# X[time][ring][cell]
X = np.zeros((tau, rings, ringsize), float)
Y = np.zeros((tau, rings, ringsize), float)
for ring in range(rings):
for cell in range(ringsize):
X[0][ring][cell] = X0[cell][ring]
Y[0][ring][cell] = Y0[cell][ring]
time = np.arange(0, h * N + h, h)
# Loop over all cells and time points
startTime = mytime.time()
for t in range(0, N):
index = t % tau
for ring in range(0, rings):
timestep.timestep(X, Y, cylinderConcPerCell, index, ring, t, tau,
omega, Am)
# Generate progress output to standard out
if t % 5 == 0 and t != 0:
elapsed = mytime.time() - startTime
ratio = 1.0 * t / N
print ('%d/%d, \t %.1f%%, \t elapsed: %.1f \t ETA: %.1f'\
% (t, N, ratio*100, elapsed, elapsed/ratio-elapsed))
# Write data output to files
if t % sampleFreq == 0:
# Write raw data to files
# writeCylinder('outputX_F_Dxr_' + str(Dxr) + '_k_' + ':', X, t)
# writeCylinder('outputY_F_Dxr_' + str(Dxr) + '_k_' + ':', Y, t)
# Write X matrix to image file
# Not standard modules: ensure these files are in the current directory import timestep as ts import simple_plot as sp import simple_animate as sa from matplotlib import pyplot # Sample initial condition and code for testing #dx = 0.025 xmax = 2.0 nx = 41 dx = xmax/(nx-1) nt = 50 test_ic = numpy.zeros((1, nx)) # put at assert to accept 1-by-nx ICs test_ic[0, 0.5/dx: 1/dx + 1] = 2 test_solution = ts.timestep(test_ic, 'fd') sp.plot_at(0, test_solution) sp.plot_at(30, test_solution) sa.animate_it(test_solution) # try a different IC - Double hat test_ic_2 = test_ic.copy() test_ic_2[0, 0.7/dx: 0.9/dx] = 0 test_solution_2 = ts.timestep(test_ic_2, 'fd') sp.plot_at(0, test_solution_2) # see initial condition sp.plot_at(24, test_solution_2) # see intermediate step sp.plot_at(49, test_solution_2) # see final state #BUG: clipped sa.animate_it(test_solution_2) pyplot.show() # to make it show after all the previous non-blocking figures
def preevolve(my_data):
"""
preevolve is called before we being the timestepping loop. For
the incompressible solver, this does an initial projection on the
velocity field and then goes through the full evolution to get the
value of phi. The fluid state (u, v) is then reset to values
before this evolve.
"""
myg = my_data.grid
u = my_data.get_var("x-velocity")
v = my_data.get_var("y-velocity")
my_data.fill_BC("x-velocity")
my_data.fill_BC("y-velocity")
# 1. do the initial projection. This makes sure that our original
# velocity field satisties div U = 0
# next create the multigrid object. We want Neumann BCs on phi
# at solid walls and periodic on phi for periodic BCs
MG = multigrid.CellCenterMG2d(myg.nx,
myg.ny,
xl_BC_type="periodic",
xr_BC_type="periodic",
yl_BC_type="periodic",
yr_BC_type="periodic",
xmin=myg.xmin,
xmax=myg.xmax,
ymin=myg.ymin,
ymax=myg.ymax,
verbose=0)
# first compute divU
divU = MG.soln_grid.scratch_array()
divU[MG.ilo:MG.ihi+1,MG.jlo:MG.jhi+1] = \
0.5*(u[myg.ilo+1:myg.ihi+2,myg.jlo:myg.jhi+1] -
u[myg.ilo-1:myg.ihi ,myg.jlo:myg.jhi+1])/myg.dx + \
0.5*(v[myg.ilo:myg.ihi+1,myg.jlo+1:myg.jhi+2] -
v[myg.ilo:myg.ihi+1,myg.jlo-1:myg.jhi ])/myg.dy
# solve L phi = DU
# initialize our guess to the solution
MG.init_zeros()
# setup the RHS of our Poisson equation
MG.init_RHS(divU)
# solve
MG.solve(rtol=1.e-10)
# store the solution in our my_data object -- include a single
# ghostcell
phi = my_data.get_var("phi")
solution = MG.get_solution()
phi[myg.ilo-1:myg.ihi+2,myg.jlo-1:myg.jhi+2] = \
solution[MG.ilo-1:MG.ihi+2,MG.jlo-1:MG.jhi+2]
# compute the cell-centered gradient of phi and update the
# velocities
gradp_x = myg.scratch_array()
gradp_y = myg.scratch_array()
gradp_x[myg.ilo:myg.ihi+1,myg.jlo:myg.jhi+1] = \
0.5*(phi[myg.ilo+1:myg.ihi+2,myg.jlo:myg.jhi+1] -
phi[myg.ilo-1:myg.ihi ,myg.jlo:myg.jhi+1])/myg.dx
gradp_y[myg.ilo:myg.ihi+1,myg.jlo:myg.jhi+1] = \
0.5*(phi[myg.ilo:myg.ihi+1,myg.jlo+1:myg.jhi+2] -
phi[myg.ilo:myg.ihi+1,myg.jlo-1:myg.jhi ])/myg.dy
u[:, :] -= gradp_x
v[:, :] -= gradp_y
# fill the ghostcells
my_data.fill_BC("x-velocity")
my_data.fill_BC("y-velocity")
# 2. now get an approximation to gradp at n-1/2 by going through the
# evolution.
# store the current solution
copyData = patch.cell_center_data_clone(my_data)
# get the timestep
dt = timestep.timestep(copyData)
# evolve
evolve.evolve(copyData, dt)
# update gradp_x and gradp_y in our main data object
gp_x = my_data.get_var("gradp_x")
gp_y = my_data.get_var("gradp_y")
new_gp_x = copyData.get_var("gradp_x")
new_gp_y = copyData.get_var("gradp_y")
gp_x[:, :] = new_gp_x[:, :]
gp_y[:, :] = new_gp_y[:, :]
print "done with the pre-evolution"
# correct C_to = C_tot(num_point, p, p0, C_atm0, f_ocean, freeze, T) #correct tau = tau_ir(p, p0, T, freeze) #tau! BTW correct infrared = OLR(sigma_SB, T, tau) # correct qq = Q(insol, albedo, infrared) # ha = hab(num_point, T, freeze, boil) deltat = timestep(diff, latt, delta_latt, num_point, C_to) """ phi += phidot*deltat diff, deltax, x, insol[i], f_ice[i], C_tot[i], albedo[i], tau_ir[i],infrared[i],Q[i],hab[i] = value_update(T,phi,deltat) deltat = timestep(diff, x, deltax, nx, C_tot) phi += phidot*deltat diff, deltax, x, insol[i], f_ice[i], C_tot[i], albedo[i], tau_ir[i],infrared[i],Q[i],hab[i] = value_update(T,phi,deltat) T_max = freeze + 0.9*(boil - freeze) timeyr = time/yr T_mean_old = np.mean(T)
def simulate(parameters,
landscape,
burnInT,
tf,
initial_ecosystem=None,
idxRun=None,
suffix=None):
'''
parameters:
dictionary, see script.py for example
landscape:
string, defines the landscape as a string of H and L for high and low-quality habitats
burnInT:
integer, some number of timesteps (generations) to not store in the pickle file
tf:
integer, number of timesteps
idxRun:
integer, when multiple runs on the same parameter-set/suffix are run,
may want to separate pickled results file by additional suffix,
like *_run0.pkl, *_run1.pkl, etc.
