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+
+
+ + +
+ +

All modules for which code is available

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+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/basel.html b/_modules/pystellibs/basel.html new file mode 100644 index 0000000..e1e32e4 --- /dev/null +++ b/_modules/pystellibs/basel.html @@ -0,0 +1,248 @@ + + + + + + + + pystellibs.basel — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.basel

+""" BaSeL 2.2 library """
+import numpy as np
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+from .stellib import Stellib
+from .config import libsdir
+
+
+
[docs]class BaSeL(Stellib):
+    """ BaSeL 2.2 library derived class
+        This library + Rauch is used in Pegase.2
+
+    The BaSeL stellar spectral energy distribution (SED) libraries are libraries
+    of theoretical stellar SEDs recalibrated using empirical photometric data.
+    Therefore, we call them semi-empirical libraries.
+
+    The BaSeL 2.2 library was calibrated using photometric data from solar
+    metallicity stars.
+
+    References
+    ----------
+    * Lejeune, Cuisiner, and Buser, 1998 A&AS, 130, 65
+    * can be downloaded http://www.astro.unibas.ch/BaSeL_files/BaSeL2_2.tar.gz
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'BaSeL 2.2'
+        self.source = libsdir + '/stellib_BaSeL_v2.2.grid.fits'
+        self._load_()
+        Stellib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Basel 2.2 library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.301 - dlogT, 5.500 + dlogg),
+                (3.301 - dlogT, 3.500 - dlogg),
+                (3.544 - dlogT, 3.500 - dlogg),
+                (3.544 - dlogT, 1.000),
+                (3.477, 0.600 + dlogg),
+                (3.447 - dlogT, 0.600 + dlogg),
+                (3.398 - dlogT, 0.280 + dlogg),
+                (3.398 - dlogT, -1.020 - dlogg),
+                (3.398, -1.020 - dlogg),
+                (3.447, -1.020 - dlogg),
+                (3.505 + dlogT, -0.700 - dlogg),
+                (3.544 + dlogT, -0.510 - dlogg),
+                (3.574 + dlogT, -0.290 - dlogg),
+                (3.602 + dlogT, 0.000 - dlogg),
+                (3.778, 0.000 - dlogg),
+                (3.778 + dlogT, 0.000),
+                (3.875 + dlogT, 0.500),
+                (3.929 + dlogT, 1.000),
+                (3.954 + dlogT, 1.500),
+                (4.021 + dlogT, 2.000 - dlogg),
+                (4.146, 2.000 - dlogg),
+                (4.146 + dlogT, 2.000),
+                (4.279 + dlogT, 2.500),
+                (4.415 + dlogT, 3.000),
+                (4.491 + dlogT, 3.500),
+                (4.544 + dlogT, 4.000),
+                (4.602 + dlogT, 4.500),
+                (4.699 + dlogT, 5.000 - dlogg),
+                (4.699 + dlogT, 5.000 + dlogg),
+                (3.525 + dlogT, 5.000 + dlogg),
+                (3.525 + dlogT, 5.500 + dlogg),
+                (3.301 - dlogT, 5.500 + dlogg) ]
+
+        return np.array(bbox)

+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def Teff(self):
+        return self.grid['Teff']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return self.grid['logZ']
+
+    @property
+    def NHI(self):
+        return self.grid['NHI']
+
+    @property
+    def NHeI(self):
+        return self.grid['NHeI']
+
+    @property
+    def NHeII(self):
+        return self.grid['NHeII']

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/btsettl.html b/_modules/pystellibs/btsettl.html new file mode 100644 index 0000000..780ef25 --- /dev/null +++ b/_modules/pystellibs/btsettl.html @@ -0,0 +1,232 @@ + + + + + + + + pystellibs.btsettl — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.btsettl

+import numpy as np
+from .stellib import AtmosphereLib
+from .config import libsdir
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+
+
[docs]class BTSettl(AtmosphereLib):
+    """
+    BT-Settl Library
+
+    References
+    ----------
+
+    Paper: Few refereed publications
+      Older Ref = http://adsabs.harvard.edu/abs/2000ApJ...539..366A
+
+    Conference Proceedings:
+      http://adsabs.harvard.edu/abs/2016sf2a.conf..223A
+      http://adsabs.harvard.edu/abs/2012RSPTA.370.2765A
+
+    Files available at: https://phoenix.ens-lyon.fr/Grids/BT-Settl/
+
+    Current Library: AGSS2009 Abundances (due to grid availability)
+    Spectra rebinned to match Kurucz, and custom 2 Ang medium resolution
+    """
+    def __init__(self, medres=True, *args, **kwargs):
+        self.name = 'BTSettl'
+        if medres:
+            self.source = libsdir + '/bt-settl.medres.grid.fits'
+        else:
+            self.source = libsdir + '/bt-settl.lowres.grid.fits'
+        self._load_()
+        AtmosphereLib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of BT-Settl library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.41497 - dlogT, 6.0 + dlogg),
+                (3.41497 - dlogT, -0.5 - dlogg),
+                (3.84510 + dlogT, -0.5 - dlogg),
+                (4.07918 + dlogT, 0.0 - dlogg),
+                (4.17609 + dlogT, 0.5 - dlogg),
+                (4.30103 + dlogT, 1.0 - dlogg),
+                (4.39794 + dlogT, 1.5 - dlogg),
+                (4.47712 + dlogT, 2.0 - dlogg),
+                (4.60206 + dlogT, 2.5 - dlogg),
+                (4.60206 + dlogT, 3.0 - dlogg),
+                (4.69897 + dlogT, 3.5 - dlogg),
+                (4.84510 + dlogT, 4.0 - dlogg),
+                (4.84510 + dlogT, 4.5 + dlogg),
+                (4.00000 + dlogT, 4.5 + dlogg),
+                (4.00000 + dlogT, 5.0 + dlogg),
+                (3.69897 + dlogT, 5.0 + dlogg),
+                (3.69897 + dlogT, 5.5 + dlogg),
+                (3.60206 + dlogT, 5.5 + dlogg),
+                (3.60206 + dlogT, 6.0 + dlogg),
+                (3.41497 - dlogT, 6.0 + dlogg)]
+            
+        return np.array(bbox)

+
+
[docs]    def get_interpolation_data(self):
+        """ interpolation needs alpha """
+        return np.array([self.logT, self.logg, self.logZ]).T

+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return self.grid['Teff']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return self.grid['logZ']

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/elodie.html b/_modules/pystellibs/elodie.html new file mode 100644 index 0000000..75cfa7e --- /dev/null +++ b/_modules/pystellibs/elodie.html @@ -0,0 +1,236 @@ + + + + + + + + pystellibs.elodie — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.elodie

+""" Elodie 3.1
+"""
+
+import numpy as np
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+from .stellib import Stellib
+from .config import libsdir
+
+
+
[docs]class Elodie(Stellib):
+    """ Elodie 3.1 stellar library derived class """
+    def __init__(self, *args, **kwargs):
+        self.name = 'ELODIE v3.1 (Prugniel et al 2007, astro-ph/703658)'
+        self.source = libsdir + '/stellib_ELODIE_3.1.fits'
+        self._load_()
+        Stellib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = np.asarray(f[0].data[-1])
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Elodie library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.301 - dlogT, 5.500 + dlogg),
+                (3.301 - dlogT, 3.500 - dlogg),
+                (3.544 - dlogT, 3.500 - dlogg),
+                (3.544 - dlogT, 1.000),
+                (3.477, 0.600 + dlogg),
+                (3.447 - dlogT, 0.600 + dlogg),
+                (3.398 - dlogT, 0.280 + dlogg),
+                (3.398 - dlogT, -1.020 - dlogg),
+                (3.398, -1.020 - dlogg),
+                (3.447, -1.020 - dlogg),
+                (3.505 + dlogT, -0.700 - dlogg),
+                (3.544 + dlogT, -0.510 - dlogg),
+                (3.574 + dlogT, -0.290 - dlogg),
+                (3.602 + dlogT, 0.000 - dlogg),
+                (3.778, 0.000 - dlogg),
+                (3.778 + dlogT, 0.000),
+                (3.875 + dlogT, 0.500),
+                (3.929 + dlogT, 1.000),
+                (3.954 + dlogT, 1.500),
+                (4.021 + dlogT, 2.000 - dlogg),
+                (4.146, 2.000 - dlogg),
+                (4.146 + dlogT, 2.000),
+                (4.279 + dlogT, 2.500),
+                (4.415 + dlogT, 3.000),
+                (4.491 + dlogT, 3.500),
+                (4.544 + dlogT, 4.000),
+                (4.602 + dlogT, 4.500),
+                (4.699 + dlogT, 5.000 - dlogg),
+                (4.699 + dlogT, 5.000 + dlogg),
+                (3.525 + dlogT, 5.000 + dlogg),
+                (3.525 + dlogT, 5.500 + dlogg),
+                (3.301 - dlogT, 5.500 + dlogg) ]
+
+        return np.array(bbox)

+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def Teff(self):
+        return 10 ** self.logT
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def NHI(self):
+        return self.grid['NHI']
+
+    @property
+    def NHeI(self):
+        return self.grid['NHeI']
+
+    @property
+    def NHeII(self):
+        return self.grid['NHeII']
+
+    @property
+    def logZ(self):
+        return np.log10(self.Z)

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/ezunits.html b/_modules/pystellibs/ezunits.html new file mode 100644 index 0000000..853fdf9 --- /dev/null +++ b/_modules/pystellibs/ezunits.html @@ -0,0 +1,133 @@ + + + + + + + + pystellibs.ezunits — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.ezunits

+# -*- coding: utf-8 -*-
+"""
+    pint
+    ~~~~
+
+    Pint is Python module/package to define, operate and manipulate
+    **physical quantities**: the product of a numerical value and a
+    unit of measurement. It allows arithmetic operations between them
+    and conversions from and to different units.
+
+    :copyright: (c) 2012 by Hernan E. Grecco.
+    :license: BSD, see LICENSE for more details.
+"""
+
+from .pint import UnitRegistry, DimensionalityError, UnitsContainer, UndefinedUnitError, logger, __version__
+
+# load a default registery.
+## Example sage unit['m * s **-1']
+unit = UnitRegistry()
+
+
+
[docs]def hasUnit(val):
+    return hasattr(val, 'units')

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/ezunits/pint.html b/_modules/pystellibs/ezunits/pint.html new file mode 100644 index 0000000..7fe790c --- /dev/null +++ b/_modules/pystellibs/ezunits/pint.html @@ -0,0 +1,1321 @@ + + + + + + + + pystellibs.ezunits.pint — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.ezunits.pint

+# -*- coding: utf-8 -*-
+"""
+    pint
+    ~~~~
+
+    Pint is Python module/package to define, operate and manipulate
+    **physical quantities**: the product of a numerical value and a
+    unit of measurement. It allows arithmetic operations between them
+    and conversions from and to different units.
+
+    :copyright: 2012 by Hernan E. Grecco.
+    :license: BSD, see LICENSE for more details.
+"""
+
+from __future__ import division, print_function, absolute_import
+
+__version__ = '0.1'
+
+import os
+import sys
+import copy
+import math
+import logging
+import operator
+import functools
+import itertools
+
+try:
+    from collections.abc import Iterable
+except ImportError:
+    from collections import Iterable
+
+from pkg_resources import resource_filename
+
+from io import BytesIO
+from numbers import Number
+import tokenize
+from tokenize import untokenize, NUMBER, STRING, NAME, OP
+
+logger = logging.getLogger(__name__)
+
+if hasattr(logging, 'NullHandler'):
+    logger.addHandler(logging.NullHandler())
+else:
+    class NullHandler(logging.Handler):
+        """
+        This handler does nothing. It's intended to be used to avoid the
+        "No handlers could be found for logger XXX" one-off warning. This is
+        important for library code, which may contain code to log events. If a user
+        of the library does not configure logging, the one-off warning might be
+        produced; to avoid this, the library developer simply needs to instantiate
+        a NullHandler and add it to the top-level logger of the library module or
+        package.
+        """
+        def handle(self, record):
+            pass
+
+        def emit(self, record):
+            pass
+
+        def createLock(self):
+            self.lock = None
+
+    logger.addHandler(NullHandler())
+
+if hasattr(functools, 'total_ordering'):
+    total_ordering = functools.total_ordering
+else:
+    def total_ordering(cls):
+        """Class decorator that fills in missing ordering methods"""
+        convert = {
+            '__lt__': [('__gt__', lambda self, other: not (self < other or self == other)),
+                       ('__le__', lambda self, other: self < other or self == other),
+                       ('__ge__', lambda self, other: not self < other)],
+            '__le__': [('__ge__', lambda self, other: not self <= other or self == other),
+                       ('__lt__', lambda self, other: self <= other and not self == other),
+                       ('__gt__', lambda self, other: not self <= other)],
+            '__gt__': [('__lt__', lambda self, other: not (self > other or self == other)),
+                       ('__ge__', lambda self, other: self > other or self == other),
+                       ('__le__', lambda self, other: not self > other)],
+            '__ge__': [('__le__', lambda self, other: (not self >= other) or self == other),
+                       ('__gt__', lambda self, other: self >= other and not self == other),
+                       ('__lt__', lambda self, other: not self >= other)]
+        }
+        roots = set(dir(cls)) & set(convert)
+        if not roots:
+            raise ValueError('must define at least one ordering operation: < > <= >=')
+        root = max(roots)       # prefer __lt__ to __le__ to __gt__ to __ge__
+        for opname, opfunc in convert[root]:
+            if opname not in roots:
+                #print (opname, opfunc)
+                opfunc.__name__ = opname
+                opfunc.__doc__ = getattr(int, opname).__doc__
+                setattr(cls, opname, opfunc)
+        return cls
+
+
+if sys.version < '3':
+    from io import open
+    from StringIO import StringIO
+    string_types = basestring
+    _tokenize = lambda input: tokenize.generate_tokens(StringIO(input).readline)
+else:
+    string_types = str
+    _tokenize = lambda input: tokenize.tokenize(BytesIO(input.encode('utf-8')).readline)
+
+PRETTY = '⁰¹²³⁴⁵⁶⁷⁸⁹·⁻'
+
+try:
+    import numpy as np
+    from numpy import ndarray
+
+    HAS_NUMPY = True
+    NUMERIC_TYPES = (Number, ndarray)
+
+    def _to_magnitude(value, force_ndarray=False):
+        if value is None:
+            return value
+        elif isinstance(value, (list, tuple)):
+            return np.asarray(value)
+        if force_ndarray:
+            return np.asarray(value)
+        return value
+
+except ImportError:
+
+    class ndarray(object):
+        pass
+
+    HAS_NUMPY = False
+    NUMERIC_TYPES = (Number, )
+
+    def _to_magnitude(value, force_ndarray=False):
+        return value
+
+
+def _eq(first, second):
+    """Comparison of scalars and arrays
+    """
+    out = first == second
+    if isinstance(out, Iterable):
+        if isinstance(out, ndarray):
+            return np.all(out)
+        else:
+            return all(out)
+    return out
+
+
+def _definitions_from_file(filename):
+    """Load definition from file.
+    """
+    with open(filename, encoding='utf-8') as fp:
+        for line in fp:
+            line = line.strip()
+            if not line or line.startswith('#'):
+                continue
+            try:
+                if ';' in line:
+                    [definition, modifiers] = line.split(';', 2)
+                    modifiers = (modifier.split('=') for modifier in modifiers.split(';'))
+                    modifiers = dict((key.strip(), float(value.strip())) for key, value in modifiers)
+                else:
+                    definition = line
+                    modifiers = {}
+                result = [res.strip() for res in definition.split('=')]
+                name, value, aliases = result[0], result[1], result[2:]
+            except Exception as ex:
+                logger.error("Exception: Cannot parse '{}' {}".format(line, ex))
+                continue
+            yield name, value, aliases, modifiers
+
+
+def _solve_dependencies(dependencies):
+    """Solve a dependency graph.
+
+    :param dependencies: dependency dictionary. For each key, the value is
+                         an iterable indicating its dependencies.
+    :return: list of sets, each containing keys of independents tasks dependent
+
+    """
+    d = dict((key, set(dependencies[key])) for key in dependencies)
+    r = []
+    while d:
+        # values not in keys (items without dep)
+        t = set(i for v in d.values() for i in v) - set(d.keys())
+        # and keys without value (items without dep)
+        t.update(k for k, v in d.items() if not v)
+        # can be done right away
+        r.append(t)
+        # and cleaned up
+        d = dict(((k, v - t) for k, v in d.items() if v))
+    return r
+
+
+class _Exception(Exception):
+
+    def __init__(self, internal):
+        self.internal = internal
+
+
+
[docs]class UndefinedUnitError(ValueError):
+    """Raised when the units are not defined in the unit registry.
+    """
+
+    def __init__(self, unit_names):
+        super(ValueError, self).__init__()
+        self.unit_names = unit_names
+
+    def __str__(self):
+        if isinstance(self.unit_names, string_types):
+            return "'{}' is not defined in the unit registry".format(self.unit_names)
+        elif isinstance(self.unit_names, (list, tuple)) and len(self.unit_names) == 1:
+            return "'{}' is not defined in the unit registry".format(self.unit_names[0])
+        elif isinstance(self.unit_names, set) and len(self.unit_names) == 1:
+            uname = list(self.unit_names)[0]
+            return "'{}' is not defined in the unit registry".format(uname)
+        else:
+            return '{} are not defined in the unit registry'.format(self.unit_names)

+
+
+
[docs]class DimensionalityError(ValueError):
+    """Raised when trying to convert between incompatible units.
+    """
+
+    def __init__(self, units1, units2, dim1=None, dim2=None):
+        super(DimensionalityError, self).__init__()
+        self.units1 = units1
+        self.units2 = units2
+        self.dim1 = dim1
+        self.dim2 = dim2
+
+    def __str__(self):
+        if self.dim1 or self.dim2:
+            dim1 = ' ({})'.format(self.dim1)
+            dim2 = ' ({})'.format(self.dim2)
+        else:
+            dim1 = ''
+            dim2 = ''
+        return "Cannot convert from '{}'{} to '{}'{}".format(self.units1, dim1, self.units2, dim2)

+
+
+
[docs]class AliasDict(dict):
+
+    def __init__(self, *args, **kwargs):
+        dict.__init__(self, *args, **kwargs)
+        self.preferred_alias = {}
+
+
[docs]    def add_alias(self, key, value, preferred=False):
+        if value not in self:
+            raise IndexError("The aliased value '{}' is not present in the dictionary".format(value))
+        self[key] = value = self.get_aliased(value)
+        if preferred:
+            self.preferred_alias[value] = key

+
+
[docs]    def get_aliased(self, key):
+        value = self[key]
+        if isinstance(value, string_types):
+            return self.get_aliased(value)
+        return key

+
+
+
[docs]class UnitsContainer(dict):
+    """The UnitsContainer stores the product of units and their respective
+    exponent and implements the corresponding operations
+    """
+
+    def __init__(self, *args, **kwargs):
+        dict.__init__(self, *args, **kwargs)
+        for key, value in self.items():
+            if not isinstance(key, string_types):
+                raise TypeError('key must be a str, not {}'.format(type(key)))
+            if not isinstance(value, NUMERIC_TYPES):
+                raise TypeError('value must be a NUMERIC_TYPES, not {}'.format(type(value)))
+            if not isinstance(value, float):
+                self[key] = float(value)
+
+    def __missing__(self, key):
+        return 0.0
+
+    def __setitem__(self, key, value):
+        if not isinstance(key, string_types):
+            raise TypeError('key must be a str, not {}'.format(type(key)))
+        if not isinstance(value, NUMERIC_TYPES):
+            raise TypeError('value must be a NUMERIC_TYPES, not {}'.format(type(value)))
+        dict.__setitem__(self, key, float(value))
+
+
[docs]    def add(self, key, value):
+        newval = self.__getitem__(key) + value
+        if newval:
+            self.__setitem__(key, newval)
+        else:
+            del self[key]

+
+    def _formatter(self, product_sign=' * ', superscript_format=' ** {:n}',
+                   as_ratio=True, single_denominator=False, short_form=False):
+        if not self:
+            return 'dimensionless'
+
+        if as_ratio:
+            fun = abs
+        else:
+            fun = lambda x: x
+
+        tmp_plus = []
+        tmp_minus = []
+        for key, value in sorted(self.items()):
+            if value == 1:
+                tmp_plus.append(key)
+            elif value > 1:
+                tmp_plus.append(key + superscript_format.format(value))
+            elif value == -1:
+                tmp_minus.append(key)
+            else:
+                tmp_minus.append(key + superscript_format.format(fun(value)))
+
+        if tmp_plus:
+            ret = product_sign.join(tmp_plus)
+        elif as_ratio:
+            ret = '1'
+        else:
+            ret = ''
+
+        if tmp_minus:
+            if as_ratio:
+                ret += ' / '
+                if single_denominator:
+                    ret += ' / '.join(tmp_minus)
+                else:
+                    ret += product_sign.join(tmp_minus)
+            else:
+                ret += product_sign.join(tmp_minus)
+
+        return ret
+
+    def __str__(self):
+        return self._formatter()
+
+    def __repr__(self):
+        tmp = '{%s}' % ', '.join(["'{}': {}".format(key, value) for key, value in sorted(self.items())])
+        return '<UnitsContainer({})>'.format(tmp)
+
+    def __format__(self, spec):
+        if spec == '!s' or spec == '':
+            return str(self)
+        elif spec == '!r':
+            return repr(self)
+        elif spec == '!l':
+            tmp = self._formatter(r' \cdot ', '^[{:n}]', True, True).replace('[', '{').replace(']', '}')
+            if '/' in tmp:
+                return r'\frac{%s}' % tmp.replace(' / ', '}{')
+        elif spec == '!p':
+            pretty = '{}'.format(self).replace(' ** ', '').replace(' * ', PRETTY[10]).replace('-', PRETTY[11]).replace(' / ', '/')
+            for n in range(10):
+                pretty = pretty.replace(str(n), PRETTY[n])
+            return pretty
+        else:
+            raise ValueError('{} is not a valid format for UnitsContainer'.format(spec))
+
+    def __copy__(self):
+        ret = self.__class__()
+        ret.update(self)
+        return ret
+
+    def __imul__(self, other):
+        if not isinstance(other, self.__class__):
+            raise TypeError('Cannot multiply UnitsContainer by {}'.format(type(other)))
+        for key, value in other.items():
+            self[key] += value
+        keys = [key for key, value in self.items() if value == 0]
+        for key in keys:
+            del self[key]
+
+        return self
+
+    def __mul__(self, other):
+        if not isinstance(other, self.__class__):
+            raise TypeError('Cannot multiply UnitsContainer by {}'.format(type(other)))
+        ret = copy.copy(self)
+        ret *= other
+        return ret
+
+    __rmul__ = __mul__
+
+    def __ipow__(self, other):
+        if not isinstance(other, NUMERIC_TYPES):
+            raise TypeError('Cannot power UnitsContainer by {}'.format(type(other)))
+        for key, value in self.items():
+            self[key] *= other
+        return self
+
+    def __pow__(self, other):
+        if not isinstance(other, NUMERIC_TYPES):
+            raise TypeError('Cannot power UnitsContainer by {}'.format(type(other)))
+        ret = copy.copy(self)
+        ret **= other
+        return ret
+
+    def __itruediv__(self, other):
+        if not isinstance(other, self.__class__):
+            raise TypeError('Cannot divide UnitsContainer by {}'.format(type(other)))
+
+        for key, value in other.items():
+            self[key] -= value
+
+        keys = [key for key, value in self.items() if value == 0]
+        for key in keys:
+            del self[key]
+
+        return self
+
+    def __truediv__(self, other):
+        if not isinstance(other, self.__class__):
+            raise TypeError('Cannot divide UnitsContainer by {}'.format(type(other)))
+
+        ret = copy.copy(self)
+        ret /= other
+        return ret
+
+    def __rtruediv__(self, other):
+        if not isinstance(other, self.__class__) and other != 1:
+            raise TypeError('Cannot divide {} by UnitsContainer'.format(type(other)))
+
+        ret = copy.copy(self)
+        ret **= -1
+        return ret

+
+
+
[docs]def converter_to_reference(scale, offset, log_base):
+    def _inner(value):
+        if log_base:
+            return log_base ** (value / scale + offset)
+        else:
+            return value * scale + offset
+    return _inner

+
+
+
[docs]def converter_from_reference(scale, offset, log_base):
+    def _inner(value):
+        if log_base:
+            return (math.log10(value) / math.log10(log_base) - offset) / scale
+        else:
+            return (value - offset) / scale
+    return _inner

+
+
+
[docs]class UnitRegistry(object):
+    """The unit registry stores the definitions and relationships between
+    units.
+
+    :param filename: path of the units definition file to load.
+                     Empty to load the default definition file.
+                     None to leave the UnitRegistry empty.
+    :param force_ndarray: convert any input, scalar or not to a numpy.ndarray.
+    """
+
+    #: Map unit name (string) to unit value (Quantity), and unit alias to canonical unit name
+    _UNITS = AliasDict()
+
+    #: Map prefix name (string) to prefix value (float), and unit alias to canonical prefix name
+    _PREFIXES = AliasDict({'': 1})
+
+    #: Map suffix name (string) to canonical , and unit alias to canonical unit name
+    _SUFFIXES = AliasDict({'': None, 's': ''})
+
+    # Location of default file
+    # _DEFAULT_FILENAME = os.path.join(os.path.dirname(__file__), 'default_en.txt')
+    _DEFAULT_FILENAME = resource_filename('pystellibs', 
+                                os.path.join('ezunits','default_en.txt'))
+
+    def __init__(self, filename='', force_ndarray=False):
+        self.Quantity = _build_quantity_class(self, force_ndarray)
+        self._definition_files = []
+        if filename == '':
+            self.add_from_file(self._DEFAULT_FILENAME)
+        elif filename is not None:
+            self.add_from_file(filename)
+
+    def __getattr__(self, item):
+        return self.Quantity(1, item)
+
+    def __getitem__(self, item):
+        return self._parse_expression(item)
+
+
[docs]    def add_unit(self, name, value, aliases=tuple(), **modifiers):
+        """Add unit to the registry.
+        """
+        if not isinstance(value, self.Quantity):
+            value = self.Quantity(value, **modifiers)
+
+        self._UNITS[name] = value
+
+        for ndx, alias in enumerate(aliases):
+            if ' ' in alias:
+                logger.warn('Alias cannot contain a space ' + alias)
+            self._UNITS.add_alias(alias.strip(), name, not ndx)

+
+
[docs]    def add_prefix(self, name, value, aliases=tuple()):
+        """Add prefix to the registry.
+        """
+
+        if not isinstance(value, NUMERIC_TYPES):
+            value = eval(value, {'__builtins__': None}, {})
+        self._PREFIXES[name] = float(value)
+
+        for ndx, alias in enumerate(aliases):
+            self._PREFIXES.add_alias(alias.strip(), name, not ndx)

+
+
[docs]    def add_from_file(self, filename):
+        """Add units and prefixes defined in a definition text file.
+        """
+        self._definition_files.append(filename)
+        pending = dict()
+        dependencies = dict()
+        conv = dict()
+        for name, value, aliases, modifiers in _definitions_from_file(filename):
+            try:
+                if name.endswith('-'):
+                    # Prefix
+                    self.add_prefix(name[:-1], value, [alias[:-1] for alias in aliases])
+                    continue
+                if '[' in value:
+                    # Reference units, indicates dimensionality
+                    value = value.strip('[]')
+                    if value:
+                        value = self.Quantity(None, UnitsContainer({value: 1}))
+                    else:
+                        value = self.Quantity(None, None)
+
+                conv[name] = name
+                for alias in aliases:
+                    conv[alias] = name
+                self.add_unit(name, value, aliases, **modifiers)
+                if modifiers:
+                    self.add_unit('delta_' + name, value, tuple('delta_' + item for item in aliases))
+            except UndefinedUnitError as ex:
+                pending[name] = (value, aliases)
+                dependencies[name] = ex.unit_names
+            except Exception as ex:
+                logger.error("Exception: Cannot add '{}' {}".format(name, ex))
+
+        dep2 = {}
+        for unit_name, deps in dependencies.items():
+            dep2[unit_name] = set(conv[dep_name] for dep_name in deps)
+
+        for unit_names in _solve_dependencies(dep2):
+            for unit_name in unit_names:
+                if not unit_name in self._UNITS:
+                    self.add_unit(unit_name, *pending[unit_name])

+
+
[docs]    def get_alias(self, name):
+        """Return the preferred alias for a unit
+        """
+        candidates = list(self._parse_candidate(name))
+        if not candidates:
+            raise UndefinedUnitError(name)
+        elif len(candidates) == 1:
+            prefix, unit_name, _ = candidates[0]
+        else:
+            logger.warning('Parsing {} yield multiple results. '
+                           'Options are: {}'.format(name, candidates))
+            prefix, unit_name, _ = candidates[0]
+
+        return self._PREFIXES.preferred_alias.get(prefix, prefix) + self._UNITS.preferred_alias.get(unit_name, unit_name)

+
+    def _to_canonical(self, candidate):
+        """Return the canonical name of a unit.
+        """
+
+        if candidate == 'dimensionless':
+            return ''
+
+        try:
+            return self._UNITS.get_aliased(candidate)
+        except KeyError:
+            pass
+
+        candidates = tuple(self._parse_candidate(candidate))
+        if not candidates:
+            raise UndefinedUnitError(candidate)
+        elif len(candidates) == 1:
+            prefix, unit_name, _ = candidates[0]
+        else:
+            logger.warning('Parsing {} yield multiple results. '
+                           'Options are: {}'.format(candidate, candidates))
+            prefix, unit_name, _ = candidates[0]
+
+        if prefix:
+            alias = self.get_alias(prefix + unit_name)
+            if prefix + unit_name == 'kilogram':
+                pass
+            self.add_unit(prefix + unit_name, self.Quantity(self._PREFIXES[prefix], unit_name), (alias, ))
+            return prefix + unit_name
+
+        return unit_name
+
+    def _parse_candidate(self, candidate):
+        """Parse a unit to identify prefix, suffix and unit name
+        by walking the list of prefix and suffix.
+        """
+
+        for suffix, prefix in itertools.product(self._SUFFIXES.keys(), self._PREFIXES.keys()):
+            if candidate.startswith(prefix) and candidate.endswith(suffix):
+                unit_name = candidate[len(prefix):]
+                if suffix:
+                    unit_name = unit_name[:-len(suffix)]
+                    if len(unit_name) == 1:
+                        continue
+                if unit_name in self._UNITS:
+                    yield (self._PREFIXES.get_aliased(prefix),
+                           self._UNITS.get_aliased(unit_name),
+                           self._SUFFIXES.get_aliased(suffix))
+
+    def _parse_candidate2(self, candidate):
+        """Parse a unit to identify prefix, suffix and unit name
+        by walking the list of units.
+        """
+        for unit_name in self._UNITS:
+            if unit_name in candidate:
+                try:
+                    [prefix, suffix] = candidate.split(unit_name)
+                    if len(unit_name) == 1 and len(suffix) == 1:
+                        continue
+                except ValueError:  # too many values to unpack
+                    continue
+                if prefix in self._PREFIXES and suffix in self._SUFFIXES:
+                    yield (self._PREFIXES.get_aliased(prefix),
+                           self._UNITS.get_aliased(unit_name),
+                           self._SUFFIXES.get_aliased(suffix))
+
+    def _parse_expression(self, input):
+        """Parse expression mathematical units and return a quantity object.
+        """
+
+        if not input:
+            return self.Quantity(1)
+
+        gen = _tokenize(input)
+        result = []
+        unknown = set()
+        for toknum, tokval, _, _, _ in gen:
+            if toknum in (STRING, NAME):  # replace NUMBER tokens
+                # TODO: Integrate math better, Replace eval
+                if tokval == 'pi':
+                    result.append((toknum, str(math.pi)))
+                    continue
+                try:
+                    tokval = self._to_canonical(tokval)
+                except UndefinedUnitError as ex:
+                    unknown.add(ex.unit_names)
+                if tokval:
+                    result.extend([
+                        (NAME, 'Q_'),
+                        (OP, '('),
+                        (NUMBER, '1'),
+                        (OP, ','),
+                        (NAME, 'U_'),
+                        (OP, '('),
+                        (STRING, tokval),
+                        (OP, '='),
+                        (NUMBER, '1'),
+                        (OP, ')'),
+                        (OP, ')')
+                    ])
+                else:
+                    result.extend([
+                        (NAME, 'Q_'),
+                        (OP, '('),
+                        (NUMBER, '1'),
+                        (OP, ','),
+                        (NAME, 'U_'),
+                        (OP, '('),
+                        (OP, ')'),
+                        (OP, ')')
+                    ])
+            else:
+                result.append((toknum, tokval))
+
+        if unknown:
+            raise UndefinedUnitError(unknown)
+
+        return eval(untokenize(result), {'__builtins__': None},
+                                        {'REGISTRY': self._UNITS,
+                                         'Q_': self.Quantity,
+                                         'U_': UnitsContainer})

+
+
+def _build_quantity_class(registry, force_ndarray):
+    """Create a Quantity Class.
+    """
+
+    @total_ordering
+    class _Quantity(object):
+        """Quantity object constituted by magnitude and units.
+
+        :param value: value of the physical quantity to be created.
+        :type value: str, Quantity or any numeric type.
+        :param units: units of the physical quantity to be created.
+        :type units: UnitsContainer, str or Quantity.
+        """
+
+        _REGISTRY = registry
+
+        def __reduce__(self):
+            return _build_quantity, (self.magnitude, self.units)
+
+        def __new__(cls, value, units=None, offset=0, log_base=0):
+            if units is None:
+                if isinstance(value, string_types):
+                    inst = cls._REGISTRY._parse_expression(value)
+                elif isinstance(value, cls):
+                    inst = copy.copy(value)
+                else:
+                    inst = object.__new__(cls)
+                    inst._magnitude = _to_magnitude(value, force_ndarray)
+                    inst._units = UnitsContainer()
+            elif isinstance(units, UnitsContainer):
+                inst = object.__new__(cls)
+                inst._magnitude = _to_magnitude(value, force_ndarray)
+                inst._units = units
+            elif isinstance(units, string_types):
+                inst = cls._REGISTRY._parse_expression(units)
+                inst._magnitude = _to_magnitude(value, force_ndarray)
+            elif isinstance(units, cls):
+                inst = copy.copy(units)
+                inst._magnitude = _to_magnitude(value, force_ndarray)
+            else:
+                raise TypeError('units must be of type str, Quantity or UnitsContainer; not {}.'.format(type(units)))
+
+            return inst
+
+        def __copy__(self):
+            return self.__class__(copy.copy(self._magnitude), copy.copy(self._units))
+
+        def __str__(self):
+            return '{} {}'.format(self._magnitude, self._units)
+
+        def __repr__(self):
+            return "<Quantity({}, '{}')>".format(self._magnitude, self._units)
+
+        def __format__(self, spec):
+            if not spec:
+                return str(self)
+            if '!' in spec:
+                fmt, conv = spec.split('!')
+                conv = '!' + conv
+            else:
+                fmt, conv = spec, ''
+
+            if conv.endswith('~'):
+                _d = {}
+                for key, value in self.units.items():
+                    _d[self._REGISTRY.get_alias(key)] = value
+                units = UnitsContainer(_d)
+                conv = conv[:-1]
+            else:
+                units = self.units
+
+            return format(self.magnitude, fmt) + ' ' + format(units, conv)
+
+        @property
+        def magnitude(self):
+            """Quantity's magnitude.
+            """
+            return self._magnitude
+
+        @property
+        def units(self):
+            """Quantity's units.
+
+            :rtype: UnitContainer
+            """
+            return self._units
+
+        @property
+        def unitless(self):
+            """Return true if the quantity does not have units.
+            """
+            return not bool(self.convert_to_reference().units)
+
+        @property
+        def dimensionless(self):
+            """Return true if the quantity is dimensionless.
+            """
+            tmp = copy.copy(self).convert_to_reference()
+
+            return not bool(tmp.dimensionality)
+
+        @property
+        def dimensionality(self):
+            """Quantity's dimensionality (e.g. {length: 1, time: -1})
+            """
+            try:
+                return self._dimensionality
+            except AttributeError:
+                if self._magnitude is None:
+                    return UnitsContainer(self.units)
+
+                tmp = UnitsContainer()
+                for key, value in self.units.items():
+                    reg = self._REGISTRY._UNITS[key]
+                    tmp = tmp * reg.dimensionality ** value
+
+                self._dimensionality = tmp
+
+            return self._dimensionality
+
+        def ito(self, other=None):
+            """Inplace rescale to different units.
+
+            :param other: destination units.
+            :type other: Quantity or str.
+            """
+            if isinstance(other, string_types):
+                other = self._REGISTRY._parse_expression(other)
+
+            if self._units == other._units:
+                return self.__class__(self._magnitude, other)
+
+            factor = self.__class__(1, self.units / other.units)
+            factor = factor.convert_to_reference()
+
+            if not factor.unitless:
+                raise DimensionalityError(self.units, other.units,
+                                          self.dimensionality, other.dimensionality)
+
+            self._magnitude *= factor.magnitude
+            self._units = copy.copy(other._units)
+            return self
+
+        def to(self, other=None):
+            """Return Quantity rescaled to different units.
+
+            :param other: destination units.
+            :type other: Quantity or str.
+            """
+            ret = copy.copy(self)
+            ret.ito(other)
+            return ret
+
+        def _convert_to_reference(self, input_units):
+
+            factor = 1
+            units = UnitsContainer()
+            for key, value in input_units.items():
+                reg = self._REGISTRY._UNITS[key]
+                if reg._magnitude is None:
+                    units.add(key, value)
+                else:
+                    fac, uni = self._convert_to_reference(reg.units)
+                    factor *= (reg._magnitude * fac) ** value
+                    units *= uni ** value
+
+            return factor, units
+
+        def convert_to_reference(self):
+            """Return Quantity rescaled to reference units.
+            """
+
+            factor, units = self._convert_to_reference(self.units)
+
+            return self.__class__(self.magnitude * factor, units)
+
+        def convert_to_reference2(self):
+            """Return Quantity rescaled to reference units.
+            """
+
+            tmp = self.__class__(self.magnitude)
+
+            for key, value in self.units.items():
+                reg = self._REGISTRY._UNITS[key]
+                if reg._magnitude is None:
+                    factor = self.__class__(1, key) ** value
+                else:
+                    factor = reg.convert_to_reference() ** value
+
+                tmp = tmp * factor
+
+            return tmp
+
+        # Mathematical operations
+        def __float__(self):
+            if self.dimensionless:
+                return float(self._magnitude)
+            raise DimensionalityError(self.units, 'dimensionless')
+
+        def __complex__(self):
+            if self.dimensionless:
+                return complex(self._magnitude)
+            raise DimensionalityError(self.units, 'dimensionless')
+
+        def iadd_sub(self, other, fun):
+            if isinstance(other, self.__class__):
+                if not self.dimensionality == other.dimensionality:
+                    raise DimensionalityError(self.units, other.units,
+                                              self.dimensionality, other.dimensionality)
+                if self._units == other._units:
+                    self._magnitude = fun(self._magnitude, other._magnitude)
+                else:
+                    self._magnitude = fun(self._magnitude, other.to(self)._magnitude)
+            else:
+                if self.dimensionless:
+                    self._magnitude = fun(self._magnitude, _to_magnitude(other, force_ndarray))
+                else:
+                    raise DimensionalityError(self.units, 'dimensionless')
+
+            return self
+
+        def add_sub(self, other, fun):
+            ret = copy.copy(self)
+            fun(ret, other)
+            return ret
+
+        def __iadd__(self, other):
+            return self.iadd_sub(other, operator.iadd)
+
+        def __add__(self, other):
+            return self.add_sub(other, operator.iadd)
+
+        __radd__ = __add__
+
+        def __isub__(self, other):
+            return self.iadd_sub(other, operator.isub)
+
+        def __sub__(self, other):
+            return self.add_sub(other, operator.isub)
+
+        __rsub__ = __sub__
+
+        def __imul__(self, other):
+            if isinstance(other, self.__class__):
+                self._magnitude *= other._magnitude
+                self._units *= other._units
+            else:
+                self._magnitude *= _to_magnitude(other, force_ndarray)
+
+            return self
+
+        def __mul__(self, other):
+            if isinstance(other, self.__class__):
+                return self.__class__(self._magnitude * other._magnitude, self._units * other._units)
+            else:
+                return self.__class__(self._magnitude * other, self._units)
+
+        __rmul__ = __mul__
+
+        def __itruediv__(self, other):
+            if isinstance(other, self.__class__):
+                self._magnitude /= other._magnitude
+                self._units /= other._units
+            else:
+                self._magnitude /= _to_magnitude(other, force_ndarray)
+
+            return self
+
+        def __truediv__(self, other):
+            ret = copy.copy(self)
+            ret /= other
+            return ret
+
+        def __rtruediv__(self, other):
+            if isinstance(other, NUMERIC_TYPES):
+                return self.__class__(other / self._magnitude, 1 / self._units)
+            raise NotImplementedError
+
+        def __ifloordiv__(self, other):
+            if isinstance(other, self.__class__):
+                self._magnitude //= other._magnitude
+                self._units /= other._units
+            else:
+                self._magnitude //= _to_magnitude(other, force_ndarray)
+
+            return self
+
+        def __floordiv__(self, other):
+            ret = copy.copy(self)
+            ret //= other
+            return ret
+
+        def __div__(self, other):
+            ret = copy.copy(self)
+            ret /= other
+            return ret
+
+        #__div__ = __floordiv__
+        __idiv__ = __ifloordiv__
+
+        def __rfloordiv__(self, other):
+            if isinstance(other, self.__class__):
+                return self.__class__(other._magnitude // self._magnitude, other._units / self._units)
+            else:
+                return self.__class__(other // self._magnitude, 1.0 / self._units)
+
+        def __ipow__(self, other):
+            self._magnitude **= _to_magnitude(other, force_ndarray)
+            self._units **= other
+            return self
+
+        def __pow__(self, other):
+            ret = copy.copy(self)
+            ret **= other
+            return ret
+
+        def __abs__(self):
+            return self.__class__(abs(self._magnitude), self._units)
+
+        def __round__(self, ndigits=0):
+            return self.__class__(round(self._magnitude, ndigits=ndigits), self._units)
+
+        def __pos__(self):
+            return self.__class__(operator.pos(self._magnitude), self._units)
+
+        def __neg__(self):
+            return self.__class__(operator.neg(self._magnitude), self._units)
+
+        def __eq__(self, other):
+            # This is class comparison by name is to bypass that
+            # each Quantity class is unique.
+            if other.__class__.__name__ != self.__class__.__name__:
+                return self.dimensionless and _eq(self.magnitude, other)
+
+            if _eq(self._magnitude, 0) and _eq(other._magnitude, 0):
+                return self.dimensionality == other.dimensionality
+
+            if self._units == other._units:
+                return _eq(self._magnitude, other._magnitude)
+
+            try:
+                return _eq(self.to(other).magnitude, other._magnitude)
+            except DimensionalityError:
+                return False
+
+        def __lt__(self, other):
+            if not isinstance(other, self.__class__):
+                if self.dimensionless:
+                    return operator.lt(self.magnitude, other)
+                else:
+                    raise ValueError('Cannot compare Quantity and {}'.format(type(other)))
+
+            if self.units == other.units:
+                return operator.lt(self._magnitude, other._magnitude)
+            if self.dimensionality != other.dimensionality:
+                raise DimensionalityError(self.units, other.units,
+                                          self.dimensionality, other.dimensionality)
+            return operator.lt(self.convert_to_reference().magnitude,
+                               self.convert_to_reference().magnitude)
+
+        def __bool__(self):
+            return bool(self._magnitude)
+
+        __nonzero__ = __bool__
+
+        # Experimental NumPy Support
+
+        #: Dictionary mapping ufunc/attributes names to the units that they
+        #: require (conversion will be tried).
+        __require_units = {'cumprod': '',
+                           'arccos': '', 'arcsin': '', 'arctan': '', 'arctan2': '',
+                           'exp': '', 'expm1': '',
+                           'log': '', 'log10': '', 'log1p': '', 'log2': '',
+                           'sin': 'radian', 'cos': 'radian', 'tan': 'radian',
+                           'sinh': 'radian', 'cosh': 'radian', 'tanh': 'radian',
+                           'radians': 'degree', 'degrees': 'radian',
+                           'add': '', 'subtract': ''}
+
+        #: Dictionary mapping ufunc/attributes names to the units that they
+        #: will set on output.
+        __set_units = {'cos': '', 'sin': '', 'tan': '',
+                       'cosh': '', 'sinh': '', 'tanh': '',
+                       'arccos': 'radian', 'arcsin': 'radian',
+                       'arctan': 'radian', 'arctan2': 'radian',
+                       'arccosh': 'radian', 'arcsinh': 'radian',
+                       'arctanh': 'radian',
+                       'degrees': 'degree', 'radians': 'radian',
+                       'expm1': '', 'cumprod': ''}
+
+        #: List of ufunc/attributes names in which units are copied from the
+        #: original.
+        __copy_units = 'clip compress conj conjugate copy cumsum diagonal flatten ' \
+                       'max mean min ptp ravel repeat reshape round ' \
+                       'squeeze std sum take trace transpose ' \
+                       'ceil diff ediff1d floor hypot rint trapz ' \
+                       'add subtract multiply'.split()
+
+        #: Dictionary mapping ufunc/attributes names to the units that they will
+        #: set on output. The value is interpreded as the power to which the unit
+        #: will be raised.
+        __prod_units = {'var': 2, 'prod': 'size', 'true_divide': -1, 'divide': -1}
+
+        def __numpy_method_wrap(self, func, *args, **kwargs):
+            """Convenience method to wrap on the fly numpy method taking
+            care of the units.
+            """
+            if func.__name__ in self.__require_units:
+                try:
+                    self.ito(self.__require_units[func.__name__])
+                except:
+                    raise ValueError('Quantity must be dimensionless.')
+
+            value = func(*args, **kwargs)
+
+            if func.__name__ in self.__copy_units:
+                return self.__class__(value, self._units)
+
+            if func.__name__ in self.__prod_units:
+                tmp = self.__prod_units[func.__name__]
+                if tmp == 'size':
+                    return self.__class__(value, self._units ** self._magnitude.size)
+                return self.__class__(value, self._units ** tmp)
+
+            return value
+
+        def __len__(self):
+            return len(self._magnitude)
+
+        def __getattr__(self, item):
+            if item.startswith('__array_'):
+                if isinstance(self._magnitude, ndarray):
+                    return getattr(self._magnitude, item)
+                else:
+                    raise AttributeError('__array_* attributes are only taken from ndarray objects.')
+            try:
+                return functools.partial(self.__numpy_method_wrap, getattr(self._magnitude, item))
+            except AttributeError:
+                raise AttributeError("Neither Quantity object nor its magnitude ({})"
+                                     "has attribute '{}'".format(self._magnitude, item))
+
+        def __getitem__(self, key):
+            try:
+                value = self._magnitude[key]
+                return self.__class__(value, self._units)
+            except TypeError:
+                raise TypeError("Neither Quantity object nor its magnitude ({})"
+                                "supports indexing".format(self._magnitude))
+
+        def __setitem__(self, key, value):
+            try:
+                if isinstance(value, self.__class__):
+                    factor = self.__class__(value.magnitude, value.units / self.units).convert_to_reference()
+                else:
+                    factor = self.__class__(value, self._units ** (-1)).convert_to_reference()
+
+                if isinstance(factor, self.__class__):
+                    if not factor.dimensionless:
+                        raise ValueError
+                    self._magnitude[key] = factor.magnitude
+                else:
+                    self._magnitude[key] = factor
+
+            except TypeError:
+                raise TypeError("Neither Quantity object nor its magnitude ({})"
+                                "supports indexing".format(self._magnitude))
+
+        def tolist(self):
+            units = self._units
+            return [self.__class__(value, units).tolist() if isinstance(value, list) else self.__class__(value, units)
+                    for value in self._magnitude.tolist()]
+
+        __array_priority__ = 21
+
+        def __array_prepare__(self, obj, context=None):
+            try:
+                uf, objs, huh = context
+                if uf.__name__ in self.__require_units:
+                    try:
+                        self.ito(self.__require_units[uf.__name__])
+                    except:
+                        raise _Exception(ValueError)
+                return self.magnitude.__array_prepare__(obj, context)
+            except _Exception as ex:
+                raise ex.internal
+            except Exception as ex:
+                print(ex)
+            return self.magnitude.__array_prepare__(obj, context)
+
+        def __array_wrap__(self, obj, context=None):
+            try:
+                uf, objs, huh = context
+                out = self.magnitude.__array_wrap__(obj, context)
+                if uf.__name__ in self.__set_units:
+                    try:
+                        out = self.__class__(out, self.__set_units[uf.__name__])
+                    except:
+                        raise _Exception(ValueError)
+                elif uf.__name__ in self.__copy_units:
+                    try:
+                        out = self.__class__(out, self.units)
+                    except:
+                        raise _Exception(ValueError)
+                elif uf.__name__ in self.__prod_units:
+                    tmp = self.__prod_units[uf.__name__]
+                    if tmp == 'size':
+                        out = self.__class__(out, self.units ** self._magnitude.size)
+                    else:
+                        out = self.__class__(out, self.units ** tmp)
+                return out
+            except _Exception as ex:
+                raise ex.internal
+            except Exception as ex:
+                print(ex)
+            return self.magnitude.__array_wrap__(obj, context)
+
+    return _Quantity
+
+
+_DEFAULT_REGISTRY = UnitRegistry()
+
+
+def _build_quantity(value, units):
+    return _DEFAULT_REGISTRY.Quantity(value, units)
+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/helpers.html b/_modules/pystellibs/helpers.html new file mode 100644 index 0000000..8e7162f --- /dev/null +++ b/_modules/pystellibs/helpers.html @@ -0,0 +1,603 @@ + + + + + + + + pystellibs.helpers — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.helpers

