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)