Support_Expander.py 文件源码

def positions(X, V_s, stoc_vector,domain_enum):

    ''' Get the positions of the previous positions. '''

    # X is the state space vector. N \times N_s

    # stoc_vector is a vector $N_s$ with 1 when a variable is stochastic and zero otherwise.

    # Initialising the positions
    ##pdb.set_trace()
    N = X.shape[1] # Number of states.

    N_s = np.sum(stoc_vector)

    N_r_s = len(V_s) # N_r_s is the number of propensities which are purely stochastic ( N_r_s = len(V_s))

    position = np.zeros((N,N_r_s),dtype=np.int64)
    valid = np.zeros((N,N_r_s),dtype=np.bool)
    #shift_M = np.zeros((N_r_s,N,N_s),dtype=np.int)

    # Loops through the stochiometry and find the coresponding indexes.
    ##pdb.set_trace()
    for i in range(N_r_s):
        pre_states = X - np.array(V_s[i])[:,np.newaxis]
        interior = domain_enum.contains(pre_states)
        #print("shape In" + str(interior.shape))
        #print("shape valid" + str(valid[:,i].shape)) 
        valid[:,i] = interior
        #exterior = np.invert(interior)
        if np.sum(valid[:,i]) >0:
            position[interior,i] = domain_enum.indices(pre_states[:,interior])

    return valid, position