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sondehub-infra/lambda/ham_predict_updater/__init__.py
Michaela Wheeler 81830c2d74
Move lambda functions to secrets manager (#126) 
Co-authored-by: xss <michaela@michaela.lgbt>
2023-10-22 14:40:16 +11:00

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Python
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import sys
sys.path.append("sns_to_mqtt/vendor")
import paho.mqtt.client as mqtt
import json
from datetime import datetime
from datetime import timedelta
import http.client
import math
import logging
from math import radians, degrees, sin, cos, atan2, sqrt, pi
import es
import asyncio
import functools
import os
import random
import time
import config_handler
TAWHIRI_SERVER = "tawhiri.v2.sondehub.org"
# FLIGHT PROFILE DEFAULTS
#
# If we have no better estimates for flight profile, use these:
PREDICT_DEFAULTS = {'ascent_rate': 5.0, 'burst_altitude': 30000.0, 'descent_rate': 5.0}
# For some sonde types we can make better assumptions
SONDE_TYPE_PREDICT_DEFAULTS = {
}
#
# LAUNCH SITE ALLOCATION SETTINGS
#
# Immediately allocate a launch site if it is within this distance (straight line)
# of a known launch site.
LAUNCH_ALLOCATE_RANGE_MIN = 4000 # metres
LAUNCH_ALLOCATE_RANGE_MAX = 30000 # metres
LAUNCH_ALLOCATE_RANGE_SCALING = 1.5 # Scaling factor - launch allocation range is min(current alt * this value , launch allocate range max)
# Do not run predictions if the ascent or descent rate is less than this value
ASCENT_RATE_THRESHOLD = 0.6
# Do not run predictions if the payload is below this altitude AGL
# Currently not used.
ALTITUDE_AGL_THRESHOLD = 150.0
# Do not run predictions if the payload is below this altitude AMSL, and has an ascent rate below the above threshold.
ALTITUDE_AMSL_THRESHOLD = 1500.0
# Setup MQTT
client = mqtt.Client(transport="websockets")
connected_flag = False
setup = False
import socket
socket.setdefaulttimeout(1)
## MQTT functions
def connect():
client.on_connect = on_connect
client.on_disconnect = on_disconnect
client.on_publish = on_publish
#client.tls_set()
client.username_pw_set(config_handler.get("MQTT","USERNAME"), password=config_handler.get("MQTT","PASSWORD"))
HOSTS = config_handler.get("MQTT","HOST").split(",")
PORT = int(config_handler.get("MQTT","PORT", default="8080"))
if PORT == 443:
client.tls_set()
HOST = random.choice(HOSTS)
print(f"Connecting to {HOST}")
client.connect(HOST, PORT, 5)
client.loop_start()
print("loop started")
def on_disconnect(client, userdata, rc):
global connected_flag
print("disconnected")
connected_flag=False #set flag
def on_connect(client, userdata, flags, rc):
global connected_flag
if rc==0:
print("connected")
connected_flag=True #set flag
else:
print("Bad connection Returned code")
def on_publish(client, userdata, mid):
pass
def get_flight_docs():
path = "flight-doc/_search"
payload = {
"aggs": {
"payload_callsign": {
"terms": {
"field": "payload_callsign.keyword",
"order": {
"_key": "desc"
},
"size": 10000
},
"aggs": {
"flight_doc": {
"top_hits": {
"_source": True,
"size": 1,
"sort": [
{
"datetime": {
"order": "desc"
}
}
]
}
}
}
}
},
"size": 0
}
logging.debug("Start ES Request")
results = es.request(json.dumps(payload), path, "POST")
logging.debug("Finished ES Request")
return { x['key'] : x['flight_doc']['hits']['hits'][0]['_source'] for x in results['aggregations']['payload_callsign']['buckets']}
def getDensity(altitude):
"""
Calculate the atmospheric density for a given altitude in metres.
