n_inputs = 2
n_outputs = 2
_separable = False
def __init__(self, crpix, a_order, b_order, a_coeff={}, b_coeff={},
ap_order=None, bp_order=None, ap_coeff={}, bp_coeff={},
n_models=None, model_set_axis=None, name=None, meta=None):
self._crpix = crpix
self._a_order = a_order
self._b_order = b_order
self._a_coeff = a_coeff
self._b_coeff = b_coeff
self._ap_order = ap_order
self._bp_order = bp_order
self._ap_coeff = ap_coeff
self._bp_coeff = bp_coeff
self.shift_a = Shift(-crpix[0])
self.shift_b = Shift(-crpix[1])
self.sip1d_a = _SIP1D(a_order, coeff_prefix='A', n_models=n_models,
model_set_axis=model_set_axis, **a_coeff)
self.sip1d_b = _SIP1D(b_order, coeff_prefix='B', n_models=n_models,
model_set_axis=model_set_axis, **b_coeff)
super().__init__(n_models=n_models, model_set_axis=model_set_axis,
name=name, meta=meta)
self._inputs = ("u", "v")
self._outputs = ("x", "y")
def __repr__(self):
return '<{}({!r})>'.format(self.__class__.__name__,
[self.shift_a, self.shift_b, self.sip1d_a, self.sip1d_b])
def __str__(self):
parts = [f'Model: {self.__class__.__name__}']
for model in [self.shift_a, self.shift_b, self.sip1d_a, self.sip1d_b]:
parts.append(indent(str(model), width=4))
parts.append('')
return '\n'.join(parts)
@property
def inverse(self):
if (self._ap_order is not None and self._bp_order is not None):
return InverseSIP(self._ap_order, self._bp_order,
self._ap_coeff, self._bp_coeff)
else:
raise NotImplementedError("SIP inverse coefficients are not available.")
def evaluate(self, x, y):
u = self.shift_a.evaluate(x, *self.shift_a.param_sets)
v = self.shift_b.evaluate(y, *self.shift_b.param_sets)
f = self.sip1d_a.evaluate(u, v, *self.sip1d_a.param_sets)
g = self.sip1d_b.evaluate(u, v, *self.sip1d_b.param_sets)
return f, g
class InverseSIP(Model):
"""
Inverse Simple Imaging Polynomial
Parameters
----------
ap_order : int
order for the inverse transformation (AP coefficients)
bp_order : int
order for the inverse transformation (BP coefficients)
ap_coeff : dict
coefficients for the inverse transform
bp_coeff : dict
coefficients for the inverse transform
"""
n_inputs = 2
n_outputs = 2
_separable = False
def __init__(self, ap_order, bp_order, ap_coeff={}, bp_coeff={},
n_models=None, model_set_axis=None, name=None, meta=None):
self._ap_order = ap_order
self._bp_order = bp_order
self._ap_coeff = ap_coeff
self._bp_coeff = bp_coeff
# define the 0th term in order to use Polynomial2D
ap_coeff.setdefault('AP_0_0', 0)
bp_coeff.setdefault('BP_0_0', 0)
ap_coeff_params = dict((k.replace('AP_', 'c'), v)
for k, v in ap_coeff.items())
bp_coeff_params = dict((k.replace('BP_', 'c'), v)
for k, v in bp_coeff.items())
self.sip1d_ap = Polynomial2D(degree=ap_order,
model_set_axis=model_set_axis,
**ap_coeff_params)
self.sip1d_bp = Polynomial2D(degree=bp_order,
model_set_axis=model_set_axis,
**bp_coeff_params)
super().__init__(n_models=n_models, model_set_axis=model_set_axis,
name=name, meta=meta)
def __repr__(self):
return f'<{self.__class__.__name__}({[self.sip1d_ap, self.sip1d_bp]!r})>'
def __str__(self):
parts = [f'Model: {self.__class__.__name__}']
for model in [self.sip1d_ap, self.sip1d_bp]:
parts.append(indent(str(model), width=4))
parts.append('')
return '\n'.join(parts)
def evaluate(self, x, y):
x1 = self.sip1d_ap.evaluate(x, y, *self.sip1d_ap.param_sets)
y1 = self.sip1d_bp.evaluate(x, y, *self.sip1d_bp.param_sets)
return x1, y1
|
fbacca35c6e23fcd604c2a0e7df1f3bf1693bfd2a6a1dd84d847757755da1a9e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Tabular models.
Tabular models of any dimension can be created using `tabular_model`.
For convenience `Tabular1D` and `Tabular2D` are provided.
Examples
--------
>>> table = np.array([[ 3., 0., 0.],
... [ 0., 2., 0.],
... [ 0., 0., 0.]])
>>> points = ([1, 2, 3], [1, 2, 3])
>>> t2 = Tabular2D(points, lookup_table=table, bounds_error=False,
... fill_value=None, method='nearest')
"""
# pylint: disable=invalid-name
import abc
import numpy as np
from astropy import units as u
from .core import Model
try:
from scipy.interpolate import interpn
has_scipy = True
except ImportError:
has_scipy = False
__all__ = ['tabular_model', 'Tabular1D', 'Tabular2D']
__doctest_requires__ = {('tabular_model'): ['scipy']}
class _Tabular(Model):
"""
Returns an interpolated lookup table value.
Parameters
----------
points : tuple of ndarray of float, optional
The points defining the regular grid in n dimensions.
ndarray must have shapes (m1, ), ..., (mn, ),
lookup_table : array-like
The data on a regular grid in n dimensions.
Must have shapes (m1, ..., mn, ...)
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest", and "splinef2d". "splinef2d" is only supported for
2-dimensional data. Default is "linear".
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then ``fill_value`` is used.
fill_value : float or `~astropy.units.Quantity`, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated. Extrapolation is not supported by method
"splinef2d". If Quantity is given, it will be converted to the unit of
``lookup_table``, if applicable.
Returns
-------
value : ndarray
Interpolated values at input coordinates.
Raises
------
ImportError
Scipy is not installed.
Notes
-----
Uses `scipy.interpolate.interpn`.
"""
linear = False
fittable = False
standard_broadcasting = False
_is_dynamic = True
_id = 0
def __init__(self, points=None, lookup_table=None, method='linear',
bounds_error=True, fill_value=np.nan, **kwargs):
n_models = kwargs.get('n_models', 1)
if n_models > 1:
raise NotImplementedError('Only n_models=1 is supported.')
super().__init__(**kwargs)
self.outputs = ("y",)
if lookup_table is None:
raise ValueError('Must provide a lookup table.')
if not isinstance(lookup_table, u.Quantity):
lookup_table = np.asarray(lookup_table)
if self.lookup_table.ndim != lookup_table.ndim:
raise ValueError("lookup_table should be an array with "
"{} dimensions.".format(self.lookup_table.ndim))
if points is None:
points = tuple(np.arange(x, dtype=float)
for x in lookup_table.shape)
else:
if lookup_table.ndim == 1 and not isinstance(points, tuple):
points = (points,)
npts = len(points)
if npts != lookup_table.ndim:
raise ValueError(
"Expected grid points in "
"{} directions, got {}.".format(lookup_table.ndim, npts))
if (npts > 1 and isinstance(points[0], u.Quantity) and
len(set([getattr(p, 'unit', None) for p in points])) > 1):
raise ValueError('points must all have the same unit.')
if isinstance(fill_value, u.Quantity):
if not isinstance(lookup_table, u.Quantity):
raise ValueError('fill value is in {} but expected to be '
'unitless.'.format(fill_value.unit))
fill_value = fill_value.to(lookup_table.unit).value
self.points = points
self.lookup_table = lookup_table
self.bounds_error = bounds_error
self.method = method
self.fill_value = fill_value
def __repr__(self):
return "<{}(points={}, lookup_table={})>".format(
self.__class__.__name__, self.points, self.lookup_table)
def __str__(self):
default_keywords = [
('Model', self.__class__.__name__),
('Name', self.name),
('N_inputs', self.n_inputs),
('N_outputs', self.n_outputs),
('Parameters', ""),
(' points', self.points),
(' lookup_table', self.lookup_table),
(' method', self.method),
(' fill_value', self.fill_value),
(' bounds_error', self.bounds_error)
]
parts = [f'{keyword}: {value}'
for keyword, value in default_keywords
if value is not None]
return '\n'.join(parts)
@property
def input_units(self):
pts = self.points[0]
if not isinstance(pts, u.Quantity):
return None
return dict([(x, pts.unit) for x in self.inputs])
@property
def return_units(self):
if not isinstance(self.lookup_table, u.Quantity):
return None
return {self.outputs[0]: self.lookup_table.unit}
@property
def bounding_box(self):
"""
Tuple defining the default ``bounding_box`` limits,
``(points_low, points_high)``.
Examples
--------
>>> from astropy.modeling.models import Tabular1D, Tabular2D
>>> t1 = Tabular1D(points=[1, 2, 3], lookup_table=[10, 20, 30])
>>> t1.bounding_box
ModelBoundingBox(
intervals={
x: Interval(lower=1, upper=3)
}
model=Tabular1D(inputs=('x',))
order='C'
)
>>> t2 = Tabular2D(points=[[1, 2, 3], [2, 3, 4]],
... lookup_table=[[10, 20, 30], [20, 30, 40]])
>>> t2.bounding_box
ModelBoundingBox(
intervals={
x: Interval(lower=1, upper=3)
y: Interval(lower=2, upper=4)
}
model=Tabular2D(inputs=('x', 'y'))
order='C'
)
"""
bbox = [(min(p), max(p)) for p in self.points][::-1]
if len(bbox) == 1:
bbox = bbox[0]
return bbox
def evaluate(self, *inputs):
"""
Return the interpolated values at the input coordinates.
Parameters
----------
inputs : list of scalar or list of ndarray
Input coordinates. The number of inputs must be equal
to the dimensions of the lookup table.
"""
inputs = np.broadcast_arrays(*inputs)
shape = inputs[0].shape
inputs = [inp.flatten() for inp in inputs[: self.n_inputs]]
inputs = np.array(inputs).T
if not has_scipy: # pragma: no cover
raise ImportError("Tabular model requires scipy.")
result = interpn(self.points, self.lookup_table, inputs,
method=self.method, bounds_error=self.bounds_error,
fill_value=self.fill_value)
# return_units not respected when points has no units
if (isinstance(self.lookup_table, u.Quantity) and
not isinstance(self.points[0], u.Quantity)):
result = result * self.lookup_table.unit
if self.n_outputs == 1:
result = result.reshape(shape)
else:
result = [r.reshape(shape) for r in result]
return result
@property
def inverse(self):
if self.n_inputs == 1:
# If the wavelength array is descending instead of ascending, both
# points and lookup_table need to be reversed in the inverse transform
# for scipy.interpolate to work properly
if np.all(np.diff(self.lookup_table) > 0):
# ascending case
points = self.lookup_table
lookup_table = self.points[0]
elif np.all(np.diff(self.lookup_table) < 0):
# descending case, reverse order
points = self.lookup_table[::-1]
lookup_table = self.points[0][::-1]
else:
# equal-valued or double-valued lookup_table
raise NotImplementedError
return Tabular1D(points=points, lookup_table=lookup_table, method=self.method,
bounds_error=self.bounds_error, fill_value=self.fill_value)
raise NotImplementedError("An analytical inverse transform "
"has not been implemented for this model.")
def tabular_model(dim, name=None):
"""
Make a ``Tabular`` model where ``n_inputs`` is
based on the dimension of the lookup_table.
This model has to be further initialized and when evaluated
returns the interpolated values.
Parameters
----------
dim : int
Dimensions of the lookup table.
name : str
Name for the class.
Examples
--------
>>> table = np.array([[3., 0., 0.],
... [0., 2., 0.],
... [0., 0., 0.]])
>>> tab = tabular_model(2, name='Tabular2D')
>>> print(tab)
<class 'astropy.modeling.tabular.Tabular2D'>
Name: Tabular2D
N_inputs: 2
N_outputs: 1
>>> points = ([1, 2, 3], [1, 2, 3])
Setting fill_value to None, allows extrapolation.
>>> m = tab(points, lookup_table=table, name='my_table',
... bounds_error=False, fill_value=None, method='nearest')
>>> xinterp = [0, 1, 1.5, 2.72, 3.14]
>>> m(xinterp, xinterp) # doctest: +FLOAT_CMP
array([3., 3., 3., 0., 0.])
"""
if dim < 1:
raise ValueError('Lookup table must have at least one dimension.')
table = np.zeros([2] * dim)
members = {'lookup_table': table, 'n_inputs': dim, 'n_outputs': 1}
if dim == 1:
members['_separable'] = True
else:
members['_separable'] = False
if name is None:
model_id = _Tabular._id
_Tabular._id += 1
name = f'Tabular{model_id}'
model_class = type(str(name), (_Tabular,), members)
model_class.__module__ = 'astropy.modeling.tabular'
return model_class
Tabular1D = tabular_model(1, name='Tabular1D')
Tabular2D = tabular_model(2, name='Tabular2D')
_tab_docs = """
method : str, optional
The method of interpolation to perform. Supported are "linear" and
"nearest", and "splinef2d". "splinef2d" is only supported for
2-dimensional data. Default is "linear".
bounds_error : bool, optional
If True, when interpolated values are requested outside of the
domain of the input data, a ValueError is raised.
If False, then ``fill_value`` is used.
fill_value : float, optional
If provided, the value to use for points outside of the
interpolation domain. If None, values outside
the domain are extrapolated. Extrapolation is not supported by method
"splinef2d".
Returns
-------
value : ndarray
Interpolated values at input coordinates.
Raises
------
ImportError
Scipy is not installed.
Notes
-----
Uses `scipy.interpolate.interpn`.
"""
Tabular1D.__doc__ = """
Tabular model in 1D.
Returns an interpolated lookup table value.
Parameters
----------
points : array-like of float of ndim=1.
The points defining the regular grid in n dimensions.
lookup_table : array-like, of ndim=1.
The data in one dimensions.
""" + _tab_docs
Tabular2D.__doc__ = """
Tabular model in 2D.
Returns an interpolated lookup table value.
Parameters
----------
points : tuple of ndarray of float, optional
The points defining the regular grid in n dimensions.
ndarray with shapes (m1, m2).
lookup_table : array-like
The data on a regular grid in 2 dimensions.
Shape (m1, m2).
""" + _tab_docs
|
0f72ef850070de7df590e1c309a56a398e6e4c1714a3299d64fdd06c5b7e533e | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Models that have physical origins.
"""
# pylint: disable=invalid-name, no-member
import warnings
import numpy as np
from astropy import constants as const
from astropy import units as u
from astropy.utils.exceptions import AstropyUserWarning
from .core import Fittable1DModel
from .parameters import Parameter, InputParameterError
__all__ = ["BlackBody", "Drude1D", "Plummer1D", "NFW"]
class BlackBody(Fittable1DModel):
"""
Blackbody model using the Planck function.
Parameters
----------
temperature : `~astropy.units.Quantity` ['temperature']
Blackbody temperature.
scale : float or `~astropy.units.Quantity` ['dimensionless']
Scale factor
Notes
-----
Model formula:
.. math:: B_{\\nu}(T) = A \\frac{2 h \\nu^{3} / c^{2}}{exp(h \\nu / k T) - 1}
Examples
--------
>>> from astropy.modeling import models
>>> from astropy import units as u
>>> bb = models.BlackBody(temperature=5000*u.K)
>>> bb(6000 * u.AA) # doctest: +FLOAT_CMP
<Quantity 1.53254685e-05 erg / (cm2 Hz s sr)>
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import BlackBody
from astropy import units as u
from astropy.visualization import quantity_support
bb = BlackBody(temperature=5778*u.K)
wav = np.arange(1000, 110000) * u.AA
flux = bb(wav)
with quantity_support():
plt.figure()
plt.semilogx(wav, flux)
plt.axvline(bb.nu_max.to(u.AA, equivalencies=u.spectral()).value, ls='--')
plt.show()
"""
# We parametrize this model with a temperature and a scale.
temperature = Parameter(default=5000.0, min=0, unit=u.K, description="Blackbody temperature")
scale = Parameter(default=1.0, min=0, description="Scale factor")
# We allow values without units to be passed when evaluating the model, and
# in this case the input x values are assumed to be frequencies in Hz.
_input_units_allow_dimensionless = True
# We enable the spectral equivalency by default for the spectral axis
input_units_equivalencies = {'x': u.spectral()}
def evaluate(self, x, temperature, scale):
"""Evaluate the model.
Parameters
----------
x : float, `~numpy.ndarray`, or `~astropy.units.Quantity` ['frequency']
Frequency at which to compute the blackbody. If no units are given,
this defaults to Hz.
temperature : float, `~numpy.ndarray`, or `~astropy.units.Quantity`
Temperature of the blackbody. If no units are given, this defaults
to Kelvin.
scale : float, `~numpy.ndarray`, or `~astropy.units.Quantity` ['dimensionless']
Desired scale for the blackbody.
Returns
-------
y : number or ndarray
Blackbody spectrum. The units are determined from the units of
``scale``.
.. note::
Use `numpy.errstate` to suppress Numpy warnings, if desired.
.. warning::
Output values might contain ``nan`` and ``inf``.
Raises
------
ValueError
Invalid temperature.
ZeroDivisionError
Wavelength is zero (when converting to frequency).
"""
if not isinstance(temperature, u.Quantity):
in_temp = u.Quantity(temperature, u.K)
else:
in_temp = temperature
# Convert to units for calculations, also force double precision
with u.add_enabled_equivalencies(u.spectral() + u.temperature()):
freq = u.Quantity(x, u.Hz, dtype=np.float64)
temp = u.Quantity(in_temp, u.K)
# check the units of scale and setup the output units
bb_unit = u.erg / (u.cm ** 2 * u.s * u.Hz * u.sr) # default unit
# use the scale that was used at initialization for determining the units to return
# to support returning the right units when fitting where units are stripped
if hasattr(self.scale, "unit") and self.scale.unit is not None:
# check that the units on scale are covertable to surface brightness units
if not self.scale.unit.is_equivalent(bb_unit, u.spectral_density(x)):
raise ValueError(
f"scale units not surface brightness: {self.scale.unit}"
)
# use the scale passed to get the value for scaling
if hasattr(scale, "unit"):
mult_scale = scale.value
else:
mult_scale = scale
bb_unit = self.scale.unit
else:
mult_scale = scale
# Check if input values are physically possible
if np.any(temp < 0):
raise ValueError(f"Temperature should be positive: {temp}")
if not np.all(np.isfinite(freq)) or np.any(freq <= 0):
warnings.warn(
"Input contains invalid wavelength/frequency value(s)",
AstropyUserWarning,
)
log_boltz = const.h * freq / (const.k_B * temp)
boltzm1 = np.expm1(log_boltz)
# Calculate blackbody flux
bb_nu = 2.0 * const.h * freq ** 3 / (const.c ** 2 * boltzm1) / u.sr
y = mult_scale * bb_nu.to(bb_unit, u.spectral_density(freq))
# If the temperature parameter has no unit, we should return a unitless
# value. This occurs for instance during fitting, since we drop the
# units temporarily.
if hasattr(temperature, "unit"):
return y
return y.value
@property
def input_units(self):
# The input units are those of the 'x' value, which should always be
# Hz. Because we do this, and because input_units_allow_dimensionless
# is set to True, dimensionless values are assumed to be in Hz.
return {self.inputs[0]: u.Hz}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {"temperature": u.K}
@property
def bolometric_flux(self):
"""Bolometric flux."""
