def test_ValueslatLon2Azi(self):
"""Test latLon2Azi function"""
[A, D] = maputils.latLon2Azi(self.lat, self.lon)
for aa, azi in zip(A, self.AZI):
self.assertAlmostEqual(aa, azi, 2)
for dd, dist in zip(D, self.Dist):
self.assertAlmostEqual(dd, dist, 2)
def test_ValueslatLon2Azi(self):
"""Test latLon2Azi function"""
[A, D] = maputils.latLon2Azi(self.lat, self.lon)
for aa, azi in zip(A, self.AZI):
self.assertAlmostEqual(aa, azi, 2)
for dd, dist in zip(D, self.Dist):
self.assertAlmostEqual(dd, dist, 2)
def interpolateTrack(configFile,
trackFile,
source,
delta=0.1,
interpolation_type=None):
"""
Interpolate the data in a track file to the time interval delta hours.
:param str configFile: Configuration file that contains information on the
source format of the track file.
:param str trackFile: Path to csv format track file.
:param str source: Name of the data source. There must be a corresponding
section in the configuration file that contains the
description of the data.
:param float delta: Time interval in hours to interpolate to. Default is
0.1 hours
:param str interpolation_type: Optionally use Akima or linear
interpolation for the track positions.
Default is linear 1-dimensional spline
interpolation.
:returns: 10 arrays (id, time, date, lon, lat, bearing, forward speed,
central pressure, environmental pressure and radius to
maximum wind) that describe the track at ``delta`` hours
intervals.
"""
logger = logging.getLogger()
indicator, year, month, day, hour, minute, lon, lat, \
pressure, speed, bearing, windspeed, rmax, penv = \
loadTrackFile(configFile, trackFile, source)
# Time between observations:
day_ = [
datetime.datetime(year[i], month[i], day[i], hour[i], minute[i])
for i in xrange(year.size)
]
time_ = date2num(day_)
dt_ = 24.0 * numpy.diff(time_)
dt = numpy.empty(hour.size, 'f')
dt[1:] = dt_
# At this stage, convert all times to a time after initial observation:
timestep = 24.0 * (time_ - time_[0])
newtime = numpy.arange(timestep[0], timestep[-1] + .01, delta)
newtime[-1] = timestep[-1]
_newtime = (newtime / 24.) + time_[0]
newdates = num2date(_newtime)
nid = numpy.ones(newtime.size)
logger.info("Interpolating data...")
if len(indicator) <= 2:
# Use linear interpolation only (only a start and end point given):
nLon = scint.interp1d(timestep, lon, kind='linear')(newtime)
nLat = scint.interp1d(timestep, lat, kind='linear')(newtime)
npCentre = scint.interp1d(timestep, pressure, kind='linear')(newtime)
npEnv = scint.interp1d(timestep, penv, kind='linear')(newtime)
nrMax = scint.interp1d(timestep, rmax, kind='linear')(newtime)
else:
if interpolation_type == 'akima':
# Use the Akima interpolation method:
try:
import _akima
except ImportError:
logger.exception(("Akima interpolation module unavailable - "
"default to scipy.interpolate"))
nLon = scint.splev(newtime,
scint.splrep(timestep, lon, s=0),
der=0)
nLat = scint.splev(newtime,
scint.splrep(timestep, lat, s=0),
der=0)
else:
nLon = _akima.interpolate(timestep, lon, newtime)
nLat = _akima.interpolate(timestep, lat, newtime)
elif interpolation_type == 'linear':
nLon = scint.interp1d(timestep, lon, kind='linear')(newtime)
nLat = scint.interp1d(timestep, lat, kind='linear')(newtime)
else:
nLon = scint.splev(newtime,
scint.splrep(timestep, lon, s=0),
der=0)
nLat = scint.splev(newtime,
scint.splrep(timestep, lat, s=0),
der=0)
npCentre = scint.interp1d(timestep, pressure, kind='linear')(newtime)
npEnv = scint.interp1d(timestep, penv, kind='linear')(newtime)
nrMax = scint.interp1d(timestep, rmax, kind='linear')(newtime)
bear_, dist_ = maputils.latLon2Azi(nLat, nLon, 1, azimuth=0)
nthetaFm = numpy.zeros(newtime.size, 'f')
nthetaFm[:-1] = bear_
nthetaFm[-1] = bear_[-1]
dist = numpy.zeros(newtime.size, 'f')
dist[:-1] = dist_
dist[-1] = dist_[-1]
nvFm = dist / delta
return nid, newtime, newdates, nLon, nLat, nthetaFm, nvFm, npCentre, npEnv, nrMax
def interpolate(track, delta, interpolation_type=None):
"""
Interpolate the records in time to have a uniform time difference between
records. Each of the input arrays represent the values for a single TC
event.
