[英]Integration of functools.partial objects
我正在尝试制作一个随机模拟器。 但是,我想在确定性的环境中进行一些完整性检查,因此需要集成ODE系统。 具体的产品self.stoich_mat
和prop_cont
作为以所述integrand
函数。 由于某种原因,被积函数起作用,给出输出[15. -15.]
[15. -15.]
,但是scipy的Integrated.odeint无法正常工作,并向我抛出错误:
[ 15. -15.] #THIS IS THE OUTPUT FROM THE INTEGRAND FUNCTION
Traceback (most recent call last):
File "C:/Users/Tomek/Documents/MasterThesis/sim_method5.py", line 474, in <module>
Simulator2.run()
File "C:/Users/Tomek/Documents/MasterThesis/sim_method5.py", line 455, in run
full_output=True)
File "C:\ProgramData\Anaconda2\lib\site-packages\scipy\integrate\odepack.py", line 215, in odeint
ixpr, mxstep, mxhnil, mxordn, mxords)
TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'
我已经建立了MarkoV
类来与不同的仿真类型一起使用,因此self.propensity_generator
看起来可能有点复杂。 下面的代码是仍然显示错误的最小数量。 我尝试使用lambda表达式而不是functools部分,但这似乎无济于事。
编码:
class MarkoV(object):
def __init__(self,rates,stoich_mat):
self.stoich_mat=stoich_mat
self.rates=rates
self.nr_reactions=rates.shape[1]
def init(self,init_state,tf,t0=0.0):
self.state=init_state
self.init_state=np.copy(init_state)
self.nr_species=init_state.shape[0]
self.tf=tf
self.t_init=t0
self.propensity_generator()
def uni_het_bi_mol_const(self,state,t,k,el):
return k*np.prod(state[el])
def uni_het_bi_mol_cat_t(self,state,t,k,cat,el):
return k*np.prod(state[el])*self.init_state[cat][0](t=t)
def propensity_generator(self):
self.propensity_funcs=range(self.stoich_mat.shape[1])
for i,reac in enumerate(self.stoich_mat.T):
j=np.where(reac<0)[0]
if isinstance(self.rates[0, i], float):
k=self.rates[0, i]
self.propensity_funcs[i] = functools.partial(self.uni_het_bi_mol_const,k=k,el=j)
else:
k,cat=self.rates[0,i]
#HERE THE DETERMINISTIC INPUT IS DEFINED, ESSENTIAL TO MY PROBLEM, BUT MAYBE A HURDLE
name_func,func_kwargs=self.init_state[cat][0]
self.init_state[cat][0]=functools.partial(getattr(self,name_func),**func_kwargs)
#self.propensity_funcs[i]=lambda state,t:self.uni_het_bi_mol_cat_t(state=state,t=t,k=k,cat=cat,el=j) #TRY LAMBDA
self.propensity_funcs[i] = functools.partial(self.uni_het_bi_mol_cat_t,k=k,cat=cat,el=j)
self.propensity_funcs=np.array(self.propensity_funcs)
class deterministic(MarkoV):
def init_sym(self,init_state,tf,species_continuous,t0=0.0):
self.species_continuous=species_continuous
self.init(init_state,tf,t0)
self.species_discrete=[i for i in np.arange(self.nr_species) if i not in self.species_continuous]
def sinput(self,t,amplitude=6.0,frequency=0.05,offset=1.0):
return amplitude*np.sin(frequency*t)+amplitude+offset
def integrand(self,state,t):
prop_cont=np.zeros(self.nr_reactions)
for i,func in enumerate(self.propensity_funcs):
prop_cont[i]=func(state=state,t=t)
return np.dot(self.stoich_mat[self.species_discrete,:],prop_cont)
def run(self,t_step=1e-2):
t=np.arange(self.t_init,self.tf,t_step)
h=integrate.odeint(func=self.integrand,
y0=self.state[self.species_discrete],
t=t,
full_output=True)
if __name__ == '__main__':
import scipy.integrate as integrate
import numpy as np
import functools
import numbers
rates=np.array([(1.2,0),6.0],dtype=object,ndmin=2)
stoich_mat=np.array([[ 0, 0], # input
[-1, 1], # A
[ 1,-1]])# A*
init_state=np.array([("sinput",{"amplitude":2.0,"frequency":0.05,"offset":0.5}),5.0,5.0],ndmin=2).T
species_continuous=[0]
Simulator2=deterministic(rates=rates,stoich_mat=stoich_mat)
Simulator2.init_sym(init_state=init_state,tf=20.0,species_continuous=species_continuous)
print Simulator2.integrand(state=init_state,t=0)
print ""
Simulator2.run()
In [126]: init_state=np.array([("sinput",{"amplitude":2.0,"frequency":0.05,"offs
...: et":0.5}),5.0,5.0],ndmin=2).T
In [127]: init_state
Out[127]:
array([[('sinput', {'frequency': 0.05, 'amplitude': 2.0, 'offset': 0.5})],
[5.0],
[5.0]], dtype=object)
In [128]: init_state.shape
Out[128]: (3, 1)
In [129]: species_discrete=[i for i in np.arange(3) if i not in [0]]
In [130]: species_discrete
Out[130]: [1, 2]
In [131]: init_state[species_discrete]
Out[131]:
array([[5.0],
[5.0]], dtype=object)
In [132]: _.shape
Out[132]: (2, 1)
尽我所能告诉您将odeint
中的y0
设置为(2,1)对象数组。
我怀疑您的代码会运行,或者至少不会出现以下错误:
In [133]: init_state[species_discrete].astype(float).ravel()
Out[133]: array([ 5., 5.])
您还想确保
Simulator2.integrand(y0, 0)
运行,并返回类似的(2,)数组。
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