def test_GetCellLonLat(self):
"""Testing getCellLonLat"""
#valid values
knownValues = ((0, 70, 0), (50, 100, -10), (150, 160, -30), (175, 175, -35))
for cell, lon, lat in knownValues:
(lon_, lat_) = statutils.getCellLonLat(cell, self.gridLimit, self.gridSpace)
self.assertEqual((lon_,lat_), (lon,lat))
invalidCells = (-10, 176, 2000)
for cell in invalidCells:
self.assertRaises(IndexError, statutils.getCellLonLat, cell, self.gridLimit, self.gridSpace)
def test_GetCellLonLat(self):
"""Testing getCellLonLat"""
#valid values
knownValues = ((0, 70, 0), (50, 100, -10), (150, 160, -30), (175, 175,
-35))
for cell, lon, lat in knownValues:
(lon_, lat_) = statutils.getCellLonLat(cell, self.gridLimit,
self.gridSpace)
self.assertEqual((lon_, lat_), (lon, lat))
invalidCells = (-10, 176, 2000)
for cell in invalidCells:
self.assertRaises(IndexError, statutils.getCellLonLat, cell,
self.gridLimit, self.gridSpace)
def extractParameters(self, cellNum):
"""
Extracts cyclone parameters for a given cell
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
raise ValueError, "Invalid input on cellNum: cell number is out of range"
lon = self.lonlat[:, 0]
lat = self.lonlat[:, 1]
wLon, nLat = stats.getCellLonLat(cellNum, self.gridLimit, self.gridSpace)
eLon = wLon + self.gridSpace["x"]
sLat = nLat - self.gridSpace["y"]
indij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon) & (lon < eLon))
self.bearing = myutils.removeNum(self.bearing_list[indij])
self.speed = myutils.removeNum(self.speed_list[indij])
self.pressure = myutils.removeNum(self.pressure_list[indij])
self.bearingRate = myutils.removeNum(self.bearingRate_list[indij])
self.speedRate = myutils.removeNum(self.speedRate_list[indij])
self.pressureRate = myutils.removeNum(self.pressureRate_list[indij])
def extractParameters(self, cellNum):
"""
Extracts cyclone parameters for a given cell
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
raise ValueError, 'Invalid input on cellNum: cell number is out of range'
lon = self.lonlat[:,0]
lat = self.lonlat[:,1]
wLon, nLat = stats.getCellLonLat(cellNum, self.gridLimit,
self.gridSpace)
eLon = wLon + self.gridSpace['x']
sLat = nLat - self.gridSpace['y']
indij = np.where(((lat >= sLat) & (lat < nLat)) & \
(lon >= wLon) & (lon < eLon))
self.bearing = myutils.removeNum(self.bearing_list[indij])
self.speed = myutils.removeNum(self.speed_list[indij])
self.pressure = myutils.removeNum(self.pressure_list[indij])
self.bearingRate = myutils.removeNum(self.bearingRate_list[indij])
self.speedRate = myutils.removeNum(self.speedRate_list[indij])
self.pressureRate = myutils.removeNum(self.pressureRate_list[indij])
def extractParameter(self, cellNum):
"""extractParameter(cellNum):
Extracts the cyclone parameter data for the given cell.
If the population of a cell is insufficient for generating a
PDF, the bounds of the cell are expanded until the population is
sufficient.
Null/missing values are removed.
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
self.logger.critical("Invalid input on cellNum: cell number %i is out of range"%cellNum)
raise InvalidArguments, 'Invalid input on cellNum: cell number is out of range'
lon = self.lonLat[:,0]
lat = self.lonLat[:,1]
cellLon, cellLat = stats.getCellLonLat(cellNum, self.gridLimit,
self.gridSpace)
wLon = cellLon
eLon = cellLon + self.gridSpace['x']
nLat = cellLat
sLat = cellLat - self.gridSpace['y']
indij = np.where(((lat >= sLat) & (lat < nLat)) &
(lon >= wLon) & (lon < eLon))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
while np.size(self.parameter) <= self.minSamplesCell:
self.logger.debug("Insufficient samples. Increasing the size of the cell")
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
if (wLon == wLon_last) & (eLon == eLon_last) & (nLat == nLat_last) & (sLat == sLat_last):
errMsg = "Insufficient grid points in selected domain to " \
+ "estimate storm statistics - please select a larger " \
+ "domain. Samples = %i / %i" % (np.size(self.parameter),
self.minSamplesCell)
self.logger.critical(errMsg)
raise StopIteration, errMsg
indij = np.where(((lat >= sLat) & (lat < nLat)) &
((lon >= wLon) & (lon < eLon)))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
# Check to see if all values in the array are the same. If the
# values are the same, bandwidth would be 0, and therefore KDE
# cannot proceed
while self.parameter.max() == self.parameter.min():
self.logger.debug("Parameter values appear to be the same. Increasing the size of the cell")
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon,
eLon, nLat, sLat)
if (wLon == wLon_last) & (eLon == eLon_last) & (nLat == nLat_last) & (sLat == sLat_last):
errMsg = "Insufficient grid points in selected domain to estimate storm statistics - please select a larger domain."