# bolometric flux in the native units of the planck function
native_bolflux = (
self.scale.value * const.sigma_sb * self.temperature ** 4 / np.pi
)
# return in more "astro" units
return native_bolflux.to(u.erg / (u.cm ** 2 * u.s))
@property
def lambda_max(self):
"""Peak wavelength when the curve is expressed as power density."""
return const.b_wien / self.temperature
@property
def nu_max(self):
"""Peak frequency when the curve is expressed as power density."""
return 2.8214391 * const.k_B * self.temperature / const.h
class Drude1D(Fittable1DModel):
"""
Drude model based one the behavior of electons in materials (esp. metals).
Parameters
----------
amplitude : float
Peak value
x_0 : float
Position of the peak
fwhm : float
Full width at half maximum
Model formula:
.. math:: f(x) = A \\frac{(fwhm/x_0)^2}{((x/x_0 - x_0/x)^2 + (fwhm/x_0)^2}
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling.models import Drude1D
fig, ax = plt.subplots()
# generate the curves and plot them
x = np.arange(7.5 , 12.5 , 0.1)
dmodel = Drude1D(amplitude=1.0, fwhm=1.0, x_0=10.0)
ax.plot(x, dmodel(x))
ax.set_xlabel('x')
ax.set_ylabel('F(x)')
plt.show()
"""
amplitude = Parameter(default=1.0, description="Peak Value")
x_0 = Parameter(default=1.0, description="Position of the peak")
fwhm = Parameter(default=1.0, description="Full width at half maximum")
@staticmethod
def evaluate(x, amplitude, x_0, fwhm):
"""
One dimensional Drude model function
"""
return (
amplitude
* ((fwhm / x_0) ** 2)
/ ((x / x_0 - x_0 / x) ** 2 + (fwhm / x_0) ** 2)
)
@staticmethod
def fit_deriv(x, amplitude, x_0, fwhm):
"""
Drude1D model function derivatives.
"""
d_amplitude = (fwhm / x_0) ** 2 / ((x / x_0 - x_0 / x) ** 2 + (fwhm / x_0) ** 2)
d_x_0 = (
-2
* amplitude
* d_amplitude
* (
(1 / x_0)
+ d_amplitude
* (x_0 ** 2 / fwhm ** 2)
* (
(-x / x_0 - 1 / x) * (x / x_0 - x_0 / x)
- (2 * fwhm ** 2 / x_0 ** 3)
)
)
)
d_fwhm = (2 * amplitude * d_amplitude / fwhm) * (1 - d_amplitude)
return [d_amplitude, d_x_0, d_fwhm]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {
"x_0": inputs_unit[self.inputs[0]],
"fwhm": inputs_unit[self.inputs[0]],
"amplitude": outputs_unit[self.outputs[0]],
}
@property
def return_units(self):
if self.amplitude.unit is None:
return None
return {self.outputs[0]: self.amplitude.unit}
@x_0.validator
def x_0(self, val):
""" Ensure `x_0` is not 0."""
if np.any(val == 0):
raise InputParameterError("0 is not an allowed value for x_0")
def bounding_box(self, factor=50):
"""Tuple defining the default ``bounding_box`` limits,
``(x_low, x_high)``.
Parameters
----------
factor : float
The multiple of FWHM used to define the limits.
"""
x0 = self.x_0
dx = factor * self.fwhm
return (x0 - dx, x0 + dx)
class Plummer1D(Fittable1DModel):
r"""One dimensional Plummer density profile model.
Parameters
----------
mass : float
Total mass of cluster.
r_plum : float
Scale parameter which sets the size of the cluster core.
Notes
-----
Model formula:
.. math::
\rho(r)=\frac{3M}{4\pi a^3}(1+\frac{r^2}{a^2})^{-5/2}
References
----------
.. [1] https://ui.adsabs.harvard.edu/abs/1911MNRAS..71..460P
"""
mass = Parameter(default=1.0, description="Total mass of cluster")
r_plum = Parameter(default=1.0, description="Scale parameter which sets the size of the cluster core")
@staticmethod
def evaluate(x, mass, r_plum):
"""
Evaluate plummer density profile model.
"""
return (3*mass)/(4 * np.pi * r_plum**3) * (1+(x/r_plum)**2)**(-5/2)
@staticmethod
def fit_deriv(x, mass, r_plum):
"""
Plummer1D model derivatives.
"""
d_mass = 3 / ((4*np.pi*r_plum**3) * (((x/r_plum)**2 + 1)**(5/2)))
d_r_plum = (6*mass*x**2-9*mass*r_plum**2) / ((4*np.pi * r_plum**6) *
(1+(x/r_plum)**2)**(7/2))
return [d_mass, d_r_plum]
@property
def input_units(self):
if self.mass.unit is None and self.r_plum.unit is None:
return None
else:
return {self.inputs[0]: self.r_plum.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'mass': outputs_unit[self.outputs[0]] * inputs_unit[self.inputs[0]] ** 3,
'r_plum': inputs_unit[self.inputs[0]]}
class NFW(Fittable1DModel):
r"""
Navarro–Frenk–White (NFW) profile - model for radial distribution of dark matter.
Parameters
----------
mass : float or `~astropy.units.Quantity` ['mass']
Mass of NFW peak within specified overdensity radius.
concentration : float
Concentration of the NFW profile.
redshift : float
Redshift of the NFW profile.
massfactor : tuple or str
Mass overdensity factor and type for provided profiles:
Tuple version:
("virial",) : virial radius
("critical", N) : radius where density is N times that of the critical density
("mean", N) : radius where density is N times that of the mean density
String version:
"virial" : virial radius
"Nc" : radius where density is N times that of the critical density (e.g. "200c")
"Nm" : radius where density is N times that of the mean density (e.g. "500m")
cosmo : :class:`~astropy.cosmology.Cosmology`
Background cosmology for density calculation. If None, the default cosmology will be used.
Notes
-----
Model formula:
.. math:: \rho(r)=\frac{\delta_c\rho_{c}}{r/r_s(1+r/r_s)^2}
References
----------
.. [1] https://arxiv.org/pdf/astro-ph/9508025
.. [2] https://en.wikipedia.org/wiki/Navarro%E2%80%93Frenk%E2%80%93White_profile
.. [3] https://en.wikipedia.org/wiki/Virial_mass
"""
# Model Parameters
# NFW Profile mass
mass = Parameter(default=1.0, min=1.0, unit=u.M_sun,
description="Peak mass within specified overdensity radius")
# NFW profile concentration
concentration = Parameter(default=1.0, min=1.0, description="Concentration")
# NFW Profile redshift
redshift = Parameter(default=0.0, min=0.0, description="Redshift")
# We allow values without units to be passed when evaluating the model, and
# in this case the input r values are assumed to be lengths / positions in kpc.
_input_units_allow_dimensionless = True
def __init__(self, mass=u.Quantity(mass.default, mass.unit),
concentration=concentration.default, redshift=redshift.default,
massfactor=("critical", 200), cosmo=None, **kwargs):
# Set default cosmology
if cosmo is None:
# LOCAL
from astropy.cosmology import default_cosmology
cosmo = default_cosmology.get()
# Set mass overdensity type and factor
self._density_delta(massfactor, cosmo, redshift)
# Establish mass units for density calculation (default solar masses)
if not isinstance(mass, u.Quantity):
in_mass = u.Quantity(mass, u.M_sun)
else:
in_mass = mass
# Obtain scale radius
self._radius_s(mass, concentration)
# Obtain scale density
self._density_s(mass, concentration)
super().__init__(mass=in_mass, concentration=concentration, redshift=redshift, **kwargs)
def evaluate(self, r, mass, concentration, redshift):
"""
One dimensional NFW profile function
Parameters
----------
r : float or `~astropy.units.Quantity` ['length']
Radial position of density to be calculated for the NFW profile.
mass : float or `~astropy.units.Quantity` ['mass']
Mass of NFW peak within specified overdensity radius.
concentration : float
Concentration of the NFW profile.
redshift : float
Redshift of the NFW profile.
Returns
-------
density : float or `~astropy.units.Quantity` ['density']
NFW profile mass density at location ``r``. The density units are:
[``mass`` / ``r`` ^3]
Notes
-----
.. warning::
Output values might contain ``nan`` and ``inf``.
"""
# Create radial version of input with dimension
if hasattr(r, "unit"):
in_r = r
else:
in_r = u.Quantity(r, u.kpc)
# Define reduced radius (r / r_{\\rm s})
# also update scale radius
radius_reduced = in_r / self._radius_s(mass, concentration).to(in_r.unit)
# Density distribution
# \rho (r)=\frac{\rho_0}{\frac{r}{R_s}\left(1~+~\frac{r}{R_s}\right)^2}
# also update scale density
density = self._density_s(mass, concentration) / (radius_reduced *
(u.Quantity(1.0) + radius_reduced) ** 2)
if hasattr(mass, "unit"):
return density
else:
return density.value
def _density_delta(self, massfactor, cosmo, redshift):
"""
Calculate density delta.
"""
# Set mass overdensity type and factor
if isinstance(massfactor, tuple):
# Tuple options
# ("virial") : virial radius
# ("critical", N) : radius where density is N that of the critical density
# ("mean", N) : radius where density is N that of the mean density
if massfactor[0].lower() == "virial":
# Virial Mass
delta = None
masstype = massfactor[0].lower()
elif massfactor[0].lower() == "critical":
# Critical or Mean Overdensity Mass
delta = float(massfactor[1])
masstype = 'c'
elif massfactor[0].lower() == "mean":
# Critical or Mean Overdensity Mass
delta = float(massfactor[1])
masstype = 'm'
else:
raise ValueError("Massfactor '" + str(massfactor[0]) + "' not one of 'critical', "
"'mean', or 'virial'")
else:
try:
# String options
# virial : virial radius
# Nc : radius where density is N that of the critical density
# Nm : radius where density is N that of the mean density
if massfactor.lower() == "virial":
# Virial Mass
delta = None
masstype = massfactor.lower()
elif massfactor[-1].lower() == 'c' or massfactor[-1].lower() == 'm':
# Critical or Mean Overdensity Mass
delta = float(massfactor[0:-1])
masstype = massfactor[-1].lower()
else:
raise ValueError("Massfactor " + str(massfactor) + " string not of the form "
"'#m', '#c', or 'virial'")
except (AttributeError, TypeError):
raise TypeError("Massfactor " + str(
massfactor) + " not a tuple or string")
# Set density from masstype specification
if masstype == "virial":
Om_c = cosmo.Om(redshift) - 1.0
d_c = 18.0 * np.pi ** 2 + 82.0 * Om_c - 39.0 * Om_c ** 2
self.density_delta = d_c * cosmo.critical_density(redshift)
elif masstype == 'c':
self.density_delta = delta * cosmo.critical_density(redshift)
elif masstype == 'm':
self.density_delta = delta * cosmo.critical_density(redshift) * cosmo.Om(redshift)
return self.density_delta
@staticmethod
def A_NFW(y):
r"""
Dimensionless volume integral of the NFW profile, used as an intermediate step in some
calculations for this model.
Notes
-----
Model formula:
.. math:: A_{NFW} = [\ln(1+y) - \frac{y}{1+y}]
"""
return np.log(1.0 + y) - (y / (1.0 + y))
def _density_s(self, mass, concentration):
"""
Calculate scale density of the NFW profile.
"""
# Enforce default units
if not isinstance(mass, u.Quantity):
in_mass = u.Quantity(mass, u.M_sun)
else:
in_mass = mass
# Calculate scale density
# M_{200} = 4\pi \rho_{s} R_{s}^3 \left[\ln(1+c) - \frac{c}{1+c}\right].
self.density_s = in_mass / (4.0 * np.pi * self._radius_s(in_mass, concentration) ** 3 *
self.A_NFW(concentration))
return self.density_s
@property
def rho_scale(self):
r"""
Scale density of the NFW profile. Often written in the literature as :math:`\rho_s`
"""
return self.density_s
def _radius_s(self, mass, concentration):
"""
Calculate scale radius of the NFW profile.
"""
# Enforce default units
if not isinstance(mass, u.Quantity):
in_mass = u.Quantity(mass, u.M_sun)
else:
in_mass = mass
# Delta Mass is related to delta radius by
# M_{200}=\frac{4}{3}\pi r_{200}^3 200 \rho_{c}
# And delta radius is related to the NFW scale radius by
# c = R / r_{\\rm s}
self.radius_s = (((3.0 * in_mass) / (4.0 * np.pi * self.density_delta)) ** (
1.0 / 3.0)) / concentration
# Set radial units to kiloparsec by default (unit will be rescaled by units of radius
# in evaluate)
return self.radius_s.to(u.kpc)
@property
def r_s(self):
"""
Scale radius of the NFW profile.
"""
return self.radius_s
@property
def r_virial(self):
"""
Mass factor defined virial radius of the NFW profile (R200c for M200c, Rvir for Mvir, etc.).
"""
return self.r_s * self.concentration
@property
def r_max(self):
"""
Radius of maximum circular velocity.
"""
return self.r_s * 2.16258
@property
def v_max(self):
"""
Maximum circular velocity.
"""
return self.circular_velocity(self.r_max)
def circular_velocity(self, r):
r"""
Circular velocities of the NFW profile.
Parameters
----------
r : float or `~astropy.units.Quantity` ['length']
Radial position of velocity to be calculated for the NFW profile.
Returns
-------
velocity : float or `~astropy.units.Quantity` ['speed']
NFW profile circular velocity at location ``r``. The velocity units are:
[km / s]
Notes
-----
Model formula:
.. math:: v_{circ}(r)^2 = \frac{1}{x}\frac{\ln(1+cx)-(cx)/(1+cx)}{\ln(1+c)-c/(1+c)}
.. math:: x = r/r_s
.. warning::
Output values might contain ``nan`` and ``inf``.
"""
# Enforce default units (if parameters are without units)
if hasattr(r, "unit"):
in_r = r
else:
in_r = u.Quantity(r, u.kpc)
# Mass factor defined velocity (i.e. V200c for M200c, Rvir for Mvir)
v_profile = np.sqrt(self.mass * const.G.to(in_r.unit**3 / (self.mass.unit * u.s**2)) /
self.r_virial)
# Define reduced radius (r / r_{\\rm s})
reduced_radius = in_r / self.r_virial.to(in_r.unit)
# Circular velocity given by:
# v^2=\frac{1}{x}\frac{\ln(1+cx)-(cx)/(1+cx)}{\ln(1+c)-c/(1+c)}
# where x=r/r_{200}
velocity = np.sqrt((v_profile**2 * self.A_NFW(self.concentration * reduced_radius)) /
(reduced_radius * self.A_NFW(self.concentration)))
return velocity.to(u.km / u.s)
@property
def input_units(self):
# The units for the 'r' variable should be a length (default kpc)
return {self.inputs[0]: u.kpc}
@property
def return_units(self):
# The units for the 'density' variable should be a matter density (default M_sun / kpc^3)
if (self.mass.unit is None):
return {self.outputs[0]: u.M_sun / self.input_units[self.inputs[0]] ** 3}
else:
return {self.outputs[0]: self.mass.unit / self.input_units[self.inputs[0]] ** 3}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'mass': u.M_sun,
"concentration": None,
"redshift": None}
|
5c9a89d66c31c31018f9af460a276471f4632bf93e50bae56ed3116a267f8b65 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Statistic functions used in `~astropy.modeling.fitting`.
"""
# pylint: disable=invalid-name
import numpy as np
from astropy.utils.decorators import format_doc
__all__ = ["leastsquare", "leastsquare_1d", "leastsquare_2d", "leastsquare_3d"]
def leastsquare(measured_vals, updated_model, weights, *x):
"""Least square statistic, with optional weights, in N-dimensions.
Parameters
----------
measured_vals : ndarray or sequence
Measured data values. Will be cast to array whose
shape must match the array-cast of the evaluated model.
updated_model : :class:`~astropy.modeling.Model` instance
Model with parameters set by the current iteration of the optimizer.
when evaluated on "x", must return array of shape "measured_vals"
weights : ndarray or None
Array of weights to apply to each residual.
*x : ndarray
Independent variables on which to evaluate the model.
Returns
-------
res : float
The sum of least squares.
See Also
--------
:func:`~astropy.modeling.statistic.leastsquare_1d`
:func:`~astropy.modeling.statistic.leastsquare_2d`
:func:`~astropy.modeling.statistic.leastsquare_3d`
Notes
-----
Models in :mod:`~astropy.modeling` have broadcasting rules that try to
match inputs with outputs with Model shapes. Numpy arrays have flexible
broadcasting rules, so mismatched shapes can often be made compatible. To
ensure data matches the model we must perform shape comparison and leverage
the Numpy arithmetic functions. This can obfuscate arithmetic computation
overrides, like with Quantities. Implement a custom statistic for more
direct control.
"""
model_vals = updated_model(*x)
if np.shape(model_vals) != np.shape(measured_vals):
message = "Shape mismatch between model ({}) and measured ({})"
raise ValueError(
message.format(np.shape(model_vals), np.shape(measured_vals))
)
if weights is None:
weights = 1.0
return np.sum(np.square(weights * np.subtract(model_vals, measured_vals)))
# -------------------------------------------------------------------
def leastsquare_1d(measured_vals, updated_model, weights, x):
"""
Least square statistic with optional weights.
Safer than the general :func:`~astropy.modeling.statistic.leastsquare`
for 1D models by avoiding numpy methods that support broadcasting.
Parameters
----------
measured_vals : ndarray
Measured data values.
updated_model : `~astropy.modeling.Model`
Model with parameters set by the current iteration of the optimizer.
weights : ndarray or None
Array of weights to apply to each residual.
x : ndarray
Independent variable "x" on which to evaluate the model.
Returns
-------
res : float
The sum of least squares.
See Also
--------
:func:`~astropy.modeling.statistic.leastsquare`
"""
model_vals = updated_model(x)
if weights is None:
return np.sum((model_vals - measured_vals) ** 2)
return np.sum((weights * (model_vals - measured_vals)) ** 2)
def leastsquare_2d(measured_vals, updated_model, weights, x, y):
"""
Least square statistic with optional weights.
Safer than the general :func:`~astropy.modeling.statistic.leastsquare`
for 2D models by avoiding numpy methods that support broadcasting.
Parameters
----------
measured_vals : ndarray
Measured data values.
updated_model : `~astropy.modeling.Model`
Model with parameters set by the current iteration of the optimizer.
weights : ndarray or None
Array of weights to apply to each residual.
x : ndarray
Independent variable "x" on which to evaluate the model.
y : ndarray
Independent variable "y" on which to evaluate the model.
Returns
-------
res : float
The sum of least squares.
See Also
--------
:func:`~astropy.modeling.statistic.leastsquare`
"""
model_vals = updated_model(x, y)
if weights is None:
return np.sum((model_vals - measured_vals) ** 2)
return np.sum((weights * (model_vals - measured_vals)) ** 2)
def leastsquare_3d(measured_vals, updated_model, weights, x, y, z):
"""
Least square statistic with optional weights.
Safer than the general :func:`~astropy.modeling.statistic.leastsquare`
for 3D models by avoiding numpy methods that support broadcasting.
Parameters
----------
measured_vals : ndarray
Measured data values.
updated_model : `~astropy.modeling.Model`
Model with parameters set by the current iteration of the optimizer.
weights : ndarray or None
Array of weights to apply to each residual.
x : ndarray
Independent variable "x" on which to evaluate the model.
y : ndarray
Independent variable "y" on which to evaluate the model.
z : ndarray
Independent variable "z" on which to evaluate the model.
