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spacebook: original code

This commit is contained in:
Erin Moon 2020-06-01 00:04:44 -05:00
parent cb7a567f44
commit a63a569a54
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63
aaaa/spacebook/README.md
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@ -19,63 +19,44 @@ We're given a boresight vector $\vec{v}\in\mathbb{R}^3$ for each star, and a mag
![Distribution of star magnitudes for catalog and unknown sets](spacebook-magnitude-distribution.png) ![Distribution of star magnitudes for catalog and unknown sets](spacebook-magnitude-distribution.png)
*Note that code from here on in this report is reconstructed, since I accidentally overwrote the original copy. I took the chance to use scipy for Kabsch this time instead of the original package I used, [`rmsd`](https://github.com/charnley/rmsd).*
My immediate suspicion was that we could take the outliers (simply by thresholding for stars with $m>500$) and, for each, directly match it to the catalog by finding the star with closest magnitude in the catalog: My immediate suspicion was that we could take the outliers (simply by thresholding for stars with $m>500$) and, for each, directly match it to the catalog by finding the star with closest magnitude in the catalog:
```{.python} ```{.python}
def match_brightest_stars(unknown, catalog): refstars_magsorted = sorted(refstars, key=lambda x:x['m'])[::-1]
brightest = []
catalog_matches = []
for star in unknown:
if star['m'] < 500:
continue
# find closest magnitude in catalog catalog_magnitudes = np.array([x['m'] for x in catalog])
match = np.argmin(np.abs(np.array([s['m'] for s in catalog]) - star['m'])) matches = []
print(f'[+] matched {star} to {catalog[match]}')
brightest.append(star)
catalog_matches.append(catalog[match])
return brightest, catalog_matches for idx, star in enumerate(refstars_magsorted):
if star['m'] <= 500:
break
match = np.argmin(np.abs(catalog_magnitudes-star['m']))
matches.append(match)
``` ```
We can then align those brightest stars to their closest magnitude matches using the Kabsch algorithm: We can then align those brightest stars to their closest magnitude matches using the Kabsch algorithm:
```{.python} ```{.python}
def orient(ref, catalog): P = np.vstack([x['v'] for x in [catalog[i] for i in matches]])
P = [s['v'] for s in catalog] Q = np.vstack([x['v'] for x in refstars_magsorted[:4]])
Q = [s['v'] for s in ref] print("rmsd: {}".format(calculate_rmsd.kabsch_rmsd(P,Q)))
rotation_mtx = calculate_rmsd.kabsch(P, Q)
rot, rmsd = Rotation.align_vectors(P, Q) rotation = Rotation.from_matrix(rotation_mtx)
print(f'[+] aligned; rmsd = {rmsd}')
return rot
``` ```
Then for each unknown star with vector $\vec{v}$, we can find its catalog index by evaluating Knowing the rotation, we can rotate all stars into the catalog reference frame and then evaluate
$$\arg\min_{\vec{u}\in\text{catalog}} \norm{\vec{u}-\vec{v}}_2$$ $$\arg\min_{\vec{u}\in\text{catalog}} \norm{\vec{u}-\vec{v}}_2$$
for every challenge: for each unknown star with vector $\vec{v}$ to find the closest star in the dictionary:
```{.python} ```{.python}
for _ in range(5): rotated = [dict(v=rotation.apply(x['v']), m=x['m']) for x in refstars_magsorted]
unknown_stars = read_starfile(r.recvuntil('\n\n').decode()) found_idxes = []
for star in rotated:
# find pairs found_idxes.append(np.argmin([np.linalg.norm(star['v']-catalogstar['v']) for catalogstar in catalog]))
unknown_ref, catalog_ref = match_brightest_stars(unknown_stars, catalog)
# get attitude
attitude = orient(unknown_ref, catalog_ref)
# rotate each star to catalog-referenced coordinates and match by L2 norm
index_guesses = []
for star in unknown_stars:
v = attitude.apply(star['v'])
index_guesses.append(np.argmin([np.linalg.norm(catalog_star['v'] - v)
for catalog_star in catalog]))
r.send(','.join(map(str, index_guesses))+'\n')
r.recvuntil('Left...\n')
print(r.clean())
``` ```
It's then trivial to send `found_idxs` back to the challenge as a comma-separated string of indices for each problem sent to us; the server happily sends us the flag after we answer its questions.
### Full code ### Full code
```{.python include=spacebook.py} ```{.python include=spacebook-original.py}
``` ```
## Resources ## Resources

54
aaaa/spacebook/spacebook-original.py Normal file
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@ -0,0 +1,54 @@
import numpy as np
from pwnlib import tubes
import time
import matplotlib.pyplot as plt
from rmsd import calculate_rmsd
from scipy.spatial.transform import Rotation
%matplotlib inline
def read_starfile(data):
stars = []
for line in data.strip().split('\n'):
[x,y,z,m] = [float(s.strip()) for s in line.split(',')]
stars.append({'v': np.array([x,y,z]), 'm':m})
return stars
with open('./spacebook-golf56788echo/test.txt') as f:
catalog = read_starfile(f.read())
TICKET = 'THE_TICKET'
r = tubes.remote.remote('spacebook.satellitesabove.me', 5015)
r.send(TICKET+'\n')
time.sleep(0.5)
r.recvuntil('Ticket please:\n', drop=True)
for _ in range(5):
refstars = read_starfile(r.recvuntil('\n\n').decode())
refstars_magsorted = sorted(refstars, key=lambda x:x['m'])[::-1]
catalog_magnitudes = np.array([x['m'] for x in catalog])
matches = []
for idx, star in enumerate(refstars_magsorted):
if star['m'] <= 500:
break
match = np.argmin(np.abs(catalog_magnitudes-star['m']))
matches.append(match)
print(matches)
P = np.vstack([x['v'] for x in [catalog[i] for i in matches]])
Q = np.vstack([x['v'] for x in refstars_magsorted[:4]])
print("rmsd: {}".format(calculate_rmsd.kabsch_rmsd(P,Q)))
rotation_mtx = calculate_rmsd.kabsch(P, Q)
rotation = Rotation.from_matrix(rotation_mtx)
rotated = [dict(v=rotation.apply(x['v']), m=x['m']) for x in refstars_magsorted]
found_idxes = []
for star in rotated:
found_idxes.append(np.argmin([np.linalg.norm(star['v']-catalogstar['v']) for catalogstar in catalog]))
r.send(','.join([str(x) for x in found_idxes]) + '\n')
time.sleep(0.1)
r.recvuntil('Left...\n')
print(r.clean())