self.logger.critical(errMsg)
raise StopIteration, errMsg
indij = np.where(((lat >= sLat) & (lat < nLat)) &
((lon >= wLon) & (lon < eLon)))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
self.logger.debug("Number of valid observations in cell %s : %s" %
(str(cellNum), str(np.size(self.parameter))))
def extractParameter(self, cellNum, onLand):
"""
Extracts the cyclone parameter data for the given cell.
If the population of a cell is insufficient for generating a
PDF, the bounds of the cell are expanded until the population is
sufficient.
Null/missing values are removed.
:param int cellNum: The cell number to process.
:returns: None. The :attr:`parameter` attribute is updated.
:raises InvalidArguments: if the cell number is not valid
(i.e. if it is outside the possible
range of cell numbers).
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
self.logger.critical("Invalid input on cellNum: cell number %i is out of range"%cellNum)
raise InvalidArguments, 'Invalid input on cellNum: cell number %i is out of range'%cellNum
cellLon, cellLat = stats.getCellLonLat(cellNum, self.gridLimit,
self.gridSpace)
wLon = cellLon
eLon = cellLon + self.gridSpace['x']
nLat = cellLat
sLat = cellLat - self.gridSpace['y']
lon = self.lonLat[:,0]
lat = self.lonLat[:,1]
lsflag = self.lonLat[:,2]
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) &
(lon >= wLon) & (lon < eLon) & (lsflag>0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) &
(lon >= wLon) & (lon < eLon) & (lsflag==0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
while np.size(p) <= self.minSample:
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
# Check if grid has reached maximum extent
if (wLon == wLon_last) & (eLon == eLon_last) & (nLat == nLat_last) & (sLat == sLat_last):
if onLand:
if not self.domain_warning_raised:
self.domain_warning_raised = True
self.logger.warning("Insufficient grid points over land in selected domain to estimate storm statistics - reverting to statistics for open ocean.")
return self.extractParameter(cellNum, False)
else:
errMsg = ("Insufficient grid points in selected "
"domain to estimate storm statistics - "
"please select a larger domain.")
self.logger.critical(errMsg)
raise StopIteration, errMsg
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon) &
(lon < eLon) & (lsflag>0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon) &
(lon < eLon) & (lsflag==0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
# Check to see if all values in the np.array are the same. If the values
# are the same, bandwidth would be 0, and therefore KDE cannot be generated
while p.max() == p.min():
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
# Check if grid has reached maximum extent
if (wLon == wLon_last) & (eLon == eLon_last) & (nLat == nLat_last) & (sLat == sLat_last):
if onLand:
if not self.domain_warning_raised:
self.domain_warning_raised = True
self.logger.warning("Insufficient grid points over land in selected domain to estimate storm statistics - reverting to statistics for open ocean.")
return self.extractParameter(cellNum, False)
else:
errMsg = "Insufficient grid points in selected domain to estimate storm statistics - please select a larger domain."
self.logger.critical(errMsg)
raise StopIteration, errMsg
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) &
(lon >= wLon) & (lon < eLon) & (lsflag>0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) &
(lon >= wLon) & (lon < eLon) & (lsflag==0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
return p
def extractParameter(self, cellNum, onLand):
"""
Extracts the cyclone parameter data for the given cell.
If the population of a cell is insufficient for generating a
PDF, the bounds of the cell are expanded until the population is
sufficient.
Null/missing values are removed.
:param int cellNum: The cell number to process.
:returns: None. The :attr:`parameter` attribute is updated.
:raises IndexError: if the cell number is not valid
(i.e. if it is outside the possible
range of cell numbers).
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
self.logger.critical(
"Invalid input on cellNum: cell number %i is out of range" %
cellNum)
raise IndexError, 'Invalid input on cellNum: cell number %i is out of range' % cellNum
cellLon, cellLat = stats.getCellLonLat(cellNum, self.gridLimit,
self.gridSpace)
wLon = cellLon
eLon = cellLon + self.gridSpace['x']
nLat = cellLat
sLat = cellLat - self.gridSpace['y']
lon = self.lonLat[:, 0]
lat = self.lonLat[:, 1]
lsflag = self.lonLat[:, 2]
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag > 0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag == 0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
while np.size(p) <= self.minSample:
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
# Check if grid has reached maximum extent
if (wLon == wLon_last) & (eLon == eLon_last) & (
nLat == nLat_last) & (sLat == sLat_last):
if onLand:
if not self.domain_warning_raised:
self.domain_warning_raised = True
self.logger.warning(
"Insufficient grid points over land in selected domain to estimate storm statistics - reverting to statistics for open ocean."
