mirror of
https://github.com/projecthorus/sondehub-infra.git
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878 lines
31 KiB
Python
878 lines
31 KiB
Python
from json.encoder import py_encode_basestring_ascii
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import boto3
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import botocore.credentials
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from botocore.awsrequest import AWSRequest
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from botocore.endpoint import URLLib3Session
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from botocore.auth import SigV4Auth
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import json
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import os
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from datetime import datetime, timedelta, timezone
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import sys, traceback
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import http.client
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import math
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import logging
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import gzip
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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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#
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# FLIGHT PROFILE DEFAULTS
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#
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# If we have no better estimates for flight profile, use these:
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PREDICT_DEFAULTS = {'ascent_rate': 5.0, 'burst_altitude': 26000.0, 'descent_rate': 6.0}
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# For some sonde types we can make better assumptions
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SONDE_TYPE_PREDICT_DEFAULTS = {
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'LMS6': {'ascent_rate': 5.0, 'burst_altitude': 32000.0, 'descent_rate': 3.0},
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}
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#
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# LAUNCH SITE ALLOCATION SETTINGS
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#
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# Immediately allocate a launch site if it is within this distance (straight line)
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# of a known launch site.
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LAUNCH_ALLOCATE_RANGE_MIN = 4000 # metres
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LAUNCH_ALLOCATE_RANGE_MAX = 30000 # metres
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LAUNCH_ALLOCATE_RANGE_SCALING = 1.5 # Scaling factor - launch allocation range is min(current alt * this value , launch allocate range max)
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# Do not run predictions if the ascent or descent rate is less than this value
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ASCENT_RATE_THRESHOLD = 0.5
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def flight_profile_by_type(sonde_type):
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"""
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Determine the appropriate flight profile based on radiosonde type
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"""
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for _def_type in SONDE_TYPE_PREDICT_DEFAULTS:
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if _def_type in sonde_type:
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return SONDE_TYPE_PREDICT_DEFAULTS[_def_type].copy()
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return PREDICT_DEFAULTS.copy()
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def getDensity(altitude):
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"""
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Calculate the atmospheric density for a given altitude in metres.
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This is a direct port of the oziplotter Atmosphere class
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"""
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# Constants
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airMolWeight = 28.9644 # Molecular weight of air
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densitySL = 1.225 # Density at sea level [kg/m3]
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pressureSL = 101325 # Pressure at sea level [Pa]
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temperatureSL = 288.15 # Temperature at sea level [deg K]
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gamma = 1.4
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gravity = 9.80665 # Acceleration of gravity [m/s2]
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tempGrad = -0.0065 # Temperature gradient [deg K/m]
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RGas = 8.31432 # Gas constant [kg/Mol/K]
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R = 287.053
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deltaTemperature = 0.0
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# Lookup Tables
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altitudes = [0, 11000, 20000, 32000, 47000, 51000, 71000, 84852]
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pressureRels = [
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1,
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2.23361105092158e-1,
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5.403295010784876e-2,
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8.566678359291667e-3,
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1.0945601337771144e-3,
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6.606353132858367e-4,
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3.904683373343926e-5,
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3.6850095235747942e-6,
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]
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temperatures = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65, 186.946]
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tempGrads = [-6.5, 0, 1, 2.8, 0, -2.8, -2, 0]
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gMR = gravity * airMolWeight / RGas
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# Pick a region to work in
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i = 0
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if altitude > 0:
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while altitude > altitudes[i + 1]:
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i = i + 1
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# Lookup based on region
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baseTemp = temperatures[i]
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tempGrad = tempGrads[i] / 1000.0
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pressureRelBase = pressureRels[i]
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deltaAltitude = altitude - altitudes[i]
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temperature = baseTemp + tempGrad * deltaAltitude
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# Calculate relative pressure
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if math.fabs(tempGrad) < 1e-10:
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pressureRel = pressureRelBase * math.exp(
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-1 * gMR * deltaAltitude / 1000.0 / baseTemp
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)
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else:
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pressureRel = pressureRelBase * math.pow(
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baseTemp / temperature, gMR / tempGrad / 1000.0
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)
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# Add temperature offset
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temperature = temperature + deltaTemperature
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# Finally, work out the density...
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speedOfSound = math.sqrt(gamma * R * temperature)
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pressure = pressureRel * pressureSL
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density = densitySL * pressureRel * temperatureSL / temperature
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return density
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def seaLevelDescentRate(descent_rate, altitude):
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""" Calculate the descent rate at sea level, for a given descent rate at altitude """
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rho = getDensity(altitude)
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return math.sqrt((rho / 1.225) * math.pow(descent_rate, 2))
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# Earthmaths code by Daniel Richman (thanks!)