Returns
-------
res : float
The sum of least squares.
See Also
--------
:func:`~astropy.modeling.statistic.leastsquare`
"""
model_vals = updated_model(x, y, z)
if weights is None:
return np.sum((model_vals - measured_vals) ** 2)
return np.sum((weights * (model_vals - measured_vals)) ** 2)
|
936124836cd19c02fedf33b2199994e3cf63e1271c8c32118b8c082a600f15ec | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Power law model variants
"""
# pylint: disable=invalid-name
import numpy as np
from astropy.units import Quantity
from .core import Fittable1DModel
from .parameters import Parameter, InputParameterError
__all__ = ['PowerLaw1D', 'BrokenPowerLaw1D', 'SmoothlyBrokenPowerLaw1D',
'ExponentialCutoffPowerLaw1D', 'LogParabola1D']
class PowerLaw1D(Fittable1DModel):
"""
One dimensional power law model.
Parameters
----------
amplitude : float
Model amplitude at the reference point
x_0 : float
Reference point
alpha : float
Power law index
See Also
--------
BrokenPowerLaw1D, ExponentialCutoffPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha``):
.. math:: f(x) = A (x / x_0) ^ {-\\alpha}
"""
amplitude = Parameter(default=1, description="Peak value at the reference point")
x_0 = Parameter(default=1, description="Reference point")
alpha = Parameter(default=1, description="Power law index")
@staticmethod
def evaluate(x, amplitude, x_0, alpha):
"""One dimensional power law model function"""
xx = x / x_0
return amplitude * xx ** (-alpha)
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha):
"""One dimensional power law derivative with respect to parameters"""
xx = x / x_0
d_amplitude = xx ** (-alpha)
d_x_0 = amplitude * alpha * d_amplitude / x_0
d_alpha = -amplitude * d_amplitude * np.log(xx)
return [d_amplitude, d_x_0, d_alpha]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class BrokenPowerLaw1D(Fittable1DModel):
"""
One dimensional power law model with a break.
Parameters
----------
amplitude : float
Model amplitude at the break point.
x_break : float
Break point.
alpha_1 : float
Power law index for x < x_break.
alpha_2 : float
Power law index for x > x_break.
See Also
--------
PowerLaw1D, ExponentialCutoffPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha_1`
for ``alpha_1`` and :math:`\\alpha_2` for ``alpha_2``):
.. math::
f(x) = \\left \\{
\\begin{array}{ll}
A (x / x_{break}) ^ {-\\alpha_1} & : x < x_{break} \\\\
A (x / x_{break}) ^ {-\\alpha_2} & : x > x_{break} \\\\
\\end{array}
\\right.
"""
amplitude = Parameter(default=1, description="Peak value at break point")
x_break = Parameter(default=1, description="Break point")
alpha_1 = Parameter(default=1, description="Power law index before break point")
alpha_2 = Parameter(default=1, description="Power law index after break point")
@staticmethod
def evaluate(x, amplitude, x_break, alpha_1, alpha_2):
"""One dimensional broken power law model function"""
alpha = np.where(x < x_break, alpha_1, alpha_2)
xx = x / x_break
return amplitude * xx ** (-alpha)
@staticmethod
def fit_deriv(x, amplitude, x_break, alpha_1, alpha_2):
"""One dimensional broken power law derivative with respect to parameters"""
alpha = np.where(x < x_break, alpha_1, alpha_2)
xx = x / x_break
d_amplitude = xx ** (-alpha)
d_x_break = amplitude * alpha * d_amplitude / x_break
d_alpha = -amplitude * d_amplitude * np.log(xx)
d_alpha_1 = np.where(x < x_break, d_alpha, 0)
d_alpha_2 = np.where(x >= x_break, d_alpha, 0)
return [d_amplitude, d_x_break, d_alpha_1, d_alpha_2]
@property
def input_units(self):
if self.x_break.unit is None:
return None
return {self.inputs[0]: self.x_break.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_break': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class SmoothlyBrokenPowerLaw1D(Fittable1DModel):
"""One dimensional smoothly broken power law model.
Parameters
----------
amplitude : float
Model amplitude at the break point.
x_break : float
Break point.
alpha_1 : float
Power law index for ``x << x_break``.
alpha_2 : float
Power law index for ``x >> x_break``.
delta : float
Smoothness parameter.
See Also
--------
BrokenPowerLaw1D
Notes
-----
Model formula (with :math:`A` for ``amplitude``, :math:`x_b` for
``x_break``, :math:`\\alpha_1` for ``alpha_1``,
:math:`\\alpha_2` for ``alpha_2`` and :math:`\\Delta` for
``delta``):
.. math::
f(x) = A \\left( \\frac{x}{x_b} \\right) ^ {-\\alpha_1}
\\left\\{
\\frac{1}{2}
\\left[
1 + \\left( \\frac{x}{x_b}\\right)^{1 / \\Delta}
\\right]
\\right\\}^{(\\alpha_1 - \\alpha_2) \\Delta}
The change of slope occurs between the values :math:`x_1`
and :math:`x_2` such that:
.. math::
\\log_{10} \\frac{x_2}{x_b} = \\log_{10} \\frac{x_b}{x_1}
\\sim \\Delta
At values :math:`x \\lesssim x_1` and :math:`x \\gtrsim x_2` the
model is approximately a simple power law with index
:math:`\\alpha_1` and :math:`\\alpha_2` respectively. The two
power laws are smoothly joined at values :math:`x_1 < x < x_2`,
hence the :math:`\\Delta` parameter sets the "smoothness" of the
slope change.
The ``delta`` parameter is bounded to values greater than 1e-3
(corresponding to :math:`x_2 / x_1 \\gtrsim 1.002`) to avoid
overflow errors.
The ``amplitude`` parameter is bounded to positive values since
this model is typically used to represent positive quantities.
Examples
--------
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models
x = np.logspace(0.7, 2.3, 500)
f = models.SmoothlyBrokenPowerLaw1D(amplitude=1, x_break=20,
alpha_1=-2, alpha_2=2)
plt.figure()
plt.title("amplitude=1, x_break=20, alpha_1=-2, alpha_2=2")
f.delta = 0.5
plt.loglog(x, f(x), '--', label='delta=0.5')
f.delta = 0.3
plt.loglog(x, f(x), '-.', label='delta=0.3')
f.delta = 0.1
plt.loglog(x, f(x), label='delta=0.1')
plt.axis([x.min(), x.max(), 0.1, 1.1])
plt.legend(loc='lower center')
plt.grid(True)
plt.show()
"""
amplitude = Parameter(default=1, min=0, description="Peak value at break point")
x_break = Parameter(default=1, description="Break point")
alpha_1 = Parameter(default=-2, description="Power law index before break point")
alpha_2 = Parameter(default=2, description="Power law index after break point")
delta = Parameter(default=1, min=1.e-3, description="Smoothness Parameter")
@amplitude.validator
def amplitude(self, value):
if np.any(value <= 0):
raise InputParameterError(
"amplitude parameter must be > 0")
@delta.validator
def delta(self, value):
if np.any(value < 0.001):
raise InputParameterError(
"delta parameter must be >= 0.001")
@staticmethod
def evaluate(x, amplitude, x_break, alpha_1, alpha_2, delta):
"""One dimensional smoothly broken power law model function"""
# Pre-calculate `x/x_b`
xx = x / x_break
# Initialize the return value
f = np.zeros_like(xx, subok=False)
if isinstance(amplitude, Quantity):
return_unit = amplitude.unit
amplitude = amplitude.value
else:
return_unit = None
# The quantity `t = (x / x_b)^(1 / delta)` can become quite
# large. To avoid overflow errors we will start by calculating
# its natural logarithm:
logt = np.log(xx) / delta
# When `t >> 1` or `t << 1` we don't actually need to compute
# the `t` value since the main formula (see docstring) can be
# significantly simplified by neglecting `1` or `t`
# respectively. In the following we will check whether `t` is
# much greater, much smaller, or comparable to 1 by comparing
# the `logt` value with an appropriate threshold.
threshold = 30 # corresponding to exp(30) ~ 1e13
i = logt > threshold
if i.max():
# In this case the main formula reduces to a simple power
# law with index `alpha_2`.
f[i] = amplitude * xx[i] ** (-alpha_2) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
i = logt < -threshold
if i.max():
# In this case the main formula reduces to a simple power
# law with index `alpha_1`.
f[i] = amplitude * xx[i] ** (-alpha_1) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
i = np.abs(logt) <= threshold
if i.max():
# In this case the `t` value is "comparable" to 1, hence we
# we will evaluate the whole formula.
t = np.exp(logt[i])
r = (1. + t) / 2.
f[i] = amplitude * xx[i] ** (-alpha_1) \
* r ** ((alpha_1 - alpha_2) * delta)
if return_unit:
return Quantity(f, unit=return_unit, copy=False)
return f
@staticmethod
def fit_deriv(x, amplitude, x_break, alpha_1, alpha_2, delta):
"""One dimensional smoothly broken power law derivative with respect
to parameters"""
# Pre-calculate `x_b` and `x/x_b` and `logt` (see comments in
# SmoothlyBrokenPowerLaw1D.evaluate)
xx = x / x_break
logt = np.log(xx) / delta
# Initialize the return values
f = np.zeros_like(xx)
d_amplitude = np.zeros_like(xx)
d_x_break = np.zeros_like(xx)
d_alpha_1 = np.zeros_like(xx)
d_alpha_2 = np.zeros_like(xx)
d_delta = np.zeros_like(xx)
threshold = 30 # (see comments in SmoothlyBrokenPowerLaw1D.evaluate)
i = logt > threshold
if i.max():
f[i] = amplitude * xx[i] ** (-alpha_2) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * alpha_2 / x_break
d_alpha_1[i] = f[i] * (-delta * np.log(2))
d_alpha_2[i] = f[i] * (-np.log(xx[i]) + delta * np.log(2))
d_delta[i] = f[i] * (-(alpha_1 - alpha_2) * np.log(2))
i = logt < -threshold
if i.max():
f[i] = amplitude * xx[i] ** (-alpha_1) \
/ (2. ** ((alpha_1 - alpha_2) * delta))
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * alpha_1 / x_break
d_alpha_1[i] = f[i] * (-np.log(xx[i]) - delta * np.log(2))
d_alpha_2[i] = f[i] * delta * np.log(2)
d_delta[i] = f[i] * (-(alpha_1 - alpha_2) * np.log(2))
i = np.abs(logt) <= threshold
if i.max():
t = np.exp(logt[i])
r = (1. + t) / 2.
f[i] = amplitude * xx[i] ** (-alpha_1) \
* r ** ((alpha_1 - alpha_2) * delta)
d_amplitude[i] = f[i] / amplitude
d_x_break[i] = f[i] * (alpha_1 - (alpha_1 - alpha_2) * t / 2. / r) / x_break
d_alpha_1[i] = f[i] * (-np.log(xx[i]) + delta * np.log(r))
d_alpha_2[i] = f[i] * (-delta * np.log(r))
d_delta[i] = f[i] * (alpha_1 - alpha_2) \
* (np.log(r) - t / (1. + t) / delta * np.log(xx[i]))
return [d_amplitude, d_x_break, d_alpha_1, d_alpha_2, d_delta]
@property
def input_units(self):
if self.x_break.unit is None:
return None
return {self.inputs[0]: self.x_break.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_break': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class ExponentialCutoffPowerLaw1D(Fittable1DModel):
"""
One dimensional power law model with an exponential cutoff.
Parameters
----------
amplitude : float
Model amplitude
x_0 : float
Reference point
alpha : float
Power law index
x_cutoff : float
Cutoff point
See Also
--------
PowerLaw1D, BrokenPowerLaw1D, LogParabola1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha``):
.. math:: f(x) = A (x / x_0) ^ {-\\alpha} \\exp (-x / x_{cutoff})
"""
amplitude = Parameter(default=1, description="Peak value of model")
x_0 = Parameter(default=1, description="Reference point")
alpha = Parameter(default=1, description="Power law index")
x_cutoff = Parameter(default=1, description="Cutoff point")
@staticmethod
def evaluate(x, amplitude, x_0, alpha, x_cutoff):
"""One dimensional exponential cutoff power law model function"""
xx = x / x_0
return amplitude * xx ** (-alpha) * np.exp(-x / x_cutoff)
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha, x_cutoff):
"""One dimensional exponential cutoff power law derivative with respect to parameters"""
xx = x / x_0
xc = x / x_cutoff
d_amplitude = xx ** (-alpha) * np.exp(-xc)
d_x_0 = alpha * amplitude * d_amplitude / x_0
d_alpha = -amplitude * d_amplitude * np.log(xx)
d_x_cutoff = amplitude * x * d_amplitude / x_cutoff ** 2
return [d_amplitude, d_x_0, d_alpha, d_x_cutoff]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'x_cutoff': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
class LogParabola1D(Fittable1DModel):
"""
One dimensional log parabola model (sometimes called curved power law).
Parameters
----------
amplitude : float
Model amplitude
x_0 : float
Reference point
alpha : float
Power law index
beta : float
Power law curvature
See Also
--------
PowerLaw1D, BrokenPowerLaw1D, ExponentialCutoffPowerLaw1D
Notes
-----
Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha`` and :math:`\\beta` for ``beta``):
.. math:: f(x) = A \\left(\\frac{x}{x_{0}}\\right)^{- \\alpha - \\beta \\log{\\left (\\frac{x}{x_{0}} \\right )}}
"""
amplitude = Parameter(default=1, description="Peak value of model")
x_0 = Parameter(default=1, description="Reference point")
alpha = Parameter(default=1, description="Power law index")
beta = Parameter(default=0, description="Power law curvature")
@staticmethod
def evaluate(x, amplitude, x_0, alpha, beta):
"""One dimensional log parabola model function"""
xx = x / x_0
exponent = -alpha - beta * np.log(xx)
return amplitude * xx ** exponent
@staticmethod
def fit_deriv(x, amplitude, x_0, alpha, beta):
"""One dimensional log parabola derivative with respect to parameters"""
xx = x / x_0
log_xx = np.log(xx)
exponent = -alpha - beta * log_xx
d_amplitude = xx ** exponent
d_beta = -amplitude * d_amplitude * log_xx ** 2
d_x_0 = amplitude * d_amplitude * (beta * log_xx / x_0 - exponent / x_0)
d_alpha = -amplitude * d_amplitude * log_xx
return [d_amplitude, d_x_0, d_alpha, d_beta]
@property
def input_units(self):
if self.x_0.unit is None:
return None
return {self.inputs[0]: self.x_0.unit}
def _parameter_units_for_data_units(self, inputs_unit, outputs_unit):
return {'x_0': inputs_unit[self.inputs[0]],
'amplitude': outputs_unit[self.outputs[0]]}
|
f13b74c859a84565f76eae729fb9305e0a7a38597c1c59479eaffe3611730865 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import os
import select
import socket
import threading
import time
import uuid
import warnings
import queue
import xmlrpc.client as xmlrpc
from urllib.parse import urlunparse
from astropy import log
from .constants import SAMP_STATUS_OK
from .constants import __profile_version__
from .errors import SAMPWarning, SAMPHubError, SAMPProxyError
from .utils import internet_on, ServerProxyPool, _HubAsClient
from .lockfile_helpers import read_lockfile, create_lock_file
from .standard_profile import ThreadingXMLRPCServer
from .web_profile import WebProfileXMLRPCServer, web_profile_text_dialog
__all__ = ['SAMPHubServer', 'WebProfileDialog']
__doctest_skip__ = ['.', 'SAMPHubServer.*']
class SAMPHubServer:
"""
SAMP Hub Server.
Parameters
----------
secret : str, optional
The secret code to use for the SAMP lockfile. If none is is specified,
the :func:`uuid.uuid1` function is used to generate one.
addr : str, optional
Listening address (or IP). This defaults to 127.0.0.1 if the internet
is not reachable, otherwise it defaults to the host name.
port : int, optional
Listening XML-RPC server socket port. If left set to 0 (the default),
the operating system will select a free port.
lockfile : str, optional
Custom lockfile name.
timeout : int, optional
Hub inactivity timeout. If ``timeout > 0`` then the Hub automatically
stops after an inactivity period longer than ``timeout`` seconds. By
default ``timeout`` is set to 0 (Hub never expires).
client_timeout : int, optional
Client inactivity timeout. If ``client_timeout > 0`` then the Hub
automatically unregisters the clients which result inactive for a
period longer than ``client_timeout`` seconds. By default
``client_timeout`` is set to 0 (clients never expire).
mode : str, optional
Defines the Hub running mode. If ``mode`` is ``'single'`` then the Hub
runs using the standard ``.samp`` lock-file, having a single instance
for user desktop session. Otherwise, if ``mode`` is ``'multiple'``,
then the Hub runs using a non-standard lock-file, placed in
``.samp-1`` directory, of the form ``samp-hub-<UUID>``, where
``<UUID>`` is a unique UUID assigned to the hub.
label : str, optional
A string used to label the Hub with a human readable name. This string
is written in the lock-file assigned to the ``hub.label`` token.
web_profile : bool, optional
Enables or disables the Web Profile support.
web_profile_dialog : class, optional
Allows a class instance to be specified using ``web_profile_dialog``
to replace the terminal-based message with e.g. a GUI pop-up. Two
`queue.Queue` instances will be added to the instance as attributes
``queue_request`` and ``queue_result``. When a request is received via
the ``queue_request`` queue, the pop-up should be displayed, and a
value of `True` or `False` should be added to ``queue_result``
depending on whether the user accepted or refused the connection.
web_port : int, optional
The port to use for web SAMP. This should not be changed except for
testing purposes, since web SAMP should always use port 21012.
pool_size : int, optional
The number of socket connections opened to communicate with the
clients.
"""
def __init__(self, secret=None, addr=None, port=0, lockfile=None,
timeout=0, client_timeout=0, mode='single', label="",
web_profile=True, web_profile_dialog=None, web_port=21012,
pool_size=20):
# Generate random ID for the hub
self._id = str(uuid.uuid1())
# General settings
self._is_running = False
self._customlockfilename = lockfile
self._lockfile = None
self._addr = addr
self._port = port
self._mode = mode
self._label = label
self._timeout = timeout
self._client_timeout = client_timeout
self._pool_size = pool_size
# Web profile specific attributes
self._web_profile = web_profile
self._web_profile_dialog = web_profile_dialog
self._web_port = web_port
self._web_profile_server = None
self._web_profile_callbacks = {}
self._web_profile_requests_queue = None
self._web_profile_requests_result = None
self._web_profile_requests_semaphore = None
self._host_name = "127.0.0.1"
if internet_on():
try:
self._host_name = socket.getfqdn()
socket.getaddrinfo(self._addr or self._host_name,
self._port or 0)
except socket.error:
self._host_name = "127.0.0.1"
# Threading stuff
self._thread_lock = threading.Lock()
self._thread_run = None
self._thread_hub_timeout = None
self._thread_client_timeout = None
self._launched_threads = []
# Variables for timeout testing:
self._last_activity_time = None
self._client_activity_time = {}
# Hub message id counter, used to create hub msg ids
self._hub_msg_id_counter = 0
# Hub secret code
self._hub_secret_code_customized = secret
self._hub_secret = self._create_secret_code()
# Hub public id (as SAMP client)
self._hub_public_id = ""
# Client ids
# {private_key: (public_id, timestamp)}
self._private_keys = {}
# Metadata per client
# {private_key: metadata}
self._metadata = {}
# List of subscribed clients per MType
# {mtype: private_key list}
self._mtype2ids = {}
# List of subscribed MTypes per client
# {private_key: mtype list}
self._id2mtypes = {}
# List of XML-RPC addresses per client
# {public_id: (XML-RPC address, ServerProxyPool instance)}
self._xmlrpc_endpoints = {}
# Synchronous message id heap
self._sync_msg_ids_heap = {}
# Public ids counter
self._client_id_counter = -1
@property
def id(self):
"""
The unique hub ID.
"""
return self._id
def _register_standard_api(self, server):
# Standard Profile only operations
server.register_function(self._ping, 'samp.hub.ping')
server.register_function(self._set_xmlrpc_callback, 'samp.hub.setXmlrpcCallback')
# Standard API operations
server.register_function(self._register, 'samp.hub.register')
server.register_function(self._unregister, 'samp.hub.unregister')
server.register_function(self._declare_metadata, 'samp.hub.declareMetadata')
server.register_function(self._get_metadata, 'samp.hub.getMetadata')
server.register_function(self._declare_subscriptions, 'samp.hub.declareSubscriptions')
server.register_function(self._get_subscriptions, 'samp.hub.getSubscriptions')
server.register_function(self._get_registered_clients, 'samp.hub.getRegisteredClients')
server.register_function(self._get_subscribed_clients, 'samp.hub.getSubscribedClients')
server.register_function(self._notify, 'samp.hub.notify')
server.register_function(self._notify_all, 'samp.hub.notifyAll')
server.register_function(self._call, 'samp.hub.call')
server.register_function(self._call_all, 'samp.hub.callAll')
server.register_function(self._call_and_wait, 'samp.hub.callAndWait')
server.register_function(self._reply, 'samp.hub.reply')
def _register_web_profile_api(self, server):
# Web Profile methods like Standard Profile
server.register_function(self._ping, 'samp.webhub.ping')
server.register_function(self._unregister, 'samp.webhub.unregister')
server.register_function(self._declare_metadata, 'samp.webhub.declareMetadata')
server.register_function(self._get_metadata, 'samp.webhub.getMetadata')
server.register_function(self._declare_subscriptions, 'samp.webhub.declareSubscriptions')
server.register_function(self._get_subscriptions, 'samp.webhub.getSubscriptions')
server.register_function(self._get_registered_clients, 'samp.webhub.getRegisteredClients')
server.register_function(self._get_subscribed_clients, 'samp.webhub.getSubscribedClients')
server.register_function(self._notify, 'samp.webhub.notify')
server.register_function(self._notify_all, 'samp.webhub.notifyAll')
server.register_function(self._call, 'samp.webhub.call')
server.register_function(self._call_all, 'samp.webhub.callAll')
server.register_function(self._call_and_wait, 'samp.webhub.callAndWait')
server.register_function(self._reply, 'samp.webhub.reply')
# Methods particularly for Web Profile
server.register_function(self._web_profile_register, 'samp.webhub.register')
server.register_function(self._web_profile_allowReverseCallbacks, 'samp.webhub.allowReverseCallbacks')
server.register_function(self._web_profile_pullCallbacks, 'samp.webhub.pullCallbacks')
def _start_standard_server(self):
self._server = ThreadingXMLRPCServer(
(self._addr or self._host_name, self._port or 0),
log, logRequests=False, allow_none=True)
prot = 'http'
self._port = self._server.socket.getsockname()[1]
addr = f"{self._addr or self._host_name}:{self._port}"
self._url = urlunparse((prot, addr, '', '', '', ''))
self._server.register_introspection_functions()
self._register_standard_api(self._server)
def _start_web_profile_server(self):
self._web_profile_requests_queue = queue.Queue(1)
self._web_profile_requests_result = queue.Queue(1)
self._web_profile_requests_semaphore = queue.Queue(1)
if self._web_profile_dialog is not None:
# TODO: Some sort of duck-typing on the web_profile_dialog object
self._web_profile_dialog.queue_request = \
self._web_profile_requests_queue
self._web_profile_dialog.queue_result = \
self._web_profile_requests_result
try:
self._web_profile_server = WebProfileXMLRPCServer(
('localhost', self._web_port), log, logRequests=False,
allow_none=True)
self._web_port = self._web_profile_server.socket.getsockname()[1]
self._web_profile_server.register_introspection_functions()
self._register_web_profile_api(self._web_profile_server)
log.info("Hub set to run with Web Profile support enabled.")
except socket.error:
log.warning("Port {} already in use. Impossible to run the "
"Hub with Web Profile support.".format(self._web_port),
SAMPWarning)
self._web_profile = False
# Cleanup
self._web_profile_requests_queue = None
self._web_profile_requests_result = None
self._web_profile_requests_semaphore = None
def _launch_thread(self, group=None, target=None, name=None, args=None):
# Remove inactive threads
remove = []
for t in self._launched_threads:
if not t.is_alive():
remove.append(t)
for t in remove:
self._launched_threads.remove(t)
# Start new thread
t = threading.Thread(group=group, target=target, name=name, args=args)
t.start()
# Add to list of launched threads
self._launched_threads.append(t)
def _join_launched_threads(self, timeout=None):
for t in self._launched_threads:
t.join(timeout=timeout)
def _timeout_test_hub(self):
if self._timeout == 0:
return
last = time.time()
while self._is_running:
time.sleep(0.05) # keep this small to check _is_running often
now = time.time()
if now - last > 1.:
with self._thread_lock:
if self._last_activity_time is not None:
if now - self._last_activity_time >= self._timeout:
warnings.warn("Timeout expired, Hub is shutting down!",
SAMPWarning)
self.stop()
return
last = now
def _timeout_test_client(self):
if self._client_timeout == 0:
return
last = time.time()
while self._is_running:
time.sleep(0.05) # keep this small to check _is_running often
now = time.time()
if now - last > 1.:
for private_key in self._client_activity_time.keys():
if (now - self._client_activity_time[private_key] > self._client_timeout
and private_key != self._hub_private_key):
warnings.warn(
f"Client {private_key} timeout expired!",
SAMPWarning)
self._notify_disconnection(private_key)
self._unregister(private_key)
last = now
def _hub_as_client_request_handler(self, method, args):
if method == 'samp.client.receiveCall':
return self._receive_call(*args)
elif method == 'samp.client.receiveNotification':
return self._receive_notification(*args)
elif method == 'samp.client.receiveResponse':
return self._receive_response(*args)
elif method == 'samp.app.ping':
return self._ping(*args)
def _setup_hub_as_client(self):
hub_metadata = {"samp.name": "Astropy SAMP Hub",
"samp.description.text": self._label,
"author.name": "The Astropy Collaboration",
"samp.documentation.url": "https://docs.astropy.org/en/stable/samp",
"samp.icon.url": self._url + "/samp/icon"}
result = self._register(self._hub_secret)
self._hub_public_id = result["samp.self-id"]
self._hub_private_key = result["samp.private-key"]
self._set_xmlrpc_callback(self._hub_private_key, self._url)
self._declare_metadata(self._hub_private_key, hub_metadata)
self._declare_subscriptions(self._hub_private_key,
{"samp.app.ping": {},
"x-samp.query.by-meta": {}})
def start(self, wait=False):
"""
Start the current SAMP Hub instance and create the lock file. Hub
start-up can be blocking or non blocking depending on the ``wait``
parameter.
Parameters
----------
wait : bool
If `True` then the Hub process is joined with the caller, blocking
the code flow. Usually `True` option is used to run a stand-alone
Hub in an executable script. If `False` (default), then the Hub
process runs in a separated thread. `False` is usually used in a
Python shell.
"""
if self._is_running:
raise SAMPHubError("Hub is already running")
if self._lockfile is not None:
raise SAMPHubError("Hub is not running but lockfile is set")
if self._web_profile:
self._start_web_profile_server()
self._start_standard_server()
self._lockfile = create_lock_file(lockfilename=self._customlockfilename,
mode=self._mode, hub_id=self.id,
hub_params=self.params)
self._update_last_activity_time()
self._setup_hub_as_client()
self._start_threads()
log.info("Hub started")
if wait and self._is_running:
self._thread_run.join()
self._thread_run = None
@property
def params(self):
"""
The hub parameters (which are written to the logfile)
"""
params = {}
# Keys required by standard profile
params['samp.secret'] = self._hub_secret
params['samp.hub.xmlrpc.url'] = self._url
params['samp.profile.version'] = __profile_version__
# Custom keys
params['hub.id'] = self.id
params['hub.label'] = self._label or f"Hub {self.id}"
return params
def _start_threads(self):
self._thread_run = threading.Thread(target=self._serve_forever)
self._thread_run.daemon = True
if self._timeout > 0:
self._thread_hub_timeout = threading.Thread(
target=self._timeout_test_hub,
name="Hub timeout test")
self._thread_hub_timeout.daemon = True
else:
self._thread_hub_timeout = None
if self._client_timeout > 0:
self._thread_client_timeout = threading.Thread(
target=self._timeout_test_client,
name="Client timeout test")
self._thread_client_timeout.daemon = True
else:
self._thread_client_timeout = None
self._is_running = True
self._thread_run.start()
if self._thread_hub_timeout is not None:
self._thread_hub_timeout.start()
if self._thread_client_timeout is not None:
self._thread_client_timeout.start()
def _create_secret_code(self):
if self._hub_secret_code_customized is not None:
return self._hub_secret_code_customized
else:
return str(uuid.uuid1())
def stop(self):
"""
Stop the current SAMP Hub instance and delete the lock file.
"""
if not self._is_running:
return
log.info("Hub is stopping...")
self._notify_shutdown()
self._is_running = False
if self._lockfile and os.path.isfile(self._lockfile):
lockfiledict = read_lockfile(self._lockfile)
if lockfiledict['samp.secret'] == self._hub_secret:
os.remove(self._lockfile)
self._lockfile = None
# Reset variables
# TODO: What happens if not all threads are stopped after timeout?
self._join_all_threads(timeout=10.)
self._hub_msg_id_counter = 0
self._hub_secret = self._create_secret_code()
self._hub_public_id = ""
self._metadata = {}
self._private_keys = {}
self._mtype2ids = {}
self._id2mtypes = {}
self._xmlrpc_endpoints = {}
self._last_activity_time = None
log.info("Hub stopped.")
def _join_all_threads(self, timeout=None):
# In some cases, ``stop`` may be called from some of the sub-threads,
# so we just need to make sure that we don't try and shut down the
# calling thread.
current_thread = threading.current_thread()
if self._thread_run is not current_thread:
self._thread_run.join(timeout=timeout)
if not self._thread_run.is_alive():
self._thread_run = None
if self._thread_hub_timeout is not None and self._thread_hub_timeout is not current_thread:
self._thread_hub_timeout.join(timeout=timeout)
if not self._thread_hub_timeout.is_alive():
self._thread_hub_timeout = None
if self._thread_client_timeout is not None and self._thread_client_timeout is not current_thread:
self._thread_client_timeout.join(timeout=timeout)
if not self._thread_client_timeout.is_alive():
self._thread_client_timeout = None
self._join_launched_threads(timeout=timeout)
@property
def is_running(self):
"""Return an information concerning the Hub running status.
Returns
-------
running : bool
Is the hub running?
"""
return self._is_running
def _serve_forever(self):
while self._is_running:
try:
read_ready = select.select([self._server.socket], [], [], 0.01)[0]
except OSError as exc:
warnings.warn(f"Call to select() in SAMPHubServer failed: {exc}",
SAMPWarning)
else:
if read_ready:
self._server.handle_request()
if self._web_profile:
# We now check if there are any connection requests from the
# web profile, and if so, we initialize the pop-up.
if self._web_profile_dialog is None:
try:
request = self._web_profile_requests_queue.get_nowait()
except queue.Empty:
pass
else:
web_profile_text_dialog(request, self._web_profile_requests_result)
# We now check for requests over the web profile socket, and we
# also update the pop-up in case there are any changes.
try:
read_ready = select.select([self._web_profile_server.socket], [], [], 0.01)[0]
except OSError as exc:
warnings.warn(f"Call to select() in SAMPHubServer failed: {exc}",
SAMPWarning)
else:
if read_ready:
self._web_profile_server.handle_request()
self._server.server_close()
if self._web_profile_server is not None:
self._web_profile_server.server_close()
def _notify_shutdown(self):
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.event.shutdown")
for mtype in msubs:
if mtype in self._mtype2ids:
for key in self._mtype2ids[mtype]:
self._notify_(self._hub_private_key,
self._private_keys[key][0],
{"samp.mtype": "samp.hub.event.shutdown",
"samp.params": {}})
def _notify_register(self, private_key):
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.event.register")
for mtype in msubs:
if mtype in self._mtype2ids:
public_id = self._private_keys[private_key][0]
for key in self._mtype2ids[mtype]:
# if key != private_key:
self._notify(self._hub_private_key,
self._private_keys[key][0],
{"samp.mtype": "samp.hub.event.register",
"samp.params": {"id": public_id}})
def _notify_unregister(self, private_key):
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.event.unregister")
for mtype in msubs:
if mtype in self._mtype2ids:
public_id = self._private_keys[private_key][0]
for key in self._mtype2ids[mtype]:
if key != private_key:
self._notify(self._hub_private_key,
self._private_keys[key][0],
{"samp.mtype": "samp.hub.event.unregister",
"samp.params": {"id": public_id}})
def _notify_metadata(self, private_key):
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.event.metadata")
for mtype in msubs:
if mtype in self._mtype2ids:
public_id = self._private_keys[private_key][0]
for key in self._mtype2ids[mtype]:
# if key != private_key:
self._notify(self._hub_private_key,
self._private_keys[key][0],
{"samp.mtype": "samp.hub.event.metadata",
"samp.params": {"id": public_id,
"metadata": self._metadata[private_key]}
})
def _notify_subscriptions(self, private_key):
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.event.subscriptions")
for mtype in msubs:
if mtype in self._mtype2ids:
public_id = self._private_keys[private_key][0]
for key in self._mtype2ids[mtype]:
self._notify(self._hub_private_key,
self._private_keys[key][0],
{"samp.mtype": "samp.hub.event.subscriptions",
"samp.params": {"id": public_id,
"subscriptions": self._id2mtypes[private_key]}
})
def _notify_disconnection(self, private_key):
def _xmlrpc_call_disconnect(endpoint, private_key, hub_public_id, message):
endpoint.samp.client.receiveNotification(private_key, hub_public_id, message)
msubs = SAMPHubServer.get_mtype_subtypes("samp.hub.disconnect")
public_id = self._private_keys[private_key][0]
endpoint = self._xmlrpc_endpoints[public_id][1]
for mtype in msubs:
if mtype in self._mtype2ids and private_key in self._mtype2ids[mtype]:
log.debug(f"notify disconnection to {public_id}")
self._launch_thread(target=_xmlrpc_call_disconnect,
args=(endpoint, private_key,
self._hub_public_id,
{"samp.mtype": "samp.hub.disconnect",
"samp.params": {"reason": "Timeout expired!"}}))
def _ping(self):
self._update_last_activity_time()
log.debug("ping")
return "1"
def _query_by_metadata(self, key, value):
public_id_list = []
for private_id in self._metadata:
if key in self._metadata[private_id]:
if self._metadata[private_id][key] == value:
public_id_list.append(self._private_keys[private_id][0])
return public_id_list
def _set_xmlrpc_callback(self, private_key, xmlrpc_addr):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
if private_key == self._hub_private_key:
public_id = self._private_keys[private_key][0]
self._xmlrpc_endpoints[public_id] = \
(xmlrpc_addr, _HubAsClient(self._hub_as_client_request_handler))
return ""
# Dictionary stored with the public id
log.debug(f"set_xmlrpc_callback: {private_key} {xmlrpc_addr}")
server_proxy_pool = None
server_proxy_pool = ServerProxyPool(self._pool_size,
xmlrpc.ServerProxy,
xmlrpc_addr, allow_none=1)
public_id = self._private_keys[private_key][0]
self._xmlrpc_endpoints[public_id] = (xmlrpc_addr,
server_proxy_pool)
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
return ""
def _perform_standard_register(self):
with self._thread_lock:
private_key, public_id = self._get_new_ids()
self._private_keys[private_key] = (public_id, time.time())
self._update_last_activity_time(private_key)
self._notify_register(private_key)
log.debug(f"register: private-key = {private_key} and self-id = {public_id}")
return {"samp.self-id": public_id,
"samp.private-key": private_key,
"samp.hub-id": self._hub_public_id}
def _register(self, secret):
self._update_last_activity_time()
if secret == self._hub_secret:
return self._perform_standard_register()
else:
# return {"samp.self-id": "", "samp.private-key": "", "samp.hub-id": ""}
raise SAMPProxyError(7, "Bad secret code")
def _get_new_ids(self):
private_key = str(uuid.uuid1())
self._client_id_counter += 1
public_id = 'cli#hub'
if self._client_id_counter > 0:
public_id = f"cli#{self._client_id_counter}"
return private_key, public_id
def _unregister(self, private_key):
self._update_last_activity_time()
public_key = ""
self._notify_unregister(private_key)
with self._thread_lock:
if private_key in self._private_keys:
public_key = self._private_keys[private_key][0]
del self._private_keys[private_key]
else:
return ""
if private_key in self._metadata:
del self._metadata[private_key]
if private_key in self._id2mtypes:
del self._id2mtypes[private_key]
for mtype in self._mtype2ids.keys():
if private_key in self._mtype2ids[mtype]:
self._mtype2ids[mtype].remove(private_key)
if public_key in self._xmlrpc_endpoints:
del self._xmlrpc_endpoints[public_key]
if private_key in self._client_activity_time:
del self._client_activity_time[private_key]
if self._web_profile:
if private_key in self._web_profile_callbacks:
del self._web_profile_callbacks[private_key]
self._web_profile_server.remove_client(private_key)
log.debug(f"unregister {public_key} ({private_key})")
return ""
def _declare_metadata(self, private_key, metadata):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
log.debug("declare_metadata: private-key = {} metadata = {}"
.format(private_key, str(metadata)))
self._metadata[private_key] = metadata
self._notify_metadata(private_key)
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
return ""
def _get_metadata(self, private_key, client_id):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
client_private_key = self._public_id_to_private_key(client_id)
log.debug("get_metadata: private-key = {} client-id = {}"
.format(private_key, client_id))
if client_private_key is not None:
if client_private_key in self._metadata:
log.debug(f"--> metadata = {self._metadata[client_private_key]}")
return self._metadata[client_private_key]
else:
return {}
else:
raise SAMPProxyError(6, "Invalid client ID")
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _declare_subscriptions(self, private_key, mtypes):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
log.debug("declare_subscriptions: private-key = {} mtypes = {}"
.format(private_key, str(mtypes)))
# remove subscription to previous mtypes
if private_key in self._id2mtypes:
prev_mtypes = self._id2mtypes[private_key]
for mtype in prev_mtypes:
try:
self._mtype2ids[mtype].remove(private_key)
except ValueError: # private_key is not in list
pass
self._id2mtypes[private_key] = copy.deepcopy(mtypes)
# remove duplicated MType for wildcard overwriting
original_mtypes = copy.deepcopy(mtypes)
for mtype in original_mtypes:
if mtype.endswith("*"):
for mtype2 in original_mtypes:
if mtype2.startswith(mtype[:-1]) and \
mtype2 != mtype:
if mtype2 in mtypes:
del(mtypes[mtype2])
log.debug("declare_subscriptions: subscriptions accepted from "
"{} => {}".format(private_key, str(mtypes)))
for mtype in mtypes:
if mtype in self._mtype2ids:
if private_key not in self._mtype2ids[mtype]:
self._mtype2ids[mtype].append(private_key)
else:
self._mtype2ids[mtype] = [private_key]
self._notify_subscriptions(private_key)
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
return ""
def _get_subscriptions(self, private_key, client_id):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
client_private_key = self._public_id_to_private_key(client_id)
if client_private_key is not None:
if client_private_key in self._id2mtypes:
log.debug("get_subscriptions: client-id = {} mtypes = {}"
.format(client_id,
str(self._id2mtypes[client_private_key])))
return self._id2mtypes[client_private_key]
else:
log.debug("get_subscriptions: client-id = {} mtypes = "
"missing".format(client_id))
return {}
else:
raise SAMPProxyError(6, "Invalid client ID")
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _get_registered_clients(self, private_key):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
reg_clients = []
for pkey in self._private_keys.keys():
if pkey != private_key:
reg_clients.append(self._private_keys[pkey][0])
log.debug("get_registered_clients: private_key = {} clients = {}"
.format(private_key, reg_clients))
return reg_clients
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _get_subscribed_clients(self, private_key, mtype):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
sub_clients = {}
for pkey in self._private_keys.keys():
if pkey != private_key and self._is_subscribed(pkey, mtype):
sub_clients[self._private_keys[pkey][0]] = {}
log.debug("get_subscribed_clients: private_key = {} mtype = {} "
"clients = {}".format(private_key, mtype, sub_clients))
return sub_clients
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
@staticmethod
def get_mtype_subtypes(mtype):
"""
Return a list containing all the possible wildcarded subtypes of MType.