This is a direct port of the oziplotter Atmosphere class
"""
# Constants
airMolWeight = 28.9644 # Molecular weight of air
densitySL = 1.225 # Density at sea level [kg/m3]
pressureSL = 101325 # Pressure at sea level [Pa]
temperatureSL = 288.15 # Temperature at sea level [deg K]
gamma = 1.4
gravity = 9.80665 # Acceleration of gravity [m/s2]
tempGrad = -0.0065 # Temperature gradient [deg K/m]
RGas = 8.31432 # Gas constant [kg/Mol/K]
R = 287.053
deltaTemperature = 0.0
# Lookup Tables
altitudes = [0, 11000, 20000, 32000, 47000, 51000, 71000, 84852]
pressureRels = [
1,
2.23361105092158e-1,
5.403295010784876e-2,
8.566678359291667e-3,
1.0945601337771144e-3,
6.606353132858367e-4,
3.904683373343926e-5,
3.6850095235747942e-6,
]
temperatures = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65, 186.946]
tempGrads = [-6.5, 0, 1, 2.8, 0, -2.8, -2, 0]
gMR = gravity * airMolWeight / RGas
# Pick a region to work in
i = 0
if altitude > 0:
while altitude > altitudes[i + 1]:
i = i + 1
# Lookup based on region
baseTemp = temperatures[i]
tempGrad = tempGrads[i] / 1000.0
pressureRelBase = pressureRels[i]
deltaAltitude = altitude - altitudes[i]
temperature = baseTemp + tempGrad * deltaAltitude
# Calculate relative pressure
if math.fabs(tempGrad) < 1e-10:
pressureRel = pressureRelBase * math.exp(
-1 * gMR * deltaAltitude / 1000.0 / baseTemp
)
else:
pressureRel = pressureRelBase * math.pow(
baseTemp / temperature, gMR / tempGrad / 1000.0
)
# Add temperature offset
temperature = temperature + deltaTemperature
# Finally, work out the density...
speedOfSound = math.sqrt(gamma * R * temperature)
pressure = pressureRel * pressureSL
density = densitySL * pressureRel * temperatureSL / temperature
return density
def seaLevelDescentRate(descent_rate, altitude):
""" Calculate the descent rate at sea level, for a given descent rate at altitude """
rho = getDensity(altitude)
return math.sqrt((rho / 1.225) * math.pow(descent_rate, 2))
# Earthmaths code by Daniel Richman (thanks!)
# Copyright 2012 (C) Daniel Richman; GNU GPL 3
def position_info(listener, balloon):
"""
Calculate and return information from 2 (lat, lon, alt) tuples
Returns a dict with:
- angle at centre
- great circle distance
- distance in a straight line
- bearing (azimuth or initial course)
- elevation (altitude)
Input and output latitudes, longitudes, angles, bearings and elevations are
in degrees, and input altitudes and output distances are in meters.
"""
# Earth:
radius = 6371000.0
(lat1, lon1, alt1) = listener
(lat2, lon2, alt2) = balloon
lat1 = radians(lat1)
lat2 = radians(lat2)
lon1 = radians(lon1)
lon2 = radians(lon2)
# Calculate the bearing, the angle at the centre, and the great circle
# distance using Vincenty's_formulae with f = 0 (a sphere). See
# http://en.wikipedia.org/wiki/Great_circle_distance#Formulas and
# http://en.wikipedia.org/wiki/Great-circle_navigation and
# http://en.wikipedia.org/wiki/Vincenty%27s_formulae
d_lon = lon2 - lon1
sa = cos(lat2) * sin(d_lon)
sb = (cos(lat1) * sin(lat2)) - (sin(lat1) * cos(lat2) * cos(d_lon))
bearing = atan2(sa, sb)
aa = sqrt((sa ** 2) + (sb ** 2))
ab = (sin(lat1) * sin(lat2)) + (cos(lat1) * cos(lat2) * cos(d_lon))
angle_at_centre = atan2(aa, ab)
great_circle_distance = angle_at_centre * radius
# Armed with the angle at the centre, calculating the remaining items
# is a simple 2D triangley circley problem:
# Use the triangle with sides (r + alt1), (r + alt2), distance in a
# straight line. The angle between (r + alt1) and (r + alt2) is the
# angle at the centre. The angle between distance in a straight line and
# (r + alt1) is the elevation plus pi/2.
# Use sum of angle in a triangle to express the third angle in terms
# of the other two. Use sine rule on sides (r + alt1) and (r + alt2),
# expand with compound angle formulae and solve for tan elevation by
# dividing both sides by cos elevation
ta = radius + alt1
tb = radius + alt2
ea = (cos(angle_at_centre) * tb) - ta
eb = sin(angle_at_centre) * tb
elevation = atan2(ea, eb)
# Use cosine rule to find unknown side.
distance = sqrt((ta ** 2) + (tb ** 2) - 2 * tb * ta * cos(angle_at_centre))
# Give a bearing in range 0 <= b < 2pi
if bearing < 0:
bearing += 2 * pi
return {
"listener": listener,
"balloon": balloon,
"listener_radians": (lat1, lon1, alt1),
"balloon_radians": (lat2, lon2, alt2),
"angle_at_centre": degrees(angle_at_centre),
"angle_at_centre_radians": angle_at_centre,
"bearing": degrees(bearing),
"bearing_radians": bearing,
"great_circle_distance": great_circle_distance,
"straight_distance": distance,
"elevation": degrees(elevation),
"elevation_radians": elevation,
}
def get_float_prediction(timestamp, latitude, longitude, altitude, current_rate=5.0, ascent_rate=PREDICT_DEFAULTS['ascent_rate'], burst_altitude=PREDICT_DEFAULTS['burst_altitude']):
"""
Request a float flight path prediction from Tawhiri.
Notes:
- The burst_altitude must be higher than the current altitude.
- Longitude is in the range 0-360.0
- All ascent/descent rates must be positive.
"""
# Shift longitude into the appropriate range for Tawhiri
if longitude < 0:
longitude += 360.0
# Generate the prediction URL
url = f"/api/v1/?launch_altitude={altitude}&launch_latitude={latitude}&launch_longitude={longitude}&launch_datetime={timestamp}&float_altitude={burst_altitude:.2f}&stop_datetime={(datetime.now() + timedelta(days=1)).isoformat()}Z&ascent_rate={ascent_rate:.2f}&profile=float_profile"
logging.debug(url)
conn = http.client.HTTPSConnection(TAWHIRI_SERVER)
conn.request("GET", url)
res = conn.getresponse()
data = res.read()
if res.code != 200:
logging.debug(data)
return None
pred_data = json.loads(data.decode("utf-8"))
path = []
if 'prediction' in pred_data:
for stage in pred_data['prediction']:
# Probably don't need to worry about this, it should only result in one or two points
# in 'ascent'.
if stage['stage'] == 'ascent' and current_rate < 0: # ignore ascent stage if we have already burst
continue
else:
for item in stage['trajectory']:
path.append({
"time": int(datetime.fromisoformat(item['datetime'].split(".")[0].replace("Z","")).timestamp()),
"lat": item['latitude'],
"lon": item['longitude'] - 360 if item['longitude'] > 180 else item['longitude'],
"alt": item['altitude'],
})
pred_data['path'] = path
return pred_data
else:
return None
def get_standard_prediction(timestamp, latitude, longitude, altitude, current_rate=5.0, ascent_rate=PREDICT_DEFAULTS['ascent_rate'], burst_altitude=PREDICT_DEFAULTS['burst_altitude'], descent_rate=PREDICT_DEFAULTS['descent_rate']):
"""
Request a standard flight path prediction from Tawhiri.
Notes:
- The burst_altitude must be higher than the current altitude.
- Longitude is in the range 0-360.0
- All ascent/descent rates must be positive.
