def time_evolve(self):
'''
Evolve the spatial grid, time grid, accumulation rate, age, density, mass, stress, temperature, and diffusivity through time
based on the user specified number of timesteps in the model run. Updates the firn density using a user specified
'''
# load in model parameters
gridLen, dx, dt, t, modeltime, years, stp, Ts, T_mean, bdot, bdotSec, rhos0, D_surf = define_parameters()
steps = 1 / t
if not self.c['physGrain']:
r2_time = None
for iter in xrange(stp):
mtime = modeltime[iter]
# getting the right physics for firn density based on user input
physics = {
'HLdynamic', HLdynamic,
'HLSigfus', HLSigfus,
'Barnola1991', Barnola1991,
'Li2004', Li2004,
'Li2011', Li2011,
'Ligtenberg2011', Ligtenberg2011,
'Arthern2010S', Arthern2010S,
'Simonsen2013', Simonsen2013,
'Morris2013', MorrisHL2013,
'Helsen2008', Helsen2008,
'Arthern2010T', Arthern2010T,
'Spencer2001', Spencer2001,
'Goujon2003', Goujon2003,
}
parameters = {
'HLdynamic', [steps, gridLen, bdotSec, self.diffu.Tz, self.rho],
'HLSigfus', [steps, gridLen, bdotSec, self.diffu.Tz, self.rho, sigma],
'Barnola1991', [steps, gridLen, bdotSec, self.diffu.Tz, self.rho, sigma],
'Li2004', [steps, gridLen, bdotSec, T_mean, self.rho],
'Li2011', [steps, gridLen, bdotSec, self.bdot_mean, self.c['bdot_type'], self.diffu.Tz, T_mean, self.rho],
'Ligtenberg2011', [steps, gridLen, bdotSec, self.bdot_mean, self.c['bdot_type'], self.diffu.Tz, T_mean, self.rho],
'Arthern2010S', [steps, gridLen, bdotSec, self.bdot_mean, self.c['bdot_type'], self.diffu.Tz, T_mean, self.rho],
'Simonsen2013', [steps, gridLen, bdotSec, self.bdot_mean, self.c['bdot_type'], self.diffu.Tz, T_mean, self.rho],
'Morris2013', [steps, gridLen, self.diffu.Tz, dt, self.rho, True, iter],
'Helsen2008', [steps, bdotSec, self.c['bdot_type'], self.bdot_mean, self.diffu.Tz, Ts, self.rho],
'Arthern2010T', [gridLen, self.diffu.Tz, self.rho, self.sigma, self.r2, self.c['physGrain']],
'Spencer2001', [],
'Goujon2003', [],
}
try:
drho_dt = physics[self.c['physRho']](parameters[self.c['physRho']])
except KeyError:
default()
# update density and age of firn
self.age = np.concatenate(([0], self.age[:-1])) + dt
self.rho = self.rho + dt * drho_dt
self.rho = np.concatenate(([rhos0[iter]], self.rho[:-1]))
self.Dcon = np.concatenate(([D_surf[iter]], self.Dcon[:-1]))
# update temperature grid and isotope grid if user specifies
if self.c['heatDiff']:
self.diffu.heatDiff(self.z, self.dz, Ts, self.rho)
if self.c['heatDiff']:
self.diffu.isoDiff(iter, self.z, self.dz, self.rho, self.c['iso'])
# update model grid
dzNew = bdotSec[iter] * RHO_I / rhos0[iter] * S_PER_YEAR
self.dz = self.mass / self.rho * dx
self.dz = np.concatenate(([dzNew], self.dz[:-1]))
self.z = self.dz.cumsum(axis = 0)
self.z = np.concatenate(([0], self.z[:-1]))
# update mass, stress, and mean accumulation rate
massNew = bdotSec[iter] * S_PER_YEAR * RHO_I
self.mass = np.concatenate(([massNew], self.mass[:-1]))
self.sigma = self.mass * dx * GRAVITY
self.sigma = self.sigma.cumsum(axis = 0)
self.mass_sum = self.mass.cumsum(axis = 0)
self.bdot_mean = np.concatenate(([mass_sum[0] / (RHO_I * S_PER_YEAR)], self.mass_sum[1:] * t / (self.age[1:] * RHO_I)))
# update grain radius
if self.c['physGrain']:
self.r2 = grainGrowth(self.diffu.Tz, Ts, iter, dt)
# write results as often as specified in the init method
if [True for iter in self.TWrite if iter == mtime] == [True]:
rho_time = np.append(mtime, self.rho)
Tz_time = np.append(mtime, self.diffu.Tz)
age_time = np.append(mtime, self.age)
