kopia lustrzana https://github.com/projecthorus/chasemapper
Added Tawhiri predictions. Added geo link for landing position
Mark Jessop
rodzic
fb39078412
commit
e6c314109a
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@ -25,7 +25,8 @@ default_config = {
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'thunderforest_api_key': 'none',
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# Predictor settings
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'pred_enabled': False, # Enable running and display of predicted flight paths.
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'pred_enabled': True, # Enable running and display of predicted flight paths.
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'offline_predictions': False, # Use an offline GFS model and predictor instead of Tawhiri.
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# Default prediction settings (actual values will be used once the flight is underway)
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'pred_model': "Disabled",
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'pred_desc_rate': 6.0,
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@ -87,6 +88,7 @@ def parse_config_file(filename):
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# Predictor
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chase_config['pred_enabled'] = config.getboolean('predictor', 'predictor_enabled')
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chase_config['offline_predictions'] = config.getboolean('predictor', 'offline_predictions')
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chase_config['pred_burst'] = config.getfloat('predictor', 'default_burst')
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chase_config['pred_desc_rate'] = config.getfloat('predictor', 'default_descent_rate')
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chase_config['pred_binary'] = config.get('predictor','pred_binary')
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@ -0,0 +1,134 @@
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#!/usr/bin/env python
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#
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# Project Horus - Browser-Based Chase Mapper - Tawhiri Interface
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#
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# Grab predictions from the Tawhiri Predictions API
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# Refer here for documentation on Tawhiri: https://tawhiri.readthedocs.io/en/latest/api.html
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#
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# Copyright (C) 2020 Mark Jessop <vk5qi@rfhead.net>
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# Released under GNU GPL v3 or later
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#
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import datetime
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import logging
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import pytz
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import requests
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import subprocess
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from dateutil.parser import parse
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from threading import Thread
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TAWHIRI_API_URL = "http://predict.cusf.co.uk/api/v1/"
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def get_tawhiri_prediction(
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launch_datetime,
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launch_latitude,
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launch_longitude,
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launch_altitude=0,
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ascent_rate=5.0,
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burst_altitude=30000.0,
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descent_rate=5.0,
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profile='standard_profile',
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dataset=None,
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timeout = 10
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):
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""" Request a Prediction from the Tawhiri Predictor API """
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# Localise supplied time to UTC if not already done
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if launch_datetime.tzinfo is None:
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launch_datetime = pytz.utc.localize(launch_datetime)
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# Create RFC3339-compliant timestamp
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_dt_rfc3339 = launch_datetime.isoformat()
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_params = {
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"launch_latitude": launch_latitude,
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"launch_longitude": launch_longitude,
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"launch_altitude": launch_altitude,
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"launch_datetime": _dt_rfc3339,
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"ascent_rate": ascent_rate,
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"descent_rate": descent_rate,
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"burst_altitude": burst_altitude,
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"profile": profile
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}
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if dataset:
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_params["dataset"] = dataset
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logging.debug("Tawhiri - Requesting prediction using parameters: %s" % str(_params))
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try:
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_r = requests.get(TAWHIRI_API_URL, params=_params, timeout=timeout)
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_json = _r.json()
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if 'error' in _json:
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# The Tawhiri API has returned an error
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_error = "%s: %s" % (_json['error']['type'], _json['error']['description'])
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logging.error("Tawhiri - %s" % _error)
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return None
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else:
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return parse_tawhiri_data(_json)
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except Exception as e:
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logging.error("Tawhiri - Error running prediction: %s" % str(e))
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return None
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def parse_tawhiri_data(data):
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""" Parse a returned flight trajectory from Tawhiri, and convert it to a cusf_predictor_wrapper compatible format """
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# Extract dataset information
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_dataset = parse(data['request']['dataset'])
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_dataset = _dataset.strftime("%Y%m%d%Hz")
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_path = []
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for _stage in data['prediction']:
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_trajectory = _stage['trajectory']
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for _point in _trajectory:
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_dt = parse(_point['datetime']).timestamp()
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_path.append([_dt, _point['latitude'], _point['longitude'], _point['altitude']])
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_output = {
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"dataset": _dataset,
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"path": _path
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}
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return _output
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if __name__ == "__main__":
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import datetime
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import pprint
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logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.INFO)
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_now = datetime.datetime.utcnow()
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# Regular complete-flightpath prediction
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_data = get_tawhiri_prediction(
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launch_datetime=_now,
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launch_latitude=-34.9499,
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launch_longitude=138.5194,
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launch_altitude=0,
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)
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pprint.pprint(_data)
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# Descent prediction
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_data = get_tawhiri_prediction(
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launch_datetime=_now,
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launch_latitude=-34.9499,
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launch_longitude=138.5194,
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launch_altitude=10000,
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burst_altitude=10001,
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descent_rate=abs(-6.0)
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)
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pprint.pprint(_data)
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@ -82,17 +82,28 @@ thunderforest_api_key = none
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# Predictor Settings
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# Use of the predictor requires installing the CUSF Predictor Python Wrapper from here:
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# https://github.com/darksidelemm/cusf_predictor_wrapper
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# You also need to compile the predictor binary, and copy it into this directory.
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# By default this will attempt to get predictions from the online Tawhiri Predictions API.
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# Optionally, you can enable offline predictions below.
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[predictor]
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# Enable Predictor (True/False) - This can also be enabled from the web client.
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predictor_enabled = False
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predictor_enabled = True
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# Predictor defaults - these can be modified at runtime in the web interface.
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default_burst = 30000
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default_descent_rate = 5.0
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# Offline Predictions
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# Use of the offline predictor requires installing the CUSF Predictor Python Wrapper from here:
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# https://github.com/darksidelemm/cusf_predictor_wrapper
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# You also need to compile the predictor binary, and copy it into this directory.
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#
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# Note: This setting turns offline predictions *on* by default, which assumes there is a valid
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# GFS dataset already present and available.
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# If you will be using the 'Download Model' button, then leave this at False, and Offline predictions
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# will be enabled once a valid model is available.
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offline_predictions = False
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# Predictory Binary Location
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# Where to find the built CUSF predictor binary. This will usually be ./pred or pred.exe (on Windows)
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pred_binary = ./pred
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195
horusmapper.py
195
horusmapper.py
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@ -29,6 +29,7 @@ from chasemapper.predictor import predictor_spawn_download, model_download_runni
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from chasemapper.habitat import HabitatChaseUploader, initListenerCallsign, uploadListenerPosition
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from chasemapper.logger import ChaseLogger
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from chasemapper.bearings import Bearings
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from chasemapper.tawhiri import get_tawhiri_prediction
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# Define Flask Application, and allow automatic reloading of templates for dev work
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@ -307,7 +308,10 @@ def run_prediction():
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''' Run a Flight Path prediction '''
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global chasemapper_config, current_payloads, current_payload_tracks, predictor, predictor_semaphore
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if (predictor == None) or (chasemapper_config['pred_enabled'] == False):
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if (chasemapper_config['pred_enabled'] == False):
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return
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if (chasemapper_config['offline_predictions'] == True) and (predictor == None):
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return
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# Set the semaphore so we don't accidentally kill the predictor object while it's running.
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@ -338,8 +342,39 @@ def run_prediction():
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else:
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_burst_alt = chasemapper_config['pred_burst']
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logging.info("Running Predictor for: %s." % _payload)
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_pred_path = predictor.predict(
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if predictor == "Tawhiri":
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logging.info("Requesting Prediction from Tawhiri for %s." % _payload)
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# Tawhiri requires that the burst altitude always be higher than the starting altitude.
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if _current_pos['is_descending']:
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_burst_alt = _current_pos['alt'] + 1
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# Tawhiri requires that the ascent rate be > 0 for standard profiles.
