def __init__(self, flux, wcs, unit=None, uncertainty=None, mask=None,
flags=None, meta=None, indexer=None):
super(Spectrum1D, self).__init__(data=flux, unit=unit, wcs=wcs, uncertainty=uncertainty,
mask=mask, flags=flags, meta=meta)
self._wcs_attributes = copy.deepcopy(self.__class__._wcs_attributes)
if indexer is None:
self.indexer = Indexer(0, len(flux))
else:
self.indexer = indexer
for key in list(self._wcs_attributes):
wcs_attribute_unit = self._wcs_attributes[key]['unit']
try:
unit_equivalent = wcs_attribute_unit.is_equivalent(self.wcs.unit, equivalencies=self.wcs.equivalencies)
except TypeError:
unit_equivalent = False
if not unit_equivalent:
#if unit is not convertible to wcs attribute - delete that wcs attribute
del self._wcs_attributes[key]
continue
if wcs_attribute_unit.physical_type == self.wcs.unit.physical_type:
self._wcs_attributes[key]['unit'] = self.wcs.unit
def test_apply_slice():
x = np.arange(1024)
ind = Indexer(2, 5, 2)
ind2 = ind[0:2]
np.testing.assert_allclose(x[2:5:2], x[ind()])
np.testing.assert_allclose(x[2:5:2], x[ind2()])
ind = Indexer(0, 1024)
ind2 = ind[::-1]
np.testing.assert_allclose(x, x[ind()])
np.testing.assert_allclose(x[::-1], x[ind2()])
np.testing.assert_allclose(x[100::-1], x[ind[100::-1]()])
np.testing.assert_allclose(x[:100:-1], x[ind[:100:-1]()])
np.testing.assert_allclose(x[200:100:-1], x[ind[200:100:-1]()])
np.testing.assert_allclose(x[242:100:-5], x[ind[242:100:-5]()])
np.testing.assert_allclose(x[242:100], x[ind[242:100]()])
np.testing.assert_allclose(x[:100], x[ind[:100]()])
np.testing.assert_allclose(x[:100:3], x[ind[:100:3]()])
np.testing.assert_allclose(x[:], x[ind[:]()])
np.testing.assert_allclose(x[50:], x[ind[50:]()])
np.testing.assert_allclose(x[50::2], x[ind[50::2]()])
np.testing.assert_allclose(x[:-1:-1], x[ind[:-1:-1]()])
np.testing.assert_allclose(x[:-100:-1], x[ind[:-100:-1]()])
np.testing.assert_allclose(x[-200:-100], x[ind[-200:-100]()])
np.testing.assert_allclose(x[-200:-100:-1], x[ind[-200:-100:-1]()])
np.testing.assert_allclose(x[-100:-200:-1], x[ind[-100:-200:-1]()])
np.testing.assert_allclose(x[-100::-1], x[ind[-100::-1]()])
np.testing.assert_allclose(x[-100:], x[ind[-100:]()])
np.testing.assert_allclose(x[-100::], x[ind[-100::]()])
def test_init_slice():
x = np.arange(101)
ind = Indexer(2, 5, 2)
np.testing.assert_allclose(x[2:5:2], x[ind()])
ind = Indexer(0, 100)
np.testing.assert_allclose(x[0:100], x[ind()])
ind = Indexer(100, -1, -1)
np.testing.assert_allclose(x[100::-1], x[ind()])
ind = Indexer(10, 3, -2)
np.testing.assert_allclose(x[10:3:-2], x[ind()])
ind = Indexer(5, 2, -2)
np.testing.assert_allclose(x[5:2:-2], x[ind()])
def __init__(self, flux, wcs, unit=None, uncertainty=None, mask=None,
meta=None, indexer=None):
super(Spectrum1D, self).__init__(data=flux, unit=unit, wcs=wcs, uncertainty=uncertainty,
mask=mask, meta=meta)
self._wcs_attributes = copy.deepcopy(self.__class__._wcs_attributes)
if indexer is None:
self.indexer = Indexer(0, len(flux))
else:
self.indexer = indexer
for key in list(self._wcs_attributes):
wcs_attribute_unit = self._wcs_attributes[key]['unit']
try:
unit_equivalent = wcs_attribute_unit.is_equivalent(self.wcs.unit, equivalencies=self.wcs.equivalencies)
except TypeError:
unit_equivalent = False
if not unit_equivalent:
#if unit is not convertible to wcs attribute - delete that wcs attribute
del self._wcs_attributes[key]
continue
if wcs_attribute_unit.physical_type == self.wcs.unit.physical_type:
self._wcs_attributes[key]['unit'] = self.wcs.unit
def test_length():
ind = Indexer(2, 5, 2)
assert ind.length == 2
ind = Indexer(0, 4, 1)
assert ind.length == 4
ind = Indexer(0, 4)
assert ind.length == 4
ind = Indexer(5, -1, -1)
assert ind.length == 6
ind = Indexer(2, 6, 2)
assert ind.length == 2
ind = Indexer(6, 2, -2)
assert ind.length == 2
ind = Indexer(10, 10, 1)
assert ind.length == 0
ind = Indexer(4, 25, -1)
assert ind.length == 0
class Spectrum1D(NDData):
"""A subclass of `NDData` for a one dimensional spectrum in Astropy.
