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sondehub-infra/predict_updater/lambda_function.py

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import boto3
import botocore.credentials
from botocore.awsrequest import AWSRequest
from botocore.endpoint import URLLib3Session
from botocore.auth import SigV4Auth
import json
import os
from datetime import datetime, timedelta, timezone
import sys, traceback
import http.client
import math
import logging
import gzip
from io import BytesIO
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from math import radians, degrees, sin, cos, atan2, sqrt, pi
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HOST = os.getenv("ES")
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#
# FLIGHT PROFILE DEFAULTS
#
# If we have no better estimates for flight profile, use these:
PREDICT_DEFAULTS = {'ascent_rate': 5.0, 'burst_altitude': 26000.0, 'descent_rate': 6.0}
# For some sonde types we can make better assumptions
SONDE_TYPE_PREDICT_DEFAULTS = {
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'LMS6-403': {'ascent_rate': 5.0, 'burst_altitude': 32000.0, 'descent_rate': 3.0},
'LMS6-1680': {'ascent_rate': 5.0, 'burst_altitude': 32000.0, 'descent_rate': 3.0},
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}
#
# 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 = 4000 # metres
# Do not run predictions if the ascent or descent rate is less than this value
ASCENT_RATE_THRESHOLD = 0.5
def flight_profile_by_type(sonde_type):
"""
Determine the appropriate flight profile based on radiosonde type
"""
for _def_type in SONDE_TYPE_PREDICT_DEFAULTS:
if _def_type in sonde_type:
return SONDE_TYPE_PREDICT_DEFAULTS[_def_type]
return PREDICT_DEFAULTS
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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))
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# 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_standard_prediction(conn, 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}"
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_launch_estimate(conn, timestamp, latitude, longitude, altitude, ascent_rate=PREDICT_DEFAULTS['ascent_rate']):
"""
Estimate the launch site of a sonde based on a current ascent position.
Notes:
- Longitude is in the range 0-360.0
- All ascent/descent rates must be positive.
UNTESTED
"""
# Bomb out if the rates are too low.
if ascent_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/?profile=reverse_profile&launch_latitude={latitude}&launch_longitude={longitude}&launch_datetime={timestamp}&launch_altitude={altitude:.2f}&ascent_rate={ascent_rate:.2f}"
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"))
if 'launch_estimate' in pred_data:
return pred_data['launch_estimate']
else:
return None
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# return a dict key'd by serial with reverse prediction data
def get_reverse_predictions():
path = "reverse-prediction-*/_search"
payload = {
"size": 1000,
"sort": [
{
"datetime": {
"order": "asc",
"unmapped_type": "boolean"
}
}
],
"query": {
"bool": {
"filter": [
{
"range": {
"datetime": {
"gte": "now-1d",
"lte": "now",
"format": "strict_date_optional_time"
}
}
}
]
}
}
}
logging.debug("Start ES Request")
results = es_request(json.dumps(payload), path, "POST")
logging.debug("Finished ES Request")
return { x['_source']['serial'] : x['_source'] for x in results['hits']['hits']}
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}/_doc/_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
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def predict(event, context):
path = "telm-*/_search"
payload = {
"aggs": {
"2": {
"terms": {
"field": "serial.keyword",
"order": {
"_key": "desc"
},
"size": 1000
},
"aggs": {
"3": {
"date_histogram": {
"field": "datetime",
"fixed_interval": "5s"
},
"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": 5
}
},
"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": [],
"must_not": [
{
"match_phrase": {
"software_name": "SondehubV1"
}
}
]
}
},
"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']/25, # 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'],
"type": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['5']['hits']['hits'][0]["_source"]["type"],
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"subtype": sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['5']['hits']['hits'][0]["_source"]["subtype"] if "subtype" in sorted(x['3']['buckets'], key=lambda k: k['key_as_string'])[-1]['5']['hits']['hits'][0]["_source"] else None
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}
except:
pass
conn = http.client.HTTPSConnection("tawhiri.v2.sondehub.org")
serial_data={}
logging.debug("Start Predict")
for serial in serials:
value = serials[serial]
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# TODO - If this serial already has a launch site allocated, get the default flight profile for it
#
# TODO - If this serial doesn't have a launch site allocated, try and allocate one.
# - Estimate the launch site using a call to tawhiri
# - For each known launch site, calculate the straight-line distance between the estimted launch location
# and the launch site (use position_info function above). Keep the smallest straight-line distance and its corresponding launch site.