+"""
+This is a first collection of tools making the design easier
+"""
+import sys
+from functools import partial, wraps, update_wrapper
+try:
+    from inspect import getargspec
+except ImportError:  # Apparently since py3.11
+    from inspect import getfullargspec as getargspec       
+from inspect import ismethod
+import warnings
+import numpy as np
+import itertools
+
+from .ezunits import unit, hasUnit
+
+
+__all__ = ['NameSpace', 'Pipe', 'Pipeable', 'Pipegroup', 'chunks',
+           'deprecated', 'generator', 'isNestedInstance', 'keywords_first',
+           'kfpartial', 'merge_records', 'missing_units_warning', 'nbytes',
+           'path_of_module', 'pretty_size_print', 'type_checker',
+           'val_in_unit']
+
+
+
[docs]class NameSpace(dict):
+    """A dict subclass that exposes its items as attributes.
+    """
+    def __init__(self, name, *args, **kwargs):
+        self.__name__ = name
+        dict.__init__(self, *args, **kwargs)
+
+    def __dir__(self):
+        return tuple(self)
+
+    def __repr__(self):
+        names = ', '.join([k for k in dir(self) if k[0] != '_'])
+        return "{s.__name__:s}: {r:s}".format(s=self, r=names)
+
+    def __getattribute__(self, name):
+        try:
+            return self[name]
+        except KeyError:
+            msg = "'{s.__name__:s}' has no attribute '{name:s}'"
+            raise AttributeError(msg.format(s=self, name=name))
+
+    def __setattr__(self, name, value):
+        self[name] = value
+
+    def __delattr__(self, name):
+        del self[name]
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        return False

+
+
+
[docs]def generator(func):
+    """ A dummy decorator that only make codes mode readable.
+    It allow to explicitly mark a function as generator (yielding values)
+    and does nothing more than calling the initial function
+    """
+    @wraps(func)
+    def deco(*args, **kwargs):
+        return func(*args, **kwargs)
+    return deco

+
+
+
[docs]def deprecated(func):
+    """ A dummy decorator that warns against using a deprecated function """
+    @wraps(func)
+    def deco(*args, **kwargs):
+        txt = 'Function {0:s} is deprecated. You should avoid its usage'
+        warnings.warn(txt.format(func.__name__))
+        return func(*args, **kwargs)
+    return deco

+
+
+
[docs]@generator
+def chunks(l, n):
+    """ Yield successive n-sized chunks from l.
+
+    Parameters
+    ----------
+    l: iterable
+        object to iter over
+
+    n: int
+        number of elements per slice
+
+    Returns
+    -------
+    chunk: tuple
+        n values from l
+    """
+    it = iter(l)
+    while True:
+        chunk = tuple(itertools.islice(it, n))
+        if chunk:
+            yield chunk
+        else:
+            raise StopIteration

+
+
+
[docs]def isNestedInstance(obj, cl):
+    """ Test for sub-classes types
+    I could not find a universal test
+
+    Parameters
+    ----------
+    obj: object instance
+        object to test
+
+    cl: Class
+        top level class to test
+
+    returns
+    -------
+    r: bool
+        True if obj is indeed an instance or subclass instance of cl
+    """
+    tree = [ cl ]
+    if hasattr(cl, '__subclasses'):
+        for k in cl.__subclasses():
+            if hasattr(k, '__subclasses'):
+                tree += k.__subclasses__()
+    return issubclass(obj.__class__, tuple(tree))

+
+
+
[docs]def type_checker(name, obj, tp):
+    """ Check a given type and raise a type error if not correct
+
+    Parameters
+    ----------
+    name: str
+        name of the variable to show in the exception text
+
+    obj: object
+        object to check
+
+    tp: type
+        expected type of obj
+
+    Raises
+    ------
+    :exc:TypeError:
+        raises a TypeError if object is not of the correct type of a subclass of it
+    """
+    if not isNestedInstance(obj, tp):
+        txt = 'Expected "{0:s}" of type {1:s}, got {2:s} instead.'
+        raise TypeError(txt.format(name, str(tp.__name__), str(type(obj).__name__)))

+
+
+
[docs]class Pipeable(object):
+    """ Decorator overloading | operator (__ror__) such that you can pipe
+    functions where the first argument is the variable on the left side of the
+    | operator.
+    This decorator allows you to use the decorated function normally and uses
+    the provided values when using in pipes.
+
+    >>> import pylab as plt
+    >>> _p = Pipeable(plt.plot, color='red', linestyle='--')
+    >>> _p(range(10), 'o-')  #  works
+    >>> range(10) | _p      #  will plot a red dashed line
+
+    """
+    def __init__(self, func, *args, **kwargs):
+        self.func = func
+        self.args = args
+        self.kwargs = kwargs
+
+    def __ror__(self, lhs):
+        return self.func(lhs, *self.args, **self.kwargs)
+
+    def __call__(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+    def __repr__(self):
+        return self.func.__repr__()

+
+
+
[docs]class Pipe(object):
+    """ Decorator overloading | operator (__ror__) such that you can pipe
+    functions where the first argument is the variable on the left side of the
+    | operator.
+    The difference with Pipeable is that you cannot use decorated function
+    outside of pipes but you gain the possibility to update the calling
+    parameters
+
+    Used with keywords_first make this a powerful Task
+    """
+    def __init__(self, func, *args, **kwargs):
+        self.func = func
+        self.args = args
+        self.kwargs = kwargs
+        update_wrapper(self, func)
+
+    def __or__(self, other):
+        if isinstance(other, Pipe):
+            return Pipegroup( (self, other), mode='multi' )
+
+    def __and__(self, other):
+        if isinstance(other, Pipe):
+            return Pipegroup( (self, other) )
+
+    def __ror__(self, other):
+            return self.func(other, *self.args, **self.kwargs)
+
+    def __call__(self, *args, **kwargs):
+        return Pipeable(self.func, *args, **kwargs)
+
+    def __repr__(self):
+        return 'Pipe: {}'.format(self.func.__repr__())
+
+    def __str__(self):
+        return '{}'.format(self.func.__name__)

+
+
+
[docs]class Pipegroup(object):
+    def __init__(self, pipes, mode='sequential'):
+        self.pipes = list(pipes)
+        self.mode = mode
+        self.func = self
+
+    def __len__(self):
+        return len(self.pipes)
+
+
[docs]    def seq_call(self, val, *args, **kwargs):
+        r = self.pipes[0].func(val)
+        if len(self) > 1:
+            for pk in self.pipes[1:]:
+                r = pk.func(r)
+        return r

+
+
[docs]    def append(self, other):
+        self.pipes.append(other)

+
+    def __or__(self, other):
+        if isinstance(other, Pipe):
+            if self.mode in ['multi', '|']:
+                self.append(other)
+                return self
+            else:
+                return Pipegroup( (self, other), mode='multi')
+
+    def __and__(self, other):
+        if isinstance(other, Pipe):
+            if self.mode in ['sequential', '&']:
+                self.append(other)
+                return self
+            else:
+                return Pipegroup( (self, other), mode='sequential')
+
+
[docs]    def multi_call(self, vals, iter=True):
+        return [ pk.func(vals) for pk in self.pipes ]

+
+    def __call__(self, val, *args, **kwargs):
+        mode = kwargs.get('mode', self.mode).lower()
+        if mode in ['sequential', '&']:
+            return self.seq_call(val, *args, **kwargs)
+        elif mode in ['multi', '|']:
+            return self.multi_call(val, *args, **kwargs)
+        else:
+            raise NotImplemented
+
+    def __ror__(self, other):
+        return self(other)
+
+    def __repr__(self):
+        txt = 'Pipegroup: mode={},\n\t | '.format(self.mode)
+
+        if self.mode == 'sequential':
+            txt += ' & '.join([str(pk) for pk in self.pipes])
+        else:
+            txt += '\n\t | '.join([str(pk) for pk in self.pipes])
+        return txt
+
+    def __str__(self):
+        if self.mode == 'sequential':
+            delim = ' & '
+        else:
+            delim = ' | '
+        return '({})'.format(delim.join([str(pk) for pk in self.pipes])).replace('Pipe: ', '')

+
+
+
[docs]def keywords_first(f):
+    """ Decorator that enables to access any argument or keyword as a keyword """
+    # http://code.activestate.com/recipes/577922/ (r2)
+    @wraps(f)
+    def wrapper(*a, **k):
+        a = list(a)
+        for idx, arg in enumerate(getargspec(f).args, -ismethod(f)):
+            if arg in k:
+                if idx < len(a):
+                    a.insert(idx, k.pop(arg))
+                else:
+                    break
+        return f(*a, **k)
+    return wrapper

+
+
+
[docs]def kfpartial(fun, *args, **kwargs):
+    """ Allows to create partial functions with arbitrary arguments/keywords """
+    return partial(keywords_first(fun), *args, **kwargs)

+
+
+def warning_on_one_line(message, category, filename, lineno, file=None,
+                        line=None):
+    return " {0:s}:{1:d} {2:s}:{3:s}".format(filename, lineno,
+                                             category.__name__, str(message))
+
+
+
[docs]def missing_units_warning(name, defaultunit):
+    """ Warn if any unit is missing
+
+    Parameters
+    ----------
+    name: str
+        name of the variable
+
+    defaultunit: str
+        default unit definition
+
+    Raises
+    ------
+    warning: warnings.warn
+        warn if units are assumed
+    """
+    warnings.formatwarning = warning_on_one_line
+    msg = 'Variable {0:s} does not have explicit units. Assuming `{1:s}`\n'
+    # stacklevel makes the correct code reference
+    warnings.warn(msg.format(name, defaultunit), stacklevel=4)

+
+
+
[docs]def val_in_unit(varname, value, defaultunit):
+    """ check units and convert to defaultunit or create the unit information
+
+    Parameters
+    ----------
+    varname: str
+        name of the variable
+
+    value: value
+        value of the variable, which may be unitless
+
+    defaultunit: str
+        default units is unitless
+
+    Returns
+    -------
+    quantity: ezunits.Quantity
+        value with units
+
+    Example
+    -------
+    >>> r = 0.5
+    >>> print(val_in_unit('r', r, 'degree'))
+    # UserWarning: Variable r does not have explicit units. Assuming `degree`
+    <Quantity(0.5, 'degree')>
+
+    >>> r = 0.5 * unit['degree']
+    >>> print(val_in_unit('r', r, 'degree'))
+    <Quantity(0.5, 'degree')>
+    """
+    if not hasUnit(value):
+        missing_units_warning(varname, defaultunit)
+        return value * unit[defaultunit]
+    else:
+        return value.to(defaultunit)

+
+
+
[docs]def merge_records(lst):
+    """ generates a stack of records even with slightly different but compatible
+    dtypes
+
+    Parameters
+    ----------
+
+    lst: sequence of np.recarray
+        sequence of individual records
+
+    Returns
+    -------
+    val: np.recarray
+        array of stacked records
+        Note if if lst is empty, returns an empty list
+    """
+    r = []
+    for rk in lst:
+        r.append(rk.tolist()[0])
+    names = rk.dtype.names
+    if len(r) > 0:
+        return np.rec.fromrecords(r, names=names)
+    else:
+        return []

+
+
+
[docs]def path_of_module(mod=None):
+    """ returns the definition code path of a given module, object or function
+    If nothing is provided, the current frame will be query, i.e., the current
+    working directory of the calling function.
+
+    Parameters
+    ----------
+    mod: module, class, function
+        object to find the defintion
+        if None, inspect.currentframe is used
+
+    returns
+    -------
+    path: str
+        path of the definition
+    """
+    import os
+    import inspect
+
+    if mod is None:
+        mod = inspect.currentframe()
+    return '/'.join(os.path.abspath(inspect.getfile(mod)).split('/')[:-1])

+
+
+
[docs]def pretty_size_print(num_bytes):
+    """
+    Output number of bytes in a human readable format
+
+    Parameters
+    ----------
+    num_bytes: int
+        number of bytes to convert
+
+    returns
+    -------
+    output: str
+        string representation of the size with appropriate unit scale
+    """
+    if num_bytes is None:
+        return
+
+    KiB = 1024
+    MiB = KiB * KiB
+    GiB = KiB * MiB
+    TiB = KiB * GiB
+    PiB = KiB * TiB
+    EiB = KiB * PiB
+    ZiB = KiB * EiB
+    YiB = KiB * ZiB
+
+    if num_bytes > YiB:
+        output = '%.3g YB' % (num_bytes / YiB)
+    elif num_bytes > ZiB:
+        output = '%.3g ZB' % (num_bytes / ZiB)
+    elif num_bytes > EiB:
+        output = '%.3g EB' % (num_bytes / EiB)
+    elif num_bytes > PiB:
+        output = '%.3g PB' % (num_bytes / PiB)
+    elif num_bytes > TiB:
+        output = '%.3g TB' % (num_bytes / TiB)
+    elif num_bytes > GiB:
+        output = '%.3g GB' % (num_bytes / GiB)
+    elif num_bytes > MiB:
+        output = '%.3g MB' % (num_bytes / MiB)
+    elif num_bytes > KiB:
+        output = '%.3g KB' % (num_bytes / KiB)
+    else:
+        output = '%.3g Bytes' % (num_bytes)
+
+    return output

+
+
+
[docs]def nbytes(obj, pprint=False):
+    """ return the number of bytes of the object, which includes size of nested
+    structures
+
+    Parameters
+    ----------
+    obj: object
+        object to find the size of
+
+    pprint: bool, optional (default=False)
+        if set, returns the result after calling pretty_size_print
+
+    returns
+    -------
+    num_bytes: int or str
+        total number of bytes or human readable corresponding string
+    """
+    num_bytes = sum(k.nbytes if hasattr(k, 'nbytes') else sys.getsizeof(k)
+                    for k in obj.__dict__.values())
+    if pprint:
+        return pretty_size_print(num_bytes)
+    else:
+        return num_bytes

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/interpolator.html b/_modules/pystellibs/interpolator.html new file mode 100644 index 0000000..ee98d15 --- /dev/null +++ b/_modules/pystellibs/interpolator.html @@ -0,0 +1,142 @@ + + + + + + + + pystellibs.interpolator — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.interpolator

+from .interpolator import BaseInterpolator
+from .lejeune import LejeuneInterpolator
+from .ndlinear import NDLinearInterpolator
+
+
+
[docs]def find_interpolator(name, osl=None, **kwargs):
+    """ Find an interpolator from its name and 
+        instanciate it if an osl was provided
+
+    Parameters
+    ----------
+    name: str
+        name of the interpolation
+    osl: Stellib instance, optional
+        library to work with
+    """
+    mapping = {"lejeune": LejeuneInterpolator,
+               "ndlinear": NDLinearInterpolator,
+               "lejeuneinterpolator": LejeuneInterpolator,
+               "ndlinearinterpolator": NDLinearInterpolator}
+
+    try:
+        cls = mapping.get(name.lower(), None)
+        if cls is not None:
+            if osl is not None:
+                return cls(osl, **kwargs)
+            else:
+                return cls
+    except AttributeError:
+        pass
+
+    return None

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/interpolator/interpolator.html b/_modules/pystellibs/interpolator/interpolator.html new file mode 100644 index 0000000..4f211da --- /dev/null +++ b/_modules/pystellibs/interpolator/interpolator.html @@ -0,0 +1,130 @@ + + + + + + + + pystellibs.interpolator.interpolator — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.interpolator.interpolator

+""" Base interpolator: a dummy class is derived by the different interpolator schemes """
+
+
+
[docs]class BaseInterpolator(object):
+    """ Base class for interpolation
+
+    It sets what can be expected as methods during the interpolation calls
+    """
+    def __init__(self, osl, *args, **kwargs):
+        pass
+
+
[docs]    def interp(self, aps, *args, **kwargs):
+        """ Interpolation over spectra """
+        raise NotImplementedError()

+
+
[docs]    def interp_other(self, aps, values, *args, **kwargs):
+        """ Interpolation over provided values """
+        raise NotImplementedError()

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/interpolator/lejeune.html b/_modules/pystellibs/interpolator/lejeune.html new file mode 100644 index 0000000..4304541 --- /dev/null +++ b/_modules/pystellibs/interpolator/lejeune.html @@ -0,0 +1,913 @@ + + + + + + + + pystellibs.interpolator.lejeune — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.interpolator.lejeune

+""" Lejeune interpolator is basically a linear interpolator for a Lejeune grid
+based spectral library.
+
+This is the simplest interpolator but most commonly used.
+
+It takes care of boundary conditions by imposing limits to extrapolation on a
+given grid.
+"""
+
+import numpy as np
+from .interpolator import BaseInterpolator
+
+
+def __det3x3__(a):
+    """ compute the 3x3 determinant of an array
+
+    Hard coded equations are 8 times faster than np.linalg.det for a matrix 3x3
+
+    Parameters
+    ----------
+    a: ndarray, shape=(3,3), dtype=float
+        array matrix
+
+    Returns
+    -------
+    val: float
+        determinant of a
+    """
+    val  = +a[0] * (a[4] * a[8] - a[7] * a[5])
+    val += -a[1] * (a[3] * a[8] - a[6] * a[5])
+    val += +a[2] * (a[3] * a[7] - a[6] * a[4])
+    return val
+
+
+def __interp__(T0, g0, T,g, dT_max=0.1, eps=1e-6):
+    """
+    Interpolation of the (T,g) grid at fixed Z
+
+    Translated from Pegase.2 fortran version
+    (this may not be pythonic though)
+
+    Note: preference is always given to the temperature over
+        the gravity when needed.
+
+    Parameters
+    ----------
+    T0: float
+        log(Teff) to obtain
+
+    g0: float
+        log(g) to obtain
+
+    T: float
+        log(Teff) of the grid
+
+    g: float
+        log(g) of the grid
+
+    dT_max: float, optional
+        If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2
+        (resp. i1) is not used.  (see below for namings)
+
+    eps: float
+        temperature sensitivity under which points are considered to have
+        the same temperature
+
+    Returns
+    -------
+    idx: ndarray, dtype=int, size=4
+        4 star indexes
+
+    w: ndarray, dtype=float, size=4
+        4 associated weights
+
+    ..note::
+        if index is -1, this means the point is rejected and the associated
+        weight is 0.
+
+
+    Naming conventions
+    ------------------
+
+    i1 = index of the star with temperature > T and gravity > g.
+    Among all such stars, one chooses the one minimizing
+    |Delta T|+kappa*|Delta g|.
+    If no star with temperature > T and gravity > g exists, i1 = -1
+
+    i2 = index of the star with temperature > T and gravity < g.
+
+    i3 = index of the star with temperature < T and gravity > g.
+
+    i4 = index of the star with temperature < T and gravity < g.
+
+    g
+    /|\
+    | i3  |
+    |     |  i1
+    | ----x------
+    |     |    i2
+    |  i4 |
+    |__________\ T
+                /
+    """
+    kappa  = 0.1
+
+    idx    = np.arange(len(g))
+    deltag = g - g0
+    deltaT = T - T0
+    dist   = kappa * abs(deltag) + abs(deltaT)
+
+    if dist.min() == 0:
+        return np.array((dist.argmin(),-1,-1,-1)), np.array((1.,0.,0.,0.))
+
+    # Looking for i_{1..4}
+    ind_dT = deltaT >= 0
+    ind_dg = deltag >= 0
+
+    # i1
+    ind = (ind_dT & ind_dg)
+    if True in ind:
+        i1  = idx[ind][dist[ind].argmin()]
+    else:
+        i1 = -1
+
+    # i2
+    ind = (ind_dT & ~ind_dg)
+    if True in ind:
+        i2  = idx[ind][dist[ind].argmin()]
+    else:
+        i2 = -1
+
+    # i3
+    ind = (~ind_dT & ind_dg)
+    if True in ind:
+        i3  = idx[ind][dist[ind].argmin()]
+    else:
+        i3 = -1
+
+    # i4
+    ind = (~ind_dT & ~ind_dg)
+    if True in ind:
+        i4  = idx[ind][dist[ind].argmin()]
+    else:
+        i4 = -1
+
+    # checking integrity
+    if ( (i1 < 0) & (i2 < 0) & (i3 < 0) & (i4 < 0) ):
+        raise ValueError("Interp. Error, could not find appropriate knots")
+
+    T1 = T[i1]
+    T2 = T[i2]
+    T3 = T[i3]
+    T4 = T[i4]
+    g1 = g[i1]
+    g2 = g[i2]
+    g3 = g[i3]
+    g4 = g[i4]
+
+    # If, T2 (resp. T1) is too far from T compared to T1
+    # (resp. T2), i2 (resp. i1) is not used.
+    # The same for i3 and i4.
+    if ( (i1 > 0) & (i2 > 0) ):
+        if (T1 < T2 - dT_max):
+            i2 = -1
+        elif (T2 < T1 - dT_max):
+            i1 = -1
+
+    if ( (i3 > 0) & (i4 > 0) ):
+        if (T3 > T4 + dT_max):
+            i4 = -1
+        elif (T4 > T3 + dT_max):
+            i3 = -1
+
+    if ( (i1 < 0) & (i2 < 0) & (i3 < 0) & (i4 < 0) ):
+        raise ValueError("Interp. Error, could not find appropriate knots")
+
+    # Interpolation in the (T, g) plane between the used points
+    # (at least 1, at most 4).
+    # Code "0110" means that i1 = i4 = 0, i2 /=0 and i3 /= 0.
+    #
+    # Note: preference is always given to the temperature over
+    #   the gravity when needed.
+    if (i1 < 0):
+        if (i2 < 0):
+            if (i3 < 0):
+                if (i4 < 0):
+                    #                   # 0000
+                    raise ValueError("Error")  # should not be possible
+                else:                   # 0001
+                    alpha1 = 0.
+                    alpha2 = 0.
+                    alpha3 = 0.
+                    alpha4 = 1.
+                # endif
+            elif (i4 < 0):              # 0010
+                alpha1 = 0.
+                alpha2 = 0.
+                alpha3 = 1.
+                alpha4 = 0.
+            else:                       # 0011
+                alpha1 = 0.
+                alpha2 = 0.
+                if ( abs(T3 - T4) < eps ):
+                    if (g3 == g4):
+                        alpha3 = 0.5
+                    else:
+                        alpha3 = (g0 - g4) / (g3 - g4)
+                    # endif
+                    alpha4 = 1. - alpha3
+                else:
+                    if (T3 > T4):
+                        alpha3 = 1.
+                        alpha4 = 0.
+                        i4 = -1
+                    else:
+                        alpha3 = 0.
+                        i3 = -1
+                        alpha4 = 1.
+                    # endif
+                # endif
+            # endif
+        elif (i3 < 0):
+            if (i4 < 0):
+                #                        # 0100
+                alpha1 = 0.
+                alpha2 = 1.
+                alpha3 = 0.
+                alpha4 = 0.
+            else:                        # 0101
+                alpha1 = 0.
+                if (T2 == T4):
+                    alpha2 = 0.5
+                else:
+                    alpha2 = (T0 - T4) / (T2 - T4)
+                # endif
+                alpha3 = 0.
+                alpha4 = 1. - alpha2
+            # endif
+        elif (i4 < 0):                   # 0110
+            alpha1 = 0.
+            if (T2 == T3):
+                alpha2 = 0.5
+            else:
+                alpha2 = (T0 - T3) / (T2 - T3)
+            # endif
+            alpha3 = 1. - alpha2
+            alpha4 = 0.
+        else:                            # 0111
+            # Assume that (T, g) is within the triangle i
+            # formed by the three points.
+
+            mat0 = np.asarray([
+                [ T2, T3, T4 ],
+                [ g2, g3, g4 ],
+                [ 1., 1., 1. ]  ])
+            mat2 = np.asarray([
+                [ T0, T3, T4 ],
+                [ g0, g3, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat3 = np.asarray([
+                [ T2, T0, T4 ],
+                [ g2, g0, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat4 = np.asarray([
+                [ T2, T3, T0 ],
+                [ g2, g3, g0 ],
+                [ 1., 1., 1. ]  ])
+            det0 = __det3x3__(mat0.ravel())
+            det2 = __det3x3__(mat2.ravel())
+            det3 = __det3x3__(mat3.ravel())
+            det4 = __det3x3__(mat4.ravel())
+            alpha1 = 0.
+            alpha2 = det2 / det0
+            alpha3 = det3 / det0
+            alpha4 = det4 / det0
+
+            # If (T, g) is outside the triangle formed
+            # by the three used points use only two points.
+            if ((alpha2 < 0.) | (alpha2 > 1. ) | (alpha3 < 0.) | (alpha3 > 1.) | 
+                (alpha4 < 0.) | (alpha4 > 1. ) ):
+                alpha1 = 0.
+                if (T2 == T3):
+                    alpha2 = 0.5
+                else:
+                    alpha2 = (T0 - T3) / (T2 - T3)
+                # endif
+                alpha3 = 1. - alpha2
+                alpha4 = 0.
+                i4 = -1
+            # endif
+        # endif
+    elif (i2 < 0):
+        if (i3 < 0):
+            if (i4 < 0):
+                #                      # 1000
+                alpha1 = 1.
+                alpha2 = 0.
+                alpha3 = 0.
+                alpha4 = 0.
+            else:                      # 1001
+                if (T1 == T4):
+                    alpha1 = 0.5
+                else:
+                    alpha1 = (T0 - T4) / (T1 - T4)
+                # endif
+                alpha2 = 0.
+                alpha3 = 0.
+                alpha4 = 1. - alpha1
+            # endif
+        elif (i4 < 0):                 # 1010
+            if (T1 == T3):
+                alpha1 = 0.5
+            else:
+                alpha1 = (T0 - T3) / (T1 - T3)
+            # endif
+            alpha2 = 0.
+            alpha3 = 1. - alpha1
+            alpha4 = 0.
+        else:                          # 1011
+            # Assume that (T, g) is within the triangle formed by the three points.
+            mat0 = np.asarray([
+                [ T1, T3, T4 ],
+                [ g1, g3, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat1 = np.asarray([
+                [ T0, T3, T4 ],
+                [ g0, g3, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat3 = np.asarray([
+                [ T1, T0, T4 ],
+                [ g1, g0, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat4 = np.asarray([
+                [ T1, T3, T0 ],
+                [ g1, g3, g0 ],
+                [ 1., 1.,  1.]  ])
+            det0 = __det3x3__(mat0.ravel())
+            det1 = __det3x3__(mat1.ravel())
+            det3 = __det3x3__(mat3.ravel())
+            det4 = __det3x3__(mat4.ravel())
+            alpha1 = det1 / det0
+            alpha2 = 0.
+            alpha3 = det3 / det0
+            alpha4 = det4 / det0
+
+            # If (T, g) is outside the triangle formed by the three used points,
+            # use only two points.
+
+            if ((alpha1 < 0.) | (alpha1 > 1.) | (alpha3 < 0.) | (alpha3 > 1.) | (alpha4 < 0.) | (alpha4 > 1.) ):
+                if (T1 == T4):
+                    alpha1 = 0.5
+                else:
+                    alpha1 = (T0 - T4) / (T1 - T4)
+                # endif
+                alpha2 = 0.
+                alpha3 = 0.
+                i3 = -1
+                alpha4 = 1. - alpha1
+            # endif
+        # endif
+    elif (i3 < 0):
+        if (i4 < 0):
+            #                       # 1100
+            if (abs(T1 - T2) < eps):
+                if (g1 == g2):
+                    alpha1 = 0.5
+                else:
+                    alpha1 = (g0 - g2) / (g1 - g2)
+                # endif
+                alpha2 = 1. - alpha1
+            else:
+                if (T1 < T2):
+                    alpha1 = 1.
+                    alpha2 = 0.
+                    i2 = -1
+                else:
+                    alpha1 = 0.
+                    i1 = -1
+                    alpha2 = 1.
+                # endif
+            # endif
+            alpha3 = 0.
+            alpha4 = 0.
+        else:                       # 1101
+            # Assume that (T, g) is within the triangle formed by the three points.
+            mat0 = np.asarray([
+                [ T1, T2, T4 ],
+                [ g1, g2, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat1 = np.asarray([
+                [ T0, T2, T4 ],
+                [ g0, g2, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat2 = np.asarray([
+                [ T1, T0, T4 ],
+                [ g1, g0, g4 ],
+                [ 1., 1.,  1.]  ])
+            mat4 = np.asarray([
+                [ T1, T2, T0 ],
+                [ g1, g2, g0 ],
+                [ 1., 1.,  1. ]  ])
+            det0 = __det3x3__(mat0.ravel())
+            det1 = __det3x3__(mat1.ravel())
+            det2 = __det3x3__(mat2.ravel())
+            det4 = __det3x3__(mat4.ravel())
+            alpha1 = det1 / det0
+            alpha2 = det2 / det0
+            alpha3 = 0.
+            alpha4 = det4 / det0
+
+            # If (T, g) is outside the triangle formed by the three used points,
+            # use only two points.
+            if ((alpha1 < 0.) | (alpha1 > 1.) | (alpha2 < 0.) | (alpha2 > 1.) | (alpha4 < 0.) | (alpha4 > 1.) ):
+                if (T1 == T4):
+                    alpha1 = 0.5
+                else:
+                    alpha1 = (T0 - T4) / (T1 - T4)
+                # endif
+                alpha2 = 0.
+                i2 = -1
+                alpha3 = 0.
+                alpha4 = 1. - alpha1
+            # endif
+        # endif
+    elif (i4 < 0):
+        #                           # 1110
+        # Assume that (T, g) is within the triangle formed by the three points.
+        mat0 = np.asarray([
+            [ T1, T2, T3 ],
+            [ g1, g2, g3 ],
+            [ 1., 1.,  1.]  ])
+        mat1 = np.asarray([
+            [ T0, T2, T3 ],
+            [ g0, g2, g3 ],
+            [ 1., 1.,  1.]  ])
+        mat2 = np.asarray([
+            [ T1, T0, T3 ],
+            [ g1, g0, g3 ],
+            [ 1., 1.,  1.]  ])
+        mat3 = np.asarray([
+            [ T1, T2, T0 ],
+            [ g1, g2, g0 ],
+            [ 1., 1.,  1.]  ])
+        det0 = __det3x3__(mat0.ravel())
+        det1 = __det3x3__(mat1.ravel())
+        det2 = __det3x3__(mat2.ravel())
+        det3 = __det3x3__(mat3.ravel())
+        alpha1 = det1 / det0
+        alpha2 = det2 / det0
+        alpha3 = det3 / det0
+        alpha4 = 0.
+
+        # If (T, g) is outside the triangle formed by the three used points,
+        # use only two points.
+        if ((alpha1 < 0.) | (alpha1 > 1.) | (alpha2 < 0.) | (alpha2 > 1.) | (alpha3 < 0.) | (alpha3 > 1.) ):
+            alpha1 = 0.
+            i1 = -1
+            if (T2 == T3):
+                alpha2 = 0.5
+            else:
+                alpha2 = (T0 - T3) / (T2 - T3)
+            # endif
+            alpha3 = 1. - alpha2
+            alpha4 = 0.
+        # endif
+    # endif
+
+    # All four points used.
+
+    if ( (i3 >= 0) & (i4 >= 0) & (i1 >= 0) & (i2 >= 0) ):
+        if (T1 != T3):
+            alpha = (T0 - T3) / (T1 - T3)
+        else:
+            alpha = 0.5
+        # endif
+        if (T2 != T4):
+            beta = (T0 - T4) / (T2 - T4)
+        else:
+            beta = 0.5
+        # endif
+        gprim = alpha * g1 + (1 - alpha) * g3
+        gsec  = beta * g2  + (1 - beta ) * g4
+        if (gprim != gsec):
+            gamma = ( g0 - gsec ) / ( gprim - gsec )
+        else:
+            gamma = 0.5
+        # endif
+        alpha1 = alpha * gamma
+        alpha2 = beta * ( 1 - gamma )
+        alpha3 = ( 1 - alpha ) * gamma
+        alpha4 = (  1 - beta ) * ( 1 - gamma )
+    # endif
+    return np.asarray((i1, i2, i3, i4)), np.asarray((alpha1, alpha2, alpha3, alpha4))
+
+
+'''
+class LejeuneInterpolator(BaseInterpolator):
+    """ Interpolation for grid based on the Lejeune library definition
+
+    The interpolation is N-D linear in log-temperature, log-gravity, and linear
+    in metallicity Z. Preference is always given to the temperature over the
+    gravity when needed.
+
+    This version is translated from Pegase
+
+    Attributes
+    ----------
+
+    dT_max: float, optional
+        If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2
+        (resp. i1) is not used.  (see below for namings)
+
+    eps: float
+        temperature sensitivity under which points are considered to have
+        the same temperature
+    """
+    def __init__(self, osl, dT_max=0.1, eps=1e-6, *args, **kwargs):
+        BaseInterpolator.__init__(self, osl, *args, **kwargs)
+        self.dlogT_max = dT_max
+        self.eps = eps
+        self.osl = osl
+        
+    def interp(self, aps, weights=None, **kwargs):
+        return self.interp_other(aps, self.osl.spectra, weights=weights, **kwargs)
+        
+    def interp_other(self, aps, other, weights=None, **kwargs):
+        # get osl data
+        osl_logT, osl_logg, osl_logZ = self.osl.get_interpolation_data().T[:3]
+        grid_logZ   = np.unique(osl_logZ)
+        if np.ndim(other) < 2:
+            values = np.atleast_2d([other]).T
+        else:
+            values = np.atleast_2d(other)
+        
+        # params
+        library_index = np.arange(len(osl_logT), dtype=int)
+        _aps = np.atleast_2d(aps)
+        if weights is None:
+            _weights = np.ones(len(_aps), dtype=float)
+        elif np.ndim(weights) == 0:
+            _weights = np.ones(len(_aps), dtype=float) * weights
+        else:
+            _weights = weights
+        
+        final_values = []
+        for current_aps, current_weight in zip(np.atleast_2d(aps), _weights):
+            logT, logg, logZ = current_aps
+            # logZ = np.log10(Z)
+        
+            # find Zsup and Zinf
+            where = np.searchsorted(grid_logZ, logZ)
+            if where >=0:
+                logZinf = grid_logZ[where]
+            else:
+                raise ValueError("Metallicity extrapolation")
+            if abs(logZinf - logZ) < 1e-4: 
+                # exact match no need to interpolate twice.
+                select = (abs(logZinf - osl_logZ) < 1e-4)
+                # call Pegase interpolation scheme
+                #   Interpolation of the (logT, logg) grid at fixed Z from pegase.2
+                #   it returns the knots'indices from the input data and their weights, resp.
+                #   the final result is then the weighted sum.
+                indices, alphas = __interp__(logT, logg, 
+                                             osl_logT[select], osl_logg[select], 
+                                             dT_max=self.dlogT_max, eps=self.eps)
+                # indices are for the selection
+                # if indices[k] = -1, then one corner is rejected
+                data_indices = library_index[select][indices[indices >= 0]]
+                data_weights = alphas[indices >= 0]
+                spectrum = np.sum(values[data_indices] * data_weights[:, None], axis=0)
+                # store the weighted sum * the input requested weight
+                final_values.append(spectrum * current_weight)
+            else:
+                logZsup = grid_logZ[where + 1]
+                # interpolate within each (therefore calling interp with Zinf, Zsup, resp.) 
+                # then linearly interpolate between logZ values.
+                inf_spectrum = self.interp_other((logT, logg, logZinf), values, weights=current_weight, **kwargs)
+                sup_spectrum = self.interp_other((logT, logg, logZsup), values, weights=current_weight, **kwargs)
+                spectrum = inf_spectrum * (logZ - logZinf) / (logZsup - logZinf) + sup_spectrum * (logZsup - logZ) / (logZsup - logZinf)
+                final_values.append(spectrum)
+        return np.squeeze(final_values)
+'''
+
+
+
[docs]class LejeuneInterpolator(BaseInterpolator):
+    """ Interpolation for grid based on the Lejeune library definition
+
+    The interpolation is N-D linear in log-temperature, log-gravity, and linear
+    in metallicity Z. Preference is always given to the temperature over the
+    gravity when needed.
+
+    This version is translated from Pegase
+
+    Attributes
+    ----------
+
+    dT_max: float, optional
+        If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2
+        (resp. i1) is not used.  (see below for namings)
+
+    eps: float
+        temperature sensitivity under which points are considered to have
+        the same temperature
+    """
+    def __init__(self, osl, dT_max=0.1, eps=1e-6, *args, **kwargs):
+        BaseInterpolator.__init__(self, osl, *args, **kwargs)
+        self.dlogT_max = dT_max
+        self.eps = eps
+        self.osl = osl
+        
+    def _osl_interp_weights(self, osl, T0, g0, Z0, dT_max=0.1, eps=1e-6):
+        """ Interpolation of the T,g grid
+
+        Interpolate on the grid and returns star indices and associated weights,
+        and Z.
+
+        3 to 12 stars are returned.
+        It calls _interp_, but reduce the output to the relevant stars.
+
+        Parameters
+        ----------
+        T0: float
+            log(Teff) to obtain
+
+        g0: float
+            log(g) to obtain
+
+        Z0: float
+            metallicity to obtain
+
+        dT_max: float, optional
+            If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2
+            (resp. i1) is not used.  (see below for namings)
+
+        eps: float
+            temperature sensitivity under which points are considered to have
+            the same temperature
+
+        Returns
+        -------
+        idx: ndarray, dtype=int, size=4
+            4 star indexes
+
+        w: ndarray, dtype=float, size=4
+            4 associated weights
+
+        Returns 3 to 12 star indexes and associated weights
+        """
+        # interpolation must be by construction from logT, logg, Z
+        # logZ could be an alternative.
+        osl_logT, osl_logg, osl_logZ = self.osl.get_interpolation_data().T[:3]
+        _Z    = 10 ** osl_logZ
+        _Zv   = np.unique(_Z)
+        _T    = np.asarray(osl_logT, dtype=np.double)
+        _g    = np.asarray(osl_logg, dtype=np.double)
+
+        bZ_m  = True in (abs(_Zv - Z0) < 1e-28)  # Z_match bool
+        r     = np.where((_Zv < Z0))[0]
+        Z_inf = _Zv[r.max()] if len(r) > 0 else -1.
+        r     = np.where((_Zv > Z0))[0]
+        Z_sup = _Zv[r.min()] if len(r) > 0 else -1.
+
+        index   = np.zeros(4 * 3) - 1
+        weights = np.zeros(4 * 3)
+        Z       = np.zeros(4 * 3)
+
+        if (bZ_m):
+            ind         = np.where((abs(_Z - Z0) < 1e-28))
+            i, w        = __interp__(T0, g0, _T[ind], _g[ind], dT_max, eps)
+            index[8:]   = ind[0][i]
+            weights[8:] = np.squeeze(w)
+            Z[8:]       = [Z0] * 4
+        else:
+            if (Z_inf > 0.):
+                ind         = np.where(_Z == Z_inf)
+                i, w        = __interp__(T0, g0, _T[ind], _g[ind], dT_max, eps)
+                index[:4]   = ind[0][i]
+                weights[:4] = np.squeeze(w)
+                Z[:4]       = [Z_inf] * 4
+
+            if (Z_sup > 0.):
+                ind          = np.where(_Z == Z_sup)
+                i, w         = __interp__(T0, g0, _T[ind], _g[ind], dT_max, eps)
+                index[4:8]   = ind[0][i]
+                weights[4:8] = np.squeeze(w)
+                Z[4:8]       = [Z_sup] * 4
+
+            if ((Z_inf > 0.) & (Z_sup > 0.)):
+                if ( Z_sup - Z_inf ) > 0.:
+                    fz = (Z0 - Z_inf) / ( Z_sup - Z_inf )
+                    weights[:4]  *= fz
+                    weights[4:8] *= ( 1. - fz )
+                else:
+                    weights[:8]  *= 0.5
+
+        ind = np.where(weights > 0)
+        return index[ind].astype(int), weights[ind]  # / (weights[ind].sum()) #, Z[ind]
+        
+        
+    def _interp_weights(self, aps, weights=None, **kwargs):
+        """ returns interpolation nodes and weights 
+        
+        Parameters
+        ----------
+        aps: ndarray
+            (logT, logg, logZ) sequence. 
+            Or appropriately defined similarly to self.osl.get_interpolation_data
+        weights: ndarray
+            optional weights of each ap vector to apply during the interpolation
+            
+        Returns
+        -------
+        node_weights: array
+            osl grid node indices and interpolation weights
+        """
+        _aps = np.atleast_2d(aps)
+        
+        if weights is None:
+            _weights = np.ones(len(_aps), dtype=float)
+        elif np.ndim(weights) == 0:
+            _weights = np.ones(len(_aps), dtype=float) * weights
+        else:
+            _weights = weights
+            
+        node_weights = []
+        for s, current_weight in zip(_aps, _weights):
+            logT, logg, logZ = s[:3]
+            Z = 10 ** logZ
+            current_nodes = np.array(self._osl_interp_weights(self.osl, logT, logg, Z, **kwargs)).T
+            current_nodes[:, 1] *= current_weight
+            node_weights.append(current_nodes)
+            
+        return node_weights
+    
+    def _evaluate_from_weights(self, r, other):
+        """ Evaluate the interpolation from interpolation nodes and weights 
+        
+        Basically do a weighted sum on the grid using the interpolation weights
+        
+        Parameters
+        ----------
+        node_weights: array
+            osl grid node indices and interpolation weights
+            result of interp_weights
+        
+        other: array
+            values to interpolate
+        
+        Returns
+        -------
+        interpolated: ndarray (size(node_weights), )
+            interpolated values
+        """
+        if np.ndim(other) < 2:
+            values = np.atleast_2d([other]).T
+        else:
+            values = np.atleast_2d(other)
+        interpolated = [(((values[rk[:, 0].astype(int)].T) * rk[:, 1])).sum(1) for rk in r]
+        return np.squeeze(interpolated)
+    
+
[docs]    def interp(self, aps, weights=None, **kwargs):
+        """
+        Interpolate spectra
+        
+        Parameters
+        ----------
+        aps: ndarray
+            (logT, logg, logZ) sequence. 
+            Or appropriately defined similarly to self.osl.get_interpolation_data
+        weights: ndarray
+            optional weights of each ap vector to apply during the interpolation
+            
+        Returns
+        -------
+        s0: ndarray (len(aps), len(l0))
+            interpolated spectra
+        """
+        s0 = self.interp_other(aps, self.osl.spectra, weights=weights, **kwargs)
+        return s0

+    
+
[docs]    def interp_other(self, aps, other, weights=None, **kwargs):
+        """ Interpolate other grid values  
+        
+        Basically do a weighted sum on the grid using the interpolation weights
+        
+        Parameters
+        ----------
+        aps: ndarray
+            (logT, logg, logZ) sequence. 
+            Or appropriately defined similarly to self.osl.get_interpolation_data
+        weights: ndarray
+            optional weights of each ap vector to apply during the interpolation
+        
+        Returns
+        -------
+        interpolated: ndarray (size(node_weights), )
+            interpolated values
+        """
+        r = self._interp_weights(aps, weights, **kwargs)
+        interpolated = self._evaluate_from_weights(r, other)
+        return interpolated

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/interpolator/ndlinear.html b/_modules/pystellibs/interpolator/ndlinear.html new file mode 100644 index 0000000..f1bfc73 --- /dev/null +++ b/_modules/pystellibs/interpolator/ndlinear.html @@ -0,0 +1,134 @@ + + + + + + + + pystellibs.interpolator.ndlinear — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.interpolator.ndlinear

+"""
+N-D linear interpolation
+"""
+from .interpolator import BaseInterpolator
+from scipy.interpolate import LinearNDInterpolator
+
+
+
[docs]class NDLinearInterpolator(BaseInterpolator):
+    def __init__(self, osl, *args, **kwargs):
+        BaseInterpolator.__init__(self, osl, *args, **kwargs)
+        data = osl.get_interpolation_data()
+        values = osl.spectra
+        self.func = LinearNDInterpolator(data, values, **kwargs)
+
+
[docs]    def interp(self, aps, weights=1., **kwargs):
+        """ Interpolate spectra """
+        return self.func(aps) * weights

+
+
[docs]    def interp_other(self, aps, values, **kwargs):
+        """ Interpolate on other values """
+        f = LinearNDInterpolator(self.func.tri, values)
+        return f(aps)

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/kurucz.html b/_modules/pystellibs/kurucz.html new file mode 100644 index 0000000..bd69e57 --- /dev/null +++ b/_modules/pystellibs/kurucz.html @@ -0,0 +1,210 @@ + + + + + + + + pystellibs.kurucz — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.kurucz

+import numpy as np
+from .stellib import AtmosphereLib
+from .config import libsdir
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+
+
[docs]class Kurucz(AtmosphereLib):
+    """
+    The stellar atmosphere models by Castelli and Kurucz 2004 or ATLAS9
+
+    * LTE
+    * PP
+    * line blanketing
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'Kurucz 2004'
+        self.source = libsdir + '/kurucz2004.grid.fits'
+        self._load_()
+        AtmosphereLib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Kurucz 2004 library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.54406 - dlogT, 5.000 + dlogg),
+                (3.55403 - dlogT, 0.000 - dlogg),
+                (3.778, 0.000 - dlogg),
+                (3.778 + dlogT, 0.000),
+                (3.875 + dlogT, 0.500),
+                (3.929 + dlogT, 1.000),
+                (3.954 + dlogT, 1.500),
+                (4.146, 2.000 - dlogg),
+                (4.146 + dlogT, 2.000),
+                (4.279 + dlogT, 2.500),
+                (4.415 + dlogT, 3.000),
+                (4.491 + dlogT, 3.500),
+                (4.591 + dlogT, 4.000),
+                (4.689 + dlogT, 4.500),
+                (4.699 + dlogT, 5.000 + dlogg),
+                (3.544 - dlogT, 5.000 + dlogg) ]
+
+        return np.array(bbox)

+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return self.grid['Teff']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return self.grid['logz']

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/marcs.html b/_modules/pystellibs/marcs.html new file mode 100644 index 0000000..942aa17 --- /dev/null +++ b/_modules/pystellibs/marcs.html @@ -0,0 +1,329 @@ + + + + + + + + pystellibs.marcs — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.marcs

+import numpy as np
+from .stellib import AtmosphereLib
+from .config import libsdir
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+
+
[docs]class Marcs(AtmosphereLib):
+    """
+    MARCS stellar atmosphere models
+
+    Gustafsson et al 2008.
+
+    http://marcs.astro.uu.se/
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'MARCS 2008'
+        self.source = libsdir + '/marcs.grid.fits'
+        self._load_()
+        AtmosphereLib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Kurucz 2004 library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.39794 - dlogT, 5.500 + dlogg),
+                (3.39794 - dlogT, 3.000 - dlogg),
+                (3.47700 - dlogT, 3.000 - dlogg),
+                (3.47700 - dlogT, 0.000 - dlogg),
+                (3.51853 - dlogT, 0.000 - dlogg),
+                (3.51853 - dlogT, -0.500 - dlogg),
+                (3.62903 - dlogT, -0.5000 - dlogg),
+                (3.62903 - dlogT, 0.0000 - dlogg),
+                (3.720, 0.000 - dlogg),
+                (3.778 + dlogT, 0.500),
+                (3.829 + dlogT, 1.000),
+                (3.860 + dlogT, 1.500),
+                (3.906, 2.000 - dlogg),
+                (3.906 + dlogT, 2.000),
+                (3.906 + dlogT, 2.500),
+                (3.906 + dlogT, 3.000),
+                (3.906 + dlogT, 3.500),
+                (3.906 + dlogT, 4.000),
+                (3.906 + dlogT, 4.500),
+                (3.906 + dlogT, 5.000 + dlogg),
+                (3.591 + dlogT, 5.000 + dlogg),
+                (3.591 + dlogT, 5.500 + dlogg)
+                ]
+
+        return np.array(bbox)

+
+
[docs]    def get_interpolation_data(self):
+        """ interpolation needs alpha """
+        return np.array([self.logT, self.logg, self.logZ, self.alpha]).T

+
+    @property
+    def logT(self):
+        return np.log10(self.grid['teff'])
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return self.grid['teff']
+
+    @property
+    def Z(self):
+        return 10 ** self.logZ
+
+    @property
+    def logZ(self):
+        return self.grid['logz']
+
+    @property
+    def alpha(self):
+        return self.grid['alpha']
+
+
[docs]    def generate_stellar_spectrum(self, logT, logg, logL, Z, alpha=0.,
+                                  raise_extrapolation=True, **kwargs):
+        """ Generates individual spectrum for the given stars APs and the
+        stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        logT: float
+            temperature
+
+        logg: float
+            log-gravity
+
+        logL: float
+            log-luminosity
+
+        Z: float
+            metallicity
+
+        alpha: float
+            alpha element
+
+        raise_extrapolation: bool
+            if set throw error on extrapolation
+
+        null: value
+            value of the flux when extrapolation and raise_extrapolation is not set
+
+        returns
+        -------
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or ergs/s/AA/Lsun
+        """
+        null_value = kwargs.pop('null', np.nan)
+
+        # weights to apply during the interpolation (note that radii must be in cm)
+        weights = self.get_weights(logT, logg, logL)
+        logZ = np.log10(Z)
+
+        l0 = self.wavelength
+
+        # check boundary conditions, keep the data but do not compute the sed
+        # if outside
+        if not self.points_inside(np.atleast_2d([logT, logg]))[0]:
+            if raise_extrapolation:
+                raise RuntimeError('Outside library interpolation range')
+            else:
+                return l0, np.full(len(self.wavelength), null_value)
+
+        aps = logT, logg, logZ, alpha
+        spec = self.interpolator.interp(aps) * weights
+
+        return spec

+
+
[docs]    def generate_individual_spectra(self, stars, nthreads=0, **kwargs):
+        """ Generates individual spectra for the given stars and stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        stars: Table
+            contains at least (logT, logg, logL, Z) of the considered stars
+
+        returns
+        -------
+        l0: ndarray, ndim=1
+            wavelength definition of the spectra
+            wavelength in AA
+
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or ergs/s/AA/Lsun
+        """
+        null_value = kwargs.pop('null', np.nan)
+        ndata = len(stars)
+        logT, logg, logL, Z = stars['logT'], stars['logg'], stars['logL'], stars['Z']
+        try:
+            alpha = stars['alpha']
+        except:
+            alpha = np.zeros_like(logT)
+
+        # weights to apply during the interpolation (note that radii must be in cm)
+        weights = self.get_weights(logT, logg, logL)
+
+        # check boundary conditions, keep the data but do not compute the sed
+        # if outside
+        bound = self.points_inside(np.array([logT, logg]).T)
+        specs = np.empty((ndata, len(self._wavelength)), dtype=float)
+        specs[~bound] = np.full(len(self.wavelength), null_value)
+
+        logZ = np.log10(Z)
+        aps = np.array([logT, logg, logZ, alpha]).T
+        s = self.interpolator.interp(aps[bound]) * weights[bound, None]
+        specs[bound] = s
+
+        l0 = self.wavelength
+        specs = specs * self.flux_units
+
+        return l0, specs

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/munari.html b/_modules/pystellibs/munari.html new file mode 100644 index 0000000..df6bd82 --- /dev/null +++ b/_modules/pystellibs/munari.html @@ -0,0 +1,212 @@ + + + + + + + + pystellibs.munari — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.munari

+import numpy as np
+from .stellib import AtmosphereLib
+from .config import libsdir
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+
+
[docs]class Munari(AtmosphereLib):
+    """
+    ATLAS9 stellar atmospheres providing higher res than Kurucz
+    medium resolution (1 Ang/pix) in optical (2500-10500 Ang)
+
+    References
+    ----------
+
+    Paper: Munari et al. 2005 A&A 442 1127
+    http://adsabs.harvard.edu/abs/2005A%26A...442.1127M
+
+    Files available at: http://archives.pd.astro.it/2500-10500/
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'Munari'
+        self.source = libsdir + '/atlas9-munari.hires.grid.fits'
+        self._load_()
+        AtmosphereLib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Munari library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(3.54407 - dlogT, 5.0 + dlogg),
+                (3.54407 - dlogT, 0.0 - dlogg),
+                (3.77815 + dlogT, 0.0 - dlogg),
+                (3.87506 + dlogT, 0.5 - dlogg),
+                (3.91645 + dlogT, 1.0 - dlogg),
+                (3.95424 + dlogT, 1.5 - dlogg),
+                (3.98900 + dlogT, 2.0 - dlogg),
+                (3.98900 + dlogT, 5.0 + dlogg),
+                (3.54407 - dlogT, 5.0 + dlogg)]
+            
+        return np.array(bbox)

+
+
[docs]    def get_interpolation_data(self):
+        """ interpolation needs alpha """
+        return np.array([self.logT, self.logg, self.logZ]).T

+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return self.grid['Teff']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return self.grid['logZ']

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/pbar.html b/_modules/pystellibs/pbar.html new file mode 100644 index 0000000..9c31882 --- /dev/null +++ b/_modules/pystellibs/pbar.html @@ -0,0 +1,420 @@ + + + + + + + + pystellibs.pbar — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.pbar

+"""
+Simple progressbar
+==================
+
+This package implement a unique progress bar class that can be used to decorate
+an iterator, a function or even standalone.
+
+The format of the meter is flexible and can display along with the progress
+meter, the running time, an eta, and the rate of the iterations.
+
+An example is::
+    description    [----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]
+"""
+import time as _time
+import sys
+import signal
+from array import array
+
+try:
+    from fcntl import ioctl
+    import termios
+except ImportError:
+    pass
+
+
+__all__ = ['Pbar']
+
+
+
[docs]class Pbar(object):
+    """
+    make a progress string  in a shape of::
+
+        [----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]
+
+
+    Attributes
+    ----------
+
+    time: bool, optional (default: True)
+        if set, add the runtime information
+
+    eta: bool, optional (default: True)
+        if set, add an estimated time to completion
+
+    rate: bool, optional (default: True)
+        if set, add the rate information
+
+    length: int, optional (default: None)
+        number of characters showing the progress meter itself
+        if None, the meter will adapt to the buffer width
+
+        TODO: make it variable with the buffer length
+
+    keep: bool, optional (default: True)
+        If not set, deletes its traces from screen after completion
+
+    file: buffer
+        the buffer to write into
+
+    mininterval: float (default: 0.5)
+        minimum time in seconds between two updates of the meter
+
+    miniters: int, optional (default: 1)
+        minimum iteration number between two updates of the meter
+
+    units: str, optional (default: 'iters')
+        unit of the iteration
+    """
+    def __init__(self, maxval=None, desc=None, time=True, eta=True, rate=True, length=None,
+                 file=None, keep=True, mininterval=0.5, miniters=1, units='iters', **kwargs):
+        self.time = time
+        self.eta = eta
+        self.rate = rate
+        self.desc = desc or ''
+        self.units = units
+        self.file = file or sys.stdout
+        self._last_print_len = 0
+        self.keep = keep
+        self.mininterval = mininterval
+        self.miniters = miniters
+        self._auto_width = True
+        self.length = 10
+        if length is not None:
+            self.length = length
+            self._auto_width = False
+        #backward compatibility
+        self._start_t = _time.time()
+        self._maxval = maxval
+        if 'txt' in kwargs:
+            self.desc = kwargs['txt']
+
+    def _buffer_width(self):
+        """ returns the width of the buffer when available """
+        try:
+            self.handle_resize(None, None)
+            signal.signal(signal.SIGWINCH, self.handle_resize)
+            self._auto_width = True
+        except:
+            self.term_width = 79
+            self._auto_width = False
+
+        return self.term_width
+
+
[docs]    def handle_resize(self, signum, frame):
+        h, w = array('h', ioctl(self.file, termios.TIOCGWINSZ, '\0' * 8))[:2]
+        self.term_width = w

+
+
[docs]    @staticmethod
+    def format_interval(t):
+        """ make a human readable time interval decomposed into days, hours,
+        minutes and seconds
+
+        Parameters
+        ----------
+        t: int
+            interval in seconds
+
+        Returns
+        -------
+        txt: str
+            string representing the interval
+            (format:  <days>d <hrs>:<min>:<sec>)
+        """
+        mins, s = divmod(int(t), 60)
+        h, m = divmod(mins, 60)
+        d, h = divmod(h, 24)
+
+        txt = '{m:02d}:{s:02d}'
+        if h:
+            txt = '{h:02d}:' + txt
+        if d:
+            txt = '{d:d}d ' + txt
+        return txt.format(d=d, h=h, m=m, s=s)

+
+
[docs]    def build_str_meter(self, n, total, elapsed):
+        """
+        make a progress string  in a shape of::
+
+            [----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]
+
+        Parameters
+        ----------
+        n: int
+            number of finished iterations
+
+        total: int
+            total number of iterations, or None
+
+        elapsed: int
+            number of seconds passed since start
+
+        Returns
+        -------
+        txt: str
+            string representing the meter
+        """
+        if n > total:
+            total = None
+
+        vals = {'n': n}
+        vals['elapsed'] = self.format_interval(elapsed)
+        vals['rate'] = '{0:5.2f}'.format((n / elapsed)) if elapsed else '?'
+        vals['units'] = self.units
+
+        if not total:
+            txt = '{n:d}'
+        else:
+            txt = '|{bar:s}| {n:d}/{total:d} {percent:s}'
+
+        if self.time or self.eta or self.rate:
+            txt += ' ['
+            info = []
+            if self.time:
+                info.append('time: {elapsed:s}')
+            if self.eta and total:
+                info.append('eta: {left:s}')
+            if self.rate:
+                info.append('{rate:s} {units:s}/sec')
+            txt += ', '.join(info) + ']'
+
+        if not total:
+            return txt.format(**vals)
+
+        frac = float(n) / total
+        bar_length = int(frac * self.length)
+        vals['bar'] = '#' * bar_length + '-' * (self.length - bar_length)
+        vals['percent'] = '{0:3.0%}'.format(frac)
+        vals['left'] = self.format_interval(elapsed / n * (total - n)) if n else '?'
+        vals['total'] = total
+
+        if self._auto_width:
+            full_length = self._buffer_width()
+            current_length = len(txt.format(**vals))
+            new_length = full_length - current_length + self.length - 1 - len(self.desc)
+            frac = float(n) / total
+            bar_length = int(frac * new_length)
+            vals['bar'] = '#' * bar_length + '-' * (new_length - bar_length)
+
+        return txt.format(**vals)

+
+
[docs]    def print_status(self, s):
+        """ print a status s  on the last file line and clean the rest of the line
+
+        Parameters
+        ----------
+        s: str
+            message to write
+        """
+        self.file.write('\r' + s + ' ' * max(self._last_print_len - len(s), 0))
+        self.file.flush()
+        self._last_print_len = len(s)

+
+
[docs]    def iterover(self, iterable, total=None):
+        """
+        Get an iterable object, and return an iterator which acts exactly like the
+        iterable, but prints a progress meter and updates it every time a value is
+        requested.
+
+        Parameters
+        ----------
+        iterable: generator or iterable object
+            object to iter over.
+
+        total: int, optional
+            the number of iterations is assumed to be the length of the
+            iterator.  But sometimes the iterable has no associated length or
+            its length is not the actual number of future iterations. In this
+            case, total can be set to define the number of iterations.
+
+        Returns
+        -------
+        gen: generator
+            pass the values from the initial iterator
+        """
+        if total is None:
+            try:
+                total = len(iterable)
+            except TypeError:
+                total = self._maxval
+
+        prefix = '{0:s}:'.format(self.desc) if self.desc else ''
+
+        self.print_status(prefix + self.build_str_meter(0, total, 0))
+        last_print_n = 0
+
+        start_t = last_print_t = _time.time()
+
+        n = 0
+        for obj in iterable:
+            yield obj
+            n += 1
+            if n - last_print_n >= self.miniters:
+                cur_t = _time.time()
+                if cur_t - last_print_t >= self.mininterval:
+                    self.print_status(prefix + self.build_str_meter(n, total, cur_t - start_t))
+                    last_print_n = n
+                    last_print_t = cur_t
+
+        if not self.keep:
+            self.print_status('')
+            sys.stdout.write('\r')
+        else:
+            if last_print_n < n:
+                cur_t = _time.time()
+                self.print_status(prefix + self.build_str_meter(n, total, cur_t - start_t))
+            self.file.write('\n')

+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        return False
+
+
[docs]    def update(self, n, desc=None, total=None):
+        """ Kept for backward compatibility and the decorator feature
+
+        Parameters
+        ----------
+        n: int
+            force iteration number n
+
+        desc: str
+            update description string
+
+        total: int
+            update the total number of iterations
+        """
+        if total is None:
+            total = self._maxval
+        if desc is not None:
+            self.desc = desc
+        prefix = '{0:s}:'.format(self.desc) if self.desc else ''
+        cur_t = _time.time()
+        self.print_status(prefix + self.build_str_meter(n, total, cur_t - self._start_t))

+
+
[docs]    def decorator(self, func):
+        """ Provide a function decorator allowing for counting calls and rates
+        """
+        self._deco_iter = 0
+        self.desc = func.__name__
+
+        def deco(*args, **kwargs):
+            # start the time at the first call
+            if self._deco_iter == 0:
+                self._start_t = _time.time()
+            self.update(self._deco_iter)
+            self._deco_iter += 1
+            return func(*args, **kwargs)
+
+        return deco

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/rauch.html b/_modules/pystellibs/rauch.html new file mode 100644 index 0000000..33cfa18 --- /dev/null +++ b/_modules/pystellibs/rauch.html @@ -0,0 +1,217 @@ + + + + + + + + pystellibs.rauch — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.rauch

+""" Rauch White Dwarfs stellar atmospheres """
+import numpy as np
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+from .stellib import Stellib
+from .config import libsdir
+
+
+
[docs]class Rauch(Stellib):
+    """
+    Rauch White Dwarfs stellar atmospheres
+
+    References
+    ----------
+
+    Rauch, T.; Werner, K.; Bohlin, R.; Kruk, J. W., "The virtual observatory service
+    TheoSSA: Establishing a database of synthetic stellar flux standards. I. NLTE
+    spectral analysis of the DA-type white dwarf G191-B2B"
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'Rauch'
+        self.source = libsdir + '/stellib_Rauch.grid.fits'
+        self._load_()
+        Stellib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Rauch library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(4.700 - dlogT, 8.000 + dlogg),
+                (4.700 - dlogT, 5.000 - dlogg),
+                (5.000 + dlogT, 5.000 - dlogg),
+                (5.280 + dlogT, 6.000 - dlogg),
+                (5.280 + dlogT, 8.000 + dlogg),
+                (4.700 - dlogT, 8.000 + dlogg) ]
+
+        return np.array(bbox)

+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return 10 ** self.grid['logT']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return np.log10(self.Z)
+
+    @property
+    def NHI(self):
+        return self.grid['NHI']
+
+    @property
+    def NHeI(self):
+        return self.grid['NHeI']
+
+    @property
+    def NHeII(self):
+        return self.grid['NHeII']

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/simpletable.html b/_modules/pystellibs/simpletable.html new file mode 100644 index 0000000..699a4ac --- /dev/null +++ b/_modules/pystellibs/simpletable.html @@ -0,0 +1,2956 @@ + + + + + + + + pystellibs.simpletable — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.simpletable

+""" This file implements a Table class
+    that is designed to be the basis of any format
+
+Requirements
+------------
+
+* FIT format:
+    * astropy:
+        provides a replacement to pyfits
+        pyfits can still be used instead but astropy is now the default
+
+* HDF5 format:
+    * pytables
+
+RuntimeError will be raised when writing to a format associated with missing
+package.
+
+
+.. code-block::python
+
+    >>> t = SimpleTable('path/mytable.csv')
+    # get a subset of columns only
+    >>> s = t.get('M_* logTe logLo U B V I J K')
+    # set some aliases
+    >>> t.set_alias('logT', 'logTe')
+    >>> t.set_alias('logL', 'logLLo')
+    # make a query on one or multiple column
+    >>> q = s.selectWhere('logT logL', '(J > 2) & (10 ** logT > 5000)')
+    # q is also a table object
+    >>> q.plot('logT', 'logL', ',')
+    # makes a simple plot
+    >>> s.write('newtable.fits')
+    # export the initial subtable to a new file
+"""
+from __future__ import (absolute_import, division, print_function)
+
+__version__ = '3.0'
+__all__ = ['AstroHelpers', 'AstroTable', 'SimpleTable', 'stats']
+
+import sys
+import math
+from copy import deepcopy
+import re
+import itertools
+from functools import wraps, partial
+import numpy as np
+from numpy import deg2rad, rad2deg, sin, cos, sqrt, arcsin, arctan2
+from numpy.lib import recfunctions
+import types
+
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+except:
+    pyfits = None
+
+try:
+    import tables
+except ImportError:
+    tables = None
+
+try:
+    import pandas as _pd
+except ImportError:
+    _pd = None
+
+
+# ==============================================================================
+# Python 3 compatibility behavior
+# ==============================================================================
+# remap some python 2 built-ins on to py3k behavior or equivalent
+# Most of them become generators
+import operator
+
+PY3 = sys.version_info[0] > 2
+
+if PY3:
+    iteritems = operator.methodcaller('items')
+    itervalues = operator.methodcaller('values')
+    basestring = (str, bytes)
+else:
+    range = xrange
+    from itertools import izip as zip
+    iteritems = operator.methodcaller('iteritems')
+    itervalues = operator.methodcaller('itervalues')
+    basestring = (str, unicode)
+
+
+# ==============================================================================
+# Specials -- special functions
+# ==============================================================================
+
+def pretty_size_print(num_bytes):
+    """
+    Output number of bytes in a human readable format
+
+    keywords
+    --------
+    num_bytes: int
+        number of bytes to convert
+
+    returns
+    -------
+    output: str
+        string representation of the size with appropriate unit scale
+    """
+    if num_bytes is None:
+        return
+
+    KiB = 1024
+    MiB = KiB * KiB
+    GiB = KiB * MiB
+    TiB = KiB * GiB
+    PiB = KiB * TiB
+    EiB = KiB * PiB
+    ZiB = KiB * EiB
+    YiB = KiB * ZiB
+
+    if num_bytes > YiB:
+        output = '%.3g YB' % (num_bytes / YiB)
+    elif num_bytes > ZiB:
+        output = '%.3g ZB' % (num_bytes / ZiB)
+    elif num_bytes > EiB:
+        output = '%.3g EB' % (num_bytes / EiB)
+    elif num_bytes > PiB:
+        output = '%.3g PB' % (num_bytes / PiB)
+    elif num_bytes > TiB:
+        output = '%.3g TB' % (num_bytes / TiB)
+    elif num_bytes > GiB:
+        output = '%.3g GB' % (num_bytes / GiB)
+    elif num_bytes > MiB:
+        output = '%.3g MB' % (num_bytes / MiB)
+    elif num_bytes > KiB:
+        output = '%.3g KB' % (num_bytes / KiB)
+    else:
+        output = '%.3g Bytes' % (num_bytes)
+
+    return output
+
+
+def _fits_read_header(hdr):
+    """
+    Convert pyfits header into dictionary with relevant values
+
+    Parameters
+    ----------
+
+    hdr: pyftis.Header
+        fits unit
+
+    Returns
+    -------
+    header: dict
+        header dictionary
+
+    alias: dict
+        aliases
+
+    units: dict
+        units
+
+    comments: dict
+        comments/description of keywords
+    """
+    header = {}
+    alias = {}
+    units = {}
+    comments = {}
+
+    # generic cards
+    genTerms = ['XTENSION', 'BITPIX', 'NAXIS', 'NAXIS1',
+                'NAXIS2', 'PCOUNT', 'GCOUNT', 'TFIELDS',
+                'EXTNAME']
+    fieldTerms = ['TTYPE', 'TFORM', 'TUNIT', 'ALIAS']
+
+    # read col comments
+    for k, name, comment in hdr.ascard['TTYPE*']:
+        comments[name] = comment
+        u = hdr.get(k.replace('TYPE', 'UNIT'), None)
+        if u is not None:
+            units[name] = u
+
+    for k, val, _ in hdr.ascard['ALIAS*']:
+        al, orig = val.split('=')
+        alias[al] = orig
+
+    # other specific keywords: COMMENT, HISTORY
+    header_comments = []
+    header_history = []
+    for k, v in hdr.items():
+        if (k not in genTerms) and (k[:5] not in fieldTerms):
+            if (k == 'COMMENT'):
+                header_comments.append(v)
+            elif (k == 'HISTORY'):
+                header_history.append(v)
+            else:
+                header[k] = v
+
+    # COMMENT, HISTORY polish
+    if len(header_comments) > 0:
+        header['COMMENT'] = '\n'.join(header_comments)
+    if len(header_history) > 0:
+        header['HISTORY'] = '\n'.join(header_history)
+
+    if 'EXTNAME' in hdr:
+        header['NAME'] = hdr['EXTNAME']
+
+    return header, alias, units, comments
+
+
+def _fits_generate_header(tab):
+    """ Generate the corresponding fits Header that contains all necessary info
+
+    Parameters
+    ----------
+
+    tab: SimpleTable instance
+        table
+
+    Returns
+    -------
+    hdr: pyfits.Header
+        header instance
+    """
+    # get column cards
+
+    cards = []
+
+    # names units and comments
+    for e, k in enumerate(tab.keys()):
+        cards.append(('TTYPE{0:d}'.format(e + 1), k, tab._desc.get(k, '')))
+        u = tab._units.get(k, '')
+        if u not in ['', 'None', None]:
+            cards.append(('TUNIT{0:d}'.format(e + 1), tab._units.get(k, ''),
+                          'unit of {0:s}'.format(k)))
+
+    # add aliases
+    for e, v in enumerate(tab._aliases.items()):
+        cards.append( ('ALIAS{0:d}'.format(e + 1), '='.join(v), '') )
+
+    if tab.header['NAME'] not in ['', 'None', None, 'No Name']:
+        cards.append(('EXTNAME', tab.header['NAME'], ''))
+
+    hdr = pyfits.Header(cards)
+
+    for k, v in tab.header.items():
+        if (v not in ['', 'None', None]) & (k != 'NAME'):
+            if (k != 'COMMENT') & (k != 'HISTORY'):
+                hdr.update(k, v)
+            else:
+                txt = v.split('\n')
+                for j in txt:
+                    if k == 'COMMENT':
+                        hdr.add_comment(j)
+                    elif k == 'HISTORY':
+                        hdr.add_history(j)
+    return hdr
+
+
+def _fits_writeto(filename, data, header=None, output_verify='exception',
+                  clobber=False, checksum=False):
+    """
+    Create a new FITS file using the supplied data/header.
+    Patched version of pyfits to correctly include provided header
+
+    Parameters
+    ----------
+    filename : file path, file object, or file like object
+        File to write to.  If opened, must be opened in a writeable binary
+        mode such as 'wb' or 'ab+'.
+
+    data : array, record array, or groups data object
+        data to write to the new file
+
+    header : `Header` object, optional
+        the header associated with ``data``. If `None`, a header
+        of the appropriate type is created for the supplied data. This
+        argument is optional.
+
+    output_verify : str
+        Output verification option.  Must be one of ``"fix"``, ``"silentfix"``,
+        ``"ignore"``, ``"warn"``, or ``"exception"``.  May also be any
+        combination of ``"fix"`` or ``"silentfix"`` with ``"+ignore"``,
+        ``+warn``, or ``+exception" (e.g. ``"fix+warn"``).  See :ref:`verify`
+        for more info.
+
+    clobber : bool, optional
+        If `True`, and if filename already exists, it will overwrite
+        the file.  Default is `False`.
+
+    checksum : bool, optional
+        If `True`, adds both ``DATASUM`` and ``CHECKSUM`` cards to the
+        headers of all HDU's written to the file
+    """
+
+    hdu = pyfits.convenience._makehdu(data, header)
+    hdu.header.update(header.cards)
+    if hdu.is_image and not isinstance(hdu, pyfits.PrimaryHDU):
+        hdu = pyfits.PrimaryHDU(data, header=header)
+    hdu.writeto(filename, clobber=clobber, output_verify=output_verify,
+                checksum=checksum)
+
+
+def _fits_append(filename, data, header=None, checksum=False, verify=True,
+                 **kwargs):
+    """
+    Append the header/data to FITS file if filename exists, create if not.
+
+    If only ``data`` is supplied, a minimal header is created.
+    Patched version of pyfits to correctly include provided header
+
+    Parameters
+    ----------
+    filename : file path, file object, or file like object
+        File to write to.  If opened, must be opened for update (rb+) unless it
+        is a new file, then it must be opened for append (ab+).  A file or
+        `~gzip.GzipFile` object opened for update will be closed after return.
+
+    data : array, table, or group data object
+        the new data used for appending
+
+    header : `Header` object, optional
+        The header associated with ``data``.  If `None`, an appropriate header
+        will be created for the data object supplied.
+
+    checksum : bool, optional
+        When `True` adds both ``DATASUM`` and ``CHECKSUM`` cards to the header
+        of the HDU when written to the file.
+
+    verify : bool, optional
+        When `True`, the existing FITS file will be read in to verify it for
+        correctness before appending.  When `False`, content is simply appended
+        to the end of the file.  Setting ``verify`` to `False` can be much
+        faster.
+
+    kwargs
+        Any additional keyword arguments to be passed to
+        `astropy.io.fits.open`.
+    """
+
+    name, closed, noexist_or_empty = pyfits.convenience._stat_filename_or_fileobj(filename)
+
+    if noexist_or_empty:
+        #
+        # The input file or file like object either doesn't exits or is
+        # empty.  Use the writeto convenience function to write the
+        # output to the empty object.
+        #
+        _fits_writeto(filename, data, header, checksum=checksum, **kwargs)
+    else:
+        hdu = pyfits.convenience._makehdu(data, header)
+        hdu.header.update(header.cards)
+
+        if isinstance(hdu, pyfits.PrimaryHDU):
+            hdu = pyfits.ImageHDU(data, header)
+
+        if verify or not closed:
+            f = pyfits.convenience.fitsopen(filename, mode='append')
+            f.append(hdu)
+
+            # Set a flag in the HDU so that only this HDU gets a checksum when
+            # writing the file.
+            hdu._output_checksum = checksum
+            f.close(closed=closed)
+        else:
+            f = pyfits.convenience._File(filename, mode='append')
+            hdu._output_checksum = checksum
+            hdu._writeto(f)
+            f.close()
+
+
+def _ascii_read_header(fname, comments='#', delimiter=None, commentedHeader=True,
+                       *args, **kwargs):
+    """
+    Read ASCII/CSV header
+
+    Parameters
+    ----------
+    fname: str or stream
+        File, filename, or generator to read.
+        Note that generators should return byte strings for Python 3k.
+
+    comments: str, optional
+        The character used to indicate the start of a comment;
+        default: '#'.
+
+    delimiter: str, optional
+        The string used to separate values.  By default, this is any
+        whitespace.
+
+    commentedHeader: bool, optional
+        if set, the last line of the header is expected to be the column titles
+
+    Returns
+    -------
+    nlines: int
+        number of lines from the header
+
+    header: dict
+        header dictionary
+
+    alias: dict
+        aliases
+
+    units: dict
+        units
+
+    comments: dict
+        comments/description of keywords
+
+    names: sequence
+        sequence or str, first data line after header, expected to be the column
+        names.
+    """
+    if hasattr(fname, 'read'):
+        stream = fname
+    else:
+        stream = open(fname, 'r')
+
+    header = {}
+    alias = {}
+    units = {}
+    desc = {}
+
+    def parseStrNone(v):
+        """ robust parse """
+        _v = v.split()
+        if (len(_v) == 0):
+            return None
+        else:
+            _v = ' '.join(_v)
+            if (_v.lower()) == 'none' or (_v.lower() == 'null'):
+                return None
+            else:
+                return _v
+
+    done = False
+    oldline = None
+    lasthdr = None
+    nlines = 0
+    header.setdefault('COMMENT', '')
+    header.setdefault('HISTORY', '')
+    while done is False:
+        line = stream.readline()[:-1]  # getting rid of '\n'
+        nlines += 1
+        if (line[0] == comments):  # header part
+            if (len(line) > 2):
+                if line[1] == comments:  # column meta data
+                    # column meta is expected to start with ##
+                    k = line[2:].split('\t')
+                    colname = k[0].strip()
+                    colunit = None
+                    colcomm = None
+                    if len(k) > 1:
+                        colunit = parseStrNone(k[1])
+                    if len(k) > 2:
+                        colcomm = parseStrNone(k[2])
+
+                    if colunit is not None:
+                        units[colname] = colunit
+                    if colcomm is not None:
+                        desc[colname] = colcomm
+                else:
+                    # header is expected as "# key \t value"
+                    k = line[1:].split('\t')
+                    if len(k) > 1:
+                        key = k[0].strip()  # remove trainling spaces
+                        val = ' '.join(k[1:]).strip()
+
+                        if key in ('', None, 'None', 'NONE', 'COMMENT'):
+                            header['COMMENT'] = header['COMMENT'] + '\n' + val
+                        if key in ('HISTORY', ):
+                            header['HISTORY'] = header['HISTORY'] + '\n' + val
+                        elif 'alias' in key.lower():
+                            # take care of aliases
+                            al, orig = val.split('=')
+                            alias[al] = orig
+                        else:
+                            header[key] = val
+                        lasthdr = key
+                    else:
+                        header['COMMENT'] = header['COMMENT'] + '\n' + line[1:]
+        else:
+            done = True
+            if commentedHeader and (oldline is not None):
+                names = oldline.split(delimiter)
+                nlines -= 1
+                if lasthdr == names[0]:
+                    header.pop(lasthdr)
+            else:
+                names = line.split(delimiter)
+        oldline = line[1:]
+
+    if not hasattr(fname, 'read'):
+        stream.close()
+    else:
+        stream.seek(stream.tell() - len(line))
+        nlines = 0  # make sure the value is set to the current position
+
+    return nlines, header, units, desc, alias, names
+
+
+def _hdf5_write_data(filename, data, tablename=None, mode='w', append=False,
+                     header={}, units={}, comments={}, aliases={}, **kwargs):
+    """ Write table into HDF format
+
+    Parameters
+    ----------
+    filename : file path, or tables.File instance
+        File to write to.  If opened, must be opened and writable (mode='w' or 'a')
+
+    data: recarray
+        data to write to the new file
+
+    tablename: str
+        path of the node including table's name
+
+    mode: str
+        in ('w', 'a') mode to open the file
+
+    append: bool
+        if set, tends to append data to an existing table
+
+    header: dict
+        table header
+
+    units: dict
+        dictionary of units
+
+    alias: dict
+        aliases
+
+    comments: dict
+        comments/description of keywords
+
+    .. note::
+        other keywords are forwarded to :func:`tables.openFile`
+    """
+
+    if hasattr(filename, 'read'):
+        raise Exception("HDF backend does not implement stream")
+
+    if append is True:
+        mode = 'a'
+    silent = kwargs.pop('silent', False)
+
+    if isinstance(filename, tables.File):
+        if (filename.mode != mode) & (mode != 'r'):
+            raise tables.FileModeError('The file is already opened in a different mode')
+        hd5 = filename
+    else:
+        hd5 = tables.openFile(filename, mode=mode)
+
+    # check table name and path
+    tablename = tablename or header.get('NAME', None)
+    if tablename in ('', None, 'Noname', 'None'):
+        tablename = '/data'
+
+    w = tablename.split('/')
+    where = '/'.join(w[:-1])
+    name = w[-1]
+    if where in ('', None):
+        where = '/'
+    if where[0] != '/':
+        where = '/' + where
+
+    if append:
+        try:
+            t = hd5.getNode(where + name)
+            t.append(data.astype(t.description._v_dtype))
+            t.flush()
+        except tables.NoSuchNodeError:
+            if not silent:
+                print(("Warning: Table {0} does not exists.  \n A new table will be created").format(where + name))
+            append = False
+
+    if not append:
+        t = hd5.createTable(where, name, data, **kwargs)
+
+        # update header
+        for k, v in header.items():
+            if (k == 'FILTERS') & (float(t.attrs['VERSION']) >= 2.0):
+                t.attrs[k.lower()] = v
+            else:
+                t.attrs[k] = v
+        if 'TITLE' not in header:
+            t.attrs['TITLE'] = name
+
+        # add column descriptions and units
+        for e, colname in enumerate(data.dtype.names):
+            _u = units.get(colname, None)
+            _d = comments.get(colname, None)
+            if _u is not None:
+                t.attrs['FIELD_{0:d}_UNIT'] = _u
+            if _d is not None:
+                t.attrs['FIELD_{0:d}_DESC'] = _d
+
+        # add aliases
+        for i, (k, v) in enumerate(aliases.items()):
+            t.attrs['ALIAS{0:d}'.format(i)] = '{0:s}={1:s}'.format(k, v)
+
+        t.flush()
+
+    if not isinstance(filename, tables.File):
+        hd5.flush()
+        hd5.close()
+
+
+def _hdf5_read_data(filename, tablename=None, silent=False, *args, **kwargs):
+    """ Generate the corresponding ascii Header that contains all necessary info
+
+    Parameters
+    ----------
+    filename: str
+        file to read from
+
+    tablename: str
+        node containing the table
+
+    silent: bool
+        skip verbose messages
+
+    Returns
+    -------
+    hdr: str
+        string that will be be written at the beginning of the file
+    """
+    source = tables.openFile(filename, *args, **kwargs)
+
+    if tablename is None:
+        node = source.listNodes('/')[0]
+        tablename = node.name
+    else:
+        if tablename[0] != '/':
+            node = source.getNode('/' + tablename)
+        else:
+            node = source.getNode(tablename)
+    if not silent:
+        print("\tLoading table: {0}".format(tablename))
+
+    hdr = {}
+    aliases = {}
+
+    # read header
+    exclude = ['NROWS', 'VERSION', 'CLASS', 'EXTNAME', 'TITLE']
+    for k in node.attrs._v_attrnames:
+        if (k not in exclude):
+            if (k[:5] != 'FIELD') & (k[:5] != 'ALIAS'):
+                hdr[k] = node.attrs[k]
+            elif k[:5] == 'ALIAS':
+                c0, c1 = node.attrs[k].split('=')
+                aliases[c0] = c1
+
+    empty_name = ['', 'None', 'Noname', None]
+    if node.attrs['TITLE'] not in empty_name:
+        hdr['NAME'] = node.attrs['TITLE']
+    else:
+        hdr['NAME'] = '{0:s}/{1:s}'.format(filename, node.name)
+
+    # read column meta
+    units = {}
+    desc = {}
+
+    for (k, colname) in enumerate(node.colnames):
+        _u = getattr(node.attrs, 'FIELD_{0:d}_UNIT'.format(k), None)
+        _d = getattr(node.attrs, 'FIELD_{0:d}_DESC'.format(k), None)
+        if _u is not None:
+            units[colname] = _u
+        if _d is not None:
+            desc[colname] = _d
+
+    data = node[:]
+
+    source.close()
+
+    return hdr, aliases, units, desc, data
+
+
+def _ascii_generate_header(tab, comments='#', delimiter=' ',
+                           commentedHeader=True):
+    """ Generate the corresponding ascii Header that contains all necessary info
+
+    Parameters
+    ----------
+
+    tab: SimpleTable instance
+        table
+
+    comments: str
+        string to prepend header lines
+
+    delimiter: str, optional
+        The string used to separate values.  By default, this is any
+        whitespace.
+
+    commentedHeader: bool, optional
+        if set, the last line of the header is expected to be the column titles
+
+    Returns
+    -------
+    hdr: str
+        string that will be be written at the beginning of the file
+    """
+    hdr = []
+
+    if comments is None:
+        comments = ''
+
+    # table header
+    length = max(map(len, tab.header.keys()))
+    fmt = '{{0:s}} {{1:{0:d}s}}\t{{2:s}}'.format(length)
+    for k, v in tab.header.items():
+        for vk in v.split('\n'):
+            if len(vk) > 0:
+                hdr.append(fmt.format(comments, k.upper(), vk.strip()))
+
+    # column metadata
+    hdr.append(comments)  # add empty line
+    length = max(map(len, tab.keys()))
+    fmt = '{{0:s}}{{0:s}} {{1:{0:d}s}}\t{{2:s}}\t{{3:s}}'.format(length)
+    for colname in tab.keys():
+        unit = tab._units.get(colname, 'None')
+        desc = tab._desc.get(colname, 'None')
+        hdr.append(fmt.format(comments, colname, unit, desc))
+
+    # aliases
+    if len(tab._aliases) > 0:
+        hdr.append(comments)  # add empty line
+        for k, v in tab._aliases.items():
+            hdr.append('{0:s} alias\t{1:s}={2:s}'.format(comments, k, v))
+
+    # column names
+    hdr.append(comments)
+    if commentedHeader:
+        hdr.append('{0:s} {1:s}'.format(comments, delimiter.join(tab.keys())))
+    else:
+        hdr.append('{0:s}'.format(delimiter.join(tab.keys())))
+
+    return '\n'.join(hdr)
+
+
+def _latex_writeto(filename, tab, comments='%'):
+    """ Write the data into a latex table format
+
+    Parameters
+    ----------
+    filename: str
+        file or unit to write into
+
+    tab: SimpleTable instance
+        table
+
+    comments: str
+        string to prepend header lines
+
+    delimiter: str, optional
+        The string used to separate values.  By default, this is any
+        whitespace.
+
+    commentedHeader: bool, optional
+        if set, the last line of the header is expected to be the column titles
+    """
+    txt = "\\begin{table}\n\\begin{center}\n"
+
+    # add caption
+    tabname = tab.header.get('NAME', None)
+    if tabname not in ['', None, 'None']:
+        txt += "\\caption{{{0:s}}}\n".format(tabname)
+
+    # tabular
+    txt += '\\begin{{tabular}}{{{0:s}}}\n'.format('c' * tab.ncols)
+    txt += tab.pprint(delim=' & ', fields='MAG*', headerChar='', endline='\\\\\n', all=True, ret=True)
+    txt += '\\end{tabular}\n'
+
+    # end table
+    txt += "\\end{center}\n"
+
+    # add notes if any
+    if len(tab._desc) > 0:
+        txt += '\% notes \n\\begin{scriptsize}\n'
+        for e, (k, v) in enumerate(tab._desc.items()):
+            if v not in (None, 'None', 'none', ''):
+                txt += '{0:d} {1:s}: {2:s} \\\\\n'.format(e, k, v)
+        txt += '\\end{scriptsize}\n'
+    txt += "\\end{table}\n"
+    if hasattr(filename, 'write'):
+        filename.write(txt)
+    else:
+        with open(filename, 'w') as unit:
+            unit.write(txt)
+
+
+def _convert_dict_to_structured_ndarray(data):
+    """convert_dict_to_structured_ndarray
+
+    Parameters
+    ----------
+
+    data: dictionary like object
+        data structure which provides iteritems and itervalues
+
+    returns
+    -------
+    tab: structured ndarray
+        structured numpy array
+    """
+    newdtype = []
+    try:
+        for key, dk in iteritems(data):
+            _dk = np.asarray(dk)
+            dtype = _dk.dtype
+            # unknown type is converted to text
+            if dtype.type == np.object_:
+                if len(data) == 0:
+                    longest = 0
+                else:
+                    longest = len(max(_dk, key=len))
+                    _dk = _dk.astype('|%iS' % longest)
+            if _dk.ndim > 1:
+                newdtype.append((str(key), _dk.dtype, (_dk.shape[1],)))
+            else:
+                newdtype.append((str(key), _dk.dtype))
+        tab = np.rec.fromarrays(itervalues(data), dtype=newdtype)
+    except AttributeError:  # not a dict
+        # hope it's a tuple ((key, value),) pairs.
+        from itertools import tee
+        d1, d2 = tee(data)
+        for key, dk in d1:
+            _dk = np.asarray(dk)
+            dtype = _dk.dtype
+            # unknown type is converted to text
+            if dtype.type == np.object_:
+                if len(data) == 0:
+                    longest = 0
+                else:
+                    longest = len(max(_dk, key=len))
+                    _dk = _dk.astype('|%iS' % longest)
+            if _dk.ndim > 1:
+                newdtype.append((str(key), _dk.dtype, (_dk.shape[1],)))
+            else:
+                newdtype.append((str(key), _dk.dtype))
+        tab = np.rec.fromarrays((dk for (_, dk) in d2), dtype=newdtype)
+
+    return tab
+
+
+def __indent__(rows, header=None, units=None, headerChar='-',
+               delim=' | ', endline='\n', **kwargs):
+    """Indents a table by column.
+
+    Parameters
+    ----------
+    rows: sequences of rows
+        one sequence per row.
+
+    header: sequence of str
+        row consists of the columns' names
+
+    units: sequence of str
+        Sequence of units
+
+    headerChar: char
+        Character to be used for the row separator line
+
+    delim: char
+        The column delimiter.
+
+    returns
+    -------
+    txt: str
+        string represation of rows
+    """
+    length_data = list(map(max, zip(*[list(map(len, k)) for k in rows])))
+    length = length_data[:]
+
+    if (header is not None):
+        length_header = list(map(len, header))
+        length = list(map(max, zip(length_data, length_header)))
+
+    if (units is not None):
+        length_units = list(map(len, units))
+        length = list(map(max, zip(length_data, length_units)))
+
+    if headerChar not in (None, '', ' '):
+        rowSeparator = headerChar * (sum(length) + len(delim) * (len(length) - 1)) + endline
+    else:
+        rowSeparator = ''
+
+    # make the format
+    fmt = ['{{{0:d}:{1:d}s}}'.format(k, l) for (k, l) in enumerate(length)]
+    fmt = delim.join(fmt) + endline
+    # write the string
+    txt = rowSeparator
+    if header is not None:
+        txt += fmt.format(*header)  # + endline
+        txt += rowSeparator
+    if units is not None:
+        txt += fmt.format(*units)  # + endline
+        txt += rowSeparator
+    for r in rows:
+        txt += fmt.format(*r)  # + endline
+    txt += rowSeparator
+    return txt
+
+
+def pprint_rec_entry(data, num=0, keys=None):
+        """ print one line with key and values properly to be readable
+
+        Parameters
+        ----------
+        data: recarray
+            data to extract entry from
+
+        num: int, slice
+            indice selection
+
+        keys: sequence or str
+            if str, can be a regular expression
+            if sequence, the sequence of keys to print
+        """
+        if (keys is None) or (keys == '*'):
+            _keys = data.dtype.names
+        elif type(keys) in basestring:
+            _keys = [k for k in data.dtype.names if (re.match(keys, k) is not None)]
+        else:
+            _keys = keys
+
+        length = max(map(len, _keys))
+        fmt = '{{0:{0:d}s}}: {{1}}'.format(length)
+        data = data[num]
+
+        for k in _keys:
+            print(fmt.format(k, data[k]))
+
+
+def pprint_rec_array(data, idx=None, fields=None, ret=False, all=False,
+                     headerChar='-', delim=' | ', endline='\n' ):
+        """ Pretty print the table content
+            you can select the table parts to display using idx to
+            select the rows and fields to only display some columns
+            (ret is only for insternal use)
+
+        Parameters
+        ----------
+        data: array
+            array to show
+
+        idx: sequence, slide
+            sub selection to print
+
+        fields: str, sequence
+            if str can be a regular expression, and/or list of fields separated
+            by spaces or commas
+
+        ret: bool
+            if set return the string representation instead of printing the result
+
+        all: bool
+            if set, force to show all rows
+
+        headerChar: char
+            Character to be used for the row separator line
+
+        delim: char
+            The column delimiter.
+        """
+        if (fields is None) or (fields == '*'):
+            _keys = data.dtype.names
+        elif type(fields) in basestring:
+            if ',' in fields:
+                _fields = fields.split(',')
+            elif ' ' in fields:
+                _fields = fields.split()
+            else:
+                _fields = [fields]
+            lbls = data.dtype.names
+            _keys = []
+            for _fk in _fields:
+                _keys += [k for k in lbls if (re.match(_fk, k) is not None)]
+        else:
+            lbls = data.dtype.names
+            _keys = []
+            for _fk in _fields:
+                _keys += [k for k in lbls if (re.match(_fk, k) is not None)]
+
+        nfields = len(_keys)
+        nrows = len(data)
+        fields = list(_keys)
+
+        if idx is None:
+            if (nrows < 10) or (all is True):
+                rows = [ [ str(data[k][rk]) for k in _keys ] for rk in range(nrows)]
+            else:
+                _idx = range(6)
+                rows = [ [ str(data[k][rk]) for k in _keys ] for rk in range(5) ]
+                if nfields > 1:
+                    rows += [ ['...' for k in range(nfields) ] ]
+                else:
+                    rows += [ ['...' for k in range(nfields) ] ]
+                rows += [ [ str(data[k][rk]) for k in fields ] for rk in range(-5, 0)]
+        elif isinstance(idx, slice):
+            _idx = range(idx.start, idx.stop, idx.step or 1)
+            rows = [ [ str(data[k][rk]) for k in fields ] for rk in _idx]
+        else:
+            rows = [ [ str(data[k][rk]) for k in fields ] for rk in idx]
+
+        out = __indent__(rows, header=_keys, units=None, delim=delim,
+                         headerChar=headerChar, endline=endline)
+        if ret is True:
+            return out
+        else:
+            print(out)
+
+
+def elementwise(func):
+    """
+    Quick and dirty elementwise function decorator it provides a quick way
+    to apply a function either on one element or a sequence of elements
+    """
+    @wraps(func)
+    def wrapper(it, **kwargs):
+        if hasattr(it, '__iter__') & (type(it) not in basestring):
+            _f = partial(func, **kwargs)
+            return map(_f, it)
+        else:
+            return func(it, **kwargs)
+    return wrapper
+
+
+
[docs]class AstroHelpers(object):
+    """ Helpers related to astronomy data """
+
+
[docs]    @staticmethod
+    @elementwise
+    def hms2deg(_str, delim=':'):
+        """ Convert hex coordinates into degrees
+
+        Parameters
+        ----------
+        str: string or sequence
+            string to convert
+
+        delimiter: str
+            character delimiting the fields
+
+        Returns
+        -------
+        deg: float
+            angle in degrees
+        """
+        if _str[0] == '-':
+            neg = -1
+            _str = _str[1:]
+        else:
+            neg = 1
+        _str = _str.split(delim)
+        return neg * ((((float(_str[-1]) / 60. +
+                         float(_str[1])) / 60. +
+                        float(_str[0])) / 24. * 360.))