This class inherits all the base class functionality from the NDData class
and is communicative with other Spectrum1D objects in ways which make sense.
Parameters
----------
data : `~numpy.ndarray`
flux of the spectrum
wcs : `spectrum1d.wcs.specwcs.BaseSpectrum1DWCS`-subclass
transformation between pixel coordinates and "dispersion" coordinates
this carries the unit of the dispersion
unit : `~astropy.unit.Unit` or None, optional
unit of the flux, default=None
mask : `~numpy.ndarray`, optional
Mask for the data, given as a boolean Numpy array with a shape
matching that of the data. The values must be ``False`` where
the data is *valid* and ``True`` when it is not (like Numpy
masked arrays). If `data` is a numpy masked array, providing
`mask` here will causes the mask from the masked array to be
ignored.
meta : `dict`-like object, optional
Metadata for this object. "Metadata" here means all information that
is included with this object but not part of any other attribute
of this particular object. e.g., creation date, unique identifier,
simulation parameters, exposure time, telescope name, etc.
"""
_wcs_attributes = {
'wavelength': {
'unit': u.m
},
'frequency': {
'unit': u.Hz
},
'energy': {
'unit': u.J
},
'velocity': {
'unit': u.m / u.s
}
}
@classmethod
def from_array(cls,
dispersion,
flux,
dispersion_unit=None,
uncertainty=None,
mask=None,
meta=None,
copy=True,
unit=None):
"""Initialize `Spectrum1D`-object from two `numpy.ndarray` objects
Parameters:
-----------
dispersion : `~astropy.units.quantity.Quantity` or `~np.array`
The dispersion for the Spectrum (e.g. an array of wavelength
points). If an array is specified `dispersion_unit` needs to be a spectral unit
flux : `~astropy.units.quantity.Quantity` or `~np.array`
The flux level for each wavelength point. Should have the same length
as `dispersion`.
dispersion_unit :
error : `~astropy.nddata.NDError`, optional
Errors on the data.
mask : `~numpy.ndarray`, optional
Mask for the data, given as a boolean Numpy array with a shape
matching that of the data. The values should be ``False`` where the
data is *valid* and ``True`` when it is not (as for Numpy masked
arrays).
meta : `dict`-like object, optional
Metadata for this object. "Metadata here means all information that
is included with this object but not part of any other attribute
of this particular object. e.g., creation date, unique identifier,
simulation parameters, exposure time, telescope name, etc.
copy : bool, optional
If True, the array will be *copied* from the provided `data`,
otherwise it will be referenced if possible (see `numpy.array` :attr:`copy`
argument for details).
Raises
------
ValueError
If the `dispersion` and `flux` arrays cannot be broadcast (e.g. their shapes
do not match), or the input arrays are not one dimensional.
