def __init__(self, data_file, sigma = 0, period=prob_spline.period(),sample=0,combine_index=[],remove_index=[],seed=None): if seed is not None: numpy.random.seed(seed) self.data_file = data_file msg = 'datafile must be a string' assert isinstance(data_file, str), msg msg = 'combine_index and remove_index cannot both be non-empty' if combine_index!=[] and remove_index!=[]: assert False, msg self.remove_index=remove_index self.combine_index=combine_index self.read_data() self.X=prob_spline.time_transform(self.time) assert (sigma >= 0), 'sigma must be nonnegative.' if sample == 1: self.generate_samples() self.splines = self.get_vector_spline(self.X,self.samples,sigma,period) else: self.splines = self.get_vector_spline(self.X,self.Y,sigma,period)
def __init__(self,
bc_splines,
bm_splines,
mos_curve,
tstart,
tend,
beta_1=1,
find_beta=0,
eps=.001,
counter=0):
self.tstart = tstart
self.tend = tend
self.time_trans = 365. / prob_spline.period()
self.eps = eps # The rate at which birds entering the population enter already infected with EEE
if find_beta == 1:
val = scipy.optimize.minimize(self.findbeta,
beta_1,
args=(bm_splines, bc_splines,
mos_curve),
method='COBYLA',
options={"disp": False})
self.beta_1 = val.x
print(counter)
else:
self.beta_1 = beta_1
self.bc_splines = bc_splines
self.bm_splines = bm_splines
self.mos_curve = mos_curve
self.Y = self.run_ode(self.beta_1, bm_splines, bc_splines, mos_curve)
def __init__(self, data_file, sigma = 0, period=prob_spline.period(), sample = 0): msg = 'datafile must be a string' assert isinstance(data_file, str), msg assert (sigma >= 0), 'sigma must be nonnegative.' self.data_file = data_file self.read_data() self.X=prob_spline.time_transform(self.time) self.sigma = sigma self.period = period self.read_data() if sample==1: self.generate_samples() self.constant = self.get_host_splines(self.samples) else: self.constant = self.get_host_splines(self.Y)
def __init__(self, data_file,sigma = 0, period=prob_spline.period(), sample = 0): msg = 'datafile must be a string' assert isinstance(data_file, str), msg assert (sigma >= 0), 'sigma must be nonnegative.' assert (n_samples >= 0), 'number of samples must be nonnegative.' assert isinstance(n_samples,int), 'number of samples must be an integer' self.data_file = data_file self.read_data() self.X=prob_spline.time_transform(self.time) if sample==1: self.generate_samples() self.splines = self.get_host_splines(self.X,self.samples,sigma,period) else: self.splines = self.get_host_splines(self.X,self.Y,sigma,period)
def __init__(self,
data_file,
MosClass,
sigma=0,
period=prob_spline.period(),
n_samples=0):
msg = 'datafile must be a string'
assert isinstance(data_file, str), msg
self.data_file = data_file
self.read_data()
self.X = prob_spline.time_transform(self.time)
assert (sigma >= 0), 'sigma must be nonnegative.'
self.splines = prob_spline.MosClass(data_file, sigma)
self.splines = self.get_host_splines(self.X, self.Y, sigma, period)
def rhs(self, Y, t, bc_splines, bm_splines, mos_curve):
# Consider adding epsilon term , for proportion infected entering population eps = .001
p = self.p
eps = self.eps
s = Y[0:p]
i = Y[p:2 * p]
r = Y[2 * p:3 * p]
sv = Y[3 * p]
iv = Y[3 * p + 1]
c = Y[3 * p + 1:4 * p + 1] #cumulative infections
e = Y[4 * p + 1:5 * p + 1] #exposure
transform_constant = 365. / prob_spline.period()
alpha_val = self.alpha_calc(bm_splines(t), bc_splines(t))
N = bc_splines(t)
N_v = mos_curve(t)
denom = numpy.dot(N, alpha_val)
lambdab = self.beta1 * self.v * iv * numpy.array(
alpha_val) * N_v / denom
lambdav = self.v * (numpy.dot(self.beta2 * i * N, alpha_val)) / denom
'''
Note that bc_splines.pos_der returns the normalized dervivative of the spline, that is
the derivative of the spline at the given time, divided by the value of the spline at
that given time.
'''
ds = bc_splines.pos_der(t) * (1 - eps - s) - lambdab * s
di = bc_splines.pos_der(t) * (eps - i) + lambdab * s - self.gammab * i
dr = self.gammab * i - r * bc_splines.pos_der(t)
dsv = mos_curve.pos_der(t) * iv - lambdav * sv + self.dv * iv
div = lambdav * sv - mos_curve.pos_der(t) * iv - self.dv * iv
dc = lambdab * s * N #cumulative infections eq
#de = numpy.sum(s*N) #exposure eq
de = bc_splines.pos_der(t) * N # proposed change
dY = numpy.hstack(
(ds, di, dr, dsv, div, dc,
de)) # the 365/2 is the rate of change of the time transform
return dY
def __init__(self, data_file, sigma = 0, period=prob_spline.period(), sample = 0, combine_index=[], remove_index=[], seed=None): if seed is not None: numpy.random.seed(seed) msg = 'datafile must be a string' assert isinstance(data_file, str), msg msg = 'combine_index and remove_index cannot both be non-empty' if combine_index!=[] and remove_index!=[]: assert False, msg self.remove_index=remove_index self.data_file = data_file self.combine_index=combine_index self.read_data() self.X=prob_spline.time_transform(self.time) ''' Index here is a list of values correlating to which bird species you want combined An empty list indicates each line in the datafile will be examined independently A list with specified indices will combine those species, and return a matrix with only the non-specified indices, and a final index of the combined species. ''' if hasattr(sigma,"__len__"): for j in sigma: assert (j >=0 ), 'sigma must be nonnegative' else: assert (sigma >= 0), 'sigma must be nonnegative.' sigma = sigma*numpy.ones(len(self.Y)) if sample==1: self.generate_samples() self.splines = self.get_host_splines(self.X,self.samples,sigma,period) else: self.splines = self.get_host_splines(self.X,self.Y,sigma,period)
def __init__(self,
data_file,
MosClass,
sigma=0,
period=prob_spline.period(),
sample=0):
msg = 'datafile must be a string'
assert isinstance(data_file, str), msg
self.data_file = data_file
self.read_data()
self.X = prob_spline.time_transform(self.time)
assert (sigma >= 0), 'sigma must be nonnegative.'
self.curves = MosClass(data_file,
sigma=sigma,
period=period,
sample=sample)