Additional way of inputting Sonde data

apps/meppen_predict.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python
+#
+#   Project Horus 
+#   CUSF Standalone Predictor Python Wrapper
+#   Radiosonde Launch Predictor
+#   Copyright 2017 Mark Jessop <[email protected]>
+#
+#   In this example we run predictions for a radiosonde launch from Meppen, West Germany
+#   for their schedule (MON-FRI 05/07/09/11Z, and write the tracks out to a KML file.
+#
+#   For this script to work correctly, we need a weeks worth of GFS data available within the gfs directory.
+#   This can be gathered using get_wind_data.py (or scripted with wind_grabber.sh), using :
+#   python get_wind_data.py --lat=54 --lon=07 --latdelta=10 --londelta=10 -f 168 -m 0p25_1hr -o gfs
+#   with lat/lon parameters chaged as appropriate.
+#   Them Germans don't work weekends mod by MikeTango
+
+
+import argparse
+import fastkml
+import datetime
+import json
+from dateutil.parser import parse
+from cusfpredict.predict import Predictor
+from pygeoif.geometry import Point, LineString
+from cusfpredict.utils import *
+
+# Predictor Parameters
+PRED_BINARY = "./pred"
+GFS_PATH = "./gfs/"
+
+# Launch Parameters
+
+# LAUNCH_COUNT = 1 # This thing counts how many launches there are per given day
+# LAUNCH_STEPS = 2 # Steps between jumps per day
+LAUNCH_TIMES = ["05:00Z", "07:00Z", "09:00Z", "11:00Z"]
+LAUNCH_DAYS = 5 # Jumps 24hrs
+LAUNCH_WEEKDAYS = [0,1,2,3,4] # This thing tells you on which weekdays to run the thing
+LAUNCH_TIME_LIMIT = 168 # Predict out this many hours into the future
+LAUNCH_TIME = "11:15Z"
+LAUNCH_LAT = 52.715
+LAUNCH_LON = 7.319
+LAUNCH_ALT = 18.0
+ASCENT_RATE = 5.0
+DESCENT_RATE = 3.5
+BURST_ALT = 25000.0
+#ARRAY = []
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-a', '--ascentrate', type=float, default=ASCENT_RATE, help="Ascent Rate (m/s). Default %.1fm/s" % ASCENT_RATE)
+parser.add_argument('-d', '--descentrate', type=float, default=DESCENT_RATE, help="Descent Rate (m/s). Default %.1dm/s" % DESCENT_RATE)
+parser.add_argument('-b', '--burstalt', type=float, default=BURST_ALT, help="Burst Altitude (m). Default %.1fm" % BURST_ALT)
+parser.add_argument('--launchalt', type=float, default=LAUNCH_ALT, help="Launch Altitude (m). Default 0m")
+parser.add_argument('--latitude', type=float, default=LAUNCH_LAT, help="Launch Latitude (dd.dddd)")
+parser.add_argument('--longitude', type=float, default=LAUNCH_LON, help="Launch Longitude (dd.dddd)")
+parser.add_argument('--time', type=str, default=LAUNCH_TIME, help="First Time of the day (string, UTC). Default = %s" % LAUNCH_TIME)
+# parser.add_argument('--steps', type=int, default=LAUNCH_STEPS, help="How many steps. Default = %s" % LAUNCH_STEPS)
+# parser.add_argument('--step', type=int, default=LAUNCH_STEP, help="Time step between predictions (Hours). Default = %d" % LAUNCH_STEP)
+parser.add_argument('--limit', type=int, default=LAUNCH_TIME_LIMIT, help="Predict up to this many hours into the future. Default = %d" % LAUNCH_TIME_LIMIT)
+parser.add_argument('-s', '--site', type=str, default="Radiosonde", help="Launch site name. Default: Radiosonde")
+parser.add_argument('-o', '--output', type=str, default='meppen_predictions', help="Output JSON File. .json will be appended. Default = sonde_predictions[.json]")
+args = parser.parse_args()
+
+OUTPUT_FILE = args.output
+LAUNCH_LAT = args.latitude
+LAUNCH_LON = args.longitude
+LAUNCH_ALT = args.launchalt
+ASCENT_RATE = args.ascentrate
+DESCENT_RATE = args.descentrate
+BURST_ALT = args.burstalt
+
+# Set the launch time to the current UTC day, but set the hours to the first sonde
+current_day = datetime.datetime.utcnow()
+
+launch_hour = parse(args.time)
+LAUNCH_TIME = datetime.datetime(current_day.year, current_day.month, current_day.day, launch_hour.hour, launch_hour.minute)
+
+
+# A list to store predicton results
+predictions = []
+
+# Separate store for JSON output data.