Parameters
----------
mtype : str
MType to be parsed.
Returns
-------
types : list
List of subtypes
Examples
--------
>>> from astropy.samp import SAMPHubServer
>>> SAMPHubServer.get_mtype_subtypes("samp.app.ping")
['samp.app.ping', 'samp.app.*', 'samp.*', '*']
"""
subtypes = []
msubs = mtype.split(".")
indexes = list(range(len(msubs)))
indexes.reverse()
indexes.append(-1)
for i in indexes:
tmp_mtype = ".".join(msubs[:i + 1])
if tmp_mtype != mtype:
if tmp_mtype != "":
tmp_mtype = tmp_mtype + ".*"
else:
tmp_mtype = "*"
subtypes.append(tmp_mtype)
return subtypes
def _is_subscribed(self, private_key, mtype):
subscribed = False
msubs = SAMPHubServer.get_mtype_subtypes(mtype)
for msub in msubs:
if msub in self._mtype2ids:
if private_key in self._mtype2ids[msub]:
subscribed = True
return subscribed
def _notify(self, private_key, recipient_id, message):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
if self._is_subscribed(self._public_id_to_private_key(recipient_id),
message["samp.mtype"]) is False:
raise SAMPProxyError(2, "Client {} not subscribed to MType {}"
.format(recipient_id, message["samp.mtype"]))
self._launch_thread(target=self._notify_, args=(private_key,
recipient_id,
message))
return {}
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _notify_(self, sender_private_key, recipient_public_id, message):
if sender_private_key not in self._private_keys:
return
sender_public_id = self._private_keys[sender_private_key][0]
try:
log.debug("notify {} from {} to {}".format(
message["samp.mtype"], sender_public_id,
recipient_public_id))
recipient_private_key = self._public_id_to_private_key(recipient_public_id)
arg_params = (sender_public_id, message)
samp_method_name = "receiveNotification"
self._retry_method(recipient_private_key, recipient_public_id, samp_method_name, arg_params)
except Exception as exc:
warnings.warn("{} notification from client {} to client {} "
"failed [{}]".format(message["samp.mtype"],
sender_public_id,
recipient_public_id, exc),
SAMPWarning)
def _notify_all(self, private_key, message):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
if "samp.mtype" not in message:
raise SAMPProxyError(3, "samp.mtype keyword is missing")
recipient_ids = self._notify_all_(private_key, message)
return recipient_ids
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _notify_all_(self, sender_private_key, message):
recipient_ids = []
msubs = SAMPHubServer.get_mtype_subtypes(message["samp.mtype"])
for mtype in msubs:
if mtype in self._mtype2ids:
for key in self._mtype2ids[mtype]:
if key != sender_private_key:
_recipient_id = self._private_keys[key][0]
recipient_ids.append(_recipient_id)
self._launch_thread(target=self._notify,
args=(sender_private_key,
_recipient_id, message)
)
return recipient_ids
def _call(self, private_key, recipient_id, msg_tag, message):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
if self._is_subscribed(self._public_id_to_private_key(recipient_id),
message["samp.mtype"]) is False:
raise SAMPProxyError(2, "Client {} not subscribed to MType {}"
.format(recipient_id, message["samp.mtype"]))
public_id = self._private_keys[private_key][0]
msg_id = self._get_new_hub_msg_id(public_id, msg_tag)
self._launch_thread(target=self._call_, args=(private_key, public_id,
recipient_id, msg_id,
message))
return msg_id
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _call_(self, sender_private_key, sender_public_id,
recipient_public_id, msg_id, message):
if sender_private_key not in self._private_keys:
return
try:
log.debug("call {} from {} to {} ({})".format(
msg_id.split(";;")[0], sender_public_id,
recipient_public_id, message["samp.mtype"]))
recipient_private_key = self._public_id_to_private_key(recipient_public_id)
arg_params = (sender_public_id, msg_id, message)
samp_methodName = "receiveCall"
self._retry_method(recipient_private_key, recipient_public_id, samp_methodName, arg_params)
except Exception as exc:
warnings.warn("{} call {} from client {} to client {} failed "
"[{},{}]".format(message["samp.mtype"],
msg_id.split(";;")[0],
sender_public_id,
recipient_public_id, type(exc), exc),
SAMPWarning)
def _call_all(self, private_key, msg_tag, message):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
if "samp.mtype" not in message:
raise SAMPProxyError(3, "samp.mtype keyword is missing in "
"message tagged as {}".format(msg_tag))
public_id = self._private_keys[private_key][0]
msg_id = self._call_all_(private_key, public_id, msg_tag, message)
return msg_id
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _call_all_(self, sender_private_key, sender_public_id, msg_tag,
message):
msg_id = {}
msubs = SAMPHubServer.get_mtype_subtypes(message["samp.mtype"])
for mtype in msubs:
if mtype in self._mtype2ids:
for key in self._mtype2ids[mtype]:
if key != sender_private_key:
_msg_id = self._get_new_hub_msg_id(sender_public_id,
msg_tag)
receiver_public_id = self._private_keys[key][0]
msg_id[receiver_public_id] = _msg_id
self._launch_thread(target=self._call_,
args=(sender_private_key,
sender_public_id,
receiver_public_id, _msg_id,
message))
return msg_id
def _call_and_wait(self, private_key, recipient_id, message, timeout):
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
timeout = int(timeout)
now = time.time()
response = {}
msg_id = self._call(private_key, recipient_id, "samp::sync::call",
message)
self._sync_msg_ids_heap[msg_id] = None
while self._is_running:
if 0 < timeout <= time.time() - now:
del(self._sync_msg_ids_heap[msg_id])
raise SAMPProxyError(1, "Timeout expired!")
if self._sync_msg_ids_heap[msg_id] is not None:
response = copy.deepcopy(self._sync_msg_ids_heap[msg_id])
del(self._sync_msg_ids_heap[msg_id])
break
time.sleep(0.01)
return response
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
def _reply(self, private_key, msg_id, response):
"""
The main method that gets called for replying. This starts up an
asynchronous reply thread and returns.
"""
self._update_last_activity_time(private_key)
if private_key in self._private_keys:
self._launch_thread(target=self._reply_, args=(private_key, msg_id,
response))
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
return {}
def _reply_(self, responder_private_key, msg_id, response):
if responder_private_key not in self._private_keys or not msg_id:
return
responder_public_id = self._private_keys[responder_private_key][0]
counter, hub_public_id, recipient_public_id, recipient_msg_tag = msg_id.split(";;", 3)
try:
log.debug("reply {} from {} to {}".format(
counter, responder_public_id, recipient_public_id))
if recipient_msg_tag == "samp::sync::call":
if msg_id in self._sync_msg_ids_heap.keys():
self._sync_msg_ids_heap[msg_id] = response
else:
recipient_private_key = self._public_id_to_private_key(recipient_public_id)
arg_params = (responder_public_id, recipient_msg_tag, response)
samp_method_name = "receiveResponse"
self._retry_method(recipient_private_key, recipient_public_id, samp_method_name, arg_params)
except Exception as exc:
warnings.warn("{} reply from client {} to client {} failed [{}]"
.format(recipient_msg_tag, responder_public_id,
recipient_public_id, exc),
SAMPWarning)
def _retry_method(self, recipient_private_key, recipient_public_id, samp_method_name, arg_params):
"""
This method is used to retry a SAMP call several times.
Parameters
----------
recipient_private_key
The private key of the receiver of the call
recipient_public_key
The public key of the receiver of the call
samp_method_name : str
The name of the SAMP method to call
arg_params : tuple
Any additional arguments to be passed to the SAMP method
"""
if recipient_private_key is None:
raise SAMPHubError("Invalid client ID")
from . import conf
for attempt in range(conf.n_retries):
if not self._is_running:
time.sleep(0.01)
continue
try:
if (self._web_profile and
recipient_private_key in self._web_profile_callbacks):
# Web Profile
callback = {"samp.methodName": samp_method_name,
"samp.params": arg_params}
self._web_profile_callbacks[recipient_private_key].put(callback)
else:
# Standard Profile
hub = self._xmlrpc_endpoints[recipient_public_id][1]
getattr(hub.samp.client, samp_method_name)(recipient_private_key, *arg_params)
except xmlrpc.Fault as exc:
log.debug("{} XML-RPC endpoint error (attempt {}): {}"
.format(recipient_public_id, attempt + 1,
exc.faultString))
time.sleep(0.01)
else:
return
# If we are here, then the above attempts failed
error_message = samp_method_name + " failed after " + str(conf.n_retries) + " attempts"
raise SAMPHubError(error_message)
def _public_id_to_private_key(self, public_id):
for private_key in self._private_keys.keys():
if self._private_keys[private_key][0] == public_id:
return private_key
return None
def _get_new_hub_msg_id(self, sender_public_id, sender_msg_id):
with self._thread_lock:
self._hub_msg_id_counter += 1
return "msg#{};;{};;{};;{}".format(self._hub_msg_id_counter,
self._hub_public_id,
sender_public_id, sender_msg_id)
def _update_last_activity_time(self, private_key=None):
with self._thread_lock:
self._last_activity_time = time.time()
if private_key is not None:
self._client_activity_time[private_key] = time.time()
def _receive_notification(self, private_key, sender_id, message):
return ""
def _receive_call(self, private_key, sender_id, msg_id, message):
if private_key == self._hub_private_key:
if "samp.mtype" in message and message["samp.mtype"] == "samp.app.ping":
self._reply(self._hub_private_key, msg_id,
{"samp.status": SAMP_STATUS_OK, "samp.result": {}})
elif ("samp.mtype" in message and
(message["samp.mtype"] == "x-samp.query.by-meta" or
message["samp.mtype"] == "samp.query.by-meta")):
ids_list = self._query_by_metadata(message["samp.params"]["key"],
message["samp.params"]["value"])
self._reply(self._hub_private_key, msg_id,
{"samp.status": SAMP_STATUS_OK,
"samp.result": {"ids": ids_list}})
return ""
else:
return ""
def _receive_response(self, private_key, responder_id, msg_tag, response):
return ""
def _web_profile_register(self, identity_info,
client_address=("unknown", 0),
origin="unknown"):
self._update_last_activity_time()
if not client_address[0] in ["localhost", "127.0.0.1"]:
raise SAMPProxyError(403, "Request of registration rejected "
"by the Hub.")
if not origin:
origin = "unknown"
if isinstance(identity_info, dict):
# an old version of the protocol provided just a string with the app name
if "samp.name" not in identity_info:
raise SAMPProxyError(403, "Request of registration rejected "
"by the Hub (application name not "
"provided).")
# Red semaphore for the other threads
self._web_profile_requests_semaphore.put("wait")
# Set the request to be displayed for the current thread
self._web_profile_requests_queue.put((identity_info, client_address,
origin))
# Get the popup dialogue response
response = self._web_profile_requests_result.get()
# OK, semaphore green
self._web_profile_requests_semaphore.get()
if response:
register_map = self._perform_standard_register()
translator_url = ("http://localhost:{}/translator/{}?ref="
.format(self._web_port, register_map["samp.private-key"]))
register_map["samp.url-translator"] = translator_url
self._web_profile_server.add_client(register_map["samp.private-key"])
return register_map
else:
raise SAMPProxyError(403, "Request of registration rejected by "
"the user.")
def _web_profile_allowReverseCallbacks(self, private_key, allow):
self._update_last_activity_time()
if private_key in self._private_keys:
if allow == "0":
if private_key in self._web_profile_callbacks:
del self._web_profile_callbacks[private_key]
else:
self._web_profile_callbacks[private_key] = queue.Queue()
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
return ""
def _web_profile_pullCallbacks(self, private_key, timeout_secs):
self._update_last_activity_time()
if private_key in self._private_keys:
callback = []
callback_queue = self._web_profile_callbacks[private_key]
try:
while self._is_running:
item_queued = callback_queue.get_nowait()
callback.append(item_queued)
except queue.Empty:
pass
return callback
else:
raise SAMPProxyError(5, f"Private-key {private_key} expired or invalid.")
class WebProfileDialog:
"""
A base class to make writing Web Profile GUI consent dialogs
easier.
The concrete class must:
1) Poll ``handle_queue`` periodically, using the timer services
of the GUI's event loop. This function will call
``self.show_dialog`` when a request requires authorization.
``self.show_dialog`` will be given the arguments:
- ``samp_name``: The name of the application making the request.
- ``details``: A dictionary of details about the client
making the request.
- ``client``: A hostname, port pair containing the client
address.
- ``origin``: A string containing the origin of the
request.
2) Call ``consent`` or ``reject`` based on the user's response to
the dialog.
"""
def handle_queue(self):
try:
request = self.queue_request.get_nowait()
except queue.Empty: # queue is set but empty
pass
except AttributeError: # queue has not been set yet
pass
else:
if isinstance(request[0], str): # To support the old protocol version
samp_name = request[0]
else:
samp_name = request[0]["samp.name"]
self.show_dialog(samp_name, request[0], request[1], request[2])
def consent(self):
self.queue_result.put(True)
def reject(self):
self.queue_result.put(False)
|
47a1ab834868aecbe78a8b223e77e7fffda6fc8c1b60c0c43bdcc9c1c4793711 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# TODO: this file should be refactored to use a more thread-safe and
# race-condition-safe lockfile mechanism.
import datetime
import os
import socket
import stat
import warnings
from contextlib import suppress
from urllib.parse import urlparse
import xmlrpc.client as xmlrpc
from astropy.config.paths import _find_home
from astropy import log
from astropy.utils.data import get_readable_fileobj
from .errors import SAMPHubError, SAMPWarning
def read_lockfile(lockfilename):
"""
Read in the lockfile given by ``lockfilename`` into a dictionary.
"""
# lockfilename may be a local file or a remote URL, but
# get_readable_fileobj takes care of this.
lockfiledict = {}
with get_readable_fileobj(lockfilename) as f:
for line in f:
if not line.startswith("#"):
kw, val = line.split("=")
lockfiledict[kw.strip()] = val.strip()
return lockfiledict
def write_lockfile(lockfilename, lockfiledict):
lockfile = open(lockfilename, "w")
lockfile.close()
os.chmod(lockfilename, stat.S_IREAD + stat.S_IWRITE)
lockfile = open(lockfilename, "w")
now_iso = datetime.datetime.now().isoformat()
lockfile.write(f"# SAMP lockfile written on {now_iso}\n")
lockfile.write("# Standard Profile required keys\n")
for key, value in lockfiledict.items():
lockfile.write(f"{key}={value}\n")
lockfile.close()
def create_lock_file(lockfilename=None, mode=None, hub_id=None,
hub_params=None):
# Remove lock-files of dead hubs
remove_garbage_lock_files()
lockfiledir = ""
# CHECK FOR SAMP_HUB ENVIRONMENT VARIABLE
if "SAMP_HUB" in os.environ:
# For the time being I assume just the std profile supported.
if os.environ["SAMP_HUB"].startswith("std-lockurl:"):
lockfilename = os.environ["SAMP_HUB"][len("std-lockurl:"):]
lockfile_parsed = urlparse(lockfilename)
if lockfile_parsed[0] != 'file':
warnings.warn("Unable to start a Hub with lockfile {}. "
"Start-up process aborted.".format(lockfilename),
SAMPWarning)
return False
else:
lockfilename = lockfile_parsed[2]
else:
# If it is a fresh Hub instance
if lockfilename is None:
log.debug("Running mode: " + mode)
if mode == 'single':
lockfilename = os.path.join(_find_home(), ".samp")
else:
lockfiledir = os.path.join(_find_home(), ".samp-1")
# If missing create .samp-1 directory
try:
os.mkdir(lockfiledir)
except OSError:
pass # directory already exists
finally:
os.chmod(lockfiledir,
stat.S_IREAD + stat.S_IWRITE + stat.S_IEXEC)
lockfilename = os.path.join(lockfiledir,
f"samp-hub-{hub_id}")
else:
log.debug("Running mode: multiple")
hub_is_running, lockfiledict = check_running_hub(lockfilename)
if hub_is_running:
warnings.warn("Another SAMP Hub is already running. Start-up process "
"aborted.", SAMPWarning)
return False
log.debug("Lock-file: " + lockfilename)
write_lockfile(lockfilename, hub_params)
return lockfilename
def get_main_running_hub():
"""
Get either the hub given by the environment variable SAMP_HUB, or the one
given by the lockfile .samp in the user home directory.
"""
hubs = get_running_hubs()
if not hubs:
raise SAMPHubError("Unable to find a running SAMP Hub.")
# CHECK FOR SAMP_HUB ENVIRONMENT VARIABLE
if "SAMP_HUB" in os.environ:
# For the time being I assume just the std profile supported.
if os.environ["SAMP_HUB"].startswith("std-lockurl:"):
lockfilename = os.environ["SAMP_HUB"][len("std-lockurl:"):]
else:
raise SAMPHubError("SAMP Hub profile not supported.")
else:
lockfilename = os.path.join(_find_home(), ".samp")
return hubs[lockfilename]
def get_running_hubs():
"""
Return a dictionary containing the lock-file contents of all the currently
running hubs (single and/or multiple mode).
The dictionary format is:
``{<lock-file>: {<token-name>: <token-string>, ...}, ...}``
where ``{<lock-file>}`` is the lock-file name, ``{<token-name>}`` and
``{<token-string>}`` are the lock-file tokens (name and content).
Returns
-------
running_hubs : dict
Lock-file contents of all the currently running hubs.
"""
hubs = {}
lockfilename = ""
# HUB SINGLE INSTANCE MODE
# CHECK FOR SAMP_HUB ENVIRONMENT VARIABLE
if "SAMP_HUB" in os.environ:
# For the time being I assume just the std profile supported.
if os.environ["SAMP_HUB"].startswith("std-lockurl:"):
lockfilename = os.environ["SAMP_HUB"][len("std-lockurl:"):]
else:
lockfilename = os.path.join(_find_home(), ".samp")
hub_is_running, lockfiledict = check_running_hub(lockfilename)
if hub_is_running:
hubs[lockfilename] = lockfiledict
# HUB MULTIPLE INSTANCE MODE
lockfiledir = ""
lockfiledir = os.path.join(_find_home(), ".samp-1")
if os.path.isdir(lockfiledir):
for filename in os.listdir(lockfiledir):
if filename.startswith('samp-hub'):
lockfilename = os.path.join(lockfiledir, filename)
hub_is_running, lockfiledict = check_running_hub(lockfilename)
if hub_is_running:
hubs[lockfilename] = lockfiledict
return hubs
def check_running_hub(lockfilename):
"""
Test whether a hub identified by ``lockfilename`` is running or not.