"""
if dispersion.ndim != 1 or dispersion.shape != flux.shape:
raise ValueError("dispersion and flux need to be one-dimensional "
"Numpy arrays with the same shape")
if hasattr(dispersion, 'unit'):
if dispersion_unit is not None:
dispersion = dispersion.to(dispersion_unit).value
else:
dispersion_unit = dispersion.unit
dispersion = dispersion.value
spec_wcs = Spectrum1DLookupWCS(dispersion, unit=dispersion_unit)
if copy:
flux = flux.copy()
return cls(flux=flux,
wcs=spec_wcs,
unit=unit,
uncertainty=uncertainty,
mask=mask,
meta=meta)
@classmethod
def from_table(cls,
table,
dispersion_column='dispersion',
flux_column='flux',
uncertainty_column=None,
flag_columns=None):
"""
Initializes a `Spectrum1D`-object from an `~astropy.table.Table` object
Parameters
----------
table : ~astropy.table.Table object
dispersion_column : str, optional
name of the dispersion column. default is 'dispersion'
flux_column : str, optional
name of the flux column. default is 'flux'
uncertainty_column : str, optional
name of the uncertainty column. If set to None uncertainty is set to None. default is None
flag_columns : str or list, optional
name or names of flag columns. If multiple names are supplied a ~astropy.nddata.FlagCollection will be built.
default is None
"""
flux = table[flux_column]
dispersion = table[dispersion_column]
if uncertainty_column is not None:
uncertainty = table[uncertainty_column]
if uncertainty.unit != flux.unit:
log.warning(
'"uncertainty"-column and "flux"-column do not share the units (%s vs %s) ',
uncertainty.unit, flux.unit)
else:
uncertainty = None
return cls.from_array(flux=flux.data,
dispersion=dispersion.data,
uncertainty=uncertainty,
dispersion_unit=dispersion.units,
unit=flux.units,
mask=table.mask,
meta=table.meta)
@classmethod
def from_ascii(cls,
filename,
uncertainty=None,
mask=None,
dtype=np.float,
comments='#',
delimiter=None,
converters=None,
skiprows=0,
usecols=None):
raw_data = np.loadtxt(filename,
dtype=dtype,
comments=comments,
delimiter=delimiter,
converters=converters,
skiprows=skiprows,
usecols=usecols,
ndmin=2)
if raw_data.shape[1] != 2:
raise ValueError(
'data contained in filename must have exactly two columns')
return cls.from_array(dispersion=raw_data[:, 0],
flux=raw_data[:, 1],
uncertainty=uncertainty,
mask=mask)
@classmethod
def from_fits(cls, filename):
"""
This function is a dummy function and will fail for now. Please use the functions provided in
`~specutils.io.read_fits` for this task.
"""
raise NotImplementedError(
'This function is not implemented. To read FITS files please refer to the'
' documentation')
def __init__(self,
flux,
wcs,
unit=None,
uncertainty=None,
mask=None,
meta=None,
indexer=None):
super(Spectrum1D, self).__init__(data=flux,
unit=unit,
wcs=wcs,
uncertainty=uncertainty,
mask=mask,
meta=meta)
self._wcs_attributes = copy.deepcopy(self.__class__._wcs_attributes)
if indexer is None:
self.indexer = Indexer(0, len(flux))
else:
self.indexer = indexer
for key in list(self._wcs_attributes):
wcs_attribute_unit = self._wcs_attributes[key]['unit']
try:
unit_equivalent = wcs_attribute_unit.is_equivalent(
self.wcs.unit, equivalencies=self.wcs.equivalencies)
except TypeError:
unit_equivalent = False
if not unit_equivalent:
#if unit is not convertible to wcs attribute - delete that wcs attribute
del self._wcs_attributes[key]
continue
if wcs_attribute_unit.physical_type == self.wcs.unit.physical_type:
self._wcs_attributes[key]['unit'] = self.wcs.unit
def flux_getter(self):
#returning the flux
return u.Quantity(self.data, self.unit, copy=False)
def flux_setter(self, flux):
if hasattr(flux, 'unit'):
if self.unit is not None:
flux = flux.to(self.unit).value
else:
raise ValueError('Attempting to set a new unit for this object'
'this is not allowed by Spectrum1D')
self._data = flux
flux = property(flux_getter, flux_setter)
def __getattr__(self, name):
if name in self._wcs_attributes:
return self.dispersion.to(self._wcs_attributes[name]['unit'],
equivalencies=self.wcs.equivalencies)
elif name[:-5] in self._wcs_attributes and name[-5:] == '_unit':
return self._wcs_attributes[name[:-5]]['unit']
else:
super(Spectrum1D, self).__getattribute__(name)
def __setattr__(self, name, value):
if name[:-5] in self._wcs_attributes and name[-5:] == '_unit':
self._wcs_attributes[name[:-5]]['unit'] = u.Unit(value)
else:
super(Spectrum1D, self).__setattr__(name, value)
def __dir__(self):
return list(self.__dict__.keys()) + list(self._wcs_attributes.keys()) + \
[item + '_unit' for item in self._wcs_attributes.keys()]
#TODO: let the WCS handle what to do with len(flux)
@property
def dispersion(self):
#returning the disp
pixel_indices = np.arange(len(self.flux))
return self.wcs(self.indexer(pixel_indices))
@property
def dispersion_unit(self):
return self.wcs.unit
def interpolate(self,
new_dispersion,
kind='linear',
bounds_error=True,
fill_value=np.nan):
"""Interpolates onto a new wavelength grid and returns a new `Spectrum1D`-object.
