1. Cheat Sheet

Document ID

PIPE-USER-105_AstrodataCheatSheet

A data package is available for download if you wish to run the examples included in this cheat sheet. Download it at:

To unpack:

$ cd <somewhere_convenient>
$ tar xvf ad_usermanual_datapkg-v1.tar
$ bunzip2 ad_usermanual/playdata/*.bz2

Then go to the ad_usermanual/playground directory to run the examples.

1.1. Imports

Import astrodata and gemini_instruments:

>>> import astrodata
>>> import gemini_instruments

1.2. Basic read and write operations

Open a file:

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')

Get path and filename:

>>> ad.path
'../playdata/N20170609S0154.fits'
>>> ad.filename
'N20170609S0154.fits'

Write to a new file:

>>> ad.write(filename='new154.fits')
>>> ad.filename
N20170609S0154.fits

Overwrite the file:

>>> adnew = astrodata.open('new154.fits')
>>> adnew.filename
new154.fits
>>> adnew.write(overwrite=True)

1.3. Object structure

1.3.1. Description

The AstroData object is assigned by “tags” that describe the type of data it contains. The tags are drawn from rules defined in gemini_instruments and are based on header information.

When mapping a FITS file, each science pixel extension is loaded as a NDAstroData object. The list is zero-indexed. So FITS extension 1 becomes element 0 of the AstroData object. If a VAR extension is present, it is loaded to the variance attribute of the NDAstroData. If a DQ extension is present, it is loaded to the .mask attribute of the NDAstroData. SCI, VAR and DQ are associated through the EXTVER keyword value.

In the file below, each AstroData “extension” contains the pixel data, then an error plane (.variance) and a bad pixel mask plane (.mask). Table can be attached to an extension, like OBJCAT, or to the AstroData object globally, like REFCAT. (In this case, OBJCAT is a catalogue of the sources detected in the image, REFCAT is a reference catalog for the area covered by the whole file.) If other 2D data needs to be associated with an extension this can also be done, like here with OBJMASK, a 2D mask matching the sources in the image.

>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
>>> ad.info()
Filename: ../playdata/N20170609S0154_varAdded.fits
Tags: ACQUISITION GEMINI GMOS IMAGE NORTH OVERSCAN_SUBTRACTED OVERSCAN_TRIMMED
    PREPARED SIDEREAL
Pixels Extensions
Index  Content                  Type              Dimensions     Format
[ 0]   science                  NDAstroData       (2112, 256)    float32
          .variance             ndarray           (2112, 256)    float32
          .mask                 ndarray           (2112, 256)    int16
          .OBJCAT               Table             (6, 43)        n/a
          .OBJMASK              ndarray           (2112, 256)    uint8
[ 1]   science                  NDAstroData       (2112, 256)    float32
          .variance             ndarray           (2112, 256)    float32
          .mask                 ndarray           (2112, 256)    int16
          .OBJCAT               Table             (8, 43)        n/a
          .OBJMASK              ndarray           (2112, 256)    uint8
[ 2]   science                  NDAstroData       (2112, 256)    float32
          .variance             ndarray           (2112, 256)    float32
          .mask                 ndarray           (2112, 256)    int16
          .OBJCAT               Table             (7, 43)        n/a
          .OBJMASK              ndarray           (2112, 256)    uint8
[ 3]   science                  NDAstroData       (2112, 256)    float32
          .variance             ndarray           (2112, 256)    float32
          .mask                 ndarray           (2112, 256)    int16
          .OBJCAT               Table             (5, 43)        n/a
          .OBJMASK              ndarray           (2112, 256)    uint8
Other Extensions
               Type        Dimensions
.REFCAT        Table       (245, 16)

1.3.2. Modifying the structure

Let’s first get our play data loaded. You are encouraged to do a info() before and after each structure-modification step, to see how things change.

