TESS Background data¶

This notebook shows how to use BackgroundCube to get TESS Full Frame Images (FFI) scatter light component for every camera/ccd in the instrument.

This data could be used to correct the scatter light contribution in TESS FFI cutouts.

In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

In [2]:

# basic imports
import os
import numpy as np
from tess_backml import BackgroundCube
import matplotlib.pyplot as plt

# increase animation frame limits
import matplotlib
matplotlib.rcParams["animation.embed_limit"] = 2**128

# basic imports import os import numpy as np from tess_backml import BackgroundCube import matplotlib.pyplot as plt # increase animation frame limits import matplotlib matplotlib.rcParams["animation.embed_limit"] = 2**128

In [3]:

# define sector/camera/ccd
sector = 2
camera = 1
ccd = 1
binsize = 8

# define sector/camera/ccd sector = 2 camera = 1 ccd = 1 binsize = 8

Initialize¶

We initialize the object with a specific Sectopr/Camera/CCD, an image bin size, and a method for downsizing (default if 'binning')

In [4]:

bkg_data = BackgroundCube(
    sector=sector, camera=camera, ccd=ccd, img_bin=binsize, downsize="binning"
)
print(bkg_data)

bkg_data = BackgroundCube( sector=sector, camera=camera, ccd=ccd, img_bin=binsize, downsize="binning" ) print(bkg_data)

TESS FFI Background object (Sector, Camera, CCD, N times): 2, 1, 1, 1235

Get Flux Data¶

Compute the binned scatter light cube which does:

• Finds the darkest frame in the sector to build a star mask and a saturated pixel mask
• Adds a strap mask
• Pulls the FFI flux data from the cloud
• Downsize the image by doing median 8x8 (effective) binning, masking out stars, straps, and saturated pixels
  • If rolling=True downsizing is done with iterative rolling windows to get to the desired output shape, this will increase ~25% more the computing time, but will give more robust estimations around bright stars, saturated pixes, and straps.
• The resulting scatter light cube has shape [nt, 256, 256]

Note: this is the most time consuming step, most of the computing time is spend in I/O (~50-60% of each iteration). This can be speed up if we run this notebook in TIKE and take advantage of AWS, if that's the case, a typical Sector/Camera/CCD should take ~30-40 minutes, depending of load. In a local machine, it can take up to 1h depending on internet connection.

In [5]:

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True, rolling=True, errors=True)

bkg_data.get_scatter_light_cube(frames=None, mask_straps=True, plot=True, rolling=True, errors=True)

INFO:tess_backml:Computing sector darkest frames...
INFO:tess_backml:Computing star mask...

INFO:tess_backml:Getting FFI flux cube...
INFO:tess_backml:Computing average static scene from darkes frames...

Iterating frames: 100%|██████████| 1245/1245 [40:34<00:00,  1.96s/it]

89
INFO:tess_backml:Filling nans with interpolation...

100%|██████████| 1235/1235 [00:15<00:00, 80.22it/s]

We can animate the scatter light cube for visualization

Time binning¶

We can do time binning to reduce size of the cubes and save memory/storage. The function bkg_data.bin_time_axis() takes the parameter bin_size as the size of the bin in hours. This creates new attributes (e.g. bkg_data.time_bin and bkg_data.scatter_cube_bin)

In [20]:

bkg_data.bin_time_axis(bin_size=2.0)
bkg_data.time_bin.shape, bkg_data.scatter_cube_bin.shape

bkg_data.bin_time_axis(bin_size=2.0) bkg_data.time_bin.shape, bkg_data.scatter_cube_bin.shape

Out[20]:

((312,), (312, 256, 256))

In [23]:

ani = bkg_data.animate_data(data="sl_bin", save=False, step=2)
ani

ani = bkg_data.animate_data(data="sl_bin", save=False, step=2) ani

INFO:matplotlib.animation:Animation.save using <class 'matplotlib.animation.HTMLWriter'>

Out[23]:

Once Loop Reflect

Save Data¶

Save only the scatter light cube in its binned version to a FITS file.

In [24]:

fname = f"./data/ffi_sl_cube_sector{sector:03}_{camera}-{ccd}.fits"
print(fname)
_ = bkg_data.save_to_fits(out_file=fname, binned=True)

fname = f"./data/ffi_sl_cube_sector{sector:03}_{camera}-{ccd}.fits" print(fname) _ = bkg_data.save_to_fits(out_file=fname, binned=True)

./data/ffi_sl_cube_sector002_1-1.fits

In [25]:

from astropy.io import fits

from astropy.io import fits

In [26]:

hdul = fits.open(fname)
hdul.info()

hdul = fits.open(fname) hdul.info()

Filename: ./data/ffi_sl_cube_sector002_1-1.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU      62   ()      
  1  SCATTER LIGHT CUBE    1 ImageHDU        10   (2, 312, 256, 256)   float32   
  2  PIXEL COUNTS    1 ImageHDU         8   (256, 256)   int64   
  3  TIME          1 BinTableHDU     13   312R x 1C   [D]   

In [ ]: