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intercept/tests/test_weather_sat_predict.py
James Smith 4607c358ed Add ground station automation with 6-phase implementation 
Phase 1 - Automated observation engine:
- utils/ground_station/scheduler.py: GroundStationScheduler fires at AOS/LOS,
  claims SDR, manages IQBus lifecycle, emits SSE events
- utils/ground_station/observation_profile.py: ObservationProfile dataclass + DB CRUD
- routes/ground_station.py: REST API for profiles, scheduler, observations, recordings,
  rotator; SSE stream; /ws/satellite_waterfall WebSocket
- DB tables: observation_profiles, ground_station_observations, ground_station_events,
  sigmf_recordings (added to utils/database.py init_db)
- app.py: ground_station_queue, WebSocket init, scheduler startup in _deferred_init
- routes/__init__.py: register ground_station_bp

Phase 2 - Doppler correction:
- utils/doppler.py: generalized DopplerTracker extracted from sstv_decoder.py;
  accepts satellite name or raw TLE tuple; thread-safe; update_tle() method
- utils/sstv/sstv_decoder.py: replace inline DopplerTracker with import from utils.doppler
- Scheduler runs 5s retune loop; calls rotator.point_to() if enabled

Phase 3 - IQ recording (SigMF):
- utils/sigmf.py: SigMFWriter writes .sigmf-data + .sigmf-meta; disk-free guard (500MB)
- utils/ground_station/consumers/sigmf_writer.py: SigMFConsumer wraps SigMFWriter

Phase 4 - Multi-decoder IQ broadcast pipeline:
- utils/ground_station/iq_bus.py: IQBus single-producer fan-out; IQConsumer Protocol
- utils/ground_station/consumers/waterfall.py: CU8→FFT→binary frames
- utils/ground_station/consumers/fm_demod.py: CU8→FM demod (numpy)→decoder subprocess
- utils/ground_station/consumers/gr_satellites.py: CU8→cf32→gr_satellites (optional)

Phase 5 - Live spectrum waterfall:
- static/js/modes/ground_station_waterfall.js: /ws/satellite_waterfall canvas renderer
- Waterfall panel in satellite dashboard sidebar, auto-shown on iq_bus_started SSE event

Phase 6 - Antenna rotator control (optional):
- utils/rotator.py: RotatorController TCP client for rotctld (Hamlib line protocol)
- Rotator panel in satellite dashboard; silently disabled if rotctld unreachable

Also fixes pre-existing test_weather_sat_predict.py breakage:
- utils/weather_sat_predict.py: rewritten with self-contained skyfield implementation
  using find_discrete (matching what committed tests expected); adds _format_utc_iso
- tests/test_weather_sat_predict.py: add _MOCK_WEATHER_SATS and @patch decorators
  for tests that assumed NOAA-18 active (decommissioned Jun 2025, now active=False)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-18 17:36:55 +00:00

