276 lines
11 KiB
Python
276 lines
11 KiB
Python
import logging
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import binascii
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from datetime import datetime, timedelta
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from db.base.models import Satellite, Transmitter, Mode, DemodData, Telemetry
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from django.db.models import Count, Max
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from django.conf import settings
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from django.utils.timezone import make_aware
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from influxdb import InfluxDBClient
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from satnogsdecoders import decoder
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from django.core.cache import cache
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logger = logging.getLogger('db')
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def calculate_statistics():
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"""View to create statistics endpoint."""
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satellites = Satellite.objects.all()
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transmitters = Transmitter.objects.all()
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modes = Mode.objects.all()
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total_satellites = satellites.count()
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total_transmitters = transmitters.count()
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total_data = DemodData.objects.all().count()
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alive_transmitters = transmitters.filter(alive=True).count()
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if alive_transmitters > 0 and total_transmitters > 0:
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try:
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alive_transmitters_percentage = '{0}%'.format(round((float(alive_transmitters)
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/ float(total_transmitters)) * 100, 2))
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except ZeroDivisionError as error:
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logger.error(error, exc_info=True)
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alive_transmitters_percentage = '0%'
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else:
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alive_transmitters_percentage = '0%'
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mode_label = []
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mode_data = []
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for mode in modes:
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tr = transmitters.filter(mode=mode).count()
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mode_label.append(mode.name)
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mode_data.append(tr)
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# needed to pass testing in a fresh environment with no modes in db
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if len(mode_label) == 0:
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mode_label = ['FM']
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if len(mode_data) == 0:
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mode_data = ['FM']
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band_label = []
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band_data = []
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# <30.000.000 - HF
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filtered = transmitters.filter(downlink_low__lt=30000000).count()
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band_label.append('HF')
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band_data.append(filtered)
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# 30.000.000 ~ 300.000.000 - VHF
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filtered = transmitters.filter(downlink_low__gte=30000000,
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downlink_low__lt=300000000).count()
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band_label.append('VHF')
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band_data.append(filtered)
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# 300.000.000 ~ 1.000.000.000 - UHF
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filtered = transmitters.filter(downlink_low__gte=300000000,
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downlink_low__lt=1000000000).count()
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band_label.append('UHF')
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band_data.append(filtered)
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# 1G ~ 2G - L
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filtered = transmitters.filter(downlink_low__gte=1000000000,
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downlink_low__lt=2000000000).count()
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band_label.append('L')
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band_data.append(filtered)
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# 2G ~ 4G - S
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filtered = transmitters.filter(downlink_low__gte=2000000000,
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downlink_low__lt=4000000000).count()
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band_label.append('S')
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band_data.append(filtered)
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# 4G ~ 8G - C
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filtered = transmitters.filter(downlink_low__gte=4000000000,
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downlink_low__lt=8000000000).count()
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band_label.append('C')
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band_data.append(filtered)
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# 8G ~ 12G - X
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filtered = transmitters.filter(downlink_low__gte=8000000000,
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downlink_low__lt=12000000000).count()
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band_label.append('X')
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band_data.append(filtered)
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# 12G ~ 18G - Ku
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filtered = transmitters.filter(downlink_low__gte=12000000000,
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downlink_low__lt=18000000000).count()
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band_label.append('Ku')
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band_data.append(filtered)
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# 18G ~ 27G - K
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filtered = transmitters.filter(downlink_low__gte=18000000000,
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downlink_low__lt=27000000000).count()
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band_label.append('K')
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band_data.append(filtered)
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# 27G ~ 40G - Ka
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filtered = transmitters.filter(downlink_low__gte=27000000000,
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downlink_low__lt=40000000000).count()
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band_label.append('Ka')
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band_data.append(filtered)
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mode_data_sorted, mode_label_sorted = zip(*sorted(zip(mode_data, mode_label), reverse=True))
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band_data_sorted, band_label_sorted = zip(*sorted(zip(band_data, band_label), reverse=True))
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statistics = {
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'total_satellites': total_satellites,
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'total_data': total_data,
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'transmitters': total_transmitters,
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'transmitters_alive': alive_transmitters_percentage,
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'mode_label': mode_label_sorted,
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'mode_data': mode_data_sorted,
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'band_label': band_label_sorted,
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'band_data': band_data_sorted
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}
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return statistics
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# kaitai does not give us a good way to export attributes when we don't know
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# what those attributes are, and here we are dealing with a lot of various
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# decoders with different attributes. This is a hacky way of getting them
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# to json. We also need to sanitize this for any binary data left over as
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# it won't export to json.