Parameters
----------
new_dispersion : `~numpy.ndarray`
The dispersion array to interpolate the flux on to.
kind : `str` or `int`, optional
Specifies the kind of interpolation as a string
('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic')
or as an integer specifying the order of the spline interpolator
to use. Default is 'linear'.
bounds_error : `bool`, optional
If True, an error is thrown any time interpolation is attempted on a
dispersion point outside of the range of the original dispersion map
(where extrapolation is necessary). If False, out of bounds values
are assigned `fill_value`. By default, an error is raised.
fill_value : `float`, optional
If provided, then this value will be used to fill in for requested
dispersion points outside of the original dispersion map. If not
provided, then the default is NaN.
Raises
------
ImportError
If the `SciPy interpolate interp1d <http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html>`_
function cannot be imported.
Notes
-----
When the `Spectrum1D` class has an associated error array, the nearest
uncertainty is taken for each new dispersion point.
"""
# Check for SciPy availability
if kind != 'linear':
raise ValueError('No other kind but linear supported')
if not isinstance(new_dispersion, BaseSpectrum1DWCS):
new_dispersion = Spectrum1DLookupWCS(np.array(new_dispersion))
new_pixel = self.wcs.invert(new_dispersion.lookup_table)
new_flux = np.interp(new_pixel,
self.wcs.pixel_index,
self.flux,
left=np.nan,
right=np.nan)
return self.__class__(new_flux, wcs=new_dispersion, meta=self.meta)
def slice_dispersion(self, start=None, stop=None):
"""Slice the spectrum within a given start and end dispersion value.
Parameters
----------
start : `float`
Starting slice point.
stop : `float`
Stopping slice point.
Notes
-----
Often it is useful to slice out a portion of a `Spectrum1D` objects
either by two dispersion points (e.g. two wavelengths) or by the indices
of the dispersion/flux arrays (see :meth:`~Spectrum1D.slice_index` for this
functionality).
Examples
--------
>>> from specutils import Spectrum1D
>>> from astropy import units
>>> import numpy as np
>>> dispersion = np.arange(4000, 5000, 0.12)
>>> flux = np.random.randn(len(dispersion))
>>> mySpectrum = Spectrum1D.from_array(dispersion,
flux,
dispersion_unit=units.m)
>>> # Now say we wanted a slice near H-beta at 4861 Angstroms
>>> hBeta = mySpectrum.slice_dispersion(4851.0, 4871.0)
>>> hBeta
<hBeta __repr__ #TODO>
See Also
--------
See `~Spectrum1D.slice_index`
"""
raise NotImplementedError(
'Waiting for slicing implementation in WCS and NDData')
# Transform the dispersion end points to index space
start_index, stop_index = self.wcs([start, stop])
#return self.slice_index(start_index, stop_index)
def slice_index(self, start=None, stop=None, step=None):
"""Slice the spectrum within a given start and end index.
Parameters
----------
start : int
Starting slice point.
stop : int
Stopping slice point.
step : int
Slice step
Notes
-----
Often it is useful to slice out a portion of a `Spectrum1D` objects
either by two index points (see :meth:`~Spectrum1D.slice_dispersion`) or by
the indices of the dispersion/flux array.