Parameters
----------
lockfilename : str
Lock-file name (path + file name) of the Hub to be tested.
Returns
-------
is_running : bool
Whether the hub is running
hub_params : dict
If the hub is running this contains the parameters from the lockfile
"""
is_running = False
lockfiledict = {}
# Check whether a lockfile already exists
try:
lockfiledict = read_lockfile(lockfilename)
except OSError:
return is_running, lockfiledict
if "samp.hub.xmlrpc.url" in lockfiledict:
try:
proxy = xmlrpc.ServerProxy(lockfiledict["samp.hub.xmlrpc.url"]
.replace("\\", ""), allow_none=1)
proxy.samp.hub.ping()
is_running = True
except xmlrpc.ProtocolError:
# There is a protocol error (e.g. for authentication required),
# but the server is alive
is_running = True
except socket.error:
pass
return is_running, lockfiledict
def remove_garbage_lock_files():
lockfilename = ""
# HUB SINGLE INSTANCE MODE
lockfilename = os.path.join(_find_home(), ".samp")
hub_is_running, lockfiledict = check_running_hub(lockfilename)
if not hub_is_running:
# If lockfilename belongs to a dead hub, then it is deleted
if os.path.isfile(lockfilename):
with suppress(OSError):
os.remove(lockfilename)
# HUB MULTIPLE INSTANCE MODE
lockfiledir = os.path.join(_find_home(), ".samp-1")
if os.path.isdir(lockfiledir):
for filename in os.listdir(lockfiledir):
if filename.startswith('samp-hub'):
lockfilename = os.path.join(lockfiledir, filename)
hub_is_running, lockfiledict = check_running_hub(lockfilename)
if not hub_is_running:
# If lockfilename belongs to a dead hub, then it is deleted
if os.path.isfile(lockfilename):
with suppress(OSError):
os.remove(lockfilename)
|
5eef71280f346091979e183d83e61ba38a80944c51fe2bc0e1677921169a78b3 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import time
import sys
import argparse
from astropy import log, __version__
from .hub import SAMPHubServer
__all__ = ['hub_script']
def hub_script(timeout=0):
"""
This main function is executed by the ``samp_hub`` command line tool.
"""
parser = argparse.ArgumentParser(prog="samp_hub " + __version__)
parser.add_argument("-k", "--secret", dest="secret", metavar="CODE",
help="custom secret code.")
parser.add_argument("-d", "--addr", dest="addr", metavar="ADDR",
help="listening address (or IP).")
parser.add_argument("-p", "--port", dest="port", metavar="PORT", type=int,
help="listening port number.")
parser.add_argument("-f", "--lockfile", dest="lockfile", metavar="FILE",
help="custom lockfile.")
parser.add_argument("-w", "--no-web-profile", dest="web_profile", action="store_false",
help="run the Hub disabling the Web Profile.", default=True)
parser.add_argument("-P", "--pool-size", dest="pool_size", metavar="SIZE", type=int,
help="the socket connections pool size.", default=20)
timeout_group = parser.add_argument_group("Timeout group",
"Special options to setup hub and client timeouts."
"It contains a set of special options that allows to set up the Hub and "
"clients inactivity timeouts, that is the Hub or client inactivity time "
"interval after which the Hub shuts down or unregisters the client. "
"Notification of samp.hub.disconnect MType is sent to the clients "
"forcibly unregistered for timeout expiration.")
timeout_group.add_argument("-t", "--timeout", dest="timeout", metavar="SECONDS",
help="set the Hub inactivity timeout in SECONDS. By default it "
"is set to 0, that is the Hub never expires.", type=int, default=0)
timeout_group.add_argument("-c", "--client-timeout", dest="client_timeout", metavar="SECONDS",
help="set the client inactivity timeout in SECONDS. By default it "
"is set to 0, that is the client never expires.", type=int, default=0)
parser.add_argument_group(timeout_group)
log_group = parser.add_argument_group("Logging options",
"Additional options which allow to customize the logging output. By "
"default the SAMP Hub uses the standard output and standard error "
"devices to print out INFO level logging messages. Using the options "
"here below it is possible to modify the logging level and also "
"specify the output files where redirect the logging messages.")
log_group.add_argument("-L", "--log-level", dest="loglevel", metavar="LEVEL",
help="set the Hub instance log level (OFF, ERROR, WARNING, INFO, DEBUG).",
type=str, choices=["OFF", "ERROR", "WARNING", "INFO", "DEBUG"], default='INFO')
log_group.add_argument("-O", "--log-output", dest="logout", metavar="FILE",
help="set the output file for the log messages.", default="")
parser.add_argument_group(log_group)
adv_group = parser.add_argument_group("Advanced group",
"Advanced options addressed to facilitate administrative tasks and "
"allow new non-standard Hub behaviors. In particular the --label "
"options is used to assign a value to hub.label token and is used to "
"assign a name to the Hub instance. "
"The very special --multi option allows to start a Hub in multi-instance mode. "
"Multi-instance mode is a non-standard Hub behavior that enables "
"multiple contemporaneous running Hubs. Multi-instance hubs place "
"their non-standard lock-files within the <home directory>/.samp-1 "
"directory naming them making use of the format: "
"samp-hub-<PID>-<ID>, where PID is the Hub process ID while ID is an "
"internal ID (integer).")
adv_group.add_argument("-l", "--label", dest="label", metavar="LABEL",
help="assign a LABEL to the Hub.", default="")
adv_group.add_argument("-m", "--multi", dest="mode",
help="run the Hub in multi-instance mode generating a custom "
"lockfile with a random name.",
action="store_const", const='multiple', default='single')
parser.add_argument_group(adv_group)
options = parser.parse_args()
try:
if options.loglevel in ("OFF", "ERROR", "WARNING", "DEBUG", "INFO"):
log.setLevel(options.loglevel)
if options.logout != "":
context = log.log_to_file(options.logout)
else:
class dummy_context:
def __enter__(self):
pass
def __exit__(self, exc_type, exc_value, traceback):
pass
context = dummy_context()
with context:
args = copy.deepcopy(options.__dict__)
del(args["loglevel"])
del(args["logout"])
hub = SAMPHubServer(**args)
hub.start(False)
if not timeout:
while hub.is_running:
time.sleep(0.01)
else:
time.sleep(timeout)
hub.stop()
except KeyboardInterrupt:
try:
hub.stop()
except NameError:
pass
except OSError as e:
print(f"[SAMP] Error: I/O error({e.errno}): {e.strerror}")
sys.exit(1)
except SystemExit:
pass
|
e3a1de16749eac98bddb4e6292abb23d3bb9fc0b7adad21b222cfae219d98b7f | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Defines custom errors and exceptions used in `astropy.samp`.
"""
import xmlrpc.client as xmlrpc
from astropy.utils.exceptions import AstropyUserWarning
__all__ = ['SAMPWarning', 'SAMPHubError', 'SAMPClientError', 'SAMPProxyError']
class SAMPWarning(AstropyUserWarning):
"""
SAMP-specific Astropy warning class
"""
class SAMPHubError(Exception):
"""
SAMP Hub exception.
"""
class SAMPClientError(Exception):
"""
SAMP Client exceptions.
"""
class SAMPProxyError(xmlrpc.Fault):
"""
SAMP Proxy Hub exception
"""
|
0553dc69292ad6900890ef95c0711f1cda27ed774cd02d171732d1ae02ad0640 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from urllib.parse import parse_qs
from urllib.request import urlopen
from astropy.utils.data import get_pkg_data_contents
from .standard_profile import (SAMPSimpleXMLRPCRequestHandler,
ThreadingXMLRPCServer)
__all__ = []
CROSS_DOMAIN = get_pkg_data_contents('data/crossdomain.xml')
CLIENT_ACCESS_POLICY = get_pkg_data_contents('data/clientaccesspolicy.xml')
class WebProfileRequestHandler(SAMPSimpleXMLRPCRequestHandler):
"""
Handler of XMLRPC requests performed through the Web Profile.
"""
def _send_CORS_header(self):
if self.headers.get('Origin') is not None:
method = self.headers.get('Access-Control-Request-Method')
if method and self.command == "OPTIONS":
# Preflight method
self.send_header('Content-Length', '0')
self.send_header('Access-Control-Allow-Origin',
self.headers.get('Origin'))
self.send_header('Access-Control-Allow-Methods', method)
self.send_header('Access-Control-Allow-Headers', 'Content-Type')
self.send_header('Access-Control-Allow-Credentials', 'true')
else:
# Simple method
self.send_header('Access-Control-Allow-Origin',
self.headers.get('Origin'))
self.send_header('Access-Control-Allow-Headers', 'Content-Type')
self.send_header('Access-Control-Allow-Credentials', 'true')
def end_headers(self):
self._send_CORS_header()
SAMPSimpleXMLRPCRequestHandler.end_headers(self)
def _serve_cross_domain_xml(self):
cross_domain = False
if self.path == "/crossdomain.xml":
# Adobe standard
response = CROSS_DOMAIN
self.send_response(200, 'OK')
self.send_header('Content-Type', 'text/x-cross-domain-policy')
self.send_header("Content-Length", f"{len(response)}")
self.end_headers()
self.wfile.write(response.encode('utf-8'))
self.wfile.flush()
cross_domain = True
elif self.path == "/clientaccesspolicy.xml":
# Microsoft standard
response = CLIENT_ACCESS_POLICY
self.send_response(200, 'OK')
self.send_header('Content-Type', 'text/xml')
self.send_header("Content-Length", f"{len(response)}")
self.end_headers()
self.wfile.write(response.encode('utf-8'))
self.wfile.flush()
cross_domain = True
return cross_domain
def do_POST(self):
if self._serve_cross_domain_xml():
return
return SAMPSimpleXMLRPCRequestHandler.do_POST(self)
def do_HEAD(self):
if not self.is_http_path_valid():
self.report_404()
return
if self._serve_cross_domain_xml():
return
def do_OPTIONS(self):
self.send_response(200, 'OK')
self.end_headers()
def do_GET(self):
if not self.is_http_path_valid():
self.report_404()
return
split_path = self.path.split('?')
if split_path[0] in [f'/translator/{clid}' for clid in self.server.clients]:
# Request of a file proxying
urlpath = parse_qs(split_path[1])
try:
proxyfile = urlopen(urlpath["ref"][0])
self.send_response(200, 'OK')
self.end_headers()
self.wfile.write(proxyfile.read())
proxyfile.close()
except OSError:
self.report_404()
return
if self._serve_cross_domain_xml():
return
def is_http_path_valid(self):
valid_paths = (["/clientaccesspolicy.xml", "/crossdomain.xml"] +
[f'/translator/{clid}' for clid in self.server.clients])
return self.path.split('?')[0] in valid_paths
class WebProfileXMLRPCServer(ThreadingXMLRPCServer):
"""
XMLRPC server supporting the SAMP Web Profile.
"""
def __init__(self, addr, log=None, requestHandler=WebProfileRequestHandler,
logRequests=True, allow_none=True, encoding=None):
self.clients = []
ThreadingXMLRPCServer.__init__(self, addr, log, requestHandler,
logRequests, allow_none, encoding)
def add_client(self, client_id):
self.clients.append(client_id)
def remove_client(self, client_id):
try:
self.clients.remove(client_id)
except ValueError:
# No warning here because this method gets called for all clients,
# not just web clients, and we expect it to fail for non-web
# clients.
pass
def web_profile_text_dialog(request, queue):
samp_name = "unknown"
if isinstance(request[0], str):
# To support the old protocol version
samp_name = request[0]
else:
samp_name = request[0]["samp.name"]
text = \
f"""A Web application which declares to be
Name: {samp_name}
Origin: {request[2]}
is requesting to be registered with the SAMP Hub.
Pay attention that if you permit its registration, such
application will acquire all current user privileges, like
file read/write.
Do you give your consent? [yes|no]"""
print(text)
answer = input(">>> ")
queue.put(answer.lower() in ["yes", "y"])
|
bc905ed75f9cbaa0ea4d8941d412e595dd77d478311d5a71074a92df058ac676 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from .client import SAMPClient
from .hub_proxy import SAMPHubProxy
__all__ = ['SAMPIntegratedClient']
__doctest_skip__ = ['SAMPIntegratedClient.*']
class SAMPIntegratedClient:
"""
A Simple SAMP client.
This class is meant to simplify the client usage providing a proxy class
that merges the :class:`~astropy.samp.SAMPClient` and
:class:`~astropy.samp.SAMPHubProxy` functionalities in a
simplified API.
Parameters
----------
name : str, optional
Client name (corresponding to ``samp.name`` metadata keyword).
description : str, optional
Client description (corresponding to ``samp.description.text`` metadata
keyword).
metadata : dict, optional
Client application metadata in the standard SAMP format.
addr : str, optional
Listening address (or IP). This defaults to 127.0.0.1 if the internet
is not reachable, otherwise it defaults to the host name.
port : int, optional
Listening XML-RPC server socket port. If left set to 0 (the default),
the operating system will select a free port.
callable : bool, optional
Whether the client can receive calls and notifications. If set to
`False`, then the client can send notifications and calls, but can not
receive any.
"""
def __init__(self, name=None, description=None, metadata=None,
addr=None, port=0, callable=True):
self.hub = SAMPHubProxy()
self.client_arguments = {
'name': name,
'description': description,
'metadata': metadata,
'addr': addr,
'port': port,
'callable': callable,
}
"""
Collected arguments that should be passed on to the SAMPClient below.
The SAMPClient used to be instantiated in __init__; however, this
caused problems with disconnecting and reconnecting to the HUB.
The client_arguments is used to maintain backwards compatibility.
"""
self.client = None
"The client will be instantiated upon connect()."
# GENERAL
@property
def is_connected(self):
"""
Testing method to verify the client connection with a running Hub.
Returns
-------
is_connected : bool
True if the client is connected to a Hub, False otherwise.
"""
return self.hub.is_connected and self.client.is_running
def connect(self, hub=None, hub_params=None, pool_size=20):
"""
Connect with the current or specified SAMP Hub, start and register the
client.
Parameters
----------
hub : `~astropy.samp.SAMPHubServer`, optional
The hub to connect to.
hub_params : dict, optional
Optional dictionary containing the lock-file content of the Hub
with which to connect. This dictionary has the form
``{<token-name>: <token-string>, ...}``.
pool_size : int, optional
The number of socket connections opened to communicate with the
Hub.
"""
self.hub.connect(hub, hub_params, pool_size)
# The client has to be instantiated here and not in __init__() because
# this allows disconnecting and reconnecting to the HUB. Nonetheless,
# the client_arguments are set in __init__() because the
# instantiation of the client used to happen there and this retains
# backwards compatibility.
self.client = SAMPClient(
self.hub,
**self.client_arguments
)
self.client.start()
self.client.register()
def disconnect(self):
"""
Unregister the client from the current SAMP Hub, stop the client and
disconnect from the Hub.
"""
if self.is_connected:
try:
self.client.unregister()
finally:
if self.client.is_running:
self.client.stop()
self.hub.disconnect()
# HUB
def ping(self):
"""
Proxy to ``ping`` SAMP Hub method (Standard Profile only).
"""
return self.hub.ping()
def declare_metadata(self, metadata):
"""
Proxy to ``declareMetadata`` SAMP Hub method.
"""
return self.client.declare_metadata(metadata)
def get_metadata(self, client_id):
"""
Proxy to ``getMetadata`` SAMP Hub method.
"""
return self.hub.get_metadata(self.get_private_key(), client_id)
def get_subscriptions(self, client_id):
"""
Proxy to ``getSubscriptions`` SAMP Hub method.
"""
return self.hub.get_subscriptions(self.get_private_key(), client_id)
def get_registered_clients(self):
"""
Proxy to ``getRegisteredClients`` SAMP Hub method.
This returns all the registered clients, excluding the current client.
"""
return self.hub.get_registered_clients(self.get_private_key())
def get_subscribed_clients(self, mtype):
"""
Proxy to ``getSubscribedClients`` SAMP Hub method.
"""
return self.hub.get_subscribed_clients(self.get_private_key(), mtype)
def _format_easy_msg(self, mtype, params):
msg = {}
if "extra_kws" in params:
extra = params["extra_kws"]
del(params["extra_kws"])
msg = {"samp.mtype": mtype, "samp.params": params}
msg.update(extra)
else:
msg = {"samp.mtype": mtype, "samp.params": params}
return msg
def notify(self, recipient_id, message):
"""
Proxy to ``notify`` SAMP Hub method.
"""
return self.hub.notify(self.get_private_key(), recipient_id, message)
def enotify(self, recipient_id, mtype, **params):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.notify`.
This is a proxy to ``notify`` method that allows to send the
notification message in a simplified way.
Note that reserved ``extra_kws`` keyword is a dictionary with the
special meaning of being used to add extra keywords, in addition to
the standard ``samp.mtype`` and ``samp.params``, to the message sent.
Parameters
----------
recipient_id : str
Recipient ID
mtype : str
the MType to be notified
params : dict or set of str
Variable keyword set which contains the list of parameters for the
specified MType.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient
>>> cli = SAMPIntegratedClient()
>>> ...
>>> cli.enotify("samp.msg.progress", msgid = "xyz", txt = "initialization",
... percent = "10", extra_kws = {"my.extra.info": "just an example"})
"""
return self.notify(recipient_id, self._format_easy_msg(mtype, params))
def notify_all(self, message):
"""
Proxy to ``notifyAll`` SAMP Hub method.
"""
return self.hub.notify_all(self.get_private_key(), message)
def enotify_all(self, mtype, **params):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.notify_all`.
This is a proxy to ``notifyAll`` method that allows to send the
notification message in a simplified way.
Note that reserved ``extra_kws`` keyword is a dictionary with the
special meaning of being used to add extra keywords, in addition to
the standard ``samp.mtype`` and ``samp.params``, to the message sent.
Parameters
----------
mtype : str
MType to be notified.
params : dict or set of str
Variable keyword set which contains the list of parameters for
the specified MType.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient
>>> cli = SAMPIntegratedClient()
>>> ...
>>> cli.enotify_all("samp.msg.progress", txt = "initialization",
... percent = "10",
... extra_kws = {"my.extra.info": "just an example"})
"""
return self.notify_all(self._format_easy_msg(mtype, params))
def call(self, recipient_id, msg_tag, message):
"""
Proxy to ``call`` SAMP Hub method.
"""
return self.hub.call(self.get_private_key(), recipient_id, msg_tag, message)
def ecall(self, recipient_id, msg_tag, mtype, **params):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.call`.
This is a proxy to ``call`` method that allows to send a call message
in a simplified way.
Note that reserved ``extra_kws`` keyword is a dictionary with the
special meaning of being used to add extra keywords, in addition to
the standard ``samp.mtype`` and ``samp.params``, to the message sent.
Parameters
----------
recipient_id : str
Recipient ID
msg_tag : str
Message tag to use
mtype : str
MType to be sent
params : dict of set of str
Variable keyword set which contains the list of parameters for
the specified MType.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient
>>> cli = SAMPIntegratedClient()
>>> ...