>>> from copy import deepcopy
>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
>>> adcopy = deepcopy(ad)
>>> advar = astrodata.open('../playdata/N20170609S0154_varAdded.fits')

Append an extension:

>>> adcopy.append(advar[3])
>>> adcopy.append(advar[3].data)

Delete an extension:

>>> del adcopy[5]

Delete and add variance and mask planes:

>>> var = adcopy[4].variance
>>> adcopy[4].variance = None
>>> adcopy[4].variance = var

Attach a table to an extension:

>>> adcopy[3].BOB = advar[0].OBJCAT

Attach a table to the AstroData object:

>>> adcopy.BILL = advar.REFCAT

Delete a table:

>>> del adcopy[3].BOB
>>> del adcopy.BILL

1.4. Astrodata tags

>>> ad = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> ad.tags
{'GMOS', 'OVERSCAN_SUBTRACTED', 'SIDEREAL', 'NORTH', 'OVERSCAN_TRIMMED',
'PREPARED', 'IMAGE', 'GEMINI'}

>>> type(ad.tags)
<class 'set'>

>>> {'IMAGE', 'PREPARED'}.issubset(ad.tags)
True
>>> 'PREPARED' in ad.tags
True

1.5. Headers

The use of descriptors is favored over direct header access when retrieving values already represented by descriptors, and when writing instrument agnostic routines.

1.5.1. Descriptors

>>> ad = astrodata.open('../playdata/N20170609S0154.fits')
>>> ad.filter_name()
'open1-6&g_G0301'
>>> ad.filter_name(pretty=True)
'g'
>>> ad.gain()   # uses a look-up table to get the correct values
[2.03, 1.97, 1.96, 2.01]
>>> ad.hdr['GAIN']
[1.0, 1.0, 1.0, 1.0]    # the wrong values contained in the raw data.
>>> ad[0].gain()
2.03
>>> ad.gain()[0]
2.03

>>> ad.descriptors
('airmass', 'amp_read_area', 'ao_seeing', ...
 ...)

1.5.2. Direct access to header keywords

>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')

1.5.2.1. Primary Header Unit

To see a print out of the full PHU:

>>> ad.phu

Get value from PHU:

>>> ad.phu['EXPTIME']
1.0

>>> default = 5.
>>> ad.phu.get('BOGUSKEY', default)
5.0

Set PHU keyword, with and without comment:

>>> ad.phu['NEWKEY'] = 50.
>>> ad.phu['ANOTHER'] = (30., 'Some comment')

Delete PHU keyword:

>>> del ad.phu['NEWKEY']

1.5.2.2. Pixel extension header

To see a print out of the full header for an extension or all the extensions:

>>> ad[0].hdr
>>> list(ad.hdr)

Get value from an extension header:

>>> ad[0].hdr['OVERSCAN']
469.7444308769482
>>> ad[0].hdr.get('OVERSCAN', default)

Get keyword value for all extensions:

>>> ad.hdr['OVERSCAN']
[469.7444308769482, 469.656175780001, 464.9815279808291, 467.5701178951787]
>>> ad.hdr.get('BOGUSKEY', 5.)
[5.0, 5.0, 5.0, 5.0]

Set extension header keyword, with and without comment:

>>> ad[0].hdr['NEWKEY'] = 50.
>>> ad[0].hdr['ANOTHER'] = (30., 'Some comment')

Delete an extension keyword:

>>> del ad[0].hdr['NEWKEY']

1.5.2.3. Table header

See the Tables section.

1.6. Pixel data

1.6.1. Arithmetics

Arithmetics with variance and mask propagation is offered for +, -, *, /, and **.

>>> ad_hcont = astrodata.open('../playdata/N20170521S0925_forStack.fits')
>>> ad_halpha = astrodata.open('../playdata/N20170521S0926_forStack.fits')

>>> adsub = ad_halpha - ad_hcont

>>> ad_halpha[0].data.mean()
646.11896
>>> ad_hcont[0].data.mean()
581.81342
>>> adsub[0].data.mean()
64.305862

>>> ad_halpha[0].variance.mean()
669.80664
>>> ad_hcont[0].variance.mean()
598.46667
>>> adsub[0].variance.mean()
1268.274


