From 3ed73eedc5f7bccb7662adf69f5c6e29fa3c7a75 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 18 Oct 2024 15:36:28 -0700
Subject: [PATCH 01/28] Making quick progress, but still much to do.

---
 starsim/calibration.py    | 116 ++++++++++++++++++++------------------
 tests/test_calibration.py |  13 ++++-
 2 files changed, 70 insertions(+), 59 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 5e17cb56..81d37e02 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -104,28 +104,33 @@ def compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=F
         return gofs
 
 
-class Calibration(sc.prettyobj): # pragma: no cover
+class Calibration(sc.prettyobj):
     """
     A class to handle calibration of Starsim simulations. Uses the Optuna hyperparameter
     optimization library (optuna.org).
 
     Args:
-        sim          (Sim)  : the simulation to calibrate
-        data         (df)   : pandas dataframe (or dataframe-compatible dict) of the data to calibrate to
-        calib_pars   (dict) : a dictionary of the parameters to calibrate of the format dict(key1=[best, low, high])
-        n_trials     (int)  : the number of trials per worker
-        n_workers    (int)  : the number of parallel workers (default: maximum number of available CPUs)
-        total_trials (int)  : if n_trials is not supplied, calculate by dividing this number by n_workers
+        sim          (Sim)  : the base simulation to calibrate
+        data         (df)   : pandas dataframe (or dataframe-compatible dict) containing calibration data
+        calib_pars   (dict) : a dictionary of the parameters to calibrate of the format dict(key1=dict(low=1, high=2, guess=1.5, **kwargs), key2=...), where kwargs can include "suggest_type" to choose the suggest method of the trial (e.g. suggest_float) and args passed to the trial suggest function like "log" and "step"
+        n_workers    (int)  : the number of parallel workers (if None, will use all available CPUs)
+        total_trials (int)  : the total number of trials to run, each worker will run approximately n_trials = total_trial / n_workers
+
         reseed       (bool) : whether to generate new random seeds for each trial
-        weights      (dict) : the relative weights of each data source
-        fit_args     (dict) : a dictionary of options that are passed to sim.compute_fit() to calculate the goodness-of-fit
-        sep          (str)  : the separate between different types of results, e.g. 'hiv.deaths' vs 'hiv_deaths'
-        name         (str)  : the name of the database (default: 'starsim_calibration')
+
+        build_fn  (callable): function that takes a sim object and calib_pars dictionary and returns a modified sim
+        build_kwargs  (dict): a dictionary of options that are passed to build_fn to aid in modifying the base simulation. The API is self.build_fn(sim, calib_pars=calib_pars, **self.build_kwargs), where sim is a copy of the base simulation to be modified with calib_pars
+
+        eval_fn   (callable): function that takes a sim object and data as arguments and returns a scalar. If None, uses built-in compute_gof function.
+        eval_kwargs  (dict) : a dictionary of options that are passed to eval_fn to calculate the goodness of fit, can include weights and "sep". The API is self.eval_fn(sim, self.data, **self.eval_kwargs), where sim is a completed sim
+
+        label        (str)  : a label for this calibration object
+        study_name   (str)  : name of the optuna study
         db_name      (str)  : the name of the database file (default: 'starsim_calibration.db')
         keep_db      (bool) : whether to keep the database after calibration (default: false)
         storage      (str)  : the location of the database (default: sqlite)
-        rand_seed    (int)  : if provided, use this random seed to initialize Optuna runs (for reproducibility)
-        label        (str)  : a label for this calibration object
+        sampler (BaseSampler): the sampler used by optuna, like optuna.samplers.TPESampler
+
         die          (bool) : whether to stop if an exception is encountered (default: false)
         debug        (bool) : if True, do not run in parallel
         verbose      (bool) : whether to print details of the calibration
@@ -133,20 +138,29 @@ class Calibration(sc.prettyobj): # pragma: no cover
     Returns:
         A Calibration object
     """
-    def __init__(self, sim, data, calib_pars, n_trials=None, n_workers=None, total_trials=None, reseed=True,
-                 weights=None, fit_args=None, sep='.', name=None, db_name=None, keep_db=None, storage=None,
-                 rand_seed=None, sampler=None, label=None, die=False, debug=False, verbose=True):
+    def __init__(self, sim, data, calib_pars, n_workers=None, total_trials=None,
+                 reseed=True,
+                 build_fn=None, build_kwargs=None, eval_fn=None, eval_kwargs=None,
+
+                 label=None, study_name=None, db_name=None, keep_db=None, storage=None,
+                 sampler=None, die=False, debug=False, verbose=True):
 
         # Handle run arguments
-        if n_trials  is None: n_trials  = 20
-        if n_workers is None: n_workers = sc.cpu_count()
-        if name      is None: name      = 'starsim_calibration'
-        if db_name   is None: db_name   = f'{name}.db'
-        if keep_db   is None: keep_db   = False
-        if storage   is None: storage   = f'sqlite:///{db_name}'
-        if total_trials is not None: n_trials = int(np.ceil(total_trials/n_workers))
-        kw = dict(n_trials=int(n_trials), n_workers=int(n_workers), debug=debug, name=name, db_name=db_name,
-                  keep_db=keep_db, storage=storage, rand_seed=rand_seed, sampler=sampler)
+        if total_trials is None: total_trials   = 100
+        if n_workers    is None: n_workers      = sc.cpu_count()
+        if study_name   is None: study_name     = 'starsim_calibration'
+        if db_name      is None: db_name        = f'{study_name}.db'
+        if keep_db      is None: keep_db        = False
+        if storage      is None: storage        = f'sqlite:///{db_name}'
+        
+        self.build_fn       = build_fn or self.translate_pars
+        self.build_kwargs   = build_kwargs or dict()
+        self.eval_fn        = eval_fn or self.compute_fit
+        self.eval_kwargs    = eval_kwargs or dict()
+
+        n_trials = int(np.ceil(total_trials/n_workers))
+        kw = dict(n_trials=n_trials, n_workers=int(n_workers), debug=debug, study_name=study_name,
+                  db_name=db_name, keep_db=keep_db, storage=storage, sampler=sampler)
         self.run_args = sc.objdict(kw)
 
         # Handle other inputs
@@ -154,9 +168,6 @@ def __init__(self, sim, data, calib_pars, n_trials=None, n_workers=None, total_t
         self.sim        = sim
         self.calib_pars = calib_pars
         self.reseed     = reseed
-        self.sep        = sep
-        self.weights    = sc.mergedicts(weights)
-        self.fit_args   = sc.mergedicts(fit_args)
         self.die        = die
         self.verbose    = verbose
         self.calibrated = False
@@ -183,7 +194,7 @@ def run_sim(self, calib_pars=None, label=None):
         sim = sc.dcp(self.sim)
         if label: sim.label = label
 
-        sim = self.translate_pars(sim, calib_pars=calib_pars)
+        sim = self.build_fn(sim, calib_pars=calib_pars, **self.build_kwargs)
 
         # Run the sim
         try:
@@ -236,11 +247,6 @@ def translate_pars(sim=None, calib_pars=None):
     def trial_to_sim_pars(self, pardict=None, trial=None):
         """
         Take in an optuna trial and sample from pars, after extracting them from the structure they're provided in
-
-        Different use cases:
-            - pardict is self.calib_pars, i.e. {'diseases':{'hiv':{'art_efficacy':[0.96, 0.9, 0.99]}}}, need to sample
-            - pardict is self.initial_pars, i.e. {'diseases':{'hiv':{'art_efficacy':[0.96, 0.9, 0.99]}}}, pull 1st vals
-            - pardict is self.best_pars, i.e. {'diseases':{'hiv':{'art_efficacy':0.96786}}}, pull single vals
         """
         pars = sc.dcp(pardict)
         for parname, spec in pars.items():
@@ -249,20 +255,21 @@ def trial_to_sim_pars(self, pardict=None, trial=None):
                 # Already have a value, likely running initial or final values as part of checking the fit
                 continue
 
-            if 'sampler' in spec:
-                sampler = spec.pop('sampler')
-                sampler_fn = getattr(trial, sampler)
+            if 'suggest_type' in spec:
+                suggest_type = spec.pop('suggest_type')
+                sampler_fn = getattr(trial, suggest_type)
             else:
                 sampler_fn = trial.suggest_float
 
             path = spec.pop('path', None) # remove path
             guess = spec.pop('guess', None) # remove guess
-            spec['value'] = sampler_fn(name=parname, **spec) # Sample!
+            spec['value'] = sampler_fn(name=parname, **spec) # suggest values!
             spec['path'] = path
             spec['guess'] = guess
 
         return pars
 
+    '''
     @staticmethod
     def sim_to_df(sim): # TODO: remove this method
         """ Convert a sim to the expected dataframe type """
@@ -271,6 +278,8 @@ def sim_to_df(sim): # TODO: remove this method
         df_res = df_res.set_index('t')
         df_res['time'] = np.floor(np.round(df_res.index, 1)).astype(int)
         return df_res
+    '''
+
 
     def run_trial(self, trial, save=False):
         """ Define the objective for Optuna """
@@ -284,6 +293,7 @@ def run_trial(self, trial, save=False):
 
         sim = self.run_sim(calib_pars)
 
+        '''
         # Export results # TODO: make more robust
         df_res = self.sim_to_df(sim)
         sim_results = sc.objdict()
@@ -300,18 +310,18 @@ def run_trial(self, trial, save=False):
         if save:
             filename = self.tmp_filename % trial.number
             sc.save(filename, sim_results)
+        '''
 
         # Compute fit
-        fit = self.compute_fit(df_res=df_res)
+        fit = self.eval_fn(sim, self.data, **self.eval_kwargs)
         return fit
 
-    def compute_fit(self, sim=None, df_res=None):
+    def compute_fit(self, sim, data, **kwargs):
         """ Compute goodness-of-fit """
         fit = 0
 
-        # TODO: reduce duplication with above
-        if df_res is None:
-            df_res = self.sim_to_df(sim)
+        df_res = sim.to_df()
+
         for skey in self.sim_result_list:
             if 'prevalence' in skey:
                 model_output = df_res.groupby(by='time')[skey].mean()
@@ -335,7 +345,7 @@ def worker(self):
             op.logging.set_verbosity(op.logging.DEBUG)
         else:
             op.logging.set_verbosity(op.logging.ERROR)
-        study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.name, sampler = self.run_args.sampler)
+        study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.study_name, sampler=self.run_args.sampler)
         output = study.optimize(self.run_trial, n_trials=self.run_args.n_trials, callbacks=None)
         return output
 
@@ -357,8 +367,8 @@ def remove_db(self):
                 if self.verbose: print(f'Removed existing calibration file {self.run_args.db_name}')
             else:
                 # Delete the study from the database e.g., mysql
-                op.delete_study(study_name=self.run_args.name, storage=self.run_args.storage)
-                if self.verbose: print(f'Deleted study {self.run_args.name} in {self.run_args.storage}')
+                op.delete_study(study_name=self.run_args.study_name, storage=self.run_args.storage)
+                if self.verbose: print(f'Deleted study {self.run_args.study_name} in {self.run_args.storage}')
         except Exception as E:
             if self.verbose:
                 print('Could not delete study, skipping...')
@@ -369,14 +379,8 @@ def make_study(self):
         """ Make a study, deleting one if it already exists """
         if not self.run_args.keep_db:
             self.remove_db()
-        if self.run_args.rand_seed is not None:
-            sampler = op.samplers.RandomSampler(self.run_args.rand_seed)
-            sampler.reseed_rng()
-            raise NotImplementedError('Implemented but does not work')
-        else:
-            sampler = None
         if self.verbose: print(self.run_args.storage)
-        output = op.create_study(storage=self.run_args.storage, study_name=self.run_args.name, sampler=sampler)
+        output = op.create_study(storage=self.run_args.storage, study_name=self.run_args.study_name)
         return output
 
     def calibrate(self, calib_pars=None, confirm_fit=False, load=False, tidyup=True, **kwargs):
@@ -400,7 +404,7 @@ def calibrate(self, calib_pars=None, confirm_fit=False, load=False, tidyup=True,
         t0 = sc.tic()
         self.make_study()
         self.run_workers()
-        study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.name, sampler = self.run_args.sampler)
+        study = op.load_study(storage=self.run_args.storage, study_name=self.run_args.study_name, sampler=self.run_args.sampler)
         self.best_pars = sc.objdict(study.best_params)
         self.elapsed = sc.toc(t0, output=True)
 
@@ -456,8 +460,8 @@ def confirm_fit(self):
 
         self.before_sim = self.run_sim(calib_pars=before_pars, label='Before calibration')
         self.after_sim  = self.run_sim(calib_pars=after_pars, label='After calibration')
-        self.before_fit = self.compute_fit(self.before_sim)
-        self.after_fit  = self.compute_fit(self.after_sim)
+        self.before_fit = self.eval_fn(self.before_sim, **self.eval_kwargs)
+        self.after_fit  = self.eval_fn(self.after_sim, **self.eval_kwargs)
 
         # Add the data to the sims
         for sim in [self.before_sim, self.after_sim]:
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 2d89ebba..61211671 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -85,8 +85,9 @@ def test_calibration(do_plot=False):
 
     # Define the calibration parameters
     calib_pars = dict(
-        init_prev = dict(low=0.01, high=0.30, guess=0.15, path=('diseases', 'hiv', 'init_prev')),
-        n_contacts = dict(low=2, high=10, guess=4, path=('networks', 'randomnet', 'n_contacts')),
+        beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', path=('diseases', 'hiv', 'beta'), log=True), # Log scale
+        init_prev = dict(low=0.01, high=0.30, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify
+        n_contacts = dict(low=2, high=10, guess=4, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo
     )
 
     # Make the sim and data
@@ -107,7 +108,13 @@ def test_calibration(do_plot=False):
         calib_pars = calib_pars,
         sim = sim,
         data = data,
-        weights = weights,
+
+        build_fn = None, # Use default builder, Calibration.translate_pars
+        build_kwargs = None,
+
+        eval_fn = None, # Use default evaluation, Calibration.compute_fit
+        eval_kwargs = dict(weights=weights), # Pass in weights
+
         total_trials = 8,
         n_workers = 2,
         die = True,

From 13ce0e19cbd2b5621d4352af840a4ce5316bcfc9 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 18 Oct 2024 16:38:04 -0700
Subject: [PATCH 02/28] Good progress on the API, almost there

---
 starsim/calibration.py    | 21 ++++++++++-----------
 tests/test_calibration.py | 20 +++++++++++++++++---
 2 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 81d37e02..25c39a14 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -214,17 +214,15 @@ def translate_pars(sim=None, calib_pars=None):
         """ Take the nested dict of calibration pars and modify the sim """
 
         if 'rand_seed' in calib_pars:
-            sim.pars['rand_seed'] = calib_pars['rand_seed']
+            sim.pars['rand_seed'] = calib_pars.pop('rand_seed')
 
         for parname, spec in calib_pars.items():
-            if parname == 'rand_seed':
-                continue
-
             if 'path' not in spec:
                 raise ValueError(f'Cannot map {parname} because "path" is missing from the parameter configuration.')
 
             p = spec['path']
 
+            # TODO: Allow longer paths
             if len(p) != 3:
                 raise ValueError(f'Cannot map {parname} because "path" must be a tuple of length 3.')
 
@@ -261,8 +259,8 @@ def trial_to_sim_pars(self, pardict=None, trial=None):
             else:
                 sampler_fn = trial.suggest_float
 
-            path = spec.pop('path', None) # remove path
-            guess = spec.pop('guess', None) # remove guess
+            path = spec.pop('path', None) # remove path for the sampler
+            guess = spec.pop('guess', None) # remove guess for the sampler
             spec['value'] = sampler_fn(name=parname, **spec) # suggest values!
             spec['path'] = path
             spec['guess'] = guess
@@ -316,20 +314,21 @@ def run_trial(self, trial, save=False):
         fit = self.eval_fn(sim, self.data, **self.eval_kwargs)
         return fit
 
-    def compute_fit(self, sim, data, **kwargs):
+    @staticmethod
+    def compute_fit(sim, data, **kwargs):
         """ Compute goodness-of-fit """
         fit = 0
 
-        df_res = sim.to_df()
+        df_res = sim.to_df(sep='.')
 
-        for skey in self.sim_result_list:
+        for skey in data.cols:
             if 'prevalence' in skey:
                 model_output = df_res.groupby(by='time')[skey].mean()
             else:
                 model_output = df_res.groupby(by='time')[skey].sum()
 
-            data = self.data[skey]
-            combined = pd.merge(data, model_output, how='left', on='time')
+            obs = data[skey]
+            combined = pd.merge(obs, model_output, how='left', on='time')
             combined['diffs'] = combined[skey+'_x'] - combined[skey+'_y']
             gofs = compute_gof(combined.dropna()[skey+'_x'], combined.dropna()[skey+'_y'])
 
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 61211671..1dbb6e24 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -78,14 +78,28 @@ def make_data():
     df = sc.dataframe(target_data[1:], columns=target_data[0])
     return df
 
-#%% Define the tests
+def build_sim(sim, calib_pars, **kwargs):
+    """ Modify the base simulation by applying calib_pars """
+
+    # Capture any parameters that need special handling here
+    if 'beta_randomnet' in calib_pars:
+        v = calib_pars.pop('beta_randomnet')['value']
+        sim.diseases.hiv.pars.beta['random'] = [ss.beta(v), ss.beta(v)]
 
+    # The remaining calib_pars should have a path and can be handled in the
+    # straighforward way by the built-in translate_pars
+    sim = ss.Calibration.translate_pars(sim, calib_pars)
+
+    return sim
+
+
+#%% Define the tests
 def test_calibration(do_plot=False):
     sc.heading('Testing calibration')
 
     # Define the calibration parameters
     calib_pars = dict(
-        beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', path=('diseases', 'hiv', 'beta'), log=True), # Log scale
+        beta_randomnet = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Log scale and no "path", will be handled by build_sim (ablve)
         init_prev = dict(low=0.01, high=0.30, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify
         n_contacts = dict(low=2, high=10, guess=4, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo
     )
@@ -109,7 +123,7 @@ def test_calibration(do_plot=False):
         sim = sim,
         data = data,
 
-        build_fn = None, # Use default builder, Calibration.translate_pars
+        build_fn = build_sim, # Use default builder, Calibration.translate_pars
         build_kwargs = None,
 
         eval_fn = None, # Use default evaluation, Calibration.compute_fit

From 7db30eeabdf19a6462cb2ce76f544c02baca056d Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 18 Oct 2024 17:18:42 -0700
Subject: [PATCH 03/28] Stopping here for now. Need a general way to know if a
 parameter is "prevalent" or "incident" as part of evaluating fit. Also need
 to perform an integration for incident parameters, in case times and observed
 times don't align.

---
 starsim/calibration.py    | 30 ++++++++++++++++++++++--------
 tests/test_calibration.py | 12 +++++++-----
 2 files changed, 29 insertions(+), 13 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 25c39a14..0949c946 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -8,6 +8,7 @@
 import optuna as op
 import matplotlib.pyplot as plt
 import starsim as ss
+import datetime as dt
 
 
 __all__ = ['Calibration', 'compute_gof']
@@ -319,18 +320,31 @@ def compute_fit(sim, data, **kwargs):
         """ Compute goodness-of-fit """
         fit = 0
 
-        df_res = sim.to_df(sep='.')
+        #df_res = sim.to_df(sep='.')
 
         for skey in data.cols:
-            if 'prevalence' in skey:
-                model_output = df_res.groupby(by='time')[skey].mean()
+            if '.' in skey:
+                module, mkey = skey.split('.')
+                res = sim.results[module]
             else:
-                model_output = df_res.groupby(by='time')[skey].sum()
+                res = sim.results
+                mkey = skey
+
+            time = np.array(res['timevec'])
+            if isinstance(sim.pars.start, dt.date):
+                time = np.array([sc.datetoyear(d) for d in time])
+
+            # Prevalent (interp) or incident (integrate interpolation over duration)
+            if mkey in ['n_alive', 'prevalence', 'n_infected']:
+                # Prevalent
+                sim_vals = np.interp(x=data.index, xp=time, fp=res[mkey])
+            elif mkey in ['new_infections', 'new_deaths']:
+                print(mkey)
+            else:
+                raise Exception(mkey)
 
-            obs = data[skey]
-            combined = pd.merge(obs, model_output, how='left', on='time')
-            combined['diffs'] = combined[skey+'_x'] - combined[skey+'_y']
-            gofs = compute_gof(combined.dropna()[skey+'_x'], combined.dropna()[skey+'_y'])
+            obs_vals = data[skey]
+            gofs = compute_gof(obs_vals, sim_vals)
 
             losses = gofs  #* self.weights[skey]
             mismatch = losses.sum()
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 1dbb6e24..c267a4c1 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -15,19 +15,21 @@
 
 def make_sim():
     hiv = ss.HIV(
-        beta = {'random': [0.01]*2, 'maternal': [1, 0]},
-        init_prev = 0.15,
+        beta = {'random': [ss.beta(0.01)]*2, 'maternal': [ss.beta(0.4), 0]},
+        init_prev = ss.bernoulli(0.15),
+
+        dt = 0.25,
     )
     pregnancy = ss.Pregnancy(fertility_rate=20)
     death = ss.Deaths(death_rate=10)
-    random = ss.RandomNet(n_contacts=4)
+    random = ss.RandomNet(n_contacts=ss.poisson(4))
     maternal = ss.MaternalNet()
 
     sim = ss.Sim(
-        dt = 1,
+        dt = 0.5,
         n_agents = n_agents,
         total_pop = 9980999,
-        start = 1990,
+        start = sc.date('1990-01-01'),
         dur = 40,
         diseases = [hiv],
         networks = [random, maternal],

From aac65c502a37bc85c06f214fbc099ac86af56840 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Sat, 19 Oct 2024 23:13:52 -0700
Subject: [PATCH 04/28] Starting work on a CalibComponent. WIP

---
 starsim/calibration.py | 226 ++++++++++++++++++++++++-----------------
 1 file changed, 133 insertions(+), 93 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 0949c946..92325c24 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -11,98 +11,7 @@
 import datetime as dt
 
 
-__all__ = ['Calibration', 'compute_gof']
-
-
-def compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=False,
-                as_scalar='none', eps=1e-9, skestimator=None, estimator=None, **kwargs):
-    """
-    Calculate the goodness of fit. By default use normalized absolute error, but
-    highly customizable. For example, mean squared error is equivalent to
-    setting normalize=False, use_squared=True, as_scalar='mean'.
-
-    Args:
-        actual      (arr):   array of actual (data) points
-        predicted   (arr):   corresponding array of predicted (model) points
-        normalize   (bool):  whether to divide the values by the largest value in either series
-        use_frac    (bool):  convert to fractional mismatches rather than absolute
-        use_squared (bool):  square the mismatches
-        as_scalar   (str):   return as a scalar instead of a time series: choices are sum, mean, median
-        eps         (float): to avoid divide-by-zero
-        skestimator (str):   if provided, use this scikit-learn estimator instead
-        estimator   (func):  if provided, use this custom estimator instead
-        kwargs      (dict):  passed to the scikit-learn or custom estimator
-
-    Returns:
-        gofs (arr): array of goodness-of-fit values, or a single value if as_scalar is True
-
-    **Examples**::
-
-        x1 = np.cumsum(np.random.random(100))
-        x2 = np.cumsum(np.random.random(100))
-
-        e1 = compute_gof(x1, x2) # Default, normalized absolute error
-        e2 = compute_gof(x1, x2, normalize=False, use_frac=False) # Fractional error
-        e3 = compute_gof(x1, x2, normalize=False, use_squared=True, as_scalar='mean') # Mean squared error
-        e4 = compute_gof(x1, x2, skestimator='mean_squared_error') # Scikit-learn's MSE method
-        e5 = compute_gof(x1, x2, as_scalar='median') # Normalized median absolute error -- highly robust
-    """
-
-    # Handle inputs
-    actual    = np.array(sc.dcp(actual), dtype=float)
-    predicted = np.array(sc.dcp(predicted), dtype=float)
-
-    # Scikit-learn estimator is supplied: use that
-    if skestimator is not None: # pragma: no cover
-        try:
-            import sklearn.metrics as sm
-            sklearn_gof = getattr(sm, skestimator) # Shortcut to e.g. sklearn.metrics.max_error
-        except ImportError as E:
-            errormsg = f'You must have scikit-learn >=0.22.2 installed: {str(E)}'
-            raise ImportError(errormsg) from E
-        except AttributeError as E:
-            errormsg = f'Estimator {skestimator} is not available; see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter for options'
-            raise AttributeError(errormsg) from E
-        gof = sklearn_gof(actual, predicted, **kwargs)
-        return gof
-
-    # Custom estimator is supplied: use that
-    if estimator is not None: # pragma: no cover
-        try:
-            gof = estimator(actual, predicted, **kwargs)
-        except Exception as E:
-            errormsg = f'Custom estimator "{estimator}" must be a callable function that accepts actual and predicted arrays, plus optional kwargs'
-            raise RuntimeError(errormsg) from E
-        return gof
-
-    # Default case: calculate it manually
-    else:
-        # Key step -- calculate the mismatch!
-        gofs = abs(np.array(actual) - np.array(predicted))
-
-        if normalize and not use_frac:
-            actual_max = abs(actual).max()
-            if actual_max > 0:
-                gofs /= actual_max
-
-        if use_frac:
-            if (actual<0).any() or (predicted<0).any():
-                print('Warning: Calculating fractional errors for non-positive quantities is ill-advised!')
-            else:
-                maxvals = np.maximum(actual, predicted) + eps
-                gofs /= maxvals
-
-        if use_squared:
-            gofs = gofs**2
-
-        if as_scalar == 'sum':
-            gofs = np.sum(gofs)
-        elif as_scalar == 'mean':
-            gofs = np.mean(gofs)
-        elif as_scalar == 'median':
-            gofs = np.median(gofs)
-
-        return gofs
+__all__ = ['Calibration', 'CalibComponent', 'compute_gof']
 
 
 class Calibration(sc.prettyobj):
@@ -586,4 +495,135 @@ def plot_trend(self, best_thresh=None, fig_kw=None):
             plt.xlabel('Trial number')
             plt.ylabel('Mismatch')
         sc.figlayout()
-        return fig
\ No newline at end of file
+        return fig
+
+
+from enum import Enum
+
+class eMode(Enum):
+    PREVALENT = 0
+    INCIDENT = 1
+
+class CalibComponent(sc.prettyobj):
+    """
+    A class to compare a single channel of observed data with output from a
+    simulation. The Calibration class can use several CalibComponent objects to
+    form an overall understanding of how will a given simulation reflects
+    observed data.
+
+    Args:
+        name (str) : the of this component. Importantly,
+            sim_extract_fn is None, the code will attempt to use the name, like
+            "hiv.prevalence" to automatically extract data from the simulation.
+        data (df) : pandas Series containing calibration data. The index should be the time in either floating point years or datetime.
+        mode (eMode): To handle misaligned timepoints between observed data and simulation output, it's important to know if the data are incident (like new cases) or prevalent (like the number infected).
+            If eMode.PREVALENT, simulation outputs will be interpolated to observed timepoints.
+            If eMode.INCIDENT, ...
+    """
+    def __init__(self, name, data, mode, likelihood, sim_extract_fn=None):
+        pass
+
+    def validate(self):
+        pass
+
+    def __call__(self):
+        pass
+
+    def __repr__(self):
+        pass
+
+    def plot(self):
+        pass
+
+
+
+def compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=False,
+                as_scalar='none', eps=1e-9, skestimator=None, estimator=None, **kwargs):
+    """
+    Calculate the goodness of fit. By default use normalized absolute error, but
+    highly customizable. For example, mean squared error is equivalent to
+    setting normalize=False, use_squared=True, as_scalar='mean'.
+
+    Args:
+        actual      (arr):   array of actual (data) points
+        predicted   (arr):   corresponding array of predicted (model) points
+        normalize   (bool):  whether to divide the values by the largest value in either series
+        use_frac    (bool):  convert to fractional mismatches rather than absolute
+        use_squared (bool):  square the mismatches
+        as_scalar   (str):   return as a scalar instead of a time series: choices are sum, mean, median
+        eps         (float): to avoid divide-by-zero
+        skestimator (str):   if provided, use this scikit-learn estimator instead
+        estimator   (func):  if provided, use this custom estimator instead
+        kwargs      (dict):  passed to the scikit-learn or custom estimator
+
+    Returns:
+        gofs (arr): array of goodness-of-fit values, or a single value if as_scalar is True
+
+    **Examples**::
+
+        x1 = np.cumsum(np.random.random(100))
+        x2 = np.cumsum(np.random.random(100))
+
+        e1 = compute_gof(x1, x2) # Default, normalized absolute error
+        e2 = compute_gof(x1, x2, normalize=False, use_frac=False) # Fractional error
+        e3 = compute_gof(x1, x2, normalize=False, use_squared=True, as_scalar='mean') # Mean squared error
+        e4 = compute_gof(x1, x2, skestimator='mean_squared_error') # Scikit-learn's MSE method
+        e5 = compute_gof(x1, x2, as_scalar='median') # Normalized median absolute error -- highly robust
+    """
+
+    # Handle inputs
+    actual    = np.array(sc.dcp(actual), dtype=float)
+    predicted = np.array(sc.dcp(predicted), dtype=float)
+
+    # Scikit-learn estimator is supplied: use that
+    if skestimator is not None: # pragma: no cover
+        try:
+            import sklearn.metrics as sm
+            sklearn_gof = getattr(sm, skestimator) # Shortcut to e.g. sklearn.metrics.max_error
+        except ImportError as E:
+            errormsg = f'You must have scikit-learn >=0.22.2 installed: {str(E)}'
+            raise ImportError(errormsg) from E
+        except AttributeError as E:
+            errormsg = f'Estimator {skestimator} is not available; see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter for options'
+            raise AttributeError(errormsg) from E
+        gof = sklearn_gof(actual, predicted, **kwargs)
+        return gof
+
+    # Custom estimator is supplied: use that
+    if estimator is not None: # pragma: no cover
+        try:
+            gof = estimator(actual, predicted, **kwargs)
+        except Exception as E:
+            errormsg = f'Custom estimator "{estimator}" must be a callable function that accepts actual and predicted arrays, plus optional kwargs'
+            raise RuntimeError(errormsg) from E
+        return gof
+
+    # Default case: calculate it manually
+    else:
+        # Key step -- calculate the mismatch!
+        gofs = abs(np.array(actual) - np.array(predicted))
+
+        if normalize and not use_frac:
+            actual_max = abs(actual).max()
+            if actual_max > 0:
+                gofs /= actual_max
+
+        if use_frac:
+            if (actual<0).any() or (predicted<0).any():
+                print('Warning: Calculating fractional errors for non-positive quantities is ill-advised!')
+            else:
+                maxvals = np.maximum(actual, predicted) + eps
+                gofs /= maxvals
+
+        if use_squared:
+            gofs = gofs**2
+
+        if as_scalar == 'sum':
+            gofs = np.sum(gofs)
+        elif as_scalar == 'mean':
+            gofs = np.mean(gofs)
+        elif as_scalar == 'median':
+            gofs = np.median(gofs)
+
+        return gofs
+

From c71e5d982fda8f5fad4fa9ddcc09e30c2edb700d Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Wed, 23 Oct 2024 21:46:03 -0700
Subject: [PATCH 05/28] WIP on components

---
 starsim/calibration.py    | 238 ++++++++------------------------------
 tests/test_calibration.py |  19 ++-
 2 files changed, 65 insertions(+), 192 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 92325c24..07490fc1 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -3,15 +3,14 @@
 """
 import os
 import numpy as np
+import optuna as op
 import pandas as pd
 import sciris as sc
-import optuna as op
-import matplotlib.pyplot as plt
 import starsim as ss
-import datetime as dt
+import matplotlib.pyplot as plt
 
 
-__all__ = ['Calibration', 'CalibComponent', 'compute_gof']
+__all__ = ['Calibration', 'CalibComponent']
 
 
 class Calibration(sc.prettyobj):
@@ -21,7 +20,6 @@ class Calibration(sc.prettyobj):
 
     Args:
         sim          (Sim)  : the base simulation to calibrate
-        data         (df)   : pandas dataframe (or dataframe-compatible dict) containing calibration data
         calib_pars   (dict) : a dictionary of the parameters to calibrate of the format dict(key1=dict(low=1, high=2, guess=1.5, **kwargs), key2=...), where kwargs can include "suggest_type" to choose the suggest method of the trial (e.g. suggest_float) and args passed to the trial suggest function like "log" and "step"
         n_workers    (int)  : the number of parallel workers (if None, will use all available CPUs)
         total_trials (int)  : the total number of trials to run, each worker will run approximately n_trials = total_trial / n_workers
@@ -31,8 +29,9 @@ class Calibration(sc.prettyobj):
         build_fn  (callable): function that takes a sim object and calib_pars dictionary and returns a modified sim
         build_kwargs  (dict): a dictionary of options that are passed to build_fn to aid in modifying the base simulation. The API is self.build_fn(sim, calib_pars=calib_pars, **self.build_kwargs), where sim is a copy of the base simulation to be modified with calib_pars
 
-        eval_fn   (callable): function that takes a sim object and data as arguments and returns a scalar. If None, uses built-in compute_gof function.
-        eval_kwargs  (dict) : a dictionary of options that are passed to eval_fn to calculate the goodness of fit, can include weights and "sep". The API is self.eval_fn(sim, self.data, **self.eval_kwargs), where sim is a completed sim
+        components (list of CalibComponent objects): CalibComponents independently assess pseudo-likelihood as part of evaluating the quality of input parameters
+
+        eval_fn  (callable): Function maping a sim to a float (e.g. negative log likelihood) to be maximized. If None, the default will use CalibComponents.
 
         label        (str)  : a label for this calibration object
         study_name   (str)  : name of the optuna study
@@ -48,9 +47,10 @@ class Calibration(sc.prettyobj):
     Returns:
         A Calibration object
     """
-    def __init__(self, sim, data, calib_pars, n_workers=None, total_trials=None,
+    def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
                  reseed=True,
-                 build_fn=None, build_kwargs=None, eval_fn=None, eval_kwargs=None,
+                 build_fn=None, build_kwargs=None, eval_fn=None,
+                 components=None,
 
                  label=None, study_name=None, db_name=None, keep_db=None, storage=None,
                  sampler=None, die=False, debug=False, verbose=True):
@@ -65,8 +65,8 @@ def __init__(self, sim, data, calib_pars, n_workers=None, total_trials=None,
         
         self.build_fn       = build_fn or self.translate_pars
         self.build_kwargs   = build_kwargs or dict()
-        self.eval_fn        = eval_fn or self.compute_fit
-        self.eval_kwargs    = eval_kwargs or dict()
+        self.eval_fn        = eval_fn or self._eval_fit
+        self.components     = components,
 
         n_trials = int(np.ceil(total_trials/n_workers))
         kw = dict(n_trials=n_trials, n_workers=int(n_workers), debug=debug, study_name=study_name,
@@ -84,9 +84,6 @@ def __init__(self, sim, data, calib_pars, n_workers=None, total_trials=None,
         self.before_sim = None
         self.after_sim  = None
 
-        # Load data -- this is expecting a dataframe with a column for 'time' and other columns for to sim results
-        self.data = ss.validate_sim_data(data, die=True)
-
         # Temporarily store a filename
         self.tmp_filename = 'tmp_calibration_%05i.obj'
 
@@ -94,9 +91,6 @@ def __init__(self, sim, data, calib_pars, n_workers=None, total_trials=None,
         if not self.sim.initialized:
             self.sim.init()
 
-        # Figure out which sim results to get
-        self.sim_result_list = self.data.cols
-
         return
 
     def run_sim(self, calib_pars=None, label=None):
@@ -152,7 +146,7 @@ def translate_pars(sim=None, calib_pars=None):
 
         return sim
 
-    def trial_to_sim_pars(self, pardict=None, trial=None):
+    def _sample_from_trial(self, pardict=None, trial=None):
         """
         Take in an optuna trial and sample from pars, after extracting them from the structure they're provided in
         """
@@ -177,88 +171,27 @@ def trial_to_sim_pars(self, pardict=None, trial=None):
 
         return pars
 
-    '''
-    @staticmethod
-    def sim_to_df(sim): # TODO: remove this method
-        """ Convert a sim to the expected dataframe type """
-        df_res = sim.to_df(sep='.')
-        df_res['t'] = df_res['timevec']
-        df_res = df_res.set_index('t')
-        df_res['time'] = np.floor(np.round(df_res.index, 1)).astype(int)
-        return df_res
-    '''
+    def _eval_fit(self, sim):
+        nll = 0 # Negative log likelihood
+        for c in self.components:
+            nll += c(sim)
 
