def __init__(self, configName):
'''
Sets up the initial spatial grid, time grid, accumulation rate, age, density, mass, stress, and temperature 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)
# create directory to store results
if os.path.exists(self.c['resultsFolder']):
rmtree(self.c['resultsFolder'])
os.makedirs(self.c['resultsFolder'])
# read in initial temperatures and accumulation rates
input_temp, input_year_temp = read_temp(self.c['InputFileNameTemp'])
input_bdot, input_year_bdot = read_bdot(self.c['InputFileNamebdot'])
# load in model parameters
self.bdot0 = input_bdot[0]
self.temp0 = input_temp[0]
gridLen, dx, dt, t, stp, Ts, T_mean, bdotSec, rhos0 = self.define_parameters()
# set up model grid
gridheight = np.linspace(c['H'], c['HbaseSpin'], gridLen)
self.z = self.c['H'] - gridHeight
self.dz = np.diff(self.z)
self.dz = np.append(self.dz, self.dz[-1])
# set up the initial age and density of the firn column using herron & langway analytic
THL = input_temp[0]
AHL = input_bdot[0]
self.age, self.rho = hl_analytic(self.c['rhos0'], self.z, THL, AHL)
# 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 initial temperature grid as well as a class to handle heat/isotope diffusion
init_Tz = input_temp[0] * np.ones(gridLen)
self.diffu = Diffusion(self.z, stp, gridLen, init_Tz)
# set up initial grain growth (if specified in config file)
if self.c['physGrain']:
if self.c['calcGrainSize']:
r02 = -2.42e-9 * (self.c['Ts0']) + 9.46e-7
self.r2 = r02 * np.ones(gridLen)
else:
self.r2 = np.linspace(self.c['r2s0'], (6 * self.c['r2s0']), gridLen)
def define_parameters(self):
'''
Return the parameters used in the model, for the initialization as well as the time evolution
:returns gridLen: size of grid used in the model run
(unit: number of boxes, type: int)
:returns dx: vector of width of each box, used for stress calculations
(unit: ???, type: array of ints)
:returns dt: number of seconds per time step
(unit: seconds, type: float)
:returns t: number of years per time step
(unit: years, type: float)
:returns modeltime: linearly spaced time vector from indicated start year to indicated end year
(unit: years, type: array of floats)
:returns years: total number of years in the model run
(unit: years, type: float)
:returns stp: total number of steps in the model run
(unit: number of steps, type: int)
:returns T_mean: interpolated temperature vector based on the model time and the initial user temperature data
(unit: ???, type: array of floats)
:returns Ts: interpolated temperature vector based on the model time & the initial user temperature data
may have a seasonal signal imposed depending on number of years per time step (< 1)
(unit: ???, type: array of floats)
:returns bdot: bdot is meters of ice equivalent/year. multiply by 0.917 for W.E. or 917.0 for kg/year
(unit: ???, type: )
:returns bdotSec: accumulation rate vector at each time step
(unit: ???, type: array of floats)
:returns rhos0: surface accumulate rate vector
(unit: ???, type: array of floats)
:returns D_surf: diffusivity tracker
(unit: ???, type: array of floats)
'''
gridLen = np.size(self.z)
dx = np.ones(gridLen)
input_temp, input_year_temp = read_temp(self.c['InputFileNameTemp'])
input_bdot, input_year_bdot = read_bdot(self.c['InputFileNamebdot'])
yr_start = max(input_year_temp[0], input_year_bdot[0])
yr_end = min(input_year_temp[-1], input_year_bdot[-1])
modeltime = np.linspace(yr_start, yr_end, stp + 1)
years = (yr_end - yr_start) * 1.0
stp = int(years * self.c['stpsPerYear'])
dt = years * S_PER_YEAR / stp
t = 1.0 / self.c['stpsPerYear']
TPeriod = years
Ts = np.interp(modeltime, input_year_temp, input_temp)
T_mean = Ts
if t < 1.0:
Ts = Ts + self.c['TAmp'] * (np.cos(2 * np.pi * np.linspace(0, TPeriod, stp + 1)) + 0.3 * np.cos(4 * np.pi * np.linspace(0, TPeriod, stp + 1)))
bdot = np.interp(modeltime, input_year_bdot, input_bdot)
bdotSec = bdot / S_PER_YEAR / (stp / years)
rhos0 = self.c['rhos0'] * np.ones(stp)
D_surf = self.c['D_surf'] * np.ones(stp)
return gridLen, dx, dt, t, modeltime, years, stp, Ts, T_mean, bdot, bdotSec, rhos0, D_surf