# In place multiplication
>>> ad_mult = deepcopy(ad)
>>> ad_mult.multiply(ad)
>>> ad_mult.multiply(5.)


# Using descriptors to operate in-place on extensions.
>>> from copy import deepcopy
>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')
>>> ad_gain = deepcopy(ad)
>>> for (ext, gain) in zip(ad_gain, ad_gain.gain()):
...     ext.multiply(gain)
>>> ad_gain[0].data.mean()
366.39545
>>> ad[0].data.mean()
180.4904
>>> ad[0].gain()
2.03

1.6.2. Other pixel data operations

>>> import numpy as np
>>> ad_halpha[0].mask[300:350,300:350] = 1
>>> np.mean(ad_halpha[0].data[ad_halpha[0].mask==0])
657.1994
>>> np.mean(ad_halpha[0].data)
646.11896

1.7. Tables

Tables are stored as astropy.table.Table class. FITS tables are represented in astrodata as Table and FITS headers are stored in the NDAstroData meta attribute. Most table access should be done through the Table interface. The best reference is the Astropy documentation itself. Below are just a few examples.

>>> ad = astrodata.open('../playdata/N20170609S0154_varAdded.fits')

Get column names:

>>> ad.REFCAT.colnames

Get column content:

>>> ad.REFCAT['zmag']
>>> ad.REFCAT['zmag', 'zmag_err']

Get content of row:

>>> ad.REFCAT[4]     # 5th row
>>> ad.REFCAT[4:6]   # 5th and 6th rows

Get content from specific row and column:

>>> ad.REFCAT['zmag'][4]

Add a column:

>>> new_column = [0] * len(ad.REFCAT)
>>> ad.REFCAT['new_column'] = new_column

Add a row:

>>> new_row = [0] * len(ad.REFCAT.colnames)
>>> ad.REFCAT.add_row(new_row)

Selecting value from criterion:

>>> ad.REFCAT['zmag'][ad.REFCAT['Cat_Id'] == '1237662500002005475']
>>> ad.REFCAT['zmag'][ad.REFCAT['zmag'] < 18.]

Rejecting numpy.nan before doing something with the values:

>>> t = ad.REFCAT   # to save typing.
>>> t['zmag'][np.where(np.isnan(t['zmag']), 99, t['zmag']) < 18.]

>>> t['zmag'].mean()
nan
>>> t['zmag'][np.where(~np.isnan(t['zmag']))].mean()
20.377306

If for some reason you need to access the FITS table headers, here is how to do it.

To see the FITS headers:

>>> ad.REFCAT.meta
>>> ad[0].OBJCAT.meta

To retrieve a specific FITS table header:

>>> ad.REFCAT.meta['header']['TTYPE3']
'RAJ2000'
>>> ad[0].OBJCAT.meta['header']['TTYPE3']
'Y_IMAGE'

To retrieve all the keyword names matching a selection:

>>> keynames = [key for key in ad.REFCAT.meta['header'] if key.startswith('TTYPE')]

1.8. Create new AstroData object

Basic header and data array set to zeros:

>>> from astropy.io import fits

>>> phu = fits.PrimaryHDU()
>>> pixel_data = np.zeros((100,100))

>>> hdu = fits.ImageHDU()
>>> hdu.data = pixel_data
>>> ad = astrodata.create(phu)
>>> ad.append(hdu, name='SCI')

or another way:

>>> hdu = fits.ImageHDU(data=pixel_data, name='SCI')
>>> ad = astrodata.create(phu, [hdu])

A Table as an AstroData object:

>>> from astropy.table import Table

>>> my_astropy_table = Table(list(np.random.rand(2,100)), names=['col1', 'col2'])
>>> phu = fits.PrimaryHDU()

>>> ad = astrodata.create(phu)
>>> ad.BOB = my_astropy_table

From a BinTableHDU:

>>> phu = fits.PrimaryHDU()
>>> ad = astrodata.create(phu)
>>> ad.BOB = my_fits_table

WARNING: This last line will not run like the others as we have not defined my_fits_table. This is nonetheless how it is done if you had a FITS table.