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# Copyright 2012 (C) Daniel Richman; GNU GPL 3
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def position_info(listener, balloon):
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"""
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Calculate and return information from 2 (lat, lon, alt) tuples
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Returns a dict with:
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- angle at centre
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- great circle distance
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- distance in a straight line
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- bearing (azimuth or initial course)
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- elevation (altitude)
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Input and output latitudes, longitudes, angles, bearings and elevations are
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in degrees, and input altitudes and output distances are in meters.
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"""
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# Earth:
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radius = 6371000.0
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(lat1, lon1, alt1) = listener
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(lat2, lon2, alt2) = balloon
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lat1 = radians(lat1)
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lat2 = radians(lat2)
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lon1 = radians(lon1)
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lon2 = radians(lon2)
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# Calculate the bearing, the angle at the centre, and the great circle
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# distance using Vincenty's_formulae with f = 0 (a sphere). See
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# http://en.wikipedia.org/wiki/Great_circle_distance#Formulas and
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# http://en.wikipedia.org/wiki/Great-circle_navigation and
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# http://en.wikipedia.org/wiki/Vincenty%27s_formulae
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d_lon = lon2 - lon1
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sa = cos(lat2) * sin(d_lon)
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sb = (cos(lat1) * sin(lat2)) - (sin(lat1) * cos(lat2) * cos(d_lon))
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bearing = atan2(sa, sb)
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aa = sqrt((sa ** 2) + (sb ** 2))
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ab = (sin(lat1) * sin(lat2)) + (cos(lat1) * cos(lat2) * cos(d_lon))
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angle_at_centre = atan2(aa, ab)
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great_circle_distance = angle_at_centre * radius
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# Armed with the angle at the centre, calculating the remaining items
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# is a simple 2D triangley circley problem:
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# Use the triangle with sides (r + alt1), (r + alt2), distance in a
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# straight line. The angle between (r + alt1) and (r + alt2) is the
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# angle at the centre. The angle between distance in a straight line and
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# (r + alt1) is the elevation plus pi/2.
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# Use sum of angle in a triangle to express the third angle in terms
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# of the other two. Use sine rule on sides (r + alt1) and (r + alt2),
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# expand with compound angle formulae and solve for tan elevation by
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# dividing both sides by cos elevation
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ta = radius + alt1
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tb = radius + alt2
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ea = (cos(angle_at_centre) * tb) - ta
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eb = sin(angle_at_centre) * tb
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elevation = atan2(ea, eb)
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# Use cosine rule to find unknown side.
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distance = sqrt((ta ** 2) + (tb ** 2) - 2 * tb * ta * cos(angle_at_centre))
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# Give a bearing in range 0 <= b < 2pi
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if bearing < 0:
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bearing += 2 * pi
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return {
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"listener": listener,
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"balloon": balloon,
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"listener_radians": (lat1, lon1, alt1),
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"balloon_radians": (lat2, lon2, alt2),
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"angle_at_centre": degrees(angle_at_centre),
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"angle_at_centre_radians": angle_at_centre,
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"bearing": degrees(bearing),
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"bearing_radians": bearing,
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"great_circle_distance": great_circle_distance,
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"straight_distance": distance,
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"elevation": degrees(elevation),
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"elevation_radians": elevation,
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}
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def compare_launch_sites(sites, launch_estimate, altitude=0):
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"""
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Compare a provided launch position estimate with all known launch sites
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If a launch site is within a threshold, return the launch site.
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"""
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launch_site = None
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launch_site_range = 999999999999999
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for _site in sites:
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try:
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_site_pos = [sites[_site]['position'][1], sites[_site]['position'][0], sites[_site]['alt']]
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_pos_info = position_info(_site_pos, launch_estimate)
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if _pos_info['straight_distance'] < launch_site_range:
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launch_site = _site
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launch_site_range = _pos_info['straight_distance']
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except Exception as e:
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logging.error(f"Error comparing launch site with estimate: {str(e)}")
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print(_site_pos)
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print(launch_estimate)
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continue
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# print(sites[launch_site])
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# print(launch_site_range)
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_allocate_range = min(LAUNCH_ALLOCATE_RANGE_MAX, max(LAUNCH_ALLOCATE_RANGE_MIN, altitude*LAUNCH_ALLOCATE_RANGE_SCALING))
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if launch_site_range < _allocate_range:
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return {'site':launch_site, 'range': launch_site_range}
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else:
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return None
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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']):
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"""
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Request a standard flight path prediction from Tawhiri.