+
+
[docs]    @staticmethod
+    @elementwise
+    def deg2dms(val, delim=':'):
+        """ Convert degrees into hex coordinates
+        Parameters
+        ----------
+        deg: float
+            angle in degrees
+
+        delimiter: str
+            character delimiting the fields
+
+        Returns
+        -------
+        str: string or sequence
+            string to convert
+        """
+        if val < 0:
+            sign = -1
+        else:
+            sign = 1
+        d = int( sign * val )
+        m = int( (sign * val - d) * 60. )
+        s = (( sign * val - d) * 60.  - m) * 60.
+        return '{0}{1}{2}{3}{4}'.format( sign * d, delim, m, delim, s)

+
+
[docs]    @staticmethod
+    @elementwise
+    def deg2hms(val, delim=':'):
+        """ Convert degrees into hex coordinates
+
+        Parameters
+        ----------
+        deg: float
+            angle in degrees
+
+        delimiter: str
+            character delimiting the fields
+
+        Returns
+        -------
+        str: string or sequence
+            string to convert
+        """
+        if val < 0:
+            sign = -1
+        else:
+            sign = 1
+        h = int( sign * val / 45. * 3.)   # * 24 / 360
+        m = int( (sign * val / 45. * 3. - h) * 60. )
+        s = (( sign * val / 45. * 3. - h) * 60.  - m) * 60.
+        return '{0}{1}{2}{3}{4}'.format( sign * h, delim, m, delim, s)

+
+
[docs]    @staticmethod
+    @elementwise
+    def dms2deg(_str, delim=':'):
+        """ Convert hex coordinates into degrees
+        Parameters
+        ----------
+        str: string or sequence
+            string to convert
+
+        delimiter: str
+            character delimiting the fields
+
+        Returns
+        -------
+        deg: float
+            angle in degrees
+        """
+        if _str[0] == '-':
+            neg = -1
+            _str = _str[1:]
+        else:
+            neg = 1
+        _str = _str.split(delim)
+        return (neg * ((float(_str[-1]) / 60. + float(_str[1])) / 60. + float(_str[0])))

+
+
[docs]    @staticmethod
+    @elementwise
+    def euler(ai_in, bi_in, select, b1950=False, dtype='f8'):
+        """
+        Transform between Galactic, celestial, and ecliptic coordinates.
+        Celestial coordinates (RA, Dec) should be given in equinox J2000
+        unless the b1950 is True.
+
+        select From           To         |   select    From          To
+        ----------------------------------------------------------------------
+        1      RA-Dec (2000)  Galactic   |     4       Ecliptic      RA-Dec
+        2      Galactic       RA-DEC     |     5       Ecliptic      Galactic
+        3      RA-Dec         Ecliptic   |     6       Galactic      Ecliptic
+
+        Parameters
+        ----------
+
+        long_in: float, or sequence
+            Input Longitude in DEGREES, scalar or vector.
+
+        lat_in: float, or sequence
+            Latitude in DEGREES
+
+        select: int
+            Integer from 1 to 6 specifying type of coordinate transformation.
+
+        b1950: bool
+            set equinox set to 1950
+
+
+        Returns
+        -------
+        long_out: float, seq
+            Output Longitude in DEGREES
+
+        lat_out: float, seq
+            Output Latitude in DEGREES
+
+
+        REVISION HISTORY:
+        Written W. Landsman,  February 1987
+        Adapted from Fortran by Daryl Yentis NRL
+        Converted to IDL V5.0   W. Landsman   September 1997
+        Made J2000 the default, added /FK4 keyword  W. Landsman December 1998
+        Add option to specify SELECT as a keyword W. Landsman March 2003
+        Converted from IDL to numerical Python: Erin Sheldon, NYU, 2008-07-02
+        """
+
+        # Make a copy as an array. ndmin=1 to avoid messed up scalar arrays
+        ai = np.array(ai_in, ndmin=1, copy=True, dtype=dtype)
+        bi = np.array(bi_in, ndmin=1, copy=True, dtype=dtype)
+
+        PI = math.pi
+        # HALFPI = PI / 2.0
+        D2R = PI / 180.0
+        R2D = 1.0 / D2R
+
+        twopi   = 2.0 * PI
+        fourpi  = 4.0 * PI
+
+        #   J2000 coordinate conversions are based on the following constants
+        #   (see the Hipparcos explanatory supplement).
+        #  eps = 23.4392911111d           Obliquity of the ecliptic
+        #  alphaG = 192.85948d            Right Ascension of Galactic North Pole
+        #  deltaG = 27.12825d             Declination of Galactic North Pole
+        #  lomega = 32.93192d             Galactic longitude of celestial equator
+        #  alphaE = 180.02322d            Ecliptic longitude of Galactic North Pole
+        #  deltaE = 29.811438523d         Ecliptic latitude of Galactic North Pole
+        #  Eomega  = 6.3839743d           Galactic longitude of ecliptic equator
+        # Parameters for all the different conversions
+        if b1950:
+            # equinox = '(B1950)'
+            psi    = np.array([ 0.57595865315, 4.9261918136,
+                                0.00000000000, 0.0000000000,
+                                0.11129056012, 4.7005372834], dtype=dtype)
+            stheta = np.array([ 0.88781538514, -0.88781538514,
+                                0.39788119938, -0.39788119938,
+                                0.86766174755, -0.86766174755], dtype=dtype)
+            ctheta = np.array([ 0.46019978478, 0.46019978478,
+                                0.91743694670, 0.91743694670,
+                                0.49715499774, 0.49715499774], dtype=dtype)
+            phi    = np.array([ 4.9261918136,  0.57595865315,
+                                0.0000000000, 0.00000000000,
+                                4.7005372834, 0.11129056012], dtype=dtype)
+        else:
+            # equinox = '(J2000)'
+            psi    = np.array([ 0.57477043300, 4.9368292465,
+                                0.00000000000, 0.0000000000,
+                                0.11142137093, 4.71279419371], dtype=dtype)
+            stheta = np.array([ 0.88998808748, -0.88998808748,
+                                0.39777715593, -0.39777715593,
+                                0.86766622025, -0.86766622025], dtype=dtype)
+            ctheta = np.array([ 0.45598377618, 0.45598377618,
+                                0.91748206207, 0.91748206207,
+                                0.49714719172, 0.49714719172], dtype=dtype)
+            phi    = np.array([ 4.9368292465,  0.57477043300,
+                                0.0000000000, 0.00000000000,
+                                4.71279419371, 0.11142137093], dtype=dtype)
+
+        # zero offset
+        i  = select - 1
+        a  = ai * D2R - phi[i]
+
+        b = bi * D2R
+        sb = sin(b)
+        cb = cos(b)
+        cbsa = cb * sin(a)
+        b  = -stheta[i] * cbsa + ctheta[i] * sb
+        w, = np.where(b > 1.0)
+        if w.size > 0:
+            b[w] = 1.0
+        bo = arcsin(b) * R2D
+        a  = arctan2( ctheta[i] * cbsa + stheta[i] * sb, cb * cos(a) )
+        ao = ( (a + psi[i] + fourpi) % twopi) * R2D
+        return ao, bo

+
+
[docs]    @staticmethod
+    def sphdist(ra1, dec1, ra2, dec2):
+        """measures the spherical distance between 2 points
+
+        Parameters
+        ----------
+        ra1: float or sequence
+            first right ascensions in degrees
+
+        dec1: float or sequence
+            first declination in degrees
+        ra2: float or sequence
+            second right ascensions in degrees
+        dec2: float or sequence
+            first declination in degrees
+
+        Returns
+        -------
+        Outputs: float or sequence
+            returns a distance in degrees
+        """
+        dec1_r = deg2rad(dec1)
+        dec2_r = deg2rad(dec2)
+        return 2. * rad2deg(arcsin(sqrt((sin((dec1_r - dec2_r) / 2)) ** 2 +
+                                        cos(dec1_r) * cos(dec2_r) * (
+                                            sin((deg2rad(ra1 - ra2)) / 2)) **
+                                        2)))

+
+
[docs]    @staticmethod
+    def conesearch(ra0, dec0, ra, dec, r, outtype=0):
+        """ Perform a cone search on a table
+
+        Parameters
+        ----------
+        ra0: ndarray[ndim=1, dtype=float]
+            column name to use as RA source in degrees
+
+        dec0: ndarray[ndim=1, dtype=float]
+            column name to use as DEC source in degrees
+
+        ra: float
+            ra to look for (in degree)
+
+        dec: float
+            ra to look for (in degree)
+
+        r: float
+            distance in degrees
+
+        outtype: int
+            type of outputs
+                0 -- minimal, indices of matching coordinates
+                1 -- indices and distances of matching coordinates
+                2 -- full, boolean filter and distances
+
+        Returns
+        -------
+        t: tuple
+            if outtype is 0:
+                only return indices from ra0, dec0
+            elif outtype is 1:
+                return indices from ra0, dec0 and distances
+            elif outtype is 2:
+                return conditional vector and distance to all ra0, dec0
+        """
+        @elementwise
+        def getDist( pk ):
+            """ get spherical distance between 2 points """
+            return AstroHelpers.sphdist(pk[0], pk[1], ra, dec)
+
+        dist = np.array(list(getDist(zip(ra0, dec0))))
+        v = (dist <= r)
+
+        if outtype == 0:
+            return np.ravel(np.where(v))
+        elif outtype == 1:
+            return np.ravel(np.where(v)), dist[v]
+        else:
+            return v, dist

+
+
+# ==============================================================================
+# SimpleTable -- provides table manipulations with limited storage formats
+# ==============================================================================
+
[docs]class SimpleTable(object):
+    """ Table class that is designed to be the basis of any format wrapping
+    around numpy recarrays
+
+    Attributes
+    ----------
+
+    fname: str or object
+        if str, the file to read from. This may be limited to the format
+        currently handled automatically. If the format is not correctly handled,
+        you can try by providing an object.__
+
+        if object with a structure like dict, ndarray, or recarray-like
+            the data will be encapsulated into a Table
+
+    caseless: bool
+        if set, column names will be caseless during operations
+
+    aliases: dict
+        set of column aliases (can be defined later :func:`set_alias`)
+
+    units: dict
+        set of column units (can be defined later :func:`set_unit`)
+
+    desc: dict
+        set of column description or comments (can be defined later :func:`set_comment`)
+
+    header: dict
+        key, value pair corresponding to the attributes of the table
+    """
+
+    def __init__(self, fname, *args, **kwargs):
+
+        dtype = kwargs.pop('dtype', None)
+        self.caseless = kwargs.get('caseless', False)
+        self._aliases = kwargs.get('aliases', {})
+        self._units = kwargs.get('units', {})
+        self._desc = kwargs.get('desc', {})
+
+        if (isinstance(fname, (dict, tuple, list, types.GeneratorType))) or (dtype in [dict, 'dict']):
+            try:
+                self.header = fname.pop('header', {})
+            except (AttributeError, TypeError):
+                self.header = kwargs.pop('header', {})
+            self.data = _convert_dict_to_structured_ndarray(fname)
+        elif (type(fname) in (str,)) or (dtype is not None):
+            if (type(fname) in (str,)):
+                extension = fname.split('.')[-1]
+            else:
+                extension = None
+            if (extension == 'csv') or dtype == 'csv':
+                kwargs.setdefault('delimiter', ',')
+                commentedHeader = kwargs.pop('commentedHeader', False)
+                n, header, units, comments, aliases, names = _ascii_read_header(fname, commentedHeader=commentedHeader, **kwargs)
+                kwargs.setdefault('names', names)
+                if _pd is not None:   # pandas is faster
+                    kwargs.setdefault('comment', '#')
+                    kwargs.setdefault('as_recarray', True)
+                    kwargs.setdefault('skiprows', n)
+                    self.data = _pd.read_csv(fname, *args, **kwargs)
+                else:
+                    kwargs.setdefault('skip_header', n)
+                    kwargs.setdefault('comments', '#')
+                    self.data = np.recfromcsv(fname, *args, **kwargs)
+                self.header = header
+                self._units.update(**units)
+                self._desc.update(**comments)
+                self._aliases.update(**aliases)
+                kwargs.setdefault('names', True)
+            elif (extension in ('tsv', 'dat', 'txt')) or dtype in ('tsv', 'dat', 'txt'):
+                commentedHeader = kwargs.pop('commentedHeader', True)
+                n, header, units, comments, aliases, names = _ascii_read_header(fname, commentedHeader=commentedHeader, **kwargs)
+                kwargs.setdefault('names', names)
+                if _pd is not None:   # pandas is faster
+                    kwargs.setdefault('delimiter', '\s+')
+                    kwargs.setdefault('comment', '#')
+                    kwargs.setdefault('as_recarray', True)
+                    self.data = _pd.read_csv(fname, *args, **kwargs)
+                else:
+                    kwargs.setdefault('delimiter', None)
+                    kwargs.setdefault('comments', '#')
+                    kwargs.setdefault('skip_header', n)
+                    self.data = np.recfromtxt(fname, *args, **kwargs)
+                self.header = header
+                self._units.update(**units)
+                self._desc.update(**comments)
+                self._aliases.update(**aliases)
+            elif (extension == 'fits') or dtype == 'fits':
+                if pyfits is None:
+                    raise RuntimeError('Cannot read this format, Astropy or pyfits not found')
+                if ('extname' not in kwargs) and ('ext' not in kwargs) and (len(args) == 0):
+                    args = (1, )
+                self.data = np.array(pyfits.getdata(fname, *args, **kwargs))
+                header, aliases, units, comments = _fits_read_header(pyfits.getheader(fname, *args, **kwargs))
+                self.header = header
+                self._desc.update(**comments)
+                self._units.update(**units)
+                self._aliases.update(**aliases)
+            elif (extension in ('hdf5', 'hd5', 'hdf')) or dtype in (extension in ('hdf5', 'hd5', 'hdf')):
+                if tables is None:
+                    raise RuntimeError('Cannot read this format, pytables not found')
+                hdr, aliases, units, desc, data = _hdf5_read_data(fname, *args, **kwargs)
+                self.data = data
+                self.header = hdr
+                self._units.update(**units)
+                self._desc.update(**desc)
+                self._aliases.update(**aliases)
+            else:
+                raise Exception('Format {0:s} not handled'.format(extension))
+        elif type(fname) == np.ndarray:
+            self.data = fname
+            self.header = {}
+        elif type(fname) == pyfits.FITS_rec:
+            self.data = np.array(fname)
+            self.header = {}
+        elif type(fname) == SimpleTable:
+            cp = kwargs.pop('copy', True)
+            if cp:
+                self.data = deepcopy(fname.data)
+                self.header = deepcopy(fname.header)
+                self._aliases = deepcopy(fname._aliases)
+                self._units = deepcopy(fname._units)
+                self._desc = deepcopy(fname._desc)
+            else:
+                self.data = fname.data
+                self.header = fname.header
+                self._aliases = fname._aliases
+                self._units = fname._units
+                self._desc = fname._desc
+        elif hasattr(fname, 'dtype'):
+            self.data = np.array(fname)
+            self.header = {}
+        else:
+            raise Exception('Type {0!s:s} not handled'.format(type(fname)))
+        if 'NAME' not in self.header:
+            if type(fname) not in basestring:
+                self.header['NAME'] = 'No Name'
+            else:
+                self.header['NAME'] = fname
+
+
[docs]    def pprint_entry(self, num, keys=None):
+        """ print one line with key and values properly to be readable
+
+        Parameters
+        ----------
+        num: int, slice
+            indice selection
+
+        keys: sequence or str
+            if str, can be a regular expression
+            if sequence, the sequence of keys to print
+        """
+        if (keys is None) or (keys == '*'):
+            _keys = self.keys()
+        elif type(keys) in basestring:
+            _keys = [k for k in (self.keys() + tuple(self._aliases.keys()))
+                     if (re.match(keys, k) is not None)]
+        else:
+            _keys = keys
+
+        length = max(map(len, _keys))
+        fmt = '{{0:{0:d}s}}: {{1}}'.format(length)
+        data = self[num]
+
+        for k in _keys:
+            print(fmt.format(k, data[self.resolve_alias(k)]))

+
+
[docs]    def pprint(self, idx=None, fields=None, ret=False, all=False,
+               full_match=False, headerChar='-', delim=' | ', endline='\n',
+               **kwargs):
+        """ Pretty print the table content
+            you can select the table parts to display using idx to
+            select the rows and fields to only display some columns
+            (ret is only for insternal use)
+
+        Parameters
+        ----------
+
+        idx: sequence, slide
+            sub selection to print
+
+        fields: str, sequence
+            if str can be a regular expression, and/or list of fields separated
+            by spaces or commas
+
+        ret: bool
+            if set return the string representation instead of printing the result
+
+        all: bool
+            if set, force to show all rows
+
+        headerChar: char
+            Character to be used for the row separator line
+
+        delim: char
+            The column delimiter.
+        """
+        if full_match is True:
+            fn = re.fullmatch
+        else:
+            fn = re.match
+
+        if (fields is None) or (fields == '*'):
+            _keys = self.keys()
+        elif type(fields) in basestring:
+            if ',' in fields:
+                _fields = fields.split(',')
+            elif ' ' in fields:
+                _fields = fields.split()
+            else:
+                _fields = [fields]
+            lbls = self.keys() + tuple(self._aliases.keys())
+            _keys = []
+            for _fk in _fields:
+                _keys += [k for k in lbls if (fn(_fk, k) is not None)]
+        else:
+            lbls = self.keys() + tuple(self._aliases.keys())
+            _keys = []
+            for _fk in _fields:
+                _keys += [k for k in lbls if (fn(_fk, k) is not None)]
+
+        nfields = len(_keys)
+
+        fields = list(map( self.resolve_alias, _keys ))
+
+        if idx is None:
+            if (self.nrows < 10) or all:
+                rows = [ [ str(self[k][rk]) for k in _keys ] for rk in range(self.nrows)]
+            else:
+                _idx = range(6)
+                rows = [ [ str(self[k][rk]) for k in _keys ] for rk in range(5) ]
+                if nfields > 1:
+                    rows += [ ['...' for k in range(nfields) ] ]
+                else:
+                    rows += [ ['...' for k in range(nfields) ] ]
+                rows += [ [ str(self[k][rk]) for k in fields ] for rk in range(-5, 0)]
+        elif isinstance(idx, slice):
+            _idx = range(idx.start, idx.stop, idx.step or 1)
+            rows = [ [ str(self[k][rk]) for k in fields ] for rk in _idx]
+        else:
+            rows = [ [ str(self[k][rk]) for k in fields ] for rk in idx]
+
+        if len(self._units) == 0:
+            units = None
+        else:
+            units = [ '(' + str( self._units.get(k, None) or '') + ')' for k in fields ]
+
+        out = __indent__(rows, header=_keys, units=units, delim=delim,
+                         headerChar=headerChar, endline=endline)
+        if ret is True:
+            return out
+        else:
+            print(out)

+
+
[docs]    def write(self, fname, **kwargs):
+        """ write table into file
+
+        Parameters
+        ----------
+        fname: str
+            filename to export the table into
+
+        .. note::
+            additional keywords are forwarded to the corresponding libraries
+            :func:`pyfits.writeto` or :func:`pyfits.append`
+            :func:`np.savetxt`
+        """
+        extension = kwargs.pop('extension', None)
+        if extension is None:
+            extension = fname.split('.')[-1]
+        if (extension == 'csv'):
+            comments = kwargs.pop('comments', '#')
+            delimiter = kwargs.pop('delimiter', ',')
+            commentedHeader = kwargs.pop('commentedHeader', False)
+            hdr = _ascii_generate_header(self, comments=comments, delimiter=delimiter,
+                                         commentedHeader=commentedHeader)
+            header = kwargs.pop('header', hdr)
+            np.savetxt(fname, self.data, delimiter=delimiter, header=header,
+                       comments='', **kwargs)
+        elif (extension in ['txt', 'dat']):
+            comments = kwargs.pop('comments', '#')
+            delimiter = kwargs.pop('delimiter', ' ')
+            commentedHeader = kwargs.pop('commentedHeader', True)
+            hdr = _ascii_generate_header(self, comments=comments, delimiter=delimiter,
+                                         commentedHeader=commentedHeader)
+            header = kwargs.pop('header', hdr)
+            np.savetxt(fname, self.data, delimiter=delimiter, header=header,
+                       comments='', **kwargs)
+        elif (extension == 'fits'):
+            hdr0 = kwargs.pop('header', None)
+            append = kwargs.pop('append', False)
+            hdr = _fits_generate_header(self)
+            if hdr0 is not None:
+                hdr.update(**hdr0)
+            if append:
+                _fits_append(fname, self.data, hdr, **kwargs)
+            else:
+                # patched version to correctly include the header
+                _fits_writeto(fname, self.data, hdr, **kwargs)
+        elif (extension in ('hdf', 'hdf5', 'hd5')):
+            _hdf5_write_data(fname, self.data, header=self.header,
+                             units=self._units, comments=self._desc,
+                             aliases=self._aliases, **kwargs)
+        else:
+            raise Exception('Format {0:s} not handled'.format(extension))

+
+
[docs]    def set_alias(self, alias, colname):
+        """
+        Define an alias to a column
+
+        Parameters
+        ----------
+        alias: str
+            The new alias of the column
+
+        colname: str
+            The column being aliased
+        """
+        if (colname not in self.keys()):
+            raise KeyError("Column {0:s} does not exist".format(colname))
+        self._aliases[alias] = colname

+
+
[docs]    def reverse_alias(self, colname):
+        """
+        Return aliases of a given column.
+
+        Given a colname, return a sequence of aliases associated to this column
+        Aliases are defined by using .define_alias()
+        """
+        _colname = self.resolve_alias(colname)
+        if (_colname not in self.keys()):
+            raise KeyError("Column {0:s} does not exist".format(colname))
+
+        return tuple([ k for (k, v) in self._aliases.iteritems() if (v == _colname) ])

+
+
[docs]    def resolve_alias(self, colname):
+        """
+        Return the name of an aliased column.
+
+        Given an alias, return the column name it aliases. This
+        function is a no-op if the alias is a column name itself.
+
+        Aliases are defined by using .define_alias()
+        """
+        # User aliases
+        if hasattr(colname, '__iter__') & (type(colname) not in basestring):
+            return [ self.resolve_alias(k) for k in colname ]
+        else:
+            if self.caseless is True:
+                maps = dict( [ (k.lower(), v) for k, v in self._aliases.items() ] )
+                maps.update( (k.lower(), k) for k in self.keys() )
+                return maps.get(colname.lower(), colname)
+            else:
+                return self._aliases.get(colname, colname)

+
+
[docs]    def set_unit(self, colname, unit):
+        """ Set the unit of a column referenced by its name
+
+        Parameters
+        ----------
+        colname: str
+            column name or registered alias
+
+        unit: str
+            unit description
+        """
+        if isinstance(unit, basestring) and isinstance(colname, basestring):
+            self._units[self.resolve_alias(colname)] = str(unit)
+        else:
+            for k, v in zip(colname, unit):
+                self._units[self.resolve_alias(k)] = str(v)

+
+
[docs]    def set_comment(self, colname, comment):
+        """ Set the comment of a column referenced by its name
+
+        Parameters
+        ----------
+        colname: str
+            column name or registered alias
+
+        comment: str
+            column description
+        """
+        if isinstance(comment, basestring) and isinstance(colname, basestring):
+            self._desc[self.resolve_alias(colname)] = str(comment)
+        else:
+            for k, v in zip(colname, comment):
+                self._desc[self.resolve_alias(k)] = str(v)

+
+
[docs]    def keys(self, regexp=None, full_match=False):
+        """
+        Return the data column names or a subset of it
+
+        Parameters
+        ----------
+        regexp: str
+            pattern to filter the keys with
+
+        full_match: bool
+            if set, use :func:`re.fullmatch` instead of :func:`re.match`
+
+        Try to apply the pattern at the start of the string, returning
+        a match object, or None if no match was found.
+
+        returns
+        -------
+        seq: sequence
+            sequence of keys
+        """
+        if (regexp is None) or (regexp == '*'):
+            return self.colnames
+        elif type(regexp) in basestring:
+            if full_match is True:
+                fn = re.fullmatch
+            else:
+                fn = re.match
+
+            if regexp.count(',') > 0:
+                _re = regexp.split(',')
+            elif regexp.count(' ') > 0:
+                _re = regexp.split()
+            else:
+                _re = [regexp]
+
+            lbls = self.colnames + tuple(self._aliases.keys())
+            _keys = []
+            for _rk in _re:
+                _keys += [k for k in lbls if (fn(_rk, k) is not None)]
+
+            return _keys
+        elif hasattr(regexp, '__iter__'):
+            _keys = []
+            for k in regexp:
+                _keys += self.keys(k)
+            return _keys
+        else:
+            raise ValueError('Unexpected type {0} for regexp'.format(type(regexp)))

+
+    @property
+    def name(self):
+        """ name of the table given by the Header['NAME'] attribute """
+        return self.header.get('NAME', None)
+
+    @property
+    def colnames(self):
+        """ Sequence of column names """
+        return self.data.dtype.names
+
+    @property
+    def ncols(self):
+        """ number of columns """
+        return len(self.colnames)
+
+    @property
+    def nrows(self):
+        """ number of lines """
+        return len(self.data)
+
+    @property
+    def nbytes(self):
+        """ number of bytes of the object """
+        n = sum(k.nbytes if hasattr(k, 'nbytes') else sys.getsizeof(k)
+                for k in self.__dict__.values())
+        return n
+
+    def __len__(self):
+        """ number of lines """
+        return self.nrows
+
+    @property
+    def shape(self):
+        """ shape of the data """
+        return self.data.shape
+
+    @property
+    def dtype(self):
+        """ dtype of the data """
+        return self.data.dtype
+
+    def __getitem__(self, v):
+        return np.asarray(self.data.__getitem__(self.resolve_alias(v)))
+
+
[docs]    def get(self, v, full_match=False):
+        """ returns a table from columns given as v
+
+        this function is equivalent to :func:`__getitem__` but preserve the
+        Table format and associated properties (units, description, header)
+
+        Parameters
+        ----------
+        v: str
+            pattern to filter the keys with
+
+        full_match: bool
+            if set, use :func:`re.fullmatch` instead of :func:`re.match`
+
+        """
+        new_keys = self.keys(v)
+        t = self.__class__(self[new_keys])
+        t.header.update(**self.header)
+        t._aliases.update((k, v) for (k, v) in self._aliases.items() if v in new_keys)
+        t._units.update((k, v) for (k, v) in self._units.items() if v in new_keys)
+        t._desc.update((k, v) for (k, v) in self._desc.items() if v in new_keys)
+        return t

+
+    def __setitem__(self, k, v):
+        if k in self:
+            return self.data.__setitem__(self.resolve_alias(k), v)
+        else:
+            object.__setitem__(self, k, v)
+
+    def __getattr__(self, k):
+        try:
+            return self.data.__getitem__(self.resolve_alias(k))
+        except:
+            return object.__getattribute__(self, k)
+
+    def __iter__(self):
+        newtab = self.select('*', [0])
+        for d in self.data:
+            newtab.data[0] = d
+            yield newtab
+        # return self.data.__iter__()
+
+
[docs]    def iterkeys(self):
+        """ Iterator over the columns of the table """
+        for k in self.colnames:
+            yield k

+
+
[docs]    def itervalues(self):
+        """ Iterator over the lines of the table """
+        for l in self.data:
+            yield l

+
+
[docs]    def info(self):
+        s = "\nTable: {name:s}\n       nrows={s.nrows:d}, ncols={s.ncols:d}, mem={size:s}"
+        s = s.format(name=self.header.get('NAME', 'Noname'), s=self,
+                     size=pretty_size_print(self.nbytes))
+
+        s += '\n\nHeader:\n'
+        vals = list(self.header.items())
+        length = max(map(len, self.header.keys()))
+        fmt = '\t{{0:{0:d}s}} {{1}}\n'.format(length)
+        for k, v in vals:
+            s += fmt.format(k, v)
+
+        vals = [(k, self._units.get(k, ''), self._desc.get(k, ''))
+                for k in self.colnames]
+        lengths = [(len(k), len(self._units.get(k, '')), len(self._desc.get(k, '')))
+                   for k in self.colnames]
+        lengths = list(map(max, (zip(*lengths))))
+
+        s += '\nColumns:\n'
+
+        fmt = '\t{{0:{0:d}s}} {{1:{1:d}s}} {{2:{2:d}s}}\n'.format(*(k + 1 for k in lengths))
+        for k, u, c in vals:
+            s += fmt.format(k, u, c)
+
+        print(s)
+
+        if len(self._aliases) > 0:
+            print("\nTable contains alias(es):")
+            for k, v in self._aliases.items():
+                print('\t{0:s} --> {1:s}'.format(k, v))

+
+    def __repr__(self):
+        s = object.__repr__(self)
+        s += "\nTable: {name:s}\n       nrows={s.nrows:d}, ncols={s.ncols:d}, mem={size:s}"
+        return s.format(name=self.header.get('NAME', 'Noname'), s=self,
+                        size=pretty_size_print(self.nbytes))
+
+    def __getslice__(self, i, j):
+        return self.data.__getslice__(i, j)
+
+    def __contains__(self, k):
+        return (k in self.colnames) or (k in self._aliases)
+
+    def __array__(self):
+        return self.data
+
+    def __call__(self, *args, **kwargs):
+        if (len(args) > 0) or (len(kwargs) > 0):
+            return self.evalexpr(*args, **kwargs)
+        else:
+            return self.info()
+
+
[docs]    def sort(self, keys, copy=False):
+        """
+        Sort the table inplace according to one or more keys. This operates on
+        the existing table (and does not return a new table).
+
+        Parameters
+        ----------
+
+        keys: str or seq(str)
+            The key(s) to order by
+
+        copy: bool
+            if set returns a sorted copy instead of working inplace
+        """
+        if not hasattr(keys, '__iter__'):
+            keys = [keys]
+
+        if copy is False:
+            self.data.sort(order=keys)
+        else:
+            t = self.__class__(self, copy=True)
+            t.sort(keys, copy=False)
+            return t

+
+
[docs]    def match(self, r2, key):
+        """ Returns the indices at which the tables match
+        matching uses 2 columns that are compared in values
+
+        Parameters
+        ----------
+        r2:  Table
+            second table to use
+
+        key: str
+            fields used for comparison.
+
+        Returns
+        -------
+        indexes: tuple
+            tuple of both indices list where the two columns match.
+        """
+        return np.where( np.equal.outer( self[key], r2[key] ) )

+
+    '''
+    def stack(self, r, defaults=None):
+        """
+        Superposes arrays fields by fields inplace
+
+        Parameters
+        ----------
+        r: Table
+        """
+        if not hasattr(r, 'data'):
+            raise AttributeError('r should be a Table object')
+        self.data = recfunctions.stack_arrays([self.data, r.data], defaults,
+                                              usemask=False, asrecarray=True)
+    '''
+
+
[docs]    def stack(self, r, *args, **kwargs):
+        """
+        Superposes arrays fields by fields inplace
+
+        t.stack(t1, t2, t3, default=None, inplace=True)
+
+        Parameters
+        ----------
+        r: Table
+        """
+        if not hasattr(r, 'data'):
+            raise AttributeError('r should be a Table object')
+        defaults = kwargs.get('defaults', None)
+        inplace = kwargs.get('inplace', False)
+
+        data = [self.data, r.data] + [k.data for k in args]
+        sdata = recfunctions.stack_arrays(data, defaults, usemask=False,
+                                          asrecarray=True)
+
+        if inplace:
+            self.data = sdata
+        else:
+            t = self.__class__(self)
+            t.data = sdata
+            return t

+
+
[docs]    def join_by(self, r2, key, jointype='inner', r1postfix='1', r2postfix='2',
+                defaults=None, asrecarray=False, asTable=True):
+        """
+        Join arrays `r1` and `r2` on key `key`.
+
+        The key should be either a string or a sequence of string corresponding
+        to the fields used to join the array.
+        An exception is raised if the `key` field cannot be found in the two input
+        arrays.
+        Neither `r1` nor `r2` should have any duplicates along `key`: the presence
+        of duplicates will make the output quite unreliable. Note that duplicates
+        are not looked for by the algorithm.
+
+        Parameters
+        ----------
+        key: str or seq(str)
+            corresponding to the fields used for comparison.
+
+        r2: Table
+            Table to join with
+
+        jointype: str in {'inner', 'outer', 'leftouter'}
+            * 'inner'     : returns the elements common to both r1 and r2.
+            * 'outer'     : returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2.
+            * 'leftouter' : returns the common elements and the elements of r1 not in r2.
+
+        r1postfix: str
+            String appended to the names of the fields of r1 that are present in r2
+
+        r2postfix:  str
+            String appended to the names of the fields of r2 that are present in r1
+
+        defaults:   dict
+            Dictionary mapping field names to the corresponding default values.
+
+        Returns
+        -------
+        tab: Table
+            joined table
+
+        .. note::
+
+            * The output is sorted along the key.
+
+            * A temporary array is formed by dropping the fields not in the key
+              for the two arrays and concatenating the result. This array is
+              then sorted, and the common entries selected. The output is
+              constructed by filling the fields with the selected entries.
+              Matching is not preserved if there are some duplicates...
+        """
+        arr = recfunctions.join_by(key, self.data, r2.data, jointype=jointype,
+                                   r1postfix=r1postfix, r2postfix=r2postfix,
+                                   defaults=defaults, usemask=False,
+                                   asrecarray=True)
+
+        return SimpleTable(arr)

+
+    @property
+    def empty_row(self):
+        """ Return an empty row array respecting the table format """
+        return np.rec.recarray(shape=(1,), dtype=self.data.dtype)
+
+
[docs]    def add_column(self, name, data, dtype=None, unit=None, description=None):
+        """
+        Add one or multiple columns to the table
+
+        Parameters
+        ----------
+        name: str or sequence(str)
+           The name(s) of the column(s) to add
+
+        data: ndarray, or sequence of ndarray
+            The column data, or sequence of columns
+
+        dtype: dtype
+            numpy dtype for the data to add
+
+        unit: str
+            The unit of the values in the column
+
+        description: str
+            A description of the content of the column
+        """
+
+        _data = np.array(data, dtype=dtype)
+        dtype = _data.dtype
+
+        # unknown type is converted to text
+        if dtype.type == np.object_:
+            if len(data) == 0:
+                longest = 0
+            else:
+                longest = len(max(data, key=len))
+                _data = np.asarray(data, dtype='|%iS' % longest)
+
+        dtype = _data.dtype
+
+        if len(self.data.dtype) > 0:
+            # existing data in the table
+            self.data = recfunctions.append_fields(self.data, name, _data,
+                                                   dtypes=dtype, usemask=False,
+                                                   asrecarray=True)
+        else:
+            if _data.ndim > 1:
+                newdtype = (str(name), _data.dtype, (_data.shape[1],))
+            else:
+                newdtype = (str(name), _data.dtype)
+            self.data = np.array(_data, dtype=[newdtype])
+
+        if unit is not None:
+            self.set_unit(name, unit)
+
+        if description is not None:
+            self.set_unit(name, description)

+
+
[docs]    def append_row(self, iterable):
+        """
+        Append one row in this table.
+
+        see also: :func:`stack`
+
+        Parameters
+        ----------
+        iterable: iterable
+            line to add
+        """
+        if (len(iterable) != self.ncols):
+            raise AttributeError('Expecting as many items as columns')
+        r = self.empty_row
+        for k, v in enumerate(iterable):
+            r[0][k] = v
+        self.stack(r)

+
+
[docs]    def remove_columns(self, names):
+        """
+        Remove several columns from the table
+
+        Parameters
+        ----------
+        names: sequence
+            A list containing the names of the columns to remove
+        """
+        self.pop_columns(names)

+
+
[docs]    def pop_columns(self, names):
+        """
+        Pop several columns from the table
+
+        Parameters
+        ----------
+
+        names: sequence
+            A list containing the names of the columns to remove
+
+        Returns
+        -------
+
+        values: tuple
+            list of columns
+        """
+
+        if not hasattr(names, '__iter__') or type(names) in basestring:
+            names = [names]
+
+        p = [self[k] for k in names]
+
+        _names = set([ self.resolve_alias(k) for k in names ])
+        self.data = recfunctions.drop_fields(self.data, _names)
+        for k in names:
+            self._aliases.pop(k, None)
+            self._units.pop(k, None)
+            self._desc.pop(k, None)
+
+        return p

+
+
[docs]    def find_duplicate(self, index_only=False, values_only=False):
+        """Find duplication in the table entries, return a list of duplicated
+        elements Only works at this time is 2 lines are *the same entry* not if
+        2 lines have *the same values*
+        """
+        dup = []
+        idd = []
+        for i in range(len(self.data)):
+            if (self.data[i] in self.data[i + 1:]):
+                if (self.data[i] not in dup):
+                    dup.append(self.data[i])
+                    idd.append(i)
+        if index_only:
+            return idd
+        elif values_only:
+            return dup
+        else:
+            return zip(idd, dup)

+
+
[docs]    def evalexpr(self, expr, exprvars=None, dtype=float):
+        """ evaluate expression based on the data and external variables
+            all np function can be used (log, exp, pi...)
+
+        Parameters
+        ----------
+        expr: str
+            expression to evaluate on the table
+            includes mathematical operations and attribute names
+
+        exprvars: dictionary, optional
+            A dictionary that replaces the local operands in current frame.
+
+        dtype: dtype definition
+            dtype of the output array
+
+        Returns
+        -------
+        out : NumPy array
+            array of the result
+        """
+        _globals = {}
+        for k in ( list(self.colnames) + list(self._aliases.keys()) ):
+            if k in expr:
+                _globals[k] = self[k]
+
+        if exprvars is not None:
+            if (not (hasattr(exprvars, 'keys') & hasattr(exprvars, '__getitem__' ))):
+                raise AttributeError("Expecting a dictionary-like as condvars")
+            for k, v in ( exprvars.items() ):
+                _globals[k] = v
+
+        # evaluate expression, to obtain the final filter
+        r    = np.empty( self.nrows, dtype=dtype)
+        r[:] = eval(expr, _globals, np.__dict__)
+
+        return r

+
+
[docs]    def where(self, condition, condvars=None, *args, **kwargs):
+        """ Read table data fulfilling the given `condition`.
+        Only the rows fulfilling the `condition` are included in the result.
+
+        Parameters
+        ----------
+        condition: str
+            expression to evaluate on the table
+            includes mathematical operations and attribute names
+
+        condvars: dictionary, optional
+            A dictionary that replaces the local operands in current frame.
+
+        Returns
+        -------
+        out: ndarray/ tuple of ndarrays
+        result equivalent to :func:`np.where`
+
+        """
+        ind = np.where(self.evalexpr(condition, condvars, dtype=bool ), *args, **kwargs)
+        return ind

+
+
[docs]    def select(self, fields, indices=None, **kwargs):
+        """
+        Select only a few fields in the table
+
+        Parameters
+        ----------
+        fields: str or sequence
+            fields to keep in the resulting table
+
+        indices: sequence or slice
+            extract only on these indices
+
+        returns
+        -------
+        tab: SimpleTable instance
+            resulting table
+        """
+        _fields = self.keys(fields)
+
+        if fields == '*':
+            if indices is None:
+                return self
+            else:
+                tab = self.__class__(self[indices])
+                for k in self.__dict__.keys():
+                    if k not in ('data', ):
+                        setattr(tab, k, deepcopy(self.__dict__[k]))
+                return tab
+        else:
+            d = {}
+            for k in _fields:
+                _k = self.resolve_alias(k)
+                if indices is not None:
+                    d[k] = self[_k][indices]
+                else:
+                    d[k] = self[_k]
+            d['header'] = deepcopy(self.header)
+            tab = self.__class__(d)
+            for k in self.__dict__.keys():
+                if k not in ('data', ):
+                    setattr(tab, k, deepcopy(self.__dict__[k]))
+            return tab

+
+
[docs]    def selectWhere(self, fields, condition, condvars=None, **kwargs):
+        """ Read table data fulfilling the given `condition`.
+            Only the rows fulfilling the `condition` are included in the result.
+
+        Parameters
+        ----------
+        fields: str or sequence
+            fields to keep in the resulting table
+
+        condition: str
+            expression to evaluate on the table
+            includes mathematical operations and attribute names
+
+        condvars: dictionary, optional
+            A dictionary that replaces the local operands in current frame.
+
+        Returns
+        -------
+        tab: SimpleTable instance
+            resulting table
+        """
+        if condition in [True, 'True', None]:
+            ind = None
+        else:
+            ind = self.where(condition, condvars, **kwargs)
+
+        tab = self.select(fields, indices=ind)
+
+        return tab

+
+
[docs]    def groupby(self, key):
+        """
+        Create an iterator which returns (key, sub-table) grouped by each value
+        of key(value)
+
+        Parameters
+        ----------
+        key: str
+            expression or pattern to filter the keys with
+
+        Returns
+        -------
+        key: str or sequence
+            group key
+
+        tab: SimpleTable instance
+           sub-table of the group
+           header, aliases and column metadata are preserved (linked to the
+           master table).
+        """
+        _key = self.keys(key)
+        getter = operator.itemgetter(*_key)
+
+        for k, grp in itertools.groupby(self.data, getter):
+            t = self.__class__(np.dstack(grp))
+            t.header = self.header
+            t._aliases = self._aliases
+            t._units = self._units
+            t._desc = self._desc
+            yield (k, t)

+
+
[docs]    def stats(self, fn=None, fields=None, fill=None):
+        """ Make statistics on columns of a table
+
+        Paramters
+        ---------
+        fn: callable or sequence of callables
+            functions to apply to each column
+            default: (np.mean, np.std, np.nanmin, np.nanmax)
+
+        fields: str or sequence
+            any key or key expression to subselect columns
+            default is all columns
+
+        fill: value
+            value when not applicable
+            default np.nan
+
+        returns
+        -------
+        tab: Table instance
+            collection of statistics, one column per function in fn and 1 ligne
+            per column in the table
+        """
+        from collections import OrderedDict
+
+        fn = (stats.mean, stats.std,
+              stats.min, stats.max,
+              stats.has_nan)
+
+        d = OrderedDict()
+        d.setdefault('FIELD', [])
+        for k in fn:
+            d.setdefault(k.__name__, [])
+
+        if fields is None:
+            fields = self.colnames
+        else:
+            fields = self.keys(fields)
+
+        if fill is None:
+            fill = np.nan
+
+        for k in fields:
+            d['FIELD'].append(k)
+            for fnk in fn:
+                try:
+                    val = fnk(self[k])
+                except:
+                    val = fill
+                d[fnk.__name__].append(val)
+
+        return self.__class__(d, dtype=dict)

+
+    # method aliases
+    remove_column = remove_columns
+
+    # deprecated methods
+    addCol = add_column
+    addLine = append_row
+    setComment = set_comment
+    setUnit = set_unit
+    delCol = remove_columns

+
+
+
[docs]class AstroTable(SimpleTable):
+    """
+    Derived from the Table, this class add implementations of common astro
+    tools especially conesearch
+    """
+    def __init__(self, *args, **kwargs):
+        super(self.__class__, self).__init__(*args, **kwargs)
+        self._ra_name, self._dec_name = self.__autoRADEC__()
+        if (len(args) > 0):
+            if isinstance(args[0], AstroTable):
+                self._ra_name = args[0]._ra_name
+                self._dec_name = args[0]._dec_name
+        self._ra_name = kwargs.get('ra_name', self._ra_name)
+        self._dec_name = kwargs.get('dec_name', self._dec_name)
+
+    def __autoRADEC__(self):
+        """ Tries to identify the columns containing RA and DEC coordinates """
+        if 'ra' in self:
+            ra_name = 'ra'
+        elif 'RA' in self:
+            ra_name = 'RA'
+        else:
+            ra_name = None
+        if 'dec' in self:
+            dec_name = 'dec'
+        elif 'DEC' in self:
+            dec_name = 'DEC'
+        else:
+            dec_name = None
+        return ra_name, dec_name
+
+
[docs]    def set_RA(self, val):
+        """ Set the column that defines RA coordinates """
+        assert(val in self), 'column name {} not found in the table'.format(val)
+        self._ra_name = val

+
+
[docs]    def set_DEC(self, val):
+        """ Set the column that defines DEC coordinates """
+        assert(val in self), 'column name {} not found in the table'.format(val)
+        self._dec_name = val

+
+
[docs]    def get_RA(self, degree=True):
+        """ Returns RA, converted from hexa/sexa into degrees """
+        if self._ra_name is None:
+            return None
+        if (not degree) or (self.dtype[self._ra_name].kind != 'S'):
+            return self[self._ra_name]
+        else:
+            if (len(str(self[0][self._ra_name]).split(':')) == 3):
+                return np.asarray(AstroHelpers.hms2deg(self[self._ra_name],
+                                                       delim=':'))
+            elif (len(str(self[0][self._ra_name]).split(' ')) == 3):
+                return np.asarray(AstroHelpers.hms2deg(self[self._ra_name],
+                                                       delim=' '))
+            else:
+                raise Exception('RA Format not understood')

+
+
[docs]    def get_DEC(self, degree=True):
+        """ Returns RA, converted from hexa/sexa into degrees """
+        if self._dec_name is None:
+            return None
+        if (not degree) or (self.dtype[self._dec_name].kind != 'S'):
+            return self[self._dec_name]
+        else:
+            if (len(str(self[0][self._dec_name]).split(':')) == 3):
+                return np.asarray(AstroHelpers.dms2deg(self[self._dec_name],
+                                                       delim=':'))
+            elif (len(str(self[0][self._dec_name]).split(' ')) == 3):
+                return np.asarray(AstroHelpers.dms2deg(self[self._dec_name],
+                                                       delim=' '))
+            else:
+                raise Exception('RA Format not understood')