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if _current_pos['ascent_rate'] < 0.1:
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_current_pos['ascent_rate'] = 0.1
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_tawhiri = get_tawhiri_prediction(
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launch_datetime=_current_pos['time'],
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launch_latitude=_current_pos['lat'],
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launch_longitude=_current_pos['lon'],
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launch_altitude=_current_pos['alt'],
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burst_altitude=_burst_alt,
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ascent_rate=_current_pos['ascent_rate'],
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descent_rate=_desc_rate,
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)
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if _tawhiri:
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_pred_path = _tawhiri['path']
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_dataset = _tawhiri['dataset'] + " (Online)"
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# Inform the client of the dataset age
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flask_emit_event('predictor_model_update',{'model':_dataset})
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else:
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_pred_path = []
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else:
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logging.info("Running Offline Predictor for %s." % _payload)
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_pred_path = predictor.predict(
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launch_lat=_current_pos['lat'],
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launch_lon=_current_pos['lon'],
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launch_alt=_current_pos['alt'],
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@ -383,17 +418,42 @@ def run_prediction():
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# Abort predictions
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if chasemapper_config['show_abort'] and (_current_pos['alt'] < chasemapper_config['pred_burst']) and (_current_pos['is_descending'] == False):
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logging.info("Running Abort Predictor for: %s." % _payload)
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_abort_pred_path = predictor.predict(
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launch_lat=_current_pos['lat'],
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launch_lon=_current_pos['lon'],
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launch_alt=_current_pos['alt'],
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if predictor == "Tawhiri":
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logging.info("Requesting Abort Prediction from Tawhiri for %s." % _payload)
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# Tawhiri requires that the ascent rate be > 0 for standard profiles.
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if _current_pos['ascent_rate'] < 0.1:
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_current_pos['ascent_rate'] = 0.1
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_tawhiri = get_tawhiri_prediction(
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launch_datetime=_current_pos['time'],
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launch_latitude=_current_pos['lat'],
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launch_longitude=_current_pos['lon'],
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launch_altitude=_current_pos['alt'],
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burst_altitude=_burst_alt + 200,
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ascent_rate=_current_pos['ascent_rate'],
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descent_rate=_desc_rate,
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burst_alt=_current_pos['alt']+200,
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launch_time=_current_pos['time'],
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descent_mode=_current_pos['is_descending'])
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)
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if _tawhiri:
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_abort_pred_path = _tawhiri['path']
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else:
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_abort_pred_path = []
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else:
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logging.info("Running Offline Abort Predictor for: %s." % _payload)
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_abort_pred_path = predictor.predict(
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launch_lat=_current_pos['lat'],
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launch_lon=_current_pos['lon'],
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launch_alt=_current_pos['alt'],
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ascent_rate=_current_pos['ascent_rate'],
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descent_rate=_desc_rate,
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burst_alt=_current_pos['alt']+200,
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launch_time=_current_pos['time'],
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descent_mode=_current_pos['is_descending'])
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if len(_pred_path) > 1:
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# Valid Prediction!
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@ -439,56 +499,73 @@ def run_prediction():
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def initPredictor():
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global predictor, predictor_thread, chasemapper_config, pred_settings
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try:
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from cusfpredict.predict import Predictor
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from cusfpredict.utils import gfs_model_age, available_gfs
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# Check if we have any GFS data
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_model_age = gfs_model_age(pred_settings['gfs_path'])
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if _model_age == "Unknown":
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logging.error("No GFS data in directory.")
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chasemapper_config['pred_model'] = "No GFS Data."
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flask_emit_event('predictor_model_update',{'model':"No GFS data."})
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chasemapper_config['pred_enabled'] = False
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else:
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# Check model contains data to at least 4 hours into the future.
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(_model_start, _model_end) = available_gfs(pred_settings['gfs_path'])
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_model_now = datetime.utcnow() + timedelta(0,60*60*4)
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if (_model_now < _model_start) or (_model_now > _model_end):
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# No suitable GFS data!
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logging.error("GFS Data in directory does not cover now!")
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chasemapper_config['pred_model'] = "Old GFS Data."