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"""Tests for weather satellite pass prediction.
Covers predict_passes() function, TLE handling, trajectory computation,
and ground track generation.
"""
from __future__ import annotations
from datetime import datetime, timedelta, timezone
from unittest.mock import MagicMock, patch
import pytest
from utils.weather_sat_predict import _format_utc_iso, predict_passes
# Controlled single-satellite config used by tests that need exactly one active satellite.
# NOAA-18 was decommissioned Jun 2025 and is inactive in the real WEATHER_SATELLITES,
# so tests that assert on satellite-specific fields patch the module-level name.
_MOCK_WEATHER_SATS = {
'NOAA-18': {
'name': 'NOAA 18',
'frequency': 137.9125,
'mode': 'APT',
'pipeline': 'noaa_apt',
'tle_key': 'NOAA-18',
'active': True,
}
}
class TestPredictPasses:
"""Tests for predict_passes() function."""
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
def test_predict_passes_no_tle_data(self, mock_tle, mock_load):
"""predict_passes() should handle missing TLE data."""
mock_tle.get.return_value = None
mock_ts = MagicMock()
mock_ts.now.return_value = MagicMock()
mock_ts.utc.return_value = MagicMock()
mock_load.timescale.return_value = mock_ts
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert passes == []
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_basic(self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load):
"""predict_passes() should predict basic passes."""
# Mock timescale
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
# Mock TLE data
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
# Mock observer
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
# Mock satellite
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
# Mock pass detection - one pass
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
# Mock topocentric calculations
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 1
pass_data = passes[0]
assert pass_data['satellite'] == 'NOAA-18'
assert pass_data['name'] == 'NOAA 18'
assert pass_data['frequency'] == 137.9125
assert pass_data['mode'] == 'APT'
assert 'maxEl' in pass_data
assert 'duration' in pass_data
assert 'quality' in pass_data
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_below_min_elevation(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should filter passes below min elevation."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
# Mock low elevation pass
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 10.0 # Below min_elevation of 15
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 0
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_with_trajectory(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should include trajectory when requested."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(
lat=51.5, lon=-0.1, hours=24, min_elevation=15, include_trajectory=True
)
assert len(passes) == 1
assert 'trajectory' in passes[0]
assert len(passes[0]['trajectory']) == 30
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_with_ground_track(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should include ground track when requested."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
# Mock geocentric position
def mock_at(t):
geocentric = MagicMock()
return geocentric
mock_satellite_obj.at.side_effect = mock_at
# Mock subpoint
mock_subpoint = MagicMock()
mock_lat = MagicMock()
mock_lat.degrees = 51.5
mock_lon = MagicMock()
mock_lon.degrees = -0.1
mock_subpoint.latitude = mock_lat
mock_subpoint.longitude = mock_lon
mock_wgs84.subpoint.return_value = mock_subpoint
passes = predict_passes(
lat=51.5, lon=-0.1, hours=24, min_elevation=15, include_ground_track=True
)
assert len(passes) == 1
assert 'groundTrack' in passes[0]
assert len(passes[0]['groundTrack']) == 60
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_quality_excellent(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should mark high elevation passes as excellent."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 75.0 # Excellent pass
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 1
assert passes[0]['quality'] == 'excellent'
assert passes[0]['maxEl'] >= 60
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_quality_good(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should mark medium elevation passes as good."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0 # Good pass
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 1
assert passes[0]['quality'] == 'good'
assert 30 <= passes[0]['maxEl'] < 60
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_quality_fair(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should mark low elevation passes as fair."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 20.0 # Fair pass
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 1
assert passes[0]['quality'] == 'fair'
assert passes[0]['maxEl'] < 30
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_inactive_satellite(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should skip inactive satellites."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_load.timescale.return_value = mock_ts
# Temporarily mark satellite as inactive
from utils.weather_sat import WEATHER_SATELLITES
original_active = WEATHER_SATELLITES['NOAA-18']['active']
WEATHER_SATELLITES['NOAA-18']['active'] = False
try:
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
# Should not include NOAA-18
noaa_18_passes = [p for p in passes if p['satellite'] == 'NOAA-18']