+
+
[docs]    def info(self):
+        s = "\nTable: {name:s}\n       nrows={s.nrows:d}, ncols={s.ncols:d}, mem={size:s}"
+        s = s.format(name=self.header.get('NAME', 'Noname'), s=self,
+                     size=pretty_size_print(self.nbytes))
+
+        s += '\n\nHeader:\n'
+        vals = list(self.header.items())
+        length = max(map(len, self.header.keys()))
+        fmt = '\t{{0:{0:d}s}} {{1}}\n'.format(length)
+        for k, v in vals:
+            s += fmt.format(k, v)
+
+        vals = [(k, self._units.get(k, ''), self._desc.get(k, ''))
+                for k in self.colnames]
+        lengths = [(len(k), len(self._units.get(k, '')), len(self._desc.get(k, '')))
+                   for k in self.colnames]
+        lengths = list(map(max, (zip(*lengths))))
+
+        if (self._ra_name is not None) & (self._dec_name is not None):
+            s += "\nPosition coordinate columns: {0}, {1}\n".format(self._ra_name,
+                                                                    self._dec_name)
+
+        s += '\nColumns:\n'
+
+        fmt = '\t{{0:{0:d}s}} {{1:{1:d}s}} {{2:{2:d}s}}\n'.format(*(k + 1 for k in lengths))
+        for k, u, c in vals:
+            s += fmt.format(k, u, c)
+
+        print(s)
+
+        if len(self._aliases) > 0:
+            print("\nTable contains alias(es):")
+            for k, v in self._aliases.items():
+                print('\t{0:s} --> {1:s}'.format(k, v))

+
+
[docs]    def coneSearch(self, ra, dec, r, outtype=0):
+        """ Perform a cone search on a table
+
+        Parameters
+        ----------
+        ra0: ndarray[ndim=1, dtype=float]
+            column name to use as RA source in degrees
+
+        dec0: ndarray[ndim=1, dtype=float]
+            column name to use as DEC source in degrees
+
+        ra: float
+            ra to look for (in degree)
+
+        dec: float
+            ra to look for (in degree)
+
+        r: float
+            distance in degrees
+
+        outtype: int
+            type of outputs
+                0 -- minimal, indices of matching coordinates
+                1 -- indices and distances of matching coordinates
+                2 -- full, boolean filter and distances
+
+        Returns
+        -------
+        t: tuple
+            if outtype is 0:
+                only return indices from ra0, dec0
+            elif outtype is 1:
+                return indices from ra0, dec0 and distances
+            elif outtype is 2:
+                return conditional vector and distance to all ra0, dec0
+        """
+        if (self._ra_name is None) or (self._dec_name is None):
+            raise AttributeError('Coordinate columns not set.')
+
+        ra0  = self.get_RA()
+        dec0 = self.get_DEC()
+        return AstroHelpers.conesearch(ra0, dec0, ra, dec, r, outtype=outtype)

+
+
[docs]    def zoneSearch(self, ramin, ramax, decmin, decmax, outtype=0):
+        """ Perform a zone search on a table, i.e., a rectangular selection
+        Parameters
+        ----------
+        ramin: float
+            minimal value of RA
+
+        ramax: float
+            maximal value of RA
+
+        decmin: float
+            minimal value of DEC
+
+        decmax: float
+            maximal value of DEC
+
+        outtype: int
+            type of outputs
+                0 or 1 -- minimal, indices of matching coordinates
+                2 -- full, boolean filter and distances
+
+        Returns
+        -------
+        r: sequence
+            indices or conditional sequence of matching values
+        """
+
+        assert( (self._ra_name is not None) & (self._dec_name is not None) ), 'Coordinate columns not set.'
+
+        ra0  = self.get_RA()
+        dec0 = self.get_DEC()
+        ind = (ra0 >= ramin) & (ra0 <= ramax) & (dec0 >= decmin) & (dec0 <= decmax)
+        if outtype <= 2:
+            return ind
+        else:
+            return np.where(ind)

+
+
[docs]    def where(self, condition=None, condvars=None, cone=None, zone=None, **kwargs):
+        """ Read table data fulfilling the given `condition`.
+        Only the rows fulfilling the `condition` are included in the result.
+
+        Parameters
+        ----------
+        condition: str
+            expression to evaluate on the table
+            includes mathematical operations and attribute names
+
+        condvars: dictionary, optional
+            A dictionary that replaces the local operands in current frame.
+
+        Returns
+        -------
+        out: ndarray/ tuple of ndarrays
+        result equivalent to :func:`np.where`
+        """
+        if cone is not None:
+            if len(cone) != 3:
+                raise ValueError('Expecting cone keywords as a triplet (ra, dec, r)')
+        if zone is not None:
+            if len(zone) != 4:
+                raise ValueError('Expecting zone keywords as a tuple of 4 elements (ramin, ramax, decmin, decmax)')
+
+        if condition is not None:
+            ind = super(self.__class__, self).where(condition, **kwargs)
+            if ind is None:
+                if (cone is None) & (zone is None):
+                    return None
+        else:
+            ind = True
+
+        blobs = []
+        if (cone is not None):
+            ra, dec, r = cone
+            _ind, d = self.coneSearch(ra, dec, r, outtype=1)
+            ind = ind & _ind.astype(bool)
+            blobs.append(d)
+        if (zone is not None):
+            _ind = self.zoneSearch(zone[0], zone[1], zone[2], zone[3], outtype=1)
+            ind = ind & _ind
+        elif (cone is not None) and (zone is not None):  # cone + zone
+            ra, dec, r = cone
+            ind, d = self.coneSearch(ra, dec, r, outtype=2)
+            ind = ind & self.zoneSearch(zone[0], zone[1], zone[2], zone[3], outtype=2)
+            d = d[ind]
+            ind = np.where(ind)
+            blobs.append(d)
+
+        return ind, blobs[0]

+
+
[docs]    def selectWhere(self, fields, condition=None, condvars=None, cone=None, zone=None, **kwargs):
+        """ Read table data fulfilling the given `condition`.
+            Only the rows fulfilling the `condition` are included in the result.
+            conesearch is also possible through the keyword cone formatted as (ra, dec, r)
+            zonesearch is also possible through the keyword zone formatted as (ramin, ramax, decmin, decmax)
+
+            Combination of multiple selections is also available.
+        """
+        ind, blobs = self.where(condition, condvars, cone, zone, **kwargs)
+        tab = self.select(fields, indices=ind)
+
+        if cone is not None:
+            tab.add_column('separation', np.asarray(blobs), unit='degree')
+
+        if self._ra_name in tab:
+            tab.set_RA(self._ra_name)
+
+        if self._dec_name in tab:
+            tab.set_DEC(self._dec_name)
+
+        return tab

+
+
+
[docs]class stats(object):
+
[docs]    @classmethod
+    def has_nan(s, v):
+        return (True in np.isnan(v))

+
+
[docs]    @classmethod
+    def mean(s, v):
+        return np.nanmean(v)

+
+
[docs]    @classmethod
+    def max(s, v):
+        return np.nanmax(v)

+
+
[docs]    @classmethod
+    def min(s, v):
+        return np.nanmin(v)

+
+
[docs]    @classmethod
+    def std(s, v):
+        return np.nanstd(v)

+
+
[docs]    @classmethod
+    def var(s, v):
+        return np.var(v)

+
+
[docs]    @classmethod
+    def p16(s, v):
+        try:
+            return np.nanpercentile(v, 16)
+        except AttributeError:
+            return np.percentile(v, 16)

+
+
[docs]    @classmethod
+    def p84(s, v):
+        try:
+            return np.nanpercentile(v, 84)
+        except AttributeError:
+            return np.percentile(v, 84)

+
+
[docs]    @classmethod
+    def p50(s, v):
+        try:
+            return np.nanmedian(v)
+        except AttributeError:
+            return np.percentile(v, 50)

+
+
+# =============================================================================
+# Adding some plotting functions
+# =============================================================================
+
+try:
+    import pylab as plt
+
+    def plot_function(tab, fn, *args, **kwargs):
+        """ Generate a plotting method of tab from a given function
+
+        Parameters
+        ----------
+        tab: SimpleTable instance
+            table instance
+
+        fn: str or callable
+            if str, will try a function in matplotlib
+            if callable, calls the function directly
+
+        xname: str
+            expecting a column name from the table
+
+        yname: str, optional
+            if provided, another column to use for the plot
+
+        onlywhere: sequence or str, optional
+            if provided, selects only data with this condition
+            the condition can be a ndarray slice or a string.
+            When a string is given, the evaluation calls :func:`SimpleTable.where`
+
+        ax: matplotlib.Axes instance
+            if provided make sure it uses the axis to do the plots if a mpl
+            function is used.
+
+        Returns
+        -------
+        r: object
+            anything returned by the called function
+        """
+        if not hasattr(fn, '__call__'):
+            ax = kwargs.pop('ax', None)
+            if ax is None:
+                ax = plt.gca()
+            _fn = getattr(ax, fn, None)
+            if _fn is None:
+                raise AttributeError('function neither callable or found in matplotlib')
+        else:
+            _fn = fn
+
+        onlywhere = kwargs.pop('onlywhere', None)
+        if type(onlywhere) in basestring:
+            select = tab.where(onlywhere)
+        else:
+            select = onlywhere
+
+        _args = ()
+        for a in args:
+            if (hasattr(a, '__iter__')):
+                try:
+                    b = tab[a]
+                    if select is not None:
+                        b = b.compress(select)
+                    if (len(b.dtype) > 1):
+                        b = list((b[k] for k in b.dtype.names))
+                    _args += (b, )
+                except Exception as e:
+                    print(e)
+                    _args += (a, )
+            else:
+                _args += (a, )
+
+        return _fn(*_args, **kwargs)
+
+    def attached_function(fn, doc=None, errorlevel=0):
+        """ eclare a function as a method to the class table"""
+
+        def _fn(self, *args, **kwargs):
+            try:
+                return plot_function(self, fn, *args, **kwargs)
+            except Exception as e:
+                if errorlevel < 1:
+                    pass
+                else:
+                    raise e
+
+        if doc is not None:
+            _fn.__doc__ = doc
+
+        return _fn
+
+    SimpleTable.plot_function = plot_function
+    SimpleTable.plot = attached_function('plot', plt.plot.__doc__)
+    SimpleTable.hist = attached_function('hist', plt.hist.__doc__)
+    SimpleTable.hist2d = attached_function('hist2d', plt.hist2d.__doc__)
+    SimpleTable.hexbin = attached_function('hexbin', plt.hexbin.__doc__)
+    SimpleTable.scatter = attached_function('scatter', plt.scatter.__doc__)
+
+    # newer version of matplotlib
+    if hasattr(plt, 'violinplot'):
+        SimpleTable.violinplot = attached_function('violinplot', plt.violinplot.__doc__)
+    if hasattr(plt, 'boxplot'):
+        SimpleTable.boxplot = attached_function('boxplot', plt.boxplot.__doc__)
+
+except Exception as e:
+    print(e)
+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/stellib.html b/_modules/pystellibs/stellib.html new file mode 100644 index 0000000..4840949 --- /dev/null +++ b/_modules/pystellibs/stellib.html @@ -0,0 +1,973 @@ + + + + + + + + pystellibs.stellib — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.stellib

+"""
+Stellar library class
+
+Intent to implement a generic module to manage stellar library from various
+sources.
+
+The interpolation is implemented from the pegase.2 fortran converted algorithm.
+(this may not be super pythonic though)
+
+.. note::
+
+    a cython version is available for speed up and should be used transparently when available
+    (run make once)
+"""
+import numpy as np
+from scipy.interpolate import interp1d
+from itertools import groupby
+
+from .ezunits import unit, hasUnit
+from .helpers import nbytes, isNestedInstance
+from .future import Path
+from .interpolator import NDLinearInterpolator, find_interpolator
+
+
+lsun = 3.839e+26   # in W (Watts)
+sig_stefan = 5.67037321 * 1e-8  # W * m**-2 * K**-4
+rsun = 6.955e8  # in meters
+_default_interpolator = find_interpolator('lejeune')
+
+
+def _drop_units(q):
+    """ Drop the unit definition silently """
+    try:
+        return q.magnitude
+    except:
+        return q
+
+
+
[docs]class Stellib(object):
+    """ Basic stellar library class
+
+    Attributes
+    ----------
+    interpolator: interpolator.BaseInterpolator
+        interpolator to use, default LeujeuneInterpolator
+    """
+    def __init__(self, *args, **kwargs):
+        """ Contructor """
+        self.interpolator = find_interpolator(
+                kwargs.pop('interpolator', None),
+                osl=self)
+        if self.interpolator is None:
+            self.interpolator = _default_interpolator(self)
+        if not hasattr(self, 'wavelength_unit'):
+            self.wavelength_unit = None
+        self._dlogT = 0.5
+        self._dlogg = 0.5
+
+
[docs]    def set_default_extrapolation_bounds(self, dlogT=None, dlogg=None):
+        if dlogT is not None:
+            self._dlogT = dlogT
+        if dlogg is not None:
+            self._dlogg = dlogg

+
+
[docs]    def get_interpolation_data(self):
+        """ Default interpolation """
+        return np.array([self.logT, self.logg, self.logZ]).T

+
+    @property
+    def wavelength(self):
+        l = np.copy(self._wavelength)
+        if self.wavelength_unit is not None:
+            return l * unit[self.wavelength_unit]
+        else:
+            return l
+
+    @property
+    def flux_units(self):
+        if self.wavelength_unit is not None:
+            return unit['erg/s/' + self.wavelength_unit]
+        else:
+            return 1.
+
+    def _load_(self):
+        """ Load the library """
+        raise NotImplementedError
+
+    @property
+    def nbytes(self):
+        """ return the number of bytes of the object """
+        return nbytes(self)
+
+
[docs]    def plot_boundary(self, ax=None, dlogT=0., dlogg=0., **kwargs):
+        """
+        Parameters
+        ----------
+
+        dlogT: float
+            margin in logT (see get_boundaries)
+
+        dlogg: float
+            margin in logg (see get_boundaries)
+
+        .. see also::
+            :func:`matplotlib.plot`
+                For additional kwargs
+        """
+        import matplotlib.patches as patches
+        from pylab import gca
+        if ax is None:
+            ax = gca()
+        p = self.get_boundaries(dlogT=dlogT, dlogg=dlogg)
+        ax.add_patch(patches.PathPatch(p, **kwargs))
+        return p

+
+    def __add__(self, other):
+        if not isNestedInstance(other, Stellib):
+            raise ValueError('expecting a Stellib object, got {0}'.format(type(other)))
+
+        return CompositeStellib([self, other])
+
+    def __repr__(self):
+        return "{0:s}, ({1:s})\n{2:s}".format(self.name, nbytes(self, pprint=True),
+                                              object.__repr__(self))
+
+
[docs]    def get_weights(self, logT, logg, logL, weights=None):
+        """ Returns the proper weights for the interpolation
+
+        in spectra libraries the default is to have Lbol=1 normalization
+
+        Parameters
+        ----------
+        logT: float or ndarray
+            log-temperatures log(T/K)
+        logg: float or ndarray
+            log-gravity  log(g)
+        logL: float or ndarray
+            bolometric luminosity (log (L/Lsun))
+        """
+        # weights to apply during the interpolation (note that radii must be in cm)
+        # Stellar library models are given in cm^-2  ( 4 pi R)
+        # Compute radii of each point using log(T) and log(L)
+        L = 10 ** logL * unit['lsun'].to("ergs/s").magnitude
+        if weights is not None:
+            weights *= L
+        else:
+            weights = L
+        return weights

+
+
[docs]    def generate_stellar_spectrum(self, logT, logg, logL, Z,
+                                  raise_extrapolation=True, **kwargs):
+        """ Generates individual spectrum for the given stars APs and the
+        stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        logT: float
+            temperature
+
+        logg: float
+            log-gravity
+
+        logL: float
+            log-luminosity
+
+        Z: float
+            metallicity
+
+        raise_extrapolation: bool
+            if set throw error on extrapolation
+
+        null: value
+            value of the flux when extrapolation and raise_extrapolation is not set
+
+        returns
+        -------
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or ergs/s/AA/Lsun
+        """
+        null_value = kwargs.pop('null', np.nan)
+
+        # weights to apply during the interpolation (note that radii must be in cm)
+        weights = self.get_weights(logT, logg, logL)
+        logZ = np.log10(Z)
+
+        l0 = self.wavelength
+
+        # check boundary conditions, keep the data but do not compute the sed
+        # if outside
+        if not self.points_inside(np.atleast_2d([logT, logg]))[0]:
+            if raise_extrapolation:
+                raise RuntimeError('Outside library interpolation range')
+            else:
+                return l0, np.full(len(self.wavelength), null_value)
+
+        aps = logT, logg, logZ
+        spec = self.interpolator.interp(aps) * weights
+
+        return spec

+
+
+
[docs]    def generate_individual_values(self, stars, values, **kwargs):
+        """ Generates individual spectra for the given stars and stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        stars: Table
+            contains at least (logT, logg, logL, Z) of the considered stars
+
+        values: sequence or attribute name
+            value to interpolate
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        values: sequence
+            value to interpolate
+        """
+        _values = np.atleast_1d(getattr(self, values, values))
+
+        null_value = kwargs.pop('null', np.nan)
+        dlogT = kwargs.pop('dlogT', self._dlogT)
+        dlogg = kwargs.pop('dlogg', self._dlogg)
+        ndata = len(stars)
+        logT, logg, logL, Z = stars['logT'], stars['logg'], stars['logL'], stars['Z']
+
+        # weights to apply during the interpolation (note that radii must be in cm)
+        weights = self.get_weights(logT, logg, logL)
+
+        # check boundary conditions, keep the data but do not compute the sed
+        # if outside
+        bound = self.points_inside(np.array([logT, logg]).T, 
+                dlogT=dlogT, dlogg=dlogg)
+        if np.ndim(_values) == 1:
+            specs = np.empty(ndata, dtype=float)
+        else:
+            specs = np.empty((ndata, _values.shape[1]), dtype=float)
+
+        specs[~bound] = null_value
+        logZ = np.log10(Z)
+        aps = np.array([logT, logg, logZ]).T
+        s = self.interpolator.interp_other(aps[bound], _values) * weights[bound]
+        specs[bound] = np.squeeze(s)
+
+        return specs

+
+
[docs]    def generate_individual_spectra(self, stars, **kwargs):
+        """ Generates individual spectra for the given stars and stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        stars: Table
+            contains at least (logT, logg, logL, Z) of the considered stars
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        l0: ndarray, ndim=1
+            wavelength definition of the spectra
+            wavelength in AA
+
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or lsun/AA
+        """
+        null_value = kwargs.pop('null', np.nan)
+        dlogT = kwargs.pop('dlogT', self._dlogT)
+        dlogg = kwargs.pop('dlogg', self._dlogg)
+        logT, logg, logL, Z = stars['logT'], stars['logg'], stars['logL'], stars['Z']
+        ndata = len(logT)
+
+        # weights to apply during the interpolation (note that radii must be in cm)
+        weights = self.get_weights(logT, logg, logL)
+
+        # check boundary conditions, keep the data but do not compute the sed
+        # if outside
+        bound = self.points_inside(np.array([logT, logg]).T, 
+                dlogT=dlogT, dlogg=dlogg)
+        specs = np.empty((ndata, len(self._wavelength)), dtype=float)
+        specs[~bound] = np.full(len(self.wavelength), null_value)
+
+        logZ = np.log10(Z)
+        aps = np.array([logT, logg, logZ]).T
+        s = self.interpolator.interp(aps[bound]) * weights[bound, None]
+        specs[bound] = s
+
+        l0 = self.wavelength
+        specs = specs * self.flux_units
+
+        return l0, specs

+
+
[docs]    def points_inside(self, xypoints, dlogT=0.1, dlogg=0.5):
+        """
+        Returns if a point is inside the polygon defined by the boundary of the library
+
+        Parameters
+        ----------
+        xypoints: sequence
+            a sequence of N logg, logT pairs.
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        r: ndarray(dtype=bool)
+            a boolean ndarray, True for points inside the polygon.
+            A point on the boundary may be treated as inside or outside.
+        """
+        p = self.get_boundaries(dlogT=dlogT, dlogg=dlogg)
+        return p.contains_points(xypoints)

+
+
[docs]    def get_radius(self, logl, logt):
+        """ Returns the radius of a star given its luminosity and temperature
+
+        Assuming a black body, it comes:
+
+        .. math::
+
+                R ^ 2 = L / ( 4 \pi \sigma T ^ 4 ),
+
+        with:
+
+            * L, luminosity in W,
+            * pi, 3.141592...
+            * sig, Stefan constant in  W * m**-2 * K**-4
+            * T, temperature in K
+
+        Parameters
+        ----------
+        logl: ndarray[float, ndim=1]
+            log luminosities from the isochrones, in Lsun
+
+        logt: ndarray[float, ndim=1]
+            log temperatures from the isochrones, in K
+
+        returns
+        -------
+        radii: ndarray[float, ndim=1]
+            array of radii in m (SI units)
+        """
+        return np.sqrt( (10 ** logl) * lsun / (4.0 * np.pi * sig_stefan * ((10 ** logt) ** 4)) )

+
+
[docs]    def get_boundaries(self, dlogT=0.1, dlogg=0.3, **kwargs):
+        """ Returns the closed boundary polygon around the stellar library with
+        given margins
+
+        Parameters
+        ----------
+        s: Stellib
+            Stellar library object
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        b: ndarray[float, ndim=2]
+            closed boundary edge points: [logT, logg]
+
+        .. note::
+
+            as computing the boundary could take time, it is saved in the object
+            and only recomputed when parameters are updated
+        """
+        # if bbox is defined then assumes it is more precise and use it instead.
+        if dlogT is None:
+            dlogT = 0.1
+        if dlogg is None:
+            dlogg = 0.3
+        if hasattr(self, 'bbox'):
+            return Path(self.bbox(dlogT, dlogg))
+
+        if getattr(self, '_bound', None) is not None:
+            # check if recomputing is needed
+            if ((self._bound[1] - dlogT) < 1e-3) and (abs(self._bound[2] - dlogg) < 1e-3):
+                return self._bound[0]
+
+        leftb   = [(np.max(self.logT[self.logg == k]) + dlogT, k ) for k in np.unique(self.logg)]
+        leftb  += [(leftb[-1][1], leftb[-1][0] + dlogg)]
+        leftb   = [(leftb[0][1], leftb[0][0] - dlogg)] + leftb
+
+        rightb  = [(np.min(self.logT[self.logg == k]) - dlogT, k) for k in np.unique(self.logg)[::-1]]
+        rightb += [(rightb[-1][1], rightb[-1][0] - dlogg)]
+        rightb  = [(rightb[0][1], rightb[0][0] + dlogg)] + rightb
+
+        b = leftb + rightb
+        b += [b[0]]
+
+        self._bound = (Path(np.array(b)), dlogT, dlogg)
+        return self._bound[0]

+
+
+
[docs]class AtmosphereLib(Stellib):
+    """
+    Almost identical to a spectral library. The difference lies into the units
+    of the input libraries.
+    """
+
[docs]    def get_weights(self, logT, logg, logL, weights=None):
+        """ Returns the proper weights for the interpolation 
+        Stellar atmospheres are normalized to Radius = 1
+
+        Parameters
+        ----------
+        logT: float or ndarray
+            log-temperatures log(T/K)
+        logg: float or ndarray
+            log-gravity  log(g)
+        logL: float or ndarray
+            bolometric luminosity (log (L/Lsun))
+        """
+        # weights to apply during the interpolation (note that radii must be in cm)
+        # Stellar library models are given in cm^-2  ( 4 pi R)
+        # Compute radii of each point using log(T) and log(L)
+        radii = self.get_radius(logL, logT)
+        if weights is not None:
+            weights *= 4. * np.pi * (radii * 1e2) ** 2
+        else:
+            weights = 4. * np.pi * (radii * 1e2) ** 2
+        return weights

+
+
+
[docs]class CompositeStellib(Stellib):
+    """ Generates an object from the union of multiple individual libraries """
+    def __init__(self, osllist, *args, **kwargs):
+        self._olist = osllist
+        self._dlogT = 0.5
+        self._dlogg = 0.5
+
+    @property
+    def name(self):
+        return ' + '.join([sl.name for sl in self._olist])
+
+
[docs]    def set_default_extrapolation_bounds(self, dlogT=None, dlogg=None):
+        if dlogT is not None:
+            self._dlogT = dlogT
+        if dlogg is not None:
+            self._dlogg = dlogg
+        for oslk in self._olist:
+            oslk.set_default_extrapolation_bounds(dlogT, dlogg)

+
+    def __add__(self, other):
+        """ Adding a library after """
+        if not isNestedInstance(other, Stellib):
+            raise ValueError('expecting a Stellib object, got {0}'.format(type(other)))
+
+        lst = [k for k in self._olist] + [other]
+        return CompositeStellib(lst)
+
+    def __radd__(self, other):
+        """ Adding a library before """
+        if not isNestedInstance(other, Stellib):
+            raise ValueError('expecting a Stellib object, got {0}'.format(type(other)))
+
+        lst = [other] + [k for k in self._olist]
+        return CompositeStellib(lst)
+
+    @property
+    def wavelength(self):
+        """ return a common wavelength sampling to all libraries. This can be
+        used to reinterpolate any spectrum onto a common definition """
+        # check units
+        has_units = [hasUnit(osl.wavelength) for osl in self._olist]
+        test = sum(has_units)
+        if (test == 0):
+            return np.unique(np.asarray([ osl._wavelength for osl in self._olist ]))
+
+        # which library sets the units
+        common_unit = self._olist[0].wavelength_unit
+        libset_unit = self._olist[0].name
+        # if some libraries do not have units... Should not happen often!
+        if (test < len(self._olist)):
+            for k, osl in enumerate(self._olist):
+                common_unit = osl.wavelength_unit
+                libset_unit = osl.name
+                if common_unit is not None:
+                    break
+            print("Warning: Some libraries do not have units. Assuming consistency with {0:s}".format(libset_unit))
+
+        wave = []
+        for osl in self._olist:
+            wave.append(osl.wavelength.to(common_unit).magnitude)
+        return np.unique(np.array(wave)) * unit[common_unit]
+
+    @property
+    def _wavelength(self):
+        return _drop_units(self.wavelength)
+
+    @property
+    def source(self):
+        return ' + '.join([k.name for k in self._olist])
+
+    @property
+    def logT(self):
+        return np.hstack([osl.logT for osl in self._olist])
+
+    @property
+    def logg(self):
+        return np.hstack([osl.logg for osl in self._olist])
+
+    @property
+    def Teff(self):
+        return np.hstack([osl.Teff for osl in self._olist])
+
+    @property
+    def Z(self):
+        return np.hstack([osl.Z for osl in self._olist])
+
+    @property
+    def logZ(self):
+        return np.hstack([osl.logZ for osl in self._olist])
+
+    @property
+    def flux_units(self):
+        return self._olist[0].flux_units
+
+
[docs]    def which_osl(self, xypoints, **kwargs):
+        """
+        Returns the library indice that contains each point in xypoints
+
+        The decision is made from a two step search:
+
+            * first, each point is checked against the strict boundary of each
+              library (i.e., dlogT = 0, dlogg = 0).
+            * second, if points are not found in strict mode, the boundary is
+              relaxed and a new search is made.
+
+        Each point is associated to the first library matching the above conditions.
+
+        Parameters
+        ----------
+        xypoints: sequence
+            a sequence of N logg, logT pairs.
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        res: ndarray(dtype=int)
+            a ndarray, 0 meaning no library covers the point, and 1, ... n, for the n-th library
+        """
+        dlogT = kwargs.pop('dlogT', self._dlogT)
+        dlogg = kwargs.pop('dlogg', self._dlogg)
+
+        xy = np.atleast_2d(xypoints)
+
+        # check that all points are in the full boundary area
+        # MF: testing why points_inside does not agree on all computers...
+        # as we do not keep individual results, no need to store then all
+        # first, collapse directly
+
+        # res_temp = np.zeros((len(xy),len(self._olist)))
+        # for ek,ok in enumerate(self._olist):
+        #    res_temp[:, ek] = ok.points_inside(xy, dlogT=dlogT, dlogg=dlogg).astype(int)
+        res_temp = np.zeros(len(xy), dtype=int)
+        for ek, ok in enumerate(self._olist):
+            res_temp += ok.points_inside(xy, dlogT=dlogT, dlogg=dlogg).astype(int)
+
+        ind = res_temp > 0
+        res = np.zeros(len(xy), dtype=int)
+        res[ind] = 1
+        res = res - 1
+
+        # res = self.points_inside(xy, dlogT=dlogT, dlogg=dlogg).astype(int) - 1
+        # if res == -1: invalid point, res == 0: proceed
+
+        if max(res) < 0:
+            # DEBUG: should generate an exeception in further functions
+            # TODO: get rid and replace
+            return res
+            # return res
+
+        # Strict mode
+        # ===========
+        # Not extrapolation allowed >> dlogT = 0, dlogg = 0
+        # 0 is used to flag points without a matching library yet
+        # libraries are then indexed from 1 to n
+        # -1 means point outside the compound library
+        for ek, ok in enumerate(self._olist):
+            if 0 in res:
+                ind = np.atleast_1d(np.squeeze(np.where(res == 0)))
+                r = ok.points_inside(xy[ind], dlogT=0., dlogg=0.)
+                res[ind[r]] = ek + 1
+
+        # Relaxed mode
+        # ============
+        # In this case we accept some flexibility in the boundary limits,
+        # which allows limited extrapolation ranges.
+        # this only affects points not already matched
+        if 0 in res:
+            for ek, ok in enumerate(self._olist):
+                if 0 in res:
+                    ind = np.atleast_1d(np.squeeze(np.where(res == 0)))
+                    r = ok.points_inside(xy[ind], dlogT=dlogT, dlogg=dlogg)
+                    res[ind[r]] = ek + 1
+        return res

+
+    def __repr__(self):
+        return "CompositeStellib, {0}\n{1}".format(object.__repr__(self), '\n'.join([k.name for k in self._olist]))
+
+
[docs]    def get_boundaries(self, **kwargs):
+        """ Returns the closed boundary polygon around the stellar library with
+        given margins
+
+        Parameters
+        ----------
+        s: Stellib
+            Stellar library object
+
+        dlogT: float
+            margin in logT
+
+        dlogg: float
+            margin in logg
+
+        returns
+        -------
+        b: ndarray[float, ndim=2]
+            (closed) boundary points: [logg, Teff] (or [Teff, logg] is swap is True)
+
+        .. note::
+
+            as computing the boundary could take time, it is saved in the object
+            and only recomputed when parameters are updated
+        """
+        dlogT = kwargs.pop('dlogT', self._dlogT)
+        dlogg = kwargs.pop('dlogg', self._dlogg)
+
+        if getattr(self, '_bound', None) is not None:
+            if ((self._bound[1] - dlogT) < 1e-3) and (abs(self._bound[2] - dlogg) < 1e-3):
+                return self._bound[0]
+
+        b = [osl.get_boundaries(dlogT=dlogT, dlogg=dlogg, **kwargs) for osl in self._olist]
+        self._bound = (Path.make_compound_path(*b), dlogT, dlogg)
+        return self._bound[0]

+
+
[docs]    def generate_stellar_spectrum(self, logT, logg, logL, Z,
+                                  raise_extrapolation=True, **kwargs):
+        """ Generates individual spectrum for the given stars APs and the
+        stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        logT: float
+            temperature
+
+        logg: float
+            log-gravity
+
+        logL: float
+            log-luminosity
+
+        Z: float
+            metallicity
+
+        raise_extrapolation: bool
+            if set throw error on extrapolation
+
+        null: value
+            value of the flux when extrapolation and raise_extrapolation is not set
+
+        returns
+        -------
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or ergs/s/AA/Lsun
+        """
+        try:
+            bounds = kwargs.pop('bounds', None)
+            dlogT = bounds.get('dlogT', self._dlogT)
+            dlogg = bounds.get('dlogg', self._dlogg)
+        except:
+            dlogT = None
+            dlogg = None
+
+        osl_index = self.which_osl(np.atleast_2d([logT, logg]), dlogT=dlogT, dlogg=dlogg)[0]
+        osl = self._olist[osl_index - 1]
+        specs = osl.generate_stellar_spectrum(logT, logg, logL, Z,
+                                              raise_extrapolation,
+                                              **kwargs)
+        specs = self.reinterpolate_spectra(osl.wavelength, specs, fill_value=0., bounds_error=False)
+        return specs

+
+
[docs]    def reinterpolate_spectra(self, l0, specs, **kwargs):
+        """ One-dimensional linear interpolation onto the common wavelength.
+
+        Returns the one-dimensional interpolated spectrum
+
+        Parameters
+        ----------
+        l0 : 1-D sequence of floats (with units or not)
+            wavelength of the spectrum to interpolate
+
+        specs : 1-D sequence of floats
+            spectrum to reinterpolate
+
+        left : float, optional
+            Value to return for `x < xp[0]`, default is `fp[0]`.
+
+        right : float, optional
+            Value to return for `x > xp[-1]`, default is `fp[-1]`.
+
+        period : None or float, optional
+            A period for the x-coordinates. This parameter allows the proper
+            interpolation of angular x-coordinates. Parameters `left` and `right`
+            are ignored if `period` is specified.
+
+        Returns
+        -------
+        spec : ndarray
+            The interpolated values
+        """
+        # TODO: proper reinterpolation that conserves energy... but makes a new
+        # resolution
+        wave = self.wavelength
+        try:
+            wave = wave.to(l0.unit)
+        except:
+            wave = _drop_units(wave)
+        func = interp1d(_drop_units(l0), _drop_units(specs), **kwargs)
+        f = func(_drop_units(wave))
+        return f

+
+
[docs]    def generate_individual_spectra(self, stars, **kwargs):
+        """ Generates individual spectra for the given stars and stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        stars: Table
+            contains at least (logT, logg, logL, Z) of the considered stars
+
+        returns
+        -------
+        l0: ndarray, ndim=1
+            wavelength definition of the spectra
+            wavelength in AA
+
+        s0: ndarray, shape=(len(stars), len(l0))
+            array of spectra, one per input star
+            Spectrum in ergs/s/AA or ergs/s/AA/Lsun
+        """
+        null_value = kwargs.pop('null', np.nan)
+        try:
+            bounds = kwargs.pop('bounds', None)
+            dlogT = bounds.get('dlogT', self._dlogT)
+            dlogg = bounds.get('dlogg', self._dlogg)
+        except:
+            dlogT = None
+            dlogg = None
+
+        ndata = len(stars)
+        logT, logg, logL, Z = stars['logT'], stars['logg'], stars['logL'], stars['Z']
+
+        # find which library per star
+        osl_index = self.which_osl(list(zip(logT, logg)), dlogT=dlogT, dlogg=dlogg)
+
+        # group calculations per library
+        groups = [(osl_i, [k[0] for k in grp]) 
+                  for osl_i, grp in groupby(enumerate(osl_index), lambda x:x[1])]
+
+        # Do the actual interpolation, avoiding exptrapolations
+        spectra = np.empty( (ndata, len(self.wavelength)), dtype=float )
+
+        for osl_i, idx_group in groups:
+            ind = np.array(idx_group)
+            if osl_i > 0:
+                _osl = self._olist[osl_i - 1]
+                lambdas, specs = _osl.generate_individual_spectra(stars[ind])
+                spectra[ind] = self.reinterpolate_spectra(
+                        _drop_units(lambdas), 
+                        _drop_units(specs),
+                        bounds_error=False,
+                        fill_value=0.,
+                        **kwargs
+                        )
+            else:
+                spectra[ind] = null_value
+
+        l0 = self.wavelength
+        spectra = spectra * self.flux_units
+
+        return l0, spectra, osl_index

+
+
[docs]    def generate_individual_values(self, stars, values, **kwargs):
+        """ Generates individual spectra for the given stars and stellar library
+
+        Returns NaN spectra if the boundary conditions are not met (no extrapolation)
+
+        Parameters
+        ----------
+        stars: Table
+            contains at least (logT, logg, logL, Z) of the considered stars
+
+        values: sequence or attribute name
+            value to interpolate
+
+        returns
+        -------
+        values: sequence
+            value to interpolate
+        """
+        null_value = kwargs.pop('null', np.nan)
+        try:
+            bounds = kwargs.pop('bounds', None)
+            dlogT = bounds.get('dlogT', self._dlogT)
+            dlogg = bounds.get('dlogg', self._dlogg)
+        except:
+            dlogT = None
+            dlogg = None
+
+        ndata = len(stars)
+        logT, logg, logL, Z = stars['logT'], stars['logg'], stars['logL'], stars['Z']
+
+        # find which library per star
+        osl_index = self.which_osl(list(zip(logT, logg)), dlogT=dlogT, dlogg=dlogg)
+
+        # group calculations per library
+        groups = [(osl_i, [k[0] for k in grp]) 
+                  for osl_i, grp in groupby(enumerate(osl_index), lambda x:x[1])]
+
+        # Do the actual interpolation, avoiding exptrapolations
+        spectra = np.empty(ndata, dtype=float )
+
+        for osl_i, idx_group in groups:
+            ind = np.array(idx_group)
+            if osl_i > 0:
+                _osl = self._olist[osl_i - 1]
+                spectra[ind] = _osl.generate_individual_values(stars[ind], values, **kwargs)
+            else:
+                spectra[ind] = null_value
+
+        return spectra, osl_index

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_modules/pystellibs/tlusty.html b/_modules/pystellibs/tlusty.html new file mode 100644 index 0000000..747e922 --- /dev/null +++ b/_modules/pystellibs/tlusty.html @@ -0,0 +1,220 @@ + + + + + + + + pystellibs.tlusty — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Source code for pystellibs.tlusty

+import numpy as np
+from .stellib import AtmosphereLib
+from .config import libsdir
+from .simpletable import SimpleTable
+try:
+    from astropy.io import fits as pyfits
+except ImportError:
+    import pyfits
+
+
+
[docs]class Tlusty(AtmosphereLib):
+    """
+    Tlusty O and B stellar atmospheres
+
+    * NLTE
+    * Parallel Planes
+    * line blanketing
+
+    References
+    ----------
+    Hubeny 1988 for initial reference
+    Lanz, T., & Hubeny, I. (2003) for more recent (NL TE) developments
+
+    * **OSTAR2002 Grid**: O-type stars, 27500 K <= Teff <= 55000 K
+        * Reference: Lanz & Hubeny (2003)
+
+    * **BSTAR2006 Grid**: Early B-type stars, 15000 K <= Teff <= 30000 K
+            * Reference: Lanz & Hubeny (2007)
+
+    files are available at: http://nova.astro.umd.edu/Tlusty2002/database/
+
+    O and B stars rebinned to nearly 20,000 frequency points (for CLOUDY usage)
+    http://nova.astro.umd.edu/Tlusty2002/database/obstar_merged_3d.ascii.gz
+    """
+    def __init__(self, *args, **kwargs):
+        self.name = 'Tlusty'
+        self.source = libsdir + '/tlusty.lowres.grid.fits'
+        self._load_()
+        AtmosphereLib.__init__(self, *args, **kwargs)
+
+    def _load_(self):
+        with pyfits.open(self.source) as f:
+            # load data
+            self._getWaveLength_(f)
+            self._getTGZ_(f)
+            self._getSpectra_(f)
+            self._getWaveLength_units(f)
+
+    def _getWaveLength_units(self, f):
+        self.wavelength_unit = 'angstrom'
+
+    def _getWaveLength_(self, f):
+        self._wavelength = f[0].data[-1]
+
+    def _getTGZ_(self, f):
+        self.grid = SimpleTable(f[1].data)
+        self.grid.header.update(f[1].header.items())
+        self.grid.header['NAME'] = 'TGZ'
+
+    def _getSpectra_(self, f):
+        self.spectra = f[0].data[:-1]
+
+
[docs]    def bbox(self, dlogT=0.05, dlogg=0.25):
+        """ Boundary of Tlusty library
+
+        Parameters
+        ----------
+        dlogT: float
+            log-temperature tolerance before extrapolation limit
+
+        dlogg: float
+            log-g tolerance before extrapolation limit
+
+        Returns
+        -------
+        bbox: ndarray
+            (logT, logg) edges of the bounding polygon
+        """
+        bbox = [(4.176 - dlogT, 4.749 + dlogg),
+                (4.176 - dlogT, 1.750 - dlogg),
+                (4.176 + dlogT, 1.750 - dlogg),
+                (4.255 + dlogT, 2.000 - dlogg),
+                (4.447 + dlogT, 2.750 - dlogg),
+                (4.478 + dlogT, 3.000 - dlogg),
+                (4.544 + dlogT, 3.250 - dlogg),
+                (4.740 + dlogT, 4.000 - dlogg),
+                (4.740 + dlogT, 4.749 + dlogg),
+                (4.176 - dlogT, 4.749 + dlogg) ]
+
+        return np.array(bbox)

+
+    @property
+    def logT(self):
+        return self.grid['logT']
+
+    @property
+    def logg(self):
+        return self.grid['logg']
+
+    @property
+    def Teff(self):
+        return self.grid['Teff']
+
+    @property
+    def Z(self):
+        return self.grid['Z']
+
+    @property
+    def logZ(self):
+        return np.log10(self.Z)