# - If the straight-line distance is < LAUNCH_ALLOCATE_RANGE, assign the sonde to that launch site.
# - Otherwise, set the serial's launch site to 'unknown'.
#
# Otherwise, fallback to using a flight profile based on the sonde type.
_flight_profile = flight_profile_by_type(value['type'])
#print(value)
#print(_flight_profile)
# Determine current ascent rate
# If the value is < 0.5 (e.g. we are on descent, or not moving), we just use a default value.
ascent_rate=value['rate'] if value['rate'] > 0.5 else _flight_profile['ascent_rate']
# If we are on descent, estimate the sea-level descent rate from the current descent rate
# Otherwise, use the flight profile descent rate
descent_rate= seaLevelDescentRate(abs(value['rate']),value['alt']) if value['rate'] < 0 else _flight_profile['descent_rate']
# If the resultant sea-level descent rate is very small, it means we're probably landed
# so dont run a prediction for this sonde.
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if descent_rate < 0.5:
continue
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# Now to determine the burst altitude
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if value['rate'] < 0:
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# On descent (rate < 0), we need to set the burst altitude just higher than our current altitude for
# the predictor to be happy
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burst_altitude = value['alt']+0.05
else:
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# Otherwise, on ascent we either use the expected burst altitude, or we
# add a little bit on to our current altitude.
burst_altitude = (value['alt']+0.05) if value['alt'] > _flight_profile['burst_altitude'] else _flight_profile['burst_altitude']
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longitude = float(value['position'][1].strip())
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latitude = float(value['position'][0].strip())
#print(f"Prediction Parameters for {serial} at {latitude}, {longitude}, {value['alt']}: {ascent_rate}/{burst_altitude}/{descent_rate}")
# Run prediction! This will return None if there is an error
serial_data[serial] = get_standard_prediction(
conn,
value['time'],
latitude,
longitude,
value['alt'],
current_rate=value['rate'],
ascent_rate=ascent_rate,
burst_altitude=burst_altitude,
descent_rate=descent_rate
)
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logging.debug("Stop Predict")
output = []
for serial in serial_data:
value = serial_data[serial]
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if value is not None:
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output.append(
{
"serial": serial,
"type": serials[serial]['type'],
"subtype": serials[serial]['subtype'],
"datetime": value['request']['launch_datetime'],
"position": [
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value['request']['launch_longitude'] - 360 if value['request']['launch_longitude'] > 180 else value['request']['launch_longitude'],
value['request']['launch_latitude']
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],
"altitude": value['request']['launch_altitude'],
"ascent_rate": value['request']['ascent_rate'],
"descent_rate": value['request']['descent_rate'],
"burst_altitude": value['request']['burst_altitude'],
"descending": True if serials[serial]['rate'] < 0 else False,
"landed": False, # I don't think this gets used anywhere?
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"data": value['path']
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}
)
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bulk_upload_es("predictions", output)
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logging.debug("Finished")
return
def es_request(params, path, method):
# get aws creds
session = boto3.Session()
compressed = BytesIO()
with gzip.GzipFile(fileobj=compressed, mode='w') as f:
f.write(params.encode('utf-8'))
params = compressed.getvalue()
headers = {"Host": HOST, "Content-Type": "application/json", "Content-Encoding":"gzip"}
request = AWSRequest(
method=method, url=f"https://{HOST}/{path}", data=params, headers=headers
)
SigV4Auth(boto3.Session().get_credentials(), "es", "us-east-1").add_auth(request)
session = URLLib3Session()
r = session.send(request.prepare())
if r.status_code != 200:
raise RuntimeError
return json.loads(r.text)
if __name__ == "__main__":
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# Predictor test
# conn = http.client.HTTPSConnection("tawhiri.v2.sondehub.org")
# _now = datetime.utcnow().isoformat() + "Z"
# _ascent = get_standard_prediction(conn, _now, -34.0, 138.0, 10.0, burst_altitude=26000)
# print(f"Got {len(_ascent)} data points for ascent prediction.")
# _descent = get_standard_prediction(conn, _now, -34.0, 138.0, 24000.0, burst_altitude=24000.5)
# print(f"Got {len(_descent)} data points for descent prediction.")
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test = predict(
{},{}
)
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# for _serial in test:
# print(f"{_serial['serial']}: {len(_serial['data'])}")
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# print(predict(
# {},{}
# ))
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# bulk_upload_es("reverse-prediction",[{
# "datetime" : "2021-10-04",
# "data" : { },
# "serial" : "R12341234",
# "station" : "-2",
# "subtype" : "RS41-SGM",
# "ascent_rate" : "5",
# "alt" : 1000,
# "position" : [
# 1,
# 2
# ],
# "type" : "RS41"
# }]
# )
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