:param track: :class:`Track` object containing all data for the track.
:param delta: `float` time difference to interpolate the dataset to. Must be
positive.
:param interpolation_type: Optional ['linear', 'akima'], specify the type
of interpolation used for the locations (i.e.
longitude and latitude) of the records.
# FIXME: Need to address masking values - scipy.interpolate.interp1d
handles numpy.ma masked arrays.
"""
day_ = [datetime(*x) for x in zip(track.Year, track.Month,
track.Day, track.Hour,
track.Minute)]
timestep = timedelta(delta/24.)
time_ = np.array([d.toordinal() + d.hour/24.0 for d in day_], dtype=float)
dt_ = 24.0 * np.diff(time_)
dt = np.empty(track.Hour.size, dtype=float)
dt[1:] = dt_
# Convert all times to a time after initial observation:
timestep = 24.0*(time_ - time_[0])
newtime = np.arange(timestep[0], timestep[-1] + .01, delta)
newtime[-1] = timestep[-1]
_newtime = (newtime / 24.) + time_[0]
newdates = num2date(_newtime)
newdates = np.array([n.replace(tzinfo=None) for n in newdates])
nid = track.trackId[0] * np.ones(newtime.size)
# Find the indices of valid pressure observations:
validIdx = np.where(track.CentralPressure < sys.maxint)[0]
# FIXME: Need to address the issue when the time between obs is less
# than delta (e.g. only two obs 5 hrs apart, but delta = 6 hrs).
if len(track.data) <= 2:
# Use linear interpolation only (only a start and end point given):
nLon = interp1d(timestep, track.Longitude, kind='linear')(newtime)
nLat = interp1d(timestep, track.Latitude, kind='linear')(newtime)
if len(validIdx) == 2:
npCentre = interp1d(timestep,
track.CentralPressure,
kind='linear')(newtime)
nwSpd = interp1d(timestep,
track.WindSpeed,
kind='linear')(newtime)
elif len(validIdx) == 1:
# If one valid observation, assume no change and
# apply value to all times
npCentre = np.ones(len(newtime)) * track.CentralPressure[validIdx]
nwSpd = np.ones(len(newtime)) * track.WindSpeed[validIdx]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npEnv = interp1d(timestep, track.EnvPressure, kind='linear')(newtime)
nrMax = interp1d(timestep, track.rMax, kind='linear')(newtime)
else:
if interpolation_type=='akima':
# Use the Akima interpolation method:
try:
import akima
except ImportError:
logger.exception( ("Akima interpolation module unavailable "
" - default to scipy.interpolate") )
nLon = splev(newtime, splrep(timestep, track.Longitude, s=0), der=0)
nLat = splev(newtime, splrep(timestep, track.Latitude, s=0), der=0)
else:
nLon = akima.interpolate(timestep, track.Longitude, newtime)
nLat = akima.interpolate(timestep, track.Latitude, newtime)
elif interpolation_type=='linear':
nLon = interp1d(timestep, track.Longitude, kind='linear')(newtime)
nLat = interp1d(timestep, track.Latitude, kind='linear')(newtime)
