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 conn.request("GET", 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}" ) 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": "" }},{} ) ) # 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