See Also
--------
See `~Spectrum1D.slice_dispersion`
"""
# We need to slice the following items:
# >> disp, flux, error, and mask
# Which are all common NDData objects, therefore I am (perhaps
# reasonably) assuming that __slice__ will be a NDData base function
# which we will inherit.
# At this time, that function raises an error if WCS is not None, so it
# cannot be used
item = slice(start, stop, step)
new_data = self.data[item]
if self.uncertainty is not None:
new_uncertainty = self.uncertainty[item]
else:
new_uncertainty = None
if self.mask is not None:
new_mask = self.mask[item]
# mask setter expects an array, always
if new_mask.shape == ():
new_mask = np.array(new_mask)
else:
new_mask = None
new_indexer = self.indexer.__getitem__(item)
new_wcs = self.wcs
return self.__class__(new_data,
new_wcs,
meta=self.meta,
unit=self.unit,
uncertainty=new_uncertainty,
mask=new_mask,
indexer=new_indexer)
class Spectrum1D(NDData):
"""A subclass of `NDData` for a one dimensional spectrum in Astropy.
This class inherits all the base class functionality from the NDData class
and is communicative with other Spectrum1D objects in ways which make sense.
Parameters
----------
data : `~numpy.ndarray`
flux of the spectrum
wcs : `spectrum1d.wcs.specwcs.BaseSpectrum1DWCS`-subclass
transformation between pixel coordinates and "dispersion" coordinates
this carries the unit of the dispersion
unit : `~astropy.unit.Unit` or None, optional
unit of the flux, default=None
mask : `~numpy.ndarray`, optional
Mask for the data, given as a boolean Numpy array with a shape
matching that of the data. The values must be ``False`` where
the data is *valid* and ``True`` when it is not (like Numpy
masked arrays). If `data` is a numpy masked array, providing
`mask` here will causes the mask from the masked array to be
ignored.
meta : `dict`-like object, optional
Metadata for this object. "Metadata" here means all information that
is included with this object but not part of any other attribute
of this particular object. e.g., creation date, unique identifier,
simulation parameters, exposure time, telescope name, etc.
"""
_wcs_attributes = {'wavelength': {'unit': u.m},
'frequency': {'unit': u.Hz},
'energy': {'unit': u.J},
'velocity': {'unit': u.m/u.s}}
@classmethod
def from_array(cls, dispersion, flux, dispersion_unit=None,
uncertainty=None, mask=None, meta=None, copy=True,
unit=None):
"""Initialize `Spectrum1D`-object from two `numpy.ndarray` objects
Parameters:
-----------
dispersion : `~astropy.units.quantity.Quantity` or `~np.array`
The dispersion for the Spectrum (e.g. an array of wavelength
points). If an array is specified `dispersion_unit` needs to be a spectral unit
flux : `~astropy.units.quantity.Quantity` or `~np.array`
The flux level for each wavelength point. Should have the same length
as `dispersion`.
dispersion_unit :
error : `~astropy.nddata.NDError`, optional
Errors on the data.
mask : `~numpy.ndarray`, optional
Mask for the data, given as a boolean Numpy array with a shape
matching that of the data. The values should be ``False`` where the
data is *valid* and ``True`` when it is not (as for Numpy masked
arrays).
meta : `dict`-like object, optional
Metadata for this object. "Metadata here means all information that
is included with this object but not part of any other attribute
of this particular object. e.g., creation date, unique identifier,
simulation parameters, exposure time, telescope name, etc.
copy : bool, optional
If True, the array will be *copied* from the provided `data`,
otherwise it will be referenced if possible (see `numpy.array` :attr:`copy`
argument for details).
Raises
------
ValueError
If the `dispersion` and `flux` arrays cannot be broadcast (e.g. their shapes
do not match), or the input arrays are not one dimensional.
"""
if dispersion.ndim != 1 or dispersion.shape != flux.shape:
raise ValueError("dispersion and flux need to be one-dimensional "
"Numpy arrays with the same shape")
if hasattr(dispersion, 'unit'):
if dispersion_unit is not None:
dispersion = dispersion.to(dispersion_unit).value
else:
dispersion_unit = dispersion.unit
dispersion = dispersion.value
spec_wcs = Spectrum1DLookupWCS(dispersion, unit=dispersion_unit)
if copy:
flux = flux.copy()
return cls(flux=flux, wcs=spec_wcs, unit=unit, uncertainty=uncertainty,
mask=mask, meta=meta)
@classmethod
def from_table(cls, table, dispersion_column='dispersion',
flux_column='flux', uncertainty_column=None,
flag_columns=None):
"""
Initializes a `Spectrum1D`-object from an `~astropy.table.Table` object
Parameters
----------
table : ~astropy.table.Table object
dispersion_column : str, optional
name of the dispersion column. default is 'dispersion'
flux_column : str, optional
name of the flux column. default is 'flux'
uncertainty_column : str, optional
name of the uncertainty column. If set to None uncertainty is set to None. default is None
flag_columns : str or list, optional
name or names of flag columns. If multiple names are supplied a ~astropy.nddata.FlagCollection will be built.