z_time = np.append(mtime, self.z)
Dcon_time = np.append(mtime, self.Dcon)
Clim_time = np.append(mtime, [bdot[iter], Ts[iter]])
bdot_time = np.append(mtime, self.bdot_mean)
if c['physGrain']:
r2_time = np.append(mtime, self.r2)
write_nospin(self.c['resultsFolder'], self.c['physGrain'], rho_time, Tz_time, age_time, z_time, D_time, Clim_time, bdot_time, r2_time)
update_BCO()
update_LIZ()
update_DIP()
# write BCO, LIZ, DIP at the end of the time evolution
write_nospin_BCO(self.c['resultsFolder'], self.bcoAgeMartAll, self.bcoDepMartAll, self.bcoAge815All, self.bcoDep815All)
write_nospin_LIZ(self.c['resultsFolder'], self.LIZAgeAll, self.LIZDepAll)
write_nospin_DIP(self.c['resultsFolder'], self.intPhiAll)
def __init__(self, configName):
'''
Sets up the initial spatial grid, time grid, accumulation rate, age, density, mass, stress, temperature, and diffusivity of the model run
:param configName: name of json config file containing model configurations
'''
# load in json config file and parses the user inputs to a dictionary
with open(configName, "r") as f:
jsonString = f.read()
self.c = json.loads(jsonString)
# read in initial depth, age, density, temperature
initDepth, initAge, initDensity, initTemp = read_init(self.c['resultsFolder'])
# set up the initial age and density of the firn column
self.age = initAge[1:]
self.rho = initDensity[1:]
# set up model grid
self.z = initDepth[1:]
self.dz = np.diff(self.z)
self.dz = np.append(self.dz, self.dz[-1])
# load in model parameters
gridLen, dx, dt, t, modeltime, years, stp, Ts, T_mean, bdot, bdotSec, rhos0, D_surf = self.define_parameters()
# set up the initial grid of diffusivity constant
self.Dcon = self.c['D_surf'] * np.ones(gridLen)
# set up vector of times data will be written
self.TWrite = modeltime[INTE::INTE]
# set up initial mass, stress, and mean accumulation rate
self.mass = self.rho * self.dz
self.sigma = self.mass * dx * GRAVITY
self.sigma = self.sigma.cumsum(axis = 0)
self.mass_sum = self.mass.cumsum(axis = 0)
self.bdot_mean = np.concatenate(([self.mass_sum[0] / (RHO_I * S_PER_YEAR)], self.mass_sum[1:] / (self.age[1:] * RHO_I / t)))
# set up class to handle heat/isotope diffusion using user provided data for initial temperature vector
self.diffu = Diffusion(self.z, stp, gridLen, initTemp[1:])
# set up initial values for density, temperature, age, depth, diffusivity, Clim??, and accumulation to write
rho_time = np.append(modeltime[0], self.rho)
Tz_time = np.append(modeltime[0], self.diffu.Tz)
age_time = np.append(modeltime[0], self.age)
z_time = np.append(modeltime[0], self.z)
D_time = np.append(modeltime[0], self.Dcon)
Clim_time = np.append(modeltime[0], [bdot[0], Ts[0]]) # not sure if bdot or bdotSec
bdot_time = np.append(modeltime[0], self.bdot_mean)
# set up initial grain growth (if specified in config file)
if self.c['physGrain']:
initr2 = np.genfromtxt(r2Path, delimiter = ',')
self.r2 = initr2
r20 = r2
r2_time = np.append(modeltime[0], self.r2)
else:
r2_time = None
# write initial values to the results folder
write_nospin(self.c['resultsFolder'], self.c['physGrain'], rho_time, Tz_time, age_time, z_time, D_time, Clim_time, bdot_time, r2_time)
# set up initial values for bubble close-off depth & age, lock-in zone depth & age, and depth integrated porosity
self.bcoAgeMartAll = []
self.bcoDepMartAll = []
self.bcoAge815All = []
self.bcoDep815All = []
self.LIZAgeAll = []
self.LIZDepAll = []
self.intPhiAll = []
update_BCO()
update_LIZ()
update_DIP()