else:
nLon = splev(newtime, splrep(timestep, track.Longitude, s=0), der=0)
nLat = splev(newtime, splrep(timestep, track.Latitude, s=0), der=0)
if len(validIdx) >= 2:
# No valid data at the final new time,
# would require extrapolation:
firsttime = np.where(newtime >= timestep[validIdx[0]])[0][0]
lasttime = np.where(newtime <= timestep[validIdx[-1]])[0][-1]
if firsttime == lasttime:
# only one valid observation:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npCentre[firsttime] = track.CentralPressure[validIdx[0]]
nwSpd[firsttime] = track.WindSpeed[validIdx[0]]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
_npCentre = interp1d(timestep[validIdx],
track.CentralPressure[validIdx],
kind='linear')(newtime[firsttime:lasttime])
_nwSpd = interp1d(timestep[validIdx],
track.WindSpeed[validIdx],
kind='linear')(newtime[firsttime:lasttime])
npCentre[firsttime:lasttime] = _npCentre
nwSpd[firsttime:lasttime] = _nwSpd
npCentre[lasttime] = _npCentre[-1]
nwSpd[lasttime] = _nwSpd[-1]
elif len(validIdx) == 1:
npCentre = np.ones(len(newtime)) * track.CentralPressure[validIdx]
nwSpd = np.ones(len(newtime)) * track.WindSpeed[validIdx]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npEnv = interp1d(timestep, track.EnvPressure, kind='linear')(newtime)
nrMax = interp1d(timestep, track.rMax, kind='linear')(newtime)
if len(nLat) >= 2:
bear_, dist_ = latLon2Azi(nLat, nLon, 1, azimuth=0)
nthetaFm = np.zeros(newtime.size, dtype=float)
nthetaFm[:-1] = bear_
nthetaFm[-1] = bear_[-1]
dist = np.zeros(newtime.size, dtype=float)
dist[:-1] = dist_
dist[-1] = dist_[-1]
nvFm = dist / delta
else:
nvFm = track.Speed[-1]
nthetaFm = track.Bearing[-1]
nYear = [date.year for date in newdates]
nMonth = [date.month for date in newdates]
nDay = [date.day for date in newdates]
nHour = [date.hour for date in newdates]
nMin = [date.minute for date in newdates]
np.putmask(npCentre, npCentre > 10e+6, sys.maxint)
np.putmask(npCentre, npCentre < 700, sys.maxint)
newindex = np.zeros(len(newtime))
newindex[0] = 1
newTCID = np.ones(len(newtime)) * track.trackId[0]
newdata = np.empty(len(newtime),
dtype={
'names': TRACKFILE_COLS,
'formats': TRACKFILE_FMTS
} )
for key, val in zip(TRACKFILE_COLS,
[newindex, newTCID, nYear, nMonth, nDay, nHour, nMin,
newtime, newdates, nLon, nLat, nvFm, nthetaFm,
npCentre, nwSpd, nrMax, npEnv]):
newdata[key] = val
newtrack = Track(newdata)
newtrack.trackId = track.trackId
newtrack.trackfile = track.trackfile
return newtrack
def processData(self, restrictToWindfieldDomain=False):
"""
Process raw data into ASCII files that can be read by the main
components of the system
:param bool restrictToWindfieldDomain: if True, only process data
within the wind field domain, otherwise, process data from
across the track generation domain.
