Spectrum, Image and Cube format¶
Attributes¶
Spectrum, Image and Cube objects all have the following items, where O denotes the name of the object:
Component | Description |
---|---|
O.filename | A FITS filename if the data were loaded from a FITS file |
O.primary_header | A FITS primary header instance |
O.wcs | World coordinate spatial information (WCS object) |
O.wave | World coordinate spectral information (WaveCoord object) |
O.shape | An array of the dimensions of the cube |
O.data | A numpy masked array of pixel values |
O.data_header | A FITS data header instance |
O.unit | The physical units of the data values |
O.dtype | The data-type of the data array (int, float) |
O.var | An optional numpy masked array of pixel variances |
O.mask | An array of the masked state of each pixel |
O.ndim | The number of dimensions in the data, variance and mask arrays |
Masked arrays are arrays that can have missing or invalid entries. The
numpy.ma module
provides a nearly work-alike replacement for numpy that supports data arrays
with masks. See the DataArray
documentation for more
details.
When an object is constructed from a MUSE FITS file, O.data
will contain the DATA extension,
O.var
will contain the STAT extension and O.mask
will contain the DQ extension if it exists.
A DQ extension contains the pixel data quality. By default all bad pixels are masked.
But it is possible for the user to create his mask by using the Python interface for euro3D convention.
Indexing¶
The format of each numpy array follows the indexing used by Python to handle 2D and 3D arrays. For an MPDAF cube C, the pixel in the bottom-lower-left corner is C[0,0,0] and pixel C[k,p,q] refers to the horizontal position q, the vertical position p, and the spectral position k, as follows:
In total, this cube C contains nq pixels in the horizontal direction, np pixels in the vertical direction and nk channels in the spectral direction. In the cube, each numpy masked array has 3 dimensions, array[k,p,q], where k is the spectral axis, and p and q are the spatial axes:
In [1]: from mpdaf.obj import Cube
# data array is read from the file (extension number 0)
In [2]: cube = Cube(filename='sdetect/minicube.fits')
# The 3 dimensions of the cube:
In [3]: cube.shape
Out[3]: (3681, 40, 40)
In [4]: cube.data.shape
Out[4]: (3681, 40, 40)
In [5]: cube.var.shape
Out[5]: (3681, 40, 40)
In [6]: cube.mask.shape
Out[6]: (3681, 40, 40)
Cube[k,p,q]
returns the value of Cube.data[k,p,q]:
In [7]: cube[3659, 8, 28]
Out[7]: 493.97262573242188
Similarly Cube[k,p,q] = value
sets the
value of Cube.data[k,p,q], and Cube[k1:k2,p1:p2,q1:q2] = array
sets the corresponding subset of
Cube.data. Finally Cube[k1:k2,p1:p2,q1:q2]
returns a sub-cube, as demonstrated in the following example:
In [8]: cube.info()
[INFO] 3681 x 40 x 40 Cube (sdetect/minicube.fits)
[INFO] .data(3681 x 40 x 40) (1e-20 erg / (Angstrom cm2 s)), .var(3681 x 40 x 40)
[INFO] center:(10:27:56.3962,04:13:25.3588) size:(8.000",8.000") step:(0.200",0.200") rot:-0.0 deg frame:FK5
[INFO] wavelength: min:4749.89 max:9349.89 step:1.25 Angstrom
In [9]: cube[3000:4000,10:20,25:40].info()
[WARNING] The wavelength coordinates and data have different dimensions. Modifying the shape of the WaveCoord object
[INFO] 681 x 10 x 15 Cube (sdetect/minicube.fits)
[INFO] .data(681 x 10 x 15) (1e-20 erg / (Angstrom cm2 s)), .var(681 x 10 x 15)
[INFO] center:(10:27:55.3962,04:13:25.1893) size:(2.000",3.000") step:(0.200",0.200") rot:-0.0 deg frame:FK5
[INFO] wavelength: min:8499.89 max:9349.89 step:1.25 Angstrom
Likewise, Cube[k,:,:]
returns an Image, as
demonstrated below:
In [10]: ima1 = cube[1000, :, :]
In [11]: plt.figure()
Out[11]: <matplotlib.figure.Figure at 0x7f42ddfb4d90>
In [12]: ima1.plot(colorbar='v', title = '$\lambda$ = %.1f (%s)' %(cube.wave.coord(1000), cube.wave.unit))
Out[12]: <matplotlib.image.AxesImage at 0x7f42de09aa90>
In [13]: ima2 = cube[3000, :, :]
In [14]: plt.figure()
Out[14]: <matplotlib.figure.Figure at 0x7f42e4504c10>
In [15]: ima2.plot(colorbar='v', title = '$\lambda$ = %.1f (%s)' %(cube.wave.coord(3000), cube.wave.unit))
Out[15]: <matplotlib.image.AxesImage at 0x7f42e40dfa90>
In [16]: plt.figure()
Out[16]: <matplotlib.figure.Figure at 0x7f42e5680690>
In [17]: ima2[5:25, 15:35].plot(colorbar='v',title = 'Zoom $\lambda$ = %.1f (%s)' %(cube.wave.coord(3000), cube.wave.unit))
Out[17]: <matplotlib.image.AxesImage at 0x7f42e420d790>
In the Image objects extracted from the cube, Image[p1:p2,q1:q2]
returns a sub-image, Image[p,q]
returns the value of pixel (p,q), Image[p,q] =
value
sets value in Image.data[p,q], and
Image[p1:p2,q1:q2] = array
sets the
corresponding part of Image.data.
Finally, Cube[:,p,q]
returns a Spectrum:
In [18]: spe = cube[:, 8, 28]
In [19]: import astropy.units as u
In [20]: from mpdaf.obj import deg2sexa
In [21]: coord_sky = cube.wcs.pix2sky([8, 28], unit=u.deg)
In [22]: dec, ra = deg2sexa(coord_sky)[0]
In [23]: plt.figure()
Out[23]: <matplotlib.figure.Figure at 0x7f42ddecb910>
In [24]: spe.plot(title = 'Spectrum ra=%s dec=%s' %(ra, dec))
In [25]: plt.figure()
Out[25]: <matplotlib.figure.Figure at 0x7f42e44ce9d0>
In [26]: spe[1640:2440].plot(title = 'Zoom Spectrum ra=%s dec=%s' %(ra, dec))
Getters and setters¶
Cube.get_step
, Image.get_step
and Spectrum.get_step
return the world-coordinate separations between pixels along each axis of a cube, image, or spectrum, respectively:
In [27]: cube.get_step(unit_wave=u.nm, unit_wcs=u.deg)
Out[27]: array([ 1.25000000e-01, 5.55555556e-05, 5.55555556e-05])
In [28]: ima1.get_step(unit=u.deg)
Out[28]: array([ 5.55555556e-05, 5.55555556e-05])
In [29]: spe.get_step(unit=u.angstrom)
Out[29]: 1.25
Cube.get_range
returns the range of wavelengths,
declinations and right ascensions in a cube. Similarly, Image.get_range
returns the range of declinations and right
ascensions in an image, and Spectrum.get_range
returns the range of wavelengths in a spectrum, as demonstrated below:
In [30]: cube.get_range(unit_wave=u.nm, unit_wcs=u.deg)
Out[30]:
array([ 474.9890625 , 10.46458228, 63.35455983, 934.9890625 ,
10.46674895, 63.35676316])
In [31]: ima1.get_range(unit=u.deg)
Out[31]: array([ 10.46458228, 63.35455983, 10.46674895, 63.35676316])
In [32]: spe.get_range(unit=u.angstrom)
Out[32]: array([ 4749.890625, 9349.890625])
The get_start
and get_end
methods of cube, image and spectrum objects, return
the world-coordinate values of the first and last pixels of each axis:
In [33]: print(cube.get_start(unit_wave=u.nm, unit_wcs=u.deg), cube.get_end(unit_wave=u.nm, unit_wcs=u.deg))
[ 474.9890625 10.46458228 63.35676315] [ 934.9890625 10.46674895 63.35455983]
In [34]: print(ima1.get_start(unit=u.deg), ima2.get_end(unit=u.deg))
[ 10.46458228 63.35676315] [ 10.46674895 63.35455983]
In [35]: print(spe.get_start(unit=u.angstrom), spe.get_end(unit=u.angstrom))
4749.890625 9349.890625
Note that when the declination axis is rotated away from the vertical axis of
the image, the coordinates returned by get_start
and get_end
are not the minimum and maximum
coordinate values within the image, so they differ from the values returned by
get_range
.
Cube.get_rot
and Image.get_rot
return the rotation angle of the declination axis to
the vertical axis of the images within these objects:
In [36]: cube.get_rot(unit=u.deg)
Out[36]: -0.0
In [37]: ima1.get_rot(unit=u.rad)
Out[37]: -0.0
Updated flux and variance values can be assigned directly to the O.data
and
O.var
attributes, respectively. Similarly, elements of the data can be
masked or unmasked by assigning True or False values to the corresponding
elements of the O.mask
attribute. Changes to the spatial world coordinates
must be performed using the set_wcs
method:
In [38]: ima2.data[0:10,0:10] = 0
In [39]: ima2.mask[0:10,0:10] = True
In [40]: plt.figure()
Out[40]: <matplotlib.figure.Figure at 0x7f42dfc67190>
In [41]: ima2.plot()
Out[41]: <matplotlib.image.AxesImage at 0x7f42e454c090>