def update(self, data2tape, time_ord):
""" Update the tape with new data"""
# ------------------------------------------------------------ #
# Check that the time_ord is in sync
if self._time_ord != time_ord:
raise ValueError('rout_var.time_ord does not match the time_ord '
'passed in by the convolution call')
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get the next timestamp
self._time_ord += self._dt / SECSPERDAY
self._timestamp = ord_to_datetime(self._time_ord,
TIMEUNITS,
calendar=self._calendar)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Advance the Count
self._count += 1
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Update the fields
for field in self._fincl:
tracer = 'LIQ'
log.debug('updating {0}'.format(field))
fdata = data2tape[field][tracer]
if self._avgflag == 'A':
self._temp_data[field] += fdata
elif self._avgflag == 'I':
if self._count == self._update_count:
self._temp_data[field] = fdata[:]
elif self._avgflag == 'X':
self._temp_data[field] = np.maximum(self._temp_data[field],
fdata)
elif self._avgflag == 'M':
self._temp_data[field] = np.minimum(self._temp_data[field],
fdata)
else:
raise ValueError('Average flag ({0}) does not match any of'
' (A,I,X,M)'.format(self._avgflag))
# ------------------------------------------------------------ #
# If count == _update_count, add to _out_data
# Average first, if necessary
if (self._avgflag == 'A' and self._count == self._update_count):
self.__average()
if self._count == self._update_count:
# move the data to the out_data structure
self.__update_out_data()
# Determine next update
self.__next_update_out_data()
# zero out temp_data
for field in self._fincl:
self._temp_data[field][:] = 0.0
def convolve(self, aggrunin, time_ord):
"""
This convoluition funciton works by looping over all points and doing
the convolution one timestep at a time. This is accomplished by
creating a convolution ring. Contributing flow from each timestep is
added to the convolution ring. The convolution ring is saved as the
state. The first column of values in the ring are the current runoff.
"""
# ------------------------------------------------------------ #
# Check that the time_ord is in sync
# This is the time at the start of the current step (end of last step)
if self.time_ord != time_ord:
log.error('rout_var.time_ord = %s, time_ord = %s', self.time_ord,
time_ord)
raise ValueError('rout_var.time_ord does not match the time_ord '
'passed in by the convolution call')
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Do the convolution
log.debug('convolving')
for tracer in RVIC_TRACERS:
# -------------------------------------------------------- #
# First update the ring
log.debug('rolling the ring')
# Zero out current ring
self.ring[tracer][0, :] = 0.
# Equivalent to Fortran 90 cshift function
self.ring[tracer] = np.roll(self.ring[tracer], -1, axis=0)
# -------------------------------------------------------- #
# -------------------------------------------------------- #
# C convolution call
rvic_convolve(self.n_sources,
self.n_outlets,
self.subset_length,
self.xsize,
self.source2outlet_ind,
self.source_y_ind,
self.source_x_ind,
self.source_time_offset,
self.unit_hydrograph[tracer][:, :],
aggrunin[tracer],
self.ring[tracer][:, :])
# -------------------------------------------------------- #
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# move the time_ord forward
self.time_ord += self.unit_hydrograph_dt / SECSPERDAY
self.timestamp = ord_to_datetime(self.time_ord, TIMEUNITS,
calendar=self._calendar)
return self.timestamp
def update(self, data2tape, time_ord):
""" Update the tape with new data"""
# ------------------------------------------------------------ #
# Check that the time_ord is in sync
if self._time_ord != time_ord:
raise ValueError('rout_var.time_ord does not match the time_ord '
'passed in by the convolution call')
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get the next timestamp
self._time_ord += self._dt / SECSPERDAY
self._timestamp = ord_to_datetime(self._time_ord, TIMEUNITS,
calendar=self._calendar)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Advance the Count
self._count += 1
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Update the fields
for field in self._fincl:
tracer = 'LIQ'
log.debug('updating {0}'.format(field))
fdata = data2tape[field][tracer]
if self._avgflag == 'A':
self._temp_data[field] += fdata
elif self._avgflag == 'I':
if self._count == self._update_count:
self._temp_data[field] = fdata[:]
elif self._avgflag == 'X':
self._temp_data[field] = np.maximum(self._temp_data[field],
fdata)
elif self._avgflag == 'M':
self._temp_data[field] = np.minimum(self._temp_data[field],
fdata)
else:
raise ValueError('Average flag ({0}) does not match any of'
' (A,I,X,M)'.format(self._avgflag))
# ------------------------------------------------------------ #
# If count == _update_count, add to _out_data
# Average first, if necessary
if (self._avgflag == 'A' and self._count == self._update_count):
self.__average()
if self._count == self._update_count:
# move the data to the out_data structure
self.__update_out_data()
# Determine next update
self.__next_update_out_data()
# zero out temp_data
for field in self._fincl:
self._temp_data[field][:] = 0.0
def convolve(self, aggrunin, time_ord):
"""
This convoluition funciton works by looping over all points and doing
the convolution one timestep at a time. This is accomplished by
creating a convolution ring. Contributing flow from each timestep is
added to the convolution ring. The convolution ring is saved as the
state. The first column of values in the ring are the current runoff.
