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Jigyasa Watwani / growth-pattern-control

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Commit 082e1178 by Jigyasa Watwani
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growing domain turing patterns with extend

parent 48472926
Inline Side-by-side
Showing with 56 additions and 37 deletions
growing_domain/growing_domain_patterns/growing_domain_patterns.py
...@@ -20,9 +20,9 @@ class GrowingDomainPatterns(object): ...@@ -20,9 +20,9 @@ class GrowingDomainPatterns(object):
#growth_direction = ('x[0]', ) #growth_direction = ('x[0]', )
self.growth_direction = df.Expression(growth_direction, degree=1) self.growth_direction = df.Expression(growth_direction, degree=1)
self.sigma = df.Expression(sigma[self.growth], self.sigma = df.Expression(sigma[self.growth],
L0=self.system_size, L0 = self.system_size,
b=self.growth_parameter, b = self.growth_parameter,
t=0, degree=1) t = 0, degree=1)
# set up mesh # set up mesh
if self.dimension==1: if self.dimension==1:
self.mesh = df.IntervalMesh(self.resolution, 0, self.system_size) self.mesh = df.IntervalMesh(self.resolution, 0, self.system_size)
...@@ -35,6 +35,7 @@ class GrowingDomainPatterns(object): ...@@ -35,6 +35,7 @@ class GrowingDomainPatterns(object):
scalar_element = df.FiniteElement('P', self.mesh.ufl_cell(), 1) scalar_element = df.FiniteElement('P', self.mesh.ufl_cell(), 1)
mixed_element = df.MixedElement([scalar_element, scalar_element]) mixed_element = df.MixedElement([scalar_element, scalar_element])
self.function_space = df.FunctionSpace(self.mesh, mixed_element) self.function_space = df.FunctionSpace(self.mesh, mixed_element)
self.f = df.Function(self.function_space) self.f = df.Function(self.function_space)
self.c1, self.c2 = df.split(self.f) self.c1, self.c2 = df.split(self.f)
self.f0 = df.Function(self.function_space) self.f0 = df.Function(self.function_space)
...@@ -44,7 +45,6 @@ class GrowingDomainPatterns(object): ...@@ -44,7 +45,6 @@ class GrowingDomainPatterns(object):
self.VFS = df.VectorFunctionSpace(self.mesh, 'P', 1) self.VFS = df.VectorFunctionSpace(self.mesh, 'P', 1)
self.velocity = df.project(self.sigma * self.growth_direction, self.VFS) self.velocity = df.project(self.sigma * self.growth_direction, self.VFS)
c1form = (df.inner((self.c1 - self.c10)/self.timestep, tc1) c1form = (df.inner((self.c1 - self.c10)/self.timestep, tc1)
+ df.inner(df.div(self.velocity*self.c10), tc1) + df.inner(df.div(self.velocity*self.c10), tc1)
+ self.Dc1 * df.inner(df.nabla_grad(self.c1), df.nabla_grad(tc1)) + self.Dc1 * df.inner(df.nabla_grad(self.c1), df.nabla_grad(tc1))
...@@ -57,28 +57,35 @@ class GrowingDomainPatterns(object): ...@@ -57,28 +57,35 @@ class GrowingDomainPatterns(object):
self.form = (c1form + c2form)*df.dx(self.mesh) self.form = (c1form + c2form)*df.dx(self.mesh)
def solve(self, extend):
def solve(self):
savesteps = int(self.savetime/self.timestep) savesteps = int(self.savetime/self.timestep)
maxsteps = int(self.maxtime/self.timestep) maxsteps = int(self.maxtime/self.timestep)
self.time = 0.0 self.time = 0.0
c1File = df.XDMFFile('%s_c1.xdmf' %params['timestamp']) # create the file here self.c1File = df.XDMFFile('%s_c1.xdmf' %params['timestamp']) # create the file here
c2File = df.XDMFFile('%s_c2.xdmf' %params['timestamp']) self.c2File = df.XDMFFile('%s_c2.xdmf' %params['timestamp'])
# initial condition if extend:
c10 = df.interpolate(df.Constant(self.a), self.function_space.sub(0).collapse()) SFS = df.FunctionSpace(self.mesh,'P',1)
noise_c1 = self.noise_level * (2*np.random.random(c10.vector().size())-1) c10, c20 = df.Function(SFS), df.Function(SFS)
c10.vector()[:] = c10.vector()[:] + noise_c1 self.c1File.read_checkpoint(c10, 'c1', -1)
self.c2File.read_checkpoint(c20, 'c2', -1)
c20 = df.interpolate(df.Constant(self.b/self.a), self.function_space.sub(1).collapse()) c10 = df.interpolate(c10, self.function_space.sub(0).collapse())
noise_c2 = self.noise_level * (2*np.random.random(c20.vector().size())-1) c20 = df.interpolate(c20, self.function_space.sub(1).collapse())
c20.vector()[:] = c20.vector()[:] + noise_c2 else:
# initial condition
c10 = df.interpolate(df.Constant(self.a), self.function_space.sub(0).collapse())
noise_c1 = self.noise_level * (2*np.random.random(c10.vector().size())-1)
c10.vector()[:] = c10.vector()[:] + noise_c1
c20 = df.interpolate(df.Constant(self.b/self.a), self.function_space.sub(1).collapse())
noise_c2 = self.noise_level * (2*np.random.random(c20.vector().size())-1)
c20.vector()[:] = c20.vector()[:] + noise_c2
df.assign(self.f0, [c10, c20]) df.assign(self.f0, [c10, c20])
# save data # save data
c1File.write_checkpoint(c10, 'c1', self.time, append=True) self.c1File.write_checkpoint(c10, 'c1', self.time, append=True)
c2File.write_checkpoint(c20, 'c2', self.time, append=True) self.c2File.write_checkpoint(c20, 'c2', self.time, append=True)
# time stepping # time stepping
for steps in progressbar.progressbar(range(1, maxsteps + 1)): for steps in progressbar.progressbar(range(1, maxsteps + 1)):
...@@ -93,36 +100,47 @@ class GrowingDomainPatterns(object): ...@@ -93,36 +100,47 @@ class GrowingDomainPatterns(object):
c1, c2 = self.f0.split(deepcopy=True) c1, c2 = self.f0.split(deepcopy=True)
if steps % savesteps == 0: if steps % savesteps == 0:
c1File.write_checkpoint(c1, 'c1', self.time, append=True) self.c1File.write_checkpoint(c1, 'c1', self.time, append=True)
c2File.write_checkpoint(c2, 'c2', self.time, append=True) self.c2File.write_checkpoint(c2, 'c2', self.time, append=True)
# move mesh # move mesh
displacement = df.project(self.velocity*self.timestep, self.VFS) displacement = df.project(self.velocity*self.timestep, self.VFS)
df.ALE.move(self.mesh, displacement) df.ALE.move(self.mesh, displacement)
self.time += self.timestep self.time += self.timestep
c1File.close() self.c1File.close()
c2File.close() self.c2File.close()
if __name__ == '__main__': if __name__ == '__main__':
import json, datetime import json, datetime
assert os.path.isfile('parameters.json'), 'parameters.json file not found' # assert that parameters.json is a valid file, otherwise import argparse
# give an error message parameters.json file not found # give an error message parameters.json file not found
parser = argparse.ArgumentParser()
# load the parameters parser.add_argument('-j','--jsonfile', help='json file', default='growing_domain_patterns_parameters.json')
with open('parameters.json') as jsonFile: parser.add_argument('-t','--time', help='time to extend', type=float, default=50)
args = parser.parse_args()
assert os.path.isfile(args.jsonfile), '%s file not found' % args.jsonfile
with open(args.jsonfile) as jsonFile:
params = json.load(jsonFile) params = json.load(jsonFile)
# parse parameters if args.jsonfile=='growing_domain_patterns_parameters.json':
assert params['dimension'] in (1,2) print('Fresh run')
assert params['growth'] in ('none','linear','exponential'), 'Unknown growth model' extend = False
timestamp = datetime.datetime.now().strftime("%d%m%y-%H%M%S")
timestamp = datetime.datetime.now().strftime("%d%m%y-%H%M%S") # current timestamp is the jobID
params['timestamp'] = timestamp params['timestamp'] = timestamp
else:
print('Extending %s' % params['timestamp'])
extend = True
oldMaxTime = params['maxtime']
params['maxtime'] = args.time
gdp = GrowingDomainPatterns(params) gdp = GrowingDomainPatterns(params)
gdp.solve() gdp.solve(extend)
if extend:
params['maxtime'] = oldMaxTime + params['maxtime']
with open(params['timestamp'] + '_parameters.json', "w") as fp: with open(params['timestamp'] + '_parameters.json', "w") as fp:
json.dump(params, fp, indent=4) json.dump(params, fp, indent=4)
\ No newline at end of file
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