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116 lines
4.0 KiB
Python
Executable File
116 lines
4.0 KiB
Python
Executable File
#!/usr/bin/python
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#
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# build databases from files systems.csv and stations.json from http://eddb.io/api
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#
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import cPickle
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import csv
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import json
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import requests
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def download(filename):
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r = requests.get('https://eddb.io/archive/v5/' + filename, stream=True)
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print '\n%s\t%dK' % (filename, len(r.content) / 1024)
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return r
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if __name__ == "__main__":
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# Ellipsoid that encompasses most of the systems in the bubble (but not outliers like Sothis)
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RX = RZ = 260
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CY = -50
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RY = 300
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RX2 = RX * RX
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RY2 = RY * RY
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RZ2 = RZ * RZ
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def inbubble(x, y, z):
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return (x * x)/RX2 + ((y - CY) * (y - CY))/RY2 + (z * z)/RZ2 <= 1
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# Sphere around Jaques
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JX, JY, JZ = -9530.50000, -910.28125, 19808.12500
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RJ2 = 80 * 80 # Furthest populated system is Pekoe at 50.16 Ly
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def around_jaques(x, y, z):
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return ((x - JX) * (x - JX) + (y - JY) * (y - JY) + (z - JZ) * (z - JZ)) <= RJ2
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# Sphere around outliers
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RO2 = 40 * 40
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def around_outlier(cx, cy, cz, x, y, z):
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return ((x - ox) * (x - ox) + (y - oy) * (y - oy) + (z - oz) * (z - oz)) <= RO2
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systems = { int(s['id']) : {
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'name' : s['name'].decode('utf-8'),
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'x' : float(s['x']),
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'y' : float(s['y']),
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'z' : float(s['z']),
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'is_populated' : int(s['is_populated']),
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} for s in csv.DictReader(download('systems.csv').iter_lines()) }
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#} for s in csv.DictReader(open('systems.csv')) }
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print '%d\tsystems' % len(systems)
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# (system_id, is_populated) by system_name (ignoring duplicate names)
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system_ids = {
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str(s['name']) : (k, s['is_populated'])
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for k,s in systems.iteritems() if inbubble(s['x'], s['y'], s['z'])
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}
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print '%d\tsystems in bubble' % len(system_ids)
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extra_ids = {
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str(s['name']) : (k, s['is_populated'])
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for k,s in systems.iteritems() if around_jaques(s['x'], s['y'], s['z'])
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}
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system_ids.update(extra_ids)
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print '%d\tsystems in Colonia' % len(extra_ids)
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cut = {
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k : s for k, s in systems.iteritems()
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if s['is_populated'] and s['name'] not in system_ids
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}
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print '%d\toutlying populated systems:' % len(cut)
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extra_ids = {}
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for k1,o in sorted(cut.iteritems()):
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ox, oy, oz = o['x'], o['y'], o['z']
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extra = {
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str(s['name']) : (k, s['is_populated'])
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for k,s in systems.iteritems() if around_outlier(ox, oy, oz, s['x'], s['y'], s['z'])
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}
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print '%-30s%7d %11.5f %11.5f %11.5f %4d' % (o['name'], k1, ox, oy, oz, len(extra))
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extra_ids.update(extra)
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print '\n%d\tsystems around outliers' % len(extra_ids)
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system_ids.update(extra_ids)
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cut = {
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k : s
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for k,s in systems.iteritems() if s['name'] in system_ids and system_ids[s['name']][0] != k
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}
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print '\n%d duplicate systems' % len(cut)
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for k,s in sorted(cut.iteritems()):
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print '%-20s%8d %8d %11.5f %11.5f %11.5f' % (s['name'], system_ids[s['name']][0], k, s['x'], s['y'], s['z'])
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# Hack - ensure duplicate system names are pointing at the more interesting system
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system_ids['Amo'] = (866, True)
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system_ids['q Velorum'] = (15843, True)
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system_ids['M Carinae'] = (22627, False)
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system_ids['HH 17'] = (61275, False)
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system_ids['K Carinae'] = (375886, False)
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system_ids['d Velorum'] = (406476, False)
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system_ids['L Velorum'] = (2016580, False)
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system_ids['N Velorum'] = (3012033, False)
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system_ids['i Velorum'] = (3387990, False)
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with open('systems.p', 'wb') as h:
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cPickle.dump(system_ids, h, protocol = cPickle.HIGHEST_PROTOCOL)
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print '\n%d saved systems' % len(system_ids)
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# station_id by (system_id, station_name)
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stations = json.loads(download('stations.json').content) # let json do the utf-8 decode
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station_ids = {
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(x['system_id'], str(x['name'])) : x['id']
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for x in stations if x['max_landing_pad_size']
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}
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with open('stations.p', 'wb') as h:
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cPickle.dump(station_ids, h, protocol = cPickle.HIGHEST_PROTOCOL)
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print '\n%d saved stations' % len(station_ids)
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