>>> msgid = cli.ecall("abc", "xyz", "samp.msg.progress",
... txt = "initialization", percent = "10",
... extra_kws = {"my.extra.info": "just an example"})
"""
return self.call(recipient_id, msg_tag, self._format_easy_msg(mtype, params))
def call_all(self, msg_tag, message):
"""
Proxy to ``callAll`` SAMP Hub method.
"""
return self.hub.call_all(self.get_private_key(), msg_tag, message)
def ecall_all(self, msg_tag, mtype, **params):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.call_all`.
This is a proxy to ``callAll`` method that allows to send the call
message in a simplified way.
Note that reserved ``extra_kws`` keyword is a dictionary with the
special meaning of being used to add extra keywords, in addition to
the standard ``samp.mtype`` and ``samp.params``, to the message sent.
Parameters
----------
msg_tag : str
Message tag to use
mtype : str
MType to be sent
params : dict of set of str
Variable keyword set which contains the list of parameters for
the specified MType.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient
>>> cli = SAMPIntegratedClient()
>>> ...
>>> msgid = cli.ecall_all("xyz", "samp.msg.progress",
... txt = "initialization", percent = "10",
... extra_kws = {"my.extra.info": "just an example"})
"""
self.call_all(msg_tag, self._format_easy_msg(mtype, params))
def call_and_wait(self, recipient_id, message, timeout):
"""
Proxy to ``callAndWait`` SAMP Hub method.
"""
return self.hub.call_and_wait(self.get_private_key(), recipient_id, message, timeout)
def ecall_and_wait(self, recipient_id, mtype, timeout, **params):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.call_and_wait`.
This is a proxy to ``callAndWait`` method that allows to send the call
message in a simplified way.
Note that reserved ``extra_kws`` keyword is a dictionary with the
special meaning of being used to add extra keywords, in addition to
the standard ``samp.mtype`` and ``samp.params``, to the message sent.
Parameters
----------
recipient_id : str
Recipient ID
mtype : str
MType to be sent
timeout : str
Call timeout in seconds
params : dict of set of str
Variable keyword set which contains the list of parameters for
the specified MType.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient
>>> cli = SAMPIntegratedClient()
>>> ...
>>> cli.ecall_and_wait("xyz", "samp.msg.progress", "5",
... txt = "initialization", percent = "10",
... extra_kws = {"my.extra.info": "just an example"})
"""
return self.call_and_wait(recipient_id, self._format_easy_msg(mtype, params), timeout)
def reply(self, msg_id, response):
"""
Proxy to ``reply`` SAMP Hub method.
"""
return self.hub.reply(self.get_private_key(), msg_id, response)
def _format_easy_response(self, status, result, error):
msg = {"samp.status": status}
if result is not None:
msg.update({"samp.result": result})
if error is not None:
msg.update({"samp.error": error})
return msg
def ereply(self, msg_id, status, result=None, error=None):
"""
Easy to use version of :meth:`~astropy.samp.integrated_client.SAMPIntegratedClient.reply`.
This is a proxy to ``reply`` method that allows to send a reply
message in a simplified way.
Parameters
----------
msg_id : str
Message ID to which reply.
status : str
Content of the ``samp.status`` response keyword.
result : dict
Content of the ``samp.result`` response keyword.
error : dict
Content of the ``samp.error`` response keyword.
Examples
--------
>>> from astropy.samp import SAMPIntegratedClient, SAMP_STATUS_ERROR
>>> cli = SAMPIntegratedClient()
>>> ...
>>> cli.ereply("abd", SAMP_STATUS_ERROR, result={},
... error={"samp.errortxt": "Test error message"})
"""
return self.reply(msg_id, self._format_easy_response(status, result, error))
# CLIENT
def receive_notification(self, private_key, sender_id, message):
return self.client.receive_notification(private_key, sender_id, message)
receive_notification.__doc__ = SAMPClient.receive_notification.__doc__
def receive_call(self, private_key, sender_id, msg_id, message):
return self.client.receive_call(private_key, sender_id, msg_id, message)
receive_call.__doc__ = SAMPClient.receive_call.__doc__
def receive_response(self, private_key, responder_id, msg_tag, response):
return self.client.receive_response(private_key, responder_id, msg_tag, response)
receive_response.__doc__ = SAMPClient.receive_response.__doc__
def bind_receive_message(self, mtype, function, declare=True, metadata=None):
self.client.bind_receive_message(mtype, function, declare=True, metadata=None)
bind_receive_message.__doc__ = SAMPClient.bind_receive_message.__doc__
def bind_receive_notification(self, mtype, function, declare=True, metadata=None):
self.client.bind_receive_notification(mtype, function, declare, metadata)
bind_receive_notification.__doc__ = SAMPClient.bind_receive_notification.__doc__
def bind_receive_call(self, mtype, function, declare=True, metadata=None):
self.client.bind_receive_call(mtype, function, declare, metadata)
bind_receive_call.__doc__ = SAMPClient.bind_receive_call.__doc__
def bind_receive_response(self, msg_tag, function):
self.client.bind_receive_response(msg_tag, function)
bind_receive_response.__doc__ = SAMPClient.bind_receive_response.__doc__
def unbind_receive_notification(self, mtype, declare=True):
self.client.unbind_receive_notification(mtype, declare)
unbind_receive_notification.__doc__ = SAMPClient.unbind_receive_notification.__doc__
def unbind_receive_call(self, mtype, declare=True):
self.client.unbind_receive_call(mtype, declare)
unbind_receive_call.__doc__ = SAMPClient.unbind_receive_call.__doc__
def unbind_receive_response(self, msg_tag):
self.client.unbind_receive_response(msg_tag)
unbind_receive_response.__doc__ = SAMPClient.unbind_receive_response.__doc__
def declare_subscriptions(self, subscriptions=None):
self.client.declare_subscriptions(subscriptions)
declare_subscriptions.__doc__ = SAMPClient.declare_subscriptions.__doc__
def get_private_key(self):
return self.client.get_private_key()
get_private_key.__doc__ = SAMPClient.get_private_key.__doc__
def get_public_id(self):
return self.client.get_public_id()
get_public_id.__doc__ = SAMPClient.get_public_id.__doc__
|
c3b5b78854348bc92fe963694ec5fff1975841f6fdd6e6a396114b54d0d2766c | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This subpackage provides classes to communicate with other applications via the
`Simple Application Messaging Protocol (SAMP)
<http://www.ivoa.net/documents/SAMP/>`_.
Before integration into Astropy it was known as
`SAMPy <https://pypi.org/project/sampy/>`_, and was developed by Luigi Paioro
(INAF - Istituto Nazionale di Astrofisica).
"""
from .constants import *
from .errors import *
from .utils import *
from .hub import *
from .client import *
from .integrated_client import *
from .hub_proxy import *
from astropy import config as _config
class Conf(_config.ConfigNamespace):
"""
Configuration parameters for `astropy.samp`.
"""
use_internet = _config.ConfigItem(
True,
"Whether to allow `astropy.samp` to use "
"the internet, if available.",
aliases=['astropy.samp.utils.use_internet'])
n_retries = _config.ConfigItem(10,
"How many times to retry communications when they fail")
conf = Conf()
|
612dac733ece31fd81ac7e6526337596616bd92277d7c20d082bf0a168689b95 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import sys
import traceback
import warnings
import socketserver
import xmlrpc.client as xmlrpc
from xmlrpc.server import SimpleXMLRPCRequestHandler, SimpleXMLRPCServer
from .constants import SAMP_ICON
from .errors import SAMPWarning
__all__ = []
class SAMPSimpleXMLRPCRequestHandler(SimpleXMLRPCRequestHandler):
"""
XMLRPC handler of Standard Profile requests.
"""
def do_GET(self):
if self.path == '/samp/icon':
self.send_response(200, 'OK')
self.send_header('Content-Type', 'image/png')
self.end_headers()
self.wfile.write(SAMP_ICON)
def do_POST(self):
"""
Handles the HTTP POST request.
Attempts to interpret all HTTP POST requests as XML-RPC calls,
which are forwarded to the server's ``_dispatch`` method for
handling.
"""
# Check that the path is legal
if not self.is_rpc_path_valid():
self.report_404()
return
try:
# Get arguments by reading body of request.
# We read this in chunks to avoid straining
# socket.read(); around the 10 or 15Mb mark, some platforms
# begin to have problems (bug #792570).
max_chunk_size = 10 * 1024 * 1024
size_remaining = int(self.headers["content-length"])
L = []
while size_remaining:
chunk_size = min(size_remaining, max_chunk_size)
L.append(self.rfile.read(chunk_size))
size_remaining -= len(L[-1])
data = b''.join(L)
params, method = xmlrpc.loads(data)
if method == "samp.webhub.register":
params = list(params)
params.append(self.client_address)
if 'Origin' in self.headers:
params.append(self.headers.get('Origin'))
else:
params.append('unknown')
params = tuple(params)
data = xmlrpc.dumps(params, methodname=method)
elif method in ('samp.hub.notify', 'samp.hub.notifyAll',
'samp.hub.call', 'samp.hub.callAll',
'samp.hub.callAndWait'):
user = "unknown"
if method == 'samp.hub.callAndWait':
params[2]["host"] = self.address_string()
params[2]["user"] = user
else:
params[-1]["host"] = self.address_string()
params[-1]["user"] = user
data = xmlrpc.dumps(params, methodname=method)
data = self.decode_request_content(data)
if data is None:
return # response has been sent
# In previous versions of SimpleXMLRPCServer, _dispatch
# could be overridden in this class, instead of in
# SimpleXMLRPCDispatcher. To maintain backwards compatibility,
# check to see if a subclass implements _dispatch and dispatch
# using that method if present.
response = self.server._marshaled_dispatch(
data, getattr(self, '_dispatch', None), self.path
)
except Exception as e:
# This should only happen if the module is buggy
# internal error, report as HTTP server error
self.send_response(500)
# Send information about the exception if requested
if hasattr(self.server, '_send_traceback_header') and \
self.server._send_traceback_header:
self.send_header("X-exception", str(e))
trace = traceback.format_exc()
trace = str(trace.encode('ASCII', 'backslashreplace'), 'ASCII')
self.send_header("X-traceback", trace)
self.send_header("Content-length", "0")
self.end_headers()
else:
# got a valid XML RPC response
self.send_response(200)
self.send_header("Content-type", "text/xml")
if self.encode_threshold is not None:
if len(response) > self.encode_threshold:
q = self.accept_encodings().get("gzip", 0)
if q:
try:
response = xmlrpc.gzip_encode(response)
self.send_header("Content-Encoding", "gzip")
except NotImplementedError:
pass
self.send_header("Content-length", str(len(response)))
self.end_headers()
self.wfile.write(response)
class ThreadingXMLRPCServer(socketserver.ThreadingMixIn, SimpleXMLRPCServer):
"""
Asynchronous multithreaded XMLRPC server.
"""
def __init__(self, addr, log=None,
requestHandler=SAMPSimpleXMLRPCRequestHandler,
logRequests=True, allow_none=True, encoding=None):
self.log = log
SimpleXMLRPCServer.__init__(self, addr, requestHandler,
logRequests, allow_none, encoding)
def handle_error(self, request, client_address):
if self.log is None:
socketserver.BaseServer.handle_error(self, request, client_address)
else:
warnings.warn("Exception happened during processing of request "
"from {}: {}".format(client_address, sys.exc_info()[1]),
SAMPWarning)
|
516410637a55041289464563427a02cbaaa17b9ac4eee7b9769d21643f68263c | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import os
import select
import socket
import threading
import warnings
from urllib.parse import urlunparse
from .constants import SAMP_STATUS_OK, SAMP_STATUS_WARNING
from .hub import SAMPHubServer
from .errors import SAMPClientError, SAMPWarning
from .utils import internet_on, get_num_args
from .standard_profile import ThreadingXMLRPCServer
__all__ = ['SAMPClient']
class SAMPClient:
"""
Utility class which provides facilities to create and manage a SAMP
compliant XML-RPC server that acts as SAMP callable client application.
Parameters
----------
hub : :class:`~astropy.samp.SAMPHubProxy`
An instance of :class:`~astropy.samp.SAMPHubProxy` to be
used for messaging with the SAMP Hub.
name : str, optional
Client name (corresponding to ``samp.name`` metadata keyword).
description : str, optional
Client description (corresponding to ``samp.description.text`` metadata
keyword).
metadata : dict, optional
Client application metadata in the standard SAMP format.
addr : str, optional
Listening address (or IP). This defaults to 127.0.0.1 if the internet
is not reachable, otherwise it defaults to the host name.
port : int, optional
Listening XML-RPC server socket port. If left set to 0 (the default),
the operating system will select a free port.
callable : bool, optional
Whether the client can receive calls and notifications. If set to
`False`, then the client can send notifications and calls, but can not
receive any.
"""
# TODO: define what is meant by callable
def __init__(self, hub, name=None, description=None, metadata=None,
addr=None, port=0, callable=True):
# GENERAL
self._is_running = False
self._is_registered = False
if metadata is None:
metadata = {}
if name is not None:
metadata["samp.name"] = name
if description is not None:
metadata["samp.description.text"] = description
self._metadata = metadata
self._addr = addr
self._port = port
self._xmlrpcAddr = None
self._callable = callable
# HUB INTERACTION
self.client = None
self._public_id = None
self._private_key = None
self._hub_id = None
self._notification_bindings = {}
self._call_bindings = {"samp.app.ping": [self._ping, {}],
"client.env.get": [self._client_env_get, {}]}
self._response_bindings = {}
self._host_name = "127.0.0.1"
if internet_on():
try:
self._host_name = socket.getfqdn()
socket.getaddrinfo(self._addr or self._host_name, self._port or 0)
except socket.error:
self._host_name = "127.0.0.1"
self.hub = hub
if self._callable:
self._thread = threading.Thread(target=self._serve_forever)
self._thread.daemon = True
self.client = ThreadingXMLRPCServer((self._addr or self._host_name,
self._port), logRequests=False, allow_none=True)
self.client.register_introspection_functions()
self.client.register_function(self.receive_notification, 'samp.client.receiveNotification')
self.client.register_function(self.receive_call, 'samp.client.receiveCall')
self.client.register_function(self.receive_response, 'samp.client.receiveResponse')
# If the port was set to zero, then the operating system has
# selected a free port. We now check what this port number is.
if self._port == 0:
self._port = self.client.socket.getsockname()[1]
protocol = 'http'
self._xmlrpcAddr = urlunparse((protocol,
'{}:{}'.format(self._addr or self._host_name,
self._port),
'', '', '', ''))
def start(self):
"""
Start the client in a separate thread (non-blocking).
This only has an effect if ``callable`` was set to `True` when
initializing the client.
"""
if self._callable:
self._is_running = True
self._run_client()
def stop(self, timeout=10.):
"""
Stop the client.
Parameters
----------
timeout : float
Timeout after which to give up if the client cannot be cleanly
shut down.
"""
# Setting _is_running to False causes the loop in _serve_forever to
# exit. The thread should then stop running. We wait for the thread to
# terminate until the timeout, then we continue anyway.
self._is_running = False
if self._callable and self._thread.is_alive():
self._thread.join(timeout)
if self._thread.is_alive():
raise SAMPClientError("Client was not shut down successfully "
"(timeout={}s)".format(timeout))
@property
def is_running(self):
"""
Whether the client is currently running.
"""
return self._is_running
@property
def is_registered(self):
"""
Whether the client is currently registered.
"""
return self._is_registered
def _run_client(self):
if self._callable:
self._thread.start()
def _serve_forever(self):
while self._is_running:
try:
read_ready = select.select([self.client.socket], [], [], 0.1)[0]
except OSError as exc:
warnings.warn(f"Call to select in SAMPClient failed: {exc}",
SAMPWarning)
else:
if read_ready:
self.client.handle_request()
self.client.server_close()
def _ping(self, private_key, sender_id, msg_id, msg_mtype, msg_params,
message):
reply = {"samp.status": SAMP_STATUS_OK, "samp.result": {}}
self.hub.reply(private_key, msg_id, reply)
def _client_env_get(self, private_key, sender_id, msg_id, msg_mtype,
msg_params, message):
if msg_params["name"] in os.environ:
reply = {"samp.status": SAMP_STATUS_OK,
"samp.result": {"value": os.environ[msg_params["name"]]}}
else:
reply = {"samp.status": SAMP_STATUS_WARNING,
"samp.result": {"value": ""},
"samp.error": {"samp.errortxt":
"Environment variable not defined."}}
self.hub.reply(private_key, msg_id, reply)
def _handle_notification(self, private_key, sender_id, message):
if private_key == self.get_private_key() and "samp.mtype" in message:
msg_mtype = message["samp.mtype"]
del message["samp.mtype"]
msg_params = message["samp.params"]
del message["samp.params"]
msubs = SAMPHubServer.get_mtype_subtypes(msg_mtype)
for mtype in msubs:
if mtype in self._notification_bindings:
bound_func = self._notification_bindings[mtype][0]
if get_num_args(bound_func) == 5:
bound_func(private_key, sender_id, msg_mtype,
msg_params, message)
else:
bound_func(private_key, sender_id, None, msg_mtype,
msg_params, message)
return ""
def receive_notification(self, private_key, sender_id, message):
"""
Standard callable client ``receive_notification`` method.
This method is automatically handled when the
:meth:`~astropy.samp.client.SAMPClient.bind_receive_notification`
method is used to bind distinct operations to MTypes. In case of a
customized callable client implementation that inherits from the
:class:`~astropy.samp.SAMPClient` class this method should be
overwritten.
.. note:: When overwritten, this method must always return
a string result (even empty).
Parameters
----------
private_key : str
Client private key.
sender_id : str
Sender public ID.
message : dict
Received message.
Returns
-------
confirmation : str
Any confirmation string.
"""
return self._handle_notification(private_key, sender_id, message)
def _handle_call(self, private_key, sender_id, msg_id, message):
if private_key == self.get_private_key() and "samp.mtype" in message:
msg_mtype = message["samp.mtype"]
del message["samp.mtype"]
msg_params = message["samp.params"]
del message["samp.params"]
msubs = SAMPHubServer.get_mtype_subtypes(msg_mtype)
for mtype in msubs:
if mtype in self._call_bindings:
self._call_bindings[mtype][0](private_key, sender_id,
msg_id, msg_mtype,
msg_params, message)
return ""
def receive_call(self, private_key, sender_id, msg_id, message):
"""
Standard callable client ``receive_call`` method.
This method is automatically handled when the
:meth:`~astropy.samp.client.SAMPClient.bind_receive_call` method is
used to bind distinct operations to MTypes. In case of a customized
callable client implementation that inherits from the
:class:`~astropy.samp.SAMPClient` class this method should be
overwritten.
.. note:: When overwritten, this method must always return
a string result (even empty).
Parameters
----------
private_key : str
Client private key.
sender_id : str
Sender public ID.
msg_id : str
Message ID received.
message : dict
Received message.
Returns
-------
confirmation : str
Any confirmation string.
"""
return self._handle_call(private_key, sender_id, msg_id, message)
def _handle_response(self, private_key, responder_id, msg_tag, response):
if (private_key == self.get_private_key() and
msg_tag in self._response_bindings):
self._response_bindings[msg_tag](private_key, responder_id,
msg_tag, response)
return ""
def receive_response(self, private_key, responder_id, msg_tag, response):
"""
Standard callable client ``receive_response`` method.