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def kaitai_to_json(structdict, satellite, telemetry, demoddata, json_obj):
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"""Recursively sends dict string/int data to the given json_obj"""
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for key, value in structdict.iteritems():
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if type(value) is dict: # recursion
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kaitai_to_json(value, satellite, telemetry, demoddata, json_obj)
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else:
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data = {
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'time': demoddata.timestamp.strftime('%Y-%m-%dT%H:%M:%SZ'),
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'measurement': key,
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'tags': {
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'satellite': satellite.name,
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'norad': satellite.norad_cat_id,
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'decoder': telemetry.decoder,
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'station': demoddata.station,
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'observer': demoddata.observer,
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'source': demoddata.source
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}
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}
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if isinstance(value, basestring): # skip binary values
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try:
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value.decode('utf-8')
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data.update({'fields': {'value': value}})
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except UnicodeError:
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continue
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else:
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data.update({'fields': {'value': value}})
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if 'value' in data['fields']:
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json_obj.append(data)
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def kaitai_to_dict(struct):
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"""Take a kaitai decode object and return a dict object"""
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structdict = struct.__dict__
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todict = {}
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for key, value in structdict.iteritems():
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if not key.startswith("_"): # kaitai objects and priv vars, skip
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if isinstance(value, basestring): # skip binary values
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try:
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value.decode('utf-8')
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todict[key] = value
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except UnicodeError:
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continue
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elif hasattr(value, '__dict__'):
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todict[key] = kaitai_to_dict(value) # recursion
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else:
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todict[key] = value
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return todict
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def write_influx(json_obj):
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"""Take a json object and send to influxdb."""
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client = InfluxDBClient(settings.INFLUX_HOST, settings.INFLUX_PORT,
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settings.INFLUX_USER, settings.INFLUX_PASS,
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settings.INFLUX_DB, ssl=settings.INFLUX_SSL)
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client.write_points(json_obj)
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def decode_data(norad, period=None):
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"""Decode data for a satellite, with an option to limit the scope."""
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sat = Satellite.objects.get(norad_cat_id=norad)
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if sat.has_telemetry_decoders:
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now = datetime.utcnow()
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if period:
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q = now - timedelta(hours=4)
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q = make_aware(q)
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data = DemodData.objects.filter(satellite__norad_cat_id=norad,
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timestamp__gte=q) \
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.filter(is_decoded=False)
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else:
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data = DemodData.objects.filter(satellite=sat) \
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.filter(is_decoded=False)
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telemetry_decoders = Telemetry.objects.filter(satellite=sat)
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# iterate over DemodData objects
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for obj in data:
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# iterate over Telemetry decoders
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for tlmdecoder in telemetry_decoders:
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try:
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decoder_class = getattr(decoder,
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tlmdecoder.decoder.capitalize())
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except AttributeError:
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continue
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try:
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with open(obj.payload_frame.path) as fp:
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# we get data frames in hex but kaitai requires binary
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hexdata = fp.read()
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bindata = binascii.unhexlify(hexdata)
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# if we are set to use InfluxDB, send the decoded data
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# there, otherwise we store it in the local DB.
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if settings.USE_INFLUX:
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try:
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frame = decoder_class.from_bytes(bindata)
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json_obj = []
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kaitai_to_json(kaitai_to_dict(frame), sat,
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tlmdecoder, obj, json_obj)
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write_influx(json_obj)
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obj.payload_decoded = 'influxdb'
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obj.is_decoded = True
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obj.save()
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break
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except Exception:
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obj.is_decoded = False
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obj.save()
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continue
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else: # store in the local db instead of influx
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try:
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frame = decoder_class.from_bytes(bindata)
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except Exception:
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obj.payload_decoded = ''
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obj.is_decoded = False
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obj.save()
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continue
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else:
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json_obj = []
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kaitai_to_json(kaitai_to_dict(frame), sat,
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tlmdecoder, obj, json_obj)
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obj.payload_decoded = json_obj
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obj.is_decoded = True
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obj.save()
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break
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except IOError:
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continue
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# Caches stats about satellites and data
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def cache_statistics():
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statistics = calculate_statistics()
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cache.set('stats_transmitters', statistics, 60 * 60 * 2)
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satellites = Satellite.objects \
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.values('name', 'norad_cat_id') \
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.annotate(count=Count('telemetry_data'),
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latest_payload=Max('telemetry_data__timestamp')) \
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.order_by('-count')
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cache.set('stats_satellites', satellites, 60 * 60 * 2)
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observers = DemodData.objects \
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.values('observer') \
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.annotate(count=Count('observer'),
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latest_payload=Max('timestamp')) \
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.order_by('-count')
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cache.set('stats_observers', observers, 60 * 60 * 2)
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