+        return nll
 
     def run_trial(self, trial, save=False):
         """ Define the objective for Optuna """
         if self.calib_pars is not None:
-            calib_pars = self.trial_to_sim_pars(self.calib_pars, trial)
+            pars = self._sample_from_trial(self.calib_pars, trial)
         else:
-            calib_pars = None
+            pars = None
 
         if self.reseed:
-            calib_pars['rand_seed'] = trial.suggest_int('rand_seed', 0, 1_000_000) # Choose a random rand_seed
-
-        sim = self.run_sim(calib_pars)
-
-        '''
-        # Export results # TODO: make more robust
-        df_res = self.sim_to_df(sim)
-        sim_results = sc.objdict()
+            pars['rand_seed'] = trial.suggest_int('rand_seed', 0, 1_000_000) # Choose a random rand_seed
 
-        for skey in self.sim_result_list:
-            if 'prevalence' in skey:
-                model_output = df_res.groupby(by='time')[skey].mean()
-            else:
-                model_output = df_res.groupby(by='time')[skey].sum()
-            sim_results[skey] = model_output.values
-
-        sim_results['time'] = model_output.index.values
-        # Store results in temporary files
-        if save:
-            filename = self.tmp_filename % trial.number
-            sc.save(filename, sim_results)
-        '''
+        sim = self.run_sim(pars)
 
         # Compute fit
-        fit = self.eval_fn(sim, self.data, **self.eval_kwargs)
-        return fit
-
-    @staticmethod
-    def compute_fit(sim, data, **kwargs):
-        """ Compute goodness-of-fit """
-        fit = 0
-
-        #df_res = sim.to_df(sep='.')
-
-        for skey in data.cols:
-            if '.' in skey:
-                module, mkey = skey.split('.')
-                res = sim.results[module]
-            else:
-                res = sim.results
-                mkey = skey
-
-            time = np.array(res['timevec'])
-            if isinstance(sim.pars.start, dt.date):
-                time = np.array([sc.datetoyear(d) for d in time])
-
-            # Prevalent (interp) or incident (integrate interpolation over duration)
-            if mkey in ['n_alive', 'prevalence', 'n_infected']:
-                # Prevalent
-                sim_vals = np.interp(x=data.index, xp=time, fp=res[mkey])
-            elif mkey in ['new_infections', 'new_deaths']:
-                print(mkey)
-            else:
-                raise Exception(mkey)
-
-            obs_vals = data[skey]
-            gofs = compute_gof(obs_vals, sim_vals)
-
-            losses = gofs  #* self.weights[skey]
-            mismatch = losses.sum()
-            fit += mismatch
-
+        fit = self.eval_fn(sim)
         return fit
 
     def worker(self):
@@ -385,10 +318,6 @@ def confirm_fit(self):
         self.before_fit = self.eval_fn(self.before_sim, **self.eval_kwargs)
         self.after_fit  = self.eval_fn(self.after_sim, **self.eval_kwargs)
 
-        # Add the data to the sims
-        for sim in [self.before_sim, self.after_sim]:
-            sim.init_data(self.data)
-
         print(f'Fit with original pars: {self.before_fit:n}')
         print(f'Fit with best-fit pars: {self.after_fit:n}')
         if self.after_fit <= self.before_fit:
@@ -520,110 +449,39 @@ class CalibComponent(sc.prettyobj):
             If eMode.PREVALENT, simulation outputs will be interpolated to observed timepoints.
             If eMode.INCIDENT, ...
     """
-    def __init__(self, name, data, mode, likelihood, sim_extract_fn=None):
-        pass
-
-    def validate(self):
+    def __init__(self, name, real_data, sim_data_fn, conform_fn, likelihood, weight=1):
+        self.name = name
+        self.real_data = real_data
+        self.sim_data_fn = sim_data_fn
+        self.conform_fn = conform_fn # e.g. prev_interp
+        self.likelihood = likelihood # Actually negative log-likelihood
+        self.weight = weight
         pass
 
-    def __call__(self):
-        pass
-
-    def __repr__(self):
-        pass
-
-    def plot(self):
-        pass
-
-
-
-def compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=False,
-                as_scalar='none', eps=1e-9, skestimator=None, estimator=None, **kwargs):
-    """
-    Calculate the goodness of fit. By default use normalized absolute error, but
-    highly customizable. For example, mean squared error is equivalent to
-    setting normalize=False, use_squared=True, as_scalar='mean'.
-
-    Args:
-        actual      (arr):   array of actual (data) points
-        predicted   (arr):   corresponding array of predicted (model) points
-        normalize   (bool):  whether to divide the values by the largest value in either series
-        use_frac    (bool):  convert to fractional mismatches rather than absolute
-        use_squared (bool):  square the mismatches
-        as_scalar   (str):   return as a scalar instead of a time series: choices are sum, mean, median
-        eps         (float): to avoid divide-by-zero
-        skestimator (str):   if provided, use this scikit-learn estimator instead
-        estimator   (func):  if provided, use this custom estimator instead
-        kwargs      (dict):  passed to the scikit-learn or custom estimator
-
-    Returns:
-        gofs (arr): array of goodness-of-fit values, or a single value if as_scalar is True
-
-    **Examples**::
+    @staticmethod
+    def interp(real_data, sim_data):
+        t = real_data.index
+        sim_t = sim_data.index
 
-        x1 = np.cumsum(np.random.random(100))
-        x2 = np.cumsum(np.random.random(100))
+        sdi = np.interp(x=t, xp=sim_t, fp=sim_data)
+        df = pd.Series(sdi, index=t)
+        return df
 
-        e1 = compute_gof(x1, x2) # Default, normalized absolute error
-        e2 = compute_gof(x1, x2, normalize=False, use_frac=False) # Fractional error
-        e3 = compute_gof(x1, x2, normalize=False, use_squared=True, as_scalar='mean') # Mean squared error
-        e4 = compute_gof(x1, x2, skestimator='mean_squared_error') # Scikit-learn's MSE method
-        e5 = compute_gof(x1, x2, as_scalar='median') # Normalized median absolute error -- highly robust
-    """
+    def eval(self, sim):
+        # Compute and return the negative log likelihood
 
-    # Handle inputs
-    actual    = np.array(sc.dcp(actual), dtype=float)
-    predicted = np.array(sc.dcp(predicted), dtype=float)
+        sim_data = self.sim_data_fn(sim)
+        sim_data = self.conform_data(sim_data)
 
-    # Scikit-learn estimator is supplied: use that
-    if skestimator is not None: # pragma: no cover
-        try:
-            import sklearn.metrics as sm
-            sklearn_gof = getattr(sm, skestimator) # Shortcut to e.g. sklearn.metrics.max_error
-        except ImportError as E:
-            errormsg = f'You must have scikit-learn >=0.22.2 installed: {str(E)}'
-            raise ImportError(errormsg) from E
-        except AttributeError as E:
-            errormsg = f'Estimator {skestimator} is not available; see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter for options'
-            raise AttributeError(errormsg) from E
-        gof = sklearn_gof(actual, predicted, **kwargs)
-        return gof
-
-    # Custom estimator is supplied: use that
-    if estimator is not None: # pragma: no cover
-        try:
-            gof = estimator(actual, predicted, **kwargs)
-        except Exception as E:
-            errormsg = f'Custom estimator "{estimator}" must be a callable function that accepts actual and predicted arrays, plus optional kwargs'
-            raise RuntimeError(errormsg) from E
-        return gof
-
-    # Default case: calculate it manually
-    else:
-        # Key step -- calculate the mismatch!
-        gofs = abs(np.array(actual) - np.array(predicted))
-
-        if normalize and not use_frac:
-            actual_max = abs(actual).max()
-            if actual_max > 0:
-                gofs /= actual_max
-
-        if use_frac:
-            if (actual<0).any() or (predicted<0).any():
-                print('Warning: Calculating fractional errors for non-positive quantities is ill-advised!')
-            else:
-                maxvals = np.maximum(actual, predicted) + eps
-                gofs /= maxvals
+        nll = self.likelihood(self.real_data, sim_data)
 
-        if use_squared:
-            gofs = gofs**2
+        return self.weight * nll
 
-        if as_scalar == 'sum':
-            gofs = np.sum(gofs)
-        elif as_scalar == 'mean':
-            gofs = np.mean(gofs)
-        elif as_scalar == 'median':
-            gofs = np.median(gofs)
+    def __call__(self, sim):
+        return self.eval(sim)
 
-        return gofs
+    def __repr__(self):
+        return f'Calibration component with name {self.name}'
 
+    def plot(self):
+        pass
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index c267a4c1..4cd6ff35 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -110,6 +110,22 @@ def test_calibration(do_plot=False):
     sim = make_sim()
     data = make_data()
 
+    prevalence = ss.CalibComponent(
+        name = 'hiv.prevalence',
+        real_data = data['hiv.prevalence'],
+        sim_data_fn = lambda sim: sim.results.hiv.prevalence,
+        likelihood = 'hmm',
+        weight = 1,
+    )
+
+    new_infections = ss.CalibComponent(
+        name = 'hiv.new_infections',
+        real_data = data['hiv.new_infections'],
+        sim_data_fn = lambda sim: sim.results.hiv.new_infections,
+        likelihood = 'hmm',
+        weight = 1,
+    )
+
     # Define weights for the data
     weights = {
         'n_alive':            1.0,
@@ -128,8 +144,7 @@ def test_calibration(do_plot=False):
         build_fn = build_sim, # Use default builder, Calibration.translate_pars
         build_kwargs = None,
 
-        eval_fn = None, # Use default evaluation, Calibration.compute_fit
-        eval_kwargs = dict(weights=weights), # Pass in weights
+        components = [prevalence],
 
         total_trials = 8,
         n_workers = 2,

From f95ff6bedcf45cd67dd3bfaef9944c221be5640b Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Wed, 23 Oct 2024 22:15:20 -0700
Subject: [PATCH 06/28] conforming seems to work

---
 starsim/calibration.py    | 30 ++++++++++++++++++++++++++----
 tests/test_calibration.py | 32 ++++++++++++++++++--------------
 2 files changed, 44 insertions(+), 18 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 07490fc1..fe82b470 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -5,6 +5,7 @@
 import numpy as np
 import optuna as op
 import pandas as pd
+import datetime as dt
 import sciris as sc
 import starsim as ss
 import matplotlib.pyplot as plt
@@ -66,7 +67,7 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         self.build_fn       = build_fn or self.translate_pars
         self.build_kwargs   = build_kwargs or dict()
         self.eval_fn        = eval_fn or self._eval_fit
-        self.components     = components,
+        self.components     = components
 
         n_trials = int(np.ceil(total_trials/n_workers))
         kw = dict(n_trials=n_trials, n_workers=int(n_workers), debug=debug, study_name=study_name,
@@ -459,19 +460,40 @@ def __init__(self, name, real_data, sim_data_fn, conform_fn, likelihood, weight=
         pass
 
     @staticmethod
-    def interp(real_data, sim_data):
+    def linear_interp(real_data, sim_data):
+        """
+        Simply interpolate
+        Use for prevalent data like prevalence
+        """
         t = real_data.index
-        sim_t = sim_data.index
+        sim_t = np.array([sc.datetoyear(t) for t in sim_data.index if isinstance(t, dt.date)])
 
         sdi = np.interp(x=t, xp=sim_t, fp=sim_data)
         df = pd.Series(sdi, index=t)
         return df
 
+    @staticmethod
+    def linear_accum(real_data, sim_data):
+        """
+        Interpolate in the accumulation, then difference.
+        Use for incident data like incidence or new_deaths
+        """
+        t = real_data.index
+        t_step = np.diff(t)
+        assert np.all(t_step == t_step[0])
+        ti = np.append(t, t[-1] + t_step) # Add one more because later we'll diff
+
+        sim_t = np.array([sc.datetoyear(t) for t in sim_data.index if isinstance(t, dt.date)])
+
+        sdi = np.interp(x=ti, xp=sim_t, fp=sim_data.cumsum())
+        df = pd.Series(sdi.diff(), index=t)
+        return df
+
     def eval(self, sim):
         # Compute and return the negative log likelihood
 
         sim_data = self.sim_data_fn(sim)
-        sim_data = self.conform_data(sim_data)
+        sim_data = self.conform_fn(self.real_data, sim_data)
 
         nll = self.likelihood(self.real_data, sim_data)
 
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 4cd6ff35..b7b20269 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -5,6 +5,7 @@
 #%% Imports and settings
 import sciris as sc
 import starsim as ss
+import pandas as pd
 
 do_plot = 1
 do_save = 0
@@ -77,7 +78,8 @@ def make_data():
     [  2021,  15085870,        0.0861733,          1300000,               19000 ,           None],
     [  2022,  15312158,        0.0848998,          1300000,               17000 ,           None],
     ]
-    df = sc.dataframe(target_data[1:], columns=target_data[0])
+    df = sc.dataframe(target_data[1:], columns=target_data[0]) \
+        .set_index('time')
     return df
 
 def build_sim(sim, calib_pars, **kwargs):
@@ -112,39 +114,41 @@ def test_calibration(do_plot=False):
 
     prevalence = ss.CalibComponent(
         name = 'hiv.prevalence',
+
+        # By default, automate these based on name
         real_data = data['hiv.prevalence'],
-        sim_data_fn = lambda sim: sim.results.hiv.prevalence,
+        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.prevalence, index=sim.results.hiv.timevec),
+
+        # Don't like this syntax
+        conform_fn = ss.CalibComponent.linear_interp,
+
         likelihood = 'hmm',
         weight = 1,
     )
 
     new_infections = ss.CalibComponent(
         name = 'hiv.new_infections',
+
+        # By default, automate these based on name
         real_data = data['hiv.new_infections'],
-        sim_data_fn = lambda sim: sim.results.hiv.new_infections,
+        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.new_infections, index=sim.results.hiv.timevec),
+
+        # Don't like this syntax
+        conform_fn = ss.CalibComponent.linear_accum,
+
         likelihood = 'hmm',
         weight = 1,
     )
 
-    # Define weights for the data
-    weights = {
-        'n_alive':            1.0,
-        'hiv.prevalence':     1.0,
-        'hiv.n_infected':     1.0,
-        'hiv.new_infections': 1.0,
-        'hiv.new_deaths':     1.0,
-    }
-
     # Make the calibration
     calib = ss.Calibration(
         calib_pars = calib_pars,
         sim = sim,
-        data = data,
 
         build_fn = build_sim, # Use default builder, Calibration.translate_pars
         build_kwargs = None,
 
-        components = [prevalence],
+        components = [prevalence, new_infections],
 
         total_trials = 8,
         n_workers = 2,

From 80af5c08705615dfd9c29c5f174a82f8049aec27 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 25 Oct 2024 09:17:59 -0700
Subject: [PATCH 07/28] WIP

---
 starsim/calibration.py    | 40 +++++++++++++++++++++++++++++++++------
 tests/test_calibration.py | 10 ++++------
 2 files changed, 38 insertions(+), 12 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index fe82b470..360e51e5 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -11,7 +11,7 @@
 import matplotlib.pyplot as plt
 
 
-__all__ = ['Calibration', 'CalibComponent']
+__all__ = ['Calibration', 'CalibComponent', 'eConform', 'eLikelihood']
 
 
 class Calibration(sc.prettyobj):
@@ -430,10 +430,13 @@ def plot_trend(self, best_thresh=None, fig_kw=None):
 
 from enum import Enum
 
-class eMode(Enum):
+class eConform(Enum):
     PREVALENT = 0
     INCIDENT = 1
 
+class eLikelihood(Enum):
+    POISSON = 0
+
 class CalibComponent(sc.prettyobj):
     """
     A class to compare a single channel of observed data with output from a
@@ -450,15 +453,40 @@ class CalibComponent(sc.prettyobj):
             If eMode.PREVALENT, simulation outputs will be interpolated to observed timepoints.
             If eMode.INCIDENT, ...
     """
-    def __init__(self, name, real_data, sim_data_fn, conform_fn, likelihood, weight=1):
+    def __init__(self, name, real_data, sim_data_fn, conform, likelihood, weight=1):
         self.name = name
         self.real_data = real_data
         self.sim_data_fn = sim_data_fn
-        self.conform_fn = conform_fn # e.g. prev_interp
-        self.likelihood = likelihood # Actually negative log-likelihood
         self.weight = weight
+
+        if isinstance(likelihood, eLikelihood):
+            if likelihood == eLikelihood.POISSON:
+                self.likelihood = self.poisson_nll # Actually negative log-likelihood
+        else:
+            if not callable(conform):
+                msg = f'The likelihood argument must be an eLikelihood or callable function, not {type(likelihood)}.'
+                raise Exception(msg)
+            self.likelihood = likelihood
+
+        if isinstance(conform, eConform):
+            if conform == eConform.INCIDENT:
+                self.conform = self.linear_accum
+            elif conform == eConform.PREVALENT:
+                self.conform = self.linear_interp
+        else:
+            if not callable(conform):
+                msg = f'The conform argument must be an eConform or callable function, not {type(conform)}.'
+                raise Exception(msg)
+            self.conform = conform
+
         pass
 
+    @staticmethod
+    def poisson_nll(real_data, sim_data):
+        print('poisson')
+
+        return 0
+
     @staticmethod
     def linear_interp(real_data, sim_data):
         """
@@ -493,7 +521,7 @@ def eval(self, sim):
         # Compute and return the negative log likelihood
 
         sim_data = self.sim_data_fn(sim)
-        sim_data = self.conform_fn(self.real_data, sim_data)
+        sim_data = self.conform(self.real_data, sim_data)
 
         nll = self.likelihood(self.real_data, sim_data)
 
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index b7b20269..a7681181 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -119,10 +119,9 @@ def test_calibration(do_plot=False):
         real_data = data['hiv.prevalence'],
         sim_data_fn = lambda sim: pd.Series(sim.results.hiv.prevalence, index=sim.results.hiv.timevec),
 
-        # Don't like this syntax
-        conform_fn = ss.CalibComponent.linear_interp,
+        conform = ss.eConform.PREVALENT,
+        likelihood = ss.eLikelihood.POISSON,
 
-        likelihood = 'hmm',
         weight = 1,
     )
 
@@ -133,10 +132,9 @@ def test_calibration(do_plot=False):
         real_data = data['hiv.new_infections'],
         sim_data_fn = lambda sim: pd.Series(sim.results.hiv.new_infections, index=sim.results.hiv.timevec),
 
-        # Don't like this syntax
-        conform_fn = ss.CalibComponent.linear_accum,
+        conform = ss.eConform.INCIDENT,
+        likelihood = ss.eLikelihood.POISSON,
 
-        likelihood = 'hmm',
         weight = 1,
     )
 

From e0b57d71efb5c3f1c52e52b48170b27be9a50599 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Thu, 31 Oct 2024 11:04:11 -0700
Subject: [PATCH 08/28] correcting a typo in a comment

---
 starsim/calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 360e51e5..778ce9f7 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -32,7 +32,7 @@ class Calibration(sc.prettyobj):
 
         components (list of CalibComponent objects): CalibComponents independently assess pseudo-likelihood as part of evaluating the quality of input parameters
 
-        eval_fn  (callable): Function maping a sim to a float (e.g. negative log likelihood) to be maximized. If None, the default will use CalibComponents.
+        eval_fn  (callable): Function mapping a sim to a float (e.g. negative log likelihood) to be maximized. If None, the default will use CalibComponents.
 
         label        (str)  : a label for this calibration object
         study_name   (str)  : name of the optuna study

From 94c0585550b085f1a088d4bc17c777bdf2053a6e Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Mon, 4 Nov 2024 11:01:50 -0800
Subject: [PATCH 09/28] Experimenting with Ax + BoTorch

---
 tests/test_axbo.py | 146 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 146 insertions(+)
 create mode 100644 tests/test_axbo.py

diff --git a/tests/test_axbo.py b/tests/test_axbo.py
new file mode 100644
index 00000000..98f36ac1
--- /dev/null
+++ b/tests/test_axbo.py
@@ -0,0 +1,146 @@
+"""
+Test calibration
+"""
+
+#%% Imports and settings
+import sciris as sc
+import starsim as ss
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from ax.plot.contour import plot_contour
+from ax.plot.trace import optimization_trace_single_method
+from ax.service.managed_loop import optimize
+from ax.utils.notebook.plotting import init_notebook_plotting, render
+
+do_plot = 1
+do_save = 0
+n_agents = 2e3
+
+#%% Helper functions
+
+def make_sim():
+    sir = ss.SIR(
+        beta = ss.beta(0.9),
+        dur_inf = ss.lognorm_ex(mean=ss.dur(6)),
+        init_prev = ss.bernoulli(0.01),
+    )
+
+    #deaths = ss.Deaths(death_rate=15)
+    #births = ss.Births(birth_rate=15)
+
+    random = ss.RandomNet(n_contacts=ss.poisson(4))
+
+    sim = ss.Sim(
+        dt = 1,
+        unit = 'day',
+        n_agents = n_agents,
+        #total_pop = 9980999,
+        start = sc.date('2024-01-01'),
+        stop = sc.date('2024-01-31'),
+        diseases = sir,
+        networks = random,
+        #demographics = [deaths, births],
+    )
+
+    return sim
+
+
+def build_sim(sim, calib_pars, **kwargs):
+    """ Modify the base simulation by applying calib_pars """
+
+    for k, v in calib_pars.items():
+        if k == 'beta':
+            sim.diseases.sir.pars['beta'] = ss.beta(v)
+        elif k == 'dur_inf':
+            sim.diseases.sir.pars['dur_inf'] = ss.lognorm_ex(mean=ss.dur(v)), #ss.dur(v)
+        elif k == 'n_contacts':
+            sim.networks.randomnet.pars.n_contacts = v # Typically a Poisson distribution, but this should set the distribution parameter value appropriately
+        else:
+            sim.pars[k] = v # Assume sim pars
+
+    return sim
+
+def eval_sim(pars):
+    sim = make_sim()
+    sim.init()
+    sim = build_sim(sim, pars)
+    sim.run()
+    #print('pars:', pars, ' --> Final prevalence:', sim.results.sir.prevalence[-1])
+    fig = sim.plot()
+    fig.suptitle(pars)
+    fig.subplots_adjust(top=0.9)
+    plt.show()
+
+    return dict(
+        prevalence_error = ((sim.results.sir.prevalence[-1] - 0.10)**2, None),
+        prevalence = (sim.results.sir.prevalence[-1], None),
+    )
+
+
+#%% Define the tests
+def test_calibration(do_plot=False):
+    sc.heading('Testing calibration')
+
+    # Define the calibration parameters
+    calib_pars = [
+        dict(name='beta', type='range', bounds=[0.01, 1.0], value_type='float', log_scale=True),
+        dict(name='dur_inf', type='range', bounds=[1, 60], value_type='float', log_scale=False),
+        #dict(name='init_prev', type='range', bounds=[0.01, 0.30], value_type='float', log_scale=False),
+        dict(name='n_contacts', type='range', bounds=[2, 10], value_type='int', log_scale=False),
+    ]
+
+    best_pars, values, exp, model = optimize(
+        experiment_name = 'starsim',
+        parameters = calib_pars,
+        evaluation_function = eval_sim,
+        objective_name = 'prevalence_error',
+        minimize = True,
+        parameter_constraints = None,
+        outcome_constraints = None,
+        total_trials = 10,
+        arms_per_trial = 3,
+    )
+
+    return best_pars, values, exp, model
+
+
+#%% Run as a script
+if __name__ == '__main__':
+
+    T = sc.timer()
+    do_plot = True
+
+    best_pars, values, exp, model = test_calibration(do_plot=do_plot)
+
+    print('best_pars:', best_pars)
+    print('values:', values)
+    print('exp:', exp)
+    print('model:', model)
+
+    render(plot_contour(model=model, param_x='beta', param_y='init_prev', metric_name='prevalence'))
+
+    # `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple
+    # optimization runs, so we wrap out best objectives array in another array.
+
+    for trial in exp.trials.values():
+        print(trial)
+        print(dir(trial))
+        print(f"Trial {trial.index} with parameters {trial.arm.parameters} "
+              f"has objective {trial.objective_mean}.") 
+
+    best_objectives = np.array(
+        [[trial.objective_mean for trial in exp.trials.values()]]
+    )
+    best_objective_plot = optimization_trace_single_method(
+        y = np.minimum.accumulate(best_objectives, axis=1),
+        optimum = 0.10, #hartmann6.fmin,
+        title = "Model performance vs. # of iterations",
+        ylabel = "Prevalence",
+    )
+    render(best_objective_plot)
+
+    plt.show()
+
+    T.toc()

From 706813957d2577deece50c308ccfc425b48b2ba0 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 8 Nov 2024 09:45:47 -0800
Subject: [PATCH 10/28] Addressing stochasticity in Births related to dicussion
 in Unstable Vital Dynamics #695

---
 starsim/demographics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/starsim/demographics.py b/starsim/demographics.py
index fd981c0e..475f2188 100644
--- a/starsim/demographics.py
+++ b/starsim/demographics.py
@@ -93,7 +93,7 @@ def get_births(self):
 
         scaled_birth_prob = this_birth_rate * p.rate_units * p.rel_birth * factor
         scaled_birth_prob = np.clip(scaled_birth_prob, a_min=0, a_max=1)
-        n_new = int(sc.randround(sim.people.alive.count() * scaled_birth_prob))
+        n_new = np.random.binomial(n=sim.people.alive.count(), p=scaled_birth_prob) # Not CRN safe, see issue #404
         return n_new
 
     def step(self):

From 9d8f87c83580116561c71c57f6a527d84cb88a6f Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 8 Nov 2024 10:36:02 -0800
Subject: [PATCH 11/28] Updating baselines

---
 tests/baseline.json  | 46 +++++++++++++++++++++++---------------------
 tests/benchmark.json |  6 +++---
 2 files changed, 27 insertions(+), 25 deletions(-)

diff --git a/tests/baseline.json b/tests/baseline.json
index 0e7720a9..5b9e462f 100644
--- a/tests/baseline.json
+++ b/tests/baseline.json
@@ -1,26 +1,28 @@
 {
   "summary": {
-    "timevec": 2020.0,
-    "births_new": 46.84158415841584,
-    "births_cumulative": 2267.7425742574255,
-    "births_cbr": 19.93833106141367,
-    "deaths_new": 9.673267326732674,
-    "deaths_cumulative": 468.5742574257426,
-    "deaths_cmr": 4.118825151847284,
-    "sir_n_susceptible": 2440.029702970297,
-    "sir_n_infected": 3630.3960396039606,
-    "sir_n_recovered": 5676.693069306931,
-    "sir_prevalence": 0.32402568798505815,
-    "sir_new_infections": 122.34653465346534,
-    "sir_cum_infections": 12357.0,
-    "sis_n_susceptible": 4784.306930693069,
-    "sis_n_infected": 6973.504950495049,
-    "sis_prevalence": 0.5720906510271361,
-    "sis_new_infections": 193.5742574257426,
-    "sis_cum_infections": 19551.0,
-    "sis_rel_sus": 0.5019197711850157,
-    "n_alive": 11747.118811881188,
-    "new_deaths": 10.693069306930694,
-    "cum_deaths": 1072.0
+    "births_new": 48.257425742574256,
+    "births_cumulative": 2343.3069306930693,
+    "births_cbr": 20.43178207644599,
+    "deaths_new": 9.712871287128714,
+    "deaths_cumulative": 470.58415841584156,
+    "deaths_cmr": 4.112394571867341,
+    "randomnet_n_edges": 58901.33663366337,
+    "mfnet_n_edges": 4004.732673267327,
+    "maternalnet_n_edges": 0.0,
+    "sir_n_susceptible": 2464.970297029703,
+    "sir_n_infected": 3658.227722772277,
+    "sir_n_recovered": 5694.504950495049,
+    "sir_prevalence": 0.32462009407521675,
+    "sir_new_infections": 122.81188118811882,
+    "sir_cum_infections": 12404.0,
+    "sis_n_susceptible": 4828.3267326732675,
+    "sis_n_infected": 7000.19801980198,
+    "sis_prevalence": 0.5702209995778549,
+    "sis_new_infections": 195.01980198019803,
+    "sis_cum_infections": 19697.0,
+    "sis_rel_sus": 0.5033450153204474,
+    "n_alive": 11817.70297029703,
+    "new_deaths": 10.821782178217822,
+    "cum_deaths": 1084.0
   }
 }
\ No newline at end of file
diff --git a/tests/benchmark.json b/tests/benchmark.json
index 00f78bb2..38baff8b 100644
--- a/tests/benchmark.json
+++ b/tests/benchmark.json
@@ -1,12 +1,12 @@
 {
   "time": {
-    "initialize": 0.055,
-    "run": 1.013
+    "initialize": 0.053,
+    "run": 0.914
   },
   "parameters": {
     "n_agents": 10000,
     "dur": 20,
     "dt": 0.2
   },
-  "cpu_performance": 0.9665005580733697
+  "cpu_performance": 0.8734404449460742
 }
\ No newline at end of file

From faa8d8d5d51f89c19af5e44a709993bf5cf5454b Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 8 Nov 2024 10:45:52 -0800
Subject: [PATCH 12/28] Addressing match_time_inds fails #750

---
 starsim/modules.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/starsim/modules.py b/starsim/modules.py
index 6ecd6e0a..642103df 100644
--- a/starsim/modules.py
+++ b/starsim/modules.py
@@ -228,13 +228,12 @@ def init_time(self, force=False):
 
     def match_time_inds(self, inds=None):
          """ Find the nearest matching sim time indices for the current module """
-         if inds is None: inds = Ellipsis
          self_tvec = self.t.abstvec
          sim_tvec = self.sim.t.abstvec
          if len(self_tvec) == len(sim_tvec): # Shortcut to avoid doing matching
-             return inds
+             return Ellipsis if inds is None else inds
          else:
-             out = sc.findnearest(sim_tvec, [inds])
+             out = sc.findnearest(sim_tvec, self_tvec)
              return out
 
     def start_step(self):

From 05bf41ce7f117c0109f4ee8147e6890771020f13 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 8 Nov 2024 10:51:27 -0800
Subject: [PATCH 13/28] Adding Ax+BoTorch example

---
 tests/test_axbo_service.py | 153 +++++++++++++++++++++++++++++++++++++
 1 file changed, 153 insertions(+)
 create mode 100644 tests/test_axbo_service.py

diff --git a/tests/test_axbo_service.py b/tests/test_axbo_service.py
new file mode 100644
index 00000000..8d4a7879
--- /dev/null
+++ b/tests/test_axbo_service.py
@@ -0,0 +1,153 @@
+"""
+Test calibration
+"""
+
+#%% Imports and settings
+import sciris as sc
+import starsim as ss
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+#from ax.plot.contour import plot_contour
+#from ax.plot.trace import optimization_trace_single_method
+#from ax.service.managed_loop import optimize
+#from ax.utils.notebook.plotting import init_notebook_plotting, render
+
+from ax.service.ax_client import AxClient, ObjectiveProperties
+from ax.utils.notebook.plotting import init_notebook_plotting, render
+
+from ax.modelbridge.cross_validation import cross_validate
+from ax.plot.contour import interact_contour
+from ax.plot.diagnostic import interact_cross_validation
+from ax.plot.scatter import interact_fitted, plot_objective_vs_constraints, tile_fitted
+from ax.plot.slice import plot_slice
+from ax.service.utils.report_utils import exp_to_df
+
+do_plot = 1
+do_save = 0
+n_agents = [2e3, 25_000][1]
+
+ax_client = AxClient(enforce_sequential_optimization=False)
+
+#%% Helper functions
+
+def make_sim(calib_pars):
+    sir = ss.SIR(
+        beta = ss.beta( calib_pars.get('beta', 0.9) ),
+        dur_inf = ss.lognorm_ex(mean=ss.dur( calib_pars.get('dur_inf', 6))),
+        init_prev = ss.bernoulli(0.01),
+    )
+
+    #deaths = ss.Deaths(death_rate=15)
+    #births = ss.Births(birth_rate=15)
+
+    random = ss.RandomNet(n_contacts=ss.poisson(calib_pars.get('n_contacts', 4)))
+
+    sim = ss.Sim(
+        dt = 1,
+        unit = 'day',
+        n_agents = n_agents,
+        #total_pop = 9980999,
+        start = sc.date('2024-01-01'),
+        stop = sc.date('2024-01-31'),
+        diseases = sir,
+        networks = random,
+        #demographics = [deaths, births],
+        rand_seed = np.random.randint(1e6),
+    )
+
+    return sim
+
+
+def eval_sim(pars):
+    sim = make_sim(pars)
+    sim.run()
+
+    if False:
+        fig = sim.plot()
+        fig.suptitle(pars)
+        fig.subplots_adjust(top=0.9)
+        plt.show()
+
+    return dict(
+        prevalence_error = (np.abs(sim.results.sir.prevalence[-1] - 0.20), None),
+        #prevalence = (sim.results.sir.prevalence[-1], None),
+    )
+
+#%% Define the tests
+def test_calibration(do_plot=False):
+    sc.heading('Testing calibration')
+
+    # Define the calibration parameters
+    calib_pars = [
+        dict(name='beta', type='range', bounds=[0.005, 0.1], value_type='float', log_scale=True),
+        #dict(name='dur_inf', type='range', bounds=[1, 120], value_type='float', log_scale=False),
+        dict(name='dur_inf', type='fixed', value=60, value_type='float'),
+        #dict(name='init_prev', type='range', bounds=[0.01, 0.30], value_type='float', log_scale=False),
+        dict(name='n_contacts', type='range', bounds=[1, 10], value_type='int', log_scale=False),
+    ]
+
+    ax_client.create_experiment(
+        name = 'starsim test',
+        parameters = calib_pars,
+        objectives={'prevalence_error': ObjectiveProperties(minimize=True)},
+        parameter_constraints = None,
+        outcome_constraints = None,
+        choose_generation_strategy_kwargs={"max_parallelism_override": 25},
+    )
+
+    print('Max parallelism:', ax_client.get_max_parallelism()) # Seems to require manual specification of generation_strategy
+
+    for i in range(5):
+        print('THINKING...')
+        trial_index_to_param, idk = ax_client.get_next_trials(max_trials=1_000)
+
+        print('STEP', i, len(trial_index_to_param))
+
+        # Does NOT work to complete_trial in the parallel loop
+        results = sc.parallelize(eval_sim, iterkwargs=dict(pars=list(trial_index_to_param.values())), serial=False)
+        for trial_index, result in zip(trial_index_to_param.keys(), results):
+            ax_client.complete_trial(trial_index=trial_index, raw_data=result)
+
+        print(exp_to_df(ax_client.experiment))
+
+
+    best_pars, values = ax_client.get_best_parameters()
+
+    return best_pars, values#, exp, model
+
+
+#%% Run as a script
+if __name__ == '__main__':
+
+
+    T = sc.timer()
+    do_plot = True
+
+    best_pars, values = test_calibration(do_plot=do_plot)
+
+    sim = make_sim(best_pars)
+    sim.run()
+    sim.plot()
+
+    print('best_pars:', best_pars)
+    print('values:', values)
+
+    #render(ax_client.get_contour_plot(param_x='beta', param_y='dur_inf', metric_name='prevalence_error'))
+    render(ax_client.get_optimization_trace(objective_optimum=0))
+
+    model = ax_client.generation_strategy.model
+    render(interact_contour(model=model, metric_name='prevalence_error'))
+
+    cv_results = cross_validate(model)
+    render(interact_cross_validation(cv_results))
+
+    render(plot_slice(model, 'beta', 'prevalence_error'))
+    render(plot_slice(model, 'n_contacts', 'prevalence_error'))
+
+    render(interact_fitted(model, rel=False))
+
+    plt.show()
+
+    T.toc()

From ddc0e25663ee69b09e07b8080ee36f6c1c7ffe3b Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 8 Nov 2024 11:00:58 -0800
Subject: [PATCH 14/28] Removing `save_results`

---
 starsim/calibration.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 3f1ea4bb..4a4a830f 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -82,7 +82,6 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         self.reseed     = reseed
         self.die        = die
         self.verbose    = verbose
-        self.save_results = save_results
         self.calibrated = False
         self.before_sim = None
         self.after_sim  = None

From 8e2dd749c96e1f253c64eadba561f77f5d6f4610 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Fri, 15 Nov 2024 14:58:53 -0800
Subject: [PATCH 15/28] Beta-Binomial likelihood working for SIR example

---
 starsim/calibration.py    | 117 +++++++++++++++++++----------
 tests/test_calibration.py | 152 ++++++++++++++++++--------------------
 2 files changed, 149 insertions(+), 120 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 4a4a830f..64cb565b 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -9,6 +9,7 @@
 import sciris as sc
 import starsim as ss
 import matplotlib.pyplot as plt
+from scipy.special import gammaln as gln
 from enum import Enum
 