"""
config = ConfigParser()
config.read(self.configFile)
self.logger.info("Running {0}".format(flModuleName()))
if config.has_option('DataProcess', 'InputFile'):
inputFile = config.get('DataProcess', 'InputFile')
if config.has_option('DataProcess', 'Source'):
source = config.get('DataProcess', 'Source')
self.logger.info('Loading %s dataset', source)
fn = config.get(source, 'filename')
path = config.get(source, 'path')
inputFile = pjoin(path, fn)
# If input file has no path information, default to tcrm input folder
if len(os.path.dirname(inputFile)) == 0:
inputFile = pjoin(self.tcrm_input_dir, inputFile)
self.logger.info("Processing {0}".format(inputFile))
self.source = config.get('DataProcess', 'Source')
inputData = colReadCSV(self.configFile, inputFile, self.source)
inputSpeedUnits = config.get(self.source, 'SpeedUnits')
inputPressureUnits = config.get(self.source, 'PressureUnits')
inputLengthUnits = config.get(self.source, 'LengthUnits')
startSeason = config.getint('DataProcess', 'StartSeason')
indicator = loadData.getInitialPositions(inputData)
lat = np.array(inputData['lat'], 'd')
lon = np.mod(np.array(inputData['lon'], 'd'), 360)
if restrictToWindfieldDomain:
# Filter the input arrays to only retain the tracks that
# pass through the windfield domain.
CD = CalcTrackDomain(self.configFile)
self.domain = CD.calcDomainFromTracks(indicator, lon, lat)
domainIndex = self.extractTracks(indicator, lon, lat)
inputData = inputData[domainIndex]
indicator = indicator[domainIndex]
lon = lon[domainIndex]
lat = lat[domainIndex]
if self.progressbar is not None:
self.progressbar.update(0.125)
# Sort date/time information
try:
dt = np.empty(indicator.size, 'f')
dt[1:] = np.diff(inputData['age'])
except (ValueError, KeyError):
try:
self.logger.info(("Filtering input data by season:"
"season > {0}".format(startSeason)))
# Find indicies that satisfy minimum season filter
idx = np.where(inputData['season'] >= startSeason)[0]
# Filter records:
inputData = inputData[idx]
indicator = indicator[idx]
lon = lon[idx]
lat = lat[idx]
except (ValueError, KeyError):
pass
year, month, day, hour, minute, datetimes \
= loadData.parseDates(inputData, indicator)
# Time between observations:
dt = loadData.getTimeDelta(year, month, day, hour, minute)
# Calculate julian days:
jdays = loadData.julianDays(year, month, day, hour, minute)
delta_lon = np.diff(lon)
delta_lat = np.diff(lat)
# Split into separate tracks if large jump occurs (delta_lon >
# 15 degrees or delta_lat > 5 degrees) This avoids two tracks
# being accidentally combined when seasons and track numbers
# match but basins are different as occurs in the IBTrACS
# dataset. This problem can also be prevented if the
# 'tcserialno' column is specified.
indicator[np.where(delta_lon > 15)[0] + 1] = 1
indicator[np.where(delta_lat > 5)[0] + 1] = 1
# Save information required for frequency auto-calculation
try:
origin_seasonOrYear = np.array(inputData['season'],
'i').compress(indicator)
header = 'Season'
except (ValueError, KeyError):
origin_seasonOrYear = year.compress(indicator)
header = 'Year'
flSaveFile(self.origin_year,
np.transpose(origin_seasonOrYear),
header,
',',
fmt='%d')
pressure = np.array(inputData['pressure'], 'd')
novalue_index = np.where(pressure == sys.maxint)
pressure = metutils.convert(pressure, inputPressureUnits, "hPa")
pressure[novalue_index] = sys.maxint
# Convert any non-physical central pressure values to maximum integer
# This is required because IBTrACS has a mix of missing value codes
# (i.e. -999, 0, 9999) in the same global dataset.
pressure = np.where((pressure < 600) | (pressure > 1100), sys.maxint,
pressure)
if self.progressbar is not None:
self.progressbar.update(0.25)
try:
vmax = np.array(inputData['vmax'], 'd')
except (ValueError, KeyError):
self.logger.warning("No max wind speed data")
vmax = np.empty(indicator.size, 'f')
else:
novalue_index = np.where(vmax == sys.maxint)
vmax = metutils.convert(vmax, inputSpeedUnits, "mps")
vmax[novalue_index] = sys.maxint
assert lat.size == indicator.size
assert lon.size == indicator.size
assert pressure.size == indicator.size
#assert vmax.size == indicator.size
try:
rmax = np.array(inputData['rmax'])
novalue_index = np.where(rmax == sys.maxint)
rmax = metutils.convert(rmax, inputLengthUnits, "km")
rmax[novalue_index] = sys.maxint
self._rmax(rmax, indicator)
self._rmaxRate(rmax, dt, indicator)
except (ValueError, KeyError):
self.logger.warning("No rmax data available")
if self.ncflag:
self.data['index'] = indicator
# ieast : parameter used in latLon2Azi
# FIXME: should be a config setting describing the input data.
ieast = 1
# Determine the index of initial cyclone observations, excluding
# those cyclones that have only one observation. This is used
# for calculating initial bearing and speed
indicator2 = np.where(indicator > 0, 1, 0)
initIndex = np.concatenate(
[np.where(np.diff(indicator2) == -1, 1, 0), [0]])
# Calculate the bearing and distance (km) of every two
# consecutive records using ll2azi
bear_, dist_ = maputils.latLon2Azi(lat, lon, ieast, azimuth=0)
assert bear_.size == indicator.size - 1
assert dist_.size == indicator.size - 1
bear = np.empty(indicator.size, 'f')
bear[1:] = bear_
dist = np.empty(indicator.size, 'f')
dist[1:] = dist_
self._lonLat(lon, lat, indicator, initIndex)
self._bearing(bear, indicator, initIndex)
self._bearingRate(bear, dt, indicator)
if self.progressbar is not None:
self.progressbar.update(0.375)
self._speed(dist, dt, indicator, initIndex)
self._speedRate(dist, dt, indicator)
self._pressure(pressure, indicator)
self._pressureRate(pressure, dt, indicator)
self._windSpeed(vmax)
try:
self._frequency(year, indicator)
self._juliandays(jdays, indicator, year)
except (ValueError, KeyError):
pass
self.logger.info("Completed {0}".format(flModuleName()))
if self.progressbar is not None:
self.progressbar.update(0.5)
def processData(self, restrictToWindfieldDomain=False):
"""
Process raw data into ASCII files that can be read by the main
components of the system
:param bool restrictToWindfieldDomain: if True, only process data
within the wind field domain, otherwise, process data from
across the track generation domain.
"""
config = ConfigParser()
config.read(self.configFile)
self.logger.info("Running %s" % flModuleName())
if config.has_option('DataProcess', 'InputFile'):
inputFile = config.get('DataProcess', 'InputFile')
if config.has_option('DataProcess', 'Source'):
source = config.get('DataProcess', 'Source')
self.logger.info('Loading %s dataset', source)
fn = config.get(source, 'filename')
path = config.get(source, 'path')
inputFile = pjoin(path, fn)
# If input file has no path information, default to tcrm input folder
if len(os.path.dirname(inputFile)) == 0:
inputFile = pjoin(self.tcrm_input_dir, inputFile)
self.logger.info("Processing %s" % inputFile)
self.source = config.get('DataProcess', 'Source')
inputData = colReadCSV(self.configFile, inputFile, self.source)
inputSpeedUnits = config.get(self.source, 'SpeedUnits')
inputPressureUnits = config.get(self.source, 'PressureUnits')
inputLengthUnits = config.get(self.source, 'LengthUnits')
startSeason = config.getint('DataProcess', 'StartSeason')
indicator = loadData.getInitialPositions(inputData)
lat = np.array(inputData['lat'], 'd')
lon = np.mod(np.array(inputData['lon'], 'd'), 360)
if restrictToWindfieldDomain:
# Filter the input arrays to only retain the tracks that
# pass through the windfield domain.
CD = CalcTrackDomain(self.configFile)
self.domain = CD.calcDomainFromTracks(indicator, lon, lat)
domainIndex = self.extractTracks(indicator, lon, lat)
inputData = inputData[domainIndex]
indicator = indicator[domainIndex]
lon = lon[domainIndex]
lat = lat[domainIndex]
if self.progressbar is not None:
self.progressbar.update(0.125)
# Sort date/time information
try:
dt = np.empty(indicator.size, 'f')
dt[1:] = np.diff(inputData['age'])
except (ValueError, KeyError):
try:
self.logger.info("Filtering input data by season: season > %d"%startSeason)
# Find indicies that satisfy minimum season filter
idx = np.where(inputData['season'] >= startSeason)[0]
# Filter records:
inputData = inputData[idx]
indicator = indicator[idx]
lon = lon[idx]
lat = lat[idx]
except (ValueError, KeyError):
pass
year, month, day, hour, minute, datetimes \
= loadData.parseDates(inputData, indicator)
# Time between observations:
dt = loadData.getTimeDelta(year, month, day, hour, minute)
# Calculate julian days:
jdays = loadData.julianDays(year, month, day, hour, minute)
delta_lon = np.diff(lon)
delta_lat = np.diff(lat)
# Split into separate tracks if large jump occurs (delta_lon >
# 15 degrees or delta_lat > 5 degrees) This avoids two tracks
# being accidentally combined when seasons and track numbers
# match but basins are different as occurs in the IBTrACS
# dataset. This problem can also be prevented if the
# 'tcserialno' column is specified.
indicator[np.where(delta_lon > 15)[0] + 1] = 1
indicator[np.where(delta_lat > 5)[0] + 1] = 1
# Save information required for frequency auto-calculation
try:
origin_seasonOrYear = np.array(
inputData['season'], 'i').compress(indicator)
header = 'Season'
except (ValueError, KeyError):
origin_seasonOrYear = year.compress(indicator)
header = 'Year'
flSaveFile(self.origin_year, np.transpose(origin_seasonOrYear),
header, ',', fmt='%d')
pressure = np.array(inputData['pressure'], 'd')
novalue_index = np.where(pressure == sys.maxint)
pressure = metutils.convert(pressure, inputPressureUnits, "hPa")
pressure[novalue_index] = sys.maxint
# Convert any non-physical central pressure values to maximum integer
# This is required because IBTrACS has a mix of missing value codes
# (i.e. -999, 0, 9999) in the same global dataset.
pressure = np.where((pressure < 600) | (pressure > 1100),
sys.maxint, pressure)
if self.progressbar is not None:
self.progressbar.update(0.25)
try:
vmax = np.array(inputData['vmax'], 'd')
except (ValueError, KeyError):
self.logger.warning("No max wind speed data")
vmax = np.empty(indicator.size, 'f')
else:
novalue_index = np.where(vmax == sys.maxint)
vmax = metutils.convert(vmax, inputSpeedUnits, "mps")
vmax[novalue_index] = sys.maxint
assert lat.size == indicator.size
assert lon.size == indicator.size
assert pressure.size == indicator.size
#assert vmax.size == indicator.size
try:
rmax = np.array(inputData['rmax'])
novalue_index = np.where(rmax == sys.maxint)
rmax = metutils.convert(rmax, inputLengthUnits, "km")
rmax[novalue_index] = sys.maxint
self._rmax(rmax, indicator)
self._rmaxRate(rmax, dt, indicator)
except (ValueError, KeyError):
self.logger.warning("No rmax data available")
if self.ncflag:
self.data['index'] = indicator
# ieast : parameter used in latLon2Azi
# FIXME: should be a config setting describing the input data.
ieast = 1
# Determine the index of initial cyclone observations, excluding
# those cyclones that have only one observation. This is used
# for calculating initial bearing and speed
indicator2 = np.where(indicator > 0, 1, 0)
initIndex = np.concatenate([np.where(np.diff(indicator2) ==
-1, 1, 0), [0]])
# Calculate the bearing and distance (km) of every two
# consecutive records using ll2azi
bear_, dist_ = maputils.latLon2Azi(lat, lon, ieast, azimuth=0)
assert bear_.size == indicator.size - 1
assert dist_.size == indicator.size - 1
bear = np.empty(indicator.size, 'f')
bear[1:] = bear_
dist = np.empty(indicator.size, 'f')
dist[1:] = dist_
self._lonLat(lon, lat, indicator, initIndex)
self._bearing(bear, indicator, initIndex)
self._bearingRate(bear, dt, indicator)
if self.progressbar is not None:
self.progressbar.update(0.375)
self._speed(dist, dt, indicator, initIndex)
self._speedRate(dist, dt, indicator)
self._pressure(pressure, indicator)
self._pressureRate(pressure, dt, indicator)
self._windSpeed(vmax)
try:
self._frequency(year, indicator)
self._juliandays(jdays, indicator, year)
except (ValueError, KeyError):
pass
self.logger.info("Completed %s" % flModuleName())
if self.progressbar is not None:
self.progressbar.update(0.5)
def interpolate(track, delta, interpolation_type=None):
"""
Interpolate the records in time to have a uniform time difference between
records. Each of the input arrays represent the values for a single TC
event.
:param track: :class:`Track` object containing all data for the track.
:param delta: `float` time difference to interpolate the dataset to. Must be
positive.
:param interpolation_type: Optional ['linear', 'akima'], specify the type
of interpolation used for the locations (i.e.
longitude and latitude) of the records.
# FIXME: Need to address masking values - scipy.interpolate.interp1d
handles numpy.ma masked arrays.
"""
LOG.debug("Performing interpolation of TC track")
if not hasattr(track, 'Datetime'):
day_ = [
datetime(*x) for x in zip(track.Year, track.Month, track.Day,
track.Hour, track.Minute)
]
else:
day_ = track.Datetime
timestep = timedelta(delta / 24.)
try:
time_ = np.array(
[d.toordinal() + (d.hour + d.minute / 60.) / 24.0 for d in day_],
dtype=float)
except AttributeError:
import cftime
if isinstance(day_[0], cftime.DatetimeJulian):
day__ = [d._to_real_datetime() for d in day_]
time_ = np.array([
d.toordinal() + (d.hour + d.minute / 60.) / 24. for d in day__
],
dtype=float)
else:
raise
dt_ = 24.0 * np.diff(time_)
dt = np.zeros(len(track.data), dtype=float)
dt[1:] = dt_
# Convert all times to a time after initial observation:
timestep = 24.0 * (time_ - time_[0])
newtime = np.arange(timestep[0], timestep[-1] + .01, delta)
newtime[-1] = timestep[-1]
_newtime = (newtime / 24.) + time_[0]
newdates = num2date(_newtime)
newdates = np.array([n.replace(tzinfo=None) for n in newdates])
if not hasattr(track, 'Speed'):
idx = np.zeros(len(track.data))
idx[0] = 1
# TODO: Possibly could change `np.mean(dt)` to `dt`?
track.WindSpeed = maxWindSpeed(idx, np.mean(dt), track.Longitude,
track.Latitude, track.CentralPressure,
track.EnvPressure)
# Find the indices of valid pressure observations:
validIdx = np.where(track.CentralPressure < sys.maxsize)[0]
