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161 lines
6.6 KiB
161 lines
6.6 KiB
import os
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import numpy as np
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from datetime import datetime
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from .gps_time import GPSTime
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from .constants import SECS_IN_YEAR
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from . import raw_gnss as raw
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from . import opt
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from .rinex_file import RINEXFile
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from .downloader import download_cors_coords
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from .helpers import get_constellation, ConstellationId
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def mean_filter(delay):
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d2 = delay.copy()
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max_step = 10
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for i in range(max_step, len(delay) - max_step):
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finite_idxs = np.where(np.isfinite(delay[i - max_step:i + max_step]))
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if max_step in finite_idxs[0]:
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step = min([max_step, finite_idxs[0][-1] - max_step, max_step - finite_idxs[0][0]])
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d2[i] = np.nanmean(delay[i - step:i + step + 1])
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return d2
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def download_and_parse_station_postions(cors_station_positions_path, cache_dir):
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if not os.path.isfile(cors_station_positions_path):
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cors_stations = {}
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coord_file_paths = download_cors_coords(cache_dir=cache_dir)
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for coord_file_path in coord_file_paths:
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try:
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station_id = coord_file_path.split('/')[-1][:4]
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with open(coord_file_path, 'r+') as coord_file:
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contents = coord_file.readlines()
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phase_center = False
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for line_number in range(len(contents)):
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if 'L1 Phase Center' in contents[line_number]:
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phase_center = True
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if not phase_center and 'ITRF2014 POSITION' in contents[line_number]:
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velocity = [float(contents[line_number+8].split()[3]),
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float(contents[line_number+9].split()[3]),
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float(contents[line_number+10].split()[3])]
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if phase_center and 'ITRF2014 POSITION' in contents[line_number]:
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epoch = GPSTime.from_datetime(datetime(2005,1,1))
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position = [float(contents[line_number+2].split()[3]),
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float(contents[line_number+3].split()[3]),
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float(contents[line_number+4].split()[3])]
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cors_stations[station_id] = [epoch, position, velocity]
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break
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except ValueError:
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pass
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cors_station_positions_file = open(cors_station_positions_path, 'wb')
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np.save(cors_station_positions_file, cors_stations)
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cors_station_positions_file.close()
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def get_closest_station_names(pos, k=5, max_distance=100000, cache_dir='/tmp/gnss/'):
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from scipy.spatial import cKDTree
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cors_station_positions_dict = load_cors_station_positions(cache_dir)
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station_ids = list(cors_station_positions_dict.keys())
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station_positions = []
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for station_id in station_ids:
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station_positions.append(cors_station_positions_dict[station_id][1])
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tree = cKDTree(station_positions)
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distances, idxs = tree.query(pos, k=k, distance_upper_bound=max_distance)
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return np.array(station_ids)[idxs]
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def load_cors_station_positions(cache_dir):
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cors_station_positions_path = cache_dir + 'cors_coord/cors_station_positions'
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download_and_parse_station_postions(cors_station_positions_path, cache_dir)
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with open(cors_station_positions_path, 'rb') as f:
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return np.load(f, allow_pickle=True).item() # pylint: disable=unexpected-keyword-arg
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def get_station_position(station_id, cache_dir='/tmp/gnss/', time=GPSTime.from_datetime(datetime.utcnow())):
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cors_station_positions_dict = load_cors_station_positions(cache_dir)
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epoch, pos, vel = cors_station_positions_dict[station_id]
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return ((time - epoch)/SECS_IN_YEAR)*np.array(vel) + np.array(pos)
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def parse_dgps(station_id, station_obs_file_path, dog, max_distance=100000, required_constellations=[ConstellationId.GPS]):
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station_pos = get_station_position(station_id, cache_dir=dog.cache_dir)
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obsdata = RINEXFile(station_obs_file_path)
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measurements = raw.read_rinex_obs(obsdata)
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# if not all constellations in first 100 epochs bail
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detected_constellations = set()
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for m in sum(measurements[:100],[]):
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detected_constellations.add(get_constellation(m.prn))
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for constellation in required_constellations:
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if constellation not in detected_constellations:
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return None
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proc_measurements = []
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for measurement in measurements:
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proc_measurements.append(raw.process_measurements(measurement, dog=dog))
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# sample at 30s
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if len(proc_measurements) > 2880:
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proc_measurements = proc_measurements[::int(len(proc_measurements)/2880)]
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if len(proc_measurements) != 2880:
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return None
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station_delays = {}
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n = len(proc_measurements)
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for signal in ['C1C', 'C2P']:
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times = []
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station_delays[signal] = {}
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for i, proc_measurement in enumerate(proc_measurements):
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times.append(proc_measurement[0].recv_time)
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Fx_pos = opt.pr_residual(proc_measurement, signal=signal)
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residual, _ = Fx_pos(list(station_pos) + [0,0])
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residual = -np.array(residual)
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for j, m in enumerate(proc_measurement):
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prn = m.prn
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if prn not in station_delays[signal]:
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station_delays[signal][prn] = np.nan*np.ones(n)
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station_delays[signal][prn][i] = residual[j]
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assert len(times) == n
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# TODO crude way to get dgps station's clock errors,
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# could this be biased? Only use GPS for convenience.
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model_delays = {}
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for prn in station_delays['C1C']:
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if get_constellation(prn) == ConstellationId.GPS:
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model_delays[prn] = np.nan*np.zeros(n)
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for i in range(n):
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model_delays[prn][i] = dog.get_delay(prn, times[i], station_pos, no_dgps=True)
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station_clock_errs = np.zeros(n)
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for i in range(n):
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station_clock_errs[i] = np.nanmean([(station_delays['C1C'][prn][i] - model_delays[prn][i]) for prn in model_delays])
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# remove clock errors and smooth out signal
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for prn in station_delays['C1C']:
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station_delays['C1C'][prn] = mean_filter(station_delays['C1C'][prn] - station_clock_errs)
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for prn in station_delays['C2P']:
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station_delays['C2P'][prn] = station_delays['C2P'][prn] - station_clock_errs
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return DGPSDelay(station_id, station_pos, station_delays,
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times, max_distance)
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class DGPSDelay:
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def __init__(self, station_id, station_pos,
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station_delays, station_delays_t, max_distance):
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self.id = station_id
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self.pos = station_pos
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self.delays = station_delays
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self.delays_t = station_delays_t
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self.max_distance = max_distance
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def get_delay(self, prn, time, signal='C1C'):
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time_index = int((time - self.delays_t[0])/30)
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assert abs(self.delays_t[time_index] - time) < 30
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if prn in self.delays[signal] and np.isfinite(self.delays[signal][prn][time_index]):
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return self.delays[signal][prn][time_index]
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return None
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def valid(self, time, recv_pos):
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return (np.linalg.norm(recv_pos - self.pos) <= self.max_distance and
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time - self.delays_t[0] > -30 and
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self.delays_t[-1] - time > -30)
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