suffix:
string, the string to identify the pickled results file i.e. ecosystems_suffix_run0.pkl
'''
# initialise ecosystem
if initial_ecosystem == None:
# random start for individuals
geneRandFnc = lambda geneType: random.randint(
0, 2**parameters['genetics'][geneType]['noLoci'] - 1)
#sexRandFnc = lambda: random.choice( parameters['sexes'] )
natalHabRandFnc = lambda: random.choice(landscape)
initial_ecosystem = [{
'adults': [{
'sex': 'h',
'natalHabType': natalHabRandFnc(),
'genotype': {
geneType: geneRandFnc(geneType)
for geneType in parameters['genetics'].keys()
}
}, {
'sex': 'h',
'natalHabType': natalHabRandFnc(),
'genotype': {
geneType: geneRandFnc(geneType)
for geneType in parameters['genetics'].keys()
}
}],
'juveniles':
list(),
} for habType in landscape]
ecosystem = copy.deepcopy(
initial_ecosystem
) # make a deep copy so we can store the initial conditions in pickle file
# simulate ecosystem for burn-in timesteps, but don't record results
t = 1
while t <= burnInT and not ecosystemIsEmpty(ecosystem):
ecosystem, _ = timestep(parameters, ecosystem, landscape)
t += 1
# simulate ecosystem for remaining timesteps and record results
ecosystems = list() # a place to store the ecosystem at each timestep
while t <= tf and not ecosystemIsEmpty(ecosystem):
# one timestep of simulation
ecosystem, _ = timestep(parameters, ecosystem, landscape)
# store info
ecosystems.append(copy.deepcopy(ecosystem))
t += 1
# pickle the info
# build the pickled results file's name
fName = 'ecosystems'
if suffix == None:
suffix = parameters2filesuffix(tf, landscape, parameters)
fName += suffix
if idxRun != None:
fName += '_run' + str(idxRun)
fName += '.pkl'
# open the file with the name fName
f = open(fName, 'wb')
# a string explaining the pickle file
path = os.path.dirname(os.path.realpath('simulate.py'))
ss = 'Created by simulate.py in ' + path + '.\n'
ss += 'Contains the following:\n'
ss += '0. ss, string: this string you are reading now.\n'
ss += '1. burnInT, integer: the number of unrecorded timesteps of burn-in.\n'
ss += '2. t, integer: the total number up to which timesteps run (so range(burnInT+1,t)).\n'
ss += '3. tf, integer: the total maximum number up to which timesteps were attempted (if pop did not go extinct, t = tf+1).\n'
ss += '4. landscape, string: a string defining the landscape habitat type composition (e.g. LLLLHHHLLLL).\n'
ss += '5. ecosystems, list of lists of dictionaries: the ecosystem at the end of each recorded timestep.\n'
ss += '6. path, string: the path in which the run was performed.\n'
ss += '7. parameters, dictionary: the parameter values with which the run was performed.\n'
ss += '8. initial_ecosystem, list of dictionaries: the initial ecosystem.\n'
pickle.dump(ss, f) # 0.
pickle.dump(burnInT, f)
pickle.dump(t, f)
pickle.dump(tf, f) # 3.
pickle.dump(landscape, f)
pickle.dump(ecosystems, f)
pickle.dump(path, f) # 6.
pickle.dump(parameters, f)
pickle.dump(initial_ecosystem, f)
f.close()
def preevolve(my_data):
"""
preevolve is called before we being the timestepping loop. For
the incompressible solver, this does an initial projection on the
velocity field and then goes through the full evolution to get the
value of phi. The fluid state (u, v) is then reset to values
before this evolve.