# FIXME: Need to address the issue when the time between obs is less
# than delta (e.g. only two obs 5 hrs apart, but delta = 6 hrs).
if len(track.data) <= 3:
# Use linear interpolation only (only a start and end point given):
nLon = interp1d(timestep, track.Longitude, kind='linear')(newtime)
nLat = interp1d(timestep, track.Latitude, kind='linear')(newtime)
if len(validIdx) >= 2:
npCentre = interp1d(timestep, track.CentralPressure,
kind='linear')(newtime)
nwSpd = interp1d(timestep, track.WindSpeed, kind='linear')(newtime)
elif len(validIdx) == 1:
# If one valid observation, assume no change and
# apply value to all times
npCentre = np.ones(len(newtime)) * track.CentralPressure[validIdx]
nwSpd = np.ones(len(newtime)) * track.WindSpeed[validIdx]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npEnv = interp1d(timestep, track.EnvPressure, kind='linear')(newtime)
nrMax = interp1d(timestep, track.rMax, kind='linear')(newtime)
else:
if interpolation_type == 'akima':
# Use the Akima interpolation method:
try:
import akima
except ImportError:
LOG.exception(("Akima interpolation module unavailable "
" - default to scipy.interpolate"))
nLon = splev(newtime,
splrep(timestep, track.Longitude, s=0),
der=0)
nLat = splev(newtime,
splrep(timestep, track.Latitude, s=0),
der=0)
else:
nLon = akima.interpolate(timestep, track.Longitude, newtime)
nLat = akima.interpolate(timestep, track.Latitude, newtime)
elif interpolation_type == 'linear':
nLon = interp1d(timestep, track.Longitude, kind='linear')(newtime)
nLat = interp1d(timestep, track.Latitude, kind='linear')(newtime)
else:
nLon = splev(newtime,
splrep(timestep, track.Longitude, s=0),
der=0)
nLat = splev(newtime, splrep(timestep, track.Latitude, s=0), der=0)
if len(validIdx) >= 2:
# No valid data at the final new time,
# would require extrapolation:
firsttime = np.where(newtime >= timestep[validIdx[0]])[0][0]
lasttime = np.where(newtime <= timestep[validIdx[-1]])[0][-1]
if firsttime == lasttime:
# only one valid observation:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npCentre[firsttime] = track.CentralPressure[validIdx[0]]
nwSpd[firsttime] = track.WindSpeed[validIdx[0]]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
_npCentre = interp1d(timestep[validIdx],
track.CentralPressure[validIdx],
kind='linear')(
newtime[firsttime:lasttime])
_nwSpd = interp1d(timestep[validIdx],
track.Speed[validIdx],
kind='linear')(newtime[firsttime:lasttime])
npCentre[firsttime:lasttime] = _npCentre
nwSpd[firsttime:lasttime] = _nwSpd
npCentre[lasttime] = _npCentre[-1]
nwSpd[lasttime] = _nwSpd[-1]
elif len(validIdx) == 1:
npCentre = np.ones(len(newtime)) * track.CentralPressure[validIdx]
nwSpd = np.ones(len(newtime)) * track.WindSpeed[validIdx]
else:
npCentre = np.zeros(len(newtime))
nwSpd = np.zeros(len(newtime))
npEnv = interp1d(timestep, track.EnvPressure, kind='linear')(newtime)
nrMax = interp1d(timestep, track.rMax, kind='linear')(newtime)
if len(nLat) >= 2:
bear_, dist_ = latLon2Azi(nLat, nLon, 1, azimuth=0)
nthetaFm = np.zeros(newtime.size, dtype=float)
nthetaFm[:-1] = bear_
nthetaFm[-1] = bear_[-1]
dist = np.zeros(newtime.size, dtype=float)
dist[:-1] = dist_
dist[-1] = dist_[-1]
nvFm = dist / delta
else:
nvFm = track.Speed[-1]
nthetaFm = track.Bearing[-1]
nYear = [date.year for date in newdates]
nMonth = [date.month for date in newdates]
nDay = [date.day for date in newdates]
nHour = [date.hour for date in newdates]
nMin = [date.minute for date in newdates]
np.putmask(npCentre, npCentre > 10e+6, sys.maxsize)
np.putmask(npCentre, npCentre < 700, sys.maxsize)
newindex = np.zeros(len(newtime))
newindex[0] = 1
newTCID = np.ones(len(newtime)) * track.trackId[0]
newdata = np.empty(len(newtime),
dtype={
'names': TRACKFILE_COLS,
'formats': TRACKFILE_FMTS
})
for key, val in zip(TRACKFILE_COLS, [
newindex, newTCID, nYear, nMonth, nDay, nHour, nMin, newtime,
newdates, nLon, nLat, nvFm, nthetaFm, npCentre, nwSpd, nrMax, npEnv
]):
newdata[key] = val
newtrack = Track(newdata)
newtrack.trackId = track.trackId
newtrack.trackfile = track.trackfile
return newtrack
