Acquiring Data for the Tutorial¶
We need to download the data we’ll need for this tutorial. From within the dawn_giant directory, make a file called
download_data.py, enter the following code into the file, then run python download_data.py (note that ~4 GB
will be downloaded to your machine, so ensure that you (a) have enough space and (b) have enough time to complete the
download). This script just uses some basic python capabilities to do the downloads so we won’t go into detail
about what it is doing here.
import requests
from bs4 import BeautifulSoup
import re
from pathlib import Path
import time
def download_images():
web_locations = ["https://sbnarchive.psi.edu/pds3/dawn/fc/DWNXFC2_1A/DATA/FITS/20071203_PERFORMANCE/",
"https://sbnarchive.psi.edu/pds3/dawn/fc/DWNXFC2_1A/DATA/FITS/20100720_CHKOUT_VIRGEOMCAL/",
"https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVFC2_1A/DATA/FITS/2011123_APPROACH/2011123_OPNAV_001/",
"https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVFC2_1A/DATA/FITS/2011123_APPROACH/2011165_OPNAV_007/",
"https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVFC2_1A/DATA/FITS/2011123_APPROACH/2011198_OPNAV_017/",
"https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVFC2_1A/DATA/FITS/2011123_APPROACH/2011218_OPNAV_023/"]
local_locations = ["cal1",
"cal2",
"opnav/2011123_OPNAV_001/",
"opnav/2011165_OPNAV_007/",
"opnav/2011198_OPNAV_017/",
"opnav/2011218_OPNAV_023/"]
filt = re.compile("FC2.*F1.*")
for addr, dest in zip(web_locations, local_locations):
page = requests.get(addr)
soup = BeautifulSoup(page.text)
local_dir = Path(dest)
local_dir.mkdir(exist_ok=True, parents=True)
for p in soup.find_all('a', href=filt):
if filt.match(p['href']):
start = time.time()
local_file = local_dir / p['href']
r = requests.get(addr+p['href'], stream=True)
if r.status_code == 200:
with local_file.open('wb') as f:
for chunk in r:
f.write(chunk)
print('{} complete in {:.3f}'.format(p['href'], time.time()-start), flush=True)
def download_spice():
base_url = "http://naif.jpl.nasa.gov/pub/naif/DAWN/kernels/"
files = ['lsk/naif0012.tls',
'pck/pck00008.tpc',
'spk/de432.bsp',
'spk/sb_vesta_ssd_120716.bsp',
'pck/dawn_vesta_v02.tpc',
'fk/dawn_v14.tf',
'fk/dawn_vesta_v00.tf',
'sclk/DAWN_203_SCLKSCET.00090.tsc',
'spk/dawn_rec_070927-070930_081218_v1.bsp',
'spk/dawn_rec_070930-071201_081218_v1.bsp',
'spk/dawn_rec_071201-080205_081218_v1.bsp',
'spk/dawn_rec_100208-100316_100323_v1.bsp',
'spk/dawn_rec_100316-100413_100422_v1.bsp',
'spk/dawn_rec_100413-100622_100830_v1.bsp',
'spk/dawn_rec_100622-100824_100830_v1.bsp',
'spk/dawn_rec_100824-101130_101202_v1.bsp',
'spk/dawn_rec_101130-110201_110201_v1.bsp',
'spk/dawn_rec_101130-110419_pred_110419-110502_110420_v1.bsp',
'spk/dawn_rec_101130-110606_pred_110606-110628_110609_v1.bsp',
'spk/dawn_rec_110201-110328_110328_v1.bsp',
'spk/dawn_rec_110328-110419_110419_v1.bsp',
'spk/dawn_rec_110328-110419_110420_v1.bsp',
'spk/dawn_rec_110416-110802_110913_v1.bsp',
'spk/dawn_rec_110802-110831_110922_v1.bsp',
'spk/dawn_rec_110831-110928_111221_v1.bsp',
'spk/dawn_rec_110928-111102_111221_v1.bsp',
'spk/dawn_rec_110928-111102_120615_v1.bsp',
'spk/dawn_rec_111102-111210_120618_v1.bsp',
'spk/dawn_rec_111211-120501_120620_v1.bsp',
'ck/dawn_fc_v3.bc',
'ck/dawn_sc_071203_071209.bc',
'ck/dawn_sc_071210_071216.bc',
'ck/dawn_sc_071217_071223.bc',
'ck/dawn_sc_071224_071230.bc',
'ck/dawn_sc_071231_080106.bc',
'ck/dawn_sc_100705_100711.bc',
'ck/dawn_sc_100712_100718.bc',
'ck/dawn_sc_100719_100725.bc',
'ck/dawn_sc_100726_100801.bc',
'ck/dawn_sc_110502_110508.bc',
'ck/dawn_sc_110509_110515.bc',
'ck/dawn_sc_110516_110522.bc',
'ck/dawn_sc_110523_110529.bc',
'ck/dawn_sc_110530_110605.bc',
'ck/dawn_sc_110606_110612.bc',
'ck/dawn_sc_110613_110619.bc',
'ck/dawn_sc_110620_110626.bc',
'ck/dawn_sc_110627_110703.bc',
'ck/dawn_sc_110704_110710.bc',
'ck/dawn_sc_110711_110717.bc',
'ck/dawn_sc_110718_110724.bc',
'ck/dawn_sc_110725_110731.bc',
'ck/dawn_sc_110801_110807.bc',
'ck/dawn_sc_110808_110814.bc',
'ck/dawn_sc_110815_110821.bc',
'ck/dawn_sc_110822_110828.bc',
'ck/dawn_sc_110829_110904.bc',
'dsk/old_versions/vesta_gaskell_512_110825.bds'
]
for file in files:
start = time.time()
local = Path('kernels').joinpath(file)
local.parent.mkdir(exist_ok=True, parents=True)
url = base_url + file
r = requests.get(url, stream=True)
if r.status_code == 200:
with local.open('wb') as f:
for chunk in r:
f.write(chunk)
print('{} done in {:.3f}'.format(file, time.time()-start), flush=True)
if __name__ == '__main__':
download_spice()
download_images()
Directories cal1 and cal2 provide images that we will use to perform geometric camera calibration of the
camera we are going to use. Directory opnav contains the OpNav images we will use GIANT to process once we have
completed our calibration. All of the image directories also include label (.LBL) files which provide meta data
about the images. Directory kernels contains NAIF Spice kernels that provide our a priori position and pointing
information to feed GIANT.
Once the download is complete, enter the following commands in the terminal to prepare the shape model and the rest of our directory.
mkdir shape_model
cd shape_model
ingest_shape ../kernels/dsk/old_versions/vesta_gaskell_512_110825.bds "Vesta SPC" -p ../kernels/pck/dawn_vesta_02.tpc -m 18 -s32 # convert the shape into the GIANT format
cd ..
mkdir scripts
Directory shape_model contains the shape model file, and function ingest_shape builds the
GIANT version of the shape model for us and stores it in this directory. Finally, directory scripts is where we will
write our scripts to use GIANT to actually process images.
Finally, we can optionally download the Gaia catalog data for local use. This is not necessary, but it will speed up
the star identification process. Simply run build_catalog -m 12 from the terminal to download the catalog data for stars
brighter than magnitude 12. This can take a while depending on your internet connection and will take up about
400 MB of space on your machine. If you want, you can increase the magnitude limit to get more stars (in case you
have other missions you plan to use GIANT for), but this will increase the download time and the space required.
Now we are ready to begin the rest of the tutorial!