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flask_emit_event('predictor_model_update',{'model':"Old GFS data."})
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chasemapper_config['pred_enabled'] = False
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if chasemapper_config['offline_predictions']:
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# Attempt to initialize an Offline Predictor instance
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try:
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from cusfpredict.predict import Predictor
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from cusfpredict.utils import gfs_model_age, available_gfs
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# Check if we have any GFS data
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_model_age = gfs_model_age(pred_settings['gfs_path'])
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if _model_age == "Unknown":
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logging.error("No GFS data in directory.")
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chasemapper_config['pred_model'] = "No GFS Data."
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flask_emit_event('predictor_model_update',{'model':"No GFS data."})
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chasemapper_config['offline_predictions'] = False
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else:
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chasemapper_config['pred_model'] = _model_age
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flask_emit_event('predictor_model_update',{'model':_model_age})
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predictor = Predictor(bin_path=pred_settings['pred_binary'], gfs_path=pred_settings['gfs_path'])
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# Check model contains data to at least 4 hours into the future.
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(_model_start, _model_end) = available_gfs(pred_settings['gfs_path'])
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_model_now = datetime.utcnow() + timedelta(0,60*60*4)
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if (_model_now < _model_start) or (_model_now > _model_end):
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# No suitable GFS data!
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logging.error("GFS Data in directory does not cover now!")
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chasemapper_config['pred_model'] = "Old GFS Data."
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flask_emit_event('predictor_model_update',{'model':"Old GFS data."})
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chasemapper_config['offline_predictions'] = False
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# Start up the predictor thread if it is not running.
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if predictor_thread == None:
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predictor_thread = Thread(target=predictorThread)
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predictor_thread.start()
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else:
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chasemapper_config['pred_model'] = _model_age + " (Offline)"
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flask_emit_event('predictor_model_update',{'model':_model_age + " (Offline)"})
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predictor = Predictor(bin_path=pred_settings['pred_binary'], gfs_path=pred_settings['gfs_path'])
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# Set the predictor to enabled, and update the clients.
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chasemapper_config['pred_enabled'] = True
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# Start up the predictor thread if it is not running.
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if predictor_thread == None:
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predictor_thread = Thread(target=predictorThread)
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predictor_thread.start()
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# Set the predictor to enabled, and update the clients.
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chasemapper_config['offline_predictions'] = True
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except Exception as e:
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traceback.print_exc()
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logging.error("Loading predictor failed: " + str(e))
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flask_emit_event('predictor_model_update',{'model':"Failed - Check Log."})
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chasemapper_config['pred_model'] = "Failed - Check Log."
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print("Loading Predictor failed.")
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predictor = None
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flask_emit_event('server_settings_update', chasemapper_config)
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else:
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# No initialization required for the online predictor
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predictor = "Tawhiri"
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flask_emit_event('predictor_model_update',{'model':"Tawhiri"})
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# Start up the predictor thread if it is not running.
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if predictor_thread == None:
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predictor_thread = Thread(target=predictorThread)
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predictor_thread.start()
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flask_emit_event('server_settings_update', chasemapper_config)
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except Exception as e:
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traceback.print_exc()
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logging.error("Loading predictor failed: " + str(e))
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flask_emit_event('predictor_model_update',{'model':"Failed - Check Log."})
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chasemapper_config['pred_model'] = "Failed - Check Log."
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print("Loading Predictor failed.")
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predictor = None
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def model_download_finished(result):
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""" Callback for when the model download is finished """
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global chasemapper_config
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if result == "OK":
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# Downloader reported OK, restart the predictor.
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chasemapper_config["offline_predictions"] = True
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initPredictor()
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else:
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# Downloader reported an error, pass on to the client.
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@ -834,15 +911,17 @@ class WebHandler(logging.Handler):
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def emit(self, record):
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""" Emit a log message via SocketIO """
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if 'socket.io' not in record.msg:
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# Convert log record into a dictionary
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log_data = {
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'level': record.levelname,
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'timestamp': datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),
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'msg': record.msg
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}
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# Emit to all socket.io clients
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socketio.emit('log_event', log_data, namespace='/chasemapper')
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# Deal with log records with no content.