"""
myg = my_data.grid
u = my_data.get_var("x-velocity")
v = my_data.get_var("y-velocity")
my_data.fill_BC("x-velocity")
my_data.fill_BC("y-velocity")
# 1. do the initial projection. This makes sure that our original
# velocity field satisties div U = 0
# next create the multigrid object. We want Neumann BCs on phi
# at solid walls and periodic on phi for periodic BCs
MG = multigrid.CellCenterMG2d(myg.nx, myg.ny,
xl_BC_type="periodic", xr_BC_type="periodic",
yl_BC_type="periodic", yr_BC_type="periodic",
xmin=myg.xmin, xmax=myg.xmax,
ymin=myg.ymin, ymax=myg.ymax,
verbose=0)
# first compute divU
divU = MG.soln_grid.scratch_array()
divU[MG.ilo:MG.ihi+1,MG.jlo:MG.jhi+1] = \
0.5*(u[myg.ilo+1:myg.ihi+2,myg.jlo:myg.jhi+1] -
u[myg.ilo-1:myg.ihi ,myg.jlo:myg.jhi+1])/myg.dx + \
0.5*(v[myg.ilo:myg.ihi+1,myg.jlo+1:myg.jhi+2] -
v[myg.ilo:myg.ihi+1,myg.jlo-1:myg.jhi ])/myg.dy
# solve L phi = DU
# initialize our guess to the solution
MG.init_zeros()
# setup the RHS of our Poisson equation
MG.init_RHS(divU)
# solve
MG.solve(rtol=1.e-10)
# store the solution in our my_data object -- include a single
# ghostcell
phi = my_data.get_var("phi")
solution = MG.get_solution()
phi[myg.ilo-1:myg.ihi+2,myg.jlo-1:myg.jhi+2] = \
solution[MG.ilo-1:MG.ihi+2,MG.jlo-1:MG.jhi+2]
# compute the cell-centered gradient of phi and update the
# velocities
gradp_x = myg.scratch_array()
gradp_y = myg.scratch_array()
gradp_x[myg.ilo:myg.ihi+1,myg.jlo:myg.jhi+1] = \
0.5*(phi[myg.ilo+1:myg.ihi+2,myg.jlo:myg.jhi+1] -
phi[myg.ilo-1:myg.ihi ,myg.jlo:myg.jhi+1])/myg.dx
gradp_y[myg.ilo:myg.ihi+1,myg.jlo:myg.jhi+1] = \
0.5*(phi[myg.ilo:myg.ihi+1,myg.jlo+1:myg.jhi+2] -
phi[myg.ilo:myg.ihi+1,myg.jlo-1:myg.jhi ])/myg.dy
u[:,:] -= gradp_x
v[:,:] -= gradp_y
# fill the ghostcells
my_data.fill_BC("x-velocity")
my_data.fill_BC("y-velocity")
# 2. now get an approximation to gradp at n-1/2 by going through the
# evolution.
# store the current solution
copyData = patch.cell_center_data_clone(my_data)
# get the timestep
dt = timestep.timestep(copyData)
# evolve
evolve.evolve(copyData, dt)
# update gradp_x and gradp_y in our main data object
gp_x = my_data.get_var("gradp_x")
gp_y = my_data.get_var("gradp_y")
new_gp_x = copyData.get_var("gradp_x")
new_gp_y = copyData.get_var("gradp_y")
gp_x[:,:] = new_gp_x[:,:]
gp_y[:,:] = new_gp_y[:,:]
print "done with the pre-evolution"
import numpy # Not standard modules: ensure these files are in the current directory import timestep as ts import simple_plot as sp import simple_animate as sa from matplotlib import pyplot # Sample initial condition and code for testing #dx = 0.025 xmax = 2.0 nx = 41 dx = xmax / (nx - 1) nt = 50 test_ic = numpy.zeros((1, nx)) # put at assert to accept 1-by-nx ICs test_ic[0, 0.5 / dx:1 / dx + 1] = 2 test_solution = ts.timestep(test_ic, 'fd') sp.plot_at(0, test_solution) sp.plot_at(30, test_solution) sa.animate_it(test_solution) # try a different IC - Double hat test_ic_2 = test_ic.copy() test_ic_2[0, 0.7 / dx:0.9 / dx] = 0 test_solution_2 = ts.timestep(test_ic_2, 'fd') sp.plot_at(0, test_solution_2) # see initial condition sp.plot_at(24, test_solution_2) # see intermediate step sp.plot_at(49, test_solution_2) # see final state #BUG: clipped sa.animate_it(test_solution_2) pyplot.show() # to make it show after all the previous non-blocking figures
def _csv_line_extract_datetime(_line):
line = timestep(_line)
return datetime(year=int(line.year), month=int(line.month), day=int(line.day), hour=int(line.hour), \
minute=int(line.minute))