"""
# # Bomb out if the rates are too low.
# if ascent_rate < ASCENT_RATE_THRESHOLD:
# return None
# if descent_rate < ASCENT_RATE_THRESHOLD:
# return None
# Shift longitude into the appropriate range for Tawhiri
if longitude < 0:
longitude += 360.0
# Generate the prediction URL
url = f"/api/v1/?launch_latitude={latitude}&launch_longitude={longitude}&launch_datetime={timestamp}&launch_altitude={altitude:.2f}&ascent_rate={ascent_rate:.2f}&burst_altitude={burst_altitude:.2f}&descent_rate={descent_rate:.2f}"
logging.debug(url)
conn = http.client.HTTPSConnection(TAWHIRI_SERVER)
conn.request("GET", url)
res = conn.getresponse()
data = res.read()
if res.code != 200:
logging.debug(data)
return None
pred_data = json.loads(data.decode("utf-8"))
path = []
if 'prediction' in pred_data:
for stage in pred_data['prediction']:
# Probably don't need to worry about this, it should only result in one or two points
# in 'ascent'.
if stage['stage'] == 'ascent' and current_rate < 0: # ignore ascent stage if we have already burst
continue
else:
for item in stage['trajectory']:
path.append({
"time": int(datetime.fromisoformat(item['datetime'].split(".")[0].replace("Z","")).timestamp()),
"lat": item['latitude'],
"lon": item['longitude'] - 360 if item['longitude'] > 180 else item['longitude'],
"alt": item['altitude'],
})
pred_data['path'] = path
return pred_data
else:
return None
# Need to mock this out if we ever use it again
#
# def get_ruaumoko(latitude, longitude):
# """
# Request the ground level from ruaumoko.
# Returns 0.0 if the ground level could not be determined, effectively
# defaulting to any checks based on this data being based on mean sea level.
# """
# # Shift longitude into the appropriate range for Tawhiri
# if longitude < 0:
# longitude += 360.0
# # Generate the prediction URL
# url = f"/api/ruaumoko/?latitude={latitude}&longitude={longitude}"
# logging.debug(url)
# conn = http.client.HTTPSConnection(TAWHIRI_SERVER)
# conn.request("GET", url)
# res = conn.getresponse()
# data = res.read()
# if res.code != 200:
# logging.debug(data)
# return None
# resp_data = json.loads(data.decode("utf-8"))
# if 'altitude' in resp_data:
# return resp_data['altitude']
# else:
# return 0.0
def bulk_upload_es(index_prefix,payloads):
body=""
for payload in payloads:
body += "{\"index\":{}}\n" + json.dumps(payload) + "\n"
body += "\n"
date_prefix = datetime.now().strftime("%Y-%m")
result = es.request(body, f"{index_prefix}-{date_prefix}/_bulk", "POST")
if 'errors' in result and result['errors'] == True:
error_types = [x['index']['error']['type'] for x in result['items'] if 'error' in x['index']] # get all the error types
error_types = [a for a in error_types if a != 'mapper_parsing_exception'] # filter out mapper failures since they will never succeed
if error_types:
print(result)
raise RuntimeError
def predict(event, context):
global setup
# Connect to MQTT
if not setup:
connect()
setup = True
# Use asyncio.run to synchronously "await" an async function
result = asyncio.run(predict_async(event, context))
time.sleep(0.5) # give paho mqtt 500ms to send messages this could be improved on but paho mqtt is a pain to interface with
return result
async def predict_async(event, context):
flight_docs = get_flight_docs()
sem = asyncio.Semaphore(5)
path = "ham-telm-*/_search"
interval = 60 # because some aprs balloons are only every minute
lag = 3 # how many samples to use
payload = {
"aggs": {
"2": {
"terms": {
"field": "payload_callsign.keyword",
"order": {
"_key": "desc"
},
"size": 1000
},
"aggs": {
"3": {
"date_histogram": {
"field": "datetime",
"fixed_interval": f"{interval}s"
},
"aggs": {
"1": {
"top_hits": {
"docvalue_fields": [
{
"field": "alt"
}
],
"_source": "alt",
"size": 1,
"sort": [
{
"datetime": {
"order": "desc"
}
}
]
}
},
"4": {
"serial_diff": {
"buckets_path": "4-metric",