 
@@ -34,6 +35,7 @@ class Calibration(sc.prettyobj):
         components (list of CalibComponent objects): CalibComponents independently assess pseudo-likelihood as part of evaluating the quality of input parameters
 
         eval_fn  (callable): Function mapping a sim to a float (e.g. negative log likelihood) to be maximized. If None, the default will use CalibComponents.
+        eval_kwargs  (dict): Additional keyword arguments to pass to the eval_fn
 
         label        (str)  : a label for this calibration object
         study_name   (str)  : name of the optuna study
@@ -51,7 +53,7 @@ class Calibration(sc.prettyobj):
     """
     def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
                  reseed=True,
-                 build_fn=None, build_kwargs=None, eval_fn=None,
+                 build_fn=None, build_kwargs=None, eval_fn=None, eval_kwargs=None,
                  components=None,
 
                  label=None, study_name=None, db_name=None, keep_db=None, storage=None,
@@ -68,6 +70,7 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         self.build_fn       = build_fn or self.translate_pars
         self.build_kwargs   = build_kwargs or dict()
         self.eval_fn        = eval_fn or self._eval_fit
+        self.eval_kwargs    = eval_kwargs or dict()
         self.components     = components
 
         n_trials = int(np.ceil(total_trials/n_workers))
@@ -83,15 +86,15 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         self.die        = die
         self.verbose    = verbose
         self.calibrated = False
-        self.before_sim = None
-        self.after_sim  = None
+        self.before_msim = None
+        self.after_msim  = None
 
         # Temporarily store a filename
         self.tmp_filename = 'tmp_calibration_%05i.obj'
 
         # Initialize sim
-        if not self.sim.initialized:
-            self.sim.init()
+        #if not self.sim.initialized:
+        #    self.sim.init()
 
         return
 
@@ -173,7 +176,7 @@ def _sample_from_trial(self, pardict=None, trial=None):
 
         return pars
 
-    def _eval_fit(self, sim):
+    def _eval_fit(self, sim, **kwargs):
         nll = 0 # Negative log likelihood
         for c in self.components:
             nll += c(sim)
@@ -193,7 +196,7 @@ def run_trial(self, trial):
         sim = self.run_sim(pars)
 
         # Compute fit
-        fit = self.eval_fn(sim)
+        fit = self.eval_fn(sim, **self.eval_kwargs)
         return fit
 
     def worker(self):
@@ -315,19 +318,28 @@ def confirm_fit(self):
         for parname, spec in after_pars.items():
             spec['value'] = self.best_pars[parname]
 
-        self.before_sim = self.run_sim(calib_pars=before_pars, label='Before calibration')
-        self.after_sim  = self.run_sim(calib_pars=after_pars, label='After calibration')
-        self.before_fit = self.eval_fn(self.before_sim, **self.eval_kwargs)
-        self.after_fit  = self.eval_fn(self.after_sim, **self.eval_kwargs)
 
-        print(f'Fit with original pars: {self.before_fit:n}')
-        print(f'Fit with best-fit pars: {self.after_fit:n}')
-        if self.after_fit <= self.before_fit:
+        n_runs = 25
+        before_sim = self.build_fn(self.sim, calib_pars=before_pars, **self.build_kwargs)
+        before_sim.label = 'Before calibration'
+        self.before_msim = ss.MultiSim(before_sim, n_runs=n_runs)
+        self.before_msim.run()
+        self.before_fits = np.array([self.eval_fn(sim, **self.eval_kwargs) for sim in self.before_msim.sims])
+
+        after_sim = self.build_fn(self.sim, calib_pars=after_pars, **self.build_kwargs)
+        after_sim.label = 'Before calibration'
+        self.after_msim = ss.MultiSim(after_sim, n_runs=n_runs)
+        self.after_msim.run()
+        self.after_fits = np.array([self.eval_fn(sim, **self.eval_kwargs) for sim in self.after_msim.sims])
+
+        print(f'Fit with original pars: {self.before_fits.mean()}')
+        print(f'Fit with best-fit pars: {self.after_fits.mean()}')
+        if self.after_fits.mean() <= self.before_fits.mean():
             print('✓ Calibration improved fit')
         else:
             print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')
 
-        return self.before_fit, self.after_fit
+        return self.before_fits, self.after_fits
 
     def parse_study(self, study):
         """Parse the study into a data frame -- called automatically """
@@ -385,11 +397,22 @@ def plot_sims(self, **kwargs):
         Args:
             kwargs (dict): passed to MultiSim.plot()
         """
-        if self.before_sim is None:
+        if self.before_msim is None:
             self.confirm_fit()
-        msim = ss.MultiSim([self.before_sim, self.after_sim])
-        fig = msim.plot(**kwargs)
-        return ss.return_fig(fig)
+
+        self.before_msim.reduce()
+        fig = self.before_msim.plot()#, label='Before calibration')
+
+        self.after_msim.reduce()
+        self.after_msim.plot(fig=fig)#, label='After calibration')
+
+        plt.legend()
+
+        return fig
+        #msim = ss.MultiSim([self.before_msim, self.after_msim])
+        #fig = msim.plot(**kwargs)
+        #plt.legend()
+        #return ss.return_fig(fig)
 
     def plot_trend(self, best_thresh=None, fig_kw=None):
         """
@@ -435,7 +458,7 @@ class eConform(Enum):
     INCIDENT = 1
 
 class eLikelihood(Enum):
-    POISSON = 0
+    BETA_BINOMIAL = 0
 
 class CalibComponent(sc.prettyobj):
     """
@@ -453,20 +476,20 @@ class CalibComponent(sc.prettyobj):
             If eMode.PREVALENT, simulation outputs will be interpolated to observed timepoints.
             If eMode.INCIDENT, ...
     """
-    def __init__(self, name, real_data, sim_data_fn, conform, likelihood, weight=1):
+    def __init__(self, name, real_data, sim_data_fn, conform, nll_fn, weight=1):
         self.name = name
         self.real_data = real_data
         self.sim_data_fn = sim_data_fn
         self.weight = weight
 
-        if isinstance(likelihood, eLikelihood):
-            if likelihood == eLikelihood.POISSON:
-                self.likelihood = self.poisson_nll # Actually negative log-likelihood
+        if isinstance(nll_fn, eLikelihood):
+            if nll_fn == eLikelihood.BETA_BINOMIAL:
+                self.nll_fn = self.beta_binomial # Actually negative log-likelihood
         else:
             if not callable(conform):
-                msg = f'The likelihood argument must be an eLikelihood or callable function, not {type(likelihood)}.'
+                msg = f'The nll_fn argument must be an eLikelihood or callable function, not {type(nll_fn)}.'
                 raise Exception(msg)
-            self.likelihood = likelihood
+            self.nll_fn = nll_fn
 
         if isinstance(conform, eConform):
             if conform == eConform.INCIDENT:
@@ -482,10 +505,26 @@ def __init__(self, name, real_data, sim_data_fn, conform, likelihood, weight=1):
         pass
 
     @staticmethod
-    def poisson_nll(real_data, sim_data):
-        print('poisson')
-
-        return 0
+    def beta_binomial(real_data, sim_data):
+        # For the beta-binomial log likelihood, we begin with a Beta(1,1) prior
+        # and subsequently observe sim_data['x'] successes (positives) in sim_data['n'] trials (total observations).
+        # The result is a Beta(sim_data['x']+1, sim_data['n']-sim_data['x']+1) posterior.
+        # We then compare this to the real data, which has real_data['x'] successes (positives) in real_data['n'] trials (total observations).
+        # To do so, we use a beta-binomial likelihood:
+        # p(x|n, x, a, b) = (n choose x) B(x+a, n-x+b) / B(a, b)
+        # where
+        #   x=real_data['x']
+        #   n=real_data['n']
+        #   a=sim_data['x']+1
+        #   b=sim_data['n']-sim_data['x']+1 
+        # and B is the beta function, B(x, y) = Gamma(x)Gamma(y)/Gamma(x+y)
+
+        # We compute the log of p(x|n, x, a, b), noting that gln is the log of the gamma function
+        logL = gln(real_data['n'] + 1) - gln(real_data['x'] + 1) - gln(real_data['n'] - real_data['x'] + 1)
+        logL += gln(real_data['x'] + sim_data['x'] + 1) + gln(real_data['n'] - real_data['x'] + sim_data['n'] - sim_data['x'] + 1) - gln(real_data['n'] + sim_data['n'] + 2)
+        logL += gln(sim_data['n'] + 2) - gln(sim_data['x'] + 1) - gln(sim_data['n'] - sim_data['x'] + 1)
+
+        return -logL
 
     @staticmethod
     def linear_interp(real_data, sim_data):
@@ -494,11 +533,13 @@ def linear_interp(real_data, sim_data):
         Use for prevalent data like prevalence
         """
         t = real_data.index
-        sim_t = np.array([sc.datetoyear(t) for t in sim_data.index if isinstance(t, dt.date)])
+        #sim_t = np.array([sc.datetoyear(t.date()) for t in sim_data.index if isinstance(t, dt.date)])
 
-        sdi = np.interp(x=t, xp=sim_t, fp=sim_data)
-        df = pd.Series(sdi, index=t)
-        return df
+        conformed = pd.DataFrame(index=real_data.index)
+        for k in sim_data:
+            conformed[k] = np.interp(x=t, xp=sim_data.index, fp=sim_data[k])
+
+        return conformed
 
     @staticmethod
     def linear_accum(real_data, sim_data):
@@ -520,12 +561,12 @@ def linear_accum(real_data, sim_data):
     def eval(self, sim):
         # Compute and return the negative log likelihood
 
-        sim_data = self.sim_data_fn(sim)
-        sim_data = self.conform(self.real_data, sim_data)
+        sim_data = self.sim_data_fn(sim) # Extract
+        sim_data = self.conform(self.real_data, sim_data) # Conform
 
-        nll = self.likelihood(self.real_data, sim_data)
+        self.nll = self.nll_fn(self.real_data, sim_data) # Negative log likelihood
 
-        return self.weight * nll
+        return self.weight * np.sum(self.nll)
 
     def __call__(self, sim):
         return self.eval(sim)
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index a7681181..43259449 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -7,6 +7,7 @@
 import starsim as ss
 import pandas as pd
 
+debug = False # If true, will run in serial
 do_plot = 1
 do_save = 0
 n_agents = 2e3
@@ -15,84 +16,47 @@
 #%% Helper functions
 
 def make_sim():
-    hiv = ss.HIV(
-        beta = {'random': [ss.beta(0.01)]*2, 'maternal': [ss.beta(0.4), 0]},
-        init_prev = ss.bernoulli(0.15),
-
-        dt = 0.25,
+    sir = ss.SIR(
+        beta = ss.beta(0.075),
+        init_prev = ss.bernoulli(0.02),
     )
-    pregnancy = ss.Pregnancy(fertility_rate=20)
-    death = ss.Deaths(death_rate=10)
     random = ss.RandomNet(n_contacts=ss.poisson(4))
-    maternal = ss.MaternalNet()
 
     sim = ss.Sim(
-        dt = 0.5,
         n_agents = n_agents,
-        total_pop = 9980999,
         start = sc.date('1990-01-01'),
         dur = 40,
-        diseases = [hiv],
-        networks = [random, maternal],
-        demographics = [pregnancy, death],
+        dt = 1,
+        unit = 'day',
+        #total_pop = 10000,
+        diseases = sir,
+        networks = random,
     )
 
     return sim
 
 
-def make_data():
-    """ Define the calibration target data """
-    target_data = [
-    ['time', 'n_alive', 'hiv.prevalence', 'hiv.n_infected', 'hiv.new_infections', 'hiv.new_deaths'],
-    [  1990,  10432409,        0.0699742,          730000 ,               210000,           25000],
-    [  1991,  10681008,        0.0851979,          910000 ,               220000,           33000],
-    [  1992,  10900511,        0.1009127,          1100000,               220000,           43000],
-    [  1993,  11092775,        0.1081785,          1200000,               210000,           53000],
-    [  1994,  11261752,        0.1154349,          1300000,               200000,           63000],
-    [  1995,  11410721,        0.1226916,          1400000,               180000,           74000],
-    [  1996,  11541215,        0.1299689,          1500000,               160000,           84000],
-    [  1997,  11653254,        0.1287194,          1500000,               150000,           94000],
-    [  1998,  11747079,        0.1362040,          1600000,               140000,           100000],
-    [  1999,  11822722,        0.1353326,          1600000,               130000,           110000],
-    [  2000,  11881482,        0.1346633,          1600000,               120000,           120000],
-    [  2001,  11923906,        0.1341842,          1600000,               110000,           130000],
-    [  2002,  11954293,        0.1254779,          1500000,               100000,           130000],
-    [  2003,  11982219,        0.1251854,          1500000,               94000 ,           130000],
-    [  2004,  12019911,        0.1164734,          1400000,               89000 ,           120000],
-    [  2005,  12076697,        0.1159257,          1400000,               83000 ,           120000],
-    [  2006,  12155496,        0.1069475,          1300000,               78000 ,           110000],
-    [  2007,  12255920,        0.1060711,          1300000,               74000 ,           93000],
-    [  2008,  12379553,        0.1050118,          1300000,               69000 ,           80000],
-    [  2009,  12526964,        0.0957933,          1200000,               65000 ,           68000],
-    [  2010,  12697728,        0.0945050,          1200000,               62000 ,           54000],
-    [  2011,  12894323,        0.0930642,          1200000,               56000 ,           42000],
-    [  2012,  13115149,        0.0914972,          1200000,               49000 ,           34000],
-    [  2013,  13350378,        0.0973755,          1300000,               47000 ,           28000],
-    [  2014,  13586710,        0.0956817,          1300000,               45000 ,           25000],
-    [  2015,  13814642,        0.0941030,          1300000,               44000 ,           24000],
-    [  2016,  14030338,        0.0926563,          1300000,               43000 ,           23000],
-    [  2017,  14236599,        0.0913139,          1300000,               34000 ,           23000],
-    [  2018,  14438812,        0.0900351,          1300000,               27000 ,           22000],
-    [  2019,  14645473,        0.0920401,          1347971,               23000 ,           None],
-    [  2020,  14862927,        0.0874659,          1300000,               20000 ,           None],
-    [  2021,  15085870,        0.0861733,          1300000,               19000 ,           None],
-    [  2022,  15312158,        0.0848998,          1300000,               17000 ,           None],
-    ]
-    df = sc.dataframe(target_data[1:], columns=target_data[0]) \
-        .set_index('time')
-    return df
-
 def build_sim(sim, calib_pars, **kwargs):
     """ Modify the base simulation by applying calib_pars """
 
-    # Capture any parameters that need special handling here
-    if 'beta_randomnet' in calib_pars:
-        v = calib_pars.pop('beta_randomnet')['value']
-        sim.diseases.hiv.pars.beta['random'] = [ss.beta(v), ss.beta(v)]
+    sir = sim.pars.diseases # There is only one disease in this simulation and it is a SIR
+    net = sim.pars.networks # There is only one network in this simulation and it is a RandomNet
 
-    # The remaining calib_pars should have a path and can be handled in the
-    # straighforward way by the built-in translate_pars
-    sim = ss.Calibration.translate_pars(sim, calib_pars)
+    # Capture any parameters that need special handling here
+    for k, pars in calib_pars.items():
+        if k == 'rand_seed':
+            sim.pars.rand_seed = v
+            continue
+
+        v = pars['value']
+        if k == 'beta':
+            sir.pars.beta = ss.beta(v)
+        elif k == 'init_prev':
+            sir.pars.init_prev = ss.bernoulli(v)
+        elif k == 'n_contacts':
+            net.pars.n_contacts = ss.poisson(v)
+        else:
+            raise NotImplementedError(f'Parameter {k} not recognized')
 
     return sim
 
@@ -103,15 +67,15 @@ def test_calibration(do_plot=False):
 
     # Define the calibration parameters
     calib_pars = dict(
-        beta_randomnet = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Log scale and no "path", will be handled by build_sim (ablve)
-        init_prev = dict(low=0.01, high=0.30, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify
-        n_contacts = dict(low=2, high=10, guess=4, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo
+        beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Log scale and no "path", will be handled by build_sim (ablve)
+        init_prev = dict(low=0.01, high=0.05, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify
+        n_contacts = dict(low=2, high=10, guess=3, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo
     )
 
     # Make the sim and data
     sim = make_sim()
-    data = make_data()
 
+    '''
     prevalence = ss.CalibComponent(
         name = 'hiv.prevalence',
 
@@ -124,16 +88,25 @@ def test_calibration(do_plot=False):
 
         weight = 1,
     )
+    '''
+
+    infectious = ss.CalibComponent(
+        name = 'Infectious',
+
+        # "real_data" actually from a simulation with pars
+        #   beta=0.075, init_prev=0.02, n_contacts=4
+        real_data = pd.DataFrame({
+            'n': [200, 197, 195], # Number of individuals sampled
+            'x': [30, 30, 10],    # Number of individuals found to be infectious
+        }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates
+        
+        sim_data_fn = lambda sim: pd.DataFrame({
+            'n': sim.results.n_alive,
+            'x': sim.results.sir.n_infected,
+        }, index=pd.Index(sim.results.timevec, name='t')),
 
-    new_infections = ss.CalibComponent(
-        name = 'hiv.new_infections',
-
-        # By default, automate these based on name
-        real_data = data['hiv.new_infections'],
-        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.new_infections, index=sim.results.hiv.timevec),
-
-        conform = ss.eConform.INCIDENT,
-        likelihood = ss.eLikelihood.POISSON,
+        conform = ss.eConform.PREVALENT,
+        nll_fn = ss.eLikelihood.BETA_BINOMIAL,
 
         weight = 1,
     )
@@ -146,12 +119,12 @@ def test_calibration(do_plot=False):
         build_fn = build_sim, # Use default builder, Calibration.translate_pars
         build_kwargs = None,
 
-        components = [prevalence, new_infections],
+        components = [infectious],
 
-        total_trials = 8,
-        n_workers = 2,
+        total_trials = 1_000,
+        n_workers = None, # None indicates to use all available CPUs
         die = True,
-        debug = False,
+        debug = debug,
     )
 
     # Perform the calibration
@@ -161,9 +134,9 @@ def test_calibration(do_plot=False):
     # Confirm
     sc.printcyan('\nConfirming fit...')
     calib.confirm_fit()
-    print(f'Fit with original pars: {calib.before_fit:n}')
-    print(f'Fit with best-fit pars: {calib.after_fit:n}')
-    if calib.after_fit <= calib.before_fit:
+    print(f'Fit with original pars: {calib.before_fits}')
+    print(f'Fit with best-fit pars: {calib.after_fits}')
+    if calib.after_fits.mean() <= calib.before_fits.mean():
         print('✓ Calibration improved fit')
     else:
         print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')
@@ -178,9 +151,24 @@ def test_calibration(do_plot=False):
 #%% Run as a script
 if __name__ == '__main__':
 
+    # Useful for generating fake "real_data"
+    if False:
+        sim = make_sim()
+        pars = {
+            'beta'      : dict(value=0.075),
+            'init_prev' : dict(value=0.02),
+            'n_contacts': dict(value=4),
+        }
+        sim = build_sim(sim, pars)
+        ms = ss.MultiSim(sim, n_runs=25)
+        ms.run().plot()
+
     T = sc.timer()
     do_plot = True
 
     sim, calib = test_calibration(do_plot=do_plot)
 
     T.toc()
+
+    import matplotlib.pyplot as plt
+    plt.show()
\ No newline at end of file

From ee1da913004658f87f68935a3887399c8605c093 Mon Sep 17 00:00:00 2001
From: Dan Klein <dklein@declinebook4.local>
Date: Sat, 16 Nov 2024 09:52:30 -0800
Subject: [PATCH 16/28] Beginning work on a calibration tutorial

---
 docs/tutorials/tut_calibration.ipynb | 310 +++++++++++++++++++++++++++
 1 file changed, 310 insertions(+)
 create mode 100644 docs/tutorials/tut_calibration.ipynb

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
new file mode 100644
index 00000000..380650bb
--- /dev/null
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T6 - Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "An interactive version of this notebook is available on [Google Colab](https://colab.research.google.com/github/starsimhub/starsim/blob/main/docs/tutorials/tut_calibration.ipynb?install=starsim) or [Binder](https://mybinder.org/v2/gh/starsimhub/starsim/HEAD?labpath=docs%2Ftutorials%2Ftut_calibration.ipynb).\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Disease models typically require contextualization to a relevant setting of interest prior to addressing \"what-if\" scenario questions. The process of tuning model input parameters so that model outputs match observed data is known as calibration. There are many approaches to model calibration, ranging from manual tuning to fully Bayesian methods.\n",
+    "\n",
+    "For many applications, we have found that an optimization-based approach is sufficient. Such methods avoid the tedious process of manual tuning and are less computationally expensive than fully Bayesian methods. One such optimization-based approach is the Optuna library, which is a Bayesian hyperparameter optimization framework. Optuna is designed for tuning hyperparameters of machine learning models, but it can also be used to calibrate disease models.\n",
+    "\n",
+    "Calibration libraries often treat the disease model as a black box, where the input parameters are the \"hyperparameters\" to be tuned. The calibration process is often iterative and requires a combination of expert knowledge and computational tools. The optimization algorithm iteratively chooses new parameter values to evaluate, and the model is run with these values to generate outputs. The outputs are compared to observed data, and a loss function is calculated to quantify the difference between the model outputs and the observed data. The optimization algorithm then uses this loss function to update its search strategy and choose new parameter values to evaluate. This process continues until the algorithm converges to a set of parameter values that minimize the loss function.\n",
+    "\n",
+    "While many optimization algorithms are available, Starsim has a built-in interface to the Optuna library, which we will demonstrate in this tutorial. We will use a simple Susceptible-Infected-Recovered (SIR) model as an example. We will tune three input parameters, the infectivity parameter, `beta`, the initial prevalence parameter, `init_prev`, and the Poisson-distributed degree distribution parameter, `n_contacts`. We will calibrate the model using a beta-binomial likelihood function so as to match prevalence at three distinct time points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin with a few imports and default settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%% Imports and settings\n",
+    "import sciris as sc\n",
+    "import starsim as ss\n",
+    "import pandas as pd\n",
+    "\n",
+    "debug = False # If true, will run in serial\n",
+    "do_plot = 1\n",
+    "do_save = 0\n",
+    "n_agents = 2e3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The calibration class will require a base `Sim` object. This `sim` will later be modified according to parameters selected by the optimization engine. The following function creates the base `Sim` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_sim():\n",
+    "    \"\"\" Helper function to create the base simulation object \"\"\"\n",
+    "    sir = ss.SIR(\n",
+    "        beta = ss.beta(0.075),\n",
+    "        init_prev = ss.bernoulli(0.02),\n",
+    "    )\n",
+    "    random = ss.RandomNet(n_contacts=ss.poisson(4))\n",
+    "\n",
+    "    sim = ss.Sim(\n",
+    "        n_agents = n_agents,\n",
+    "        start = sc.date('1990-01-01'),\n",
+    "        dur = 40,\n",
+    "        dt = 1,\n",
+    "        unit = 'day',\n",
+    "        diseases = sir,\n",
+    "        networks = random,\n",
+    "    )\n",
+    "\n",
+    "    # Remember to return the sim object\n",
+    "    return sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's define the calibration parameters. These are the inputs that Optuna will be able to modify. Here, we define three such parameters, `beta`, `init_prev`, and `n_contacts`.\n",
+    "\n",
+    "Each parameter entry should have range defined by `low` and `high` as well as a `guess` values. The `guess` value is not used by Optuna, rather only for a check after calibration completes to see if the new parameters are better than the `guess` values.\n",
+    "\n",
+    "You'll notice there are a few other parameters that can be specified. For example, the data type of the parameter appears in `suggest_type`. Possible values are listed in the Optuna documentation, and include suggest_float (https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_float) for float values and suggest_int (https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_int) for integer types.\n",
+    "\n",
+    "To make things easier for the search algorithm, it's helpful to indicate how outputs are expected to change with inputs. For example, increasing `beta` from 0.01 to 0.02 should double disease transmission, but increasing from 0.11 to 0.12 will have a small effect. Thus, we indicate that this parameter should be calibrated with `log=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the calibration parameters\n",
+    "calib_pars = dict(\n",
+    "    beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Note the log scale\n",
+    "    init_prev = dict(low=0.01, high=0.05, guess=0.15), # Default type is suggest_float, no need to re-specify\n",
+    "    n_contacts = dict(low=2, high=10, guess=3, suggest_type='suggest_int'), # Suggest int just for this demo\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optimization engine iteratively chooses input parameters to simulate. Those parameters are passed into the following `build_sim` function as a dictionary of `calib_pars` along with the base `sim` and any other key word arguments.\n",
+    "\n",
+    "When modifying a `sim`, it is important to realize that the simulation has not been initialized yet. Nonetheless, the configuration is available for modification at `sim.pars`, as demonstrated in the function below for the SIR example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_sim(sim, calib_pars, **kwargs):\n",
+    "    \"\"\" Modify the base simulation by applying calib_pars \"\"\"\n",
+    "\n",
+    "    sir = sim.pars.diseases # There is only one disease in this simulation and it is a SIR\n",
+    "    net = sim.pars.networks # There is only one network in this simulation and it is a RandomNet\n",
+    "\n",
+    "    for k, pars in calib_pars.items(): # Loop over the calibration parameters\n",
+    "        if k == 'rand_seed':\n",
+    "            sim.pars.rand_seed = v\n",
+    "            continue\n",
+    "\n",
+    "        # Each item in calib_pars is a dictionary with keys like 'low', 'high',\n",
+    "        # 'guess', 'suggest_type', and importantly 'value'. The 'value' key is\n",
+    "        # the one we want to use as that's the one selected by the algorithm\n",
+    "        v = pars['value']\n",
+    "        if k == 'beta':\n",
+    "            sir.pars.beta = ss.beta(v)\n",
+    "        elif k == 'init_prev':\n",
+    "            sir.pars.init_prev = ss.bernoulli(v)\n",
+    "        elif k == 'n_contacts':\n",
+    "            net.pars.n_contacts = ss.poisson(v)\n",
+    "        else:\n",
+    "            raise NotImplementedError(f'Parameter {k} not recognized')\n",
+    "\n",
+    "    return sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "#%% Define the tests\n",
+    "def test_calibration(do_plot=False):\n",
+    "    sc.heading('Testing calibration')\n",
+    "\n",
+    "    # Define the calibration parameters\n",
+    "    calib_pars = dict(\n",
+    "        beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Log scale and no \"path\", will be handled by build_sim (ablve)\n",
+    "        init_prev = dict(low=0.01, high=0.05, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify\n",
+    "        n_contacts = dict(low=2, high=10, guess=3, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo\n",
+    "    )\n",
+    "\n",
+    "    # Make the sim and data\n",
+    "    sim = make_sim()\n",
+    "\n",
+    "    '''\n",
+    "    prevalence = ss.CalibComponent(\n",
+    "        name = 'hiv.prevalence',\n",
+    "\n",
+    "        # By default, automate these based on name\n",
+    "        real_data = data['hiv.prevalence'],\n",
+    "        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.prevalence, index=sim.results.hiv.timevec),\n",
+    "\n",
+    "        conform = ss.eConform.PREVALENT,\n",
+    "        likelihood = ss.eLikelihood.POISSON,\n",
+    "\n",
+    "        weight = 1,\n",
+    "    )\n",
+    "    '''\n",
+    "\n",
+    "    infectious = ss.CalibComponent(\n",
+    "        name = 'Infectious',\n",
+    "\n",
+    "        # \"real_data\" actually from a simulation with pars\n",
+    "        #   beta=0.075, init_prev=0.02, n_contacts=4\n",
+    "        real_data = pd.DataFrame({\n",
+    "            'n': [200, 197, 195], # Number of individuals sampled\n",
+    "            'x': [30, 30, 10],    # Number of individuals found to be infectious\n",
+    "        }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates\n",
+    "        \n",
+    "        sim_data_fn = lambda sim: pd.DataFrame({\n",
+    "            'n': sim.results.n_alive,\n",
+    "            'x': sim.results.sir.n_infected,\n",
+    "        }, index=pd.Index(sim.results.timevec, name='t')),\n",
+    "\n",
+    "        conform = ss.eConform.PREVALENT,\n",
+    "        nll_fn = ss.eLikelihood.BETA_BINOMIAL,\n",
+    "\n",
+    "        weight = 1,\n",
+    "    )\n",
+    "\n",
+    "    # Make the calibration\n",
+    "    calib = ss.Calibration(\n",
+    "        calib_pars = calib_pars,\n",
+    "        sim = sim,\n",
+    "\n",
+    "        build_fn = build_sim, # Use default builder, Calibration.translate_pars\n",
+    "        build_kwargs = None,\n",
+    "\n",
+    "        components = [infectious],\n",
+    "\n",
+    "        total_trials = 1_000,\n",
+    "        n_workers = None, # None indicates to use all available CPUs\n",
+    "        die = True,\n",
+    "        debug = debug,\n",
+    "    )\n",
+    "\n",
+    "    # Perform the calibration\n",
+    "    sc.printcyan('\\nPeforming calibration...')\n",
+    "    calib.calibrate(confirm_fit=False)\n",
+    "\n",
+    "    # Confirm\n",
+    "    sc.printcyan('\\nConfirming fit...')\n",
+    "    calib.confirm_fit()\n",
+    "    print(f'Fit with original pars: {calib.before_fits}')\n",
+    "    print(f'Fit with best-fit pars: {calib.after_fits}')\n",
+    "    if calib.after_fits.mean() <= calib.before_fits.mean():\n",
+    "        print('✓ Calibration improved fit')\n",
+    "    else:\n",
+    "        print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')\n",
+    "\n",
+    "    if do_plot:\n",
+    "        calib.plot_sims()\n",
+    "        calib.plot_trend()\n",
+    "\n",
+    "    return sim, calib\n",
+    "\n",
+    "\n",
+    "#%% Run as a script\n",
+    "if __name__ == '__main__':\n",
+    "\n",
+    "    # Useful for generating fake \"real_data\"\n",
+    "    if False:\n",
+    "        sim = make_sim()\n",
+    "        pars = {\n",
+    "            'beta'      : dict(value=0.075),\n",
+    "            'init_prev' : dict(value=0.02),\n",
+    "            'n_contacts': dict(value=4),\n",
+    "        }\n",
+    "        sim = build_sim(sim, pars)\n",
+    "        ms = ss.MultiSim(sim, n_runs=25)\n",
+    "        ms.run().plot()\n",
+    "\n",
+    "    T = sc.timer()\n",
+    "    do_plot = True\n",
+    "\n",
+    "    sim, calib = test_calibration(do_plot=do_plot)\n",
+    "\n",
+    "    T.toc()\n",
+    "\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py312",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From acffe5025301cb04fc5de7dceafb2db9bb7aae6b Mon Sep 17 00:00:00 2001
From: Dan Klein <dklein@declinebook4.local>
Date: Sat, 16 Nov 2024 09:56:24 -0800
Subject: [PATCH 17/28] Continued work on the calibration tutorial

---
 docs/tutorials/tut_calibration.ipynb | 169 ++++++++++++---------------
 1 file changed, 74 insertions(+), 95 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 380650bb..6758029d 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -121,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The optimization engine iteratively chooses input parameters to simulate. Those parameters are passed into the following `build_sim` function as a dictionary of `calib_pars` along with the base `sim` and any other key word arguments.\n",
+    "The optimization engine iteratively chooses input parameters to simulate. Those parameters are passed into the following `build_sim` function as a dictionary of `calib_pars` along with the base `sim` and any other key word arguments. The `calib_pars` will be as above, but importantly will have an additional key named `value` containing the value selected by Optuna.\n",
     "\n",
     "When modifying a `sim`, it is important to realize that the simulation has not been initialized yet. Nonetheless, the configuration is available for modification at `sim.pars`, as demonstrated in the function below for the SIR example."
    ]
@@ -160,9 +160,32 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "infectious = ss.CalibComponent(\n",
+    "    name = 'Infectious',\n",
+    "\n",
+    "    # \"real_data\" actually from a simulation with pars\n",
+    "    #   beta=0.075, init_prev=0.02, n_contacts=4\n",
+    "    real_data = pd.DataFrame({\n",
+    "        'n': [200, 197, 195], # Number of individuals sampled\n",
+    "        'x': [30, 30, 10],    # Number of individuals found to be infectious\n",
+    "    }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates\n",
+    "    \n",
+    "    sim_data_fn = lambda sim: pd.DataFrame({\n",
+    "        'n': sim.results.n_alive,\n",
+    "        'x': sim.results.sir.n_infected,\n",
+    "    }, index=pd.Index(sim.results.timevec, name='t')),\n",
+    "\n",
+    "    conform = ss.eConform.PREVALENT,\n",
+    "    nll_fn = ss.eLikelihood.BETA_BINOMIAL,\n",
+    "\n",
+    "    weight = 1,\n",
+    ")"
+   ]
   },
   {
    "cell_type": "code",
@@ -172,117 +195,73 @@
    "source": [
     "\n",
     "\n",
-    "#%% Define the tests\n",
-    "def test_calibration(do_plot=False):\n",
-    "    sc.heading('Testing calibration')\n",
+    "sc.heading('Beginning calibration')\n",
     "\n",
-    "    # Define the calibration parameters\n",
-    "    calib_pars = dict(\n",
-    "        beta = dict(low=0.01, high=0.30, guess=0.15, suggest_type='suggest_float', log=True), # Log scale and no \"path\", will be handled by build_sim (ablve)\n",
-    "        init_prev = dict(low=0.01, high=0.05, guess=0.15, path=('diseases', 'hiv', 'init_prev')), # Default type is suggest_float, no need to re-specify\n",
-    "        n_contacts = dict(low=2, high=10, guess=3, suggest_type='suggest_int', path=('networks', 'randomnet', 'n_contacts')), # Suggest int just for demo\n",
-    "    )\n",
-    "\n",
-    "    # Make the sim and data\n",
-    "    sim = make_sim()\n",
-    "\n",
-    "    '''\n",
-    "    prevalence = ss.CalibComponent(\n",
-    "        name = 'hiv.prevalence',\n",
-    "\n",
-    "        # By default, automate these based on name\n",
-    "        real_data = data['hiv.prevalence'],\n",
-    "        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.prevalence, index=sim.results.hiv.timevec),\n",
+    "# Make the sim and data\n",
+    "sim = make_sim()\n",
     "\n",
-    "        conform = ss.eConform.PREVALENT,\n",
-    "        likelihood = ss.eLikelihood.POISSON,\n",
     "\n",
-    "        weight = 1,\n",
-    "    )\n",
-    "    '''\n",
-    "\n",
-    "    infectious = ss.CalibComponent(\n",
-    "        name = 'Infectious',\n",
-    "\n",
-    "        # \"real_data\" actually from a simulation with pars\n",
-    "        #   beta=0.075, init_prev=0.02, n_contacts=4\n",
-    "        real_data = pd.DataFrame({\n",
-    "            'n': [200, 197, 195], # Number of individuals sampled\n",
-    "            'x': [30, 30, 10],    # Number of individuals found to be infectious\n",
-    "        }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates\n",
-    "        \n",
-    "        sim_data_fn = lambda sim: pd.DataFrame({\n",
-    "            'n': sim.results.n_alive,\n",
-    "            'x': sim.results.sir.n_infected,\n",
-    "        }, index=pd.Index(sim.results.timevec, name='t')),\n",
-    "\n",
-    "        conform = ss.eConform.PREVALENT,\n",
-    "        nll_fn = ss.eLikelihood.BETA_BINOMIAL,\n",
-    "\n",
-    "        weight = 1,\n",
-    "    )\n",
+    "# Make the calibration\n",
+    "calib = ss.Calibration(\n",
+    "    calib_pars = calib_pars,\n",
+    "    sim = sim,\n",
     "\n",
-    "    # Make the calibration\n",
-    "    calib = ss.Calibration(\n",
-    "        calib_pars = calib_pars,\n",
-    "        sim = sim,\n",
+    "    build_fn = build_sim, # Use default builder, Calibration.translate_pars\n",
+    "    build_kwargs = None,\n",
     "\n",
-    "        build_fn = build_sim, # Use default builder, Calibration.translate_pars\n",
-    "        build_kwargs = None,\n",
+    "    components = [infectious],\n",
     "\n",
-    "        components = [infectious],\n",
+    "    total_trials = 1_000,\n",
+    "    n_workers = None, # None indicates to use all available CPUs\n",
+    "    die = True,\n",
+    "    debug = debug,\n",
+    ")\n",
     "\n",
-    "        total_trials = 1_000,\n",
-    "        n_workers = None, # None indicates to use all available CPUs\n",
-    "        die = True,\n",
-    "        debug = debug,\n",
-    "    )\n",
+    "# Perform the calibration\n",
+    "sc.printcyan('\\nPeforming calibration...')\n",
+    "calib.calibrate(confirm_fit=False)\n",
     "\n",
-    "    # Perform the calibration\n",
-    "    sc.printcyan('\\nPeforming calibration...')\n",
-    "    calib.calibrate(confirm_fit=False)\n",
+    "# Confirm\n",
+    "sc.printcyan('\\nConfirming fit...')\n",
+    "calib.confirm_fit()\n",
+    "print(f'Fit with original pars: {calib.before_fits}')\n",
+    "print(f'Fit with best-fit pars: {calib.after_fits}')\n",
+    "if calib.after_fits.mean() <= calib.before_fits.mean():\n",
+    "    print('✓ Calibration improved fit')\n",
+    "else:\n",
+    "    print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')\n",
     "\n",
-    "    # Confirm\n",
-    "    sc.printcyan('\\nConfirming fit...')\n",
-    "    calib.confirm_fit()\n",
-    "    print(f'Fit with original pars: {calib.before_fits}')\n",
-    "    print(f'Fit with best-fit pars: {calib.after_fits}')\n",
-    "    if calib.after_fits.mean() <= calib.before_fits.mean():\n",
-    "        print('✓ Calibration improved fit')\n",
-    "    else:\n",
-    "        print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')\n",
+    "if do_plot:\n",
+    "    calib.plot_sims()\n",
+    "    calib.plot_trend()\n",
     "\n",
-    "    if do_plot:\n",
-    "        calib.plot_sims()\n",
-    "        calib.plot_trend()\n",
-    "\n",
-    "    return sim, calib\n",
+    "return sim, calib\n",
     "\n",
     "\n",
     "#%% Run as a script\n",
     "if __name__ == '__main__':\n",
     "\n",
-    "    # Useful for generating fake \"real_data\"\n",
-    "    if False:\n",
-    "        sim = make_sim()\n",
-    "        pars = {\n",
-    "            'beta'      : dict(value=0.075),\n",
-    "            'init_prev' : dict(value=0.02),\n",
-    "            'n_contacts': dict(value=4),\n",
-    "        }\n",
-    "        sim = build_sim(sim, pars)\n",
-    "        ms = ss.MultiSim(sim, n_runs=25)\n",
-    "        ms.run().plot()\n",
+    "# Useful for generating fake \"real_data\"\n",
+    "if False:\n",
+    "    sim = make_sim()\n",
+    "    pars = {\n",
+    "        'beta'      : dict(value=0.075),\n",
+    "        'init_prev' : dict(value=0.02),\n",
+    "        'n_contacts': dict(value=4),\n",
+    "    }\n",
+    "    sim = build_sim(sim, pars)\n",
+    "    ms = ss.MultiSim(sim, n_runs=25)\n",
+    "    ms.run().plot()\n",
     "\n",
-    "    T = sc.timer()\n",
-    "    do_plot = True\n",
+    "T = sc.timer()\n",
+    "do_plot = True\n",
     "\n",
-    "    sim, calib = test_calibration(do_plot=do_plot)\n",
+    "sim, calib = test_calibration(do_plot=do_plot)\n",
     "\n",
-    "    T.toc()\n",
+    "T.toc()\n",
     "\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    plt.show()"
+    "import matplotlib.pyplot as plt\n",
+    "plt.show()"
    ]
   }
  ],

From dd9a1846efce5574b149bff0897397e497ecbb85 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Sat, 16 Nov 2024 09:59:40 -0800
Subject: [PATCH 18/28] Needs cleanup and additional comments, but working.

