def __init__(self, ps, landscape):
self._ps = ps
self._landscape = landscape
self._nx, self._ny = landscape.shape
#Check if the simulation should end if an individual leaves the landscape bounds
self._break_on_leave = ps.max_distance and (
ps.max_distance < self._nx / 2. or ps.max_distance < self._ny / 2.)
#Calculate the number of individuals to seed each of the initial source locations.
self._ips = np.floor(self._ps.ips / len(self._ps.isl))
#Construct the population and dispersal sub-models
self._pmodel = Population_Model(ps)
self._dmodel = Dispersal_Model(ps, landscape)
def __init__(self, ps, landscape): self._ps = ps self._landscape = landscape self._nx, self._ny = landscape.shape #Check if the simulation should end if an individual leaves the landscape bounds self._break_on_leave = ps.max_distance and ( ps.max_distance < self._nx / 2. or ps.max_distance < self._ny / 2.) #Calculate the number of individuals to seed each of the initial source locations. self._ips = np.floor(self._ps.ips / len(self._ps.isl)) #Construct the population and dispersal sub-models self._pmodel = Population_Model(ps) self._dmodel = Dispersal_Model(ps, landscape)
class GMBI(object):
def __init__(self, ps, landscape):
self._ps = ps
self._landscape = landscape
self._nx, self._ny = landscape.shape
#Check if the simulation should end if an individual leaves the landscape bounds
self._break_on_leave = ps.max_distance and (
ps.max_distance < self._nx / 2. or ps.max_distance < self._ny / 2.)
#Calculate the number of individuals to seed each of the initial source locations.
self._ips = np.floor(self._ps.ips / len(self._ps.isl))
#Construct the population and dispersal sub-models
self._pmodel = Population_Model(ps)
self._dmodel = Dispersal_Model(ps, landscape)
#Perform a single time step
def step(self, N):
#Simulate growth
nac, nx, ny = N.shape
for i in xrange(nx):
for j in xrange(ny):
if np.any(N[:, i, j] > 0):
self._pmodel.step(N[:, i, j], self._landscape[i, j])
#Simulate dispersal
age = 0
for life_stage in xrange(self._ps.nls):
self._dmodel.disperse(N[age, :, :], life_stage)
age += self._ps.tsfls[life_stage]
#Run a single simulation
def run(self):
print "new run"
#Setup the initial population array, this is a three dimensional
#array, indexed as (k, i, j) where each element represents the number of individuals
#in age class k, in the sub-population at landscape coordinates (i, j)
N = np.zeros((self._ps.nac, self._nx, self._ny), dtype=np.int32)
#Seed the array with the initial populations
for i, j in self._ps.isl:
N[:, i, j] = self._ips
#Store a summary of the initial model state in a list. In each time step, we add
#a summary snapshot to this list and then it gets returned at the end
Ns = [np.sum(N, 0)]
#Run the simulation
for t in range(1, self._ps.max_time_steps):
print "time", t
#Perform a time step, this modifes N
self.step(N)
#Store a summary snapshot for the timestep
Ns.append(np.sum(N, 0))
#Test if the model should continue to run
if (self._break_on_leave
and not self._dmodel.all_in_bounds()) or np.sum(
Ns[-1]) == 0:
break
return Ns
class GMBI(object):
def __init__(self, ps, landscape):
self._ps = ps
self._landscape = landscape
self._nx, self._ny = landscape.shape
#Check if the simulation should end if an individual leaves the landscape bounds
self._break_on_leave = ps.max_distance and (
ps.max_distance < self._nx / 2. or ps.max_distance < self._ny / 2.)
#Calculate the number of individuals to seed each of the initial source locations.
self._ips = np.floor(self._ps.ips / len(self._ps.isl))
#Construct the population and dispersal sub-models
self._pmodel = Population_Model(ps)
self._dmodel = Dispersal_Model(ps, landscape)
#Perform a single time step
def step(self, N):
#Simulate growth
nac, nx, ny = N.shape
for i in xrange(nx):
for j in xrange(ny):
if np.any(N[:, i, j] > 0):
self._pmodel.step(N[:, i, j], self._landscape[i,j])
#Simulate dispersal
age = 0
for life_stage in xrange(self._ps.nls):
self._dmodel.disperse(N[age,:,:], life_stage)
age += self._ps.tsfls[life_stage]
#Run a single simulation
def run(self):
print "new run"
#Setup the initial population array, this is a three dimensional
#array, indexed as (k, i, j) where each element represents the number of individuals
#in age class k, in the sub-population at landscape coordinates (i, j)
N = np.zeros((self._ps.nac, self._nx, self._ny), dtype=np.int32)
#Seed the array with the initial populations
for i, j in self._ps.isl:
N[:, i, j] = self._ips
#Store a summary of the initial model state in a list. In each time step, we add
#a summary snapshot to this list and then it gets returned at the end
Ns = [np.sum(N, 0)]
#Run the simulation
for t in range(1, self._ps.max_time_steps):
print "time",t
#Perform a time step, this modifes N
self.step(N)
#Store a summary snapshot for the timestep
Ns.append(np.sum(N, 0))
#Test if the model should continue to run
if (self._break_on_leave and not self._dmodel.all_in_bounds()) or np.sum(Ns[-1]) == 0:
break
return Ns