+
+ +
+ +
+
+ +
+
+
+ ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
+ + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index 68ae867..22089cc 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -1,15 +1,125 @@ -.. No Errors Test Project documentation master file, created by - sphinx-quickstart on Fri Aug 30 17:07:56 2019. +.. pystellibs documentation master file, created by + sphinx-quickstart on Mon Oct 17 14:28:28 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Welcome to No Errors Test Project's documentation! -================================================== +pystellibs - making synthetic spectra +====================================== + +This is a set of tools to compute synthetic spectra in a simple way, ideal to +integrate in larger projects. + +This package provides a common interface to compute a single or a collection of +spectra using spectral libraries or atmospheres. + +Package main content +~~~~~~~~~~~~~~~~~~~~ + +The main package articulates around 2 parts: + +* :class:`pystellibs.stellib.Stellib` a stellar spectra library, commonly normalized to + bolometric luminosities +* :class:`pystellibs.stellib.AtmosphereLib` a stellar atmosphere library, commonly normalized to + stellar radii. + +The interpolation within libraries is left as a separated component. + +This package provides already multiple input libraries: + +* :class:`pystellibs.basel.BaSeL`: BaSeL 2.2, ~ Atlas 9 empirically recalibrated (Leujeune et al 1998) +* :class:`pystellibs.rauch.Rauch`: a White dwarf library +* :class:`pystellibs.kurucz.Kurucz`: Castelli and Kurucz 2004 or ATLAS9 +* :class:`pystellibs.tlusty.Tlusty`: NLTE O, B stars [Lanz, T., & Hubeny, I. (2003)] +* :class:`pystellibs.elodie.Elodie`: version 3.1, high resolution optical library. +* :class:`pystellibs.munari.Munari`: extended ATLAS9 stellar atmospheres (Munari et al. 2005 A&A 442 1127) +* :class:`pystellibs.btsettl.BTSettl`: BT-Settl Library (Allard, Hauschildt and Schweitzer 2000) + + +Future libraries to be included: + +* XSL: Xshooter spectral library +* PHOENIX +* MARCS + +References (TBU) +---------------- + +* Leujeune et al 1998: http://cdsads.u-strasbg.fr/abs/1998yCat..41300065L + + +API +~~~ + +The API has been reduced to minimum to make it easy to use. Mostly 2 functions: + +* :func:`Stellib.generate_stellar_spectrum` + that computes one spectrum for one set of stellar parameters +* :func:`Stellib.generate_individual_spectra` + that computes a spectrum for each of many sets of paramaters + +Contents: .. toctree:: :maxdepth: 2 - :caption: Hello World! + modules + + + +Quick Start +~~~~~~~~~~~ + +* This example shows how to use one of 2 libraries to compute a spectrum + +.. code-block:: python + + import pylab as plt + from pystellibs import BaSeL, Kurucz + + # load 2 libraries + basel = BaSeL() + kurucz = Kurucz() + ap = (4., 3.5, 0., 0.02) + sb = basel.generate_stellar_spectrum(*ap) + sk = kurucz.generate_stellar_spectrum(*ap) + plt.figure() + plt.loglog(osl._wavelength, sb, label='BaSel') + plt.loglog(osl._wavelength, sk, label='Kurucz') + plt.legend(frameon=False, loc='lower right') + plt.xlabel("Wavelength [{0}]".format(basel.wavelength_unit)) + plt.ylabel("Flux [{0}]".format(basel.flux_units)) + plt.xlim(800, 5e4) + plt.ylim(1e25, 5e30) + plt.tight_layout() + +.. image:: single_spec_libs.png + :scale: 70 % + +* Combining multiple libraries (with priority): the following example combines + BaSeL with white-dwarf models. + +.. code-block:: python + + from pystellibs import BaSeL, Rauch + + # Combine 2 libraries by priority order + lib = BaSeL() + Rauch() + + for osl in lib._olist: + l = plt.plot(osl.logT, osl.logg, 'o')[0] + osl.plot_boundary(color=l.get_color(), dlogT=0.1, dlogg=0.3, alpha=0.3, + label=osl.name) + + plt.xlim(5.6, 2.8) + plt.ylim(8.5, -2) + plt.xlabel('log T$_{eff}$') + plt.ylabel('log g') + plt.tight_layout() + + plt.legend(frameon=False, loc='upper left') + +.. image:: combined_libs.png + :scale: 70 % Indices and tables @@ -18,3 +128,4 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` + diff --git a/_sources/modules.rst.txt b/_sources/modules.rst.txt new file mode 100644 index 0000000..97cd888 --- /dev/null +++ b/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +pystellibs +========== + +.. toctree:: + :maxdepth: 4 + + pystellibs diff --git a/_sources/pystellibs.ezunits.rst.txt b/_sources/pystellibs.ezunits.rst.txt new file mode 100644 index 0000000..c625969 --- /dev/null +++ b/_sources/pystellibs.ezunits.rst.txt @@ -0,0 +1,22 @@ +pystellibs.ezunits package +========================== + +Submodules +---------- + +pystellibs.ezunits.pint module +------------------------------ + +.. automodule:: pystellibs.ezunits.pint + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: pystellibs.ezunits + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/pystellibs.future.rst.txt b/_sources/pystellibs.future.rst.txt new file mode 100644 index 0000000..4e903d6 --- /dev/null +++ b/_sources/pystellibs.future.rst.txt @@ -0,0 +1,22 @@ +pystellibs.future package +========================= + +Submodules +---------- + +pystellibs.future.path module +----------------------------- + +.. automodule:: pystellibs.future.path + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: pystellibs.future + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/pystellibs.interpolator.rst.txt b/_sources/pystellibs.interpolator.rst.txt new file mode 100644 index 0000000..45ebf2b --- /dev/null +++ b/_sources/pystellibs.interpolator.rst.txt @@ -0,0 +1,38 @@ +pystellibs.interpolator package +=============================== + +Submodules +---------- + +pystellibs.interpolator.interpolator module +------------------------------------------- + +.. automodule:: pystellibs.interpolator.interpolator + :members: + :undoc-members: + :show-inheritance: + +pystellibs.interpolator.lejeune module +-------------------------------------- + +.. automodule:: pystellibs.interpolator.lejeune + :members: + :undoc-members: + :show-inheritance: + +pystellibs.interpolator.ndlinear module +--------------------------------------- + +.. automodule:: pystellibs.interpolator.ndlinear + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: pystellibs.interpolator + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/pystellibs.rst.txt b/_sources/pystellibs.rst.txt new file mode 100644 index 0000000..b06b0c0 --- /dev/null +++ b/_sources/pystellibs.rst.txt @@ -0,0 +1,135 @@ +pystellibs package +================== + +Subpackages +----------- + +.. toctree:: + + pystellibs.ezunits + pystellibs.future + pystellibs.interpolator + +Submodules +---------- + +pystellibs\.basel module +------------------------ + +.. automodule:: pystellibs.basel + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.btsettl module +-------------------------- + +.. automodule:: pystellibs.btsettl + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.config module +------------------------- + +.. automodule:: pystellibs.config + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.elodie module +------------------------- + +.. automodule:: pystellibs.elodie + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.ezmap module +------------------------ + +.. automodule:: pystellibs.ezmap + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.helpers module +-------------------------- + +.. automodule:: pystellibs.helpers + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.kurucz module +------------------------- + +.. automodule:: pystellibs.kurucz + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.marcs module +------------------------ + +.. automodule:: pystellibs.marcs + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.munari module +------------------------- + +.. automodule:: pystellibs.munari + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.pbar module +----------------------- + +.. automodule:: pystellibs.pbar + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.rauch module +------------------------ + +.. automodule:: pystellibs.rauch + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.simpletable module +------------------------------ + +.. automodule:: pystellibs.simpletable + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.stellib module +-------------------------- + +.. automodule:: pystellibs.stellib + :members: + :undoc-members: + :show-inheritance: + +pystellibs\.tlusty module +------------------------- + +.. automodule:: pystellibs.tlusty + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: pystellibs + :members: + :undoc-members: + :show-inheritance: diff --git a/_static/basic.css b/_static/basic.css index 0119285..7577acb 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -15,6 +15,12 @@ div.clearer { clear: both; } +div.section::after { + display: block; + content: ''; + clear: left; +} + /* -- relbar ---------------------------------------------------------------- */ div.related { @@ -124,7 +130,7 @@ ul.search li a { font-weight: bold; } -ul.search li div.context { +ul.search li p.context { color: #888; margin: 2px 0 0 30px; text-align: left; @@ -216,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 450px; + min-width: 360px; max-width: 800px; } @@ -231,16 +237,6 @@ a.headerlink { visibility: hidden; } -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -271,25 +267,25 @@ p.rubric { font-weight: bold; } -img.align-left, .figure.align-left, object.align-left { +img.align-left, figure.align-left, .figure.align-left, object.align-left { clear: left; float: left; margin-right: 1em; } -img.align-right, .figure.align-right, object.align-right { +img.align-right, figure.align-right, .figure.align-right, object.align-right { clear: right; float: right; margin-left: 1em; } -img.align-center, .figure.align-center, object.align-center { +img.align-center, figure.align-center, .figure.align-center, object.align-center { display: block; margin-left: auto; margin-right: auto; } -img.align-default, .figure.align-default { +img.align-default, figure.align-default, .figure.align-default { display: block; margin-left: auto; margin-right: auto; @@ -313,24 +309,35 @@ img.align-default, .figure.align-default { /* -- sidebars -------------------------------------------------------------- */ -div.sidebar { +div.sidebar, +aside.sidebar { margin: 0 0 0.5em 1em; border: 1px solid #ddb; - padding: 7px 7px 0 7px; + padding: 7px; background-color: #ffe; width: 40%; float: right; + clear: right; + overflow-x: auto; } p.sidebar-title { font-weight: bold; } +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + /* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, div.topic { border: 1px solid #ccc; - padding: 7px 7px 0 7px; + padding: 7px; margin: 10px 0 10px 0; } @@ -352,10 +359,6 @@ div.admonition dt { font-weight: bold; } -div.admonition dl { - margin-bottom: 0; -} - p.admonition-title { margin: 0px 10px 5px 0px; font-weight: bold; @@ -366,9 +369,34 @@ div.body p.centered { margin-top: 25px; } +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + /* -- tables ---------------------------------------------------------------- */ table.docutils { + margin-top: 10px; + margin-bottom: 10px; border: 0; border-collapse: collapse; } @@ -398,10 +426,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -416,32 +440,34 @@ table.citation td { border-bottom: none; } -th > p:first-child, -td > p:first-child { +th > :first-child, +td > :first-child { margin-top: 0px; } -th > p:last-child, -td > p:last-child { +th > :last-child, +td > :last-child { margin-bottom: 0px; } /* -- figures --------------------------------------------------------------- */ -div.figure { +div.figure, figure { margin: 0.5em; padding: 0.5em; } -div.figure p.caption { +div.figure p.caption, figcaption { padding: 0.3em; } -div.figure p.caption span.caption-number { +div.figure p.caption span.caption-number, +figcaption span.caption-number { font-style: italic; } -div.figure p.caption span.caption-text { +div.figure p.caption span.caption-text, +figcaption span.caption-text { } /* -- field list styles ----------------------------------------------------- */ @@ -468,10 +494,71 @@ table.field-list td, table.field-list th { /* -- hlist styles ---------------------------------------------------------- */ +table.hlist { + margin: 1em 0; +} + table.hlist td { vertical-align: top; } +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + /* -- other body styles ----------------------------------------------------- */ @@ -495,26 +582,53 @@ ol.upperroman { list-style: upper-roman; } -li > p:first-child { +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { margin-top: 0px; } -li > p:last-child { +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { margin-bottom: 0px; } -dl.footnote > dt, -dl.citation > dt { - float: left; +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; } -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; } -dl.footnote > dd:after, -dl.citation > dd:after { +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { content: ""; clear: both; } @@ -531,10 +645,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -546,7 +656,7 @@ dl { margin-bottom: 15px; } -dd > p:first-child { +dd > :first-child { margin-top: 0px; } @@ -560,6 +670,11 @@ dd { margin-left: 30px; } +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + dt:target, span.highlighted { background-color: #fbe54e; } @@ -573,14 +688,6 @@ dl.glossary dt { font-size: 1.1em; } -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - .versionmodified { font-style: italic; } @@ -621,8 +728,9 @@ dl.glossary dt { .classifier:before { font-style: normal; - margin: 0.5em; + margin: 0 0.5em; content: ":"; + display: inline-block; } abbr, acronym { @@ -637,29 +745,69 @@ pre { overflow-y: hidden; /* fixes display issues on Chrome browsers */ } +pre, div[class*="highlight-"] { + clear: both; +} + span.pre { -moz-hyphens: none; -ms-hyphens: none; -webkit-hyphens: none; hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; } td.linenos pre { - padding: 5px 0px; border: 0; background-color: transparent; color: #aaa; } table.highlighttable { - margin-left: 0.5em; + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; } table.highlighttable td { - padding: 0 0.5em 0 0.5em; + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; } div.code-block-caption { + margin-top: 1em; padding: 2px 5px; font-size: small; } @@ -668,12 +816,14 @@ div.code-block-caption code { background-color: transparent; } -div.code-block-caption + div > div.highlight > pre { - margin-top: 0; -} - -div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ } div.code-block-caption span.caption-number { @@ -685,21 +835,7 @@ div.code-block-caption span.caption-text { } div.literal-block-wrapper { - padding: 1em 1em 0; -} - -div.literal-block-wrapper div.highlight { - margin: 0; -} - -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; + margin: 1em 0; } code.xref, a code { @@ -740,8 +876,7 @@ span.eqno { } span.eqno a.headerlink { - position: relative; - left: 0px; + position: absolute; z-index: 1; } diff --git a/_static/doctools.js b/_static/doctools.js index daccd20..d06a71d 100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -2,314 +2,155 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - */ -jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return result; }; -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } }, - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + * helper function to focus on search bar */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * highlight the search words provided in the url in the text + * Initialise the domain index toggle buttons */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, + }; - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); } + break; } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js index 4790c4d..a7f754b 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,11 +1,14 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', + VERSION: '1.0', + LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, }; \ No newline at end of file diff --git a/_static/language_data.js b/_static/language_data.js index d2b4ee9..250f566 100644 --- a/_static/language_data.js +++ b/_static/language_data.js @@ -5,15 +5,16 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ -var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"]; +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version JS is _stemmer.js if file is provided */ +/* Non-minified version is copied as a separate JS file, is available */ + /** * Porter Stemmer */ @@ -196,102 +197,3 @@ var Stemmer = function() { } } - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if (start !== -1) { - result.push(query.slice(start)); - } - return result; -} - - diff --git a/_static/pygments.css b/_static/pygments.css index dd6621d..0d49244 100644 --- a/_static/pygments.css +++ b/_static/pygments.css @@ -1,77 +1,75 @@ +pre { line-height: 125%; } +td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +td.linenos .special { color: #000000; background-color: #ffffc0; 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-if (!Scorer) { - /** - * Simple result scoring code. - */ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { var Scorer = { // Implement the following function to further tweak the score for each result - // The function takes a result array [filename, title, anchor, descr, score] + // The function takes a result array [docname, title, anchor, descr, score, filename] // and returns the new score. /* - score: function(result) { - return result[4]; + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score }, */ @@ -28,9 +30,11 @@ if (!Scorer) { // or matches in the last dotted part of the object name objPartialMatch: 6, // Additive scores depending on the priority of the object - objPrio: {0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5}, // used to be unimportantResults + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, // Used when the priority is not in the mapping. objPrioDefault: 0, @@ -39,442 +43,495 @@ if (!Scorer) { partialTitle: 7, // query found in terms term: 5, - partialTerm: 2 + partialTerm: 2, }; } -if (!splitQuery) { - function splitQuery(query) { - return query.split(/\s+/); +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings } /** * Search Module */ -var Search = { - - _index : null, - _queued_query : null, - _pulse_status : -1, - - htmlToText : function(htmlString) { - var htmlElement = document.createElement('span'); - htmlElement.innerHTML = htmlString; - $(htmlElement).find('.headerlink').remove(); - docContent = $(htmlElement).find('[role=main]')[0]; - if(docContent === undefined) { - console.warn("Content block not found. Sphinx search tries to obtain it " + - "via '[role=main]'. Could you check your theme or template."); - return ""; - } - return docContent.textContent || docContent.innerText; +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; }, - init : function() { - var params = $.getQueryParameters(); - if (params.q) { - var query = params.q[0]; - $('input[name="q"]')[0].value = query; - this.performSearch(query); - } + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); }, - loadIndex : function(url) { - $.ajax({type: "GET", url: url, data: null, - dataType: "script", cache: true, - complete: function(jqxhr, textstatus) { - if (textstatus != "success") { - document.getElementById("searchindexloader").src = url; - } - }}); - }, + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), - setIndex : function(index) { - var q; - this._index = index; - if ((q = this._queued_query) !== null) { - this._queued_query = null; - Search.query(q); + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); } }, - hasIndex : function() { - return this._index !== null; - }, + hasIndex: () => Search._index !== null, - deferQuery : function(query) { - this._queued_query = query; - }, + deferQuery: (query) => (Search._queued_query = query), - stopPulse : function() { - this._pulse_status = 0; - }, + stopPulse: () => (Search._pulse_status = -1), - startPulse : function() { - if (this._pulse_status >= 0) - return; - function pulse() { - var i; + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { Search._pulse_status = (Search._pulse_status + 1) % 4; - var dotString = ''; - for (i = 0; i < Search._pulse_status; i++) - dotString += '.'; - Search.dots.text(dotString); - if (Search._pulse_status > -1) - window.setTimeout(pulse, 500); - } + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; pulse(); }, /** * perform a search for something (or wait until index is loaded) */ - performSearch : function(query) { + performSearch: (query) => { // create the required interface elements - this.out = $('#search-results'); - this.title = $('

' + _('Searching') + '

').appendTo(this.out); - this.dots = $('').appendTo(this.title); - this.status = $('

 

').appendTo(this.out); - this.output = $('
    ').appendTo(this.out); - - $('#search-progress').text(_('Preparing search...')); - this.startPulse(); + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); // index already loaded, the browser was quick! - if (this.hasIndex()) - this.query(query); - else - this.deferQuery(query); + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); }, /** * execute search (requires search index to be loaded) */ - query : function(query) { - var i; - - // stem the searchterms and add them to the correct list - var stemmer = new Stemmer(); - var searchterms = []; - var excluded = []; - var hlterms = []; - var tmp = splitQuery(query); - var objectterms = []; - for (i = 0; i < tmp.length; i++) { - if (tmp[i] !== "") { - objectterms.push(tmp[i].toLowerCase()); - } + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; - if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) || - tmp[i] === "") { - // skip this "word" - continue; - } // stem the word - var word = stemmer.stemWord(tmp[i].toLowerCase()); - // prevent stemmer from cutting word smaller than two chars - if(word.length < 3 && tmp[i].length >= 3) { - word = tmp[i]; - } - var toAppend; + let word = stemmer.stemWord(queryTermLower); // select the correct list - if (word[0] == '-') { - toAppend = excluded; - word = word.substr(1); - } + if (word[0] === "-") excludedTerms.add(word.substr(1)); else { - toAppend = searchterms; - hlterms.push(tmp[i].toLowerCase()); + searchTerms.add(word); + highlightTerms.add(queryTermLower); } - // only add if not already in the list - if (!$u.contains(toAppend, word)) - toAppend.push(word); - } - var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" ")); + }); - // console.debug('SEARCH: searching for:'); - // console.info('required: ', searchterms); - // console.info('excluded: ', excluded); + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } - // prepare search - var terms = this._index.terms; - var titleterms = this._index.titleterms; + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } - // array of [filename, title, anchor, descr, score] - var results = []; - $('#search-progress').empty(); + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } // lookup as object - for (i = 0; i < objectterms.length; i++) { - var others = [].concat(objectterms.slice(0, i), - objectterms.slice(i+1, objectterms.length)); - results = results.concat(this.performObjectSearch(objectterms[i], others)); - } + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); // lookup as search terms in fulltext - results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms)); + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) { - for (i = 0; i < results.length; i++) - results[i][4] = Scorer.score(results[i]); - } + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); // now sort the results by score (in opposite order of appearance, since the // display function below uses pop() to retrieve items) and then // alphabetically - results.sort(function(a, b) { - var left = a[4]; - var right = b[4]; - if (left > right) { - return 1; - } else if (left < right) { - return -1; - } else { + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { // same score: sort alphabetically - left = a[1].toLowerCase(); - right = b[1].toLowerCase(); - return (left > right) ? -1 : ((left < right) ? 1 : 0); + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional } + return leftScore > rightScore ? 1 : -1; }); + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + // for debugging //Search.lastresults = results.slice(); // a copy - //console.info('search results:', Search.lastresults); + // console.info("search results:", Search.lastresults); // print the results - var resultCount = results.length; - function displayNextItem() { - // results left, load the summary and display it - if (results.length) { - var item = results.pop(); - var listItem = $('
  • '); - var requestUrl = ""; - if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') { - // dirhtml builder - var dirname = item[0] + '/'; - if (dirname.match(/\/index\/$/)) { - dirname = dirname.substring(0, dirname.length-6); - } else if (dirname == 'index/') { - dirname = ''; - } - requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname; - - } else { - // normal html builders - requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX; - } - listItem.append($('').attr('href', - requestUrl + - highlightstring + item[2]).html(item[1])); - if (item[3]) { - listItem.append($(' (' + item[3] + ')')); - Search.output.append(listItem); - listItem.slideDown(5, function() { - displayNextItem(); - }); - } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { - $.ajax({url: requestUrl, - dataType: "text", - complete: function(jqxhr, textstatus) { - var data = jqxhr.responseText; - if (data !== '' && data !== undefined) { - listItem.append(Search.makeSearchSummary(data, searchterms, hlterms)); - } - Search.output.append(listItem); - listItem.slideDown(5, function() { - displayNextItem(); - }); - }}); - } else { - // no source available, just display title - Search.output.append(listItem); - listItem.slideDown(5, function() { - displayNextItem(); - }); - } - } - // search finished, update title and status message - else { - Search.stopPulse(); - Search.title.text(_('Search Results')); - if (!resultCount) - Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.')); - else - Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount)); - Search.status.fadeIn(500); - } - } - displayNextItem(); + _displayNextItem(results, results.length, searchTerms); }, /** * search for object names */ - performObjectSearch : function(object, otherterms) { - var filenames = this._index.filenames; - var docnames = this._index.docnames; - var objects = this._index.objects; - var objnames = this._index.objnames; - var titles = this._index.titles; - - var i; - var results = []; - - for (var prefix in objects) { - for (var name in objects[prefix]) { - var fullname = (prefix ? prefix + '.' : '') + name; - var fullnameLower = fullname.toLowerCase() - if (fullnameLower.indexOf(object) > -1) { - var score = 0; - var parts = fullnameLower.split('.'); - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower == object || parts[parts.length - 1] == object) { - score += Scorer.objNameMatch; - // matches in last name - } else if (parts[parts.length - 1].indexOf(object) > -1) { - score += Scorer.objPartialMatch; - } - var match = objects[prefix][name]; - var objname = objnames[match[1]][2]; - var title = titles[match[0]]; - // If more than one term searched for, we require other words to be - // found in the name/title/description - if (otherterms.length > 0) { - var haystack = (prefix + ' ' + name + ' ' + - objname + ' ' + title).toLowerCase(); - var allfound = true; - for (i = 0; i < otherterms.length; i++) { - if (haystack.indexOf(otherterms[i]) == -1) { - allfound = false; - break; - } - } - if (!allfound) { - continue; - } - } - var descr = objname + _(', in ') + title; - - var anchor = match[3]; - if (anchor === '') - anchor = fullname; - else if (anchor == '-') - anchor = objnames[match[1]][1] + '-' + fullname; - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) { - score += Scorer.objPrio[match[2]]; - } else { - score += Scorer.objPrioDefault; - } - results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]); - } + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; } - } + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); return results; }, /** * search for full-text terms in the index */ - performTermsSearch : function(searchterms, excluded, terms, titleterms) { - var docnames = this._index.docnames; - var filenames = this._index.filenames; - var titles = this._index.titles; + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; - var i, j, file; - var fileMap = {}; - var scoreMap = {}; - var results = []; + const scoreMap = new Map(); + const fileMap = new Map(); // perform the search on the required terms - for (i = 0; i < searchterms.length; i++) { - var word = searchterms[i]; - var files = []; - var _o = [ - {files: terms[word], score: Scorer.term}, - {files: titleterms[word], score: Scorer.title} + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, ]; // add support for partial matches if (word.length > 2) { - for (var w in terms) { - if (w.match(word) && !terms[word]) { - _o.push({files: terms[w], score: Scorer.partialTerm}) - } - } - for (var w in titleterms) { - if (w.match(word) && !titleterms[word]) { - _o.push({files: titleterms[w], score: Scorer.partialTitle}) - } - } + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); } // no match but word was a required one - if ($u.every(_o, function(o){return o.files === undefined;})) { - break; - } + if (arr.every((record) => record.files === undefined)) return; + // found search word in contents - $u.each(_o, function(o) { - var _files = o.files; - if (_files === undefined) - return - - if (_files.length === undefined) - _files = [_files]; - files = files.concat(_files); - - // set score for the word in each file to Scorer.term - for (j = 0; j < _files.length; j++) { - file = _files[j]; - if (!(file in scoreMap)) - scoreMap[file] = {}; - scoreMap[file][word] = o.score; - } + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); }); // create the mapping - for (j = 0; j < files.length; j++) { - file = files[j]; - if (file in fileMap && fileMap[file].indexOf(word) === -1) - fileMap[file].push(word); - else - fileMap[file] = [word]; - } - } + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); // now check if the files don't contain excluded terms - for (file in fileMap) { - var valid = true; - + const results = []; + for (const [file, wordList] of fileMap) { // check if all requirements are matched - var filteredTermCount = // as search terms with length < 3 are discarded: ignore - searchterms.filter(function(term){return term.length > 2}).length + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; if ( - fileMap[file].length != searchterms.length && - fileMap[file].length != filteredTermCount - ) continue; + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; // ensure that none of the excluded terms is in the search result - for (i = 0; i < excluded.length; i++) { - if (terms[excluded[i]] == file || - titleterms[excluded[i]] == file || - $u.contains(terms[excluded[i]] || [], file) || - $u.contains(titleterms[excluded[i]] || [], file)) { - valid = false; - break; - } - } + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; - // if we have still a valid result we can add it to the result list - if (valid) { - // select one (max) score for the file. - // for better ranking, we should calculate ranking by using words statistics like basic tf-idf... - var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]})); - results.push([docnames[file], titles[file], '', null, score, filenames[file]]); - } + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); } return results; }, @@ -482,31 +539,28 @@ var Search = { /** * helper function to return a node containing the * search summary for a given text. keywords is a list - * of stemmed words, hlwords is the list of normal, unstemmed - * words. the first one is used to find the occurrence, the - * latter for highlighting it. + * of stemmed words. */ - makeSearchSummary : function(htmlText, keywords, hlwords) { - var text = Search.htmlToText(htmlText); - var textLower = text.toLowerCase(); - var start = 0; - $.each(keywords, function() { - var i = textLower.indexOf(this.toLowerCase()); - if (i > -1) - start = i; - }); - start = Math.max(start - 120, 0); - var excerpt = ((start > 0) ? '...' : '') + - $.trim(text.substr(start, 240)) + - ((start + 240 - text.length) ? '...' : ''); - var rv = $('
    ').text(excerpt); - $.each(hlwords, function() { - rv = rv.highlightText(this, 'highlighted'); - }); - return rv; - } + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, }; -$(document).ready(function() { - Search.init(); -}); +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

    ' + + '' + + _("Hide Search Matches") + + "

    " + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/genindex.html b/genindex.html index 4eb1e1f..4db5565 100644 --- a/genindex.html +++ b/genindex.html @@ -1,18 +1,16 @@ - - + - Index — No Errors Test Project documentation - - - - - + + Index — pystellibs 1.0 documentation + + + - + @@ -35,8 +33,929 @@

    Index

    + A + | B + | C + | D + | E + | F + | G + | H + | I + | J + | K + | L + | M + | N + | P + | R + | S + | T + | U + | V + | W + | Z
    +

    A

    + + + +
    + +

    B

    + + + +
    + +

    C

    + + + +
    + +

    D

    + + + +
    + +

    E

    + + + +
    + +

    F

    + + + +
    + +

    G

    + + + +
    + +

    H

    + + + +
    + +

    I

    + + + +
    + +

    J

    + + +
    + +

    K

    + + + +
    + +

    L

    + + + +
    + +

    M

    + + + +
    + +

    N

    + + + +
    + +

    P

    + + + +
      +
    • + pystellibs.future + +
      • +
    • + pystellibs.future.path + +
      • +
    • + pystellibs.helpers + +
      • +
    • + pystellibs.interpolator + +
      • +
    • + pystellibs.interpolator.interpolator + +
      • +
    • + pystellibs.interpolator.lejeune + +
      • +
    • + pystellibs.interpolator.ndlinear + +
      • +
    • + pystellibs.kurucz + +
      • +
    • + pystellibs.marcs + +
      • +
    • + pystellibs.munari + +
      • +
    • + pystellibs.pbar + +
      • +
    • + pystellibs.rauch + +
      • +
    • + pystellibs.simpletable + +
      • +
    • + pystellibs.stellib + +
      • +
    • + pystellibs.tlusty + +
      • +
    + +

    R

    + + + +
    + +

    S

    + + + +
    + +

    T

    + + + +
    + +

    U

    + + + +
    + +

    V

    + + + +
    + +

    W

    + + + +
    + +

    Z

    + + + +
    + @@ -45,16 +964,26 @@

    Index

    - ©2019, sphinx-action Test Suite. + ©2016, M. Fouesneau. | - Powered by Sphinx 2.4.4 + Powered by Sphinx 6.2.1 & Alabaster 0.7.12 | @@ -104,6 +221,10 @@

    Quick search

    + + Fork me on GitHub + + diff --git a/modules.html b/modules.html new file mode 100644 index 0000000..0bc0f91 --- /dev/null +++ b/modules.html @@ -0,0 +1,438 @@ + + + + + + + + + pystellibs — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    pystellibs

    +
    + +
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    + +
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    + +
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    +
    + ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + + | + Page source +
    + + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv index 8c8a34fe028d0d8118c92d3cd44d32e53008dc3f..280c840f84393854592591d341793f32e1306af9 100644 GIT binary patch delta 1978 zcmV;r2Sxai0)h~bC>d~hb97~FY-wV13L_v^WpZykm3x7 zLy?MXv;oHGT+d|lu@XhiVD`=)Z%-$ssn~Dbh3nsE*H?o}bE1KT>XOgo3GSUbXGH7S z`y=srRr2ul&MBo55|n>we8mu{j*vgYViB%R!=>}N*tE6+Q;Nb*%f#|Iykx6Rqtr`J zwb5WqkYjermT;+aj+G&1J|LuVa&L1V=$BwJ$_*$f7%^1vtRRNO5Cfd(n2<&j zJe?^Qd_k81z64AZ6oQ4xm!1AX7aZyQBjlkRqV-V#{|+?Pr!jwG-_A2u`#buPK8@6S zIyt=7oSL&KK!17CX}7l!{lgwO(xT?M3moI(N31gtX#)>JT^uI)#3<3XG&g5@(xvs; zgA!5{8F9bPxkv!hh1-@1MKtx6R=~Awc?&en7o~)9OxWZg!wr)B-~}uwhr$^ckxI>X zSW<0N6KkR&tF?d9LWwF{PzKR$dW4qBHpT{@Tlc*OkGgX$6s*1x+z{&q3d34^ntS-@ z3fjSXZ6hA2&hpkopn&HPAC3lSBVxge+SKZ7YS3Ms3UK`$l9)0aQUbH1L&&tRS0)dn zf+tCGpCo0geWWr}M&>M3x>TZ;G1`Pk3B2&h4UQRA%?y9da>7^~N5g-?l)S9@N}qS% zDQy8Z_~pcOLzTaN3AVoz(~Tega;hJ|o-FRTHe|97PEl6Sya-ijJ3J_D0vQWQl%a%$ z$tzGxu(2d}^$0{zttnr&qlV62h~oBF7>dsu1A*0Wkth?3tPdn%3}t6%O{VE9*uk~W z0R1thk&u7oCFCmz^shBxcILYqh%X21r^}sdp9-tPd!}OS=sufu71<&EbiI!GQ{XqN z@#x2Ao7LVOlyo8hhgbcmg#PNez=&T`CQRA+TYPy8enQOw)gC=Fx$pj>TVsNcYKvc& zgJ}s~Fq@hZ+^(^OCJ(pw?E$(mCQCpy$0a328f1S}t2%!T$Wl9D^3P6xVa_+w1-ynD z>(dyq?`x>B+W$4wkv@&od%lLMh{{7IB&{y_wpCwf=z%K0%tLh=E+tHsBwX(6AwzAS zhJ(#43c996ZSx*2AvcOdBipM)^}Mt@=W*8L0f1D#w3fh*U?&{bS@%orX(&g1D+OycYQt@nkVdJf@M1VGz4Pv2`Uw8U9l0m~0XrO{X=7OT=9 zEDwbR)43~qtMzF!c2)5 z44NJ1B@+e%Wgn4~s$1d-8h=N$0vM7d3%6v0Qz@vVX|_#|fU`3Q*hq!(l=-76w!QIb|6HE+IYb03&74K0NSp<$ zH`(q(D+D0Y4h}DVzJMC{l&}SI(wTDIRKxgbJ7@pF=6w@4x_5 zuXOoa0Si(yJLTF~>TZ`uYi=A~HwKI^0SmGzVp zWy$7xd!d7U>pZP1wev1LEVn;(Sn|~X^otT68*5QME@P zG+_7<@a)w8TQ~;x-v*!N4ZiA~o%o*km^S*r>ooJ|3yj&>PudoGe!uBy)ZpE@PTy}h Mcy#yv50T_`rg}fk@&Et; delta 187 zcmV;s07U67M%?I=BM(B$;AUaMUW!f^WO5q~3hbXm$PjLFU-GyGgAt9^P pp7YEk_J5|&*XAA6+Z*;n#Po!~7T5g_HmX + + + + + + Python Module Index — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +

    Python Module Index

    + +
    + p +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
     
    + p
    + pystellibs +
        + pystellibs.basel +
        + pystellibs.btsettl +
        + pystellibs.config +
        + pystellibs.elodie +
        + pystellibs.ezunits +
        + pystellibs.ezunits.pint +
        + pystellibs.future +
        + pystellibs.future.path +
        + pystellibs.helpers +
        + pystellibs.interpolator +
        + pystellibs.interpolator.interpolator +
        + pystellibs.interpolator.lejeune +
        + pystellibs.interpolator.ndlinear +
        + pystellibs.kurucz +
        + pystellibs.marcs +
        + pystellibs.munari +
        + pystellibs.pbar +
        + pystellibs.rauch +
        + pystellibs.simpletable +
        + pystellibs.stellib +
        + pystellibs.tlusty +
    + + +
    + +
    +
    + +
    +
    +
    + ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + +
    + + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/pystellibs.ezunits.html b/pystellibs.ezunits.html new file mode 100644 index 0000000..8f98da7 --- /dev/null +++ b/pystellibs.ezunits.html @@ -0,0 +1,376 @@ + + + + + + + + + pystellibs.ezunits package — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    pystellibs.ezunits package

    +
    +

    Submodules

    +
    +
    +

    pystellibs.ezunits.pint module

    +
    +

    pint

    +

    Pint is Python module/package to define, operate and manipulate +physical quantities: the product of a numerical value and a +unit of measurement. It allows arithmetic operations between them +and conversions from and to different units.

    +
    +
    copyright:
    +

    2012 by Hernan E. Grecco.

    +
    +
    license:
    +

    BSD, see LICENSE for more details.

    +
    +
    +
    +
    +
    +class pystellibs.ezunits.pint.AliasDict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list.  For example:  dict(one=1, two=2)[source]
    +

    Bases: dict

    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    clear()

    copy()

    fromkeys(iterable[, value])

    Create a new dictionary with keys from iterable and values set to value.

    get(key[, default])

    Return the value for key if key is in the dictionary, else default.

    items()

    keys()

    pop(k[,d])

    If key is not found, d is returned if given, otherwise KeyError is raised

    popitem(/)

    Remove and return a (key, value) pair as a 2-tuple.

    setdefault(key[, default])

    Insert key with a value of default if key is not in the dictionary.

    update([E, ]**F)

    If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

    values()

    + + + + + + + + + +

    add_alias

    get_aliased

    +
    +
    +add_alias(key, value, preferred=False)[source]
    +
    + +
    +
    +get_aliased(key)[source]
    +
    + +
    + +
    +
    +exception pystellibs.ezunits.pint.DimensionalityError(units1, units2, dim1=None, dim2=None)[source]
    +

    Bases: ValueError

    +

    Raised when trying to convert between incompatible units.

    +
    + +
    +
    +exception pystellibs.ezunits.pint.UndefinedUnitError(unit_names)[source]
    +

    Bases: ValueError

    +

    Raised when the units are not defined in the unit registry.

    +
    + +
    +
    +class pystellibs.ezunits.pint.UnitRegistry(filename='', force_ndarray=False)[source]
    +

    Bases: object

    +

    The unit registry stores the definitions and relationships between +units.

    +
    +
    Parameters:
    +
      +

    • filename – path of the units definition file to load. +Empty to load the default definition file. +None to leave the UnitRegistry empty.

      • +

    • force_ndarray – convert any input, scalar or not to a numpy.ndarray.

      • +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + +

    add_from_file(filename)

    Add units and prefixes defined in a definition text file.

    add_prefix(name, value[, aliases])

    Add prefix to the registry.

    add_unit(name, value[, aliases])

    Add unit to the registry.

    get_alias(name)

    Return the preferred alias for a unit

    +
    +
    +add_from_file(filename)[source]
    +

    Add units and prefixes defined in a definition text file.

    +
    + +
    +
    +add_prefix(name, value, aliases=())[source]
    +

    Add prefix to the registry.

    +
    + +
    +
    +add_unit(name, value, aliases=(), **modifiers)[source]
    +

    Add unit to the registry.

    +
    + +
    +
    +get_alias(name)[source]
    +

    Return the preferred alias for a unit

    +
    + +
    + +
    +
    +class pystellibs.ezunits.pint.UnitsContainer(*args, **kwargs)[source]
    +

    Bases: dict

    +

    The UnitsContainer stores the product of units and their respective +exponent and implements the corresponding operations

    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    clear()

    copy()

    fromkeys(iterable[, value])

    Create a new dictionary with keys from iterable and values set to value.

    get(key[, default])

    Return the value for key if key is in the dictionary, else default.

    items()

    keys()

    pop(k[,d])

    If key is not found, d is returned if given, otherwise KeyError is raised

    popitem(/)

    Remove and return a (key, value) pair as a 2-tuple.

    setdefault(key[, default])

    Insert key with a value of default if key is not in the dictionary.

    update([E, ]**F)

    If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

    values()

    + + + + + + +

    add

    +
    +
    +add(key, value)[source]
    +
    + +
    + +
    +
    +pystellibs.ezunits.pint.converter_from_reference(scale, offset, log_base)[source]
    +
    + +
    +
    +pystellibs.ezunits.pint.converter_to_reference(scale, offset, log_base)[source]
    +
    + +
    +
    +

    Module contents

    +
    +

    pint

    +

    Pint is Python module/package to define, operate and manipulate +physical quantities: the product of a numerical value and a +unit of measurement. It allows arithmetic operations between them +and conversions from and to different units.

    +
    +
    copyright:
    +
      +

    1. 2012 by Hernan E. Grecco.

      2. +
    +
    +
    license:
    +

    BSD, see LICENSE for more details.

    +
    +
    +
    +
    +
    +pystellibs.ezunits.hasUnit(val)[source]
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + + | + Page source +
    + + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/pystellibs.future.html b/pystellibs.future.html new file mode 100644 index 0000000..26b48c6 --- /dev/null +++ b/pystellibs.future.html @@ -0,0 +1,135 @@ + + + + + + + + + pystellibs.future package — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    pystellibs.future package

    +
    +

    Submodules

    +
    +
    +

    pystellibs.future.path module

    +

    Contains a class for managing paths (polylines).

    +
    +
    +

    Module contents

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + + | + Page source +
    + + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/pystellibs.html b/pystellibs.html new file mode 100644 index 0000000..fe5ce9b --- /dev/null +++ b/pystellibs.html @@ -0,0 +1,5361 @@ + + + + + + + + + pystellibs package — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    pystellibs package

    +
    +

    Subpackages

    +
    + +
    +
    +
    +

    Submodules

    +
    +
    +

    pystellibs.basel module

    +

    BaSeL 2.2 library

    +
    +
    +class pystellibs.basel.BaSeL(*args, **kwargs)[source]
    +

    Bases: Stellib

    +
    +
    BaSeL 2.2 library derived class

    This library + Rauch is used in Pegase.2

    +
    +
    +

    The BaSeL stellar spectral energy distribution (SED) libraries are libraries +of theoretical stellar SEDs recalibrated using empirical photometric data. +Therefore, we call them semi-empirical libraries.

    +

    The BaSeL 2.2 library was calibrated using photometric data from solar +metallicity stars.

    +

    References

    + +
    +
    Attributes:
    +
    +
    NHI
    +
    NHeI
    +
    NHeII
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Basel 2.2 library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property NHI
    +
    + +
    +
    +property NHeI
    +
    + +
    +
    +property NHeII
    +
    + +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Basel 2.2 library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.btsettl module

    +
    +
    +class pystellibs.btsettl.BTSettl(medres=True, *args, **kwargs)[source]
    +

    Bases: AtmosphereLib

    +

    BT-Settl Library

    +

    References

    +
    +
    Paper: Few refereed publications

    Older Ref = http://adsabs.harvard.edu/abs/2000ApJ…539..366A

    +
    +
    Conference Proceedings:

    http://adsabs.harvard.edu/abs/2016sf2a.conf..223A +http://adsabs.harvard.edu/abs/2012RSPTA.370.2765A

    +
    +
    +

    Files available at: https://phoenix.ens-lyon.fr/Grids/BT-Settl/

    +

    Current Library: AGSS2009 Abundances (due to grid availability) +Spectra rebinned to match Kurucz, and custom 2 Ang medium resolution

    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of BT-Settl library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    interpolation needs alpha

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of BT-Settl library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +get_interpolation_data()[source]
    +

    interpolation needs alpha

    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.config module

    +
    +
    +

    pystellibs.elodie module

    +

    Elodie 3.1

    +
    +
    +class pystellibs.elodie.Elodie(*args, **kwargs)[source]
    +

    Bases: Stellib

    +

    Elodie 3.1 stellar library derived class

    +
    +
    Attributes:
    +
    +
    NHI
    +
    NHeI
    +
    NHeII
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Elodie library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property NHI
    +
    + +
    +
    +property NHeI
    +
    + +
    +
    +property NHeII
    +
    + +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Elodie library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.ezmap module

    +
    +
    +

    pystellibs.helpers module

    +

    This is a first collection of tools making the design easier

    +
    +
    +class pystellibs.helpers.NameSpace(name, *args, **kwargs)[source]
    +

    Bases: dict

    +

    A dict subclass that exposes its items as attributes.

    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    clear()

    copy()

    fromkeys(iterable[, value])

    Create a new dictionary with keys from iterable and values set to value.

    get(key[, default])

    Return the value for key if key is in the dictionary, else default.

    items()

    keys()

    pop(k[,d])

    If key is not found, d is returned if given, otherwise KeyError is raised

    popitem(/)

    Remove and return a (key, value) pair as a 2-tuple.

    setdefault(key[, default])

    Insert key with a value of default if key is not in the dictionary.

    update([E, ]**F)

    If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

    values()

    +
    + +
    +
    +class pystellibs.helpers.Pipe(func, *args, **kwargs)[source]
    +

    Bases: object

    +

    Decorator overloading | operator (__ror__) such that you can pipe +functions where the first argument is the variable on the left side of the +| operator. +The difference with Pipeable is that you cannot use decorated function +outside of pipes but you gain the possibility to update the calling +parameters

    +

    Used with keywords_first make this a powerful Task

    +

    Methods

    + + + + + + +

    __call__(*args, **kwargs)

    Call self as a function.

    +
    + +
    +
    +class pystellibs.helpers.Pipeable(func, *args, **kwargs)[source]
    +

    Bases: object

    +

    Decorator overloading | operator (__ror__) such that you can pipe +functions where the first argument is the variable on the left side of the +| operator. +This decorator allows you to use the decorated function normally and uses +the provided values when using in pipes.

    +
    >>> import pylab as plt
+>>> _p = Pipeable(plt.plot, color='red', linestyle='--')
+>>> _p(range(10), 'o-')  #  works
+>>> range(10) | _p      #  will plot a red dashed line
+
    +
    +

    Methods

    + + + + + + +

    __call__(*args, **kwargs)

    Call self as a function.

    +
    + +
    +
    +class pystellibs.helpers.Pipegroup(pipes, mode='sequential')[source]
    +

    Bases: object

    +

    Methods

    + + + + + + +

    __call__(val, *args, **kwargs)

    Call self as a function.

    + + + + + + + + + + + + +

    append

    multi_call

    seq_call

    +
    +
    +append(other)[source]
    +
    + +
    +
    +multi_call(vals, iter=True)[source]
    +
    + +
    +
    +seq_call(val, *args, **kwargs)[source]
    +
    + +
    + +
    +
    +pystellibs.helpers.chunks(l, n)[source]
    +

    Yield successive n-sized chunks from l.

    +
    +
    Parameters:
    +
    +
    l: iterable

    object to iter over

    +
    +
    n: int

    number of elements per slice

    +
    +
    +
    +
    Returns:
    +
    +
    chunk: tuple

    n values from l

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.deprecated(func)[source]
    +

    A dummy decorator that warns against using a deprecated function

    +
    + +
    +
    +pystellibs.helpers.generator(func)[source]
    +

    A dummy decorator that only make codes mode readable. +It allow to explicitly mark a function as generator (yielding values) +and does nothing more than calling the initial function

    +
    + +
    +
    +pystellibs.helpers.isNestedInstance(obj, cl)[source]
    +

    Test for sub-classes types +I could not find a universal test

    +
    +
    Parameters:
    +
    +
    obj: object instance

    object to test

    +
    +
    cl: Class

    top level class to test

    +
    +
    +
    +
    Returns:
    +
    +
    r: bool

    True if obj is indeed an instance or subclass instance of cl

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.keywords_first(f)[source]
    +

    Decorator that enables to access any argument or keyword as a keyword

    +
    + +
    +
    +pystellibs.helpers.kfpartial(fun, *args, **kwargs)[source]
    +

    Allows to create partial functions with arbitrary arguments/keywords

    +
    + +
    +
    +pystellibs.helpers.merge_records(lst)[source]
    +

    generates a stack of records even with slightly different but compatible +dtypes

    +
    +
    Parameters:
    +
    +
    lst: sequence of np.recarray

    sequence of individual records

    +
    +
    +
    +
    Returns:
    +
    +
    val: np.recarray

    array of stacked records +Note if if lst is empty, returns an empty list

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.missing_units_warning(name, defaultunit)[source]
    +

    Warn if any unit is missing

    +
    +
    Parameters:
    +
    +
    name: str

    name of the variable

    +
    +
    defaultunit: str

    default unit definition

    +
    +
    +
    +
    Raises:
    +
    +
    warning: warnings.warn

    warn if units are assumed

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.nbytes(obj, pprint=False)[source]
    +

    return the number of bytes of the object, which includes size of nested +structures

    +
    +
    Parameters:
    +
    +
    obj: object

    object to find the size of

    +
    +
    pprint: bool, optional (default=False)

    if set, returns the result after calling pretty_size_print

    +
    +
    +
    +
    Returns:
    +
    +
    num_bytes: int or str

    total number of bytes or human readable corresponding string

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.path_of_module(mod=None)[source]
    +

    returns the definition code path of a given module, object or function +If nothing is provided, the current frame will be query, i.e., the current +working directory of the calling function.

    +
    +
    Parameters:
    +
    +
    mod: module, class, function

    object to find the defintion +if None, inspect.currentframe is used

    +
    +
    +
    +
    Returns:
    +
    +
    path: str

    path of the definition

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.pretty_size_print(num_bytes)[source]
    +

    Output number of bytes in a human readable format

    +
    +
    Parameters:
    +
    +
    num_bytes: int

    number of bytes to convert

    +
    +
    +
    +
    Returns:
    +
    +
    output: str

    string representation of the size with appropriate unit scale

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.type_checker(name, obj, tp)[source]
    +

    Check a given type and raise a type error if not correct

    +
    +
    Parameters:
    +
    +
    name: str

    name of the variable to show in the exception text

    +
    +
    obj: object

    object to check

    +
    +
    tp: type

    expected type of obj

    +
    +
    +
    +
    Raises:
    +
    +
    exc:TypeError:
    +

    raises a TypeError if object is not of the correct type of a subclass of it

    +
    +
    +
    +
    +
    + +
    +
    +pystellibs.helpers.val_in_unit(varname, value, defaultunit)[source]
    +

    check units and convert to defaultunit or create the unit information

    +
    +
    Parameters:
    +
    +
    varname: str

    name of the variable

    +
    +
    value: value

    value of the variable, which may be unitless

    +
    +
    defaultunit: str

    default units is unitless

    +
    +
    +
    +
    Returns:
    +
    +
    quantity: ezunits.Quantity

    value with units

    +
    +
    +
    +
    +
    + +
    +
    +

    pystellibs.kurucz module

    +
    +
    +class pystellibs.kurucz.Kurucz(*args, **kwargs)[source]
    +

    Bases: AtmosphereLib

    +

    The stellar atmosphere models by Castelli and Kurucz 2004 or ATLAS9

    +
      +

    • LTE

      • +

    • PP

      • +

    • line blanketing

      • +
    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Kurucz 2004 library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Kurucz 2004 library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.marcs module

    +
    +
    +class pystellibs.marcs.Marcs(*args, **kwargs)[source]
    +

    Bases: AtmosphereLib

    +

    MARCS stellar atmosphere models

    +

    Gustafsson et al 2008.

    +

    http://marcs.astro.uu.se/

    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    alpha
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Kurucz 2004 library

    generate_individual_spectra(stars[, nthreads])

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    interpolation needs alpha

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +property alpha
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Kurucz 2004 library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +generate_individual_spectra(stars, nthreads=0, **kwargs)[source]
    +

    Generates individual spectra for the given stars and stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    stars: Table

    contains at least (logT, logg, logL, Z) of the considered stars

    +
    +
    +
    +
    Returns:
    +
    +
    l0: ndarray, ndim=1

    wavelength definition of the spectra +wavelength in AA

    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or ergs/s/AA/Lsun

    +
    +
    +
    +
    +
    + +
    +
    +generate_stellar_spectrum(logT, logg, logL, Z, alpha=0.0, raise_extrapolation=True, **kwargs)[source]
    +

    Generates individual spectrum for the given stars APs and the +stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    logT: float

    temperature

    +
    +
    logg: float

    log-gravity

    +
    +
    logL: float

    log-luminosity

    +
    +
    Z: float

    metallicity

    +
    +
    alpha: float

    alpha element

    +
    +
    raise_extrapolation: bool

    if set throw error on extrapolation

    +
    +
    null: value

    value of the flux when extrapolation and raise_extrapolation is not set

    +
    +
    +
    +
    Returns:
    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or ergs/s/AA/Lsun

    +
    +
    +
    +
    +
    + +
    +
    +get_interpolation_data()[source]
    +

    interpolation needs alpha

    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.munari module

    +
    +
    +class pystellibs.munari.Munari(*args, **kwargs)[source]
    +

    Bases: AtmosphereLib

    +

    ATLAS9 stellar atmospheres providing higher res than Kurucz +medium resolution (1 Ang/pix) in optical (2500-10500 Ang)

    +

    References

    +

    Paper: Munari et al. 2005 A&A 442 1127 +http://adsabs.harvard.edu/abs/2005A%26A…442.1127M

    +

    Files available at: http://archives.pd.astro.it/2500-10500/

    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Munari library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    interpolation needs alpha

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Munari library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +get_interpolation_data()[source]
    +

    interpolation needs alpha

    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.pbar module

    +
    +

    Simple progressbar

    +

    This package implement a unique progress bar class that can be used to decorate +an iterator, a function or even standalone.

    +

    The format of the meter is flexible and can display along with the progress +meter, the running time, an eta, and the rate of the iterations.