---
 docs/tutorials/tut_calibration.ipynb | 36 +++-------------------------
 1 file changed, 3 insertions(+), 33 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 6758029d..3cb2405b 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,6 +84,7 @@
     "        unit = 'day',\n",
     "        diseases = sir,\n",
     "        networks = random,\n",
+    "        verbose = 0,\n",
     "    )\n",
     "\n",
     "    # Remember to return the sim object\n",
@@ -193,14 +194,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
     "sc.heading('Beginning calibration')\n",
     "\n",
     "# Make the sim and data\n",
     "sim = make_sim()\n",
     "\n",
-    "\n",
     "# Make the calibration\n",
     "calib = ss.Calibration(\n",
     "    calib_pars = calib_pars,\n",
@@ -233,35 +231,7 @@
     "\n",
     "if do_plot:\n",
     "    calib.plot_sims()\n",
-    "    calib.plot_trend()\n",
-    "\n",
-    "return sim, calib\n",
-    "\n",
-    "\n",
-    "#%% Run as a script\n",
-    "if __name__ == '__main__':\n",
-    "\n",
-    "# Useful for generating fake \"real_data\"\n",
-    "if False:\n",
-    "    sim = make_sim()\n",
-    "    pars = {\n",
-    "        'beta'      : dict(value=0.075),\n",
-    "        'init_prev' : dict(value=0.02),\n",
-    "        'n_contacts': dict(value=4),\n",
-    "    }\n",
-    "    sim = build_sim(sim, pars)\n",
-    "    ms = ss.MultiSim(sim, n_runs=25)\n",
-    "    ms.run().plot()\n",
-    "\n",
-    "T = sc.timer()\n",
-    "do_plot = True\n",
-    "\n",
-    "sim, calib = test_calibration(do_plot=do_plot)\n",
-    "\n",
-    "T.toc()\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.show()"
+    "    calib.plot_trend()"
    ]
   }
  ],

From a601aab20951dfac3f7d56279b2ac8f83a68a811 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Sat, 16 Nov 2024 21:40:23 -0800
Subject: [PATCH 19/28] Calib tutorial improvements

---
 docs/tutorials/tut_calibration.ipynb | 392 +++++++++++++++++++++++++--
 starsim/calibration.py               |  12 +-
 2 files changed, 371 insertions(+), 33 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 3cb2405b..4b5a24cc 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -40,19 +40,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dklein/miniforge3/envs/py312/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "#%% Imports and settings\n",
     "import sciris as sc\n",
     "import starsim as ss\n",
     "import pandas as pd\n",
     "\n",
-    "debug = False # If true, will run in serial\n",
-    "do_plot = 1\n",
-    "do_save = 0\n",
-    "n_agents = 2e3"
+    "n_agents = 2e3\n",
+    "debug = False # If true, will run in serial"
    ]
   },
   {
@@ -64,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,9 +167,24 @@
     "    return sim"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall that an optimization-based approach to calibration minimizes a function of the input parameters. We compose the goodness-of-fit function using \"components.\" Each component has real data, for example from a survey, that is compared against simulation data from the model. Several components and be used at the same time. Each computes a likelihood of the data given the input parameters, as assess via simulation. Components are assumed to be independent.\n",
+    "\n",
+    "When defining a `CalibComponent`, we give it a `name` and pass in `real_data`. The required data fields depend on the likelihood function. Importantly, the functional form of the negative log likelihood, or nll, is defined by the `nll_fn`. The value for `nll_fn` can be any value of the eLikelihood enumeration, like `BETA_BINOMIAL`, or a negative log likelihood function of your creation. If designing your own function for `nll_fn`, it should take two arguments: `real_data` and `sim_data`. For a Beta binomial, the data must define `n` and `x`, where `n` is the number of individuals that were sampled and `x` is the number that were found, e.g. identified as positive.\n",
+    "\n",
+    "Output from the simulation is obtained via a function. The function takes a completed `sim` object as input and returns a dictionary with fields as required for the `nll_fn` function. In the example below, we use an in-line lambda function. Like the `real_data`, the `sim_data_fn` for a Beta binomial requires `n` and `x`.\n",
+    "\n",
+    "Each component has a `weight`. The final goodness of fit is a weighted sum of negative log likelihoods.\n",
+    "\n",
+    "Finally, the `conform` argument describes how the simulation output is adjusted to align with the real data. For example, if the real data is a prevalence measurement, choosing `ss.eConform.PREVALENT` will interpolate the simulation output at the time points of the real data. Choosing `ss.eConform.INCIDENT`, the simulation output will be aggregated between time points of the real data."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,11 +210,168 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can bring all the pieces together. We make a single base simulation and create an instance of a Starsim Calibration object. This object requires a few arguments, like the `calib_pars` and `sim`. We also pass in the function that modifies the base `sim`, here our `build_sim` function. No additional `build_kwargs` are required in this example.\n",
+    "\n",
+    "We also pass in a list of `components`. Instead of using this \"component-based\" system, a user could simply provide an `eval_fn`, which takes in a completed sim an any `eval_kwargs` and returns a \"goodness of fit\" score to be maximized.\n",
+    "\n",
+    "We can also specify the total number of trial to run, the number of parallel works, and a few other parameters."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[36m\n",
+      "\n",
+      "—————————————————————\n",
+      "Beginning calibration\n",
+      "—————————————————————\n",
+      "\u001b[0m\n",
+      "\u001b[36m\n",
+      "Peforming calibration...\u001b[0m\n",
+      "Removed existing calibration file starsim_calibration.db\n",
+      "sqlite:///starsim_calibration.db\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2024-11-16 21:37:43,528] A new study created in RDB with name: starsim_calibration\n",
+      "[I 2024-11-16 21:37:43,863] Trial 2 finished with value: 107.30873053298149 and parameters: {'beta': 0.287649229557548, 'init_prev': 0.01459633116681542, 'n_contacts': 8, 'rand_seed': 786992}. Best is trial 2 with value: 107.30873053298149.\n",
+      "[I 2024-11-16 21:37:43,903] Trial 1 finished with value: 141.71274040920855 and parameters: {'beta': 0.016131876512529307, 'init_prev': 0.03095733037020489, 'n_contacts': 6, 'rand_seed': 576498}. Best is trial 2 with value: 107.30873053298149.\n",
+      "[I 2024-11-16 21:37:43,905] Trial 7 finished with value: 115.95181064486735 and parameters: {'beta': 0.012305576988698542, 'init_prev': 0.03462649160173581, 'n_contacts': 9, 'rand_seed': 150095}. Best is trial 2 with value: 107.30873053298149.\n",
+      "[I 2024-11-16 21:37:43,937] Trial 5 finished with value: 85.2177244223318 and parameters: {'beta': 0.04392452421102194, 'init_prev': 0.03539336109748194, 'n_contacts': 3, 'rand_seed': 860382}. Best is trial 5 with value: 85.2177244223318.\n",
+      "[I 2024-11-16 21:37:43,943] Trial 4 finished with value: 141.4032426402198 and parameters: {'beta': 0.10963499388291496, 'init_prev': 0.03448486835886308, 'n_contacts': 8, 'rand_seed': 795307}. Best is trial 5 with value: 85.2177244223318.\n",
+      "[I 2024-11-16 21:37:43,946] Trial 12 finished with value: 161.00743770722488 and parameters: {'beta': 0.21478157849320906, 'init_prev': 0.014787577120824387, 'n_contacts': 5, 'rand_seed': 299437}. Best is trial 5 with value: 85.2177244223318.\n",
+      "[I 2024-11-16 21:37:43,956] Trial 15 finished with value: 167.1689297427771 and parameters: {'beta': 0.010781806464073448, 'init_prev': 0.031988405042653455, 'n_contacts': 3, 'rand_seed': 462562}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,957] Trial 11 finished with value: 11.274790395672198 and parameters: {'beta': 0.15424596603654206, 'init_prev': 0.04235611504022485, 'n_contacts': 2, 'rand_seed': 57778}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,969] Trial 8 finished with value: 24.248931515507707 and parameters: {'beta': 0.07449241390265154, 'init_prev': 0.04632856000297287, 'n_contacts': 3, 'rand_seed': 358196}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,974] Trial 14 finished with value: 13.793599248582382 and parameters: {'beta': 0.0813407183574054, 'init_prev': 0.02755289202257546, 'n_contacts': 3, 'rand_seed': 119810}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,981] Trial 9 finished with value: 10.740445158890566 and parameters: {'beta': 0.029326423384490093, 'init_prev': 0.033352301494229825, 'n_contacts': 10, 'rand_seed': 142129}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,985] Trial 6 finished with value: 105.75928870031805 and parameters: {'beta': 0.02885125074711628, 'init_prev': 0.021422368191646127, 'n_contacts': 5, 'rand_seed': 977498}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:43,995] Trial 10 finished with value: 127.75817726084074 and parameters: {'beta': 0.17569583999951785, 'init_prev': 0.023040758393914024, 'n_contacts': 6, 'rand_seed': 246148}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,005] Trial 13 finished with value: 143.48354567052831 and parameters: {'beta': 0.011558791210643644, 'init_prev': 0.04307378005123698, 'n_contacts': 8, 'rand_seed': 531030}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,010] Trial 16 finished with value: 109.4864810805908 and parameters: {'beta': 0.19583827567604442, 'init_prev': 0.025276824412756804, 'n_contacts': 10, 'rand_seed': 35747}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,010] Trial 18 finished with value: 30.614211395920165 and parameters: {'beta': 0.13020052150000808, 'init_prev': 0.01461961770819753, 'n_contacts': 3, 'rand_seed': 169282}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,029] Trial 0 finished with value: 153.44036639517492 and parameters: {'beta': 0.01481688380683361, 'init_prev': 0.02350258323813991, 'n_contacts': 5, 'rand_seed': 539193}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,034] Trial 17 finished with value: 117.56518839114926 and parameters: {'beta': 0.29643533691386287, 'init_prev': 0.02899021764520581, 'n_contacts': 7, 'rand_seed': 405144}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,048] Trial 19 finished with value: 75.95675162025566 and parameters: {'beta': 0.016609043731855854, 'init_prev': 0.019080559494204365, 'n_contacts': 9, 'rand_seed': 447055}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,048] Trial 3 finished with value: 164.82089971605512 and parameters: {'beta': 0.01135815148799859, 'init_prev': 0.0253449834811098, 'n_contacts': 4, 'rand_seed': 297913}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,077] Trial 24 finished with value: 12.079318129832814 and parameters: {'beta': 0.028595589249682203, 'init_prev': 0.04487650284870446, 'n_contacts': 10, 'rand_seed': 1414}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,099] Trial 23 finished with value: 15.395707324162345 and parameters: {'beta': 0.028810351060067654, 'init_prev': 0.04372465728229667, 'n_contacts': 10, 'rand_seed': 20492}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,107] Trial 21 finished with value: 103.38286597867284 and parameters: {'beta': 0.14664293718672586, 'init_prev': 0.046407651006863775, 'n_contacts': 10, 'rand_seed': 105406}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,109] Trial 26 finished with value: 162.73080227118282 and parameters: {'beta': 0.028646024044356105, 'init_prev': 0.042339141673771764, 'n_contacts': 2, 'rand_seed': 74531}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,112] Trial 27 finished with value: 22.184885441704637 and parameters: {'beta': 0.03144626830848546, 'init_prev': 0.04064091250085091, 'n_contacts': 10, 'rand_seed': 4944}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,115] Trial 22 finished with value: 12.262457760569873 and parameters: {'beta': 0.02850146546662436, 'init_prev': 0.04438139452126155, 'n_contacts': 10, 'rand_seed': 27467}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,121] Trial 31 finished with value: 162.73080227118282 and parameters: {'beta': 0.028417220128461814, 'init_prev': 0.041257449184238806, 'n_contacts': 2, 'rand_seed': 6884}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,122] Trial 29 finished with value: 167.13709732073278 and parameters: {'beta': 0.024310974004789525, 'init_prev': 0.04065352400298506, 'n_contacts': 2, 'rand_seed': 48083}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,149] Trial 33 finished with value: 83.06035415674944 and parameters: {'beta': 0.07382601249314252, 'init_prev': 0.04019273248689236, 'n_contacts': 2, 'rand_seed': 10324}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,168] Trial 34 finished with value: 70.76338318565524 and parameters: {'beta': 0.08066917648627793, 'init_prev': 0.039899025250897724, 'n_contacts': 2, 'rand_seed': 2825}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,183] Trial 20 finished with value: 32.089930843958996 and parameters: {'beta': 0.01905966723421134, 'init_prev': 0.0420857622990075, 'n_contacts': 10, 'rand_seed': 41185}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,190] Trial 35 finished with value: 70.76338318565524 and parameters: {'beta': 0.08041537274195945, 'init_prev': 0.03997403827295326, 'n_contacts': 2, 'rand_seed': 821}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,190] Trial 30 finished with value: 164.82089971605512 and parameters: {'beta': 0.02721320675346273, 'init_prev': 0.04129427168152436, 'n_contacts': 2, 'rand_seed': 27035}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,198] Trial 36 finished with value: 10.964537464703085 and parameters: {'beta': 0.027829741985744366, 'init_prev': 0.04011257513821645, 'n_contacts': 10, 'rand_seed': 27044}. Best is trial 9 with value: 10.740445158890566.\n",
+      "[I 2024-11-16 21:37:44,214] Trial 28 finished with value: 10.182025720756997 and parameters: {'beta': 0.027747520761274944, 'init_prev': 0.039512448108991745, 'n_contacts': 10, 'rand_seed': 5112}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,217] Trial 32 finished with value: 115.70891322491173 and parameters: {'beta': 0.05945414963129977, 'init_prev': 0.04129525599059517, 'n_contacts': 2, 'rand_seed': 154}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,223] Trial 37 finished with value: 45.89697968481846 and parameters: {'beta': 0.041694160307547315, 'init_prev': 0.03864291169320181, 'n_contacts': 9, 'rand_seed': 205798}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,237] Trial 40 finished with value: 85.62233878240909 and parameters: {'beta': 0.0477217348149753, 'init_prev': 0.04892148557563158, 'n_contacts': 9, 'rand_seed': 213495}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,238] Trial 39 finished with value: 60.2276818782791 and parameters: {'beta': 0.04585997885121268, 'init_prev': 0.0381185580542134, 'n_contacts': 9, 'rand_seed': 192829}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,238] Trial 43 finished with value: 111.60699261972763 and parameters: {'beta': 0.05467495594617335, 'init_prev': 0.03731338398960968, 'n_contacts': 9, 'rand_seed': 204931}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,276] Trial 42 finished with value: 46.415029191358144 and parameters: {'beta': 0.04181007421545325, 'init_prev': 0.03681108492775422, 'n_contacts': 9, 'rand_seed': 207399}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,276] Trial 41 finished with value: 73.94995937945873 and parameters: {'beta': 0.0446154856694533, 'init_prev': 0.04996558005938069, 'n_contacts': 9, 'rand_seed': 196099}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,287] Trial 25 finished with value: 15.292149813431593 and parameters: {'beta': 0.028968625417551172, 'init_prev': 0.044878282635886224, 'n_contacts': 10, 'rand_seed': 61013}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,297] Trial 45 finished with value: 62.825141595206105 and parameters: {'beta': 0.04250945809634435, 'init_prev': 0.04946189211688657, 'n_contacts': 9, 'rand_seed': 209682}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,316] Trial 38 finished with value: 129.25762907255444 and parameters: {'beta': 0.059448007114808285, 'init_prev': 0.04925496738535662, 'n_contacts': 9, 'rand_seed': 243572}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,329] Trial 48 finished with value: 73.90148866518825 and parameters: {'beta': 0.04440412706115726, 'init_prev': 0.048798446675135145, 'n_contacts': 9, 'rand_seed': 209619}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,331] Trial 49 finished with value: 45.698146174701606 and parameters: {'beta': 0.04250583649572974, 'init_prev': 0.036684112254986995, 'n_contacts': 9, 'rand_seed': 621640}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,342] Trial 50 finished with value: 26.743431673640544 and parameters: {'beta': 0.04157706933561373, 'init_prev': 0.04992858693961394, 'n_contacts': 7, 'rand_seed': 659651}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,348] Trial 52 finished with value: 84.07503615644293 and parameters: {'beta': 0.037156229195977684, 'init_prev': 0.03260309486400948, 'n_contacts': 4, 'rand_seed': 689364}. Best is trial 28 with value: 10.182025720756997.\n",
+      "[I 2024-11-16 21:37:44,354] Trial 53 finished with value: 8.669427305735212 and parameters: {'beta': 0.036336486559483704, 'init_prev': 0.03628739942352108, 'n_contacts': 8, 'rand_seed': 599372}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,391] Trial 51 finished with value: 64.9909820586023 and parameters: {'beta': 0.0210798597941547, 'init_prev': 0.03254032152013679, 'n_contacts': 8, 'rand_seed': 117188}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,396] Trial 47 finished with value: 53.166391709701884 and parameters: {'beta': 0.04014414561140875, 'init_prev': 0.049782809844158414, 'n_contacts': 9, 'rand_seed': 219720}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,410] Trial 56 finished with value: 67.76372462174243 and parameters: {'beta': 0.02108306705681977, 'init_prev': 0.03330398875316214, 'n_contacts': 7, 'rand_seed': 651963}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,411] Trial 44 finished with value: 69.10983038270274 and parameters: {'beta': 0.04489768401651496, 'init_prev': 0.049803258145886584, 'n_contacts': 9, 'rand_seed': 201940}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,422] Trial 46 finished with value: 80.27784123448384 and parameters: {'beta': 0.02021443161195256, 'init_prev': 0.03238930129275948, 'n_contacts': 7, 'rand_seed': 658066}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,424] Trial 58 finished with value: 72.60977726982367 and parameters: {'beta': 0.022308675733040552, 'init_prev': 0.031465309920818044, 'n_contacts': 7, 'rand_seed': 636166}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,437] Trial 59 finished with value: 64.45355099338178 and parameters: {'beta': 0.02165148798399229, 'init_prev': 0.032345096280218616, 'n_contacts': 8, 'rand_seed': 115142}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,449] Trial 55 finished with value: 77.26910665397065 and parameters: {'beta': 0.02144114630425907, 'init_prev': 0.03296934619918316, 'n_contacts': 7, 'rand_seed': 108559}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,457] Trial 60 finished with value: 132.61506686052837 and parameters: {'beta': 0.022962834395949573, 'init_prev': 0.03434964231059928, 'n_contacts': 4, 'rand_seed': 116312}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,462] Trial 61 finished with value: 64.7052972361513 and parameters: {'beta': 0.02081243546109354, 'init_prev': 0.03350615905935427, 'n_contacts': 8, 'rand_seed': 122014}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,471] Trial 57 finished with value: 73.35016687878363 and parameters: {'beta': 0.02173885910268392, 'init_prev': 0.032561115968953225, 'n_contacts': 7, 'rand_seed': 633090}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,495] Trial 64 finished with value: 78.23365806680215 and parameters: {'beta': 0.019434238633552255, 'init_prev': 0.03419532312315708, 'n_contacts': 8, 'rand_seed': 122228}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,496] Trial 63 finished with value: 58.65329552030437 and parameters: {'beta': 0.022540468605080306, 'init_prev': 0.03408513569420744, 'n_contacts': 8, 'rand_seed': 122706}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,517] Trial 62 finished with value: 63.929063254240305 and parameters: {'beta': 0.02183074304580968, 'init_prev': 0.03314924410716158, 'n_contacts': 8, 'rand_seed': 109302}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,535] Trial 65 finished with value: 14.004756804448903 and parameters: {'beta': 0.03447837266510164, 'init_prev': 0.03426800420636183, 'n_contacts': 7, 'rand_seed': 110175}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,538] Trial 70 finished with value: 30.841045617808618 and parameters: {'beta': 0.03472908476125748, 'init_prev': 0.035091324960792536, 'n_contacts': 6, 'rand_seed': 317590}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,552] Trial 66 finished with value: 11.52069994121723 and parameters: {'beta': 0.03548939976284397, 'init_prev': 0.035219365985743586, 'n_contacts': 7, 'rand_seed': 96842}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,559] Trial 73 finished with value: 32.23200258925749 and parameters: {'beta': 0.033129773943628704, 'init_prev': 0.04530997552081485, 'n_contacts': 10, 'rand_seed': 62676}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,561] Trial 69 finished with value: 157.6369759673313 and parameters: {'beta': 0.013671833200131898, 'init_prev': 0.029614509161837742, 'n_contacts': 6, 'rand_seed': 95826}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,564] Trial 67 finished with value: 47.37048323088368 and parameters: {'beta': 0.03315665213467724, 'init_prev': 0.0349527254983216, 'n_contacts': 6, 'rand_seed': 94727}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,575] Trial 71 finished with value: 143.54604098115908 and parameters: {'beta': 0.016055810599959364, 'init_prev': 0.029418003426029814, 'n_contacts': 6, 'rand_seed': 298839}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,590] Trial 76 finished with value: 45.591426306323 and parameters: {'beta': 0.034949335178451384, 'init_prev': 0.04450989930492606, 'n_contacts': 10, 'rand_seed': 73739}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,596] Trial 75 finished with value: 35.865739872788936 and parameters: {'beta': 0.03406030354813161, 'init_prev': 0.04630751778929916, 'n_contacts': 10, 'rand_seed': 73367}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,601] Trial 68 finished with value: 31.2190548512159 and parameters: {'beta': 0.03470805669285768, 'init_prev': 0.03463638774142812, 'n_contacts': 6, 'rand_seed': 88980}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,637] Trial 77 finished with value: 54.96435323775643 and parameters: {'beta': 0.016289921602386955, 'init_prev': 0.045018042809617964, 'n_contacts': 10, 'rand_seed': 524051}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,640] Trial 80 finished with value: 80.53387138953497 and parameters: {'beta': 0.013982987953245481, 'init_prev': 0.046078960122572565, 'n_contacts': 10, 'rand_seed': 159022}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,647] Trial 54 finished with value: 64.9909820586023 and parameters: {'beta': 0.021206285122736198, 'init_prev': 0.03205791664546167, 'n_contacts': 8, 'rand_seed': 617060}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,656] Trial 78 finished with value: 133.74224774808556 and parameters: {'beta': 0.2488054995091254, 'init_prev': 0.044900002130169224, 'n_contacts': 10, 'rand_seed': 538357}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,660] Trial 72 finished with value: 37.480776662929884 and parameters: {'beta': 0.03375664451553922, 'init_prev': 0.04625750747727689, 'n_contacts': 10, 'rand_seed': 338210}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,682] Trial 74 finished with value: 66.61003065309035 and parameters: {'beta': 0.016029361966139983, 'init_prev': 0.02949970297986432, 'n_contacts': 10, 'rand_seed': 549696}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,682] Trial 81 finished with value: 10.748501102658906 and parameters: {'beta': 0.025648357344235356, 'init_prev': 0.043662882795621534, 'n_contacts': 10, 'rand_seed': 543600}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,693] Trial 82 finished with value: 135.43289637051805 and parameters: {'beta': 0.25393530988710483, 'init_prev': 0.04327271866699147, 'n_contacts': 10, 'rand_seed': 164971}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,698] Trial 85 finished with value: 11.569354489664534 and parameters: {'beta': 0.025140144150853, 'init_prev': 0.04345651883695237, 'n_contacts': 10, 'rand_seed': 165388}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,706] Trial 83 finished with value: 132.17356571888797 and parameters: {'beta': 0.242519746876748, 'init_prev': 0.042995088720121095, 'n_contacts': 10, 'rand_seed': 744397}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,714] Trial 86 finished with value: 10.551486202267029 and parameters: {'beta': 0.02601053854500832, 'init_prev': 0.03919199826806074, 'n_contacts': 10, 'rand_seed': 152291}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,717] Trial 79 finished with value: 48.69258693568611 and parameters: {'beta': 0.016924314062392063, 'init_prev': 0.029154712270699153, 'n_contacts': 10, 'rand_seed': 530800}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,741] Trial 88 finished with value: 119.18988792613948 and parameters: {'beta': 0.025361542737863386, 'init_prev': 0.0392008605076073, 'n_contacts': 5, 'rand_seed': 35918}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,745] Trial 87 finished with value: 10.838879429592112 and parameters: {'beta': 0.024968961111384753, 'init_prev': 0.04324131752588966, 'n_contacts': 10, 'rand_seed': 548478}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,761] Trial 89 finished with value: 127.2529763877543 and parameters: {'beta': 0.2326453583393903, 'init_prev': 0.042885409981106355, 'n_contacts': 10, 'rand_seed': 33404}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,784] Trial 93 finished with value: 94.7855791178323 and parameters: {'beta': 0.02563878861182197, 'init_prev': 0.047753015261329815, 'n_contacts': 5, 'rand_seed': 42173}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,796] Trial 90 finished with value: 120.00208547903878 and parameters: {'beta': 0.02475565705408111, 'init_prev': 0.03880725801201955, 'n_contacts': 5, 'rand_seed': 38281}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,800] Trial 84 finished with value: 10.876239173827912 and parameters: {'beta': 0.025936664523625582, 'init_prev': 0.042917775015213294, 'n_contacts': 10, 'rand_seed': 165055}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,804] Trial 95 finished with value: 118.40938907258953 and parameters: {'beta': 0.025712382991296856, 'init_prev': 0.038420756604738183, 'n_contacts': 5, 'rand_seed': 491426}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,805] Trial 94 finished with value: 10.67278345771831 and parameters: {'beta': 0.026498770533806622, 'init_prev': 0.039049096425636774, 'n_contacts': 10, 'rand_seed': 39432}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,815] Trial 91 finished with value: 120.84819888790355 and parameters: {'beta': 0.0244653607936612, 'init_prev': 0.038821202470739134, 'n_contacts': 5, 'rand_seed': 45862}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,831] Trial 96 finished with value: 119.18988792613948 and parameters: {'beta': 0.02531293721776069, 'init_prev': 0.03893779229688157, 'n_contacts': 5, 'rand_seed': 458606}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,839] Trial 99 finished with value: 119.18988792613948 and parameters: {'beta': 0.02546988212942166, 'init_prev': 0.03889071437668847, 'n_contacts': 5, 'rand_seed': 467107}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,840] Trial 92 finished with value: 11.07039559034149 and parameters: {'beta': 0.026857438317141817, 'init_prev': 0.0390037559742509, 'n_contacts': 10, 'rand_seed': 444792}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,846] Trial 98 finished with value: 130.55735547164818 and parameters: {'beta': 0.10391322637894684, 'init_prev': 0.03840198479343909, 'n_contacts': 5, 'rand_seed': 415671}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,858] Trial 101 finished with value: 11.837359420735652 and parameters: {'beta': 0.026802202648908795, 'init_prev': 0.03610301503785394, 'n_contacts': 9, 'rand_seed': 456107}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,875] Trial 97 finished with value: 10.518363293546486 and parameters: {'beta': 0.025480814966709963, 'init_prev': 0.03910067862139324, 'n_contacts': 10, 'rand_seed': 473114}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,880] Trial 102 finished with value: 103.42742533879994 and parameters: {'beta': 0.16994581574699957, 'init_prev': 0.03605075102022744, 'n_contacts': 9, 'rand_seed': 485830}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,880] Trial 103 finished with value: 11.336166529018215 and parameters: {'beta': 0.031047077592668396, 'init_prev': 0.03756243168276404, 'n_contacts': 9, 'rand_seed': 431491}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,901] Trial 104 finished with value: 11.626501871617279 and parameters: {'beta': 0.027304783798220652, 'init_prev': 0.03629956668994116, 'n_contacts': 9, 'rand_seed': 924775}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,903] Trial 100 finished with value: 8.817064992009023 and parameters: {'beta': 0.029888727364717083, 'init_prev': 0.03602754614579327, 'n_contacts': 9, 'rand_seed': 466692}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,921] Trial 105 finished with value: 102.84989958351309 and parameters: {'beta': 0.11542522502972458, 'init_prev': 0.04149655648809396, 'n_contacts': 10, 'rand_seed': 577011}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,926] Trial 106 finished with value: 46.38794078408739 and parameters: {'beta': 0.01811566561254877, 'init_prev': 0.04169825527728097, 'n_contacts': 10, 'rand_seed': 586819}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,940] Trial 107 finished with value: 16.597173508290098 and parameters: {'beta': 0.031103049487183516, 'init_prev': 0.03605890120005514, 'n_contacts': 10, 'rand_seed': 578535}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,942] Trial 108 finished with value: 15.112986809854874 and parameters: {'beta': 0.029696023473114026, 'init_prev': 0.040862329216186453, 'n_contacts': 10, 'rand_seed': 590144}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:44,964] Trial 109 finished with value: 16.58917435323997 and parameters: {'beta': 0.03032406929416657, 'init_prev': 0.04198176806377586, 'n_contacts': 10, 'rand_seed': 565997}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:45,004] Trial 110 finished with value: 10.942846995467903 and parameters: {'beta': 0.028627193243528082, 'init_prev': 0.04033781766802849, 'n_contacts': 10, 'rand_seed': 593886}. Best is trial 53 with value: 8.669427305735212.\n",
+      "[I 2024-11-16 21:37:45,080] Trial 111 finished with value: 52.79770239101322 and parameters: {'beta': 0.03820150318055076, 'init_prev': 0.039878110513149684, 'n_contacts': 10, 'rand_seed': 371808}. Best is trial 53 with value: 8.669427305735212.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Making results structure...\n",
+      "Processed 112 trials; 0 failed\n",
+      "Best pars: {'beta': 0.036336486559483704, 'init_prev': 0.03628739942352108, 'n_contacts': 8, 'rand_seed': 599372}\n",
+      "Removed existing calibration file starsim_calibration.db\n"
+     ]
+    }
+   ],
    "source": [
     "sc.heading('Beginning calibration')\n",
     "\n",
@@ -209,7 +388,7 @@
     "\n",
     "    components = [infectious],\n",
     "\n",
-    "    total_trials = 1_000,\n",
+    "    total_trials = 100,\n",
     "    n_workers = None, # None indicates to use all available CPUs\n",
     "    die = True,\n",
     "    debug = debug,\n",
@@ -217,22 +396,187 @@
     "\n",
     "# Perform the calibration\n",
     "sc.printcyan('\\nPeforming calibration...')\n",
-    "calib.calibrate(confirm_fit=False)\n",
-    "\n",
+    "calib.calibrate(confirm_fit=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the best parameters that were found. Note that the `rand_seed` was selected at random, but the other parameters are meaningful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'beta': 0.036336486559483704,\n",
+       " 'init_prev': 0.03628739942352108,\n",
+       " 'n_contacts': 8,\n",
+       " 'rand_seed': 599372}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calib.best_pars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the calibration is complete, we can compare the `guess` values to the best values found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[36m\n",
+      "Confirming fit...\u001b[0m\n",
+      "\n",
+      "Confirming fit...\n",
+      "Elapsed time: 0.534 s\n",
+      "Elapsed time: 0.549 s\n",
+      "Fit with original pars: [102.88081432 103.17668444 101.76293553 101.73024849 101.42137967\n",
+      " 101.11678233 107.0659899  101.23460891  97.16017623  93.18140814\n",
+      "  96.57275981 104.53172647 100.64051879  97.88797276  98.78717759\n",
+      "  98.17993622  99.27916165 100.44520885  96.67673033 107.43027621\n",
+      "  89.47519502  82.59693358 105.32194039  98.86668559  95.6485363 ]\n",
+      "Fit with best-fit pars: [21.28778526  9.87210691 13.04061953  8.54594516 17.0739946  13.30003328\n",
+      " 13.85241925  8.66942731  8.90524631  7.94490608 11.10528388 10.93048302\n",
+      "  8.9045451  15.02814999 27.54693223 11.2977381  13.8170572   9.09956846\n",
+      " 11.8616321  11.43284868  8.37589914  7.48790625 24.09745799 16.4165328\n",
+      " 19.73485543]\n",
+      "✓ Calibration improved fit\n",
+      "Figure(933.333x700)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module randomnet to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n",
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module sir to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n",
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module sim to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 933.333x700 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module randomnet to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n",
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module sir to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n",
+      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
+      "You are adding a result from module sim to module MultiSim; check that this is intentional.\n",
+      "  ss.warn(warnmsg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Figure(933.333x700)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 933.333x700 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/dklein/GIT/starsim/starsim/calibration.py:409: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "# Confirm\n",
     "sc.printcyan('\\nConfirming fit...')\n",
     "calib.confirm_fit()\n",
-    "print(f'Fit with original pars: {calib.before_fits}')\n",
-    "print(f'Fit with best-fit pars: {calib.after_fits}')\n",
-    "if calib.after_fits.mean() <= calib.before_fits.mean():\n",
-    "    print('✓ Calibration improved fit')\n",
-    "else:\n",
-    "    print('✗ Calibration did not improve fit, but this sometimes happens stochastically and is not necessarily an error')\n",
     "\n",
-    "if do_plot:\n",
-    "    calib.plot_sims()\n",
-    "    calib.plot_trend()"
+    "calib.plot_sims()\n",
+    "calib.plot_trend()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/starsim/calibration.py b/starsim/calibration.py
index 64cb565b..b5e51c3d 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -332,8 +332,8 @@ def confirm_fit(self):
         self.after_msim.run()
         self.after_fits = np.array([self.eval_fn(sim, **self.eval_kwargs) for sim in self.after_msim.sims])
 
-        print(f'Fit with original pars: {self.before_fits.mean()}')
-        print(f'Fit with best-fit pars: {self.after_fits.mean()}')
+        print(f'Fit with original pars: {self.before_fits}')
+        print(f'Fit with best-fit pars: {self.after_fits}')
         if self.after_fits.mean() <= self.before_fits.mean():
             print('✓ Calibration improved fit')
         else:
@@ -405,14 +405,8 @@ def plot_sims(self, **kwargs):
 
         self.after_msim.reduce()
         self.after_msim.plot(fig=fig)#, label='After calibration')
-
-        plt.legend()
-
+        fig.legend()
         return fig
-        #msim = ss.MultiSim([self.before_msim, self.after_msim])
-        #fig = msim.plot(**kwargs)
-        #plt.legend()
-        #return ss.return_fig(fig)
 
     def plot_trend(self, best_thresh=None, fig_kw=None):
         """

From 7eaa99ad32b85c94d2193a96f9f9694035b0573b Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Sat, 16 Nov 2024 21:56:17 -0800
Subject: [PATCH 20/28] Adding Gamma Poisson likelihood

---
 starsim/calibration.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 64cb565b..0c4315f5 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -459,6 +459,7 @@ class eConform(Enum):
 
 class eLikelihood(Enum):
     BETA_BINOMIAL = 0
+    GAMMA_POISSON = 1
 
 class CalibComponent(sc.prettyobj):
     """
@@ -484,7 +485,9 @@ def __init__(self, name, real_data, sim_data_fn, conform, nll_fn, weight=1):
 
         if isinstance(nll_fn, eLikelihood):
             if nll_fn == eLikelihood.BETA_BINOMIAL:
-                self.nll_fn = self.beta_binomial # Actually negative log-likelihood
+                self.nll_fn = self.beta_binomial
+            elif nll_fn == eLikelihood.GAMMA_POISSON:
+                self.nll_fn = self.gamma_poisson
         else:
             if not callable(conform):
                 msg = f'The nll_fn argument must be an eLikelihood or callable function, not {type(nll_fn)}.'
@@ -526,6 +529,25 @@ def beta_binomial(real_data, sim_data):
 
         return -logL
 
+    @staticmethod
+    def gamma_poisson(real_data, sim_data):
+        # Also called negative binomial, but parameterized differently
+        # The gamma-poisson likelihood is a Poisson likelihood with a gamma-distributed rate parameter
+        #
+
+        logL = gammaln(real_data['x'] + sim_data['x'] + 1) \
+            - gammaln(real_data['x'] + 1) \
+            - gammaln(sim_data['x'] + 1)
+
+        logL += (real_data['x'] + 1) * np.log(real_data['n'])
+
+        logL += (sim_data['x'] + 1) * np.log(sim_data['n'])
+
+        logL -= (real_data['x'] + sim_data['x'] + 1) \
+                  * np.log(real_data['n'] + sim_data['n'])
+
+        return -logL
+
     @staticmethod
     def linear_interp(real_data, sim_data):
         """

From de626cdd022ee2c84625d20d23076dcba5a46e97 Mon Sep 17 00:00:00 2001
From: Daniel Klein <daniel.klein@gatesfoundation.org>
Date: Sun, 17 Nov 2024 09:36:50 -0800
Subject: [PATCH 21/28] Minor text and plotting improvements.