assert len(noaa_18_passes) == 0
finally:
WEATHER_SATELLITES['NOAA-18']['active'] = original_active
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_exception_handling(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should handle exceptions gracefully."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
# Make find_discrete raise exception
mock_find.side_effect = Exception('Computation error')
# Should not raise, just skip this satellite
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
# May include passes from other satellites or be empty
assert isinstance(passes, list)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
def test_predict_passes_uses_tle_cache(self, mock_tle, mock_load):
"""predict_passes() should use live TLE cache if available."""
with patch('utils.weather_sat_predict._tle_cache', {'NOAA-18': ('NOAA-18', 'line1', 'line2')}):
mock_ts = MagicMock()
mock_ts.now.return_value = MagicMock()
mock_ts.utc.return_value = MagicMock()
mock_load.timescale.return_value = mock_ts
# Even though TLE_SATELLITES is mocked, should use _tle_cache
with patch('utils.weather_sat_predict.wgs84'), \
patch('utils.weather_sat_predict.EarthSatellite'), \
patch('utils.weather_sat_predict.find_discrete', return_value=([], [])):
predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
# Should not raise
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_predict_passes_sorted_by_time(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""predict_passes() should return passes sorted by start time."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: self._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
# Two passes
rise1 = MagicMock()
rise1.utc_datetime.return_value = now + timedelta(hours=4)
set1 = MagicMock()
set1.utc_datetime.return_value = now + timedelta(hours=4, minutes=15)
rise2 = MagicMock()
rise2.utc_datetime.return_value = now + timedelta(hours=2)
set2 = MagicMock()
set2.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
# Return in non-chronological order
mock_find.return_value = ([rise1, set1, rise2, set2], [True, False, True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
# Should be sorted with earliest pass first
if len(passes) >= 2:
assert passes[0]['startTimeISO'] < passes[1]['startTimeISO']
@staticmethod
def _mock_time(dt):
"""Helper to create mock time object."""
mock_t = MagicMock()
if isinstance(dt, datetime):
mock_t.utc_datetime.return_value = dt
else:
mock_t.utc_datetime.return_value = datetime.now(timezone.utc)
return mock_t
class TestPassDataStructure:
"""Tests for pass data structure."""
@patch('utils.weather_sat_predict.WEATHER_SATELLITES', _MOCK_WEATHER_SATS)
@patch('utils.weather_sat_predict.load')
@patch('utils.weather_sat_predict.TLE_SATELLITES')
@patch('utils.weather_sat_predict.wgs84')
@patch('utils.weather_sat_predict.EarthSatellite')
@patch('utils.weather_sat_predict.find_discrete')
def test_pass_data_fields(
self, mock_find, mock_sat, mock_wgs84, mock_tle, mock_load
):
"""Pass data should contain all required fields."""
mock_ts = MagicMock()
now = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
mock_now = MagicMock()
mock_now.utc_datetime.return_value = now
mock_ts.now.return_value = mock_now
mock_ts.utc.side_effect = lambda dt: TestPredictPasses._mock_time(dt)
mock_load.timescale.return_value = mock_ts
mock_tle.get.return_value = (
'NOAA-18',
'1 28654U 05018A 24001.50000000 .00000000 00000-0 00000-0 0 9999',
'2 28654 98.7000 100.0000 0001000 0.0000 0.0000 14.12500000000000'
)
mock_observer = MagicMock()
mock_wgs84.latlon.return_value = mock_observer
mock_satellite_obj = MagicMock()
mock_sat.return_value = mock_satellite_obj
rise_time = MagicMock()
rise_time.utc_datetime.return_value = now + timedelta(hours=2)
set_time = MagicMock()
set_time.utc_datetime.return_value = now + timedelta(hours=2, minutes=15)
mock_find.return_value = ([rise_time, set_time], [True, False])
def mock_topocentric(t):
topo = MagicMock()
alt = MagicMock()
alt.degrees = 45.0
az = MagicMock()
az.degrees = 180.0
topo.altaz.return_value = (alt, az, MagicMock())
return topo
mock_diff = MagicMock()
mock_diff.at.side_effect = mock_topocentric
mock_satellite_obj.__sub__.return_value = mock_diff
passes = predict_passes(lat=51.5, lon=-0.1, hours=24, min_elevation=15)
assert len(passes) == 1
pass_data = passes[0]
# Check all required fields
required_fields = [
'id', 'satellite', 'name', 'frequency', 'mode',
'startTime', 'startTimeISO', 'endTimeISO',
'maxEl', 'maxElAz', 'riseAz', 'setAz',
'duration', 'quality'
]
for field in required_fields:
assert field in pass_data, f"Missing required field: {field}"
def test_import_error_propagates(self):
"""predict_passes() should raise ImportError if skyfield unavailable."""
with patch.dict('sys.modules', {'skyfield': None, 'skyfield.api': None}):
with pytest.raises((ImportError, AttributeError)):
predict_passes(lat=51.5, lon=-0.1)
class TestTimestampFormatting:
"""Tests for UTC timestamp serialization helpers."""
def test_format_utc_iso_from_aware_datetime(self):
"""Aware UTC datetimes should not get a duplicate UTC suffix."""
dt = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
value = _format_utc_iso(dt)
assert value == '2024-01-01T12:00:00Z'
assert '+00:00Z' not in value
def test_format_utc_iso_from_naive_datetime(self):
"""Naive datetimes should be treated as UTC and serialized consistently."""
dt = datetime(2024, 1, 1, 12, 0, 0)
value = _format_utc_iso(dt)
assert value == '2024-01-01T12:00:00Z'
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