"""
# ------------------------------------------------------------ #
# Check that the time_ord is in sync
# This is the time at the start of the current step (end of last step)
if self.time_ord != time_ord:
log.error('rout_var.time_ord = %s, time_ord = %s', self.time_ord,
time_ord)
raise ValueError('rout_var.time_ord does not match the time_ord '
'passed in by the convolution call')
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Do the convolution
log.debug('convolving')
for tracer in RVIC_TRACERS:
# -------------------------------------------------------- #
# First update the ring
log.debug('rolling the ring')
# Zero out current ring
self.ring[tracer][0, :] = 0.
# Equivalent to Fortran 90 cshift function
self.ring[tracer] = np.roll(self.ring[tracer], -1, axis=0)
# -------------------------------------------------------- #
# -------------------------------------------------------- #
# C convolution call
rvic_convolve(self.n_sources, self.n_outlets, self.subset_length,
self.xsize, self.source2outlet_ind,
self.source_y_ind, self.source_x_ind,
self.source_time_offset,
self.unit_hydrograph[tracer][:, :], aggrunin[tracer],
self.ring[tracer][:, :])
# -------------------------------------------------------- #
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# move the time_ord forward
self.time_ord += self.unit_hydrograph_dt / SECSPERDAY
self.timestamp = ord_to_datetime(self.time_ord,
TIMEUNITS,
calendar=self._calendar)
return self.timestamp
def init_state(self, state_file, run_type, timestamp):
if run_type in ['startup', 'restart']:
log.info('reading state_file: %s', state_file)
f = Dataset(state_file, 'r')
for tracer in RVIC_TRACERS:
self.ring[tracer] = f.variables['{0}_ring'.format(tracer)][:]
file_timestamp = ord_to_datetime(
f.variables['time'][:],
f.variables['time'].units,
calendar=f.variables['time'].calendar)
if run_type == 'restart':
self.timestamp = file_timestamp
elif run_type == 'startup':
self.timestamp = timestamp
if timestamp != file_timestamp:
log.warning('restart timestamps do not match (%s, %s',
file_timestamp, self.timestamp)
log.warning('Runtype is startup so model will continue')
else:
raise ValueError('unknown run_type: {0}'.format(run_type))
# Check that timestep and outlet_decomp_ids match ParamFile
if f.variables['unit_hydrograph_dt'][:] != self.unit_hydrograph_dt:
raise ValueError('Timestep in Statefile does not match '
'timestep in ParamFile')
if not np.array_equal(f.variables['outlet_decomp_ind'][:],
self.outlet_decomp_ind):
raise ValueError('outlet_decomp_ind in Statefile does not '
'match ParamFile')
if f.RvicDomainFile != self.RvicDomainFile:
raise ValueError('RvicDomainFile in StateFile does not match '
'ParamFile')
f.close()
elif run_type == 'drystart':
log.info('run_type is drystart so no state_file will be read')
self.timestamp = timestamp
self.time_ord = date2num(self.timestamp,
TIMEUNITS,
calendar=self._calendar)
self._start_date = self.timestamp
self._start_ord = self.time_ord
def init_state(self, state_file, run_type, timestamp):
if run_type in ['startup', 'restart']:
log.info('reading state_file: %s', state_file)
f = Dataset(state_file, 'r')
for tracer in RVIC_TRACERS:
self.ring[tracer] = f.variables['{0}_ring'.format(tracer)][:]
file_timestamp = ord_to_datetime(f.variables['time'][:],
f.variables['time'].units,
calendar=f.variables['time'].calendar)
if run_type == 'restart':
self.timestamp = file_timestamp
elif run_type == 'startup':
self.timestamp = timestamp
if timestamp != file_timestamp:
log.warning('restart timestamps do not match (%s, %s',
file_timestamp, self.timestamp)
log.warning('Runtype is startup so model will continue')
else:
raise ValueError('unknown run_type: {0}'.format(run_type))
# Check that timestep and outlet_decomp_ids match ParamFile
if f.variables['unit_hydrograph_dt'][:] != self.unit_hydrograph_dt:
raise ValueError('Timestep in Statefile does not match '
'timestep in ParamFile')
if not np.array_equal(f.variables['outlet_decomp_ind'][:],
self.outlet_decomp_ind):
raise ValueError('outlet_decomp_ind in Statefile does not '
'match ParamFile')
if f.RvicDomainFile != self.RvicDomainFile:
raise ValueError('RvicDomainFile in StateFile does not match '
'ParamFile')
f.close()
elif run_type == 'drystart':
log.info('run_type is drystart so no state_file will be read')
self.timestamp = timestamp
self.time_ord = date2num(self.timestamp, TIMEUNITS,
calendar=self._calendar)
self._start_date = self.timestamp
self._start_ord = self.time_ord
def convolve(self, aggrunin, time_ord):
"""
This convoluition funciton works by looping over all points and doing the
convolution one timestep at a time. This is accomplished by creating an
convolution ring. Contributing flow from each timestep is added to the
convolution ring. The convolution ring is saved as the state. The first
column of values in the ring are the current runoff.
"""
# ------------------------------------------------------------ #
# Check that the time_ord is in sync
# This is the time at the start of the current step (end of last step)
if self.time_ord != time_ord:
log.error('rout_var.time_ord = %s, time_ord = %s' %(self.time_ord, time_ord))
raise ValueError('rout_var.time_ord does not match the time_ord passed in by the convolution call')
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# First update the ring
log.debug('rolling the ring')
self.ring[0, :, 0] = 0 # Zero out current ring
self.ring = np.roll(self.ring, 1, axis=0) # Equivalent to Fortran 90 cshift function
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Do the convolution
log.debug('convolving')
# this matches the fortran implementation, it may be faster to use np.convolve but testing
# can be done later
# also this is where the parallelization will happen
for nt, tracer in enumerate(RVIC_TRACERS):
for s, outlet in enumerate(self.source2outlet_ind): # loop over all source points
y = self.source_y_ind[s]
x = self.source_x_ind[s]
for i in xrange(self.subset_length):
j = i + self.source_time_offset[s]
self.ring[j, outlet, nt] = self.ring[j, outlet, nt] + (self.unit_hydrograph[i, s, nt] * aggrunin[tracer][y, x])
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# move the time_ord forward
self.time_ord += self.unit_hydrograph_dt / SECSPERDAY
self.timestamp = ord_to_datetime(self.time_ord, TIMEUNITS, calendar=self._calendar)
return self.timestamp
def __init__(self, time_ord, caseid, Rvar, tape_num=0,
fincl=['streamflow'], mfilt=1, ndens=2, nhtfrq=0,
avgflag='A', units='kg m-2 s-1',
file_format='NETCDF4_CLASSIC', outtype='grid',
grid_lons=False, grid_lats=False, grid_area=None, out_dir='.',
calendar=None, glob_ats=None, zlib=True, complevel=4,
least_significant_digit=None):
self._tape_num = tape_num
self._time_ord = time_ord # Days since basetime
self._caseid = caseid # Case ID and prefix for outfiles
self._fincl = list(fincl) # Fields to include in history file
self._mfilt = mfilt # Maximum number of time samples
self._ndens = ndens
if self._ndens == 1: # Output file precision
self._ncprec = NC_FLOAT
else:
self._ncprec = NC_DOUBLE
self._nhtfrq = nhtfrq # Write frequency
self._avgflag = avgflag # Average Flag (A,I,X,M)
self._outtype = outtype # Outfile type (grid, array)
self._count = 0
self.files_count = 0
self._file_format = file_format
self._calendar = calendar
self._out_dir = out_dir
self._glob_ats = glob_ats
self.__get_rvar(Rvar) # Get the initial Rvar fields
self._grid_shape = grid_area.shape
self._out_data = {}
# ------------------------------------------------------------ #
# calculate the step size for each out_data timestep (units=days)
if self._nhtfrq > 0:
# If some number of timesteps
self._out_data_stepsize = self._nhtfrq * self._dt / SECSPERDAY
elif self._nhtfrq < 0:
# If some number hours
self._out_data_stepsize = -1 * self._nhtfrq / HOURSPERDAY
else:
# If monthly
self._out_data_stepsize = None # varies by month
log.debug('_out_data_stepsize: %s', self._out_data_stepsize)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get Grid Lons/Lats if outtype is grid
if outtype == 'grid':
self._out_data_shape = self._grid_shape
if type(grid_lons) == np.ndarray and type(grid_lats) == np.ndarray:
self._grid_lons = grid_lons
self._grid_lats = grid_lats
else:
raise ValueError('Must include grid lons / lats if '
'outtype == grid')
else:
self._out_data_shape = (self._num_outlets, )
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get units multiplier (size of noutlets)
self._units = units
if units in ['kg/m2/s', 'kg m-2 s-1', 'kg m^-2 s^-1',
'kg*m-2*s-1', 'kg s-1 m-2']:
self._units_mult = np.ones_like(self._outlet_y_ind,
dtype=np.float64)
elif units in ['m3/s', 'm^3/s', 'm3 s-1']:
# kg/m2/s --> m3/s
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
elif units in ['mm/day', 'mm d-1', 'mm d^-1', 'mm/day']:
# kg/m2/s --> mm/day over basin area
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult *= SECSPERDAY
self._units_mult /= WATERDENSITY
self._units_mult /= self._outlet_upstream_area
elif units in ['gal/day', 'gpd', 'gal d-1']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 2.28E7
elif units in ['cfs', 'ft^3 s-1', 'f3/s']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 35.3
elif units in ['acre-ft/d']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 70.0
else:
raise ValueError('{0} is not a valid units string'.format(units))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# netCDF variable options
self.ncvaropts = {'zlib': zlib,
'complevel': complevel,
'least_significant_digit': least_significant_digit}
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# get current timestamp
self._timestamp = ord_to_datetime(self._time_ord, TIMEUNITS,
self._calendar)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Initialize the temporary history fields
self._temp_data = {}
for field in self._fincl:
self._temp_data[field] = np.zeros(self._num_outlets,
dtype=np.float64)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine the format of the output filename
if self._avgflag == 'I':
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
else:
if self._nhtfrq == 0:
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
elif (self._nhtfrq == -24) or (nhtfrq*self._dt == SECSPERDAY):
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
else:
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d-%%H.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
self._rest_fname_format = os.path.join(out_dir,
"%s.rvic.rh%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" % (self._caseid, self._tape_num))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine when the next write should be and initialize out_data
self.__next_write_out_data()
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine when the update of out_data should be
self.__next_update_out_data()
# ------------------------------------------------------------ #
log.debug(self.__repr__())
def __init__(self, time_ord, caseid, Rvar, tape_num=0,
fincl=['streamflow'], mfilt=1, ndens=2, nhtfrq=0,
avgflag='A', units='kg m-2 s-1',
file_format='NETCDF4_CLASSIC', outtype='grid',
grid_lons=False, grid_lats=False, grid_area=None, out_dir='.',
calendar=None, glob_ats=None):
self._tape_num = tape_num
self._time_ord = time_ord # Days since basetime
self._caseid = caseid # Case ID and prefix for outfiles
self._fincl = fincl # Fields to include in history file
self._mfilt = mfilt # Maximum number of time samples
self._ndens = ndens
if self._ndens == 1: # Output file precision