---
 docs/tutorials/tut_calibration.ipynb | 363 ++++-----------------------
 starsim/calibration.py               |  27 +-
 2 files changed, 60 insertions(+), 330 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 4b5a24cc..50cc7d17 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -40,18 +40,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/dklein/miniforge3/envs/py312/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#%% Imports and settings\n",
     "import sciris as sc\n",
@@ -71,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,11 +162,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Recall that an optimization-based approach to calibration minimizes a function of the input parameters. We compose the goodness-of-fit function using \"components.\" Each component has real data, for example from a survey, that is compared against simulation data from the model. Several components and be used at the same time. Each computes a likelihood of the data given the input parameters, as assess via simulation. Components are assumed to be independent.\n",
+    "The Starsim framework has been integrated with the Optuna hyperparameter optimization algorithm to facilitate calibration through the `Calibration` class. Recall that an optimization-based approach to calibration minimizes a function of the input parameters. This function is key to achieving an acceptable calibration.\n",
     "\n",
-    "When defining a `CalibComponent`, we give it a `name` and pass in `real_data`. The required data fields depend on the likelihood function. Importantly, the functional form of the negative log likelihood, or nll, is defined by the `nll_fn`. The value for `nll_fn` can be any value of the eLikelihood enumeration, like `BETA_BINOMIAL`, or a negative log likelihood function of your creation. If designing your own function for `nll_fn`, it should take two arguments: `real_data` and `sim_data`. For a Beta binomial, the data must define `n` and `x`, where `n` is the number of individuals that were sampled and `x` is the number that were found, e.g. identified as positive.\n",
+    "There are two ways to describe the goodness-of-fit function for the `Calibration`. The first method is to directly provide a function that the algorithm will call. The `eval_fn` will be passed each completed `sim` after running, and is expected to return a float representing the goodness of fit (higher is better). Data can be passed into the `eval_fn` via `eval_kwargs`.\n",
     "\n",
-    "Output from the simulation is obtained via a function. The function takes a completed `sim` object as input and returns a dictionary with fields as required for the `nll_fn` function. In the example below, we use an in-line lambda function. Like the `real_data`, the `sim_data_fn` for a Beta binomial requires `n` and `x`.\n",
+    "As an alternative to directly specifying the evaluation function, you can use `CalibComponent`s. Each component includes real data, for example from a survey, that is compared against simulation data from the model. Several components and be used at the same time, for example one for disease prevalence and another for treatment coverage. Each component computes a likelihood of the data given the input parameters, as assess via simulation. Components are combined assuming independence.\n",
+    "\n",
+    "When defining a `CalibComponent`, we give it a `name` and pass in `real_data`. The required data fields depend on the likelihood function. Importantly, the functional form of the negative log likelihood, or nll, is defined by the `nll_fn`. The value for `nll_fn` can be any value of the `eLikelihood` enumeration, like `BETA_BINOMIAL`, or a negative log likelihood function of your own creation. If designing your own function for `nll_fn`, it should take two arguments: `real_data` and `sim_data`. For a Beta binomial, the data must define `n` and `x`, where `n` is the number of individuals that were sampled and `x` is the number that were found, e.g. identified as positive.\n",
+    "\n",
+    "Output from the simulation is obtained via a function. The function takes a completed `sim` object as input and returns a dictionary with fields as required for the evaluation function of your choice. In the example below, we use an in-line lambda function to extract `n` and `x` from the simulation, as required by the Beta binomial component.\n",
     "\n",
     "Each component has a `weight`. The final goodness of fit is a weighted sum of negative log likelihoods.\n",
     "\n",
@@ -184,29 +179,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "infectious = ss.CalibComponent(\n",
     "    name = 'Infectious',\n",
     "\n",
-    "    # \"real_data\" actually from a simulation with pars\n",
+    "    # For this example, the \"real_data\" comes from a simulation with pars\n",
     "    #   beta=0.075, init_prev=0.02, n_contacts=4\n",
     "    real_data = pd.DataFrame({\n",
     "        'n': [200, 197, 195], # Number of individuals sampled\n",
     "        'x': [30, 30, 10],    # Number of individuals found to be infectious\n",
     "    }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates\n",
-    "    \n",
+    "\n",
     "    sim_data_fn = lambda sim: pd.DataFrame({\n",
-    "        'n': sim.results.n_alive,\n",
-    "        'x': sim.results.sir.n_infected,\n",
-    "    }, index=pd.Index(sim.results.timevec, name='t')),\n",
+    "        'n': sim.results.n_alive, # Number of individuals sampled\n",
+    "        'x': sim.results.sir.n_infected, # Number of individuals found to be infectious\n",
+    "    }, index=pd.Index(sim.results.timevec, name='t')), # Index is time\n",
     "\n",
     "    conform = ss.eConform.PREVALENT,\n",
     "    nll_fn = ss.eLikelihood.BETA_BINOMIAL,\n",
     "\n",
-    "    weight = 1,\n",
+    "    weight = 1, # Not required if only one component\n",
     ")"
    ]
   },
@@ -223,155 +218,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[36m\n",
-      "\n",
-      "—————————————————————\n",
-      "Beginning calibration\n",
-      "—————————————————————\n",
-      "\u001b[0m\n",
-      "\u001b[36m\n",
-      "Peforming calibration...\u001b[0m\n",
-      "Removed existing calibration file starsim_calibration.db\n",
-      "sqlite:///starsim_calibration.db\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-11-16 21:37:43,528] A new study created in RDB with name: starsim_calibration\n",
-      "[I 2024-11-16 21:37:43,863] Trial 2 finished with value: 107.30873053298149 and parameters: {'beta': 0.287649229557548, 'init_prev': 0.01459633116681542, 'n_contacts': 8, 'rand_seed': 786992}. Best is trial 2 with value: 107.30873053298149.\n",
-      "[I 2024-11-16 21:37:43,903] Trial 1 finished with value: 141.71274040920855 and parameters: {'beta': 0.016131876512529307, 'init_prev': 0.03095733037020489, 'n_contacts': 6, 'rand_seed': 576498}. Best is trial 2 with value: 107.30873053298149.\n",
-      "[I 2024-11-16 21:37:43,905] Trial 7 finished with value: 115.95181064486735 and parameters: {'beta': 0.012305576988698542, 'init_prev': 0.03462649160173581, 'n_contacts': 9, 'rand_seed': 150095}. Best is trial 2 with value: 107.30873053298149.\n",
-      "[I 2024-11-16 21:37:43,937] Trial 5 finished with value: 85.2177244223318 and parameters: {'beta': 0.04392452421102194, 'init_prev': 0.03539336109748194, 'n_contacts': 3, 'rand_seed': 860382}. Best is trial 5 with value: 85.2177244223318.\n",
-      "[I 2024-11-16 21:37:43,943] Trial 4 finished with value: 141.4032426402198 and parameters: {'beta': 0.10963499388291496, 'init_prev': 0.03448486835886308, 'n_contacts': 8, 'rand_seed': 795307}. Best is trial 5 with value: 85.2177244223318.\n",
-      "[I 2024-11-16 21:37:43,946] Trial 12 finished with value: 161.00743770722488 and parameters: {'beta': 0.21478157849320906, 'init_prev': 0.014787577120824387, 'n_contacts': 5, 'rand_seed': 299437}. Best is trial 5 with value: 85.2177244223318.\n",
-      "[I 2024-11-16 21:37:43,956] Trial 15 finished with value: 167.1689297427771 and parameters: {'beta': 0.010781806464073448, 'init_prev': 0.031988405042653455, 'n_contacts': 3, 'rand_seed': 462562}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,957] Trial 11 finished with value: 11.274790395672198 and parameters: {'beta': 0.15424596603654206, 'init_prev': 0.04235611504022485, 'n_contacts': 2, 'rand_seed': 57778}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,969] Trial 8 finished with value: 24.248931515507707 and parameters: {'beta': 0.07449241390265154, 'init_prev': 0.04632856000297287, 'n_contacts': 3, 'rand_seed': 358196}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,974] Trial 14 finished with value: 13.793599248582382 and parameters: {'beta': 0.0813407183574054, 'init_prev': 0.02755289202257546, 'n_contacts': 3, 'rand_seed': 119810}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,981] Trial 9 finished with value: 10.740445158890566 and parameters: {'beta': 0.029326423384490093, 'init_prev': 0.033352301494229825, 'n_contacts': 10, 'rand_seed': 142129}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,985] Trial 6 finished with value: 105.75928870031805 and parameters: {'beta': 0.02885125074711628, 'init_prev': 0.021422368191646127, 'n_contacts': 5, 'rand_seed': 977498}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:43,995] Trial 10 finished with value: 127.75817726084074 and parameters: {'beta': 0.17569583999951785, 'init_prev': 0.023040758393914024, 'n_contacts': 6, 'rand_seed': 246148}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,005] Trial 13 finished with value: 143.48354567052831 and parameters: {'beta': 0.011558791210643644, 'init_prev': 0.04307378005123698, 'n_contacts': 8, 'rand_seed': 531030}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,010] Trial 16 finished with value: 109.4864810805908 and parameters: {'beta': 0.19583827567604442, 'init_prev': 0.025276824412756804, 'n_contacts': 10, 'rand_seed': 35747}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,010] Trial 18 finished with value: 30.614211395920165 and parameters: {'beta': 0.13020052150000808, 'init_prev': 0.01461961770819753, 'n_contacts': 3, 'rand_seed': 169282}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,029] Trial 0 finished with value: 153.44036639517492 and parameters: {'beta': 0.01481688380683361, 'init_prev': 0.02350258323813991, 'n_contacts': 5, 'rand_seed': 539193}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,034] Trial 17 finished with value: 117.56518839114926 and parameters: {'beta': 0.29643533691386287, 'init_prev': 0.02899021764520581, 'n_contacts': 7, 'rand_seed': 405144}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,048] Trial 19 finished with value: 75.95675162025566 and parameters: {'beta': 0.016609043731855854, 'init_prev': 0.019080559494204365, 'n_contacts': 9, 'rand_seed': 447055}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,048] Trial 3 finished with value: 164.82089971605512 and parameters: {'beta': 0.01135815148799859, 'init_prev': 0.0253449834811098, 'n_contacts': 4, 'rand_seed': 297913}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,077] Trial 24 finished with value: 12.079318129832814 and parameters: {'beta': 0.028595589249682203, 'init_prev': 0.04487650284870446, 'n_contacts': 10, 'rand_seed': 1414}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,099] Trial 23 finished with value: 15.395707324162345 and parameters: {'beta': 0.028810351060067654, 'init_prev': 0.04372465728229667, 'n_contacts': 10, 'rand_seed': 20492}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,107] Trial 21 finished with value: 103.38286597867284 and parameters: {'beta': 0.14664293718672586, 'init_prev': 0.046407651006863775, 'n_contacts': 10, 'rand_seed': 105406}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,109] Trial 26 finished with value: 162.73080227118282 and parameters: {'beta': 0.028646024044356105, 'init_prev': 0.042339141673771764, 'n_contacts': 2, 'rand_seed': 74531}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,112] Trial 27 finished with value: 22.184885441704637 and parameters: {'beta': 0.03144626830848546, 'init_prev': 0.04064091250085091, 'n_contacts': 10, 'rand_seed': 4944}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,115] Trial 22 finished with value: 12.262457760569873 and parameters: {'beta': 0.02850146546662436, 'init_prev': 0.04438139452126155, 'n_contacts': 10, 'rand_seed': 27467}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,121] Trial 31 finished with value: 162.73080227118282 and parameters: {'beta': 0.028417220128461814, 'init_prev': 0.041257449184238806, 'n_contacts': 2, 'rand_seed': 6884}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,122] Trial 29 finished with value: 167.13709732073278 and parameters: {'beta': 0.024310974004789525, 'init_prev': 0.04065352400298506, 'n_contacts': 2, 'rand_seed': 48083}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,149] Trial 33 finished with value: 83.06035415674944 and parameters: {'beta': 0.07382601249314252, 'init_prev': 0.04019273248689236, 'n_contacts': 2, 'rand_seed': 10324}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,168] Trial 34 finished with value: 70.76338318565524 and parameters: {'beta': 0.08066917648627793, 'init_prev': 0.039899025250897724, 'n_contacts': 2, 'rand_seed': 2825}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,183] Trial 20 finished with value: 32.089930843958996 and parameters: {'beta': 0.01905966723421134, 'init_prev': 0.0420857622990075, 'n_contacts': 10, 'rand_seed': 41185}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,190] Trial 35 finished with value: 70.76338318565524 and parameters: {'beta': 0.08041537274195945, 'init_prev': 0.03997403827295326, 'n_contacts': 2, 'rand_seed': 821}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,190] Trial 30 finished with value: 164.82089971605512 and parameters: {'beta': 0.02721320675346273, 'init_prev': 0.04129427168152436, 'n_contacts': 2, 'rand_seed': 27035}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,198] Trial 36 finished with value: 10.964537464703085 and parameters: {'beta': 0.027829741985744366, 'init_prev': 0.04011257513821645, 'n_contacts': 10, 'rand_seed': 27044}. Best is trial 9 with value: 10.740445158890566.\n",
-      "[I 2024-11-16 21:37:44,214] Trial 28 finished with value: 10.182025720756997 and parameters: {'beta': 0.027747520761274944, 'init_prev': 0.039512448108991745, 'n_contacts': 10, 'rand_seed': 5112}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,217] Trial 32 finished with value: 115.70891322491173 and parameters: {'beta': 0.05945414963129977, 'init_prev': 0.04129525599059517, 'n_contacts': 2, 'rand_seed': 154}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,223] Trial 37 finished with value: 45.89697968481846 and parameters: {'beta': 0.041694160307547315, 'init_prev': 0.03864291169320181, 'n_contacts': 9, 'rand_seed': 205798}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,237] Trial 40 finished with value: 85.62233878240909 and parameters: {'beta': 0.0477217348149753, 'init_prev': 0.04892148557563158, 'n_contacts': 9, 'rand_seed': 213495}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,238] Trial 39 finished with value: 60.2276818782791 and parameters: {'beta': 0.04585997885121268, 'init_prev': 0.0381185580542134, 'n_contacts': 9, 'rand_seed': 192829}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,238] Trial 43 finished with value: 111.60699261972763 and parameters: {'beta': 0.05467495594617335, 'init_prev': 0.03731338398960968, 'n_contacts': 9, 'rand_seed': 204931}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,276] Trial 42 finished with value: 46.415029191358144 and parameters: {'beta': 0.04181007421545325, 'init_prev': 0.03681108492775422, 'n_contacts': 9, 'rand_seed': 207399}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,276] Trial 41 finished with value: 73.94995937945873 and parameters: {'beta': 0.0446154856694533, 'init_prev': 0.04996558005938069, 'n_contacts': 9, 'rand_seed': 196099}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,287] Trial 25 finished with value: 15.292149813431593 and parameters: {'beta': 0.028968625417551172, 'init_prev': 0.044878282635886224, 'n_contacts': 10, 'rand_seed': 61013}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,297] Trial 45 finished with value: 62.825141595206105 and parameters: {'beta': 0.04250945809634435, 'init_prev': 0.04946189211688657, 'n_contacts': 9, 'rand_seed': 209682}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,316] Trial 38 finished with value: 129.25762907255444 and parameters: {'beta': 0.059448007114808285, 'init_prev': 0.04925496738535662, 'n_contacts': 9, 'rand_seed': 243572}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,329] Trial 48 finished with value: 73.90148866518825 and parameters: {'beta': 0.04440412706115726, 'init_prev': 0.048798446675135145, 'n_contacts': 9, 'rand_seed': 209619}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,331] Trial 49 finished with value: 45.698146174701606 and parameters: {'beta': 0.04250583649572974, 'init_prev': 0.036684112254986995, 'n_contacts': 9, 'rand_seed': 621640}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,342] Trial 50 finished with value: 26.743431673640544 and parameters: {'beta': 0.04157706933561373, 'init_prev': 0.04992858693961394, 'n_contacts': 7, 'rand_seed': 659651}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,348] Trial 52 finished with value: 84.07503615644293 and parameters: {'beta': 0.037156229195977684, 'init_prev': 0.03260309486400948, 'n_contacts': 4, 'rand_seed': 689364}. Best is trial 28 with value: 10.182025720756997.\n",
-      "[I 2024-11-16 21:37:44,354] Trial 53 finished with value: 8.669427305735212 and parameters: {'beta': 0.036336486559483704, 'init_prev': 0.03628739942352108, 'n_contacts': 8, 'rand_seed': 599372}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,391] Trial 51 finished with value: 64.9909820586023 and parameters: {'beta': 0.0210798597941547, 'init_prev': 0.03254032152013679, 'n_contacts': 8, 'rand_seed': 117188}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,396] Trial 47 finished with value: 53.166391709701884 and parameters: {'beta': 0.04014414561140875, 'init_prev': 0.049782809844158414, 'n_contacts': 9, 'rand_seed': 219720}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,410] Trial 56 finished with value: 67.76372462174243 and parameters: {'beta': 0.02108306705681977, 'init_prev': 0.03330398875316214, 'n_contacts': 7, 'rand_seed': 651963}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,411] Trial 44 finished with value: 69.10983038270274 and parameters: {'beta': 0.04489768401651496, 'init_prev': 0.049803258145886584, 'n_contacts': 9, 'rand_seed': 201940}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,422] Trial 46 finished with value: 80.27784123448384 and parameters: {'beta': 0.02021443161195256, 'init_prev': 0.03238930129275948, 'n_contacts': 7, 'rand_seed': 658066}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,424] Trial 58 finished with value: 72.60977726982367 and parameters: {'beta': 0.022308675733040552, 'init_prev': 0.031465309920818044, 'n_contacts': 7, 'rand_seed': 636166}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,437] Trial 59 finished with value: 64.45355099338178 and parameters: {'beta': 0.02165148798399229, 'init_prev': 0.032345096280218616, 'n_contacts': 8, 'rand_seed': 115142}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,449] Trial 55 finished with value: 77.26910665397065 and parameters: {'beta': 0.02144114630425907, 'init_prev': 0.03296934619918316, 'n_contacts': 7, 'rand_seed': 108559}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,457] Trial 60 finished with value: 132.61506686052837 and parameters: {'beta': 0.022962834395949573, 'init_prev': 0.03434964231059928, 'n_contacts': 4, 'rand_seed': 116312}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,462] Trial 61 finished with value: 64.7052972361513 and parameters: {'beta': 0.02081243546109354, 'init_prev': 0.03350615905935427, 'n_contacts': 8, 'rand_seed': 122014}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,471] Trial 57 finished with value: 73.35016687878363 and parameters: {'beta': 0.02173885910268392, 'init_prev': 0.032561115968953225, 'n_contacts': 7, 'rand_seed': 633090}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,495] Trial 64 finished with value: 78.23365806680215 and parameters: {'beta': 0.019434238633552255, 'init_prev': 0.03419532312315708, 'n_contacts': 8, 'rand_seed': 122228}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,496] Trial 63 finished with value: 58.65329552030437 and parameters: {'beta': 0.022540468605080306, 'init_prev': 0.03408513569420744, 'n_contacts': 8, 'rand_seed': 122706}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,517] Trial 62 finished with value: 63.929063254240305 and parameters: {'beta': 0.02183074304580968, 'init_prev': 0.03314924410716158, 'n_contacts': 8, 'rand_seed': 109302}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,535] Trial 65 finished with value: 14.004756804448903 and parameters: {'beta': 0.03447837266510164, 'init_prev': 0.03426800420636183, 'n_contacts': 7, 'rand_seed': 110175}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,538] Trial 