"gap_policy": "skip",
"lag": lag
}
},
"5": {
"top_hits": {
"docvalue_fields": [
{
"field": "position"
}
],
"_source": {"includes": ["position", "type", "subtype"]},
"size": 1,
"sort": [
{
"datetime": {
"order": "desc"
}
}
]
}
},
"4-metric": {
"avg": {
"field": "alt"
}
}
}
}
}
}
},
"size": 0,
"stored_fields": [
"*"
],
"script_fields": {},
"docvalue_fields": [
{
"field": "@timestamp",
"format": "date_time"
},
{
"field": "datetime",
"format": "date_time"
},
{
"field": "log_date",
"format": "date_time"
},
{
"field": "time_received",
"format": "date_time"
},
{
"field": "time_server",
"format": "date_time"
},
{
"field": "time_uploaded",
"format": "date_time"
}
],
"_source": {
"excludes": []
},
"query": {
"bool": {
"must": [],
"filter": [
{
"match_all": {}
},
{
"range": {
"datetime": {
"gte": "now-10m",
"lte": "now",
"format": "strict_date_optional_time"
}
}
}
],
"should": []
}
},
"size": 0
}
logging.debug("Start ES Request")
results = es.request(json.dumps(payload), path, "GET")
logging.debug("Finished ES Request")
serials = { }
for x in results['aggregations']['2']['buckets']:
try:
serials[x['key']] = {
"alt": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['1']['hits']['hits'][0]['fields']['alt'][0],
"position": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['5']['hits']['hits'][0]['fields']['position'][0].split(","),
"rate": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['4']['value']/(lag*interval), # as we bucket for every 5 seconds with a lag of 5
"time": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['key_as_string']
}
except:
pass
serial_data={}
logging.debug("Start Predict")
jobs=[]
for serial in serials:
jobs.append(run_predictions_for_serial(sem, flight_docs, serial, serials[serial]))
output = await asyncio.gather(*jobs)
for data in output:
if data:
serial_data[data[0]] = data[1]
logging.debug("Stop Predict")
# Collate and upload forward predictions
output = []
for serial in serial_data:
value = serial_data[serial]
if value is not None:
output.append(
{
"payload_callsign": serial,
"datetime": value['request']['launch_datetime'],
"position": [
value['request']['launch_longitude'] - 360 if value['request']['launch_longitude'] > 180 else value['request']['launch_longitude'],
value['request']['launch_latitude']
],
"altitude": value['request']['launch_altitude'],
"ascent_rate": value['request']['ascent_rate'],
"descent_rate": value['request']['descent_rate'] if 'descent_rate' in value['request'] else None,
"burst_altitude": value['request']['burst_altitude'] if 'burst_altitude' in value['request'] else None,
"descending": True if serials[serial]['rate'] < 0 else False,
"landed": False, # I don't think this gets used anywhere?
"data": value['path']
}
)
if len(output) > 0:
bulk_upload_es("ham-predictions", output)
# upload to mqtt
while not connected_flag:
time.sleep(0.01) # wait until connected
for prediction in output:
logging.debug(f'Publishing prediction for {prediction["payload_callsign"]} to MQTT')
client.publish(
topic=f'amateur-prediction/{prediction["payload_callsign"]}',
payload=json.dumps(prediction),
qos=0,
retain=False
)
logging.debug(f'Published prediction for {prediction["payload_callsign"]} to MQTT')
logging.debug("Finished")
return
async def run_predictions_for_serial(sem, flight_docs, serial, value):
async with sem:
loop = asyncio.get_event_loop()
logging.debug(f"Starting prediction for {serial}")
try:
_flight_doc = flight_docs[serial]
logging.debug(f"Found flight doc for {serial}")
except:
logging.debug(f"Using default flight doc for {serial}")
# Some default parameters, including a flag to indicate there is no flight doc available.
_flight_doc = {
"no_flight_doc": True,
"float_expected": False,
"peak_altitude": 30000,
"descent_rate": 5
}
longitude = float(value['position'][1].strip())
latitude = float(value['position'][0].strip())