default is None
"""
flux = table[flux_column]
dispersion = table[dispersion_column]
if uncertainty_column is not None:
uncertainty = table[uncertainty_column]
if uncertainty.unit != flux.unit:
log.warning('"uncertainty"-column and "flux"-column do not share the units (%s vs %s) ',
uncertainty.unit, flux.unit)
else:
uncertainty = None
return cls.from_array(flux=flux.data, dispersion=dispersion.data,
uncertainty=uncertainty, dispersion_unit=dispersion.units,
unit=flux.units, mask=table.mask, meta=table.meta)
@classmethod
def from_ascii(cls, filename, uncertainty=None, mask=None, dtype=np.float, comments='#',
delimiter=None, converters=None, skiprows=0,
usecols=None):
raw_data = np.loadtxt(filename, dtype=dtype, comments=comments,
delimiter=delimiter, converters=converters,
skiprows=skiprows, usecols=usecols, ndmin=2)
if raw_data.shape[1] != 2:
raise ValueError('data contained in filename must have exactly two columns')
return cls.from_array(dispersion=raw_data[:,0], flux=raw_data[:,1], uncertainty=uncertainty, mask=mask)
@classmethod
def from_fits(cls, filename):
"""
This function is a dummy function and will fail for now. Please use the functions provided in
`~specutils.io.read_fits` for this task.
"""
raise NotImplementedError('This function is not implemented. To read FITS files please refer to the'
' documentation')
def __init__(self, flux, wcs, unit=None, uncertainty=None, mask=None,
meta=None, indexer=None):
super(Spectrum1D, self).__init__(data=flux, unit=unit, wcs=wcs, uncertainty=uncertainty,
mask=mask, meta=meta)
self._wcs_attributes = copy.deepcopy(self.__class__._wcs_attributes)
if indexer is None:
self.indexer = Indexer(0, len(flux))
else:
self.indexer = indexer
for key in list(self._wcs_attributes):
wcs_attribute_unit = self._wcs_attributes[key]['unit']
try:
unit_equivalent = wcs_attribute_unit.is_equivalent(self.wcs.unit, equivalencies=self.wcs.equivalencies)
except TypeError:
unit_equivalent = False
if not unit_equivalent:
#if unit is not convertible to wcs attribute - delete that wcs attribute
del self._wcs_attributes[key]
continue
if wcs_attribute_unit.physical_type == self.wcs.unit.physical_type:
self._wcs_attributes[key]['unit'] = self.wcs.unit
def flux_getter(self):
#returning the flux
return u.Quantity(self.data, self.unit, copy=False)
def flux_setter(self, flux):
if hasattr(flux, 'unit'):
if self.unit is not None:
flux = flux.to(self.unit).value
else:
raise ValueError('Attempting to set a new unit for this object'
'this is not allowed by Spectrum1D')
self._data = flux
flux = property(flux_getter, flux_setter)
def __getattr__(self, name):
if name in self._wcs_attributes:
return self.dispersion.to(self._wcs_attributes[name]['unit'], equivalencies=self.wcs.equivalencies)
elif name[:-5] in self._wcs_attributes and name[-5:] == '_unit':
return self._wcs_attributes[name[:-5]]['unit']
else:
super(Spectrum1D, self).__getattribute__(name)
def __setattr__(self, name, value):
if name[:-5] in self._wcs_attributes and name[-5:] == '_unit':
self._wcs_attributes[name[:-5]]['unit'] = u.Unit(value)
else:
super(Spectrum1D, self).__setattr__(name, value)
def __dir__(self):
return list(self.__dict__.keys()) + list(self._wcs_attributes.keys()) + \
[item + '_unit' for item in self._wcs_attributes.keys()]
#TODO: let the WCS handle what to do with len(flux)
@property
def dispersion(self):
#returning the disp
pixel_indices = np.arange(len(self.flux))
return self.wcs(self.indexer(pixel_indices))
@property
def dispersion_unit(self):
return self.wcs.unit
def interpolate(self, new_dispersion, kind='linear', bounds_error=True, fill_value=np.nan):
"""Interpolates onto a new wavelength grid and returns a new `Spectrum1D`-object.
