Legacy API

This section describes the uses of the class Q3DataCube, which is a backwards compatibility layer for older scripts, see the examples in the methods below.

The main use for the old API is to convert from zone to XY notation, and produce radial profiles.

califa.Q3DataCube

class califa.Q3DataCube.Q3DataCube(synthesisFile, circularProfile=True, runId=None, califaId=None, smoothDezonification=True)

getZoneToYXTensor(extensive=True, dezonification=True)

Returns a tensor to transform from zone notation to spatial notation, with shape (zone, y, x). The normalization of the pixels can take into account the area of the zones or the luminosity weigth, using the following parameters:

Parameters :

extensive : boolean

A.K.A. “normalize by zone area”. Use surface density when calculating the transformation from zone to spatial coordinates.

dezonification : boolean

Use image at 5650AA to weight pixels in the zones. This is used only when extensive=True.

Returns :

zoneToYX : 3-D array

The transform tensor, shape (nZones, y, x).

See also

getYXToRadialBinsTensor, getZoneToRadialBinsTensor

Examples

To transform a cube named mycube, with axes (age, zone), into a cube with axes (age, y, x), one can perform the operation

>>> K = Q3DataCube(filename)
>>> zyx = K.getZoneToYXTensor()
>>> numpy.tensordot(mycube, zyx, (1, 0))

where 1 is the number of the zone axis in mycube, and 0 is the zone axis number in zyx.

getYXToRadialBinsTensor(d_r, rad_bin_ini=0, rad_bin_fin=None, use_HLR_units=True, normalize_area=True)

Compute a tensor that transforms an array from zone binning notation to radial binning notation. Given the radial profile for this galaxy, this method calculates a tensor similar to the zonesToYXTensor, it transforms an image from spatial coordinates (x,y) to a radial profile.

Parameters :

d_r : float

Radial step of the rings, in units of HLR.

rad_bin_ini : float, defaults to 0.0

Initial value for rad bins, in units of HLR.

rad_bin_fin : float, defaults to infinity

Final value for rad bins, in units of HLR.

use_HLR_units : boolean

If True, use the bins in HLR units. If False, use pixels.

normalize_area : boolean

If True, divide the tensor by the bin area.

Returns :

YXToRad : array, shape (rad. bins, y, x).

The transform tensor.

bin_R : array of float

Radial bin outer edges.

bin_area : array of float

Radial bin area, in pixels.

See also

getZoneToRadialBinsTensor, getZoneToYXTensor

Examples

>>> c = Q3DataCube(file)
>>> yxToRad, bins, unused = c.getYXToRadialBinsTensor(d_r=0.2)

c.LobnSD__yx is the luminosity surface desity in each pixel. Compute the radial profile of the average luminosity surface density.

>>> radProf__R = np.tensordot(Lobn_tot__yx, yxToRad, [(0,1),(1,2)])
>>> plot(bins, radProf__R)  

getZoneToRadialBinsTensor(d_r, rad_bin_ini=0, rad_bin_fin=None, use_HLR_units=True, extensive=True, dezonification=False)

Return a tensor that transforms an array from zone binning notation to radial binning notation. Given the radial profile for this galaxy, this method calculates a tensor similar to the zonesToYXTensor, it transforms an image from spatial coordinates (x,y) to a radial profile.

Parameters :

d_r : float

Radial step of the rings, in units of HLR_pix.

rad_bin_ini : float, defaults to 0.0

Initial value for rad bins, in units of HLR_pix.

rad_bin_fin : float, defaults to infinity

Final value for rad bins, in units of HLR_pix.

use_HLR_units : boolean

If True, use the bins in HLR units. If False, use pixels.

extensive : boolean

Use surface density when calculating the transformation from zone to spatial coordinates.

dezonification : boolean

Use image at 5650AA to weight pixels in the zones.

Returns :

YXToRad : array, shape (y, x, zone)

The transform tensor.

bin_R : array of float

Radial bin outer edges.

bin_area : array of float

Radial bin area, in pixels.

See also

getYXToRadialBinsTensor, getZoneToYXTensor

Examples

>>> c = Q3DataCube(file)
>>> zoneToRad, bins, unused = c.getZoneToRadialBinsTensor(d_r=0.2)

c.popx is luminosity fraction per zone. c.popx.shape is (ageBase, metBase, zone). Get the flux per unit area (?) for each zone.

>>> Lobn = c.popx / 100.0 * c.Lobs_norm

Total luminosity in each zone.

>>> Lobn_tot = Lobn.sum(axis=1).sum(axis=0)
>>> radProf = np.tensordot(Lobn_tot, zoneToRad, (1,1))
>>> plot(bins, radProf)

findHLR_pix_CID()

TODO: Deprecated findHLR_pix_CID(). Find the half light radius using the image at 5650 Angstrom. Using radial bins of 1 pixel, calculate the cumulative sum of luminosity. The HLR is the radius where the cum. sum reaches 50% of its peak value.

Returns :

HLR : float

The half light radius, in pixels.

See also

setGeometry

Notes

This value should be close to $dfrac{HLR_{circular}}{sqrt{b/a}}$.