sondehub-infra/predict/lambda_function.py

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


HOST = os.getenv("ES")
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))


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": "position",
                                "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-1h",
                                    "lte": "now",
                            "format": "strict_date_optional_time"
                            }
                        }
                        }
                    ],
                    "should": [],
                    "must_not": [
                    {
                        "match_phrase": {
                            "software_name": "SondehubV1"
                        }
                    }
                ]
                    }
                }
            }
    if "queryStringParameters" in event:
        if "vehicles" in event["queryStringParameters"] and event["queryStringParameters"]["vehicles"] != "RS_*;*chase"  and event["queryStringParameters"]["vehicles"] != "":
            payload["query"]["bool"]["filter"].append(
                {
                    "match_phrase": {
                        "serial": str(event["queryStringParameters"]["vehicles"])
                    }
                }
            )
    results = es_request(payload, path, "GET")
    


    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']
            }
        except:
            pass

    conn = http.client.HTTPSConnection("predict.cusf.co.uk")
    serial_data={}
    for serial in serials:
        value = serials[serial]
        ascent_rate=value['rate'] if value['rate'] > 0 else 5 # this shouldn't really be used but it makes the API happy
        descent_rate= seaLevelDescentRate(abs(value['rate']),value['alt']) if value['rate'] < 0 else 6
        if value['rate'] < 0:
            burst_altitude = value['alt']+0.05
        else:
            burst_altitude = (value['alt']+0.05) if value['alt'] > 26000 else 26000
        url = f"/api/v1/?launch_latitude={value['position'][0].strip()}&launch_longitude={float(value['position'][1].strip())+ 180}&launch_datetime={value['time']}&launch_altitude={value['alt']}&ascent_rate={ascent_rate}&burst_altitude={burst_altitude}&descent_rate={descent_rate}"
        print(url)
        conn.request("GET", url
            
        )
        res = conn.getresponse()
        data = res.read()
        serial_data[serial] = json.loads(data.decode("utf-8"))

    output = []
    for serial in serial_data:
        value = serial_data[serial]

        
        data = []

        for stage in value['prediction']:
            if stage['stage'] == 'ascent' and serials[serial]['rate'] < 0: # ignore ascent stage if we have already burst
                continue
            else:
                for item in stage['trajectory']:
                    data.append({
                        "time": int(datetime.fromisoformat(item['datetime'].split(".")[0].replace("Z","")).timestamp()),
                        "lat": item['latitude'],
                        "lon": item['longitude']-180,
                        "alt": item['altitude'],
                    })

        output.append({
            "vehicle": serial,
            "time": value['request']['launch_datetime'],
            "latitude": value['request']['launch_latitude'],
            "longitude": value['request']['launch_longitude'],
            "altitude": value['request']['launch_altitude'],
            "ascent_rate":value['request']['ascent_rate'],
            "descent_rate":value['request']['descent_rate'],
            "burst_altitude": value['request']['burst_altitude'],
            "landed": 0,
            "data":  json.dumps(data)
        })

    return json.dumps(output)

def es_request(payload, path, method):
    # get aws creds
    session = boto3.Session()

    params = json.dumps(payload)
    headers = {"Host": HOST, "Content-Type": "application/json"}
    request = AWSRequest(
        method="POST", 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())
    return json.loads(r.text)


if __name__ == "__main__":
    # print(get_sondes({"queryStringParameters":{"lat":"-28.22717","lon":"153.82996","distance":"50000"}}, {}))
    # mode: 6hours
# type: positions
# format: json
# max_positions: 0
# position_id: 0
# vehicles: RS_*;*chase
    print(
        predict(
          {"queryStringParameters" : {
"vehicles": "P4930339"
}},{}
        )
    )


# get list of sondes,    serial, lat,lon, alt
   #           and        current rate
# for each one, request http://predict.cusf.co.uk/api/v1/?launch_latitude=-37.8136&launch_longitude=144.9631&launch_datetime=2021-02-22T00:15:18.513413Z&launch_altitude=30000&ascent_rate=5&burst_altitude=30000.1&descent_rate=5
   # have to set the burst alt slightly higher than the launch