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if record.msg:
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if 'socket.io' not in record.msg:
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# Convert log record into a dictionary
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log_data = {
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'level': record.levelname,
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'timestamp': datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),
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'msg': record.msg
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}
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# Emit to all socket.io clients
|
||||
socketio.emit('log_event', log_data, namespace='/chasemapper')
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -56,6 +56,12 @@ html, body, #map {
|
|||
width: 10em;
|
||||
}
|
||||
|
||||
.predictorModelValue {
|
||||
display: inline-block;
|
||||
margin-left: auto;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
.timeToLanding {
|
||||
color:red;
|
||||
font-weight: bold;
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ function serverSettingsUpdate(data){
|
|||
// Accept a json blob of settings data from the client, and update our local store.
|
||||
chase_config = data;
|
||||
// Update a few fields based on this data.
|
||||
$("#predictorModel").html("<b>Current Model: </b>" + chase_config.pred_model);
|
||||
$("#predictorModelValue").text(chase_config.pred_model);
|
||||
$('#burstAlt').val(chase_config.pred_burst.toFixed(0));
|
||||
$('#descentRate').val(chase_config.pred_desc_rate.toFixed(1));
|
||||
$('#predUpdateRate').val(chase_config.pred_update_rate.toFixed(0));
|
||||
|
|
|
|||
|
|
@ -99,6 +99,13 @@ function telemetryTableDialog(e, row){
|
|||
var _last_pos = balloon_positions[callsign].latest_data.position;
|
||||
$('#telemDialogPosition').html("<a href='geo:" + _last_pos[0].toFixed(5) + "," + _last_pos[1].toFixed(5) + "'>" + _last_pos[0].toFixed(5) + ", " + _last_pos[1].toFixed(5) + "</a>");
|
||||
|
||||
if(balloon_positions[callsign].pred_marker != null){
|
||||
var _pred_latlng = balloon_positions[callsign].pred_marker.getLatLng();
|
||||
$('#telemDialogPredPosition').html("<a href='geo:" + _pred_latlng.lat.toFixed(5) + "," + _pred_latlng.lng.toFixed(5) + "'>" + _pred_latlng.lat.toFixed(5) + ", " + _pred_latlng.lng.toFixed(5) + "</a>");
|
||||
|
||||
}else{
|
||||
$('#telemDialogPredPosition').html("Unknown");
|
||||
}
|
||||
|
||||
var _buttons = {
|
||||
"Follow": function() {
|
||||
|
|
|
|||
|
|
@ -451,7 +451,7 @@
|
|||
// Predictor Functions
|
||||
socket.on('predictor_model_update', function(data){
|
||||
var _model_data = data.model;
|
||||
$("#predictorModel").html("<b>Current Model: </b>" + _model_data);
|
||||
$("#predictorModelValue").text(_model_data);
|
||||
});
|
||||
|
||||
socket.on('predictor_update', function(data){
|
||||
|
|
@ -611,6 +611,9 @@
|
|||
<div class="paramRow">
|
||||
<b>Current Position:</b> <div style='float:right;' id='telemDialogPosition'></div><br/>
|
||||
</div>
|
||||
<div class="paramRow">
|
||||
<b>Predicted Landing:</b> <div style='float:right;' id='telemDialogPredPosition'></div><br/>
|
||||
</div>
|
||||
<div class="paramRow">
|
||||
<b>Select Action:</b><br/>
|
||||
</div>
|
||||
|
|
@ -676,7 +679,7 @@
|
|||
</hr>
|
||||
<h3>Predictor</h3>
|
||||
<div class="paramRow" id="predictorModel">
|
||||
<b>Current Model: </b> Predictor Disabled
|
||||
<b>Current Model: </b> <div class="predictorModelValue" id="predictorModelValue">Disabled</div>
|
||||
</div>
|
||||
<div class="paramRow">
|
||||
<b>Download Model</b> <button type="button" class="paramSelector" id="downloadModel">Download</button>
|
||||
|
|
|
|||
Ładowanie…
Reference in New Issue