    +
    +
    An example is::

    description [———-] k/n 10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]

    +
    +
    +
    +
    +
    +class pystellibs.pbar.Pbar(maxval=None, desc=None, time=True, eta=True, rate=True, length=None, file=None, keep=True, mininterval=0.5, miniters=1, units='iters', **kwargs)[source]
    +

    Bases: object

    +

    make a progress string in a shape of:

    +
    [----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]
+
    +
    +
    +
    Attributes:
    +
    +
    time: bool, optional (default: True)

    if set, add the runtime information

    +
    +
    eta: bool, optional (default: True)

    if set, add an estimated time to completion

    +
    +
    rate: bool, optional (default: True)

    if set, add the rate information

    +
    +
    length: int, optional (default: None)

    number of characters showing the progress meter itself +if None, the meter will adapt to the buffer width

    +

    TODO: make it variable with the buffer length

    +
    +
    keep: bool, optional (default: True)

    If not set, deletes its traces from screen after completion

    +
    +
    file: buffer

    the buffer to write into

    +
    +
    mininterval: float (default: 0.5)

    minimum time in seconds between two updates of the meter

    +
    +
    miniters: int, optional (default: 1)

    minimum iteration number between two updates of the meter

    +
    +
    units: str, optional (default: ‘iters’)

    unit of the iteration

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + +

    build_str_meter(n, total, elapsed)

    make a progress string in a shape of.

    decorator(func)

    Provide a function decorator allowing for counting calls and rates

    format_interval(t)

    make a human readable time interval decomposed into days, hours, minutes and seconds

    iterover(iterable[, total])

    Get an iterable object, and return an iterator which acts exactly like the iterable, but prints a progress meter and updates it every time a value is requested.

    print_status(s)

    print a status s on the last file line and clean the rest of the line

    update(n[, desc, total])

    Kept for backward compatibility and the decorator feature

    + + + + + + +

    handle_resize

    +
    +
    +build_str_meter(n, total, elapsed)[source]
    +

    make a progress string in a shape of:

    +
    [----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]
+
    +
    +
    +
    Parameters:
    +
    +
    n: int

    number of finished iterations

    +
    +
    total: int

    total number of iterations, or None

    +
    +
    elapsed: int

    number of seconds passed since start

    +
    +
    +
    +
    Returns:
    +
    +
    txt: str

    string representing the meter

    +
    +
    +
    +
    +
    + +
    +
    +decorator(func)[source]
    +

    Provide a function decorator allowing for counting calls and rates

    +
    + +
    +
    +static format_interval(t)[source]
    +

    make a human readable time interval decomposed into days, hours, +minutes and seconds

    +
    +
    Parameters:
    +
    +
    t: int

    interval in seconds

    +
    +
    +
    +
    Returns:
    +
    +
    txt: str

    string representing the interval +(format: <days>d <hrs>:<min>:<sec>)

    +
    +
    +
    +
    +
    + +
    +
    +handle_resize(signum, frame)[source]
    +
    + +
    +
    +iterover(iterable, total=None)[source]
    +

    Get an iterable object, and return an iterator which acts exactly like the +iterable, but prints a progress meter and updates it every time a value is +requested.

    +
    +
    Parameters:
    +
    +
    iterable: generator or iterable object

    object to iter over.

    +
    +
    total: int, optional

    the number of iterations is assumed to be the length of the +iterator. But sometimes the iterable has no associated length or +its length is not the actual number of future iterations. In this +case, total can be set to define the number of iterations.

    +
    +
    +
    +
    Returns:
    +
    +
    gen: generator

    pass the values from the initial iterator

    +
    +
    +
    +
    +
    + +
    +
    +print_status(s)[source]
    +

    print a status s on the last file line and clean the rest of the line

    +
    +
    Parameters:
    +
    +
    s: str

    message to write

    +
    +
    +
    +
    +
    + +
    +
    +update(n, desc=None, total=None)[source]
    +

    Kept for backward compatibility and the decorator feature

    +
    +
    Parameters:
    +
    +
    n: int

    force iteration number n

    +
    +
    desc: str

    update description string

    +
    +
    total: int

    update the total number of iterations

    +
    +
    +
    +
    +
    + +
    + +
    +
    +

    pystellibs.rauch module

    +

    Rauch White Dwarfs stellar atmospheres

    +
    +
    +class pystellibs.rauch.Rauch(*args, **kwargs)[source]
    +

    Bases: Stellib

    +

    Rauch White Dwarfs stellar atmospheres

    +

    References

    +

    Rauch, T.; Werner, K.; Bohlin, R.; Kruk, J. W., “The virtual observatory service +TheoSSA: Establishing a database of synthetic stellar flux standards. I. NLTE +spectral analysis of the DA-type white dwarf G191-B2B”

    +
    +
    Attributes:
    +
    +
    NHI
    +
    NHeI
    +
    NHeII
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Rauch library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property NHI
    +
    + +
    +
    +property NHeI
    +
    + +
    +
    +property NHeII
    +
    + +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Rauch library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    pystellibs.simpletable module

    +
    +
    This file implements a Table class

    that is designed to be the basis of any format

    +
    +
    +
    +

    Requirements

    +
      +
    • +
      FIT format:
        +
      • +
        astropy:

        provides a replacement to pyfits +pyfits can still be used instead but astropy is now the default

        +
        +
        +
        • +
      +
      +
      +
      • +
    • +
      HDF5 format:
        +

      • pytables

        • +
      +
      +
      +
      • +
    +

    RuntimeError will be raised when writing to a format associated with missing +package.

    +
    +
    +
    +class pystellibs.simpletable.AstroHelpers[source]
    +

    Bases: object

    +

    Helpers related to astronomy data

    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + +

    conesearch(ra0, dec0, ra, dec, r[, outtype])

    Perform a cone search on a table

    deg2dms(val[, delim])

    Convert degrees into hex coordinates Parameters ---------- deg: float angle in degrees

    deg2hms(val[, delim])

    Convert degrees into hex coordinates

    dms2deg(_str[, delim])

    Convert hex coordinates into degrees Parameters ---------- str: string or sequence string to convert

    euler(ai_in, bi_in, select[, b1950, dtype])

    Transform between Galactic, celestial, and ecliptic coordinates.

    hms2deg(_str[, delim])

    Convert hex coordinates into degrees

    sphdist(ra1, dec1, ra2, dec2)

    measures the spherical distance between 2 points

    +
    +
    +static conesearch(ra0, dec0, ra, dec, r, outtype=0)[source]
    +

    Perform a cone search on a table

    +
    +
    Parameters:
    +
    +
    ra0: ndarray[ndim=1, dtype=float]

    column name to use as RA source in degrees

    +
    +
    dec0: ndarray[ndim=1, dtype=float]

    column name to use as DEC source in degrees

    +
    +
    ra: float

    ra to look for (in degree)

    +
    +
    dec: float

    ra to look for (in degree)

    +
    +
    r: float

    distance in degrees

    +
    +
    outtype: int
    +
    type of outputs

    0 – minimal, indices of matching coordinates +1 – indices and distances of matching coordinates +2 – full, boolean filter and distances

    +
    +
    +
    +
    +
    +
    Returns:
    +
    +
    t: tuple
    +
    if outtype is 0:

    only return indices from ra0, dec0

    +
    +
    elif outtype is 1:

    return indices from ra0, dec0 and distances

    +
    +
    elif outtype is 2:

    return conditional vector and distance to all ra0, dec0

    +
    +
    +
    +
    +
    +
    +
    + +
    +
    +static deg2dms(val, delim=':')[source]
    +

    Convert degrees into hex coordinates +Parameters +———- +deg: float

    +
    +

    angle in degrees

    +
    +
    +
    delimiter: str

    character delimiting the fields

    +
    +
    +
    +
    Returns:
    +
    +
    str: string or sequence

    string to convert

    +
    +
    +
    +
    +
    + +
    +
    +static deg2hms(val, delim=':')[source]
    +

    Convert degrees into hex coordinates

    +
    +
    Parameters:
    +
    +
    deg: float

    angle in degrees

    +
    +
    delimiter: str

    character delimiting the fields

    +
    +
    +
    +
    Returns:
    +
    +
    str: string or sequence

    string to convert

    +
    +
    +
    +
    +
    + +
    +
    +static dms2deg(_str, delim=':')[source]
    +

    Convert hex coordinates into degrees +Parameters +———- +str: string or sequence

    +
    +

    string to convert

    +
    +
    +
    delimiter: str

    character delimiting the fields

    +
    +
    +
    +
    Returns:
    +
    +
    deg: float

    angle in degrees

    +
    +
    +
    +
    +
    + +
    +
    +static euler(ai_in, bi_in, select, b1950=False, dtype='f8')[source]
    +

    Transform between Galactic, celestial, and ecliptic coordinates. +Celestial coordinates (RA, Dec) should be given in equinox J2000 +unless the b1950 is True.

    +
    +
    Parameters:
    +
    +
    long_in: float, or sequence

    Input Longitude in DEGREES, scalar or vector.

    +
    +
    lat_in: float, or sequence

    Latitude in DEGREES

    +
    +
    select: int

    Integer from 1 to 6 specifying type of coordinate transformation.

    +
    +
    b1950: bool

    set equinox set to 1950

    +
    +
    +
    +
    Returns:
    +
    +
    long_out: float, seq

    Output Longitude in DEGREES

    +
    +
    lat_out: float, seq

    Output Latitude in DEGREES

    +
    +
    REVISION HISTORY:
    +
    Written W. Landsman, February 1987
    +
    Adapted from Fortran by Daryl Yentis NRL
    +
    Converted to IDL V5.0 W. Landsman September 1997
    +
    Made J2000 the default, added /FK4 keyword W. Landsman December 1998
    +
    Add option to specify SELECT as a keyword W. Landsman March 2003
    +
    Converted from IDL to numerical Python: Erin Sheldon, NYU, 2008-07-02
    +
    +
    +
    +
    + +
    +
    +static hms2deg(_str, delim=':')[source]
    +

    Convert hex coordinates into degrees

    +
    +
    Parameters:
    +
    +
    str: string or sequence

    string to convert

    +
    +
    delimiter: str

    character delimiting the fields

    +
    +
    +
    +
    Returns:
    +
    +
    deg: float

    angle in degrees

    +
    +
    +
    +
    +
    + +
    +
    +static sphdist(ra1, dec1, ra2, dec2)[source]
    +

    measures the spherical distance between 2 points

    +
    +
    Parameters:
    +
    +
    ra1: float or sequence

    first right ascensions in degrees

    +
    +
    dec1: float or sequence

    first declination in degrees

    +
    +
    ra2: float or sequence

    second right ascensions in degrees

    +
    +
    dec2: float or sequence

    first declination in degrees

    +
    +
    +
    +
    Returns:
    +
    +
    Outputs: float or sequence

    returns a distance in degrees

    +
    +
    +
    +
    +
    + +
    + +
    +
    +class pystellibs.simpletable.AstroTable(*args, **kwargs)[source]
    +

    Bases: SimpleTable

    +

    Derived from the Table, this class add implementations of common astro +tools especially conesearch

    +
    +
    Attributes:
    +
    +
    colnames

    Sequence of column names

    +
    +
    dtype

    dtype of the data

    +
    +
    empty_row

    Return an empty row array respecting the table format

    +
    +
    name

    name of the table given by the Header[‘NAME’] attribute

    +
    +
    nbytes

    number of bytes of the object

    +
    +
    ncols

    number of columns

    +
    +
    nrows

    number of lines

    +
    +
    shape

    shape of the data

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    __call__(*args, **kwargs)

    Call self as a function.

    addCol(name, data[, dtype, unit, description])

    Add one or multiple columns to the table

    addLine(iterable)

    Append one row in this table.

    add_column(name, data[, dtype, unit, ...])

    Add one or multiple columns to the table

    append_row(iterable)

    Append one row in this table.

    boxplot(*args, **kwargs)

    Draw a box and whisker plot.

    coneSearch(ra, dec, r[, outtype])

    Perform a cone search on a table

    delCol(names)

    Remove several columns from the table

    evalexpr(expr[, exprvars, dtype])

    evaluate expression based on the data and external variables

    find_duplicate([index_only, values_only])

    Find duplication in the table entries, return a list of duplicated elements Only works at this time is 2 lines are the same entry not if 2 lines have the same values

    get(v[, full_match])

    returns a table from columns given as v

    get_DEC([degree])

    Returns RA, converted from hexa/sexa into degrees

    get_RA([degree])

    Returns RA, converted from hexa/sexa into degrees

    groupby(key)

    Create an iterator which returns (key, sub-table) grouped by each value of key(value)

    hexbin(*args, **kwargs)

    Make a 2D hexagonal binning plot of points x, y.

    hist(*args, **kwargs)

    Compute and plot a histogram.

    hist2d(*args, **kwargs)

    Make a 2D histogram plot.

    iterkeys()

    Iterator over the columns of the table

    itervalues()

    Iterator over the lines of the table

    join_by(r2, key[, jointype, r1postfix, ...])

    Join arrays r1 and r2 on key key.

    keys([regexp, full_match])

    Return the data column names or a subset of it

    match(r2, key)

    Returns the indices at which the tables match matching uses 2 columns that are compared in values

    plot(*args, **kwargs)

    Plot y versus x as lines and/or markers.

    plot_function(fn, *args, **kwargs)

    Generate a plotting method of tab from a given function

    pop_columns(names)

    Pop several columns from the table

    pprint([idx, fields, ret, all, full_match, ...])

    Pretty print the table content

    pprint_entry(num[, keys])

    print one line with key and values properly to be readable

    remove_column(names)

    Remove several columns from the table

    remove_columns(names)

    Remove several columns from the table

    resolve_alias(colname)

    Return the name of an aliased column.

    reverse_alias(colname)

    Return aliases of a given column.

    scatter(*args, **kwargs)

    A scatter plot of y vs.

    select(fields[, indices])

    Select only a few fields in the table

    selectWhere(fields[, condition, condvars, ...])

    Read table data fulfilling the given condition.

    setComment(colname, comment)

    Set the comment of a column referenced by its name

    setUnit(colname, unit)

    Set the unit of a column referenced by its name

    set_DEC(val)

    Set the column that defines DEC coordinates

    set_RA(val)

    Set the column that defines RA coordinates

    set_alias(alias, colname)

    Define an alias to a column

    set_comment(colname, comment)

    Set the comment of a column referenced by its name

    set_unit(colname, unit)

    Set the unit of a column referenced by its name

    sort(keys[, copy])

    Sort the table inplace according to one or more keys.

    stack(r, *args, **kwargs)

    Superposes arrays fields by fields inplace

    stats([fn, fields, fill])

    Make statistics on columns of a table

    violinplot(*args, **kwargs)

    Make a violin plot.

    where([condition, condvars, cone, zone])

    Read table data fulfilling the given condition.

    write(fname, **kwargs)

    write table into file

    zoneSearch(ramin, ramax, decmin, decmax[, ...])

    Perform a zone search on a table, i.e., a rectangular selection Parameters ---------- ramin: float minimal value of RA

    + + + + + + +

    info

    +
    +
    +coneSearch(ra, dec, r, outtype=0)[source]
    +

    Perform a cone search on a table

    +
    +
    Parameters:
    +
    +
    ra0: ndarray[ndim=1, dtype=float]

    column name to use as RA source in degrees

    +
    +
    dec0: ndarray[ndim=1, dtype=float]

    column name to use as DEC source in degrees

    +
    +
    ra: float

    ra to look for (in degree)

    +
    +
    dec: float

    ra to look for (in degree)

    +
    +
    r: float

    distance in degrees

    +
    +
    outtype: int
    +
    type of outputs

    0 – minimal, indices of matching coordinates +1 – indices and distances of matching coordinates +2 – full, boolean filter and distances

    +
    +
    +
    +
    +
    +
    Returns:
    +
    +
    t: tuple
    +
    if outtype is 0:

    only return indices from ra0, dec0

    +
    +
    elif outtype is 1:

    return indices from ra0, dec0 and distances

    +
    +
    elif outtype is 2:

    return conditional vector and distance to all ra0, dec0

    +
    +
    +
    +
    +
    +
    +
    + +
    +
    +get_DEC(degree=True)[source]
    +

    Returns RA, converted from hexa/sexa into degrees

    +
    + +
    +
    +get_RA(degree=True)[source]
    +

    Returns RA, converted from hexa/sexa into degrees

    +
    + +
    +
    +info()[source]
    +
    + +
    +
    +selectWhere(fields, condition=None, condvars=None, cone=None, zone=None, **kwargs)[source]
    +

    Read table data fulfilling the given condition. +Only the rows fulfilling the condition are included in the result. +conesearch is also possible through the keyword cone formatted as (ra, dec, r) +zonesearch is also possible through the keyword zone formatted as (ramin, ramax, decmin, decmax)

    +

    Combination of multiple selections is also available.

    +
    + +
    +
    +set_DEC(val)[source]
    +

    Set the column that defines DEC coordinates

    +
    + +
    +
    +set_RA(val)[source]
    +

    Set the column that defines RA coordinates

    +
    + +
    +
    +where(condition=None, condvars=None, cone=None, zone=None, **kwargs)[source]
    +

    Read table data fulfilling the given condition. +Only the rows fulfilling the condition are included in the result.

    +
    +
    Parameters:
    +
    +
    condition: str

    expression to evaluate on the table +includes mathematical operations and attribute names

    +
    +
    condvars: dictionary, optional

    A dictionary that replaces the local operands in current frame.

    +
    +
    +
    +
    Returns:
    +
    +
    out: ndarray/ tuple of ndarrays
    +
    result equivalent to np.where()
    +
    +
    +
    +
    + +
    +
    +zoneSearch(ramin, ramax, decmin, decmax, outtype=0)[source]
    +

    Perform a zone search on a table, i.e., a rectangular selection +Parameters +———- +ramin: float

    +
    +

    minimal value of RA

    +
    +
    +
    ramax: float

    maximal value of RA

    +
    +
    decmin: float

    minimal value of DEC

    +
    +
    decmax: float

    maximal value of DEC

    +
    +
    outtype: int
    +
    type of outputs

    0 or 1 – minimal, indices of matching coordinates +2 – full, boolean filter and distances

    +
    +
    +
    +
    +
    +
    Returns:
    +
    +
    r: sequence

    indices or conditional sequence of matching values

    +
    +
    +
    +
    +
    + +
    + +
    +
    +class pystellibs.simpletable.SimpleTable(fname, *args, **kwargs)[source]
    +

    Bases: object

    +

    Table class that is designed to be the basis of any format wrapping +around numpy recarrays

    +
    +
    Attributes:
    +
    +
    fname: str or object

    if str, the file to read from. This may be limited to the format +currently handled automatically. If the format is not correctly handled, +you can try by providing an object.__

    +
    +
    if object with a structure like dict, ndarray, or recarray-like

    the data will be encapsulated into a Table

    +
    +
    +
    +
    caseless: bool

    if set, column names will be caseless during operations

    +
    +
    aliases: dict

    set of column aliases (can be defined later set_alias())

    +
    +
    units: dict

    set of column units (can be defined later set_unit())

    +
    +
    desc: dict

    set of column description or comments (can be defined later set_comment())

    +
    +
    header: dict

    key, value pair corresponding to the attributes of the table

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    __call__(*args, **kwargs)

    Call self as a function.

    addCol(name, data[, dtype, unit, description])

    Add one or multiple columns to the table

    addLine(iterable)

    Append one row in this table.

    add_column(name, data[, dtype, unit, ...])

    Add one or multiple columns to the table

    append_row(iterable)

    Append one row in this table.

    boxplot(*args, **kwargs)

    Draw a box and whisker plot.

    delCol(names)

    Remove several columns from the table

    evalexpr(expr[, exprvars, dtype])

    evaluate expression based on the data and external variables

    find_duplicate([index_only, values_only])

    Find duplication in the table entries, return a list of duplicated elements Only works at this time is 2 lines are the same entry not if 2 lines have the same values

    get(v[, full_match])

    returns a table from columns given as v

    groupby(key)

    Create an iterator which returns (key, sub-table) grouped by each value of key(value)

    hexbin(*args, **kwargs)

    Make a 2D hexagonal binning plot of points x, y.

    hist(*args, **kwargs)

    Compute and plot a histogram.

    hist2d(*args, **kwargs)

    Make a 2D histogram plot.

    iterkeys()

    Iterator over the columns of the table

    itervalues()

    Iterator over the lines of the table

    join_by(r2, key[, jointype, r1postfix, ...])

    Join arrays r1 and r2 on key key.

    keys([regexp, full_match])

    Return the data column names or a subset of it

    match(r2, key)

    Returns the indices at which the tables match matching uses 2 columns that are compared in values

    plot(*args, **kwargs)

    Plot y versus x as lines and/or markers.

    plot_function(fn, *args, **kwargs)

    Generate a plotting method of tab from a given function

    pop_columns(names)

    Pop several columns from the table

    pprint([idx, fields, ret, all, full_match, ...])

    Pretty print the table content

    pprint_entry(num[, keys])

    print one line with key and values properly to be readable

    remove_column(names)

    Remove several columns from the table

    remove_columns(names)

    Remove several columns from the table

    resolve_alias(colname)

    Return the name of an aliased column.

    reverse_alias(colname)

    Return aliases of a given column.

    scatter(*args, **kwargs)

    A scatter plot of y vs.

    select(fields[, indices])

    Select only a few fields in the table

    selectWhere(fields, condition[, condvars])

    Read table data fulfilling the given condition.

    setComment(colname, comment)

    Set the comment of a column referenced by its name

    setUnit(colname, unit)

    Set the unit of a column referenced by its name

    set_alias(alias, colname)

    Define an alias to a column

    set_comment(colname, comment)

    Set the comment of a column referenced by its name

    set_unit(colname, unit)

    Set the unit of a column referenced by its name

    sort(keys[, copy])

    Sort the table inplace according to one or more keys.

    stack(r, *args, **kwargs)

    Superposes arrays fields by fields inplace

    stats([fn, fields, fill])

    Make statistics on columns of a table

    violinplot(*args, **kwargs)

    Make a violin plot.

    where(condition[, condvars])

    Read table data fulfilling the given condition.

    write(fname, **kwargs)

    write table into file

    + + + + + + +

    info

    +
    +
    +addCol(name, data, dtype=None, unit=None, description=None)
    +

    Add one or multiple columns to the table

    +
    +
    Parameters:
    +
    +
    name: str or sequence(str)

    The name(s) of the column(s) to add

    +
    +
    data: ndarray, or sequence of ndarray

    The column data, or sequence of columns

    +
    +
    dtype: dtype

    numpy dtype for the data to add

    +
    +
    unit: str

    The unit of the values in the column

    +
    +
    description: str

    A description of the content of the column

    +
    +
    +
    +
    +
    + +
    +
    +addLine(iterable)
    +

    Append one row in this table.

    +

    see also: stack()

    +
    +
    Parameters:
    +
    +
    iterable: iterable

    line to add

    +
    +
    +
    +
    +
    + +
    +
    +add_column(name, data, dtype=None, unit=None, description=None)[source]
    +

    Add one or multiple columns to the table

    +
    +
    Parameters:
    +
    +
    name: str or sequence(str)

    The name(s) of the column(s) to add

    +
    +
    data: ndarray, or sequence of ndarray

    The column data, or sequence of columns

    +
    +
    dtype: dtype

    numpy dtype for the data to add

    +
    +
    unit: str

    The unit of the values in the column

    +
    +
    description: str

    A description of the content of the column

    +
    +
    +
    +
    +
    + +
    +
    +append_row(iterable)[source]
    +

    Append one row in this table.

    +

    see also: stack()

    +
    +
    Parameters:
    +
    +
    iterable: iterable

    line to add

    +
    +
    +
    +
    +
    + +
    +
    +boxplot(*args, **kwargs)
    +

    Draw a box and whisker plot.

    +

    The box extends from the first quartile (Q1) to the third +quartile (Q3) of the data, with a line at the median. The +whiskers extend from the box by 1.5x the inter-quartile range +(IQR). Flier points are those past the end of the whiskers. +See https://en.wikipedia.org/wiki/Box_plot for reference.

    +
         Q1-1.5IQR   Q1   median  Q3   Q3+1.5IQR
+                  |-----:-----|
+  o      |--------|     :     |--------|    o  o
+                  |-----:-----|
+flier             <----------->            fliers
+                       IQR
+
    +
    +
    +
    Parameters:
    +
    +
    xArray or a sequence of vectors.

    The input data. If a 2D array, a boxplot is drawn for each column +in x. If a sequence of 1D arrays, a boxplot is drawn for each +array in x.

    +
    +
    notchbool, default: False

    Whether to draw a notched boxplot (True), or a rectangular +boxplot (False). The notches represent the confidence interval +(CI) around the median. The documentation for bootstrap +describes how the locations of the notches are computed by +default, but their locations may also be overridden by setting the +conf_intervals parameter.

    +
    +

    Note

    +

    In cases where the values of the CI are less than the +lower quartile or greater than the upper quartile, the +notches will extend beyond the box, giving it a +distinctive “flipped” appearance. This is expected +behavior and consistent with other statistical +visualization packages.

    +
    +
    +
    symstr, optional

    The default symbol for flier points. An empty string (‘’) hides +the fliers. If None, then the fliers default to ‘b+’. More +control is provided by the flierprops parameter.

    +
    +
    vertbool, default: True

    If True, draws vertical boxes. +If False, draw horizontal boxes.

    +
    +
    whisfloat or (float, float), default: 1.5

    The position of the whiskers.

    +

    If a float, the lower whisker is at the lowest datum above +Q1 - whis*(Q3-Q1), and the upper whisker at the highest datum +below Q3 + whis*(Q3-Q1), where Q1 and Q3 are the first and +third quartiles. The default value of whis = 1.5 corresponds +to Tukey’s original definition of boxplots.

    +

    If a pair of floats, they indicate the percentiles at which to +draw the whiskers (e.g., (5, 95)). In particular, setting this to +(0, 100) results in whiskers covering the whole range of the data.

    +

    In the edge case where Q1 == Q3, whis is automatically set +to (0, 100) (cover the whole range of the data) if autorange is +True.

    +

    Beyond the whiskers, data are considered outliers and are plotted +as individual points.

    +
    +
    bootstrapint, optional

    Specifies whether to bootstrap the confidence intervals +around the median for notched boxplots. If bootstrap is +None, no bootstrapping is performed, and notches are +calculated using a Gaussian-based asymptotic approximation +(see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and +Kendall and Stuart, 1967). Otherwise, bootstrap specifies +the number of times to bootstrap the median to determine its +95% confidence intervals. Values between 1000 and 10000 are +recommended.

    +
    +
    usermedians1D array-like, optional

    A 1D array-like of length len(x). Each entry that is not +None forces the value of the median for the corresponding +dataset. For entries that are None, the medians are computed +by Matplotlib as normal.

    +
    +
    conf_intervalsarray-like, optional

    A 2D array-like of shape (len(x), 2). Each entry that is not +None forces the location of the corresponding notch (which is +only drawn if notch is True). For entries that are None, +the notches are computed by the method specified by the other +parameters (e.g., bootstrap).

    +
    +
    positionsarray-like, optional

    The positions of the boxes. The ticks and limits are +automatically set to match the positions. Defaults to +range(1, N+1) where N is the number of boxes to be drawn.

    +
    +
    widthsfloat or array-like

    The widths of the boxes. The default is 0.5, or 0.15*(distance +between extreme positions), if that is smaller.

    +
    +
    patch_artistbool, default: False

    If False produces boxes with the Line2D artist. Otherwise, +boxes are drawn with Patch artists.

    +
    +
    labelssequence, optional

    Labels for each dataset (one per dataset).

    +
    +
    manage_ticksbool, default: True

    If True, the tick locations and labels will be adjusted to match +the boxplot positions.

    +
    +
    autorangebool, default: False

    When True and the data are distributed such that the 25th and +75th percentiles are equal, whis is set to (0, 100) such +that the whisker ends are at the minimum and maximum of the data.

    +
    +
    meanlinebool, default: False

    If True (and showmeans is True), will try to render the +mean as a line spanning the full width of the box according to +meanprops (see below). Not recommended if shownotches is also +True. Otherwise, means will be shown as points.

    +
    +
    zorderfloat, default: Line2D.zorder = 2

    The zorder of the boxplot.

    +
    +
    +
    +
    Returns:
    +
    +
    dict

    A dictionary mapping each component of the boxplot to a list +of the .Line2D instances created. That dictionary has the +following keys (assuming vertical boxplots):

    +
      +

    • boxes: the main body of the boxplot showing the +quartiles and the median’s confidence intervals if +enabled.

      • +

    • medians: horizontal lines at the median of each box.

      • +

    • whiskers: the vertical lines extending to the most +extreme, non-outlier data points.

      • +

    • caps: the horizontal lines at the ends of the +whiskers.

      • +

    • fliers: points representing data that extend beyond +the whiskers (fliers).

      • +

    • means: points or lines representing the means.

      • +
    +
    +
    +
    +
    Other Parameters:
    +
    +
    showcapsbool, default: True

    Show the caps on the ends of whiskers.

    +
    +
    showboxbool, default: True

    Show the central box.

    +
    +
    showfliersbool, default: True

    Show the outliers beyond the caps.

    +
    +
    showmeansbool, default: False

    Show the arithmetic means.

    +
    +
    cappropsdict, default: None

    The style of the caps.

    +
    +
    capwidthsfloat or array, default: None

    The widths of the caps.

    +
    +
    boxpropsdict, default: None

    The style of the box.

    +
    +
    whiskerpropsdict, default: None

    The style of the whiskers.

    +
    +
    flierpropsdict, default: None

    The style of the fliers.

    +
    +
    medianpropsdict, default: None

    The style of the median.

    +
    +
    meanpropsdict, default: None

    The style of the mean.

    +
    +
    dataindexable object, optional

    If given, all parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception).

    +
    +
    +
    +
    +
    +

    See also

    +
    +
    violinplot

    Draw an estimate of the probability density function.

    +
    +
    +
    +
    + +
    +
    +property colnames
    +

    Sequence of column names

    +
    + +
    +
    +delCol(names)
    +

    Remove several columns from the table

    +
    +
    Parameters:
    +
    +
    names: sequence

    A list containing the names of the columns to remove

    +
    +
    +
    +
    +
    + +
    +
    +property dtype
    +

    dtype of the data

    +
    + +
    +
    +property empty_row
    +

    Return an empty row array respecting the table format

    +
    + +
    +
    +evalexpr(expr, exprvars=None, dtype=<class 'float'>)[source]
    +
    +
    evaluate expression based on the data and external variables

    all np function can be used (log, exp, pi…)

    +
    +
    +
    +
    Parameters:
    +
    +
    expr: str

    expression to evaluate on the table +includes mathematical operations and attribute names

    +
    +
    exprvars: dictionary, optional

    A dictionary that replaces the local operands in current frame.

    +
    +
    dtype: dtype definition

    dtype of the output array

    +
    +
    +
    +
    Returns:
    +
    +
    outNumPy array

    array of the result

    +
    +
    +
    +
    +
    + +
    +
    +find_duplicate(index_only=False, values_only=False)[source]
    +

    Find duplication in the table entries, return a list of duplicated +elements Only works at this time is 2 lines are the same entry not if +2 lines have the same values

    +
    + +
    +
    +get(v, full_match=False)[source]
    +

    returns a table from columns given as v

    +

    this function is equivalent to __getitem__() but preserve the +Table format and associated properties (units, description, header)

    +
    +
    Parameters:
    +
    +
    v: str

    pattern to filter the keys with

    +
    +
    full_match: bool

    if set, use re.fullmatch() instead of re.match()

    +
    +
    +
    +
    +
    + +
    +
    +groupby(key)[source]
    +

    Create an iterator which returns (key, sub-table) grouped by each value +of key(value)

    +
    +
    Parameters:
    +
    +
    key: str

    expression or pattern to filter the keys with

    +
    +
    +
    +
    Returns:
    +
    +
    key: str or sequence

    group key

    +
    +
    tab: SimpleTable instance

    sub-table of the group +header, aliases and column metadata are preserved (linked to the +master table).

    +
    +
    +
    +
    +
    + +
    +
    +hexbin(*args, **kwargs)
    +

    Make a 2D hexagonal binning plot of points x, y.

    +

    If C is None, the value of the hexagon is determined by the number +of points in the hexagon. Otherwise, C specifies values at the +coordinate (x[i], y[i]). For each hexagon, these values are reduced +using reduce_C_function.

    +
    +
    Parameters:
    +
    +
    x, yarray-like

    The data positions. x and y must be of the same length.

    +
    +
    Carray-like, optional

    If given, these values are accumulated in the bins. Otherwise, +every point has a value of 1. Must be of the same length as x +and y.

    +
    +
    gridsizeint or (int, int), default: 100

    If a single int, the number of hexagons in the x-direction. +The number of hexagons in the y-direction is chosen such that +the hexagons are approximately regular.

    +

    Alternatively, if a tuple (nx, ny), the number of hexagons +in the x-direction and the y-direction. In the +y-direction, counting is done along vertically aligned +hexagons, not along the zig-zag chains of hexagons; see the +following illustration.

    +

    To get approximately regular hexagons, choose +\(n_x = \sqrt{3}\,n_y\).

    +
    +
    bins‘log’ or int or sequence, default: None

    Discretization of the hexagon values.

    +
      +

    • If None, no binning is applied; the color of each hexagon +directly corresponds to its count value.

      • +

    • If ‘log’, use a logarithmic scale for the colormap. +Internally, \(log_{10}(i+1)\) is used to determine the +hexagon color. This is equivalent to norm=LogNorm().

      • +

    • If an integer, divide the counts in the specified number +of bins, and color the hexagons accordingly.

      • +

    • If a sequence of values, the values of the lower bound of +the bins to be used.

      • +
    +
    +
    xscale{‘linear’, ‘log’}, default: ‘linear’

    Use a linear or log10 scale on the horizontal axis.

    +
    +
    yscale{‘linear’, ‘log’}, default: ‘linear’

    Use a linear or log10 scale on the vertical axis.

    +
    +
    mincntint > 0, default: None

    If not None, only display cells with more than mincnt +number of points in the cell.

    +
    +
    marginalsbool, default: False

    If marginals is True, plot the marginal density as +colormapped rectangles along the bottom of the x-axis and +left of the y-axis.

    +
    +
    extent4-tuple of float, default: None

    The limits of the bins (xmin, xmax, ymin, ymax). +The default assigns the limits based on +gridsize, x, y, xscale and yscale.

    +

    If xscale or yscale is set to ‘log’, the limits are +expected to be the exponent for a power of 10. E.g. for +x-limits of 1 and 50 in ‘linear’ scale and y-limits +of 10 and 1000 in ‘log’ scale, enter (1, 50, 1, 3).

    +
    +
    +
    +
    Returns:
    +
    +
    ~matplotlib.collections.PolyCollection

    A .PolyCollection defining the hexagonal bins.

    +
      +

    • .PolyCollection.get_offsets contains a Mx2 array containing +the x, y positions of the M hexagon centers.

      • +

    • .PolyCollection.get_array contains the values of the M +hexagons.

      • +
    +

    If marginals is True, horizontal +bar and vertical bar (both PolyCollections) will be attached +to the return collection as attributes hbar and vbar.

    +
    +
    +
    +
    Other Parameters:
    +
    +
    cmapstr or ~matplotlib.colors.Colormap, default: :rc:`image.cmap`

    The Colormap instance or registered colormap name used to map scalar data +to colors.

    +
    +
    normstr or ~matplotlib.colors.Normalize, optional

    The normalization method used to scale scalar data to the [0, 1] range +before mapping to colors using cmap. By default, a linear scaling is +used, mapping the lowest value to 0 and the highest to 1.

    +

    If given, this can be one of the following:

    +
      +

    • An instance of .Normalize or one of its subclasses +(see /tutorials/colors/colormapnorms).

      • +

    • A scale name, i.e. one of “linear”, “log”, “symlog”, “logit”, etc. For a +list of available scales, call matplotlib.scale.get_scale_names(). +In that case, a suitable .Normalize subclass is dynamically generated +and instantiated.

      • +
    +
    +
    vmin, vmaxfloat, optional

    When using scalar data and no explicit norm, vmin and vmax define +the data range that the colormap covers. By default, the colormap covers +the complete value range of the supplied data. It is an error to use +vmin/vmax when a norm instance is given (but using a str norm +name together with vmin/vmax is acceptable).

    +
    +
    alphafloat between 0 and 1, optional

    The alpha blending value, between 0 (transparent) and 1 (opaque).

    +
    +
    linewidthsfloat, default: None

    If None, defaults to 1.0.

    +
    +
    edgecolors{‘face’, ‘none’, None} or color, default: ‘face’

    The color of the hexagon edges. Possible values are:

    +
      +

    • ‘face’: Draw the edges in the same color as the fill color.

      • +

    • ‘none’: No edges are drawn. This can sometimes lead to unsightly +unpainted pixels between the hexagons.

      • +

    • None: Draw outlines in the default color.

      • +

    • An explicit color.

      • +
    +
    +
    reduce_C_functioncallable, default: numpy.mean

    The function to aggregate C within the bins. It is ignored if +C is not given. This must have the signature:

    +
    def reduce_C_function(C: array) -> float
+
    +
    +

    Commonly used functions are:

    +
      +

    • numpy.mean: average of the points

      • +

    • numpy.sum: integral of the point values

      • +

    • numpy.amax: value taken from the largest point

      • +
    +
    +
    dataindexable object, optional

    If given, the following parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception):

    +

    x, y, C

    +
    +
    **kwargs~matplotlib.collections.PolyCollection properties

    All other keyword arguments are passed on to .PolyCollection:

    +

    Properties: +agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image +alpha: array-like or scalar or None +animated: bool +antialiased or aa or antialiaseds: bool or list of bools +array: array-like or None +capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} +clim: (vmin: float, vmax: float) +clip_box: .Bbox +clip_on: bool +clip_path: Patch or (Path, Transform) or None +cmap: .Colormap or str or None +color: color or list of RGBA tuples +edgecolor or ec or edgecolors: color or list of colors or ‘face’ +facecolor or facecolors or fc: color or list of colors +figure: .Figure +gid: str +hatch: {‘/’, ‘\’, ‘|’, ‘-’, ‘+’, ‘x’, ‘o’, ‘O’, ‘.’, ‘*’} +in_layout: bool +joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} +label: object +linestyle or dashes or linestyles or ls: str or tuple or list thereof +linewidth or linewidths or lw: float or list of floats +mouseover: bool +norm: .Normalize or str or None +offset_transform or transOffset: unknown +offsets: (N, 2) or (2,) array-like +path_effects: .AbstractPathEffect +paths: list of array-like +picker: None or bool or float or callable +pickradius: unknown +rasterized: bool +sizes: numpy.ndarray or None +sketch_params: (scale: float, length: float, randomness: float) +snap: bool or None +transform: .Transform +url: str +urls: list of str or None +verts: list of array-like +verts_and_codes: unknown +visible: bool +zorder: float

    +
    +
    +
    +
    +
    +

    See also

    +
    +
    hist2d

    2D histogram rectangular bins

    +
    +
    +
    +
    + +
    +
    +hist(*args, **kwargs)
    +

    Compute and plot a histogram.

    +

    This method uses numpy.histogram to bin the data in x and count the +number of values in each bin, then draws the distribution either as a +.BarContainer or .Polygon. The bins, range, density, and +weights parameters are forwarded to numpy.histogram.

    +

    If the data has already been binned and counted, use ~.bar or +~.stairs to plot the distribution:

    +
    counts, bins = np.histogram(x)
+plt.stairs(counts, bins)
+
    +
    +

    Alternatively, plot pre-computed bins and counts using hist() by +treating each bin as a single point with a weight equal to its count:

    +
    plt.hist(bins[:-1], bins, weights=counts)
+
    +
    +

    The data input x can be a singular array, a list of datasets of +potentially different lengths ([x0, x1, …]), or a 2D ndarray in +which each column is a dataset. Note that the ndarray form is +transposed relative to the list form. If the input is an array, then +the return value is a tuple (n, bins, patches); if the input is a +sequence of arrays, then the return value is a tuple +([n0, n1, …], bins, [patches0, patches1, …]).

    +

    Masked arrays are not supported.

    +
    +
    Parameters:
    +
    +
    x(n,) array or sequence of (n,) arrays

    Input values, this takes either a single array or a sequence of +arrays which are not required to be of the same length.

    +
    +
    binsint or sequence or str, default: :rc:`hist.bins`

    If bins is an integer, it defines the number of equal-width bins +in the range.

    +

    If bins is a sequence, it defines the bin edges, including the +left edge of the first bin and the right edge of the last bin; +in this case, bins may be unequally spaced. All but the last +(righthand-most) bin is half-open. In other words, if bins is:

    +
    [1, 2, 3, 4]
+
    +
    +

    then the first bin is [1, 2) (including 1, but excluding 2) and +the second [2, 3). The last bin, however, is [3, 4], which +includes 4.

    +

    If bins is a string, it is one of the binning strategies +supported by numpy.histogram_bin_edges: ‘auto’, ‘fd’, ‘doane’, +‘scott’, ‘stone’, ‘rice’, ‘sturges’, or ‘sqrt’.

    +
    +
    rangetuple or None, default: None

    The lower and upper range of the bins. Lower and upper outliers +are ignored. If not provided, range is (x.min(), x.max()). +Range has no effect if bins is a sequence.

    +

    If bins is a sequence or range is specified, autoscaling +is based on the specified bin range instead of the +range of x.

    +
    +
    densitybool, default: False

    If True, draw and return a probability density: each bin +will display the bin’s raw count divided by the total number of +counts and the bin width +(density = counts / (sum(counts) * np.diff(bins))), +so that the area under the histogram integrates to 1 +(np.sum(density * np.diff(bins)) == 1).

    +

    If stacked is also True, the sum of the histograms is +normalized to 1.

    +
    +
    weights(n,) array-like or None, default: None

    An array of weights, of the same shape as x. Each value in +x only contributes its associated weight towards the bin count +(instead of 1). If density is True, the weights are +normalized, so that the integral of the density over the range +remains 1.

    +
    +
    cumulativebool or -1, default: False

    If True, then a histogram is computed where each bin gives the +counts in that bin plus all bins for smaller values. The last bin +gives the total number of datapoints.

    +

    If density is also True then the histogram is normalized such +that the last bin equals 1.

    +

    If cumulative is a number less than 0 (e.g., -1), the direction +of accumulation is reversed. In this case, if density is also +True, then the histogram is normalized such that the first bin +equals 1.

    +
    +
    bottomarray-like, scalar, or None, default: None

    Location of the bottom of each bin, i.e. bins are drawn from +bottom to bottom + hist(x, bins) If a scalar, the bottom +of each bin is shifted by the same amount. If an array, each bin +is shifted independently and the length of bottom must match the +number of bins. If None, defaults to 0.

    +
    +
    histtype{‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, default: ‘bar’

    The type of histogram to draw.

    +
      +

    • ‘bar’ is a traditional bar-type histogram. If multiple data +are given the bars are arranged side by side.

      • +

    • ‘barstacked’ is a bar-type histogram where multiple +data are stacked on top of each other.

      • +

    • ‘step’ generates a lineplot that is by default unfilled.

      • +

    • ‘stepfilled’ generates a lineplot that is by default filled.

      • +
    +
    +
    align{‘left’, ‘mid’, ‘right’}, default: ‘mid’

    The horizontal alignment of the histogram bars.

    +
      +

    • ‘left’: bars are centered on the left bin edges.

      • +

    • ‘mid’: bars are centered between the bin edges.

      • +

    • ‘right’: bars are centered on the right bin edges.

      • +
    +
    +
    orientation{‘vertical’, ‘horizontal’}, default: ‘vertical’

    If ‘horizontal’, ~.Axes.barh will be used for bar-type histograms +and the bottom kwarg will be the left edges.

    +
    +
    rwidthfloat or None, default: None

    The relative width of the bars as a fraction of the bin width. If +None, automatically compute the width.

    +

    Ignored if histtype is ‘step’ or ‘stepfilled’.

    +
    +
    logbool, default: False

    If True, the histogram axis will be set to a log scale.

    +
    +
    colorcolor or array-like of colors or None, default: None

    Color or sequence of colors, one per dataset. Default (None) +uses the standard line color sequence.

    +
    +
    labelstr or None, default: None

    String, or sequence of strings to match multiple datasets. Bar +charts yield multiple patches per dataset, but only the first gets +the label, so that ~.Axes.legend will work as expected.

    +
    +
    stackedbool, default: False

    If True, multiple data are stacked on top of each other If +False multiple data are arranged side by side if histtype is +‘bar’ or on top of each other if histtype is ‘step’

    +
    +
    +
    +
    Returns:
    +
    +
    narray or list of arrays

    The values of the histogram bins. See density and weights for a +description of the possible semantics. If input x is an array, +then this is an array of length nbins. If input is a sequence of +arrays [data1, data2, ...], then this is a list of arrays with +the values of the histograms for each of the arrays in the same +order. The dtype of the array n (or of its element arrays) will +always be float even if no weighting or normalization is used.

    +
    +
    binsarray

    The edges of the bins. Length nbins + 1 (nbins left edges and right +edge of last bin). Always a single array even when multiple data +sets are passed in.

    +
    +
    patches.BarContainer or list of a single .Polygon or list of such objects

    Container of individual artists used to create the histogram +or list of such containers if there are multiple input datasets.

    +
    +
    +
    +
    Other Parameters:
    +
    +
    dataindexable object, optional

    If given, the following parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception):

    +

    x, weights

    +
    +
    **kwargs

    ~matplotlib.patches.Patch properties

    +
    +
    +
    +
    +
    +

    See also

    +
    +
    hist2d

    2D histogram with rectangular bins

    +
    +
    hexbin

    2D histogram with hexagonal bins

    +
    +
    stairs

    Plot a pre-computed histogram

    +
    +
    bar

    Plot a pre-computed histogram

    +
    +
    +
    +

    Notes

    +

    For large numbers of bins (>1000), plotting can be significantly +accelerated by using ~.Axes.stairs to plot a pre-computed histogram +(plt.stairs(*np.histogram(data))), or by setting histtype to +‘step’ or ‘stepfilled’ rather than ‘bar’ or ‘barstacked’.

    +
    + +
    +
    +hist2d(*args, **kwargs)
    +

    Make a 2D histogram plot.

    +
    +
    Parameters:
    +
    +
    x, yarray-like, shape (n, )

    Input values

    +
    +
    binsNone or int or [int, int] or array-like or [array, array]

    The bin specification:

    +
      +

    • If int, the number of bins for the two dimensions +(nx=ny=bins).

      • +

    • If [int, int], the number of bins in each dimension +(nx, ny = bins).

      • +

    • If array-like, the bin edges for the two dimensions +(x_edges=y_edges=bins).

      • +

    • If [array, array], the bin edges in each dimension +(x_edges, y_edges = bins).

      • +
    +

    The default value is 10.

    +
    +
    rangearray-like shape(2, 2), optional

    The leftmost and rightmost edges of the bins along each dimension +(if not specified explicitly in the bins parameters): [[xmin, +xmax], [ymin, ymax]]. All values outside of this range will be +considered outliers and not tallied in the histogram.

    +
    +
    densitybool, default: False

    Normalize histogram. See the documentation for the density +parameter of ~.Axes.hist for more details.

    +
    +
    weightsarray-like, shape (n, ), optional

    An array of values w_i weighing each sample (x_i, y_i).

    +
    +
    cmin, cmaxfloat, default: None

    All bins that has count less than cmin or more than cmax will +not be displayed (set to NaN before passing to imshow) and these +count values in the return value count histogram will also be set +to nan upon return.

    +
    +
    +
    +
    Returns:
    +
    +
    h2D array

    The bi-dimensional histogram of samples x and y. Values in x are +histogrammed along the first dimension and values in y are +histogrammed along the second dimension.

    +
    +
    xedges1D array

    The bin edges along the x-axis.

    +
    +
    yedges1D array

    The bin edges along the y-axis.

    +
    +
    image~.matplotlib.collections.QuadMesh
    +
    +
    +
    Other Parameters:
    +
    +
    cmapstr or ~matplotlib.colors.Colormap, default: :rc:`image.cmap`

    The Colormap instance or registered colormap name used to map scalar data +to colors.

    +
    +
    normstr or ~matplotlib.colors.Normalize, optional

    The normalization method used to scale scalar data to the [0, 1] range +before mapping to colors using cmap. By default, a linear scaling is +used, mapping the lowest value to 0 and the highest to 1.

    +

    If given, this can be one of the following:

    +
      +

    • An instance of .Normalize or one of its subclasses +(see /tutorials/colors/colormapnorms).

      • +

    • A scale name, i.e. one of “linear”, “log”, “symlog”, “logit”, etc. For a +list of available scales, call matplotlib.scale.get_scale_names(). +In that case, a suitable .Normalize subclass is dynamically generated +and instantiated.