This method is automatically handled when the
:meth:`~astropy.samp.client.SAMPClient.bind_receive_response` method
is used to bind distinct operations to MTypes. In case of a customized
callable client implementation that inherits from the
:class:`~astropy.samp.SAMPClient` class this method should be
overwritten.
.. note:: When overwritten, this method must always return
a string result (even empty).
Parameters
----------
private_key : str
Client private key.
responder_id : str
Responder public ID.
msg_tag : str
Response message tag.
response : dict
Received response.
Returns
-------
confirmation : str
Any confirmation string.
"""
return self._handle_response(private_key, responder_id, msg_tag,
response)
def bind_receive_message(self, mtype, function, declare=True,
metadata=None):
"""
Bind a specific MType to a function or class method, being intended for
a call or a notification.
The function must be of the form::
def my_function_or_method(<self,> private_key, sender_id, msg_id,
mtype, params, extra)
where ``private_key`` is the client private-key, ``sender_id`` is the
notification sender ID, ``msg_id`` is the Hub message-id (calls only,
otherwise is `None`), ``mtype`` is the message MType, ``params`` is the
message parameter set (content of ``"samp.params"``) and ``extra`` is a
dictionary containing any extra message map entry. The client is
automatically declared subscribed to the MType by default.
Parameters
----------
mtype : str
MType to be caught.
function : callable
Application function to be used when ``mtype`` is received.
declare : bool, optional
Specify whether the client must be automatically declared as
subscribed to the MType (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
metadata : dict, optional
Dictionary containing additional metadata to declare associated
with the MType subscribed to (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
"""
self.bind_receive_call(mtype, function, declare=declare,
metadata=metadata)
self.bind_receive_notification(mtype, function, declare=declare,
metadata=metadata)
def bind_receive_notification(self, mtype, function, declare=True, metadata=None):
"""
Bind a specific MType notification to a function or class method.
The function must be of the form::
def my_function_or_method(<self,> private_key, sender_id, mtype,
params, extra)
where ``private_key`` is the client private-key, ``sender_id`` is the
notification sender ID, ``mtype`` is the message MType, ``params`` is
the notified message parameter set (content of ``"samp.params"``) and
``extra`` is a dictionary containing any extra message map entry. The
client is automatically declared subscribed to the MType by default.
Parameters
----------
mtype : str
MType to be caught.
function : callable
Application function to be used when ``mtype`` is received.
declare : bool, optional
Specify whether the client must be automatically declared as
subscribed to the MType (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
metadata : dict, optional
Dictionary containing additional metadata to declare associated
with the MType subscribed to (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
"""
if self._callable:
if not metadata:
metadata = {}
self._notification_bindings[mtype] = [function, metadata]
if declare:
self._declare_subscriptions()
else:
raise SAMPClientError("Client not callable.")
def bind_receive_call(self, mtype, function, declare=True, metadata=None):
"""
Bind a specific MType call to a function or class method.
The function must be of the form::
def my_function_or_method(<self,> private_key, sender_id, msg_id,
mtype, params, extra)
where ``private_key`` is the client private-key, ``sender_id`` is the
notification sender ID, ``msg_id`` is the Hub message-id, ``mtype`` is
the message MType, ``params`` is the message parameter set (content of
``"samp.params"``) and ``extra`` is a dictionary containing any extra
message map entry. The client is automatically declared subscribed to
the MType by default.
Parameters
----------
mtype : str
MType to be caught.
function : callable
Application function to be used when ``mtype`` is received.
declare : bool, optional
Specify whether the client must be automatically declared as
subscribed to the MType (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
metadata : dict, optional
Dictionary containing additional metadata to declare associated
with the MType subscribed to (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
"""
if self._callable:
if not metadata:
metadata = {}
self._call_bindings[mtype] = [function, metadata]
if declare:
self._declare_subscriptions()
else:
raise SAMPClientError("Client not callable.")
def bind_receive_response(self, msg_tag, function):
"""
Bind a specific msg-tag response to a function or class method.
The function must be of the form::
def my_function_or_method(<self,> private_key, responder_id,
msg_tag, response)
where ``private_key`` is the client private-key, ``responder_id`` is
the message responder ID, ``msg_tag`` is the message-tag provided at
call time and ``response`` is the response received.
Parameters
----------
msg_tag : str
Message-tag to be caught.
function : callable
Application function to be used when ``msg_tag`` is received.
"""
if self._callable:
self._response_bindings[msg_tag] = function
else:
raise SAMPClientError("Client not callable.")
def unbind_receive_notification(self, mtype, declare=True):
"""
Remove from the notifications binding table the specified MType and
unsubscribe the client from it (if required).
Parameters
----------
mtype : str
MType to be removed.
declare : bool
Specify whether the client must be automatically declared as
unsubscribed from the MType (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
"""
if self._callable:
del self._notification_bindings[mtype]
if declare:
self._declare_subscriptions()
else:
raise SAMPClientError("Client not callable.")
def unbind_receive_call(self, mtype, declare=True):
"""
Remove from the calls binding table the specified MType and unsubscribe
the client from it (if required).
Parameters
----------
mtype : str
MType to be removed.
declare : bool
Specify whether the client must be automatically declared as
unsubscribed from the MType (see also
:meth:`~astropy.samp.client.SAMPClient.declare_subscriptions`).
"""
if self._callable:
del self._call_bindings[mtype]
if declare:
self._declare_subscriptions()
else:
raise SAMPClientError("Client not callable.")
def unbind_receive_response(self, msg_tag):
"""
Remove from the responses binding table the specified message-tag.
Parameters
----------
msg_tag : str
Message-tag to be removed.
"""
if self._callable:
del self._response_bindings[msg_tag]
else:
raise SAMPClientError("Client not callable.")
def declare_subscriptions(self, subscriptions=None):
"""
Declares the MTypes the client wishes to subscribe to, implicitly
defined with the MType binding methods
:meth:`~astropy.samp.client.SAMPClient.bind_receive_notification`
and :meth:`~astropy.samp.client.SAMPClient.bind_receive_call`.
An optional ``subscriptions`` map can be added to the final map passed
to the :meth:`~astropy.samp.hub_proxy.SAMPHubProxy.declare_subscriptions`
method.
Parameters
----------
subscriptions : dict, optional
Dictionary containing the list of MTypes to subscribe to, with the
same format of the ``subscriptions`` map passed to the
:meth:`~astropy.samp.hub_proxy.SAMPHubProxy.declare_subscriptions`
method.
"""
if self._callable:
self._declare_subscriptions(subscriptions)
else:
raise SAMPClientError("Client not callable.")
def register(self):
"""
Register the client to the SAMP Hub.
"""
if self.hub.is_connected:
if self._private_key is not None:
raise SAMPClientError("Client already registered")
result = self.hub.register(self.hub.lockfile["samp.secret"])
if result["samp.self-id"] == "":
raise SAMPClientError("Registration failed - "
"samp.self-id was not set by the hub.")
if result["samp.private-key"] == "":
raise SAMPClientError("Registration failed - "
"samp.private-key was not set by the hub.")
self._public_id = result["samp.self-id"]
self._private_key = result["samp.private-key"]
self._hub_id = result["samp.hub-id"]
if self._callable:
self._set_xmlrpc_callback()
self._declare_subscriptions()
if self._metadata != {}:
self.declare_metadata()
self._is_registered = True
else:
raise SAMPClientError("Unable to register to the SAMP Hub. "
"Hub proxy not connected.")
def unregister(self):
"""
Unregister the client from the SAMP Hub.
"""
if self.hub.is_connected:
self._is_registered = False
self.hub.unregister(self._private_key)
self._hub_id = None
self._public_id = None
self._private_key = None
else:
raise SAMPClientError("Unable to unregister from the SAMP Hub. "
"Hub proxy not connected.")
def _set_xmlrpc_callback(self):
if self.hub.is_connected and self._private_key is not None:
self.hub.set_xmlrpc_callback(self._private_key,
self._xmlrpcAddr)
def _declare_subscriptions(self, subscriptions=None):
if self.hub.is_connected and self._private_key is not None:
mtypes_dict = {}
# Collect notification mtypes and metadata
for mtype in self._notification_bindings.keys():
mtypes_dict[mtype] = copy.deepcopy(self._notification_bindings[mtype][1])
# Collect notification mtypes and metadata
for mtype in self._call_bindings.keys():
mtypes_dict[mtype] = copy.deepcopy(self._call_bindings[mtype][1])
# Add optional subscription map
if subscriptions:
mtypes_dict.update(copy.deepcopy(subscriptions))
self.hub.declare_subscriptions(self._private_key, mtypes_dict)
else:
raise SAMPClientError("Unable to declare subscriptions. Hub "
"unreachable or not connected or client "
"not registered.")
def declare_metadata(self, metadata=None):
"""
Declare the client application metadata supported.
Parameters
----------
metadata : dict, optional
Dictionary containing the client application metadata as defined in
the SAMP definition document. If omitted, then no metadata are
declared.
"""
if self.hub.is_connected and self._private_key is not None:
if metadata is not None:
self._metadata.update(metadata)
self.hub.declare_metadata(self._private_key, self._metadata)
else:
raise SAMPClientError("Unable to declare metadata. Hub "
"unreachable or not connected or client "
"not registered.")
def get_private_key(self):
"""
Return the client private key used for the Standard Profile
communications obtained at registration time (``samp.private-key``).
Returns
-------
key : str
Client private key.
"""
return self._private_key
def get_public_id(self):
"""
Return public client ID obtained at registration time
(``samp.self-id``).
Returns
-------
id : str
Client public ID.
"""
return self._public_id
|
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 | |
d4b2d908564f4283553e0acfe0aee4cd353942913916962cd668019019533108 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Utility functions and classes
"""
import queue
import inspect
import traceback
from io import StringIO
import xmlrpc.client as xmlrpc
from urllib.request import urlopen
from .constants import SAMP_STATUS_ERROR
from .errors import SAMPProxyError
def internet_on():
from . import conf
if not conf.use_internet:
return False
else:
try:
urlopen('http://google.com', timeout=1.)
return True
except Exception:
return False
__all__ = ["SAMPMsgReplierWrapper"]
__doctest_skip__ = ['.']
def getattr_recursive(variable, attribute):
"""
Get attributes recursively.
"""
if '.' in attribute:
top, remaining = attribute.split('.', 1)
return getattr_recursive(getattr(variable, top), remaining)
else:
return getattr(variable, attribute)
class _ServerProxyPoolMethod:
# some magic to bind an XML-RPC method to an RPC server.
# supports "nested" methods (e.g. examples.getStateName)
def __init__(self, proxies, name):
self.__proxies = proxies
self.__name = name
def __getattr__(self, name):
return _ServerProxyPoolMethod(self.__proxies, f"{self.__name}.{name}")
def __call__(self, *args, **kwrds):
proxy = self.__proxies.get()
function = getattr_recursive(proxy, self.__name)
try:
response = function(*args, **kwrds)
except xmlrpc.Fault as exc:
raise SAMPProxyError(exc.faultCode, exc.faultString)
finally:
self.__proxies.put(proxy)
return response
class ServerProxyPool:
"""
A thread-safe pool of `xmlrpc.ServerProxy` objects.
"""
def __init__(self, size, proxy_class, *args, **keywords):
self._proxies = queue.Queue(size)
for i in range(size):
self._proxies.put(proxy_class(*args, **keywords))
def __getattr__(self, name):
# magic method dispatcher
return _ServerProxyPoolMethod(self._proxies, name)
def shutdown(self):
"""Shut down the proxy pool by closing all active connections."""
while True:
try:
proxy = self._proxies.get_nowait()
except queue.Empty:
break
# An undocumented but apparently supported way to call methods on
# an ServerProxy that are not dispatched to the remote server
proxy('close')
class SAMPMsgReplierWrapper:
"""
Function decorator that allows to automatically grab errors and returned
maps (if any) from a function bound to a SAMP call (or notify).
Parameters
----------
cli : :class:`~astropy.samp.SAMPIntegratedClient` or :class:`~astropy.samp.SAMPClient`
SAMP client instance. Decorator initialization, accepting the instance
of the client that receives the call or notification.
"""
def __init__(self, cli):
self.cli = cli
def __call__(self, f):
def wrapped_f(*args):
if get_num_args(f) == 5 or args[2] is None: # notification
f(*args)
else: # call
try:
result = f(*args)
if result:
self.cli.hub.reply(self.cli.get_private_key(), args[2],
{"samp.status": SAMP_STATUS_ERROR,
"samp.result": result})
except Exception:
err = StringIO()
traceback.print_exc(file=err)
txt = err.getvalue()
self.cli.hub.reply(self.cli.get_private_key(), args[2],
{"samp.status": SAMP_STATUS_ERROR,
"samp.result": {"txt": txt}})
return wrapped_f
class _HubAsClient:
def __init__(self, handler):
self._handler = handler
def __getattr__(self, name):
# magic method dispatcher
return _HubAsClientMethod(self._handler, name)
class _HubAsClientMethod:
def __init__(self, send, name):
self.__send = send
self.__name = name
def __getattr__(self, name):
return _HubAsClientMethod(self.__send, f"{self.__name}.{name}")
def __call__(self, *args):
return self.__send(self.__name, args)
def get_num_args(f):
"""
Find the number of arguments a function or method takes (excluding ``self``).
"""
if inspect.ismethod(f):
return f.__func__.__code__.co_argcount - 1
elif inspect.isfunction(f):
return f.__code__.co_argcount
else:
raise TypeError("f should be a function or a method")
|
89a53222e00e9d3347d99c8ee5cbc65e130c496efc3fd77c78a488510f7e21a0 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Defines constants used in `astropy.samp`.
"""
from astropy.utils.data import get_pkg_data_filename
__all__ = ['SAMP_STATUS_OK', 'SAMP_STATUS_WARNING', 'SAMP_STATUS_ERROR',
'SAFE_MTYPES', 'SAMP_ICON']
__profile_version__ = "1.3"
#: General constant for samp.ok status string
SAMP_STATUS_OK = "samp.ok"
#: General constant for samp.warning status string
SAMP_STATUS_WARNING = "samp.warning"
#: General constant for samp.error status string
SAMP_STATUS_ERROR = "samp.error"
SAFE_MTYPES = ["samp.app.*", "samp.msg.progress", "table.*", "image.*",
"coord.*", "spectrum.*", "bibcode.*", "voresource.*"]
with open(get_pkg_data_filename('data/astropy_icon.png'), 'rb') as f:
SAMP_ICON = f.read()
|
708d2d2c8983f1cecde9057ba246bf7f7ce13f5277063e72370f15ed2c0907f8 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import xmlrpc.client as xmlrpc
from .errors import SAMPHubError
from .utils import ServerProxyPool
from .lockfile_helpers import get_main_running_hub
__all__ = ['SAMPHubProxy']
class SAMPHubProxy:
"""
Proxy class to simplify the client interaction with a SAMP hub (via the
standard profile).
"""
def __init__(self):
self.proxy = None
self._connected = False
@property
def is_connected(self):
"""
Whether the hub proxy is currently connected to a hub.
"""
return self._connected
def connect(self, hub=None, hub_params=None, pool_size=20):
"""
Connect to the current SAMP Hub.
Parameters
----------
hub : `~astropy.samp.SAMPHubServer`, optional
The hub to connect to.
hub_params : dict, optional
Optional dictionary containing the lock-file content of the Hub
with which to connect. This dictionary has the form
``{<token-name>: <token-string>, ...}``.
pool_size : int, optional
The number of socket connections opened to communicate with the
Hub.
"""
self._connected = False
self.lockfile = {}
if hub is not None and hub_params is not None:
raise ValueError("Cannot specify both hub and hub_params")
if hub_params is None:
if hub is not None:
if not hub.is_running:
raise SAMPHubError("Hub is not running")
else:
hub_params = hub.params
else:
hub_params = get_main_running_hub()
try:
url = hub_params["samp.hub.xmlrpc.url"].replace("\\", "")
self.proxy = ServerProxyPool(pool_size, xmlrpc.ServerProxy,
url, allow_none=1)
self.ping()
self.lockfile = copy.deepcopy(hub_params)
self._connected = True
except xmlrpc.ProtocolError as p:
# 401 Unauthorized
if p.errcode == 401:
raise SAMPHubError("Unauthorized access. Basic Authentication "
"required or failed.")
else:
raise SAMPHubError(f"Protocol Error {p.errcode}: {p.errmsg}")
def disconnect(self):
"""
Disconnect from the current SAMP Hub.
"""
if self.proxy is not None:
self.proxy.shutdown()
self.proxy = None
self._connected = False
self.lockfile = {}
@property
def _samp_hub(self):
"""
Property to abstract away the path to the hub, which allows this class
to be used for other profiles.
"""
return self.proxy.samp.hub
def ping(self):
"""
Proxy to ``ping`` SAMP Hub method (Standard Profile only).
"""
return self._samp_hub.ping()
def set_xmlrpc_callback(self, private_key, xmlrpc_addr):
"""
Proxy to ``setXmlrpcCallback`` SAMP Hub method (Standard Profile only).
"""
return self._samp_hub.setXmlrpcCallback(private_key, xmlrpc_addr)
def register(self, secret):
"""
Proxy to ``register`` SAMP Hub method.
"""
return self._samp_hub.register(secret)
def unregister(self, private_key):
"""
Proxy to ``unregister`` SAMP Hub method.
"""
return self._samp_hub.unregister(private_key)
def declare_metadata(self, private_key, metadata):
"""
Proxy to ``declareMetadata`` SAMP Hub method.
"""
return self._samp_hub.declareMetadata(private_key, metadata)
def get_metadata(self, private_key, client_id):
"""
Proxy to ``getMetadata`` SAMP Hub method.
"""
return self._samp_hub.getMetadata(private_key, client_id)
def declare_subscriptions(self, private_key, subscriptions):
"""
Proxy to ``declareSubscriptions`` SAMP Hub method.
"""
return self._samp_hub.declareSubscriptions(private_key, subscriptions)
def get_subscriptions(self, private_key, client_id):
"""
Proxy to ``getSubscriptions`` SAMP Hub method.
"""
return self._samp_hub.getSubscriptions(private_key, client_id)
def get_registered_clients(self, private_key):
"""
Proxy to ``getRegisteredClients`` SAMP Hub method.
"""
return self._samp_hub.getRegisteredClients(private_key)
def get_subscribed_clients(self, private_key, mtype):
"""
Proxy to ``getSubscribedClients`` SAMP Hub method.
"""
return self._samp_hub.getSubscribedClients(private_key, mtype)
def notify(self, private_key, recipient_id, message):
"""
Proxy to ``notify`` SAMP Hub method.
"""
return self._samp_hub.notify(private_key, recipient_id, message)
def notify_all(self, private_key, message):
"""
Proxy to ``notifyAll`` SAMP Hub method.
"""
return self._samp_hub.notifyAll(private_key, message)
def call(self, private_key, recipient_id, msg_tag, message):
"""
Proxy to ``call`` SAMP Hub method.
"""
return self._samp_hub.call(private_key, recipient_id, msg_tag, message)
def call_all(self, private_key, msg_tag, message):
"""
Proxy to ``callAll`` SAMP Hub method.
"""
return self._samp_hub.callAll(private_key, msg_tag, message)
def call_and_wait(self, private_key, recipient_id, message, timeout):
"""
Proxy to ``callAndWait`` SAMP Hub method.
"""
return self._samp_hub.callAndWait(private_key, recipient_id, message,
timeout)
def reply(self, private_key, msg_id, response):
"""
Proxy to ``reply`` SAMP Hub method.
"""
return self._samp_hub.reply(private_key, msg_id, response)
|