#
# AGL Threshold check - currently not used due to the ruamoko request blocking
#
# # Attempt to get the ground level.
# try:
# ground_level = get_ruaumoko(latitude, longitude)
# except:
# ground_level = 0.0
# # Don't run a prediction if the altitude is less than our AGL threshold.
# if (value['alt'] - ground_level) < ALTITUDE_AGL_THRESHOLD:
# return None
#
# AMSL + Float check.
#
if (abs(value['rate']) <= ASCENT_RATE_THRESHOLD) and (value['alt'] < ALTITUDE_AMSL_THRESHOLD):
# Payload is 'floating' (e.g. is probably on the ground), and is below 1500m AMSL.
# Don't run a prediction in this case. It probably just hasn't been launched yet.
logging.debug(f"{serial} is floating and alt is low so not running prediction")
return None
# Now to determine the burst altitude
if value['rate'] > 0:
if value['alt'] > _flight_doc['peak_altitude']:
burst_altitude = value['alt']+0.05 # balloon past the prediction - use just a little bit higher
else:
burst_altitude = _flight_doc['peak_altitude']
else: #descending
burst_altitude = value['alt']+0.05
logging.debug(f"Burst alt for {serial} is {burst_altitude}")
# Balloon is on Descent. Doesn't matter what the flight document says at this point,
# we just run a descent prediction using the current descent rate.
if value['rate'] < -ASCENT_RATE_THRESHOLD:
logging.debug(f"{serial} running normal descent profile")
descent_rate= seaLevelDescentRate(abs(value['rate']),value['alt'])
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_standard_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=5, # this doesn't matter because we are burst
burst_altitude=burst_altitude,
descent_rate=descent_rate
)
)
]
# The balloon is either floating, or ascending.
if ("no_flight_doc" in _flight_doc) and (_flight_doc["no_flight_doc"] == True) :
# We don't have any information provided, so we need to make some assumptions.
# First up - are we floating?
if abs(value['rate']) <= ASCENT_RATE_THRESHOLD:
# If we are, run a float prediction, based off the current altitude.
logging.debug(f"{serial} - no flight doc, running float profile")
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_float_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=1, # this doesn't matter because we are floating
burst_altitude=value['alt']+1,
)
)
]
else:
# We are ascending, run an ascent prediction using the standard parameters.
logging.debug(f"{serial} - no flight doc, running default ascent profile")
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_standard_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=value['rate'],
burst_altitude=burst_altitude,
descent_rate=_flight_doc['descent_rate']
)
)
]
else:
# We have a flight document specified!
if _flight_doc["float_expected"]:
# The flight document has specified that we are expecting a float.
if abs(value['rate']) <= ASCENT_RATE_THRESHOLD:
# We are in float - run a float prediction based on the current altitude.
logging.debug(f"{serial} running float profile")
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_float_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=1, # this doesn't matter because we are floating
burst_altitude=value['alt']+1,
)
)
]
else:
# We are still ascending, on the way to an expected float
# Run a float prediction using the specified expected float altitude.
logging.debug(f"{serial} running ascending float profile")
if abs(value['rate']) > ASCENT_RATE_THRESHOLD:
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_float_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=value['rate'],
burst_altitude=burst_altitude,
)
)
]
else:
# The flight document has not specified that a float is expected.
if abs(value['rate']) <= ASCENT_RATE_THRESHOLD:
# We didn't expect a float, but we are in one anyway.
# Run an ascent prediction, but with with an imminent burst.
logging.debug(f"{serial} - flight doc available but in float, near-burst ascent prediction")
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_standard_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=1,
burst_altitude=value['alt']+1,
descent_rate=_flight_doc['descent_rate']
)
)
]
else:
# We are on ascent, run a normal ascent prediction.
logging.debug(f"{serial} - flight doc available, running standard ascent profile")
return [
serial,
await loop.run_in_executor(
None,
functools.partial(
get_standard_prediction,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=value['rate'],
burst_altitude=burst_altitude,
descent_rate=_flight_doc['descent_rate']
)
)
]
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