def collection():
global gps_times,lats,lons,alts
global lati,loni,alti
global lastlen
global tmin,tmax,dt
global counts,tbins#,weights
global yourThread
with dataLock: # Wait for lock on current thread
gps_times,lats,lons,alts = get_data(opts.gps_file,filetype='gps')
lati,loni,alti = interp_pos(gps_times.gps[:,0],lats,lons,alts)
currlen = gps_times.shape[0]
if currlen == lastlen:
sleep(POOL_TIME)
elif currlen > lastlen:
lastlen = currlen
tmin,tmax = gps_times.gps.min(),gps_times.gps.max()
# Create weights array for check of GPS data when user queries server
counts,tbins = np.histogram(gps_times.gps,bins=int((tmax-tmin)/dt))
# Start the next thread
yourThread = threading.Timer(POOL_TIME, collection, ())
yourThread.start()
def collection():
global gps_times, lats, lons, alts
global lati, loni, alti
global lastlen
global tmin, tmax, dt
global counts, tbins #,weights
global yourThread
with dataLock: # Wait for lock on current thread
gps_times, lats, lons, alts = get_data(opts.gps_file,
filetype='gps')
lati, loni, alti = interp_pos(gps_times.gps[:, 0], lats, lons,
alts)
currlen = gps_times.shape[0]
if currlen == lastlen:
sleep(POOL_TIME)
elif currlen > lastlen:
lastlen = currlen
tmin, tmax = gps_times.gps.min(), gps_times.gps.max()
# Create weights array for check of GPS data when user queries server
counts, tbins = np.histogram(gps_times.gps,
bins=int((tmax - tmin) / dt))
# Start the next thread
yourThread = threading.Timer(POOL_TIME, collection, ())
yourThread.start()
yourThread = threading.Timer(POOL_TIME, collection, ())
yourThread.start()
# Initiate
initialize()
# When you kill Flask (SIGTERM), clear the trigger for the next thread
atexit.register(interrupt)
# Return app with get function
return app
# Global variables
gps_times, lats, lons, alts = get_data(opts.gps_file, filetype='gps')
print gps_times.shape, lats.shape, lons.shape, alts.shape
lati, loni, alti = interp_pos(gps_times.gps[:, 0], lats, lons, alts)
# Get current number of GPS data points for monitoring of opts.gps_file
lastlen = gps_times.shape[0]
tmin, tmax = gps_times.gps.min(), gps_times.gps.max()
# Create weights array for check of GPS data when user queries server
dt = opts.dt
counts, tbins = np.histogram(gps_times.gps, bins=int((tmax - tmin) / dt))
# Create Lock object to access variables on an individual thread
dataLock = threading.Lock()
# Thread handler
yourThread = threading.Thread()
yourThread = threading.Timer(POOL_TIME, collection, ())
yourThread.start()
# Initiate
initialize()
# When you kill Flask (SIGTERM), clear the trigger for the next thread
atexit.register(interrupt)
# Return app with get function
return app
# Global variables
gps_times,lats,lons,alts = get_data(opts.gps_file,filetype='gps')
print gps_times.shape,lats.shape,lons.shape,alts.shape
lati,loni,alti = interp_pos(gps_times.gps[:,0],lats,lons,alts)
# Get current number of GPS data points for monitoring of opts.gps_file
lastlen = gps_times.shape[0]
tmin,tmax = gps_times.gps.min(),gps_times.gps.max()
# Create weights array for check of GPS data when user queries server
dt = opts.dt
counts,tbins = np.histogram(gps_times.gps,bins=int((tmax-tmin)/dt))
# Create Lock object to access variables on an individual thread
dataLock = threading.Lock()
# Thread handler
yourThread = threading.Thread()
print 'Read in %s GPS positions' % lats.shape[0]
minTime = times.gps.min()
maxTime = times.gps.max()
# Left off here !!!
#print "Before time filter: ",spec_times.shape
specInds = np.where(
np.logical_and(spec_times.gps > minTime, spec_times.gps < maxTime))[0]
spec_times, spec_raw = spec_times[specInds], spec_raw[specInds]
#print "After time filter: ",spec_times.shape
# Interpolate GPS positions
print '\nInterpolating...'
start = time.time()
latsi, lonsi, altsi = interp_pos(times.gps, lats, lons, alts)
stop = time.time()
print 'Interpolation finished in %.1e seconds' % (stop - start)
latsi = latsi(spec_times.gps)
lonsi = lonsi(spec_times.gps)
altsi = altsi(spec_times.gps)
# Zip everything together for start/stop and waypt filtering
spec_times = np.expand_dims(spec_times.gps, axis=1)
latsi = np.expand_dims(latsi, axis=1)
lonsi = np.expand_dims(lonsi, axis=1)
altsi = np.expand_dims(altsi, axis=1)
all_Data = np.concatenate((spec_times, latsi, lonsi, altsi, spec_raw),
axis=1)
print '\nZipped data shape: ' + str(all_Data.shape)
minTime = times.gps.min()
maxTime = times.gps.max()
# Left off here !!!
#print "Before time filter: ",spec_times.shape
specInds = np.where(np.logical_and(spec_times.gps>minTime,
spec_times.gps<maxTime))[0]
spec_times,spec_raw = spec_times[specInds],spec_raw[specInds]
#print "After time filter: ",spec_times.shape
# Interpolate GPS positions
print '\nInterpolating...'
start = time.time()
latsi,lonsi,altsi = interp_pos(times.gps,lats,lons,alts)
stop = time.time()
print 'Interpolation finished in %.1e seconds' %(stop-start)
latsi = latsi(spec_times.gps)
lonsi = lonsi(spec_times.gps)
altsi = altsi(spec_times.gps)
# Zip everything together for start/stop and waypt filtering
spec_times = np.expand_dims(spec_times.gps,axis=1)
latsi = np.expand_dims(latsi,axis=1)
lonsi = np.expand_dims(lonsi,axis=1)
altsi = np.expand_dims(altsi,axis=1)
all_Data = np.concatenate((spec_times,latsi,lonsi,altsi,spec_raw),
axis=1)
print '\nZipped data shape: '+str(all_Data.shape)