)
return self.extractParameter(cellNum, False)
else:
errMsg = ("Insufficient grid points in selected "
"domain to estimate storm statistics - "
"please select a larger domain.")
self.logger.critical(errMsg)
raise StopIteration, errMsg
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag > 0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag == 0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
# Check to see if all values in the np.array are the same. If the values
# are the same, bandwidth would be 0, and therefore KDE cannot be generated
while p.max() == p.min():
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
# Check if grid has reached maximum extent
if (wLon == wLon_last) & (eLon == eLon_last) & (
nLat == nLat_last) & (sLat == sLat_last):
if onLand:
if not self.domain_warning_raised:
self.domain_warning_raised = True
self.logger.warning(
"Insufficient grid points over land in selected domain to estimate storm statistics - reverting to statistics for open ocean."
)
return self.extractParameter(cellNum, False)
else:
errMsg = "Insufficient grid points in selected domain to estimate storm statistics - please select a larger domain."
self.logger.critical(errMsg)
raise StopIteration, errMsg
if onLand:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag > 0))
else:
ij = np.where(((lat >= sLat) & (lat < nLat)) & (lon >= wLon)
& (lon < eLon) & (lsflag == 0))
p_ = self.param[ij]
p = stats.statRemoveNum(np.array(p_), self.missingValue)
return p
def extractParameter(self, cellNum):
"""
Extracts the cyclone parameter data for the given cell.
If the population of a cell is insufficient for generating a
PDF, the bounds of the cell are expanded until the population is
sufficient.
Null/missing values are removed.
:param int cellNum: The cell number to process.
:returns: None. The :attr:`parameter` attribute is updated.
:raises IndexError: if the cell number is not valid
(i.e. if it is outside the possible
range of cell numbers).
"""
if not stats.validCellNum(cellNum, self.gridLimit, self.gridSpace):
self.logger.critical(("Invalid input on cellNum: "
"cell number %i is out of range")%cellNum)
raise IndexError('Invalid input on cellNum: '
'cell number is out of range')
lon = self.lonLat[:, 0]
lat = self.lonLat[:, 1]
cellLon, cellLat = stats.getCellLonLat(cellNum, self.gridLimit,
self.gridSpace)
wLon = cellLon
eLon = cellLon + self.gridSpace['x']
nLat = cellLat
sLat = cellLat - self.gridSpace['y']
indij = np.where(((lat >= sLat) & (lat < nLat)) &
((lon >= wLon) & (lon < eLon)))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
while np.size(self.parameter) <= self.minSamplesCell:
self.logger.debug(("Insufficient samples. Increasing the "
"size of the cell"))
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon, eLon,
nLat, sLat)
if ((wLon == wLon_last) & (eLon == eLon_last) &
(nLat == nLat_last) & (sLat == sLat_last)):
errMsg = ("Insufficient grid points in selected domain to "
"estimate storm statistics - please select a larger "
"domain. Samples = %i / %i")%(np.size(self.parameter),
self.minSamplesCell)
self.logger.critical(errMsg)
raise StopIteration(errMsg)
indij = np.where(((lat >= sLat) & (lat < nLat)) &
((lon >= wLon) & (lon < eLon)))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
# Check to see if all values in the array are the same. If the
# values are the same, bandwidth would be 0, and therefore KDE
# cannot proceed
while self.parameter.max() == self.parameter.min():
self.logger.debug(("Parameter values appear to be the same. "
"Increasing the size of the cell"))
wLon_last = wLon
eLon_last = eLon
nLat_last = nLat
sLat_last = sLat
wLon, eLon, nLat, sLat = self._expandCell(lon, lat, wLon,
eLon, nLat, sLat)
if ((wLon == wLon_last) & (eLon == eLon_last) &
(nLat == nLat_last) & (sLat == sLat_last)):
errMsg = ("Insufficient grid points in selected domain "
"to estimate storm statistics - "
"please select a larger domain.")
self.logger.critical(errMsg)
raise StopIteration(errMsg)
indij = np.where(((lat >= sLat) & (lat < nLat)) &
((lon >= wLon) & (lon < eLon)))
parameter_ = self.pList[indij]
self.parameter = stats.statRemoveNum(np.array(parameter_),
self.missingValue)
self.logger.debug("Number of valid observations in cell %s : %s",
str(cellNum), str(np.size(self.parameter)))