self._ncprec = NC_FLOAT
else:
self._ncprec = NC_DOUBLE
self._nhtfrq = nhtfrq # Write frequency
self._avgflag = avgflag # Average Flag (A,I,X,M)
self._outtype = outtype # Outfile type (grid, array)
self._count = 0
self.files_count = 0
self._file_format = file_format
self._calendar = calendar
self._out_dir = out_dir
self._glob_ats = glob_ats
self._out_data_i = 0 # position counter for out_data array
self._out_times = np.zeros(self._mfilt, dtype=np.float64)
self._out_time_bnds = np.zeros((self._mfilt, 2), dtype=np.float64)
self.__get_rvar(Rvar) # Get the initial Rvar fields
self._grid_shape = grid_area.shape
# ------------------------------------------------------------ #
# Get Grid Lons/Lats if outtype is grid, setup out_data
self._out_data = {}
if outtype == 'grid':
if type(grid_lons) == np.ndarray and type(grid_lats) == np.ndarray:
self._grid_lons = grid_lons
self._grid_lats = grid_lats
for field in self._fincl:
if grid_lons.ndim == 1:
shape = (self._mfilt,) + self._grid_shape
else:
shape = (self._mfilt,) + self._grid_shape
self._out_data[field] = np.zeros(shape, dtype=np.float64)
else:
raise ValueError('Must include grid lons / lats if '
'outtype == grid')
else:
for field in self._fincl:
self._out_data[field] = np.zeros((self._mfilt,
self._num_outlets))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get units multiplier
self._units = units
if units in ['kg/m2/s', 'kg m-2 s-1', 'kg m^-2 s^-1',
'kg*m-2*s-1', 'kg s-1 m-2']:
self._units_mult = 1.0
elif units in ['m3/s', 'm^3/s', 'm3 s-1']:
self._units_mult = grid_area / WATERDENSITY
else:
raise ValueError('{0} is not a valid units string'.format(units))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# get current timestamp
self._timestamp = ord_to_datetime(self._time_ord, TIMEUNITS,
self._calendar)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Initialize the temporary history fields
self._temp_data = {}
for field in self._fincl:
self._temp_data[field] = np.zeros(self._num_outlets,
dtype=np.float64)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine the format of the output filename
if self._avgflag == 'I':
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
else:
if nhtfrq == 0:
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
elif (nhtfrq == -24) or (nhtfrq*self._dt == SECSPERDAY):
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
else:
self._fname_format = os.path.join(out_dir,
"%s.rvic.h%s%s.%%Y-%%m-%%d-%%H.nc" % (self._caseid, self._tape_num, self._avgflag.lower()))
self._rest_fname_format = os.path.join(out_dir,
"%s.rvic.rh%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" % (self._caseid, self._tape_num))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine when the next write should be
self.__next_update_out_data()
def __init__(self,
time_ord,
caseid,
Rvar,
tape_num=0,
fincl=['streamflow'],
mfilt=1,
ndens=2,
nhtfrq=0,
avgflag='A',
units='kg m-2 s-1',
file_format='NETCDF4_CLASSIC',
outtype='grid',
grid_lons=False,
grid_lats=False,
grid_area=None,
out_dir='.',
calendar=None,
glob_ats=None,
zlib=True,
complevel=4,
least_significant_digit=None):
self._tape_num = tape_num
self._time_ord = time_ord # Days since basetime
self._caseid = caseid # Case ID and prefix for outfiles
self._fincl = list(fincl) # Fields to include in history file
self._mfilt = mfilt # Maximum number of time samples
self._ndens = ndens
if self._ndens == 1: # Output file precision
self._ncprec = NC_FLOAT
else:
self._ncprec = NC_DOUBLE
self._nhtfrq = nhtfrq # Write frequency
self._avgflag = avgflag # Average Flag (A,I,X,M)
self._outtype = outtype # Outfile type (grid, array)
self._count = 0
self.files_count = 0
self._file_format = file_format
self._calendar = calendar
self._out_dir = out_dir
self._glob_ats = glob_ats
self.__get_rvar(Rvar) # Get the initial Rvar fields
self._grid_shape = grid_area.shape
self._out_data = {}
# ------------------------------------------------------------ #
# calculate the step size for each out_data timestep (units=days)
if self._nhtfrq > 0:
# If some number of timesteps
self._out_data_stepsize = self._nhtfrq * self._dt / SECSPERDAY
elif self._nhtfrq < 0:
# If some number hours
self._out_data_stepsize = -1 * self._nhtfrq / HOURSPERDAY
else:
# If monthly
self._out_data_stepsize = None # varies by month
log.debug('_out_data_stepsize: %s', self._out_data_stepsize)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get Grid Lons/Lats if outtype is grid
if outtype == 'grid':
self._out_data_shape = self._grid_shape
if type(grid_lons) == np.ndarray and type(grid_lats) == np.ndarray:
self._grid_lons = grid_lons
self._grid_lats = grid_lats
else:
raise ValueError('Must include grid lons / lats if '
'outtype == grid')
else:
self._out_data_shape = (self._num_outlets, )
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Get units multiplier (size of noutlets)
self._units = units
if units in [
'kg/m2/s', 'kg m-2 s-1', 'kg m^-2 s^-1', 'kg*m-2*s-1',
'kg s-1 m-2'
]:
self._units_mult = np.ones_like(self._outlet_y_ind,
dtype=np.float64)
elif units in ['m3/s', 'm^3/s', 'm3 s-1']:
# kg/m2/s --> m3/s
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
elif units in ['mm/day', 'mm d-1', 'mm d^-1', 'mm/day']:
# kg/m2/s --> mm/day over basin area
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult *= SECSPERDAY
self._units_mult /= WATERDENSITY
self._units_mult /= self._outlet_upstream_area
elif units in ['gal/day', 'gpd', 'gal d-1']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 2.28E7
elif units in ['cfs', 'ft^3 s-1', 'f3/s']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 35.3
elif units in ['acre-ft/d']:
self._units_mult = grid_area[self._outlet_y_ind,
self._outlet_x_ind]
self._units_mult /= WATERDENSITY
self._units_mult *= 70.0
else:
raise ValueError('{0} is not a valid units string'.format(units))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# netCDF variable options
self.ncvaropts = {
'zlib': zlib,
'complevel': complevel,
'least_significant_digit': least_significant_digit
}
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# get current timestamp
self._timestamp = ord_to_datetime(self._time_ord, TIMEUNITS,
self._calendar)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Initialize the temporary history fields
self._temp_data = {}
for field in self._fincl:
self._temp_data[field] = np.zeros(self._num_outlets,
dtype=np.float64)
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine the format of the output filename
if self._avgflag == 'I':
self._fname_format = os.path.join(
out_dir, "%s.rvic.h%s%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" %
(self._caseid, self._tape_num, self._avgflag.lower()))
else:
if self._nhtfrq == 0:
self._fname_format = os.path.join(
out_dir, "%s.rvic.h%s%s.%%Y-%%m.nc" %
(self._caseid, self._tape_num, self._avgflag.lower()))
elif (self._nhtfrq == -24) or (nhtfrq * self._dt == SECSPERDAY):
self._fname_format = os.path.join(
out_dir, "%s.rvic.h%s%s.%%Y-%%m-%%d.nc" %
(self._caseid, self._tape_num, self._avgflag.lower()))
else:
self._fname_format = os.path.join(
out_dir, "%s.rvic.h%s%s.%%Y-%%m-%%d-%%H.nc" %
(self._caseid, self._tape_num, self._avgflag.lower()))
self._rest_fname_format = os.path.join(
out_dir, "%s.rvic.rh%s.%%Y-%%m-%%d-%%H-%%M-%%S.nc" %
(self._caseid, self._tape_num))
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine when the next write should be and initialize out_data
self.__next_write_out_data()
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Determine when the update of out_data should be
self.__next_update_out_data()
# ------------------------------------------------------------ #
log.debug(self.__repr__())