Parameters
----------
new_dispersion : `~numpy.ndarray`
The dispersion array to interpolate the flux on to.
kind : `str` or `int`, optional
Specifies the kind of interpolation as a string
('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic')
or as an integer specifying the order of the spline interpolator
to use. Default is 'linear'.
bounds_error : `bool`, optional
If True, an error is thrown any time interpolation is attempted on a
dispersion point outside of the range of the original dispersion map
(where extrapolation is necessary). If False, out of bounds values
are assigned `fill_value`. By default, an error is raised.
fill_value : `float`, optional
If provided, then this value will be used to fill in for requested
dispersion points outside of the original dispersion map. If not
provided, then the default is NaN.
Raises
------
ImportError
If the `SciPy interpolate interp1d <http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html>`_
function cannot be imported.
Notes
-----
When the `Spectrum1D` class has an associated error array, the nearest
uncertainty is taken for each new dispersion point.
"""
# Check for SciPy availability
if kind != 'linear':
raise ValueError('No other kind but linear supported')
if not isinstance(new_dispersion, BaseSpectrum1DWCS):
new_dispersion = Spectrum1DLookupWCS(np.array(new_dispersion))
new_pixel = self.wcs.invert(new_dispersion.lookup_table)
new_flux = np.interp(new_pixel, self.wcs.pixel_index, self.flux, left=np.nan, right=np.nan)
return self.__class__(new_flux, wcs=new_dispersion, meta=self.meta)
def slice_dispersion(self, start=None, stop=None):
"""Slice the spectrum within a given start and end dispersion value.
Parameters
----------
start : `float`
Starting slice point.
stop : `float`
Stopping slice point.
Notes
-----
Often it is useful to slice out a portion of a `Spectrum1D` objects
either by two dispersion points (e.g. two wavelengths) or by the indices
of the dispersion/flux arrays (see :meth:`~Spectrum1D.slice_index` for this
functionality).
Examples
--------
>>> from specutils import Spectrum1D
>>> from astropy import units
>>> import numpy as np
>>> dispersion = np.arange(4000, 5000, 0.12)
>>> flux = np.random.randn(len(dispersion))
>>> mySpectrum = Spectrum1D.from_array(dispersion,
flux,
dispersion_unit=units.m)
>>> # Now say we wanted a slice near H-beta at 4861 Angstroms
>>> hBeta = mySpectrum.slice_dispersion(4851.0, 4871.0)
>>> hBeta
<hBeta __repr__ #TODO>
See Also
--------
See `~Spectrum1D.slice_index`
"""
raise NotImplementedError('Waiting for slicing implementation in WCS and NDData')
# Transform the dispersion end points to index space
start_index, stop_index = self.wcs([start, stop])
#return self.slice_index(start_index, stop_index)
def slice_index(self, start=None, stop=None, step=None):
"""Slice the spectrum within a given start and end index.
Parameters
----------
start : int
Starting slice point.
stop : int
Stopping slice point.
step : int
Slice step
Notes
-----
Often it is useful to slice out a portion of a `Spectrum1D` objects
either by two index points (see :meth:`~Spectrum1D.slice_dispersion`) or by
the indices of the dispersion/flux array.
See Also
--------
See `~Spectrum1D.slice_dispersion`
"""
# We need to slice the following items:
# >> disp, flux, error, and mask
# Which are all common NDData objects, therefore I am (perhaps
# reasonably) assuming that __slice__ will be a NDData base function
# which we will inherit.
# At this time, that function raises an error if WCS is not None, so it
# cannot be used
item = slice(start, stop, step)
new_data = self.data[item]
if self.uncertainty is not None:
new_uncertainty = self.uncertainty[item]
else:
new_uncertainty = None
if self.mask is not None:
new_mask = self.mask[item]
# mask setter expects an array, always
if new_mask.shape == ():
new_mask = np.array(new_mask)
else:
new_mask = None
new_indexer = self.indexer.__getitem__(item)
new_wcs = self.wcs
return self.__class__(new_data, new_wcs, meta=self.meta, unit=self.unit
, uncertainty=new_uncertainty, mask=new_mask,
indexer=new_indexer)
