import predict import json import sys import datetime frequencies = {'NOAA-15':'137.620M', 'NOAA-18':'137.9125M', 'NOAA-19':'137.10M', 'METEOR-M2':'137.925M', 'ISS':'145.80M' } #Meteor M2 is also on 137.1M tle_data = json.load(open('sat_tle.txt')) default_predictions = 1 default_min_elevation = 30 suppress_low_passes = False pass_elevation = default_min_elevation if( len(sys.argv) > 1 ): pass_elevation = int(sys.argv[1]) if( pass_elevation == '' ): pass_elevation = default_min_elevation predictions = default_predictions if( len(sys.argv) > 2): predictions = int(sys.argv[2]) if( predictions == '' ): predictions = default_predictions if( len(sys.argv) > 3 ): suppress_low_passes = True def convert_time( unixtime ): return datetime.datetime.fromtimestamp( int(unixtime) ).strftime('%H:%M %Y-%m-%d') def convert_time_short( unixtime ): return datetime.datetime.fromtimestamp( int(unixtime) ).strftime('%M') def get_sat( sat ): return sat + '\n' + tle_data[sat] qth = (49.32, 123.42, 49) # lat (N), long (W), alt (meters) data = {} for sat, freq in frequencies.iteritems(): name = predict.observe(get_sat(sat), qth)['name'].strip() t = predict.transits(get_sat(sat), qth) count = 0 while ( count < predictions ): p = t.next() p_nw = t_nw.next() p_se = t_se.next() while (p.peak()['elevation'] < default_min_elevation): p = t.next() p_nw = t_nw.next() p_se = t_se.next() if( not suppress_low_passes ): print name, "pass too low, recalculating" data[name] = {} data[name]['frequency'] = freq data[name]['start_unix'] = p.start data[name]['start'] = convert_time(data[name]['start_unix']) data[name]['duration_seconds'] = int(p.duration()) data[name]['duration_minutes'] = convert_time_short(data[name]['duration_seconds']) data[name]['elevation'] = p.peak()['elevation'] print name, 'next pass at:', data[name]['start'], 'at', data[name]['elevation'], 'degrees.', data[name]['direction'] + '-bound' count = count + 1 import json with open('satpredict.txt', 'w') as fp: json.dump(data, fp, indent=4)