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Notes:
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- The burst_altitude must be higher than the current altitude.
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- Longitude is in the range 0-360.0
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- All ascent/descent rates must be positive.
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"""
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# Bomb out if the rates are too low.
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if ascent_rate < ASCENT_RATE_THRESHOLD:
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return None
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if descent_rate < ASCENT_RATE_THRESHOLD:
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return None
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# Shift longitude into the appropriate range for Tawhiri
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if longitude < 0:
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longitude += 360.0
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# Generate the prediction URL
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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}"
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conn.request("GET", url)
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res = conn.getresponse()
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data = res.read()
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if res.code != 200:
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logging.debug(data)
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return None
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pred_data = json.loads(data.decode("utf-8"))
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path = []
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if 'prediction' in pred_data:
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for stage in pred_data['prediction']:
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# Probably don't need to worry about this, it should only result in one or two points
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# in 'ascent'.
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if stage['stage'] == 'ascent' and current_rate < 0: # ignore ascent stage if we have already burst
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continue
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else:
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for item in stage['trajectory']:
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path.append({
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"time": int(datetime.fromisoformat(item['datetime'].split(".")[0].replace("Z","")).timestamp()),
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"lat": item['latitude'],
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"lon": item['longitude'] - 360 if item['longitude'] > 180 else item['longitude'],
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"alt": item['altitude'],
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})
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pred_data['path'] = path
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return pred_data
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else:
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return None
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def get_launch_estimate(conn, timestamp, latitude, longitude, altitude, ascent_rate=PREDICT_DEFAULTS['ascent_rate'], current_rate=5.0):
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"""
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Estimate the launch site of a sonde based on a current ascent position.
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Notes:
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- Longitude is in the range 0-360.0
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- All ascent/descent rates must be positive.
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UNTESTED
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"""
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# Bomb out if the rates are too low.
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if ascent_rate < ASCENT_RATE_THRESHOLD:
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return None
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# Shift longitude into the appropriate range for Tawhiri
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if longitude < 0:
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longitude += 360.0
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# Generate the prediction URL
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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}"
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conn.request("GET", url)
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res = conn.getresponse()
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data = res.read()
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if res.code != 200:
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logging.debug(data)
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return None
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pred_data = json.loads(data.decode("utf-8"))
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path = []
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if 'prediction' in pred_data:
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for stage in pred_data['prediction']:
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# Probably don't need to worry about this, it should only result in one or two points
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# in 'ascent'.
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if stage['stage'] == 'ascent' and current_rate < 0: # ignore ascent stage if we have already burst
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continue
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else:
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for item in stage['trajectory']:
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path.append({
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"time": int(datetime.fromisoformat(item['datetime'].split(".")[0].replace("Z","")).timestamp()),
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"lat": item['latitude'],
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"lon": item['longitude'] - 360 if item['longitude'] > 180 else item['longitude'],
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"alt": item['altitude'],
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})
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pred_data['path'] = path
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return pred_data
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else:
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return None
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# return a dict key'd by serial with reverse prediction data
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def get_reverse_predictions():
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path = "reverse-prediction-*/_search"
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payload = {
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"size": 1000,
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"sort": [
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{
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"datetime": {
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"order": "asc",
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"unmapped_type": "boolean"
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}
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}
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],
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"query": {
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"bool": {
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"filter": [
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{
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"range": {
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"datetime": {
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"gte": "now-1d",
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"lte": "now",
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"format": "strict_date_optional_time"
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}
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}
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}
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]
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}
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}
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}
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logging.debug("Start ES Request")
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results = es_request(json.dumps(payload), path, "POST")
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logging.debug("Finished ES Request")
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return { x['_source']['serial'] : x['_source'] for x in results['hits']['hits']}
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# Example data structure from get_launch_sites
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# {
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# '01028': {'station': '01028', 'rs_types': ['23'], 'position': [19.0012, 74.5038], 'alt': 20, 'station_name': 'Bjornoya (Norway)', 'times': ['0:00:00', '0:06:00', '0:12:00', '0:18:00']},
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# '-3': {'station': '-3', 'rs_types': ['17'], 'position': [-1.23813, 44.35714], 'alt': 15, 'station_name': 'DGA Essais de missiles (France)', 'burst_altitude': 20000},
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# '-2': {'station': '-2', 'rs_types': ['63', '77'], 'position': [2.60012, 48.337861], 'alt': 118, 'station_name': 'METEOMODEM Headquarters (France)'},
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# ...