      • +
    +
    +
    vmin, vmaxfloat, optional

    When using scalar data and no explicit norm, vmin and vmax define +the data range that the colormap covers. By default, the colormap covers +the complete value range of the supplied data. It is an error to use +vmin/vmax when a norm instance is given (but using a str norm +name together with vmin/vmax is acceptable).

    +
    +
    alpha0 <= scalar <= 1 or None, optional

    The alpha blending value.

    +
    +
    dataindexable object, optional

    If given, the following parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception):

    +

    x, y, weights

    +
    +
    **kwargs

    Additional parameters are passed along to the +~.Axes.pcolormesh method and ~matplotlib.collections.QuadMesh +constructor.

    +
    +
    +
    +
    +
    +

    See also

    +
    +
    hist

    1D histogram plotting

    +
    +
    hexbin

    2D histogram with hexagonal bins

    +
    +
    +
    +

    Notes

    +
      +

    • Currently hist2d calculates its own axis limits, and any limits +previously set are ignored.

      • +

    • Rendering the histogram with a logarithmic color scale is +accomplished by passing a .colors.LogNorm instance to the norm +keyword argument. Likewise, power-law normalization (similar +in effect to gamma correction) can be accomplished with +.colors.PowerNorm.

      • +
    +
    + +
    +
    +info()[source]
    +
    + +
    +
    +iterkeys()[source]
    +

    Iterator over the columns of the table

    +
    + +
    +
    +itervalues()[source]
    +

    Iterator over the lines of the table

    +
    + +
    +
    +join_by(r2, key, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, asrecarray=False, asTable=True)[source]
    +

    Join arrays r1 and r2 on key key.

    +

    The key should be either a string or a sequence of string corresponding +to the fields used to join the array. +An exception is raised if the key field cannot be found in the two input +arrays. +Neither r1 nor r2 should have any duplicates along key: the presence +of duplicates will make the output quite unreliable. Note that duplicates +are not looked for by the algorithm.

    +
    +
    Parameters:
    +
    +
    key: str or seq(str)

    corresponding to the fields used for comparison.

    +
    +
    r2: Table

    Table to join with

    +
    +
    jointype: str in {‘inner’, ‘outer’, ‘leftouter’}
      +

    • ‘inner’ : returns the elements common to both r1 and r2.

      • +

    • ‘outer’ : returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2.

      • +

    • ‘leftouter’ : returns the common elements and the elements of r1 not in r2.

      • +
    +
    +
    r1postfix: str

    String appended to the names of the fields of r1 that are present in r2

    +
    +
    r2postfix: str

    String appended to the names of the fields of r2 that are present in r1

    +
    +
    defaults: dict

    Dictionary mapping field names to the corresponding default values.

    +
    +
    +
    +
    Returns:
    +
    +
    tab: Table

    joined table

    +
    +
    +
    +

    Note

    +
      +

    • The output is sorted along the key.

      • +

    • A temporary array is formed by dropping the fields not in the key +for the two arrays and concatenating the result. This array is +then sorted, and the common entries selected. The output is +constructed by filling the fields with the selected entries. +Matching is not preserved if there are some duplicates…

      • +
    +
    +
    +
    +
    + +
    +
    +keys(regexp=None, full_match=False)[source]
    +

    Return the data column names or a subset of it

    +
    +
    Parameters:
    +
    +
    regexp: str

    pattern to filter the keys with

    +
    +
    full_match: bool

    if set, use re.fullmatch() instead of re.match()

    +
    +
    Try to apply the pattern at the start of the string, returning
    +
    a match object, or None if no match was found.
    +
    +
    +
    Returns:
    +
    +
    seq: sequence

    sequence of keys

    +
    +
    +
    +
    +
    + +
    +
    +match(r2, key)[source]
    +

    Returns the indices at which the tables match +matching uses 2 columns that are compared in values

    +
    +
    Parameters:
    +
    +
    r2: Table

    second table to use

    +
    +
    key: str

    fields used for comparison.

    +
    +
    +
    +
    Returns:
    +
    +
    indexes: tuple

    tuple of both indices list where the two columns match.

    +
    +
    +
    +
    +
    + +
    +
    +property name
    +

    name of the table given by the Header[‘NAME’] attribute

    +
    + +
    +
    +property nbytes
    +

    number of bytes of the object

    +
    + +
    +
    +property ncols
    +

    number of columns

    +
    + +
    +
    +property nrows
    +

    number of lines

    +
    + +
    +
    +plot(*args, **kwargs)
    +

    Plot y versus x as lines and/or markers.

    +

    Call signatures:

    +
    plot([x], y, [fmt], *, data=None, **kwargs)
+plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
+
    +
    +

    The coordinates of the points or line nodes are given by x, y.

    +

    The optional parameter fmt is a convenient way for defining basic +formatting like color, marker and linestyle. It’s a shortcut string +notation described in the Notes section below.

    +
    >>> plot(x, y)        # plot x and y using default line style and color
+>>> plot(x, y, 'bo')  # plot x and y using blue circle markers
+>>> plot(y)           # plot y using x as index array 0..N-1
+>>> plot(y, 'r+')     # ditto, but with red plusses
+
    +
    +

    You can use .Line2D properties as keyword arguments for more +control on the appearance. Line properties and fmt can be mixed. +The following two calls yield identical results:

    +
    >>> plot(x, y, 'go--', linewidth=2, markersize=12)
+>>> plot(x, y, color='green', marker='o', linestyle='dashed',
+...      linewidth=2, markersize=12)
+
    +
    +

    When conflicting with fmt, keyword arguments take precedence.

    +

    Plotting labelled data

    +

    There’s a convenient way for plotting objects with labelled data (i.e. +data that can be accessed by index obj['y']). Instead of giving +the data in x and y, you can provide the object in the data +parameter and just give the labels for x and y:

    +
    >>> plot('xlabel', 'ylabel', data=obj)
+
    +
    +

    All indexable objects are supported. This could e.g. be a dict, a +pandas.DataFrame or a structured numpy array.

    +

    Plotting multiple sets of data

    +

    There are various ways to plot multiple sets of data.

    +
      +

    • The most straight forward way is just to call plot multiple times. +Example:

      +
      >>> plot(x1, y1, 'bo')
+>>> plot(x2, y2, 'go')
+
      +
      +
      • +

    • If x and/or y are 2D arrays a separate data set will be drawn +for every column. If both x and y are 2D, they must have the +same shape. If only one of them is 2D with shape (N, m) the other +must have length N and will be used for every data set m.

      +

      Example:

      +
      >>> x = [1, 2, 3]
+>>> y = np.array([[1, 2], [3, 4], [5, 6]])
+>>> plot(x, y)
+
      +
      +

      is equivalent to:

      +
      >>> for col in range(y.shape[1]):
+...     plot(x, y[:, col])
+
      +
      +
      • +

    • The third way is to specify multiple sets of [x], y, [fmt] +groups:

      +
      >>> plot(x1, y1, 'g^', x2, y2, 'g-')
+
      +
      +

      In this case, any additional keyword argument applies to all +datasets. Also, this syntax cannot be combined with the data +parameter.

      +
      • +
    +

    By default, each line is assigned a different style specified by a +‘style cycle’. The fmt and line property parameters are only +necessary if you want explicit deviations from these defaults. +Alternatively, you can also change the style cycle using +:rc:`axes.prop_cycle`.

    +
    +
    Parameters:
    +
    +
    x, yarray-like or scalar

    The horizontal / vertical coordinates of the data points. +x values are optional and default to range(len(y)).

    +

    Commonly, these parameters are 1D arrays.

    +

    They can also be scalars, or two-dimensional (in that case, the +columns represent separate data sets).

    +

    These arguments cannot be passed as keywords.

    +
    +
    fmtstr, optional

    A format string, e.g. ‘ro’ for red circles. See the Notes +section for a full description of the format strings.

    +

    Format strings are just an abbreviation for quickly setting +basic line properties. All of these and more can also be +controlled by keyword arguments.

    +

    This argument cannot be passed as keyword.

    +
    +
    dataindexable object, optional

    An object with labelled data. If given, provide the label names to +plot in x and y.

    +
    +

    Note

    +

    Technically there’s a slight ambiguity in calls where the +second label is a valid fmt. plot('n', 'o', data=obj) +could be plt(x, y) or plt(y, fmt). In such cases, +the former interpretation is chosen, but a warning is issued. +You may suppress the warning by adding an empty format string +plot('n', 'o', '', data=obj).

    +
    +
    +
    +
    +
    Returns:
    +
    +
    list of .Line2D

    A list of lines representing the plotted data.

    +
    +
    +
    +
    Other Parameters:
    +
    +
    scalex, scaleybool, default: True

    These parameters determine if the view limits are adapted to the +data limits. The values are passed on to +~.axes.Axes.autoscale_view.

    +
    +
    **kwargs~matplotlib.lines.Line2D properties, optional

    kwargs are used to specify properties like a line label (for +auto legends), linewidth, antialiasing, marker face color. +Example:

    +
    >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)
+>>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')
+
    +
    +

    If you specify multiple lines with one plot call, the kwargs apply +to all those lines. In case the label object is iterable, each +element is used as labels for each set of data.

    +

    Here is a list of available .Line2D properties:

    +

    Properties: +agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image +alpha: scalar or None +animated: bool +antialiased or aa: bool +clip_box: .Bbox +clip_on: bool +clip_path: Patch or (Path, Transform) or None +color or c: color +dash_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} +dash_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} +dashes: sequence of floats (on/off ink in points) or (None, None) +data: (2, N) array or two 1D arrays +drawstyle or ds: {‘default’, ‘steps’, ‘steps-pre’, ‘steps-mid’, ‘steps-post’}, default: ‘default’ +figure: .Figure +fillstyle: {‘full’, ‘left’, ‘right’, ‘bottom’, ‘top’, ‘none’} +gapcolor: color or None +gid: str +in_layout: bool +label: object +linestyle or ls: {‘-’, ‘–’, ‘-.’, ‘:’, ‘’, (offset, on-off-seq), …} +linewidth or lw: float +marker: marker style string, ~.path.Path or ~.markers.MarkerStyle +markeredgecolor or mec: color +markeredgewidth or mew: float +markerfacecolor or mfc: color +markerfacecoloralt or mfcalt: color +markersize or ms: float +markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool] +mouseover: bool +path_effects: .AbstractPathEffect +picker: float or callable[[Artist, Event], tuple[bool, dict]] +pickradius: unknown +rasterized: bool +sketch_params: (scale: float, length: float, randomness: float) +snap: bool or None +solid_capstyle: .CapStyle or {‘butt’, ‘projecting’, ‘round’} +solid_joinstyle: .JoinStyle or {‘miter’, ‘round’, ‘bevel’} +transform: unknown +url: str +visible: bool +xdata: 1D array +ydata: 1D array +zorder: float

    +
    +
    +
    +
    +
    +

    See also

    +
    +
    scatter

    XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart).

    +
    +
    +
    +

    Notes

    +

    Format Strings

    +

    A format string consists of a part for color, marker and line:

    +
    fmt = '[marker][line][color]'
+
    +
    +

    Each of them is optional. If not provided, the value from the style +cycle is used. Exception: If line is given, but no marker, +the data will be a line without markers.

    +

    Other combinations such as [color][marker][line] are also +supported, but note that their parsing may be ambiguous.

    +

    Markers

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    character

    description

    '.'

    point marker

    ','

    pixel marker

    'o'

    circle marker

    'v'

    triangle_down marker

    '^'

    triangle_up marker

    '<'

    triangle_left marker

    '>'

    triangle_right marker

    '1'

    tri_down marker

    '2'

    tri_up marker

    '3'

    tri_left marker

    '4'

    tri_right marker

    '8'

    octagon marker

    's'

    square marker

    'p'

    pentagon marker

    'P'

    plus (filled) marker

    '*'

    star marker

    'h'

    hexagon1 marker

    'H'

    hexagon2 marker

    '+'

    plus marker

    'x'

    x marker

    'X'

    x (filled) marker

    'D'

    diamond marker

    'd'

    thin_diamond marker

    '|'

    vline marker

    '_'

    hline marker

    +

    Line Styles

    + + + + + + + + + + + + + + + + + + + + +

    character

    description

    '-'

    solid line style

    '--'

    dashed line style

    '-.'

    dash-dot line style

    ':'

    dotted line style

    +

    Example format strings:

    +
    'b'    # blue markers with default shape
+'or'   # red circles
+'-g'   # green solid line
+'--'   # dashed line with default color
+'^k:'  # black triangle_up markers connected by a dotted line
+
    +
    +

    Colors

    +

    The supported color abbreviations are the single letter codes

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    character

    color

    'b'

    blue

    'g'

    green

    'r'

    red

    'c'

    cyan

    'm'

    magenta

    'y'

    yellow

    'k'

    black

    'w'

    white

    +

    and the 'CN' colors that index into the default property cycle.

    +

    If the color is the only part of the format string, you can +additionally use any matplotlib.colors spec, e.g. full names +('green') or hex strings ('#008000').

    +
    + +
    +
    +plot_function(fn, *args, **kwargs)
    +

    Generate a plotting method of tab from a given function

    +
    +
    Parameters:
    +
    +
    tab: SimpleTable instance

    table instance

    +
    +
    fn: str or callable

    if str, will try a function in matplotlib +if callable, calls the function directly

    +
    +
    xname: str

    expecting a column name from the table

    +
    +
    yname: str, optional

    if provided, another column to use for the plot

    +
    +
    onlywhere: sequence or str, optional

    if provided, selects only data with this condition +the condition can be a ndarray slice or a string. +When a string is given, the evaluation calls SimpleTable.where()

    +
    +
    ax: matplotlib.Axes instance

    if provided make sure it uses the axis to do the plots if a mpl +function is used.

    +
    +
    +
    +
    Returns:
    +
    +
    r: object

    anything returned by the called function

    +
    +
    +
    +
    +
    + +
    +
    +pop_columns(names)[source]
    +

    Pop several columns from the table

    +
    +
    Parameters:
    +
    +
    names: sequence

    A list containing the names of the columns to remove

    +
    +
    +
    +
    Returns:
    +
    +
    values: tuple

    list of columns

    +
    +
    +
    +
    +
    + +
    +
    +pprint(idx=None, fields=None, ret=False, all=False, full_match=False, headerChar='-', delim=' | ', endline='\n', **kwargs)[source]
    +
    +
    Pretty print the table content

    you can select the table parts to display using idx to +select the rows and fields to only display some columns +(ret is only for insternal use)

    +
    +
    +
    +
    Parameters:
    +
    +
    idx: sequence, slide

    sub selection to print

    +
    +
    fields: str, sequence

    if str can be a regular expression, and/or list of fields separated +by spaces or commas

    +
    +
    ret: bool

    if set return the string representation instead of printing the result

    +
    +
    all: bool

    if set, force to show all rows

    +
    +
    headerChar: char

    Character to be used for the row separator line

    +
    +
    delim: char

    The column delimiter.

    +
    +
    +
    +
    +
    + +
    +
    +pprint_entry(num, keys=None)[source]
    +

    print one line with key and values properly to be readable

    +
    +
    Parameters:
    +
    +
    num: int, slice

    indice selection

    +
    +
    keys: sequence or str

    if str, can be a regular expression +if sequence, the sequence of keys to print

    +
    +
    +
    +
    +
    + +
    +
    +remove_column(names)
    +

    Remove several columns from the table

    +
    +
    Parameters:
    +
    +
    names: sequence

    A list containing the names of the columns to remove

    +
    +
    +
    +
    +
    + +
    +
    +remove_columns(names)[source]
    +

    Remove several columns from the table

    +
    +
    Parameters:
    +
    +
    names: sequence

    A list containing the names of the columns to remove

    +
    +
    +
    +
    +
    + +
    +
    +resolve_alias(colname)[source]
    +

    Return the name of an aliased column.

    +

    Given an alias, return the column name it aliases. This +function is a no-op if the alias is a column name itself.

    +

    Aliases are defined by using .define_alias()

    +
    + +
    +
    +reverse_alias(colname)[source]
    +

    Return aliases of a given column.

    +

    Given a colname, return a sequence of aliases associated to this column +Aliases are defined by using .define_alias()

    +
    + +
    +
    +scatter(*args, **kwargs)
    +

    A scatter plot of y vs. x with varying marker size and/or color.

    +
    +
    Parameters:
    +
    +
    x, yfloat or array-like, shape (n, )

    The data positions.

    +
    +
    sfloat or array-like, shape (n, ), optional

    The marker size in points**2 (typographic points are 1/72 in.). +Default is rcParams['lines.markersize'] ** 2.

    +
    +
    carray-like or list of colors or color, optional

    The marker colors. Possible values:

    +
      +

    • A scalar or sequence of n numbers to be mapped to colors using +cmap and norm.

      • +

    • A 2D array in which the rows are RGB or RGBA.

      • +

    • A sequence of colors of length n.

      • +

    • A single color format string.

      • +
    +

    Note that c should not be a single numeric RGB or RGBA sequence +because that is indistinguishable from an array of values to be +colormapped. If you want to specify the same RGB or RGBA value for +all points, use a 2D array with a single row. Otherwise, +value-matching will have precedence in case of a size matching with +x and y.

    +

    If you wish to specify a single color for all points +prefer the color keyword argument.

    +

    Defaults to None. In that case the marker color is determined +by the value of color, facecolor or facecolors. In case +those are not specified or None, the marker color is determined +by the next color of the Axes’ current “shape and fill” color +cycle. This cycle defaults to :rc:`axes.prop_cycle`.

    +
    +
    marker~.markers.MarkerStyle, default: :rc:`scatter.marker`

    The marker style. marker can be either an instance of the class +or the text shorthand for a particular marker. +See matplotlib.markers for more information about marker +styles.

    +
    +
    cmapstr or ~matplotlib.colors.Colormap, default: :rc:`image.cmap`

    The Colormap instance or registered colormap name used to map scalar data +to colors.

    +

    This parameter is ignored if c is RGB(A).

    +
    +
    normstr or ~matplotlib.colors.Normalize, optional

    The normalization method used to scale scalar data to the [0, 1] range +before mapping to colors using cmap. By default, a linear scaling is +used, mapping the lowest value to 0 and the highest to 1.

    +

    If given, this can be one of the following:

    +
      +

    • An instance of .Normalize or one of its subclasses +(see /tutorials/colors/colormapnorms).

      • +

    • A scale name, i.e. one of “linear”, “log”, “symlog”, “logit”, etc. For a +list of available scales, call matplotlib.scale.get_scale_names(). +In that case, a suitable .Normalize subclass is dynamically generated +and instantiated.

      • +
    +

    This parameter is ignored if c is RGB(A).

    +
    +
    vmin, vmaxfloat, optional

    When using scalar data and no explicit norm, vmin and vmax define +the data range that the colormap covers. By default, the colormap covers +the complete value range of the supplied data. It is an error to use +vmin/vmax when a norm instance is given (but using a str norm +name together with vmin/vmax is acceptable).

    +

    This parameter is ignored if c is RGB(A).

    +
    +
    alphafloat, default: None

    The alpha blending value, between 0 (transparent) and 1 (opaque).

    +
    +
    linewidthsfloat or array-like, default: :rc:`lines.linewidth`

    The linewidth of the marker edges. Note: The default edgecolors +is ‘face’. You may want to change this as well.

    +
    +
    edgecolors{‘face’, ‘none’, None} or color or sequence of color, default: :rc:`scatter.edgecolors`

    The edge color of the marker. Possible values:

    +
      +

    • ‘face’: The edge color will always be the same as the face color.

      • +

    • ‘none’: No patch boundary will be drawn.

      • +

    • A color or sequence of colors.

      • +
    +

    For non-filled markers, edgecolors is ignored. Instead, the color +is determined like with ‘face’, i.e. from c, colors, or +facecolors.

    +
    +
    plotnonfinitebool, default: False

    Whether to plot points with nonfinite c (i.e. inf, -inf +or nan). If True the points are drawn with the bad +colormap color (see .Colormap.set_bad).

    +
    +
    +
    +
    Returns:
    +
    +
    ~matplotlib.collections.PathCollection
    +
    +
    +
    Other Parameters:
    +
    +
    dataindexable object, optional

    If given, the following parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception):

    +

    x, y, s, linewidths, edgecolors, c, facecolor, facecolors, color

    +
    +
    **kwargs~matplotlib.collections.Collection properties
    +
    +
    +
    +
    +

    See also

    +
    +
    plot

    To plot scatter plots when markers are identical in size and color.

    +
    +
    +
    +

    Notes

    +
      +

    • The .plot function will be faster for scatterplots where markers +don’t vary in size or color.

      • +

    • Any or all of x, y, s, and c may be masked arrays, in which +case all masks will be combined and only unmasked points will be +plotted.

      • +

    • Fundamentally, scatter works with 1D arrays; x, y, s, and c +may be input as N-D arrays, but within scatter they will be +flattened. The exception is c, which will be flattened only if its +size matches the size of x and y.

      • +
    +
    + +
    +
    +select(fields, indices=None, **kwargs)[source]
    +

    Select only a few fields in the table

    +
    +
    Parameters:
    +
    +
    fields: str or sequence

    fields to keep in the resulting table

    +
    +
    indices: sequence or slice

    extract only on these indices

    +
    +
    +
    +
    Returns:
    +
    +
    tab: SimpleTable instance

    resulting table

    +
    +
    +
    +
    +
    + +
    +
    +selectWhere(fields, condition, condvars=None, **kwargs)[source]
    +
    +
    Read table data fulfilling the given condition.

    Only the rows fulfilling the condition are included in the result.

    +
    +
    +
    +
    Parameters:
    +
    +
    fields: str or sequence

    fields to keep in the resulting table

    +
    +
    condition: str

    expression to evaluate on the table +includes mathematical operations and attribute names

    +
    +
    condvars: dictionary, optional

    A dictionary that replaces the local operands in current frame.

    +
    +
    +
    +
    Returns:
    +
    +
    tab: SimpleTable instance

    resulting table

    +
    +
    +
    +
    +
    + +
    +
    +setComment(colname, comment)
    +

    Set the comment of a column referenced by its name

    +
    +
    Parameters:
    +
    +
    colname: str

    column name or registered alias

    +
    +
    comment: str

    column description

    +
    +
    +
    +
    +
    + +
    +
    +setUnit(colname, unit)
    +

    Set the unit of a column referenced by its name

    +
    +
    Parameters:
    +
    +
    colname: str

    column name or registered alias

    +
    +
    unit: str

    unit description

    +
    +
    +
    +
    +
    + +
    +
    +set_alias(alias, colname)[source]
    +

    Define an alias to a column

    +
    +
    Parameters:
    +
    +
    alias: str

    The new alias of the column

    +
    +
    colname: str

    The column being aliased

    +
    +
    +
    +
    +
    + +
    +
    +set_comment(colname, comment)[source]
    +

    Set the comment of a column referenced by its name

    +
    +
    Parameters:
    +
    +
    colname: str

    column name or registered alias

    +
    +
    comment: str

    column description

    +
    +
    +
    +
    +
    + +
    +
    +set_unit(colname, unit)[source]
    +

    Set the unit of a column referenced by its name

    +
    +
    Parameters:
    +
    +
    colname: str

    column name or registered alias

    +
    +
    unit: str

    unit description

    +
    +
    +
    +
    +
    + +
    +
    +property shape
    +

    shape of the data

    +
    + +
    +
    +sort(keys, copy=False)[source]
    +

    Sort the table inplace according to one or more keys. This operates on +the existing table (and does not return a new table).

    +
    +
    Parameters:
    +
    +
    keys: str or seq(str)

    The key(s) to order by

    +
    +
    copy: bool

    if set returns a sorted copy instead of working inplace

    +
    +
    +
    +
    +
    + +
    +
    +stack(r, *args, **kwargs)[source]
    +

    Superposes arrays fields by fields inplace

    +

    t.stack(t1, t2, t3, default=None, inplace=True)

    +
    +
    Parameters:
    +
    +
    r: Table
    +
    +
    +
    +
    + +
    +
    +stats(fn=None, fields=None, fill=None)[source]
    +

    Make statistics on columns of a table

    +
    +
    Returns:
    +
    +
    tab: Table instance

    collection of statistics, one column per function in fn and 1 ligne +per column in the table

    +
    +
    +
    +
    +
    + +
    +
    +violinplot(*args, **kwargs)
    +

    Make a violin plot.

    +

    Make a violin plot for each column of dataset or each vector in +sequence dataset. Each filled area extends to represent the +entire data range, with optional lines at the mean, the median, +the minimum, the maximum, and user-specified quantiles.

    +
    +
    Parameters:
    +
    +
    datasetArray or a sequence of vectors.

    The input data.

    +
    +
    positionsarray-like, default: [1, 2, …, n]

    The positions of the violins. The ticks and limits are +automatically set to match the positions.

    +
    +
    vertbool, default: True.

    If true, creates a vertical violin plot. +Otherwise, creates a horizontal violin plot.

    +
    +
    widthsarray-like, default: 0.5

    Either a scalar or a vector that sets the maximal width of +each violin. The default is 0.5, which uses about half of the +available horizontal space.

    +
    +
    showmeansbool, default: False

    If True, will toggle rendering of the means.

    +
    +
    showextremabool, default: True

    If True, will toggle rendering of the extrema.

    +
    +
    showmediansbool, default: False

    If True, will toggle rendering of the medians.

    +
    +
    quantilesarray-like, default: None

    If not None, set a list of floats in interval [0, 1] for each violin, +which stands for the quantiles that will be rendered for that +violin.

    +
    +
    pointsint, default: 100

    Defines the number of points to evaluate each of the +gaussian kernel density estimations at.

    +
    +
    bw_methodstr, scalar or callable, optional

    The method used to calculate the estimator bandwidth. This can be +‘scott’, ‘silverman’, a scalar constant or a callable. If a +scalar, this will be used directly as kde.factor. If a +callable, it should take a matplotlib.mlab.GaussianKDE instance as +its only parameter and return a scalar. If None (default), ‘scott’ +is used.

    +
    +
    dataindexable object, optional

    If given, the following parameters also accept a string s, which is +interpreted as data[s] (unless this raises an exception):

    +

    dataset

    +
    +
    +
    +
    Returns:
    +
    +
    dict

    A dictionary mapping each component of the violinplot to a +list of the corresponding collection instances created. The +dictionary has the following keys:

    +
      +

    • bodies: A list of the ~.collections.PolyCollection +instances containing the filled area of each violin.

      • +

    • cmeans: A ~.collections.LineCollection instance that marks +the mean values of each of the violin’s distribution.

      • +

    • cmins: A ~.collections.LineCollection instance that marks +the bottom of each violin’s distribution.

      • +

    • cmaxes: A ~.collections.LineCollection instance that marks +the top of each violin’s distribution.

      • +

    • cbars: A ~.collections.LineCollection instance that marks +the centers of each violin’s distribution.

      • +

    • cmedians: A ~.collections.LineCollection instance that +marks the median values of each of the violin’s distribution.

      • +

    • cquantiles: A ~.collections.LineCollection instance created +to identify the quantile values of each of the violin’s +distribution.

      • +
    +
    +
    +
    +
    +
    + +
    +
    +where(condition, condvars=None, *args, **kwargs)[source]
    +

    Read table data fulfilling the given condition. +Only the rows fulfilling the condition are included in the result.

    +
    +
    Parameters:
    +
    +
    condition: str

    expression to evaluate on the table +includes mathematical operations and attribute names

    +
    +
    condvars: dictionary, optional

    A dictionary that replaces the local operands in current frame.

    +
    +
    +
    +
    Returns:
    +
    +
    out: ndarray/ tuple of ndarrays
    +
    result equivalent to np.where()
    +
    +
    +
    +
    + +
    +
    +write(fname, **kwargs)[source]
    +

    write table into file

    +
    +
    Parameters:
    +
    +
    fname: str

    filename to export the table into

    +
    +
    .. note::

    additional keywords are forwarded to the corresponding libraries +pyfits.writeto() or pyfits.append() +np.savetxt()

    +
    +
    +
    +
    +
    + +
    + +
    +
    +class pystellibs.simpletable.stats[source]
    +

    Bases: object

    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    has_nan

    max

    mean

    min

    p16

    p50

    p84

    std

    var

    +
    +
    +classmethod has_nan(v)[source]
    +
    + +
    +
    +classmethod max(v)[source]
    +
    + +
    +
    +classmethod mean(v)[source]
    +
    + +
    +
    +classmethod min(v)[source]
    +
    + +
    +
    +classmethod p16(v)[source]
    +
    + +
    +
    +classmethod p50(v)[source]
    +
    + +
    +
    +classmethod p84(v)[source]
    +
    + +
    +
    +classmethod std(v)[source]
    +
    + +
    +
    +classmethod var(v)[source]
    +
    + +
    + +
    +
    +

    pystellibs.stellib module

    +

    Stellar library class

    +

    Intent to implement a generic module to manage stellar library from various +sources.

    +

    The interpolation is implemented from the pegase.2 fortran converted algorithm. +(this may not be super pythonic though)

    +
    +

    Note

    +

    a cython version is available for speed up and should be used transparently when available +(run make once)

    +
    +
    +
    +class pystellibs.stellib.AtmosphereLib(*args, **kwargs)[source]
    +

    Bases: Stellib

    +

    Almost identical to a spectral library. The difference lies into the units +of the input libraries.

    +
    +
    Attributes:
    +
    +
    flux_units
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +get_weights(logT, logg, logL, weights=None)[source]
    +

    Returns the proper weights for the interpolation +Stellar atmospheres are normalized to Radius = 1

    +
    +
    Parameters:
    +
    +
    logT: float or ndarray

    log-temperatures log(T/K)

    +
    +
    logg: float or ndarray

    log-gravity log(g)

    +
    +
    logL: float or ndarray

    bolometric luminosity (log (L/Lsun))

    +
    +
    +
    +
    +
    + +
    + +
    +
    +class pystellibs.stellib.CompositeStellib(osllist, *args, **kwargs)[source]
    +

    Bases: Stellib

    +

    Generates an object from the union of multiple individual libraries

    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    name
    +
    nbytes

    return the number of bytes of the object

    +
    +
    source
    +
    wavelength

    return a common wavelength sampling to all libraries. This can be

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries(**kwargs)

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    reinterpolate_spectra(l0, specs, **kwargs)

    One-dimensional linear interpolation onto the common wavelength.

    which_osl(xypoints, **kwargs)

    Returns the library indice that contains each point in xypoints

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +property flux_units
    +
    + +
    +
    +generate_individual_spectra(stars, **kwargs)[source]
    +

    Generates individual spectra for the given stars and stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    stars: Table

    contains at least (logT, logg, logL, Z) of the considered stars

    +
    +
    +
    +
    Returns:
    +
    +
    l0: ndarray, ndim=1

    wavelength definition of the spectra +wavelength in AA

    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or ergs/s/AA/Lsun

    +
    +
    +
    +
    +
    + +
    +
    +generate_individual_values(stars, values, **kwargs)[source]
    +

    Generates individual spectra for the given stars and stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    stars: Table

    contains at least (logT, logg, logL, Z) of the considered stars

    +
    +
    values: sequence or attribute name

    value to interpolate

    +
    +
    +
    +
    Returns:
    +
    +
    values: sequence

    value to interpolate

    +
    +
    +
    +
    +
    + +
    +
    +generate_stellar_spectrum(logT, logg, logL, Z, raise_extrapolation=True, **kwargs)[source]
    +

    Generates individual spectrum for the given stars APs and the +stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    logT: float

    temperature

    +
    +
    logg: float

    log-gravity

    +
    +
    logL: float

    log-luminosity

    +
    +
    Z: float

    metallicity

    +
    +
    raise_extrapolation: bool

    if set throw error on extrapolation

    +
    +
    null: value

    value of the flux when extrapolation and raise_extrapolation is not set

    +
    +
    +
    +
    Returns:
    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or ergs/s/AA/Lsun

    +
    +
    +
    +
    +
    + +
    +
    +get_boundaries(**kwargs)[source]
    +

    Returns the closed boundary polygon around the stellar library with +given margins

    +
    +
    Parameters:
    +
    +
    s: Stellib

    Stellar library object

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    b: ndarray[float, ndim=2]

    (closed) boundary points: [logg, Teff] (or [Teff, logg] is swap is True)

    +
    +
    +
    +

    Note

    +

    as computing the boundary could take time, it is saved in the object +and only recomputed when parameters are updated

    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    +
    +property name
    +
    + +
    +
    +reinterpolate_spectra(l0, specs, **kwargs)[source]
    +

    One-dimensional linear interpolation onto the common wavelength.

    +

    Returns the one-dimensional interpolated spectrum

    +
    +
    Parameters:
    +
    +
    l01-D sequence of floats (with units or not)

    wavelength of the spectrum to interpolate

    +
    +
    specs1-D sequence of floats

    spectrum to reinterpolate

    +
    +
    leftfloat, optional

    Value to return for x < xp[0], default is fp[0].

    +
    +
    rightfloat, optional

    Value to return for x > xp[-1], default is fp[-1].

    +
    +
    periodNone or float, optional

    A period for the x-coordinates. This parameter allows the proper +interpolation of angular x-coordinates. Parameters left and right +are ignored if period is specified.

    +
    +
    +
    +
    Returns:
    +
    +
    specndarray

    The interpolated values

    +
    +
    +
    +
    +
    + +
    +
    +set_default_extrapolation_bounds(dlogT=None, dlogg=None)[source]
    +
    + +
    +
    +property source
    +
    + +
    +
    +property wavelength
    +

    return a common wavelength sampling to all libraries. This can be +used to reinterpolate any spectrum onto a common definition

    +
    + +
    +
    +which_osl(xypoints, **kwargs)[source]
    +

    Returns the library indice that contains each point in xypoints

    +

    The decision is made from a two step search:

    +
    +
      +

    • first, each point is checked against the strict boundary of each +library (i.e., dlogT = 0, dlogg = 0).

      • +

    • second, if points are not found in strict mode, the boundary is +relaxed and a new search is made.

      • +
    +
    +

    Each point is associated to the first library matching the above conditions.

    +
    +
    Parameters:
    +
    +
    xypoints: sequence

    a sequence of N logg, logT pairs.

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    res: ndarray(dtype=int)

    a ndarray, 0 meaning no library covers the point, and 1, … n, for the n-th library

    +
    +
    +
    +
    +
    + +
    + +
    +
    +class pystellibs.stellib.Stellib(*args, **kwargs)[source]
    +

    Bases: object

    +

    Basic stellar library class

    +
    +
    Attributes:
    +
    +
    interpolator: interpolator.BaseInterpolator

    interpolator to use, default LeujeuneInterpolator

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property flux_units
    +
    + +
    +
    +generate_individual_spectra(stars, **kwargs)[source]
    +

    Generates individual spectra for the given stars and stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    stars: Table

    contains at least (logT, logg, logL, Z) of the considered stars

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    l0: ndarray, ndim=1

    wavelength definition of the spectra +wavelength in AA

    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or lsun/AA

    +
    +
    +
    +
    +
    + +
    +
    +generate_individual_values(stars, values, **kwargs)[source]
    +

    Generates individual spectra for the given stars and stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    stars: Table

    contains at least (logT, logg, logL, Z) of the considered stars

    +
    +
    values: sequence or attribute name

    value to interpolate

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    values: sequence

    value to interpolate

    +
    +
    +
    +
    +
    + +
    +
    +generate_stellar_spectrum(logT, logg, logL, Z, raise_extrapolation=True, **kwargs)[source]
    +

    Generates individual spectrum for the given stars APs and the +stellar library

    +

    Returns NaN spectra if the boundary conditions are not met (no extrapolation)

    +
    +
    Parameters:
    +
    +
    logT: float

    temperature

    +
    +
    logg: float

    log-gravity

    +
    +
    logL: float

    log-luminosity

    +
    +
    Z: float

    metallicity

    +
    +
    raise_extrapolation: bool

    if set throw error on extrapolation

    +
    +
    null: value

    value of the flux when extrapolation and raise_extrapolation is not set

    +
    +
    +
    +
    Returns:
    +
    +
    s0: ndarray, shape=(len(stars), len(l0))

    array of spectra, one per input star +Spectrum in ergs/s/AA or ergs/s/AA/Lsun

    +
    +
    +
    +
    +
    + +
    +
    +get_boundaries(dlogT=0.1, dlogg=0.3, **kwargs)[source]
    +

    Returns the closed boundary polygon around the stellar library with +given margins

    +
    +
    Parameters:
    +
    +
    s: Stellib

    Stellar library object

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    b: ndarray[float, ndim=2]

    closed boundary edge points: [logT, logg]

    +
    +
    +
    +

    Note

    +

    as computing the boundary could take time, it is saved in the object +and only recomputed when parameters are updated

    +
    +
    +
    +
    + +
    +
    +get_interpolation_data()[source]
    +

    Default interpolation

    +
    + +
    +
    +get_radius(logl, logt)[source]
    +

    Returns the radius of a star given its luminosity and temperature

    +

    Assuming a black body, it comes:

    +
    +\[R ^ 2 = L / ( 4 \pi \sigma T ^ 4 ),\]
    +

    with:

    +
    +
      +

    • L, luminosity in W,

      • +

    • pi, 3.141592…

      • +

    • sig, Stefan constant in W * m**-2 * K**-4

      • +

    • T, temperature in K

      • +
    +
    +
    +
    Parameters:
    +
    +
    logl: ndarray[float, ndim=1]

    log luminosities from the isochrones, in Lsun

    +
    +
    logt: ndarray[float, ndim=1]

    log temperatures from the isochrones, in K

    +
    +
    +
    +
    Returns:
    +
    +
    radii: ndarray[float, ndim=1]

    array of radii in m (SI units)

    +
    +
    +
    +
    +
    + +
    +
    +get_weights(logT, logg, logL, weights=None)[source]
    +

    Returns the proper weights for the interpolation

    +

    in spectra libraries the default is to have Lbol=1 normalization

    +
    +
    Parameters:
    +
    +
    logT: float or ndarray

    log-temperatures log(T/K)

    +
    +
    logg: float or ndarray

    log-gravity log(g)

    +
    +
    logL: float or ndarray

    bolometric luminosity (log (L/Lsun))

    +
    +
    +
    +
    +
    + +
    +
    +property nbytes
    +

    return the number of bytes of the object

    +
    + +
    +
    +plot_boundary(ax=None, dlogT=0.0, dlogg=0.0, **kwargs)[source]
    +
    +
    Parameters:
    +
    +
    dlogT: float

    margin in logT (see get_boundaries)

    +
    +
    dlogg: float

    margin in logg (see get_boundaries)

    +
    +
    .. see also::
    +
    matplotlib.plot()

    For additional kwargs

    +
    +
    +
    +
    +
    +
    +
    + +
    +
    +points_inside(xypoints, dlogT=0.1, dlogg=0.5)[source]
    +

    Returns if a point is inside the polygon defined by the boundary of the library

    +
    +
    Parameters:
    +
    +
    xypoints: sequence

    a sequence of N logg, logT pairs.

    +
    +
    dlogT: float

    margin in logT

    +
    +
    dlogg: float

    margin in logg

    +
    +
    +
    +
    Returns:
    +
    +
    r: ndarray(dtype=bool)

    a boolean ndarray, True for points inside the polygon. +A point on the boundary may be treated as inside or outside.

    +
    +
    +
    +
    +
    + +
    +
    +set_default_extrapolation_bounds(dlogT=None, dlogg=None)[source]
    +
    + +
    +
    +property wavelength
    +
    + +
    + +
    +
    +

    pystellibs.tlusty module

    +
    +
    +class pystellibs.tlusty.Tlusty(*args, **kwargs)[source]
    +

    Bases: AtmosphereLib

    +

    Tlusty O and B stellar atmospheres

    +
      +

    • NLTE

      • +

    • Parallel Planes

      • +

    • line blanketing

      • +
    +

    References

    +

    Hubeny 1988 for initial reference +Lanz, T., & Hubeny, I. (2003) for more recent (NL TE) developments

    +
      +
    • +
      OSTAR2002 Grid: O-type stars, 27500 K <= Teff <= 55000 K
        +

      • Reference: Lanz & Hubeny (2003)

        • +
      +
      +
      +
      • +
    • +
      BSTAR2006 Grid: Early B-type stars, 15000 K <= Teff <= 30000 K
        +

      • Reference: Lanz & Hubeny (2007)

        • +
      +
      +
      +
      • +
    +

    files are available at: http://nova.astro.umd.edu/Tlusty2002/database/

    +

    O and B stars rebinned to nearly 20,000 frequency points (for CLOUDY usage) +http://nova.astro.umd.edu/Tlusty2002/database/obstar_merged_3d.ascii.gz

    +
    +
    Attributes:
    +
    +
    Teff
    +
    Z
    +
    flux_units
    +
    logT
    +
    logZ
    +
    logg
    +
    nbytes

    return the number of bytes of the object

    +
    +
    wavelength
    +
    +
    +
    +

    Methods

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    bbox([dlogT, dlogg])

    Boundary of Tlusty library

    generate_individual_spectra(stars, **kwargs)

    Generates individual spectra for the given stars and stellar library

    generate_individual_values(stars, values, ...)

    Generates individual spectra for the given stars and stellar library

    generate_stellar_spectrum(logT, logg, logL, Z)

    Generates individual spectrum for the given stars APs and the stellar library

    get_boundaries([dlogT, dlogg])

    Returns the closed boundary polygon around the stellar library with given margins

    get_interpolation_data()

    Default interpolation

    get_radius(logl, logt)

    Returns the radius of a star given its luminosity and temperature

    get_weights(logT, logg, logL[, weights])

    Returns the proper weights for the interpolation Stellar atmospheres are normalized to Radius = 1

    plot_boundary([ax, dlogT, dlogg])

    +
    Parameters:
    +

    +
    +

    points_inside(xypoints[, dlogT, dlogg])

    Returns if a point is inside the polygon defined by the boundary of the library

    + + + + + + +

    set_default_extrapolation_bounds

    +
    +
    +property Teff
    +
    + +
    +
    +property Z
    +
    + +
    +
    +bbox(dlogT=0.05, dlogg=0.25)[source]
    +

    Boundary of Tlusty library

    +
    +
    Parameters:
    +
    +
    dlogT: float

    log-temperature tolerance before extrapolation limit

    +
    +
    dlogg: float

    log-g tolerance before extrapolation limit

    +
    +
    +
    +
    Returns:
    +
    +
    bbox: ndarray

    (logT, logg) edges of the bounding polygon

    +
    +
    +
    +
    +
    + +
    +
    +property logT
    +
    + +
    +
    +property logZ
    +
    + +
    +
    +property logg
    +
    + +
    + +
    +
    +

    Module contents

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2016, M. Fouesneau. + + | + Powered by Sphinx 6.2.1 + & Alabaster 0.7.12 + + | + Page source +
    + + + + Fork me on GitHub + + + + + + \ No newline at end of file diff --git a/pystellibs.interpolator.html b/pystellibs.interpolator.html new file mode 100644 index 0000000..0ff9b76 --- /dev/null +++ b/pystellibs.interpolator.html @@ -0,0 +1,305 @@ + + + + + + + + + pystellibs.interpolator package — pystellibs 1.0 documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    pystellibs.interpolator package

    +
    +

    Submodules

    +
    +
    +

    pystellibs.interpolator.interpolator module

    +

    Base interpolator: a dummy class is derived by the different interpolator schemes

    +
    +
    +class pystellibs.interpolator.interpolator.BaseInterpolator(osl, *args, **kwargs)[source]
    +

    Bases: object

    +

    Base class for interpolation

    +

    It sets what can be expected as methods during the interpolation calls

    +

    Methods

    + + + + + + + + + +

    interp(aps, *args, **kwargs)

    Interpolation over spectra

    interp_other(aps, values, *args, **kwargs)

    Interpolation over provided values

    +
    +
    +interp(aps, *args, **kwargs)[source]
    +

    Interpolation over spectra

    +
    + +
    +
    +interp_other(aps, values, *args, **kwargs)[source]
    +

    Interpolation over provided values

    +
    + +
    + +
    +
    +

    pystellibs.interpolator.lejeune module

    +

    Lejeune interpolator is basically a linear interpolator for a Lejeune grid +based spectral library.

    +

    This is the simplest interpolator but most commonly used.

    +

    It takes care of boundary conditions by imposing limits to extrapolation on a +given grid.

    +
    +
    +class pystellibs.interpolator.lejeune.LejeuneInterpolator(osl, dT_max=0.1, eps=1e-06, *args, **kwargs)[source]
    +

    Bases: BaseInterpolator

    +

    Interpolation for grid based on the Lejeune library definition

    +

    The interpolation is N-D linear in log-temperature, log-gravity, and linear +in metallicity Z. Preference is always given to the temperature over the +gravity when needed.

    +

    This version is translated from Pegase

    +
    +
    Attributes:
    +
    +
    dT_max: float, optional

    If, T2 (resp. T1) is too far from T compared to T1 (resp. T2), i2 +(resp. i1) is not used. (see below for namings)

    +
    +
    eps: float

    temperature sensitivity under which points are considered to have +the same temperature

    +
    +
    +
    +
    +

    Methods

    + + + + + + + + + +

    interp(aps[, weights])

    Interpolate spectra

    interp_other(aps, other[, weights])

    Interpolate other grid values

    +
    +
    +interp(aps, weights=None, **kwargs)[source]
    +

    Interpolate spectra

    +
    +
    Parameters:
    +
    +
    aps: ndarray

    (logT, logg, logZ) sequence. +Or appropriately defined similarly to self.osl.get_interpolation_data

    +
    +
    weights: ndarray

    optional weights of each ap vector to apply during the interpolation

    +
    +
    +
    +
    Returns:
    +
    +
    s0: ndarray (len(aps), len(l0))

    interpolated spectra

    +
    +
    +
    +
    +
    + +
    +
    +interp_other(aps, other, weights=None, **kwargs)[source]
    +

    Interpolate other grid values

    +

    Basically do a weighted sum on the grid using the interpolation weights

    +
    +
    Parameters:
    +
    +
    aps: ndarray

    (logT, logg, logZ) sequence. +Or appropriately defined similarly to self.osl.get_interpolation_data

    +
    +
    weights: ndarray

    optional weights of each ap vector to apply during the interpolation

    +
    +
    +
    +
    Returns:
    +
    +
    interpolated: ndarray (size(node_weights), )

    interpolated values

    +
    +
    +
    +
    +
    + +
    + +
    +
    +

    pystellibs.interpolator.ndlinear module

    +

    N-D linear interpolation

    +
    +
    +class pystellibs.interpolator.ndlinear.NDLinearInterpolator(osl, *args, **kwargs)[source]
    +

    Bases: BaseInterpolator

    +

    Methods

    + + + + + + + + + +

    interp(aps[, weights])

    Interpolate spectra

    interp_other(aps, values, **kwargs)

    Interpolate on other values

    +
    +
    +interp(aps, weights=1.0, **kwargs)[source]
    +

    Interpolate spectra

    +
    + +
    +
    +interp_other(aps, values, **kwargs)[source]
    +

    Interpolate on other values

    +
    + +
    + +
    +
    +

    Module contents

    +
    +
    +pystellibs.interpolator.find_interpolator(name, osl=None, **kwargs)[source]
    +
    +
    Find an interpolator from its name and

    instanciate it if an osl was provided

    +
    +
    +
    +
    Parameters:
    +
    +
    name: str

    name of the interpolation

    +
    +
    osl: Stellib instance, optional

    library to work with

    +
    +
    +
    +
    +
    + +
    +
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