+json_out = {
+    'dataset': gfs_model_age(GFS_PATH),
+    'predictions':{},
+    'site': args.site,
+    'launch_lat': LAUNCH_LAT,
+    'launch_lon': LAUNCH_LON
+}
+
+# Create the predictor object.
+pred = Predictor(bin_path=PRED_BINARY, gfs_path=GFS_PATH)
+
+for _delta_datum in range (0, LAUNCH_TIME_LIMIT, 24): # this limits the time to whatever model time we have before running anything, incrementing in 24hr steps
+
+    _lunch_time = LAUNCH_TIME + datetime.timedelta(seconds=_delta_datum*3600) # this sets the steps to 24 hrs
+
+     # print(_lunch_time)
+
+    for _delta_werktag in range(0, len(LAUNCH_WEEKDAYS),1): #hello working day
+        if _lunch_time.weekday() == LAUNCH_WEEKDAYS[_delta_werktag]: # if this step indeed has hit a working weekday, we shall
+            for delta_launchtimes in range(0, len(LAUNCH_TIMES),1): # go through the array filled with launch times
+                lunch_time_hour = parse (LAUNCH_TIMES[delta_launchtimes]) # parse the string in the array at the given increment
+                _lunch_time = datetime.datetime(_lunch_time.year, _lunch_time.month, _lunch_time.day, lunch_time_hour.hour, lunch_time_hour.minute) #and append the model run at that time
+                print(_lunch_time, _lunch_time.weekday()) # this just prints out which model time it is running and on which working day
+
+               # Run the prediction
+                flight_path = pred.predict(
+                launch_lat=LAUNCH_LAT,
+                launch_lon=LAUNCH_LON,
+                launch_alt=LAUNCH_ALT,
+                ascent_rate=ASCENT_RATE,
+                descent_rate=DESCENT_RATE,
+                burst_alt=BURST_ALT,
+                launch_time=_lunch_time)
+
+            # If we only get a single entry in the output array, it means we don't have any wind data for this time
+            # Continue on to the next launch time.
+                if len(flight_path) == 1:
+                    continue
+
+            # Generate a descriptive comment for the track and placemark.
+                pred_time_string = _lunch_time.strftime("%Y%m%d-%H%M")
+                pred_comment = "%s %.1f/%.1f/%.1f" % (pred_time_string, ASCENT_RATE, BURST_ALT, DESCENT_RATE)
+
+            # Add the track and placemark to our list of predictions
+                predictions.append(flight_path_to_geometry(flight_path, comment=pred_comment))
+                predictions.append(flight_path_landing_placemark(flight_path, comment=pred_comment))
+
+                json_out['predictions'][pred_comment] = {
+                    'timestamp': _lunch_time.strftime("%Y-%m-%d %H:%M:%S"),
+                    'path':flight_path_to_polyline(flight_path),
+                    'burst_alt': BURST_ALT
+                }
+
+                print("Prediction Run: %s" % pred_comment)
+
+
+# Write out the prediction data to the KML file
+kml_comment = "Sonde Predictions - %s" % gfs_model_age()
+write_flight_path_kml(predictions, filename=OUTPUT_FILE+".kml", comment=kml_comment)
+
+# Write out the JSON blob.
+with open(OUTPUT_FILE+".json",'w') as f:
+    f.write(json.dumps(json_out))