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# }
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def get_launch_sites():
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path = "sites/_search"
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payload = {
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"size": 10000
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}
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logging.debug("Start ES Request")
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results = es_request(json.dumps(payload), path, "POST")
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logging.debug("Finished ES Request")
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return {x['_source']['station']: x['_source'] for x in results['hits']['hits']}
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def bulk_upload_es(index_prefix,payloads):
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body=""
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for payload in payloads:
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body += "{\"index\":{}}\n" + json.dumps(payload) + "\n"
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body += "\n"
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date_prefix = datetime.now().strftime("%Y-%m")
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result = es_request(body, f"{index_prefix}-{date_prefix}/_doc/_bulk", "POST")
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if 'errors' in result and result['errors'] == True:
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error_types = [x['index']['error']['type'] for x in result['items'] if 'error' in x['index']] # get all the error types
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error_types = [a for a in error_types if a != 'mapper_parsing_exception'] # filter out mapper failures since they will never succeed
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if error_types:
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print(result)
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raise RuntimeError
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def predict(event, context):
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path = "telm-*/_search"
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payload = {
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"aggs": {
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"2": {
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"terms": {
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"field": "serial.keyword",
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"order": {
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"_key": "desc"
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},
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"size": 1000
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},
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"aggs": {
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"3": {
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"date_histogram": {
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"field": "datetime",
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"fixed_interval": "5s"
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},
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"aggs": {
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"1": {
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"top_hits": {
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"docvalue_fields": [
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{
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"field": "alt"
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}
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],
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"_source": "alt",
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"size": 1,
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"sort": [
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{
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"datetime": {
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"order": "desc"
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}
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}
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]
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}
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},
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"4": {
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"serial_diff": {
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"buckets_path": "4-metric",
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"gap_policy": "skip",
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"lag": 5
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}
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},
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"5": {
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"top_hits": {
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"docvalue_fields": [
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{
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"field": "position"
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}
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],
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"_source": {"includes": ["position", "type", "subtype"]},
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"size": 1,
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"sort": [
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{
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"datetime": {
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"order": "desc"
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}
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}
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]
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}
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},
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"4-metric": {
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"avg": {
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"field": "alt"
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}
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}
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}
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}
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}
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}
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},
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"size": 0,
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"stored_fields": [
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"*"
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],
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"script_fields": {},
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"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"],
|
|
"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
|
|
}
|
|
except:
|
|
pass
|
|
|
|
|
|
launch_sites = get_launch_sites()
|
|
reverse_predictions = get_reverse_predictions()
|
|
|
|
|
|
conn = http.client.HTTPSConnection("tawhiri.v2.sondehub.org")
|
|
serial_data={}
|
|
reverse_serial_data = {}
|
|
logging.debug("Start Predict")
|
|
for serial in serials:
|
|
|
|
value = serials[serial]
|
|
|
|
#
|
|
# Flight Profile selection
|
|
#
|
|
# Fallback Option - use flight profile data based on sonde type.
|
|
_flight_profile = flight_profile_by_type(value['type'])
|
|
|
|
|
|
# Check if we have already run a reverse prediction on this serial
|
|
if serial in reverse_predictions:
|
|
logging.debug(f"Found reverse prediction for {serial}.")
|
|
_rev_pred = reverse_predictions[serial]
|
|
|
|
#print(_rev_pred)
|
|
|
|
if 'launch_site' in _rev_pred:
|
|
# This serial number has been assigned to a launch site!
|
|
# Grab the launch site information
|
|
_site_info = launch_sites[_rev_pred['launch_site']]
|
|
|
|
# If we have flight profile data, update the default flight profile
|
|
if 'ascent_rate' in _site_info:
|
|
_flight_profile['ascent_rate'] = _site_info['ascent_rate']
|
|
|
|
if 'burst_altitude' in _site_info:
|
|
_flight_profile['burst_altitude'] = _site_info['burst_altitude']
|
|
|
|
if 'descent_rate' in _site_info:
|
|
_flight_profile['descent_rate'] = _site_info['descent_rate']
|
|
|
|
logging.debug(f"{serial} - Using Flight Profile data for Launch site: {_site_info['station_name']}")
|
|
else:
|
|
# No launch site was allocated...
|
|
# TODO - Try again?
|
|
pass
|
|
|
|
else:
|
|
# No reverse prediction data!
|
|
# We can only run a reverse prediction with a sonde on ascent.
|
|
#print(f"{serial}: {value['rate']}")
|
|
if value['rate'] > 0.5:
|
|
|
|
# Try and run a reverse prediction
|
|
logging.info(f"Running reverse predict for {serial}")
|
|
|
|
longitude = float(value['position'][1].strip())
|
|
latitude = float(value['position'][0].strip())
|
|
|
|
_rev_pred = get_launch_estimate(
|
|
conn,
|
|
value['time'],
|
|
latitude,
|
|
longitude,
|
|
value['alt'],
|
|
current_rate=value['rate'],
|
|
ascent_rate=value['rate'],
|
|
)
|
|
|
|
if _rev_pred:
|
|
|
|
# Attempt to find a launch site near to the launch estimate.
|
|
_launch_estimate = [_rev_pred['launch_estimate']['latitude'], _rev_pred['launch_estimate']['longitude'], _rev_pred['launch_estimate']['altitude']]
|
|
_alloc_site = compare_launch_sites(launch_sites, _launch_estimate, value['alt'])
|
|
|
|
if _alloc_site:
|
|
# We have found the launch site!
|
|
# {'site':_site, 'range': launch_site_range}
|
|
logging.info(f"Allocated {serial} to launch site {launch_sites[_alloc_site['site']]['station_name']} ({_alloc_site['site']}) with range {_alloc_site['range']:.1f}.")
|
|
|
|
# Add launch site into the prediction data
|
|
_rev_pred['launch_site'] = _alloc_site['site']
|
|
_rev_pred['launch_site_range_estimate'] = _alloc_site['range']
|
|
|
|
# If we have flight profile data, update the default flight profile
|
|
_site_info = launch_sites[_alloc_site['site']]
|
|
if 'ascent_rate' in _site_info:
|
|
_flight_profile['ascent_rate'] = _site_info['ascent_rate']
|
|
|
|
if 'burst_altitude' in _site_info:
|
|
_flight_profile['burst_altitude'] = _site_info['burst_altitude']
|
|
|
|
if 'descent_rate' in _site_info:
|
|
_flight_profile['descent_rate'] = _site_info['descent_rate']
|
|
|
|
|
|
# Add to dict for upload later.
|
|
reverse_serial_data[serial] = _rev_pred
|
|
|
|
else:
|
|
# Launch estimate prediction failed.
|
|
pass
|
|
|
|
|
|
|
|
#print(value)
|
|
#print(_flight_profile)
|
|
logging.debug(f"Running prediction for {serial} using flight profile {str(_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.
|
|
if descent_rate < 0.5:
|
|
continue
|
|
|
|
# Now to determine the burst altitude
|
|
if value['rate'] < 0:
|
|
# On descent (rate < 0), we need to set the burst altitude just higher than our current altitude for
|
|
# the predictor to be happy
|
|
burst_altitude = value['alt']+0.05
|
|
else:
|
|
# 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']
|
|
|
|
longitude = float(value['position'][1].strip())
|
|
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
|
|
)
|
|
|
|
|
|
|
|
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(
|
|
{
|
|
"serial": serial,
|
|
"type": serials[serial]['type'],
|
|
"subtype": serials[serial]['subtype'],
|
|
"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'],
|
|
"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?
|
|
"data": value['path']
|
|
}
|
|
)
|
|
|
|
# Collate and upload reverse predictions
|
|
output_reverse = []
|
|
for serial in reverse_serial_data:
|
|
value = reverse_serial_data[serial]
|
|
|
|
if value is not None:
|
|
|
|
_tmp = {
|
|
"serial": serial,
|
|
"type": serials[serial]['type'],
|
|
"subtype": serials[serial]['subtype'],
|
|
"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'],
|
|
"data": value['path']
|
|
}
|
|
if 'launch_site' in value:
|
|
_tmp['launch_site'] = value['launch_site']
|
|
|
|
if 'launch_site_range_estimate' in value:
|
|
_tmp['launch_site_range_estimate'] = value['launch_site_range_estimate']
|
|
|
|
output_reverse.append(_tmp)
|
|
|
|
|
|
if len(output) > 0:
|
|
bulk_upload_es("predictions", output)
|
|
|
|
if len(output_reverse) > 0:
|
|
bulk_upload_es("reverse-prediction", output_reverse)
|
|
|
|
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__":
|
|
|
|
# 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.")
|
|
|
|
# test = predict(
|
|
# {},{}
|
|
# )
|
|
#print(get_launch_sites())
|
|
#print(get_reverse_predictions())
|
|
# for _serial in test:
|
|
# print(f"{_serial['serial']}: {len(_serial['data'])}")
|
|
|
|
|
|
logging.basicConfig(
|
|
format="%(asctime)s %(levelname)s:%(message)s", level=logging.DEBUG
|
|
)
|
|
|
|
print(predict(
|
|
{},{}
|
|
))
|
|
# 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"
|
|
# }]
|
|
# )
|
|
|