SMS_BP
Single Molecule Simulations -BP
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Classes | |
class | FBM_BP |
Functions | |
MCMC_state_selection (int initial_state_index, np.ndarray transition_matrix, np.ndarray possible_states, int n) | |
_boundary_conditions (float fbm_store_last, float fbm_candidate, np.ndarray space_lim, str condition_type) | |
Variables | |
dict | BOUNDARY_CONDITIONS |
transition_matrix = np.array([[0.4, 0.6], [0.2, 0.8]]) | |
possible_states = np.array([1, 2]) | |
int | n = 50000 |
int | initial_state_index = 1 |
state_select | |
state_probability = np.zeros(len(possible_states)) | |
total_rate = np.sum(transition_matrix) | |
true_state_probability = np.sum(transition_matrix, axis=0)/total_rate | |
label | |
alpha | |
int | state_1_to_2 = np.zeros(n) - 1 |
int | state_2_to_1 = np.zeros(n) - 1 |
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protected |
Boundary conditions for the FBM Parameters: ----------- fbm_store_last : float Last value of the FBM fbm_candidate : float Candidate value of the FBM space_lim : np.ndarray Space limit (min, max) for the FBM\ condition_type : str Type of boundary condition takes values in REFLECTING_CONDITIONS Returns: -------- float New value of the FBM
SMS_BP.fbm_BP.MCMC_state_selection | ( | int | initial_state_index, |
np.ndarray | transition_matrix, | ||
np.ndarray | possible_states, | ||
int | n ) |
Markov Chain Monte Carlo state selection Parameters: ----------- initial_state_index : int Initial state index, this is the index of the initial state in the possible states transition_matrix : np.ndarray Transition matrix, this is the prbability at a time step. (time step is 1) possible_states : np.ndarray possible states n : int Number of iterations Returns: -------- np.ndarray State selection at each iteration
SMS_BP.fbm_BP.alpha |
dict SMS_BP.fbm_BP.BOUNDARY_CONDITIONS |
int SMS_BP.fbm_BP.initial_state_index = 1 |
SMS_BP.fbm_BP.label |
int SMS_BP.fbm_BP.n = 50000 |
SMS_BP.fbm_BP.possible_states = np.array([1, 2]) |
int SMS_BP.fbm_BP.state_1_to_2 = np.zeros(n) - 1 |
int SMS_BP.fbm_BP.state_2_to_1 = np.zeros(n) - 1 |
SMS_BP.fbm_BP.state_probability = np.zeros(len(possible_states)) |
SMS_BP.fbm_BP.state_select |
SMS_BP.fbm_BP.total_rate = np.sum(transition_matrix) |
SMS_BP.fbm_BP.transition_matrix = np.array([[0.4, 0.6], [0.2, 0.8]]) |
SMS_BP.fbm_BP.true_state_probability = np.sum(transition_matrix, axis=0)/total_rate |