refactor of t1dmodel + sensor lifetime

Author: gcappon

py_replay_bg/identification/map.py

@@ -1,31 +1,19 @@
 import os
-
-import matplotlib.pyplot as plt
-from matplotlib import pylab
-
-
-import numpy as np
-
-from multiprocessing import Pool
-
+import warnings
 import pickle
-
-import copy
+import numpy as np
 import pandas as pd
 
-from py_replay_bg.data import ReplayBGData
-
-from datetime import datetime, timedelta
-
+from multiprocessing import Pool
 from tqdm import tqdm
-
 from scipy.optimize import minimize
 
-import warnings
+from py_replay_bg.data import ReplayBGData
 
 # Suppress all RuntimeWarnings
 warnings.filterwarnings("ignore", category=RuntimeWarning)
 
+
 class MAP:
     """
     A class that orchestrates the identification process via MAP.
@@ -40,7 +28,7 @@ def __init__(self, model,
         ----------
         model: Model
             An object that represents the physiological model hyperparameters to be used by ReplayBG.
-        max_iter: int, optional, default : 1000
+        max_iter: int, optional, default : 100000
             Maximum number of iterations.
 
         Returns
@@ -122,7 +110,7 @@ def identify(self, data, rbg_data, rbg):
         options['disp'] = False
 
         # Select the function to minimize
-        neg_log_posterior_func = self.model.neg_log_posterior_single_meal if self.model.is_single_meal else self.model.neg_log_posterior_multi_meal
+        neg_log_posterior_func = self.model.neg_log_posterior
 
         # Initialize results
         results = []
@@ -175,6 +163,7 @@ def identify(self, data, rbg_data, rbg):
 
         return draws
 
+
 def run_map(start, func, rbg_data, options):
     result = minimize(func, start, method='Powell', args=(rbg_data,), options=options)
     ret = dict()

py_replay_bg/identification/mcmc.py

@@ -145,7 +145,7 @@ def identify(self, data, rbg_data, rbg):
         if rbg.environment.parallelize:
             pool = Pool(processes=rbg.environment.n_processes)
 
-        log_posterior_func = self.model.log_posterior_single_meal if self.model.is_single_meal else self.model.log_posterior_multi_meal
+        log_posterior_func = self.model.log_posterior
         sampler = emcee.EnsembleSampler(self.n_walkers, self.n_dim, log_posterior_func, moves=[(emcee.moves.DEMove(sigma=1.0e-3), 0.2), (emcee.moves.DESnookerMove(gammas=0.1), 0.8)], pool=pool, args=[rbg_data])
 
         # Run the burn-in chain
@@ -189,7 +189,7 @@ def identify(self, data, rbg_data, rbg):
             for i in range(0, len(tbs)):
 
                 #...check if it is ok. If so...
-                if self.model.check_copula_extraction(samples[i]):
+                if self.model.check_extraction(samples[i]):
 
                     # ...flag it and fill the final vector
                     to_be_sampled[tbs[i]] = False