70 finished with value: 30.841045617808618 and parameters: {'beta': 0.03472908476125748, 'init_prev': 0.035091324960792536, 'n_contacts': 6, 'rand_seed': 317590}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,552] Trial 66 finished with value: 11.52069994121723 and parameters: {'beta': 0.03548939976284397, 'init_prev': 0.035219365985743586, 'n_contacts': 7, 'rand_seed': 96842}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,559] Trial 73 finished with value: 32.23200258925749 and parameters: {'beta': 0.033129773943628704, 'init_prev': 0.04530997552081485, 'n_contacts': 10, 'rand_seed': 62676}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,561] Trial 69 finished with value: 157.6369759673313 and parameters: {'beta': 0.013671833200131898, 'init_prev': 0.029614509161837742, 'n_contacts': 6, 'rand_seed': 95826}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,564] Trial 67 finished with value: 47.37048323088368 and parameters: {'beta': 0.03315665213467724, 'init_prev': 0.0349527254983216, 'n_contacts': 6, 'rand_seed': 94727}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,575] Trial 71 finished with value: 143.54604098115908 and parameters: {'beta': 0.016055810599959364, 'init_prev': 0.029418003426029814, 'n_contacts': 6, 'rand_seed': 298839}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,590] Trial 76 finished with value: 45.591426306323 and parameters: {'beta': 0.034949335178451384, 'init_prev': 0.04450989930492606, 'n_contacts': 10, 'rand_seed': 73739}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,596] Trial 75 finished with value: 35.865739872788936 and parameters: {'beta': 0.03406030354813161, 'init_prev': 0.04630751778929916, 'n_contacts': 10, 'rand_seed': 73367}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,601] Trial 68 finished with value: 31.2190548512159 and parameters: {'beta': 0.03470805669285768, 'init_prev': 0.03463638774142812, 'n_contacts': 6, 'rand_seed': 88980}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,637] Trial 77 finished with value: 54.96435323775643 and parameters: {'beta': 0.016289921602386955, 'init_prev': 0.045018042809617964, 'n_contacts': 10, 'rand_seed': 524051}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,640] Trial 80 finished with value: 80.53387138953497 and parameters: {'beta': 0.013982987953245481, 'init_prev': 0.046078960122572565, 'n_contacts': 10, 'rand_seed': 159022}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,647] Trial 54 finished with value: 64.9909820586023 and parameters: {'beta': 0.021206285122736198, 'init_prev': 0.03205791664546167, 'n_contacts': 8, 'rand_seed': 617060}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,656] Trial 78 finished with value: 133.74224774808556 and parameters: {'beta': 0.2488054995091254, 'init_prev': 0.044900002130169224, 'n_contacts': 10, 'rand_seed': 538357}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,660] Trial 72 finished with value: 37.480776662929884 and parameters: {'beta': 0.03375664451553922, 'init_prev': 0.04625750747727689, 'n_contacts': 10, 'rand_seed': 338210}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,682] Trial 74 finished with value: 66.61003065309035 and parameters: {'beta': 0.016029361966139983, 'init_prev': 0.02949970297986432, 'n_contacts': 10, 'rand_seed': 549696}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,682] Trial 81 finished with value: 10.748501102658906 and parameters: {'beta': 0.025648357344235356, 'init_prev': 0.043662882795621534, 'n_contacts': 10, 'rand_seed': 543600}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,693] Trial 82 finished with value: 135.43289637051805 and parameters: {'beta': 0.25393530988710483, 'init_prev': 0.04327271866699147, 'n_contacts': 10, 'rand_seed': 164971}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,698] Trial 85 finished with value: 11.569354489664534 and parameters: {'beta': 0.025140144150853, 'init_prev': 0.04345651883695237, 'n_contacts': 10, 'rand_seed': 165388}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,706] Trial 83 finished with value: 132.17356571888797 and parameters: {'beta': 0.242519746876748, 'init_prev': 0.042995088720121095, 'n_contacts': 10, 'rand_seed': 744397}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,714] Trial 86 finished with value: 10.551486202267029 and parameters: {'beta': 0.02601053854500832, 'init_prev': 0.03919199826806074, 'n_contacts': 10, 'rand_seed': 152291}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,717] Trial 79 finished with value: 48.69258693568611 and parameters: {'beta': 0.016924314062392063, 'init_prev': 0.029154712270699153, 'n_contacts': 10, 'rand_seed': 530800}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,741] Trial 88 finished with value: 119.18988792613948 and parameters: {'beta': 0.025361542737863386, 'init_prev': 0.0392008605076073, 'n_contacts': 5, 'rand_seed': 35918}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,745] Trial 87 finished with value: 10.838879429592112 and parameters: {'beta': 0.024968961111384753, 'init_prev': 0.04324131752588966, 'n_contacts': 10, 'rand_seed': 548478}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,761] Trial 89 finished with value: 127.2529763877543 and parameters: {'beta': 0.2326453583393903, 'init_prev': 0.042885409981106355, 'n_contacts': 10, 'rand_seed': 33404}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,784] Trial 93 finished with value: 94.7855791178323 and parameters: {'beta': 0.02563878861182197, 'init_prev': 0.047753015261329815, 'n_contacts': 5, 'rand_seed': 42173}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,796] Trial 90 finished with value: 120.00208547903878 and parameters: {'beta': 0.02475565705408111, 'init_prev': 0.03880725801201955, 'n_contacts': 5, 'rand_seed': 38281}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,800] Trial 84 finished with value: 10.876239173827912 and parameters: {'beta': 0.025936664523625582, 'init_prev': 0.042917775015213294, 'n_contacts': 10, 'rand_seed': 165055}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,804] Trial 95 finished with value: 118.40938907258953 and parameters: {'beta': 0.025712382991296856, 'init_prev': 0.038420756604738183, 'n_contacts': 5, 'rand_seed': 491426}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,805] Trial 94 finished with value: 10.67278345771831 and parameters: {'beta': 0.026498770533806622, 'init_prev': 0.039049096425636774, 'n_contacts': 10, 'rand_seed': 39432}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,815] Trial 91 finished with value: 120.84819888790355 and parameters: {'beta': 0.0244653607936612, 'init_prev': 0.038821202470739134, 'n_contacts': 5, 'rand_seed': 45862}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,831] Trial 96 finished with value: 119.18988792613948 and parameters: {'beta': 0.02531293721776069, 'init_prev': 0.03893779229688157, 'n_contacts': 5, 'rand_seed': 458606}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,839] Trial 99 finished with value: 119.18988792613948 and parameters: {'beta': 0.02546988212942166, 'init_prev': 0.03889071437668847, 'n_contacts': 5, 'rand_seed': 467107}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,840] Trial 92 finished with value: 11.07039559034149 and parameters: {'beta': 0.026857438317141817, 'init_prev': 0.0390037559742509, 'n_contacts': 10, 'rand_seed': 444792}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,846] Trial 98 finished with value: 130.55735547164818 and parameters: {'beta': 0.10391322637894684, 'init_prev': 0.03840198479343909, 'n_contacts': 5, 'rand_seed': 415671}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,858] Trial 101 finished with value: 11.837359420735652 and parameters: {'beta': 0.026802202648908795, 'init_prev': 0.03610301503785394, 'n_contacts': 9, 'rand_seed': 456107}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,875] Trial 97 finished with value: 10.518363293546486 and parameters: {'beta': 0.025480814966709963, 'init_prev': 0.03910067862139324, 'n_contacts': 10, 'rand_seed': 473114}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,880] Trial 102 finished with value: 103.42742533879994 and parameters: {'beta': 0.16994581574699957, 'init_prev': 0.03605075102022744, 'n_contacts': 9, 'rand_seed': 485830}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,880] Trial 103 finished with value: 11.336166529018215 and parameters: {'beta': 0.031047077592668396, 'init_prev': 0.03756243168276404, 'n_contacts': 9, 'rand_seed': 431491}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,901] Trial 104 finished with value: 11.626501871617279 and parameters: {'beta': 0.027304783798220652, 'init_prev': 0.03629956668994116, 'n_contacts': 9, 'rand_seed': 924775}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,903] Trial 100 finished with value: 8.817064992009023 and parameters: {'beta': 0.029888727364717083, 'init_prev': 0.03602754614579327, 'n_contacts': 9, 'rand_seed': 466692}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,921] Trial 105 finished with value: 102.84989958351309 and parameters: {'beta': 0.11542522502972458, 'init_prev': 0.04149655648809396, 'n_contacts': 10, 'rand_seed': 577011}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,926] Trial 106 finished with value: 46.38794078408739 and parameters: {'beta': 0.01811566561254877, 'init_prev': 0.04169825527728097, 'n_contacts': 10, 'rand_seed': 586819}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,940] Trial 107 finished with value: 16.597173508290098 and parameters: {'beta': 0.031103049487183516, 'init_prev': 0.03605890120005514, 'n_contacts': 10, 'rand_seed': 578535}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,942] Trial 108 finished with value: 15.112986809854874 and parameters: {'beta': 0.029696023473114026, 'init_prev': 0.040862329216186453, 'n_contacts': 10, 'rand_seed': 590144}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:44,964] Trial 109 finished with value: 16.58917435323997 and parameters: {'beta': 0.03032406929416657, 'init_prev': 0.04198176806377586, 'n_contacts': 10, 'rand_seed': 565997}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:45,004] Trial 110 finished with value: 10.942846995467903 and parameters: {'beta': 0.028627193243528082, 'init_prev': 0.04033781766802849, 'n_contacts': 10, 'rand_seed': 593886}. Best is trial 53 with value: 8.669427305735212.\n",
-      "[I 2024-11-16 21:37:45,080] Trial 111 finished with value: 52.79770239101322 and parameters: {'beta': 0.03820150318055076, 'init_prev': 0.039878110513149684, 'n_contacts': 10, 'rand_seed': 371808}. Best is trial 53 with value: 8.669427305735212.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Making results structure...\n",
-      "Processed 112 trials; 0 failed\n",
-      "Best pars: {'beta': 0.036336486559483704, 'init_prev': 0.03628739942352108, 'n_contacts': 8, 'rand_seed': 599372}\n",
-      "Removed existing calibration file starsim_calibration.db\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sc.heading('Beginning calibration')\n",
     "\n",
@@ -408,23 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'beta': 0.036336486559483704,\n",
-       " 'init_prev': 0.03628739942352108,\n",
-       " 'n_contacts': 8,\n",
-       " 'rand_seed': 599372}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "calib.best_pars"
    ]
@@ -433,142 +268,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the calibration is complete, we can compare the `guess` values to the best values found."
+    "Once the calibration is complete, we can compare the `guess` values to the best values found by calling `confirm_fit`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[36m\n",
-      "Confirming fit...\u001b[0m\n",
-      "\n",
-      "Confirming fit...\n",
-      "Elapsed time: 0.534 s\n",
-      "Elapsed time: 0.549 s\n",
-      "Fit with original pars: [102.88081432 103.17668444 101.76293553 101.73024849 101.42137967\n",
-      " 101.11678233 107.0659899  101.23460891  97.16017623  93.18140814\n",
-      "  96.57275981 104.53172647 100.64051879  97.88797276  98.78717759\n",
-      "  98.17993622  99.27916165 100.44520885  96.67673033 107.43027621\n",
-      "  89.47519502  82.59693358 105.32194039  98.86668559  95.6485363 ]\n",
-      "Fit with best-fit pars: [21.28778526  9.87210691 13.04061953  8.54594516 17.0739946  13.30003328\n",
-      " 13.85241925  8.66942731  8.90524631  7.94490608 11.10528388 10.93048302\n",
-      "  8.9045451  15.02814999 27.54693223 11.2977381  13.8170572   9.09956846\n",
-      " 11.8616321  11.43284868  8.37589914  7.48790625 24.09745799 16.4165328\n",
-      " 19.73485543]\n",
-      "✓ Calibration improved fit\n",
-      "Figure(933.333x700)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module randomnet to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n",
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module sir to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n",
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module sim to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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I119/PS677LLBrBsAGIfyeqmouLg45s6dG01NTX1jvb290dTUFDU1NWfMv/LKK+PVV1+NlpaWvtunP/3puPnmm6OlpcV7VwCAvOT9UlF9fX0sXbo05s2bF/Pnz4/169dHZ2dnLFu2LCIilixZEjNnzozGxsYoKSmJq666qt/xU6ZMiYg4YxwA4L3kHS6LFi2Ko0ePxpo1a6K1tTXmzJkTO3bs6HvD7qFDh6Kw0BfyAgBDryDLsmy0F/FeOjo6oqysLNrb26O0tHS0lwMAnIfheP52aQQASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQIFwAgGcIFAEiGcAEAkiFcAIBkCBcAIBnCBQBIhnABAJIhXACAZAgXACAZwgUASIZwAQCSIVwAgGQMKlw2btwYs2bNipKSkqiuro5du3adde7mzZvjxhtvjKlTp8bUqVOjtrb2nPMBAM4m73DZunVr1NfXR0NDQ+zZsydmz54ddXV1ceTIkQHn79y5M2677bb46U9/Gs3NzVFVVRWf+tSn4te//vX7XjwAML4UZFmW5XNAdXV1XHfddbFhw4aIiOjt7Y2qqqq49957Y+XKle95fE9PT0ydOjU2bNgQS5YsGXBOV1dXdHV19f3c0dERVVVV0d7eHqWlpfksFwAYJR0dHVFWVjakz995XXHp7u6O3bt3R21t7R/voLAwamtro7m5+bzu4+TJk3Hq1Kn44Ac/eNY5jY2NUVZW1nerqqrKZ5kAwBiVV7gcO3Ysenp6ory8vN94eXl5tLa2ntd93H///TFjxox+8fOnVq1aFe3t7X23w4cP57NMAGCMmjCSJ1u3bl1s2bIldu7cGSUlJWedl8vlIpfLjeDKAIAU5BUu06ZNi6Kiomhra+s33tbWFhUVFec89tFHH41169bFT37yk7jmmmvyXykAMO7l9VJRcXFxzJ07N5qamvrGent7o6mpKWpqas563COPPBIPP/xw7NixI+bNmzf41QIA41reLxXV19fH0qVLY968eTF//vxYv359dHZ2xrJlyyIiYsmSJTFz5sxobGyMiIh//ud/jjVr1sT3v//9mDVrVt97YT7wgQ/EBz7wgSF8KADAWJd3uCxatCiOHj0aa9asidbW1pgzZ07s2LGj7w27hw4disLCP17I+fa3vx3d3d3x2c9+tt/9NDQ0xFe/+tX3t3oAYFzJ+3tcRsNwfA4cABheo/49LgAAo0m4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMkQLgBAMoQLAJAM4QIAJEO4AADJEC4AQDIGFS4bN26MWbNmRUlJSVRXV8euXbvOOf/ZZ5+NK6+8MkpKSuLqq6+O7du3D2qxAMD4lne4bN26Nerr66OhoSH27NkTs2fPjrq6ujhy5MiA81955ZW47bbb4o477oi9e/fGwoULY+HChfHLX/7yfS8eABhfCrIsy/I5oLq6Oq677rrYsGFDRET09vZGVVVV3HvvvbFy5coz5i9atCg6Ozvjxz/+cd/YX/7lX8acOXNi06ZNA56jq6srurq6+n5ub2+PSy65JA4fPhylpaX5LBcAGCUdHR1RVVUVx48fj7KysiG5zwn5TO7u7o7du3fHqlWr+sYKCwujtrY2mpubBzymubk56uvr+43V1dXFD3/4w7Oep7GxMR566KEzxquqqvJZLgBwAfjv//7v0QmXY8eORU9PT5SXl/cbLy8vj/379w94TGtr64DzW1tbz3qeVatW9Yud48ePx4c+9KE4dOjQkD1wBufdenb1a/TZiwuHvbiw2I8Lx7uvmHzwgx8csvvMK1xGSi6Xi1wud8Z4WVmZ/wgvEKWlpfbiAmEvLhz24sJiPy4chYVD9yHmvO5p2rRpUVRUFG1tbf3G29raoqKiYsBjKioq8poPAHA2eYVLcXFxzJ07N5qamvrGent7o6mpKWpqagY8pqampt/8iIiXXnrprPMBAM4m75eK6uvrY+nSpTFv3ryYP39+rF+/Pjo7O2PZsmUREbFkyZKYOXNmNDY2RkTEfffdFzfddFM89thjceutt8aWLVviF7/4RTz55JPnfc5cLhcNDQ0DvnzEyLIXFw57ceGwFxcW+3HhGI69yPvj0BERGzZsiG9+85vR2toac+bMiX/5l3+J6urqiIj4q7/6q5g1a1Y888wzffOfffbZeOCBB+Ktt96Kyy+/PB555JG45ZZbhuxBAADjw6DCBQBgNPhdRQBAMoQLAJAM4QIAJEO4AADJuGDCZePGjTFr1qwoKSmJ6urq2LVr1znnP/vss3HllVdGSUlJXH311bF9+/YRWunYl89ebN68OW688caYOnVqTJ06NWpra99z7zh/+f65eNeWLVuioKAgFi5cOLwLHEfy3Yvjx4/H8uXLo7KyMnK5XFxxxRX+nhoi+e7F+vXr4yMf+UhMmjQpqqqqYsWKFfGHP/xhhFY7dv3sZz+LBQsWxIwZM6KgoOCcv4PwXTt37oxPfOITkcvl4sMf/nC/TyCft+wCsGXLlqy4uDh7+umns//6r//K7rrrrmzKlClZW1vbgPN//vOfZ0VFRdkjjzySvfbaa9kDDzyQTZw4MXv11VdHeOVjT757cfvtt2cbN27M9u7dm+3bty/7u7/7u6ysrCx75513RnjlY0++e/GugwcPZjNnzsxuvPHG7DOf+czILHaMy3cvurq6snnz5mW33HJL9vLLL2cHDx7Mdu7cmbW0tIzwyseefPfie9/7XpbL5bLvfe972cGDB7MXX3wxq6yszFasWDHCKx97tm/fnq1evTp7/vnns4jIXnjhhXPOP3DgQDZ58uSsvr4+e+2117JvfetbWVFRUbZjx468zntBhMv8+fOz5cuX9/3c09OTzZgxI2tsbBxw/uc+97ns1ltv7TdWXV2d/f3f//2wrnM8yHcv/tTp06eziy66KPvud787XEscNwazF6dPn86uv/767Dvf+U62dOlS4TJE8t2Lb3/729mll16adXd3j9QSx41892L58uXZX//1X/cbq6+vz2644YZhXed4cz7h8uUvfzn7+Mc/3m9s0aJFWV1dXV7nGvWXirq7u2P37t1RW1vbN1ZYWBi1tbXR3Nw84DHNzc395kdE1NXVnXU+52cwe/GnTp48GadOnRrS3wQ6Hg12L772ta/F9OnT44477hiJZY4Lg9mLH/3oR1FTUxPLly+P8vLyuOqqq2Lt2rXR09MzUssekwazF9dff33s3r277+WkAwcOxPbt230J6igYqufuUf/t0MeOHYuenp4oLy/vN15eXh779+8f8JjW1tYB57e2tg7bOseDwezFn7r//vtjxowZZ/zHSX4Gsxcvv/xyPPXUU9HS0jICKxw/BrMXBw4ciP/4j/+Iz3/+87F9+/Z4880344tf/GKcOnUqGhoaRmLZY9Jg9uL222+PY8eOxSc/+cnIsixOnz4d99xzT3zlK18ZiSXz/5ztubujoyN+//vfx6RJk87rfkb9igtjx7p162LLli3xwgsvRElJyWgvZ1w5ceJELF68ODZv3hzTpk0b7eWMe729vTF9+vR48sknY+7cubFo0aJYvXp1bNq0abSXNu7s3Lkz1q5dG0888UTs2bMnnn/++di2bVs8/PDDo700BmnUr7hMmzYtioqKoq2trd94W1tbVFRUDHhMRUVFXvM5P4PZi3c9+uijsW7duvjJT34S11xzzXAuc1zIdy9+9atfxVtvvRULFizoG+vt7Y2IiAkTJsTrr78el1122fAueowazJ+LysrKmDhxYhQVFfWNffSjH43W1tbo7u6O4uLiYV3zWDWYvXjwwQdj8eLFceedd0ZExNVXXx2dnZ1x9913x+rVq6Ow0P+/j5SzPXeXlpae99WWiAvgiktxcXHMnTs3mpqa+sZ6e3ujqakpampqBjympqam3/yIiJdeeums8zk/g9mLiIhHHnkkHn744dixY0fMmzdvJJY65uW7F1deeWW8+uqr0dLS0nf79Kc/HTfffHO0tLREVVXVSC5/TBnMn4sbbrgh3nzzzb54jIh44403orKyUrS8D4PZi5MnT54RJ+8GZeZX9Y2oIXvuzu99w8Njy5YtWS6Xy5555pnstddey+6+++5sypQpWWtra5ZlWbZ48eJs5cqVffN//vOfZxMmTMgeffTRbN++fVlDQ4OPQw+RfPdi3bp1WXFxcfbcc89lv/nNb/puJ06cGK2HMGbkuxd/yqeKhk6+e3Ho0KHsoosuyv7hH/4he/3117Mf//jH2fTp07Ovf/3ro/UQxox896KhoSG76KKLsn/7t3/LDhw4kP37v/97dtlll2Wf+9znRushjBknTpzI9u7dm+3duzeLiOzxxx/P9u7dm7399ttZlmXZypUrs8WLF/fNf/fj0P/0T/+U7du3L9u4cWO6H4fOsiz71re+lV1yySVZcXFxNn/+/Ow///M/+/7ZTTfdlC1durTf/B/84AfZFVdckRUXF2cf//jHs23bto3wiseufPbiQx/6UBYRZ9waGhpGfuFjUL5/Lv4/4TK08t2LV155Jauurs5yuVx26aWXZt/4xjey06dPj/Cqx6Z89uLUqVPZV7/61eyyyy7LSkpKsqqqquyLX/xi9j//8z8jv/Ax5qc//emAf/+/++9/6dKl2U033XTGMXPmzMmKi4uzSy+9NPvXf/3XvM9bkGWulQEAaRj197gAAJwv4QIAJEO4AADJEC4AQDKECwCQDOECACRDuAAAyRAuAEAyhAsAkAzhAgAkQ7gAAMn4XzGb8sUnbYifAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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AdDpNV1cX7e3tnseJRCJ5/Vk64538Z/1/OGr6Ua7rBTGNLMtEw2Hz4WyoiaBoKdKIVffASpKMLAtEQmHC4RDNdVF6Y2kSikBTnrZKUgpZ1gjLIsm0iiDKQ3ZtoVBoyPstpQnmx1OUBd/nU1TN3C8cChWMrKyJqMgxFUSp6p4FN4a67xVFQRAERFFEFKtMnT0C/OpXv+KGG26gt7eXxsbGqu4TQVVBMCLCvaOKRVFEEIRheY8rxdbU1tFG0PfFU0x/Ve+I4sKnn35KZ2cnEyZMAGD//fenu7ub119/3dzm6aefRlVV5s6dW9a58gVRKJZgBIPaKg6gMIMzM801kioXKi9mpEkZLUl3bVGxRVyKNc2Dn3QZRi7D1FYebBIQEIRPBARsfYyoxq6/v5+lS5eav1esWMFbb71Fa2srra2tXHPNNcyfP5/29naWLVvGFVdcwcyZMzn66KMBvZzPMcccw4UXXsjNN99MKpXi0ksv5cwzz2TiRO/gBz+83PEyK3pWsF3Tdjnr0mZUbPYjb0SbxqpQsDMGZKO59VH9thcqL2YIQnqZLGWrT99hjWzVivhIGZftNxtF2BTstm5BOCDAJtZt3a9/QMA2w4hq7F577TXmzJnDnDlzAFiwYAFz5szh6quvRpIk3nnnHU488URmzZrFBRdcwN57781zzz1nM6P+9a9/Zfbs2Rx++OEcd9xxHHjggdxyyy0ltacp3GT7ff8n97tuZySetVZhqAlJCIKuzfOTI244MQQTIwdbRJYIy2LB8mKGpiosj45qCtYPUzEfKb/JiQ2M52K0lGEL2Ibx+fiqqu6Lp2zlk7+AgNHAiGrsDj300Lwfvn//+98Fj9Ha2spdd91VkfYcO/1Y/rbmb+bvB5c+yNfnfJ2obHcGTjvy2IHufxINicSSKrGkYgpD1YCbYFIXkUimVQYTimd5MbvGLiuolJpIdaRxVp4o9loE/G1raHI1TX9WQlt5GbaAgEKkVRVF1eiPp6irrd6AoYCAbYHqkT6qgBNnnGj73Zvs5enVT+ds5+ZjBxDNVKCIVVlpMbeqCYZQmi/xriEIWTWTW/OM3Nl0v8o0o4/8+rgLgmD22dau5QzYtvHtYZdZGTzuAQEjTyDYWZjSMIW5E+xBF+93vm/7ba0TKzsEOyN1SLUJdm4aO6PpeQW7zF9JEEyhZmv2s3Nqh/1Wk/BbTsyKWX1iKw84CQjwg/EmBa4HAQEjTyDYOdh9zO6236v7Vtt+G5GhgpAbIZn1raquj7kx1lpNiYYJMt84bBUIDXPs1izYOZvu91LMfijiXIZGNNDYBQw3giDw4IMPVvy4Rxx2GJdddlnebbbi4SEgYNQQCHYOpjRMsf3+tO9T22/jQx1yKT8QEqszzUVW45Rd5ktjZ9FUGWbnrblebMkau8zfYvzxQnIQGTta2LRpE1/72teYOnWqWbv66KOP5vnnnx/Rdv3oRz9izz33HNJz+FXAGZsVW4M5ICCg8mxVCYqHg6mNU22/1/StQdVURMGusXJLEWIKP1U0bVUtbbEKJoZZMV9TrRo7w+y8NeeyK/WuaJlLLiZmJJTpr0Cw2/qZP38+yWSSO++8kxkzZrBhwwYWLlxIZ2fnSDdtGPDpZZdZpVbR2BcQsK0SaOwcODV2CSXBpsFN5m/FJYedgTUaslqEO2sr7Bo7o62FNXYCQlUKrcXi1Cb419gZeQD9S3ZGkuLAFOuCqsLA5pH953OC0t3dzXPPPcd///d/M2/ePKZNm8a+++7LlVdeyYkn6sFWgiDwhz/8geOPP57a2lp22mknXnzxRZYuXcqhhx5KXV0dBxxwAMuWLbMd+09/+hM77LAD4XCYHXfckb/85S+29atXr+akk06ivr6exsZGTj/9dDZs2ADAHXfcwTXXXMPbb7+NIAgIgsAdd9xh7rt582ZOOeUUamtr2WGHHXjooYdsx37vvfc49thjqa+vZ/z48Zxzzjls3rzZXD8wMMC5557L2NZmZkybwq9vvLFAT+nPeaCwCwgYeQKNnYOxNWOJSlHiStxctrpvNePrxgNZjZXsksJCFPUgA1XVt5NEaXganQer1s2qscsoIPNq7KwmXMP0nNqqBbv8vwvtV5SP3Sip1jEkxLrg+u1Htg3fXgZ1YwpuVl9fT319PQ8++CD77befZynCn/zkJ9xwww3ccMMNfOc73+ELX/gCM2bM4Morr2Tq1Kl8+ctf5tJLL+Wxxx4D4B//+AdXXnklN954I0ceeSSPPPII559/PpMnT2bevHmoqmoKdc8++yzpdJpLLrmEM844g0WLFnHGGWfw3nvv8fjjj/PUU08B0NSUzcN5zTXXcN1113H99dfz29/+lrPPPptVq1bR2tpKd3c3hx12GF/5yle48cYbicVifOc73+H000/n6af1LADf/va3efbZZ7n3/gcYO24sP7rqKt58800z56gTM3hilFSnUFWNzQMJ6sIydZHgMxmwdRFo7BwIgsDkhsm2ZVY/O0MDI3vkvqg2p3kvrVQhjZ3ThLst+9iZ6U6K0NiFg7JiowJZlrnjjju48847aW5u5rOf/Szf+973eOedd2zbnX/++Zx++unMmjWL73znO6xcuZKzzz6bo48+mp122olvfvObLFq0yNz+hhtu4Atf+AJf+9rXmDVrFgsWLODUU0/ll7/8JQALFy7k3Xff5a677mLvvfdm7ty5/PnPf+bZZ5/l1Vdfpaamhvr6emRZpr29nfb2dmpqsvnjzjvvPM466yxmzpzJz3/+c/r7+3nllVcAuOmmm5gzZw4///nPmT17NnPmzOG2227jmWee4ZNPPqG/v58//elPXH/99cw77DB23XU3bvnTbaTT7snMre+VMkpUdr3xFBt6EizfNMD6nlhgYg7YqggEOwviG7eDpjG1we5nZ42M9cphZyCZvmjVMRB4peowfnk102nCHRU+diXmsTNN0kWo7Mz+UoLqE1s78+fPZ926dTz00EMcc8wxLFq0iL322stm+tx992w0/fjxunZ/t912sy2Lx+P09vYC8OGHH+bUs/7sZz/Lhx9+aK6fMmUKU6ZkXUN23nlnmpubzW3yYW1PXV0djY2NbNy4EYC3336bZ555xtRG1tfXM3v2bACWLVvGsmXLSCaTtva1trYya9aOBc87ko96Iq2wdGM/PYOpso9ldTnZ3Jdk6ab+Ki0XGRCQSyDYWZCe/jHc+0Wm1Iy1LV/dmxXs3OrEWglJ1eWL5iWUZIMnPDR2mofGrkquqxRyBTu/eeyKKykGuo+dsX2gtdv6iUajHHnkkVx11VW88MILnHfeefzwhz8014dC2eothsuD2zLVMjEayrga67mN8xvn7u/v54QTTuCtt96y/VuyZAkHH3ywuY/fp9a63UgOD33xNLGkwpbBZNnHMq4jGhKRJYFESmXZpn429MaDiVpA1RM4Dzj56BGmdr0DddlFa/rWmP9X8vjYgUVjVyXRkF5mROOnl2DnFAhDo8C0mBs84W8/Y7NiTLGgPyOptEZaVQkHc6gsNa26j9tIt6EMdt5555JzxWmaxo47zubll1/m4q991Vz+/PPPs/POOwOw0047sWbNGtasWWNq7T744AO6u7vNbcLhMIpSvBZpr7324u9//zvTp09HlnM/Adtvvz2hUIiXX3qZE045FYAtW7awZMknHHroIS4XlP3vSJosjXNXwhxsCG+1EZnxDRHWdcfpiaXY2JsgIos014bLPkdAwFARCHYuTOndAJlgCdAFO6OuaMqvj12VaLayQol9edbHzn0/p0A4GjR2pUbFulXu8IMsiqRQtmpheEgQRV+BC9VAZ2cnp512Gl/+8pfZfffdaWho4LXXXuO6667jpJNOKumYGnDZgm9xztlnsd/cz3DUUUfx8MMP88ADD5iBEEcccQS77bYbZ599Nr/61a9Ip9NcfPHFHHLIIeyzzz4ATJ8+nRUrVvDWW28xefJkGhoaPIM7rFxyySX88Y9/5KyzzuKKK66gtbWVpUuXcs8993DrrbdSX1/PBRdcwHe+cwUNzS2MHTeWa66+GtFjzLNp7EYweMIQ6CohXBqHEAVd+z61rZY1XYN0D6ZIpKtj0l5NaJrG+p449VHZs/Z4wPARqBFcmOJwEu5P9dOd6AYK+9hVW41QL6HEaL6muZsknRo7q+m5WrSRxZJNNJz5XaSPXbEau5A0MtrbtKLSM5gKTEYVoL6+nrlz53LjjTdy8MEHs+uuu3LVVVdx4YUXctNNN5V2UA1OOPEkfvqza7nhhhvYZZdd+MMf/sDtt9/OoYceCuim03/+85+0tLRw8MEHc8QRRzBjxgzuvfde8zDz58/nmGOOYd68eYwdO5a7777b1+knTpzI888/j6IoHHXUUey2225cdtllNDc3m8Lb9ddfz4EHHsRpp57M8ccew/4HfJY5e+1V+NJG8JFTKqixc7N0GGN7kIQ5l4GkQmd/kjVdg0GgSRUQaOwsqJP3hU2v0p5WkDWNtOWlXt2zksZws2edWINqCzLICmj29loHLE3LFfycudsEwZrKRUMe+UwuRWMIOoaJ1K/gU0pJMRi5Mmwb+hJ09SeZ3FJDS11gMiqHSCTCtddey7XXXuu5jfM5mj59es6yQw891FxmCCDnffkCFiy4DFlyf5mmTp3KP//5z7xtu//++wu2B/R8fFZ22GEHHnjgAc9j19fXc+ef7+SW9O0I6JOiBd/6FjVht0+GZvuvYd0YbowhtxJWBff62oWTum+rGH2uqtAdS9EajDsjSqCxs6Cc/lc4+ApkBCY5tHarF15FOqXntnOrE2sw3CbLjb1xlm7s8zyf5jLzBHv73WagZu42y26GmXlrNccazZaKHKC9hONCGDP85DCbblKZ8wVVL6oTa663alb+mE0r8Nw7r2GkxgdDU1eJPnXT0pt+ycN4fQOJNINJ9zQz1YR1MtHZnxjBlgRAINjZEWU47Psw/1ampOxOyZ9ufBv5vi8ipOOudWINhjvIoDuWIpZUPV/+fMl1swEUueuyAmF2WbWZmYvFGHsM4dt35YkS0p1AtnbwcGvsjA9ctfh5BjjQXP9bfTjGDg1/keQj9dgZAqWmlS98uWnpC/klV5pYUmH5pgGWbxognqruVCtWYT6eUhlIVL8wOpoJBDs3dvs8U7Y7zLZodSiEtOwppj9xHpLmnSfJqrEbDh8nYwDyzEeXJ7luvsjYrMbO4mNSZWbmYjGu07hHfu9OKQmKwSoID29/mdGBgWBXlWxtd6XQU++8npHyQbOet1w/u2zwhFt97eG5vrXdMUAXJD/dEqtqn1nnUNPZX37KmYDSCQQ7D6ZOPcj2e00mLUD9uhdoX3SF57TN6ns3HB9WQ8byrCCRR9uUbwaqucxYt/bIWKdg53dW7xVZXIiRShFjfNS21vs06rFq7Kr4FmVNsS7L8jBSgp31eS/32XebzIl5JsKVZstAklhSybj96Nq7TVVs4jS+FzVh3V+0N54KXEFGkECw82BKwxTb7zWhrNNw/cd/g8XXu+5nTeY7HKYwvxq7fIKd20Dl5mNSbalcisVpivU7PmfNMsVGxWZ9Eodztm181LbW+zTa0fL8qkYEigscGmlTrN6G8hphjpuWL6QwTMETiqqnDgEY3xhlYpNeKm5jb6JqTbJGn9SGJWojEpoGXQOB1m6kCAQ7D6Y02gW7Lkmi3yodPfMzeOdvrvtmC8AP7QigaZopnHgmGs78dTMj5puBukWFjRofO0NT6fOjavrYFfm2SKIwItUnDC1ukJahOrEFT4xgO/xjEe1ctfv23yPx3KmqZmtH+Ro7/a+bxm6oJ2kbeuMoqkYkJDKmPkxLXZj6qFzVJlmrhnNMnZ5LsWsgWZVt3RYIBDsPJtdPztHQrA47Qrj/eTGsejFnX9NkOcQfc+vY5WVWzJdcN98M1E0g3Jp97Kz9kw2e8Levm1naL1lz7PD0mfWDtrUK4KOercQUaxPiinj4RyKPmdOnrtwhKiuoZJcNR7qTeEoxNV0TmqLmGD2pucY0yW528V8baQHK2l+NNTKyJJBWNHpi5dftDSieQLDzICyFaa9rty37cNez7BspSbjnC9BpL49kREOmhlgAss6MvU2x+t98Gju3WbjbrHtr9rGztrjoqNjM32KDJ2D4tZyVNEcFDA1by10x2ikI9shY7y11RmJ4cI5J5T77xtA93MET67pjaJouHDVYKjiEZZEJGZPsht44m/sTrOuOsXxTPx+s6+X9db30xkdOiLKmhBIEgbZMHrvOwBw7IgSCXR6mNky1/V46Zhabdz7PvlGsSxfukoPmIkkaHgHILtgVr7Hz5WMnWjV2W6+PnfUaRdPHzt91lFpSDIZPyDewXqembZ1C+GjHekeqW/Z2a5zLWOH4PSKmWMc5y4+KzX3nC9XXLpeewRQDCT1gwhDirLTWhanL+K+t747T2Z9kIKFkfHipeIoRTdNIpBV6YqmCQqNTw9lSF0YQYDChEEtWp1/gaCYQ7PIwuWGy7fe6gU9Zv9/VKDOPsm+46SP49/fMn/IwBU9YxxdPHztjJuViS/EXPJFdJg+TwDoUWAfqYvNRuc3e/TLcGrucD9xWeK9GPY57NNJmtGJwbWkV+NjlaOzKfO7dLB1DncfOiHod1xAhLLt/mie31FIbkaiLSLTVh5nUUkNzra7Zq8SrnkyrrOkaZMmGPt5f18snHf2s7hxk1ebBvImSnT6JIUk0a8YG5tjhJxDs8jC10a6x6xhcC6KMcsqtMH5X+8av3w4f6OV/TMFuiP2qnNoZN9wENIN8CYrdIkFlSzSp9dpiSYVVndWdRNM6UBtX5NvHrgzj2Uj62Ln9DiiO8847zzQvCYJAW1sbxxxzDO+8805Jx9M0+9P0s5/8mDlz5lSmsRXGNMVicbHL44+brcwwpM1yxXnOcjR2VkHbPXhiaIRxw3e5Pupd6TMsi2w/tp4ZY+uZ2FxDa12YSEgfYyrh29g1kKR7MEU8pZqlJs0AsLT38d3Sw0RDhoVn6/PJ3toJBLs8OFOedAyuBUCuaYTT7oBQrX2Hh74O3atLMlmWkhLDFjxRwBSbL0Gx23mzfmXW7fV6sZC9Nk3TWLNlkN5YuqrD263VI4r1lcnnp1iIUEZjN1yCXSU/cAE6xxxzDOvXr2f9+vUsXLgQWZY5/vjjR7pZQ0+Jj86IaOwqqKm27uoWPOHcplKUahmQKqhJNPqxuTbErPZ6dp3URH1Etq1zwy09jFhkWqmAyhEIdnlw+th1JTaSUhP6AztmBzjOkcsu3gN/vxBJ0DVXfgeXtKLyyYY+lm7sL6p9foIn/PnYueznUYvMWS92c3+SREq1LatGrAJusb4y+bSehZCl4fVLzPnAVWFkrKqpdMW7RvSfqvkXtCORCO3t7bS3t7Pnnnvy3e9+lzVr1rBp0yYA1qxZw+mnn05zczOtra2cdNJJrFy50tx/0aJF7LvvvtTV1dHS0sLhhx7M6lWruOfuu/j5z37C22+/bWoE77jjjgr3dunYnhzvbCfOTarEFFv6saxjprXyTqH62uVSaoUbY/tKTOKMcT8akojIku34+a65Gip1BGTx1vnmYWBggF/84hcsXLiQjRs3ojreouXLl1ekcSON08cOoDO5Hhin/9jzbFj2DLx3f3aDNS8Rff56mP0N31qazoEkaUUjrehaO7/F5q3fpkJ57NyOWWyCYsCWfDmlqGzsixdsQzVg1UAW4ytjNW/4vS9WDI1dMj1CptgqvCfdiW4OufeQEW3Ds2c8S2u0tej9+vv7+b//+z9mzpxJW1sbqVSKo48+mv3335/nnnsOWZb56U9/apprRVHk5JNP5sILL+Tuu+8mFk/wwksvIYoCJ598CsuWLWXhk0/w1FNPAdDU1FTpSy0bQQAhz2NkrhpJU6whFIn6+ct57vOniMrUoq3we2XNSVrsBLKSApRbmhc/E2H39DD632qe8I9WShLsvvKVr/Dss89yzjnnMGHChJI+eFsDdaE62qJtdMY7zWWbYuuAPfQfggDH3wCfvgrdq8xtpP/8D3VN+zIwYb+Cgpqiamy2lIpRVM10uC+E3cfOK3gin8Yu9zjmfrjPHkOSQAxdy9jRk0JVrYOdr2aPCPZZuL7Mzzho3aQUjV1YEs3+SaZVT6foSuG8l4F/S/k88sgj1NfXA/qkdsKECTzyyCOIoshdd92Fqqrceuut5nt+++2309zczKJFi9hnn33o6enh+OOPZ/vtt0dRVWbsMAsBXUisr6tHkmXa29vztGCkyZ/sxHiRBAQ0RlZjF5ZE4qpaljCRP0WUgKJpFTcv2s2/xQ00hvmzEn5/Rjsky2Dnp1KPmxl5uCp1BORSkmD32GOP8eijj/LZz3620u2pOqY2TrUJdhtja+0bRJvg87fDbUeBqkcNCWhMfvZbLJn/BGm1wdTauNE1kLTNcBVN831T/Jli9b/uPnbeL6yHJdZ8yfviafri+vWObYiwsTdR1TMzQ7spCILdV0bVbCYWJ9Y+LmUCIwgCYVkkkVJJKkMv2FXSJBWgM2/ePP73f/8XgC1btvD73/+eY489lldeeYW3336bpUuX0tDQYNsnHo+zbNkyjjrqKM477zyOPvpojjzySOYddjgnnTqfSRMnjMSlFIXb2+xLyz2Cgl1IEjOO/+Vr7FwFOxEUtfLX6JaOyS9SBQWo7AQ416Sab3x303JKRaaVCqgcJX1lWlpaaG0t3oyxNZITQBH7NHejyXvDYVfZFoX719D+6i/yprnQNLu2Tl/mv23W98zrpcun3hfzaK40j8HN8LEzhLqWuhB1Gefa6jbFZq/H2heFWmwNuiiVcMbPLjEMUcPGc2C0dyg0dhv74qzYPDAiFQZGgrq6OmbOnMnMmTP5zGc+w6233srAwAB//OMf6e/vZ++99+att96y/fvkk0/4whe+AOgavBdffJEDDjiAv913H3vuujOvvPxSSZVMhhXL5M7r+bdG+VaDj10oM3EqzxSr/3UfM4dGC1VOrsyKmmJVb5NqvuMbqySbQGjsV3azAoqkJI3dT37yE66++mruvPNOamtrC++wFeMU7DYMrnXf8IBvwJInYNXz5qK2D+5kcI+TYcfDXHfZMpgireimV0HQw8mL0XoVlaC4yDx2qnO0zmBV0YsitDdGzTqo1ayxU20fKbsTtJTnE1vOgGsQCYn0xSE5DJGxRnsjsq65GAqNXWe/7hM6mFLMiLliaI408+wZz1a+YUW2oVT06HCRWCzGXnvtxb333su4ceNobGz03GfOnDnMmTOH/7riOxz42QO49557uebHuxIOh1HS1ZomyPLSaI5lbozgh9wQ5EIVyLXpprUy8CPklIIpGJXg72E0sxLjr5uFJ59lB+x+yG7BE9X8XRit+B6V58yZY3vQly5dyvjx45k+fTqhUMi27RtvvFG5Fo4wTsFu3YCLxg50Keekm+D3B0A6Zi6O/OsymP4CROptm1u1dWPqI/TEkqTQipppFpPHzssR2Hkca/sgd9ZqNSuPb4wiSyKKVlwU8Ejg1EAW6wRdSqoTg6zGbugFO6dJaig0dsY5UmkVIsXvLwpiSYELI0UikaCjowPQTbE33XQT/f39nHDCCey7775cf/31nHTSSfz4xz9m8uTJrFq1igceeIArrriCVCrFLbfcwoknnsjEiRN574MPWbZ0KV/84jkIwNRp01i5cgVvvfUWkydPpqGhgUikhE4dAlxNsXl+j6TGzhAujHetnMdeM/3FcteZQk6FXytFtY9PxVDJxMnGvXPzsSukPIDhrdQR4I1vwe7kk08ewmZUL86UJxsG13Plc1d6bi/uchB7LH+Bz/f1IwJSzypYeE1OapTeWJpESkUU9VIxfZmSLcUMGFY5ykia6Zxl5s9j521WMBY59zNC4GvColkP0JpHqZio3uFEdQi4fp2gK6Ox0/tsODV2hi9fpYVtVc322XDl5htpHn/8cSZM0H3iGhoamD17Nn/729849NBDAVi8eDHf+c53OPXUU+nr62PSpEkcfvjhNDY2EovF+Oijj7jzzjvp7OxkwoQJXPT/vsaFF13EQP8AJ59yKg//80HmzZtHd3c3t99+O+edd97IXWwGq1+UQP4ExWBX7I/EBC+rsct6FymqVpIGLK+P3RCl8HCLKvWLNbih3PHX6cphbZPXfbWOrW6+eYFcN/z4Fux++MMfDmU7qhanxk7RFB5Z/kjefR4a00qvKPKVnl59wSu3wM4nwfQDzW029etpQsbUR5DErEN/URq7nMLX4IzTyKexy/rYuZli3QWamrDEzHH1hGXRfImtg6dbG6oBZ3Sw4QRdqLvzRcj5xdAiJNPqkAu+hqxlfOAqne7Emo9vOATVkeaOO+4omFuuvb2dO++803VdY2Mj//jHP8zfiZSComUEDkHPkXfXvfcRzQj/Wx3m45B9pkfiQ24IHZIomNr4oRHs7NtUCtMEWkJ7rbuUO/66jXeFolu9vhVWf7tCQWoBlaWk4IlXX32Vl19+OWf5yy+/zGuvvVZ2o6qJpkgTTZGmovf7V73d9Mo/L4HkAAB98RSxpIogkNV6icX7IzjHFudg41Uax7nMVWOXR6CpCUu2AVOwJP2tVnOsUwNp+BwWGqCzPoqlowvBmZQnQywMmWkfMhq7Steotd7f4ap/O5owXVeF8p6pocbqYitYGutlitVfK7vmaDgxTK/WSXKpwpdTu29lqIInvILV/GAdf8sROL195extdOLlHzjUCZ0DvClJsLvkkktYs2ZNzvK1a9dyySWXlN2oakIQBE7c/sSi91sWDjFgfUm3rISnrgH0ag2gm2CNygTZ8iul+di5/87+P3+El7fGzi/VnmXcOQv3O/PODvLlfYYjclZrN5SYptjMc1Xpj6xVA7itmGKHhpHVcBWL99Pv7l81nPM762RDEoSCPmGFyJf7Uygg5JRK1seutP0rMbG2pVyxtCPbn/n3y1e2skrn+6OWkqJiP/jgA/baa6+c5XPmzOGDDz4ou1HVxrf2/hY7t+3Mki1LPLfR0PjL+38hrelpQFQ0Xm/fnYPXv53d6JU/oM4+gYHI7oAu2BmYfgxlCHbOXW1+MnlfutxzFmuClEQBRS0uqnc4cc7CfSfPNPuhvPOHM1GqibRKQ+HNS8Kavd4a5FJM0utCWEuUbQum2EpjukZgTftbhe9MjonV/fnJablFwCjFDFoKVr8wURQw3OxKHYvy5f4cunQn3uf0gygIqJSXOLmQr5y3j523UGr4MlfrhH+0UpJgF4lE2LBhAzNmzLAtX79+PbJc0iGrGkmUOH5G4aLfL69/mQ86s4LtC9sfyYGbPkZMZ8tu8dClcNLjyNE6m1+NVODlccO5qZfGzmusyOfcmi//nRvGYFruC5xMq2zqT9BWF66o31GOj132q5qXfKkPikEPOkkPqcbOprkQBbO8UlrVkCvUldYoW1UNfGeKRbOmELEur7KgI6dc5/m6OC5nJCIhnZGcpvBV4qs2EsETpim2xHdJb1d5ApTXdRe6p/msGqUkdNY0jXU9cerDMk21ocI7OPbVtNL7cbRQkin2qKOO4sorr6Snp8dc1t3dzfe+9z2OPPJI38dZvHgxJ5xwAhMnTkQQBB588EHbek3TuPrqq5kwYQI1NTUcccQRLFli15p1dXVx9tln09jYSHNzMxdccAH9/f2lXFbZ7DZmN9vvjxLr2bDPFbZlYvdK2l/9BbVh+5c2a4r1fz6nOcBL0PMW7IzjOPYroT5quYOpwZbBJF39SboGkuUdyIFTAyn4HKCzvnnlnd/weUsMpWBnud+CIJjJpCupRXUeqxit3baegT4n0tTyTFVrz+Q89h4NNQRWKc9kcaiwBk6ARcNUso9dPg2UfZtKoeQ5px8qMbG21tu1Uii6NZ9/YCkazoGkQld/kg2WOuR+Wbapn4839G0zydO9KEmw++Uvf8maNWuYNm0a8+bNY968eWy33XZ0dHTwP//zP76PMzAwwB577MHvfvc71/XXXXcdv/nNb7j55pt5+eWXqaur4+ijjyYez97ws88+m/fff58nn3ySRx55hMWLF3PRRReVcllls8fYPWy/l/V+wKadzkObsp9t+ZgP7qBpwyu2ZaUkcyyksXMeO99y64ug2bbx15ZyB1MDI+qy4mk6HEEQ/n3sjP2q38fO6kCu/9V/VzIyNu24L3787Iw8l4ODgxVrx9aI0XPWJ8l8BavuO+RokOC+3PgVj+m5OyVZv9fDqbEznkljDColEM1KNpOAm/vK0Aiu5ZpihQpMrM06sY42OKNbvfYr1o/bC6XEb4CmacSSKmlF2+bdREqym06aNIl33nmHv/71r7z99tvU1NRw/vnnc9ZZZ+UkK87Hsccey7HHHuu6TtM0fvWrX/GDH/yAk046CYA///nPjB8/ngcffJAzzzyTDz/8kMcff5xXX32VffbZB4Df/va3HHfccfzyl79k4sSJpVxeyTg1dt3JTjpTm0mdcBPhWw6yJS5ufOIymPkChOsAiym2BB87w78tN/2J90wKnM7OGqJLpKhfjV25g6mB4cM1VJnds8ET/maS+dLFAKCkoeNtWPm8XnXk01dBroEdjoQDvg5t2wNZjd1QpjxRLM+D/lcEVJtfXNnnyBHsCh9bkiSam5vZuHEjALW1tVVldhwuVFUjkVYQBAEtLZBMJkGMowGaLGbuV3WQVlRSiqq/J4qEqmlmSTxBzX42kmmF/oEBujZvpq21hQFJt71VOs1OPlSnxq7M4ImR0NgZ11Dqa1EJE3HWV9HeCGd0q+iY5PpKD1OMwqJEwc66uXMCuq1RkmC3ePFiDjjggBzNWDqdZvHixRx88MFlN2zFihV0dHRwxBFHmMuampqYO3cuL774ImeeeSYvvvgizc3NplAHcMQRRyCKIi+//DKnnHKK67ETiQSJRLZGa2+vnm8ulUqRSqVKbvPEmok0hhvpTfaayz7uepe9JnwOad73kZ78gblc7F6J8sTVqEf/AgBFSZNOp0kKqu82JJIpNA0kWSSdVkkkU6RCgmW9fkwJ0fOYipJG0zLHsggf6XRaL3Pmsy1qpv2JZJJUyv8Hyji+8TeRSpJOKySSWln3wkkylSKdVlCUNKmUYPa3fs+926vvl0ZRBHt7BruQnvkxwgcPIiRdTP+v3472+h1oOx6Hut+lMPkzqGoaVYWBWMJMWlxJEgm9rSFRIpVK2e9JOHfQdfa9r3MkU+azoWkQS7gf20lbWxuKorBhwwb/FzTK0DTdR1FAd/CPx+OEwhE07Gk6qgFV0wOhdJO+3i5DO+tMAqyoGpG6etra2ujrHCSdVkgmU6SGKTVfIpkknU6jKjiee5GUSyMKPff2scI+NpjjdGooxqc0qiKXdFyjXYlUilSqtOcomXm3VSn32ozvRDyZMq0Pzv1yxkhAVZTMvdC/TX7GnESmLwptl9P+zHcLIJ5IEhFHl3BXTF+UJNjNmzeP9evXM27cONvynp4e5s2bh6KUX/vQKOEzfvx42/Lx48eb6zo6OnLaIMsyra2t5jZuXHvttVxzzTU5y5955pmya9+OV8fTS1awe+6jp2heE6JGmsz+tTswbjDrIyi9diuvdjWwoWkOCQU2xnTz2Sc+m7AmI0+EJUgq0ByBBovCNJ6GTXEIibDE45ifDugfnGW1+nYAKRU6BvXZ46o6f23pTkBfSj9/cwkVkZ588kkANsT0awmJ8GEFyxAbx22LQq0MXQkYSEFjGJrC3vsZ11UfgpbMdUVSPey/9Dqa4rkpf6wIaAgfP4r48aNsrtuRD8d9mc3yBD7KtKHSDKT064pI8HGN/2s0+t4P1vuTUqEuBK1F3G9BEJCkrTQZb5nE0tCdhLCoP4cAWxIQV/T7MxTPRKkMpqEnCVFJf+41DToyBodxNdkkuD1J6I4r1Ekan7z3Npti+vW0RvRnYzjoSUJvMvss9ib1ZbVytp/d8HrunWOFlcE0dMb1MXd8TeWuYWMMEmX0W2dcb1tTWH/fS6E/pT+PUQk+dFzb2gFdI7a0Rr92K0b/W8dIg81x/bl3fpvyjTnG/QNYWeffHSip6PcO9HbUD9PzN1wU48pS0lDiZUrq7Oykrs6nJDCCXHnllSxYsMD83dvby5QpU5g3bx5tbW1lHXv1O6tZ8l5WeIs19HDYYUfQUhumY+cZqPcehahkfQTnrrud9HHnEq+dyNKNA0iiwE4TCifEUFWND9b3AdBQI9MXSzO+McLYhuyb1RtLsborRk1YYvux7vflo44+0orG9mPrqMm8sbGkwrJNA8iSwOx2f8k5NvUl2NCboKUuxKRm/yNeKpXiySef5MgjjyQUCvHJhj6SaY2ILLLD+PrCB/DJ0o39xFMq09pqaYjKrO+J09mfpK0+zIQm79E/Z7u+9ch/PRWhgFDnZMzAx5zWcR2fHPUXmqfvwZj6ytcD7exPsL4nQWONzNTWWjb0xtnU532Nzr73g3F/mmtDdA+mqI9KTG+r/ne+GugcSLK+O05jjcyEhhBPPvkkc/c7iP6kxrjGCOMaqqNGLMDm/gQdPQmaamWmtOgzrPfX9aJpMGt8velasLY7xpaBlNn+1V2D9MbSTGiK0DYEz7gbznfU2s9TW3Nnh4We++xYUUND1L6+L55iVWeMaEhk5rjKjU/LNw0wmFSY0lpDU03xEsm67hhdlvtQCsb4Yb3nBh939JFSNGaMraU2bBcbjP4f0xCmvdE+zhjPh/Ft8jPmdPTG2dynS3bWZ60Qg8k0yzfpws/YhjDjG/NI9VshhmXRD0UJdqeeeiqgz7rPO+88W7FqRVF45513OOCAA4o5pCft7e0AbNiwwazTaPzec889zW0Mvx2DdDpNV1eXub8bkUjEtdB2KBQqykfQjTntc+C97O+V/Z+QRiMUCjHYOIOOfb/HxBevNtcL8W5C//gK2rn/QpZlBAFfbUgrqplaJhoOEUuBJMu2feUUyHKKcEjyPGY4FAJBRQ7JhEL68VKagCzLhGXRd3+EwyqyrCCKckl9aPS9IMrIsoYgCWXfCyuSLCNrKpFwiFBIJhJWkGU1p89y90sjy5n9BjrgLyfClhW5G46ZBdM+C9MOgA3vw2u3Q6LHfqxYJzs8fjad8+8j1LJvxa7NQJQUZFnJXGOISFhFllUE0fv+Q3HPvXF/Gmoj9Cc1NMH/M7KtI4oKsiyb9wcgGgkRV1VEKf89Gm6kzLMUtjwboZCMqqKPFZn8OZKUQpY185rCoRByCsQC71UlEaUUsiwTzbQhGtaQ5TSilL8NXs+9MVaEw2FzTDQIqwKynEIqYmz0gyRLyKpAOFzaNygcTiMntLKeI9HlnhtEwiG0lIokh3L6xOj/iMt+4VAaWdZyxtl8Y44k6WMuGN80fxp+UcH8JgoF7v3WSDHXU5S3blNTE01NTWiaRkNDg/m7qamJ9vZ2LrroIv7v//6v6Aa7sd1229He3s7ChQvNZb29vbz88svsv//+AOy///50d3fz+uuvm9s8/fTTqKrK3LlzK9KOYnEGUKTUJEu6PyGlqCTTKp07fwl155PtO619HfmpqwD/lQKsOeq8Mq0XCp7Q19mP5zy2X0oJ/nCj1IioQjhTv5jBiIXSnRgBCb2r4I7jcoW6lunw9Tfg0lfhhF/B7qfDkdfA5e/BUT+Dxkm2zeXEFsb8/TRY+0aZV5SLM3hCrlBAi+0cmWMZOQaD6hP+Me6PbAmSMN6baivP5hal6Zb2IlsxwR6ROpzpToxIUNERPFF6guIRCJ7wiEj1i59gMD3Hm/cG+SJzDQudW5/miyIuJduDddti+tkaEVzJgLGtkaI0drfffjsA06dP57/+67/KNrv29/ezdOlS8/eKFSt46623aG1tZerUqVx22WX89Kc/ZYcddmC77bbjqquuYuLEiZx88skA7LTTThxzzDFceOGF3HzzzaRSKS699FLOPPPMYY+INWiKNDG9cTore1eay97f/C57jtUFvmhYQjzxt9DxDnQtN7cRX72FxoY96Z1xvK9CzlZBxeulLpTHTl9nhMmr0L0GUjG0uil6m4oYY8odTJ37VvrD4JXHrtB5NA3kwY20PHQy9K+zr2ybCec+BE2TcneMNsIBl8LeX4K/ng6rXzBXSYlu+PPJcM4DMHmf3H1LxMznJdg/cJWKEEtbhLiajGCnqsNbZWBrxhDerMGvlYomrzRGXrpcwc6eBNcpBJXyIS+XrMDsjHgvUbAzBMV8edkqPJ9xCsjFkm2X+zVrmsYnG/oRBJg13t29xk90q5tgWGlB2P58+d7NXu6w0jdoK6MkH7sf/vCHFTn5a6+9xrx588zfht/bl770Je644w6uuOIKBgYGuOiii+ju7ubAAw/k8ccfJxrN2s7/+te/cumll3L44YcjiiLz58/nN7/5TUXaVyq7jdnNJth9tOV9BpJ6tE5tRIZoDZz+Z7j1CLBUpZj83BUsbdsZpX2Pgh9Kq6Di9VIbv1wHi40fwdKnGLf6HeTOj6npXgKpAQDqw/VMG78vySmfhd2OhPG75WatdFBufUbI/RhUUmBwCrl+BxxNg4nP/wDJKdSNna0LdQ3j3Xc0iDTAF++Hu86Alc9llyd6dOHuvIdh4pwirsQbp+ZCrsA9sWImURXtlS1SiookbpsBEcVgPN82jZ0h2FVZ8ma33GT5tfuCY5thFOwMochI81OmcJlvQjxUlTX8TMLz4ZVs3iCtamYOTa9x1StBsX58b42gH01fMd1lPUdxeV2z21bbRGm4KTkO6/777+e+++5j9erVej4mC2+84c/MdOihh+ZVDQuCwI9//GN+/OMfe27T2trKXXfd5a/Rw8RuY3fj4eUPm78/7n6fwYxgV2eEFLXvBsddDw993dxOSvUz/fFz0cY9BGO3y3sO6+zKs4KEc7DQNFixGF74DSx9CoBGl2MLyX4a1zwNa56GF34C9ePh0Cth7/M8Rx6pzFkyDK1gZ5oLKC6PXXTJIzStety+cPxucO6DUDfG38nDdfCF++Ces2D5ouzyZB/c/QW46Blo8PYJ9YtpinWYxSpl5nNm+A9LInFVJamoFS3/Nlpxmsqt/6+2D1E2r1q2rW7VWjSHtqaUCjrlojqee0MwKbdP82nshtqiUCyFEsRb+8JTsMujqcw3cc+n6Stlwm8zxZaQ/w6qz7VhuCkpI+ZvfvMbzj//fMaPH8+bb77JvvvuS1tbG8uXL/dMOLwtsfvY3W2/1w2sZvNgN4A9omjOObDHF2zbRvpWE/rzcbDZXjrNiVX97WV6MIUZTYF374dbDoE/n2gKdb7p3wCPXAb3fhEGu1w3qcRg6hyUKjkrzg6c+l/BFIbznCO2hdbF37cvq20rTqgzCNfCWfcyMHWefXnfOrj3HEgn3PcrguyM2y7YVUpoSJsaJ/24Rj6zbX0Q9YtTMIah8YOsBM73xfp/zWLlcmprRsIUazx/kkNj59df2YpRaxTyC3ZQnNDh95ylTmQLmZ/dzOde27hr3jLbuPrYZSYBrpo+49ju7S61rW5Yvx+Kmt+fcLRTkmD3+9//nltuuYXf/va3hMNhrrjiCp588km+8Y1v2OrHbqvMaplFRLJH3S7t+YCQLNhDtwUBPvdLGLeLbVuxbx3cfix0vIcX5oBqSWzqFIw0VaNxxb8Y/5dD4O8XwPq3y7gq4KNH4H8/q2v9HFTC98Tp8Fopwc46GOXWis2z4xM/QB60R11z7HXFC3UGoShdx99Of7u9xByfvgKPXF62GsCZgd/qiF2JD61ifkD1ZziUeZaDAAp/KA7BGEZGEPKD20feTWNnrX6jb0/ONkONs61W4ajYbrVun89nzHrecil0Tj8YQpWXMOPHvOnUfFop1xRbskm1KN88++9tufpESYLd6tWrzbQmNTU19PXp+dTOOecc7r777sq1bislJIbYuW1n27KlPR9QF3axfIfrdA2QQ7hjYBPc8Tn49PXcfbAXXnZ9qVcspvXuY5m28P8hb1nm3tDaNgZ2PpN1c6+m+9R7YcGH8F9L6D3+Fjpnf4FU84zcffrWwZ0nwlPX6OW0MlRCiHC+xJX62NlLpOl/8zkDA7rJ9E1HhPcOR8Ou88tqSyRaw+ojbibdOMW+4q2/wkv/W9axnaZYURTM662IYOdwUg9l/g5l/dvRhJuDvGwxXVZT4XI3wc5NaMuWocpsI/qYMFUQVc3VdglC6c+91X3Fq1Zs1s+utDY7sfZhqaX2pAITVZsptoDw565582OKddtP/1uM9sxuivW9W877U22TpeGkJMGuvb2dri7dJDd16lReeuklQI9q3ZbVn1acaU+W9r5PrTNlt0H9ODjvERLj9rQvj3frptNF/51jArU6N5svnaLoZta/nAp3nkB4w5vu52vZDj73P3DZe/Qc9Ss6d/sK8WmHQuNEqB9HfMeTWHfgL+g493k4617d/GhDg//cAP+82NQyWYWIUmeyacdbXKn30jiMdeA0fWXcdkgOwsPftB8jXA/H31C6d3OGiCyhRFtZd8xtEHJElT/xfVi60H1HH2SdyLPLKmmOdTqpG6bYQGNXGKsAYtPYWd6batIwuH3k3bQ2TrNl1gdteK7FKqRILprQYscif5kE7NuWS7mBE1BY86vahKXiTbHGmOIq2PnyzfNouGtbc9vkB+e1V9P7NNyUJNgddthhPPTQQwCcf/75XH755Rx55JGcccYZnvVZtzV2G2sX7Jb1fOgt2AHUtrLxlPsYaHckrk32w6Kfw692gyeugj69VJr1JZQ6P2H8q79gu7/sC/83H5Z5CAcT9oDT7oSvvw6f+QqEay0CjnW01v+IogA7HgNfewFmHJp7vHfuhYXXmD/LNSs5Z2eV0mC4DQ55B+dFP4ctK22LkodeDU2Ty26LYYrvb54Np9xsX6mpcP/5sHmpy56FcRtgDSHCKTSXQo6PXeZatuUB1C9pi1ZGdKg2qjGAwvSbsixz+qW6uTgYlzZcUb5ukxkovU/9BDFUOoCi3MAJKCxsOv3P3MivefN2tcknmBYrYJejdXOeI70NTzhLioq95ZZb9LxnwCWXXEJbWxsvvPACJ554Il/96lcr2sCtlT3G7GH73Z/u5bv/+RZhybuQX3NoAocf/nvm/Oe/iKxaZF+Z7NejWV++GRraaVFUmpQ0IgrS4CbyFfFSmrdDOvKHsPPJOW9fvhQG5gve0A5f/Ae8eBMs/DGolmLE/7lRT8K774WIIihq6TPZHFNshUZOt4HTc6Da+BG8+DvbooHxn0Hc+8sVaYtRQFtRNZTZJyAdeiUsuja7QbwH7joNvrIQalt9H9c6AFrN4qZprAJjnOJwUg9JgSnWL26BEwaSKJBWtKpKeZIvQbGxzvqeO/PYDVcaMaePn4FkBHOVqLErLNhpFdfYlZMBoJCwaRWYPE2xfvL3ueybTzAt1iReTgCdVcDUtG17wlmSYCeKIqJlinTmmWdy5plnVqxRo4H2unbG1Ixhc2yzuezpNU8X3O/dze/wqxP/TPvi78LbLmlclCR0r0YCCiWYSNeOY8Ocb1Iz93xaG92TSWcTFFtU9eZs3fKiiiJ89hswbic9J5umZNf969vQMAGp9RBSaKX72A1R8IQ58DjiVlzP8fRPbGF/qhhm7UH/zdQCefz8IooCsqR/yBNphdqDr9BLkH34UHajruV6BPI5D4Lsr6K34qER8tLYqarG0o39bI7jm1wfO71PNC1T4k6qTB+NRpx9Z8XULlVRdLG7j539457NYZfr4jBcwRPOpNwGhRL2euGWv89JpQNEzPyQZZhirRU/3Gq5WwUmL6HbT4LinOpGLlpb+37FaTed346iEhRnrisii8RTalVpwIebkvPYxeNx3nnnHTZu3Ghq7wxOPPHEshu2tSMIAruP2d2XMGfl/S2vM6CpcMr/wv6X6Bqx9x+w5xgoxIQ9Yc+z+XTqKfQpYabI3jXmsuaV7DLjv64DzQ5Hwgm/hocuxbbH3y+g5sR7iLfuVfJs3ZrnS1G1is363QRV1xn5mlf1yF8LnXv8PxLNM8t1rbMRkUXSikIyrerpb065WTf9dryT3WjV87qf38m/9+V84zUoe0VMD6YU4imVWFovnt3kow6hkrkh1tJNxr1KqxpyFaay60/oAT71kZKHuoqgmFUncu+lmfKkqjR2ueY158c93zZeAkbF2+lIym1Qqik2e03e7fYVUV8ExtBeTl/Zo3VzKxcVKtNlT/PicnwPXzk3ra29XdmFfoRsZ9NKMcWGM4Ldtuz7W9Jo9/jjj3PuueeyefPmnHWCIKAoiste2x6fn/V5nlnzjN1/zQfd8S1AC7TvCp//E8z7nm6GfesuXWPnQio6hu4dTqXtgC8hTtgVAGVTPyiKXfPmwG2Gbb6AXrvtdQ70rrWbENNxJjx6PgMn/RO1ZSff12rFEBzCskAsWTnTlJugavzX/AABPPUj+441rWza/auZ7Sv3gQrLIgMJhYRhwgzXwRfuhT8eBn3rsxu+fReMmQkHfavgMb1MfbLk/oGLp7LvaNdAiqa6fMZ8HaePnX4t+r2qxiTFaUVl5Wa9msouExuHXMjIRz6NnfEOVsIPslK4mdecVQSc0ajO7d0EjErjjAR3tqPYMSQrZHlvU0qkZz7ypRnxixGtq2nuCYhtdVRdhCV7yhX/plg3ra19P+u2hfurHFOscV0RWQLSgcauWL7+9a9z2mmncfXVVzN+fIGSStswB00+iD8f+2deXP8iaTXtud2f3v0TisW02Z3cYt+gbXtdS3bY1bDuTdAUNvUn6U9qtNRFaGpp4yN1OoghWsY1mBExxjvhFr5ukM+skNeZ95Dv6MLdG382F0mJLUx55psMfvERwJ8J0YoxwQpJIjHUigdPWAeenA/QsoWw6j/2HQ/+L5RQQ2b7ijQFMAaeFLGkZQLUOBHOukfPX5gazC5f+GNonQG7nJL3mFltp325l+bCeu6eWMqXKdWZCBay9ypVhX52AwnFfAcUVTOF3JHAENrc3ikv4Xuk8NLeOMeKrAnR7tNpCBiqpiFVcELkhteExmqaLAb/PnYVTHfi45x+sPa71zkgf8oSt+AevW3u+xaK6LUKnKrmrSsw22lxKfG6Fjesz2w4COoqTbDbsGEDCxYsCIQ6H+w5bk/2dKYxcfD3T/5OZ7zT/N0d73bfsK4NdjgCgMHOAfpjaRqbowj1EYS1PebLY6D5GDDczAp+Bjc9ufKNepTukifMxbWb3kR78QY45mrvfT0wPn5GGo2K+di5zMKtg5emKrDwR/adGiej7v1l2JjM7Fu5D1RDVKajB/riaVKKal4vE/eE+bfCPWdjS8Ty96+AqsBun/c8piEEO++ZV91Mq8ZO02DLYIqxDfak2lasA6e11qlspjypvkG0P5mdTCmaVrrfSQUwNCZuwmW5tU0rjfW1c/OxM9Z7RVEW+1EuB+/gifJMsfl97CrrR1iJdCegt0tFcxVmnSXFim2DV1CM3yhiRdMygn5+jG+WLAmk0v79ta2bGUFd23JFnJLGus9//vMsWrSI7bffvtLt2SZpibbYBbvEljxb6zij1qwvj3ObfOOFm1nBz34ASDJ8/nb4w8HQlU2CXPvyjbDzUTB1vzw756JaNHZQuQ+dYQp3Dj7GB4j3/wEd79p3mvc9NDkKJDP7VqQpAERDErURicGEwpbBJOMaotmVsz8HR/4Ynrwqu0xN68JdbAvse6HrMT1NsWJuX2qaZpqBGzOK1a6BZF7Bzjr7tWvs9P/78WeJpxQ6B5KMb4gMS6DFQMIi2I2w0GRMWtzMbdWW7sTmN5UnN5yXb5ghYAyHZTnt8dzni8Zc3xOnY9Dd5ytfFQXnsSsf3FXkIKOqsPY1+PhfsO4tpg/0QCpGREtAOqZv0zQFWqbTEp5IpG4KyaYZaBN2BepzDgXe1+1tivWh4bRkSyikNM9WZxFJofh+hqyaPrPUYRW5Ngw3JQl2N910E6eddhrPPfccu+22GyGH4/U3vvGNijRuW6E50mz73ZPsLriPsy6o8fLYgyBKMyv40fSZROph/h/hT0fpAgggaCo8cCH8v+ch2lj4GNgH4HCFNXZekW6CAKSTiIt+Zl8xdjbscaajYkVlTUqttWEGEzG2DKTsgh3AAV/XI2Nfv92yUIN//RcMdupmcEd7vD5IbjV84ykVTdPXNYT0v8m0Sl88RUPUPYjCS3AMF5GkeENvnN5YGkkQaG+KFty+HNKKSiKVbdNIC0350p24Cd8jidVvyorh0uFMd+IesFO5dCD5KKSpdmtD10CSlAqxlELEMZfRfGjPxBLNvF4U5WOnpGDZ0/DRo/DJ43od7wyuXrJ96+HTV2gDjDTzmijD+F1g0t76vylzUeunA/kEu8y+mj0oxr+GU9Ofm0KCnREAIYnE0K0KqqoVFHqtz6Lhx6qqwxPAU42UJNjdfffdPPHEE0SjURYtWmTrOEEQAsGuSFqiLbbffgQ7zfFCGS+PW1h7fkfgzCBlMf2Z3xe/78OkveHQK/VUIQbdq/U0KKf+wdchrAlcDXNVpb5zrulb0K+98eN7EbessO9w+NUgSmgZrdZQjAtNNSHW9cRIplX6E2l71KYgwOduACkMrzj6b9G1unB3zH/b8rd4maRMoUGzCnb6gFkTkhAFaKkN0xNX6RpIegp2ZuCEY8od8mmK1TTNjFC1moGHioGE/RwjPXm3aiKcuAnfxbBlIEla1fJqXIuhUIS1maDY46MumQLg0At2hXzsnH2aTKs5pmQrfjR2FU93ohYWjgBY9ow+uessLYG5gaCm9brh69+G124DoKZ+PJPHH0ByygEQOQpaptv28QqKyU4CKtNfZkCOZZxRNA2xwMfIOv7JkmjLZRcaQd/akaIke8j3v/99rrnmGnp6eli5ciUrVqww/y1fvrzSbRz1tETsgl1voqfgPrmmWGO5VUArPPs0zQq2D5/PgcbKgZeTmuwwvb5zD7x7v6/drQXsK10Y3QwicX6A0jHGvfkr+8LJ+8KOx+ltqpDvixuiKNBcq9tBtwy4RDqLIhz73zDv+7nrXrkFbj0cVr9kLvL6wBlCg9XfJJYRrIwo1pZaXZgzfP7ccKtzCllBr5DGbjCZNavE00Mv2Fn966D46MjeeIp13bGKBfB4VUiArLBXirO3omp8uiVGR0+8YukdsqZB+3JnMnMvISibG7MizcmLZ1SsmZ7DIdhZ+sitv0cieEIrJBz1roe/nQ9/Oblsoc4LsX8DLcv+wfhF34Zf7wG/3Qee/qnuoqLZNWbWPnUqGFyPbUwIfDwP1ryE+cqYee1ntMMYB7dVP7uSNHbJZJIzzjjDlqQ4oHSao822372p7oL7OAcgIc/L42+QytXYFRWlJUrEjv9fxNsOQUr2Zpc/sgCm7g9Nk4in9CjFGpfSatZUGuVqMJyYg49j9Gl57zZCsU32jY/4kSnJ+XEMLoeW2hBd/Ul6YikmuqQoQBDgkCv0ChSP/he2gIp1b8BtR+vRskdcg6KNdW2rbEkibJg0soKdmPmb9fnrGkgyvjHXTKpY7o8VwxSrafmTFPdb/N0Mp+hyMu0XwvCvy+bZ8y9laJrGp10xPZJWFBjn0h/Fkk13kts/5fjYDSTtfoSVyDjjJdwYGm9nHjvnezWcSYpVD4E5G5BiX56waIvdfewKCyrZyXClXEU8zqmk4dU/wtM/g2Sf9wHGzoYdj6UzNJFBLUxzUyMNDY2QTkL3KtSuFfSvX0q4dxWR3uW6q0whOpfA4uv1f63bw84nEZ10MvGG6a7finzvsj31TP533joBKsZX05nPUM5Uc9Hf++pKwzQclCTYfelLX+Lee+/le9/7XqXbs03i1Nj1J3sKfviszqLg4thsiWLM9yoZ+1vH4FI1VULzFNZ+9udMfcaSvDjRAw9/E+0L97FsUz+aBjtPaMz5GFg/EpJDsCoX12CQ2BZa3/pf+4Yzj4DpnzV/+vFRLIfasEw0pCfT7B5M0lbvYUr7zFegphUeuAhbOTfQAz8++hfNO5+JOv5A5B0PgoYJ5mrrM2SYNKymWIO2Ot3nr2sgybiGSI72wHT+z3FSz1bSSCneSYr74nYNWjylUDdESYOt/nUNUZnuwVRR2qPeWDYH1qb+BG31kbKF0LTiLoCAPTWHH38iK9YAkUqld8gX7Wpd72mKHUbBzprU3NYGD2E5kc6vsSuoPSM3OrhcXIWjwS6471xY+Zz7Tu27wW6n68FWbdtndhmkezBFtClKg8Usn06rrOro03PNJfqp6XyPafEPkNa9AWtegf6O/A3sWgb/uYGZ3EjPdsfBvG/B1L0zbS9Gw1lYsDNNsYJQVKk9p5+iLImQUgONXTEoisJ1113Hv//9b3bfffec4IkbbrihIo3bVnBq7PpShQU7pzbJaYr1SlngxJkZXBSFkjVVkijQs/2J9Hz6NE1LHsiuWPokypt3o44/HtDNIVHRLgFYNULW81ZCs+MqoD3/aySnyftwe4oWLyfyStJSF2Z9d5wt+QQ7gF1PhfrxejWKziX2dUqChnfvpOHdO+EpYOxOMHWuLgxKYcYPqihCGJZJpBM9tHdtQk72UKf1c9CGNchrf0FTapD6eD9COo4mRxAi9RBpyPxrpLZhOs31s4hO2QMadwc529ZQRrBLKio1LrNjRdXMvHmGIJtPsIunFERBMPNRFYvhXxcNieYxijHFdg1mTeOqCpv7E65aTL+oqnuqGAPJkvvNjz+RFasvYaVKkmXlHS8fO/t2btHmMDyVNLxcBLy0hta6xm5ysB+NXaUF1+wkPXPSjR/C3Wfq1WicRJvhyGtgzrk5swQv87NV+NKi9QxM2I90+xFIsqTfzM5l9H20EGXZYho6XkSKdeKGgEbzikdhxaOw3SFw4GWoY/fPtN37+oqJIrbm9CtG8+vMBZgtpRgIdr559913mTNnDgDvvfeebd22GIFSLk6NXV+qJ+/D7JZANF+B7vzBE9n/q5mPSqkDltGGdQf8iKb1L9hmgtKTVyKfsg/punZSLlUKrAN0pZOc5kTF9nXASzfbN9rlVJiwh22RH/+RcmmuCdHREyeWVIklFVcztcn0z8LFL+oOz8/8HLzyHW76UP+XYZxjdZvl/60AmXzI5mCQ6gfH4F6PJUGCKOvmn5lHwE4nEorOIoa3n11/RlsXCYk0REPEUwniHgmN04rKsk39iILA7PaGksYTw7+uLiKbkwK/ZrNkWjXbO74pwoaeBJv7E4xx0dqlFJW1W2LmtUVkiQgpIls+Rh7YCAObYGATWv8mJvYNEG+ZhSQeqEckOjDSFRVjTlVVzRaIUqn0Dl7PvTMyUnVYDcztKhw16oXeBv3/ThcBr9qpdo1dbn/5C56orGBn6+9P/g33X+Buet3zi7pQVzfGo124tsvqf6uhPzfmpQsCjJnJ4O6T2Tj9TFrrZCYNfKhbAj54CHpWuzd6xbOw4lmaph9O134/RazfzvP6ikkWnTXFCtnr8fHuOk3y1ZZCaLgpSbB75plnKt2ObZpcjV13pryW+whvfVazPnb672zEGubyQnUPs0KUcQz7sf1ivkzhZjj+RrjnrOx54j1Mev77rDryVlf1uNOkYmowKuA3lFMibfH12TxPgCZICIf9IHc/D81FJZElkaaaEN2DKboGk0wKFyjtJYVg7ldht9Pg2et0H5w8VU2GBDUNG97T/z3/KybXT2DL1KMRdz4Rdjks50vfl9DNx/UR2fTr84qMHcgEWajoufZKKVNmmCfrIrJ57/3O3Lsz2rq6iMS4hig9gyniKTVHa6eqGqs6B4glVQQlCev+Q3TZQ0RXPYmUsn+UJSzC9POAFEFq341dUmOhcxa074Qs6b6AxXyIBpJp28eyUh8xz9Q5jshIr3FiuEyx1svNCRqy/DSqjmiaZtfYucjBfkyLzrQv5aJqgKYRfuX38PQPwVmCsmGiXlpy2gF5j+MV1GGtSqNpAilyzZtZVxgRJu+j/zvqp3qlo3fv1ysMuQibkZUL2WHtS/QfdDUceJGrr0ExgrBm+Q4UI5w5zdl+g7pGKxWJfujt7eXBBx/ko48+qsThtjmcGjtFU+hNeDvLuiUQNTUThnCGfz85UyjEbsYtVlli9Y1TZx2rCx8WGlc/SdOyf7q+bE7nfMnDrFAOoiBk8sPdYVue2O0Lpp+KDaemb4hozkSldg8mGUikbf88B6baVjj2F3DJK3Do9+ifdCCqNLS54byQ+tcz5oM7aL3/VPj9fvDa7ZDKCs5G4ER9VDYFNS/BzlrqLFFCmbKUxb+uLiyZ74dfoccww7bW6RHLRo7Bzf0J0pl7oWkaa7YMoq17h8nPXcHOd+/D9H+fR8vSB3KEOleUBOLa15i58THkPxwAf/8KkS26eb0Y09Fg0t6HlTJ9eqY7cURGeuVfMzYbam2J1c/YOXk1JqyQ7Rfn85TXxy5vGUZjnKtQf6eTTPrPd4g8fTU5Qt3EveCiZwoKddZ2OTVc1lx/Rj85740pGFn7URBg0l5wzM/h8vfo2v9KUjVjc84rpQZoevo7cMfnYHNu1G4xz4Ni0V4WE32ca4qtrtyQw01JGrvTTz+dgw8+mEsvvZRYLMY+++zDypUr0TSNe+65h/nz51e6naMaZ4JigC2JbibohrIc3IIbcoMn9OV+itdbS9FYB6tiNXbWgV/RNMRjr4Pli3STVIaJL17NxpmHQuM0275WFTzoA0zKSGpZJrYP1TPX2jRcqhShf78FuIlEbjVmh4KGaIiQrJfQWb5pwLZOEGB2e4N3pYa27eHQ77Byh/8H6SQ7assJffoibF4CSgKUJLFYjHQqQUSWiMkNpEONNLSMQapp4t1PVrHrXnORaxohVMvKXg01FWdSbZqIEtNn6X0b6F/zNuHNHxDuW5P/YjZ9BI9cpte43ed84nueTyrdiCBAfVjOfIR1bUkyreb40VmjPBMpBWrc8+p5MWjxr5MlEUnUP+Z+Jgh98RSptIYoQmMmn19TbYhon+4X2JmJGN68/C2an/tvmlY+VlTb3BA0Fd79G1PfvV93TD/427DdHF/7GgJzJCSSqKCjeL7gKUOTbvgDgne6k6E2xXrlbjQwnO8NzZxTsHMz8flJzu5M+1IW8V4mP3YeDWsX567b9fNw0k0QKqDFd7TL2e9WU6xguOp4CH+eY11NM337fJ11O57P9LUPUf/iL3ODLla/ADd/Vs/BOedsc3Exz4PTJQf8vbvORNVmupNAsPPP4sWL+f739fxa//jHP9A0je7ubu68805++tOfBoJdkdTINUSkCAklYS7bEtviub2bCcTpoGpu40Mna80Ubzfz+mq+/Vii/tFWVI1QbSt87n/06K4McqJbn92dc7fty6G/0Nms4cVqWvJhzkY3fQDv/s22rnPn89DqJ3rsN/Q+dgYTGmvY0Be3DX4pRU+mmsyTRgQszvlSGHHivjB9rm19V3eMrv4krfVhuvp1jdTOExtRlTSruv7FLrseB5kAKG3zAAPxNAMtNUQyWiuANet7SSsaM5tUaro+1OsDf/iwd16tWBc89z9EXvgtE2Z/kYF9v4EoNgEQkTMBFGnFJthpmmbT2MVTxWvsrP51kNUA+3mOtgzoJuPm2rBtkjKuMcrqzkF6Pv2Ypnd/zZgPH0BwalYsaAik6tpRom2Em8aTrmkjFotT1/kuoZ4VrvsYjunain/BfhfD4Vfl/aCrloCUxmiITalExfM+uheDz5YuNKs0OFONDIG23Q2vcmJmO1CROz9A2LgG1EHEvh7G9XUjpQfYf/M66j/ZBPJ+MGYWyOFMm/V9/eRlK/v6etbCX0+jYeP7uesOuwoO+lZRZhN7WpEsVgFccCwz8FtBQpOjxHY/l/q9ToMnfgBv/sW+UToO/7xY9/E94hoQpdISFFuyI/gzxdqfBbNe7EhnJh8hShLsenp6aG3VtUmPP/448+fPp7a2ls997nN8+9vfrmgDtwUEQaA50syGwWx5mK5Et+f2WX+I7DJnkWY/viLZ8xv7OIMuipdoJFHQnXON4+x8Eux8MnzwoLlN3fLH4O27Yc8vmMsMwc4skeZhVigFLaNiqF1k919Rww1s2uNiWj1OYSz2o/Usl6baEE21du3U0o39xJJKwVmndZB2L1mlLzN8z8KyqN8nF2toNCTSH881lZqz/pom3Sw07QA4/Iew6WOUDx4i9da9RLtzhTxBSTLm/dto+/hu2P9iOODrREMRMzK20VLpYjCp2ATbRAmJjK3+dWD52BXow7Si0hvXBbvW2rBtXRMDTH75GprfuxNBy9OmyfvCrqci7HwyG9JNdA+mqAlLNGUCZJprQ0yJxmHdmyhLnkJ79TZkNW47hIAGL/0Oli2EU26Gie7au8FMTkhZEsyAm6FOdwLYShd6++IZx6lIczzJiYhV0rD2dVj1PKx6ge1Xv4Rk8QtryPwDGAOw+BFYDIghGLsjTNqL6IRjSY7fL7+PXRGCiifr34G7TtdLflmRo3DqLfq4WSSe9Vwz3wRJzAbG5WjsPO6l7fhmEIQGNc1w0k10TP0crQu/Tbjfocl/4bew6ROYfyuSGLWdwwtr9LiusTPa5sOEGyQotlGSYDdlyhRefPFFWltbefzxx7nnnnsA2LJlC9HoyPj5bO20RFtsgl13wltj5/YSOl/qbBmtwlhnVKX61xm4mlCP+yXp5YuR413ZZf+6QhcO6ifZ2mv62HnMPktB1aBp+cOEVi6yLR/Y+2KUaIvnwDGUlSf8IPscnPJVNYDsIGf4nhkBDG5E5VwfOMWWrsPhgzNuNtK42XTscSnJjxcy9v1bqV+zKOe4QjoGz/0PvHorLftcSs/ML5FI2QVZw2esJiwSS6ok0mpRtR6d/nXWa9e0/DniumOpTPJsMRuZrKrw9l3w5A9pGdzsftJoM+x/CexxJjRPNRe3ZwTFWFKxVwWpbYWZh6NOO5gnB3fh6KblSK/equd7tLLpI7j1CL0u8IELQLIP1YOGABuWi9JK+iHfR96u3XcXAL38uLwotZ6nqmqgpqlb+yK8/JgexRnLjjG+w27UlBkMNI0/k6ybgLjHGTDnC7rA56CsPHaapmu5HvsupOxuF1rtGISz7oEpnynhwNn33+n7Zw1MM0yx3sETxZmgBycfxOb5T7LDO9cRefM2+w5L/g1/Ogrx1L8A4wo+D84JqlNZkQ9n+90Ss29LlBQ8cdlll3H22WczefJkJk6cyKGHHgroJtrddtutku3bZnD62XV7pbLAXRuXk8cus9zPgGmtWlGuMONqQq0fy7oDr7VvmOyDB76KoTZKO2bfxczWCiHEe5jw0jX2hY2TiO/91fznyGOSGg6yfiL5Rzar+cL1OI6bWZMn0jSSEfqs/khWJ3WvvpjQVIMy41BWHP1nVp25CG2v89CkcO6G8R7q//MzZv3tUELv3WsbtQczZtTGmhCiqF9XMQEUTv86yE3Q7IVR1q3F0NatexNuOwr+eQm4CXXhBjjku3DZO3p1EItQB3oNXSPwwojEdKbkSMkNqIdcCZe9w8AB30aVHHkM1TQ88zO9HV12E26/qZmUKp7aQcszBljHGVUFVAVxcDNs+AA+fQ22rEJUk7bj5GMgkeb9db1s7IsX3NZEScGyZ4g+8W1m37UvE/55hh4QZRHqyiE8sB75hV/B7/aFPx4Gb98D6aybjFWwKyqAon8j3H0WPPT1HKEu0bQ9wleeKlmos7bL+RhY/c+8zJuqQ+OV7/i2euSahhaqJXH0dXDCb3Ttp5VNH1L/56Oo2fR2wb5yfnuKmdw7gz+M3JCwbfrZlaSxu/jii9l3331Zs2YNRx55pFlabMaMGfz0pz+taAO3FZyRsfk0dkZFGLvyxP5Su23jhZsKv9RqC24mVEXV6Jl+LF2zzqD1k3uzG695CfHF36BpO6KqujBnfKSKma0VovWV63JLhx373xCph3jcc+btzJIy3Bgh+35nuk4BzkByFMGO5smVZ2js0opmlgjzqkNrRRQFprTWsnRjP731M+g45BfIe12KtPh6Wpb8LaeMUXhgHe1PX4b2wW0IR/0UZhxiJtutC8v0ymliSYVEyn/KE6d/XbZtFr9Pl0MNJtPEUyqCAM1aDzz0E3jjL+REKIJuKpv7VfjsZbr2LQ9j6sNsGUyaWkTPCUJNM6kDr2DthKOZtvhyIpveta9f+zr84WA44dew66lommZqN625+nTNamnaLytuPrwApBPUrl5Ey9LHqe18g1l9G5DiXTn+hg3A7OgYlPp2GDMdxu8KE3aH9t2habJNYuxP6Clb+uJpxjXgTaIflj0NHz0CnzwO8R78hRSA0jQVsaGdfi2CGqqnvr6B1SuWMJGNRAbW5t957evwj6/q/mR7nw/7fBmxod1crWrgq8b8Bw/pQUWDucl/+9vn8ulRf2R26zSfV+SOl9uBVbOqeYyrfkyxpglatQp2lv32/hK0zYT7zrFdpxjrYrt/ncXaY++A1kM9j281GQNFBU+4JaqWJT0gLa2qhCuTAGSroeSaPvvssw/77LOPbdnnPve5shu0reLMZded7PbcNp/GLpvHzpj9FB51bLPwMjV2brMsI2XHxgN+RH3HS4R7V2XPvfi/aZx5FXAgkJvupGxT7No3aH7vTvuyWcfA7OMRBgzNgvuuQ11SrBB+/UScEcU5x3G0P+pV9ytzjLAskkyrxNMq9ZJoagydGicnEVlickstqzsH2dyXRBLHoxx8PdKB36Tp5ev1pKcOhI534M8nosw4nNDul6OO3Z3asEQ0JBJLKsTTCk34i4wdTLgLdrIoklRVzw9EbyyNoCSZtPT/kF65MdcsajD7eDj6Z9Ay3Vd7BEFgQlOUlZsHM+3w7j9JFEi0zOKTEx5k/Fu/YeybN9n9+RK9cP/5sGIxscN+gqbp+0RDkk0TYuRsKwfb+JIchI8e1QWqpQuZmK9mqYVQfDOh+GbY/J6+r0FNC4zbBVqnQ+sMpPBkopGJusCs1OgzUk3RS2pteE8vQt/xLmz+xF8VedCPP+0Ausbuw4amObS0T6OxRmblxgFkSWDmmBr+ff+/mLv/gezQIlDf/RF8+irau39D6HjX/ZgDm2DxdfCfGxBmHknL+MPonXoEqtbgnUA90Qcf/QveuUcXSl1I73kOK+dcjRwp34XJy/fPlqBYc9/Gj0+2W6nHHHP89M/ChU/DXWfakqRLqX4m/+uLqLV/9jy+M5dpMQmK3dxRZFF3Cwo0dnlYsGABP/nJT6irq2PBggV5tw1KihVPS9Susev1Ezzh6mOn/9bM5YXPbZoW8Ddzy3ssw4Sq5gp2Uk0Dm474NRP/caqpwRHUNJ9ZdTPL9v08QihbaaAiwROqAo9cbtcoyDVw7HUgFC5ZY03yPBL4zcVk9JGnxs7yEEhi4VJd0VBGsEsp1EdkXxo7g6aaEGMawmzuS5r71U7aCU67Q9dyPfED1/qX0vKF7LB8If3bfw7hmB8SiU4HUqa2yw/JzHMWcVyfEVDsOsBrGtLSJ5i5+EdEe5a5H7h1e/2Z2eEI320xaIiGaK4N0RNL5a0qUhuWqQlLxFOwYa9v0Tv5cKY8exmRnuX2DV+/nfCql4gcchORiTsBugBpaCXTqne9Xr+oGkjxLmpeuAve/JOrlqlkYltg1X/0f+hBDO51FIpkwh569ZhdTjYF71RvnHRvAkXTzOfIeDbM3GqhBjMYKL3f11n53is0L3uAscsfhP4NuedR0/DJY0z+5DE0QUSbPBd2OFz3tZTC+j9NhaVPwceP2RKh26gbCyf+lvj0I9E2DXi+u8Xg5ftn9T8z1nn72BU+vnW8VFX7OkDv/wue0LMhLH8mu386jvC3L9I+9WvAcTnHdwqJfif31kpM1n6URBFQK1Zqb2vCt2D35ptvkkqlzP8HVBanKbYnj8ZOcdGqeQdP+PGxw9yn3DJazkTJAKnMiyVLIurkuWzc8+uMf/PX5vqGxHqmvHgVm474leV6sm0qmVf/BOvfsi875ApomebrHPl8jYYDv7mYvPKJOY8D+QMnsttI9MbSZgBFMYIdQHtjlMGkwmBCIRoSCRmS1cQ94UsPw5InSD3+fUJdS3L2rV/2KPz+MZp3OY0ts75CfPxOvs6ZVlTzIxN2pIZxnSSoKnzyGCy+nrHrPMazUB0c/C3Y/1JbbdximdJay6QCDtySKDBzXD2aplfcSLQeQO/Upwj9+9s0L/m7bVt584fMfPA4YvtdphdklyOmVrJsP7uuFbQ++ysaPrgHUSnC7w10oUZJFt6uQiTG7cmWqUcR2eNUWqbkPifW+24K/RlbvPGEOP3F4m07sWHM9xl70s/15+PlP7hOREDPQSiseRHWvFhcw2cfr5vV68agZiKxK5Er06uMmpHjXBIENA8tmKuA5sDLx05vv2PjaCN84V64/8s2ja2gJPnMit+ivjdbD06xtcHhZ+3THceeostiis30R2obTHniW7CzlhELSopVHqcptjePYGf6wFg+FM5gg2KiW60zvexLUtpA42aKNTL2hyQBWRTZOOcbtKx7lvCGt8xt2pY9oAtcx/9IP065DuHda+Dpn9gWqWNmI+5/qfnbDBrxOITZhyPkZec32tFZJ9HrOED+WrQZspGxqu38fgU7QRCY2lprpvhwrIRZR9PbfiDxl++g/c0bkQYd/o+aSui9e5n13r30T9gf7cD/hzD7+JzIUCvZyYOQI0DZniVV0VPvLP4fcMsfZrD7GXDEj6DRPcdhsfgNwBEE3bwaDUlQ20bstD/y6fMHMPH5HyBatD+ikqDu+f+Gj/8Bx/8KqXZ3oAzXhbWv6ykqPvgnTXlMnhoCg+P2Ir3DMXQ27oxa08bM7baD2jYQZYh3o3avZdXKZYQG1jMptQphw7vQ8Q7EPUzcfhEkmH4g7HQC7Hgc61LN9MfTTGl197az3ndDY2cI/cbtsAYm2XwLJVk/z04nwIb3dQHvnfu8NXB+iDbDMb/QI6gdk41KxGdZj2H1/XMzxdqEM8v44qeUmtX07+mPCfpk6LQ7UP/xNcT3svlDRVSEf14MaLZExs5xxm++QK/gLr8+yqORonzsvvzlLxfcRhAE/vSnP5XcoG0Vp8auN+k9CLqlGXCq4U3Nm48Rw/YCmS+qr2bnILhoR1KZ/4ckURcyxBCbjvwtk+49GlKD5nZtr90Ik2bCnC9aHGdLaESsG/56mu6XZEE97peIcjZS0xiLvKK1hjNBsRt+o2KdvilOrPWA80XEGkQc9VwNjaHsJ9t1hpAkMqW11nN9NBJh3U5fpH/2fGau+Cv851dIyd6c7erXvwh/exEaJ8GeZ8PMw2HS3nrNXAtJc/KQ20YJhbq1z1H7yr9h6b/co1wNJs7Rza5T9vV5pUNLTViiaf8vsWzsnkxZeDHRLR/bN9j8CdxxHON3OpO1u12C0jLb/8FVFZY+Cc//xjSNejJjHuxyCh3th7JZa6axRmYgltafuYZGS4NbEGta6FemADB+QoN+TzQNulfr/nJdy6BrBUrnMpTNywn1r0VAQ0NAECVdgJMjMGYHaN9ND7po3x3G7wzhOvNUysZ+oLBvqapppjuI8Wxn/bcs3eFljhy/C5z4GzjyGvjk3/DRo6hLnkJMD1IQQdKf2d1Ogx2P0wO2LJTr+mI7leU9V1QtJ1G0UWEI7MKOPWjO+/jZ/szs50cglEIIp9xMV0qm9eO7s21F0yPO1bQedEFuX3hpIJ14+Qduy7nsihLs7rjjDqZNm8acOXMqVicvQMeZ7qQ/1YuiKkhi7oc4Xx474yUoJqIza4otLujCDTdNWyqd/egas6hY43Yw/09o955tj5h8+JvQMAFxyiGZNhX5nKUTcO8XbY67AFt2+DxN0w+0LStUizDvbHQYMDRtqpp/YDNnunnaGQ3p1R78aOwismh+IJJptWiNnR+MSNekUEPfPl9n7aTTaP/gVtreuTUnFQQAvWt15/XF1+mpRqYfCDMOgZbtoLYVRa1HStVSI8iw9mPYslJPEbJ5CRM/eQLRmkPRhUTjNKRDvoM856z8jkYjQEM0hDJjd5bWP0z7a9fR9v5tOVHGDR/ew+wP7yE1cV+Ycybscop71G6iH1b+R/cBW/qk3k8eaKKMsNtpcMDXdeEGoCcGfclseiKPrjKeH/P9FQRdI59xgwDoG0yypiuWCYoQaKwNMa2tzv2ALhR67o22Kapmps2JyCJoqqvGrqCQVdOia9v2OJMV6zYjrVzMxI2LCPeu1lOwZEr4kU5CQzvsdLyenL3O24uwUFm0YnH2uzXpr72kmLUN2X3zjfte7j76ujxtkmTWHfQL1FAdY9671bJGg4e/oQt3n7kgJxed9Zj5goK8BPJQZkEQPFGAr33ta9x9992sWLGC888/ny9+8YtmBYqA8nAGT2ho9CZ7c5ZDdqZk97GzrNeKi+i0hrGXq6VyNcUaUZWSYL5sKUWD2cehHnUt0r+/Y2l8Gu77EvI5j4IwvTg1uqrCPy/N8YmJN81k3X5X0+y4pkI+diOeoFjKClhpVTPL5Dgx/WPy3LRpbXWkFY2ID896QRBsZb8UU2NXuY6QRAFZ0mt5dg4kUSNNxA78Lsz7OrzyBz0v2cAm952Tfbr/0yfZWq2tmX9u5BPTtLGzWbPLxfTMOJ5dJreOnHq2AM21YdJjmlm/39VsmXkK01/8HqENb+dsF1r3Cqx7BR77jq7tkqO6dlOOQHIA1rwCairvuZRQHV07foGGeV8n2jbNts6ZUiNfwE5ayQoVbhj5/UIhiVRaKypnoa0NXho7IzF3Wi/NJwi6KTadVvNr7Hw8AkIoSt/Uw4nteQJhp7tBEbiN5eVgrfsNuUl/Bcv1Glo9v1WKjNVGv/sVCAFEUWT93KtorKsh/PJv7SsfXQCqgrLjOXo7M8eyaSA1zVNY8XoWJdMUu+352BU1Nf3d737H+vXrueKKK3j44YeZMmUKp59+Ov/+978DDV6ZODV2AFs8ctm5aZKMlwCMtCXGNoXPbdP2OZYVi1v282Q6Y4oVxWwNP0UP1FD3uYD32hwRUsk+IvedQc3Gt4rT2D39Y3j3PtsirX48K4+5Ey3anDP4GL5z3ulOMtuNlGSHd24qK4Xy2IGuLfWjrTMwNGrxlGLRzlS2H4xzDFry11E/Fg77AVz+Pn3H/Z7BcXtV9JwmE/eC0/9M8qL/0DPzZARJrvrs9GPqI4xviiBM3APtgoW6yThc776xmoJ1b+iF2Vc8q9f2XfV8fqGuvh2O+BEfn/USHftdhdA0OWcT4xEzzFte74af59YQ5BoyZeWSmUojfikkkDjdU8KymI26z2xj87HL/Ndfiih//l+FqKQpFnLr9DrTgIiWxL1ZAS2/9tXA2Z/FTHxFUd8wdtD3+Lj9pNwNHvs2ta/fnDlP7vXk62azDx3vr1m5ZxvU2BVtc4hEIpx11lk8+eSTfPDBB+yyyy5cfPHFTJ8+nf7+/qFo4zZBWApTF7KbIbpi7oKd14BmfWFN/4ciBDtrHrtSyZpi9d+appkDSEgSTC0UZB3eXx13OlumH287jtC3nu0fPoUxr/6PbubIh5KG526A/9xoXx6qI3XGPaQaprgOPlZB2I2RjoqFrANwvsGpUB67UjArUKTUIdHYQa6/n03wlCMIe5zBshMfZOWpj+qRqeN3Let8iXF76AER33gTLnoGdj6JtKpfk5tvXjUyriHKzHENhMMhPVHyJa/Avhehumj2fTN2Npz0O72KxoGXo4SbAHdhwymweT0SfqLaDb/IurBkamb8au2cJkb3NtiXW6Ol3dpXTH3tygl2/s/ph5wKRC59lGtStS/3wunzVoyrinlOBD6aMB/l4O/kbNP83I8Y98aNrr7j+SYIzmhaA8MnOPCxKxJR1GdAmqahKMUX7A6w0xxpZsDiX9QVzy/YOTU0TjW8sawQtsHAY/bjF+eLmFKywpFR5ikk6XnSUopKWAQVkVUHXk99upPQp9nUAYKmMP7NX6N1LEI45RYY53AMV1U9wvGZn0GnowC9IMHpd6K27wEb+l0jWwvVfBxpHzvQhakE5M3FVGk/HXBq7NSKH18/R/ZDayTbta3P5Bzra90NddfP6s9k/0ZY/qyeH6vjXT2RbazLFoSDIEHzFD2fVst04i2zWNl2CHLbNGY6yhukC5jzqp6mSXDc9fQe9EO633mMMcv/Qf3Kp3R/Ly9EGabsBzMPg+0P13PAuQQ95RPs8m0D+Ap+Spp+b0ZCar0+sJ9KI04ToxvO5ZFQrmBnnTAVZYo1x8zC2+bDr7bML9ngNf23mzZfEgV98u/wlSs0zjndfYoThPW/ZgL9g76NJIfgaXulqvFv3EhcGITP/QJE0cxBmS/a28tiIVsEUWswybZA0YJdIpHggQce4LbbbuM///kPxx9/PDfddBPHHHOMWVosoDRaIi2s7c+WuPHW2Ol/BUd3uxfoLvwwW9N+lFtGyzkbNKLRrI6vsiSQTOszqbCY8QmUIwyecidND54Da162t2/923pJpe0O1j9kjZN0R+Y3/qynUXDj+BthhyPRMqWX3B7N7GDjHpww0iXFwJh1KnlzMfkJnigWI+WJ4Z+kt2VoTLGg1zx1Ikui+RFKpDOBH/XjYPfT9H8Z0orKR2s2ISW62XFsFLFpki01ipJIk9o0YPMvMtcNkTZyuJFCEfqmHUVq5jHs0KjofqaxLXowkeHYryowbieYfpCeZ8wF6+fTrUucywqZQb0SjKuqZmpSwrJIRJYygp0CPiqNGBHb+T7WOYKdxb/UTFDsVR6rAKIpNJSpsfORP64YnONvNhWIVbDDts5vyhVn1G1xgrCLoH/wt/Xch09ebds2+vofQBmAE36dnXznUeR6CceG2Vn3UVZdAxFHK0UJdhdffDH33HMPU6ZM4ctf/jJ33303Y8ZUJG94AC5lxTyqT2geQptV81aMUGKdTZVrGrCq662Dt9XUFZZEBlFIKiq1ITHb1rpWOO9fukn12V/ogRQGSkKP4vPDvO9bQui9r8c62BnO1VZGuqQYWB2A/ZhiK3fesCUy1mxLhYUfa/Rtbdh9KIqGRAYSCvGU4ukjmFI0NDkKkQmIrbkCS76ciGmXicfWiGyNAKxp1vOvlYDVb8rN18y5zOuZK2SKNcywkiggiYJZEcJvpZENvbpGMidPorMdYlZ4slYksQZPGJO6Ylwvstfnq7meVNoUKzkEO9VFI+1M/FuMQGuNus0GTxTez9kuk89+U/cTffRb2KYVb/0fJHoRD/01IOXV2OUTjg3r0FDlsounFGJJhWhIKsqHeagp6lNw880309jYyIwZM3j22We56KKLOPXUU3P+VYof/ehHmVlC9t/s2VlzXDwe55JLLqGtrY36+nrmz5/Phg0uZWC2Epy57LxNsfpf53fWOivyEv7csKb90MxlPhudcyxrOzVT0xSyfAGMj6ihzTOuRxYFXdNyyLfhK0+RaNmhuJNPnAPnPKhXl7C0AdwFXGdbnaimM3VxzagkhRyAvcrpVAKrRk0UKx9EIgiCORg2RN0FO6NSQD7fq3w57CDXodxKKTn6qpGyE3pnKOQQX7TGzqM5xv00yttF5ML32aA3niKWVBAEGNuQvyKIVaCxltKz3m1nlOfI+NiVdRgTp4nYyxRrXVeKb6Fm+8YUbldeDe5nLoBT/4gmOASjDx+i/Z9nIcW3+Asec2mIsSw1RH52/Yk0n26Jsakvj+vDCFCUxu7cc88d9gjBXXbZhaeeesr8LcvZJl9++eU8+uij/O1vf6OpqYlLL72UU089leeff35Y21gpnBq7LR6CXdZx2V1jZ81j50dlZ4umLdP+aK1bqWjZxKAhOXtA4wNsaPOModx2PRPnsObz/6LpxesY896t9nqvTsbsqEdS7nRCzhfJ7AYP7UN2Bpp72JFOdwKWQdhjYLK2eyh84GIZU/ZQCT5TW2tJK5qnX5XhZ2eY3txIWnOUuWAtXq6q9tJepil2q9fYeV9jMRTyKy3Wx87LVKmbXLP3zAzWSRf21d7Yq5c6a6sPFwx6MdxTRNEu+AtCVtto1Nctzn1F/1uxqNgKvbuiYxLjVpXG6QddjJ+fkcbGnnmhmP7y2GD301gzIDL5qa8hWvxDo+teYvuHTqJ//l3QsIvrrl7BE+C/ek+puH3fqoGiExQPN7Is097enrO8p6eHP/3pT9x1110cdthhANx+++3stNNOvPTSS+y3336ex0wkEiQS2Yent1fPeJ9Kpcx6uCNBY8huRtoS73JtTzKV0v0G0mlSZGe4qqKQTqdJJPXrSKdVlHSaQpekpPX9NFUgLGqk02nUtFRyX6iKQlrRSCRSxBJJ0uk0qNnjCap+vsGERjKpmdeiKfbrSQthPt3nSuS5F9Kw4WWE3rXQtw6hdx1C33q0urGou8xH2+10ECVIp3Pakkym9OuRNNfrSafTeiLeVApBE13XKek0qXxOHkOIlrmn8aRGKpVrdkqmVdLpNIKgt7cYjP7wus8yqnlMWRCH7N2QBUh5mOCkTBv6YwqpVNh1G/MZ07zbqCj6vYwlkjbNjb6vgqYow/ruF+r7UvC6xmJIJNOk02lEBM/3xfqcKYr7OKFktosnRVKpXKF9MK7fMyFzzwRVM487GE94Cmy9sRR9g0kEAZoj0YL9p4+Jes1iZ59rikJahUQyiYRMMqWPFV7XZDtu5vqSqfLuoT4++Run/WD0eyLT73Fj/FOy168qxjZJUimRhMs2+Y+vkkgmzbFHUQr3gXHOZOY9d9u+a+KhJI64nZnPXIiQzAYRRnpXIt91LOnT7kSbdmDOfgnzvqVJpRzPjZYZPxNJUuHKC1/Gc2z9vg0VxRxf0Ko4Ad2PfvQjrr/+epqamohGo+y///5ce+21TJ06laeffprDDz+cLVu20NzcbO4zbdo0LrvsMi6//PK8x73mmmtylt91113U1nqXQRpqXk28yj9j/zR/TxAnc0nj/8vZbk0mq8zE2qwjLEBnHAbT0ByB/hSkVRhXAy5+6TbSKqwf1GdVUQliaWiJQH2JeTfXD+rHHFsDPUlIKtAagbrM8RIKbIzptQzH18K6zDs8uc6uHdsY07e17lssAynoSuh9MM6lpOTaAX0WOb4GrC4SmgafZto1sS5bd3G4iadhUxxCIrS7PJpJBTbEdG3tJP9J+/2dW4FNmdKYXv031Cha9vmYVOdu9tkU09ua75n1us8dg5BSYWwUPKzBWw1e11gMxj0v9LwZNIWh0UXe7klCb1J/b1tdrKVu7/a6QT1N0tgafRxyw7hfDSF9nCuEcZ5aGdqi7scaE4UaOTt+el2Tlf4UbEno+42J5t82H+sG9GfczzjtB2e/dyX0MbAxrF8XQHcC+lL6u9IS0a+jP+WvTzfE9GegLaqP8T1J9751YpzT6xzW8XZnYSX7r7iRmpTdYqUi8fbU81jddohteb7vhPNaK421P2qHePwYHBzkC1/4Aj09PTQ2ugc/GVT1UDZ37lzuuOMOdtxxR9avX88111zDQQcdxHvvvUdHRwfhcNgm1AGMHz+ejo6OvMe98sorWbBggfm7t7eXKVOmMG/ePNra2obiUnwRXRPln8/907JA47jj7Ml7VVXjg/V9AOw0ocFmflvbHWPLQIrxjRG6BpKkFI0ZY2s9HdMNUorKxx26tFgfleiPK0xqidJSW2B082DZpgFiSYWprTV09MZJpjW2G1NLXUS2nU8QYFpLmL8+/DT77bcfe0y11w5Y3TVIbyzNhOYobXWltaVrIMm67jgNNTLTXGqXftzR59pP1n7eeULDiCWvjSUVlm0aQJYEZrc35KwfSKRZsXmQsCwwa3zu+nykUimefPJJjjzySEKhXIkorah8lHkummplprSMzKTno44+0nme5SUb+0mkVKa11Xr66n2yoS/nObQee+a4Ol9pNipFob4vBT/9UIi+eIpVnTFqwhLbj82dKSTSKks2ZPOVer2bm/sTdPQkaK4NMbkld0ZgvHfW+7Gqc5C+uPf73hNLsaYrhijCrHH1ZvqkfBhjyLjGCOMy/nhG3x904GdJKAKTW2porg0VNd50D6b4dEuM+qjE9CLKoDn5cH0filq558/Z7+Y1NUVoq9evf1Nfgg292W2s341CPosrOwfM70MyrbKpL0lrXYiJzflnfRv7EmzsTdAQEXj/ledynnvrWLPLxGMR+k5B+9vZCJasByIKc1b/id3b0qhH/UyvqgIs3dhPPKUyra3GTHbt7I+hGr+8vh9DgWFZ9ENVC3bHHnus+f/dd9+duXPnMm3aNO677z5qakpXH0QiESKR3Ac4FApVbJAthTGOmoI9ye6c9qQV1fQzjIRDNt+xcCiNLGtIsowkq2iCRiQcJlRgwBAlzTymIErIskAkFC65LyLhEClVQJRkNEFClqEmGiaUcZCWZY1QSNb9gdCXRcO5fR8OhZBTIEpSyW2RZL2/wrL7vQ2HQ2gpFVkOEQplXwdrP4cd/Tyc6P2XQBBwbb+o6O4KkbBYch95PfehEETCCRRVIxou/Xkol/qaCP3xNAruz4EmiMiySG3U+1mPhMOoKIiybD9G5vmMRgr7aw0FlRxzouEQiqb4fl+6BpJ09ieY2lZrBi9IKZDlFOGQxzFE1ebn7PbeAoRDKrLs3hZN08xxoa4mYvZ7XU2YWFrPa+m2T1csjizLjGuMUBP1p34Z31yHKCUY0xg1xx+DSDiEkgIh00ZJkpFliPi4J+Gw3k/ljE2gj22CqD+foRLN57Z2OfpdNK7J8v5Gwo5txBSyrBH2uJdWIqEw8XQKSZKRUJFl1dd+2Xbpv53PvSooyLKsl30Lh6FtKnz5cZL3fYXw0n/ZjiW9eSfS+jfgtDuhbXskWUbWVMLhsG0MB6iJaMiygiDKFR+/NE0DUUIWoDYaGfLxo5j2b1WhYM3NzcyaNYulS5fS3t5OMpmku7vbts2GDRtcffK2BpxRsYPpAVKOqgv56vNZnWKLKvfiFh1ahhxjOKunlGwONGtUrCAIprN6POP47qYRM6MZy3BvK9QPgmM7g2zQxciWFHMm2XSSz3G4EhhJhEcyuaeZCsPFsT6tqObzEc4zsLoFoRipTmDrz2MHxUfGdg0kiadUtgxkx5js++IveKLQdm5NMSJfBcEe0GAIl0mXyNieWIpESkUU9dJqfqmPyGw3ps61RnJ2fCklOlT/W44nk716Q8mHseHsd7eqNM7npJSgEaXI4IlC6WFcq4iE64idcgebds91R6LjXbjlUPjgn3nzeBrHs77rlSKVqYXsfI6rgepqTQH6+/tZtmwZEyZMYO+99yYUCrFw4UJz/ccff8zq1avZf//9R7CVpeOMioXcXHb5BBVr7qisfObnZc3WDzQiVcsZaIxjGQO4JAoudfzs0Y5uH9Z8aSp8UyDqzDVxZrnnrCDW2o5pFwl3qCsnNNWEEARs5svhxjBRGRG6Vow0BrKU+4xZMQZ9az6stCVicCSF90pRrGBnCFB98VzBrlCpMK/f5vI8766RnsYZxZwV4O3PuaZpbMykkxhbH6nYs25+9J1F7X18FQulc/GDdd+hSlDsVpUmJ3K2mLQljuhr6zn9tMtLEPbM9CCJdOz7PToO/zWEHKbURC/cdy7jnvsBQjrmGtVrCFzJIRHsqjcHZlULdv/1X//Fs88+y8qVK3nhhRc45ZRTkCSJs846i6amJi644AIWLFjAM888w+uvv87555/P/vvvnzcitpppDDfmCGJbEnYH0nzpCMwKEholzwQLzdj9YAwihoYl5PLgG9oVIyGp20fZnB2WMXoWyt7iNfOuhnJiBnKeJMXG4FJqFGQh2uoj7DKxkfoRFOxqM5EAg0kl5z4VymFnYAz61jxaxge92mbbpWJLUlwARc3WcI6nVPNdLaSFsU4C822XTQDsIthZSolZMQS7ZFq17Wdo6yRRMP3EKoEp7GfOVVruz3LGpuy+lUt3ov81rsVNmyU5hNJiUq64KQ+KEQi9rC/ZXHTO8+n79ewwHy58Rk9t5aDtgzvZ4R/HIq17I2edkQRdVbNjZaVwS75fLVRfiyx8+umnnHXWWey4446cfvrptLW18dJLLzF27FgAbrzxRo4//njmz5/PwQcfTHt7Ow888MAIt7p0ZFGmMWKPdumOd9t+51Obu5XJ8SuYOOumljPOGIJdPOX90TWEFWN27qqxq+DgWSgvl/MUpsZz5OW6vEmKjY9kPjNkuYy0NisakpBEAU3ThTsrhXLYGRhCj1VjZ5hlR0sNyWI0ds6PXF9cTzXip/qCP8HOW6OVTLtPRmRJNAUTq4bF0NaNqQ9X9F7lmiSNthfe18zLVgE3kUqmiPQ2xWa3KccUaxUKSzFde1WQMLJJOY9la+u42XDRM7D7mTn7R3qWI91+tF57Np00lwuCYD5n+XJhloLxjA7l2FsqVR08cc899+RdH41G+d3vfsfvfve7YWrR0NMSaaEn0WP+dmrsVI+ZDWRfCqsA4PebLIp6qgHnsUrBKSy5qaqdwp7b+SqRTd/Y02uw9ko0Wg3lxAx0oUR1TVLs9ZEcbdRHZHpiKQaSaZtZOFWkxi5t9bFTvScVWyOFqpRYcZo7++JpxtRHfPlNiYKASn4BMJ8bRb5nVq8Zq5BIqURDEj2DWd+6SmrrrG0spwJDOZPOobAKWP2svarSeCYo9iXQGpq34spPFjTFehwrp5/DdXDKzTDtAHjsCkjHs23TFFh8PXzybzj5f6F9V0Cf9CVSKom0SnF5A/Ljd+wZCaqvRds4zZFm2+9cjZ3+102L4nxhi3H8r+Tg4pxVuz34zlmO28fVy/+tGApV0vDSLFiDVEYasyyOi3rAWZpptFKbSfI1kHDX2LmZ+624aX/NcmJVODCXgljERMjoNyM4ZiCR9v2xtq7z0qDlc6PI98w6A2U29ukf7jEV9K0z8NJcFRNwVo4rrpdfWTlYJ6pePnzWet6apuWtterEtR55BXwSTZOx6BTssLVVP6Gg1wL/6nOoE/bKPVjHO3DLIfDkDyE5WFS5umLICnZV8JFwMDpGtFFETlmxHB8778HAeMHcnOwL4bcGZCnHymeKNfcZouCJQrNiwRw47OeodA3HcvDysbNGHVejOaCSGD5+A4m07V759TF00/5m68RWwU2uAMWUTzLMSA3REGFZRNOgL5H29dxb1xXv4pAtM+j2zGZLi6n0xFLES4iE9Uuuj5297fmwbuNa/9QHQzHGWIU26wTfOr5az6eUqHkruh654ePqZYo1zdLupljjnDbGziL+pcfo2Pu/0ESH8VFNw/O/gv/dn7o1zwKQqLApNltOrPrG3upr0TaOM+VJblSs/tdtMDC1EkXMwAycmr1yJpE5EbA+TLGuoeqC/w+VF4VMquYHyLmfuWDkP/rZkH1H4IChrZKFEfeDG2q8/Oz8Bk+4TRJGq4+dn4md1RxqJDPui6csvqXefWJd59V1kofgk8xMRgTB2xQLusZu0xBq68Au7NtTj/gRVKwCR6mCXe6xysUqUHtpBK0BMEqpQRC2dCfFtcsNxUPItbXVLd2TILNpzjdYc+rDMHan3ANvWUnD/acz5ZlvkO5eV7ihRZBMZ4InhqiOdjlUX4u2cXI0dnF3Hzv34InShTO/xb394BTS3GbmTvW128BdaJbnh0JRsZ4+dtWksTMc/1V3wW60a+sMrFo78J/DDnJTW0DWtF2NA3MpGBo7VS2cX82q6cwKdv40dsY7k8/Vw0vwKeQTaphiY0mVWFJFECi56kwhrNot63Ph953Pjh2lnb8YjZdfrG03nm+3sdVtsliUKbZkHzt34S5fLrp8/ozGfslxu8NFi+DgK0DMTeTbvOxBtr/3EJRF10EqlrO+WFRLVHlgig0oiFNjt8XpY2dRrztxLitmwPCbn8oPzoHEy3/OqsnLFwxSkcizIqP3qindiZtQApaorCo0BQwFdRk/u/6MYOc3hx3k+p9a/y9V4cBcCtb3Lp+WW9M026SgPqJn/E8rmqkN9eNjV+jVMNZbfZsKTUaM9BQGY+ojQ+YDKYm5+TuheL/kUieeWY1aSbu74paT1K37TL9dS8Scv3Qn2fGy5CT4LuvN8bZIlxzjeyiJAoSicNj34f/9B6bm5rKV0oNIi34GN+0L7z1QloOkITQLQnX66FZfi7ZxnMETW+Jdtt/5otZytW7+z+s3o3yxx8r34Idsgl3uNl7mnGIwTUseT7o563Ycv5hBa6jx8p3aViJiDYxoWCOfnV8zLLhXMTE+fKPFx04QhGz0b573xZ4xXzfjG1q7bILyfIIdBbcBzGOu7ho0gyEKTUas6SkEQU9xMpQY12C0qygrh5kzrrRzF1O5oRiMwxlCW75vhTE58qu0tgqzxbj8WAXOfBo7v2m8zP2MLBHW/cbNhvP+BSf8GqJNuSfrWQ33nw+3HQ3LninpBhr9Vq1jb3W2ahumJerQ2HlUnsj3AhiUOl6UO85YNQf5PrrWdW5KE+vszSv/USHMvFwe670GJa3A+uHEy3fK0IREpOErXj+SREMSspT1s/Obww7swpvhU2V8KEaLYAfeZnsrVoHYmMA5i6fni3Q03olC78bkllqiIZG0orFy8yApRfU1GYlm/Oza6sNDrg0xrAbpPEKQF+Vq7LwCBsolK7T5MMWqxV23IQCWkivVFOxc1rkKaOY5vS03nhNwUYS9z4NLX4M556C5fQHWvAx/OVkX8JY+VZSAl0r7n1SOBNXZqm0Yp2DnVVLMbSwoR+tmHVzKlWWsbctXbsU6aHs5R7sNJMVQMJO+uZ27xq4a8PKd2tY0dgB14ayfXTF5pJxO2FaN1mgJngBvs70Vt+fG0K4Z5PtYG6sKdbskCkwfU0dYFkmmVVZ1DphJy/MJ4+ObIrQ3RRnfEM1/ggpQquZK31f/W7Ipdoj8eJ3+c645QoXC27jhFgTht/1Wn0YnWSHXZb88ArQh7Hm+w/Xj4KSb6DnnKfrb57pvs+Zl+L/5cOsR8MFD4KjP7oZZTqxKx45t54uwleD0sUsocWLprLNnvqg158yvVB+7crVU1o9oPsd0wxSbzwm73JQnfqNic76DQxCxVipWAdj4YFtLQm1Tgp3Fz85vDjsDaxSkNW/WaIoo9pPyxC1FTEgSqQlnf+dPd2K8t4X7LSSJTB9TiyQKxJL+NHYRWWJsQ2RY3j2jv/KZLb0oN8/mUJlijW5L5wmeMIbl7HX7PbZTeVCCT6LLOiWPWdf0j3ULntCy73E+QpP3ZMXn7uPTo26B5mnuG619De47B27YGZ76EXQt9zxetfs3V2ertmGcUbFgT1JcKGrN+l4UM1w4/eLKxXjRQrL3wQwH6nwPYbnFto0Zndc1eZlTCkXTDjfOPGzGB1IShVGlcSqE1c+u2OTM1koD1ZxctBz8VGvxCmCwmmP9BE/4FUgissR2Y+pMYUIQqieS2xlEUIpfcqEIZC/yBcKVgyFopfKk88led3Hm4HLcffIldfZKUKy3Tf/r5met5vHNsxKRRRAEtkw9BvWSV3X/u+ap7hsPbIT/3Ai/mQN3HA8v/S9s+sTW8GquEwtVXlJsW6Qh1IAkSChaNlfXlsQWJtRPALICh6fpUhA8y7Pkw7ppJWaQkiiQVjTT58eNaEjXvuT7LrtFMxZDoSAIw5fIOdgUE8o/HMiSYDMhbotmWMj62aUVzaKx89cHznJLMLrMsOBPsPMSiBujITb26nVZ8z322eAJ/+2qCUtMa6tj5eYBasJS1WhJnabrotxXTFNsaed2K/dVCczSknnMrE4/PP9+cro1ppSsAUbfunVXvvE2a7XJt1/+c8uSiCTqY2hCk6nZ+zzY82x4+25Y/EvoXuW+48rn9H8ATVNg+3kw7UBEcRJi3XRCUm3+E48QgWBXZQiCQHOkmc54p7nMTWPnnT8qq9YubjYlWP7vf79Cx8s3M4+GJGaMrWVFHleabDRjeT5vhXzsnLPubPBEWaetGOYHOzNYJxRd8PcTODDaqI/IdA9m/WD8an9ky7NkTH7yTTy2RrL1Yr1zBHkFndSEJSIhkZSi5nWhMJ7FYoXi+ojM7PaGqhKmjbYYfTKcwROlTMD94BQU8/ljF5Oc2LpvKW3PV40kXzvymmLzaPqcREIigwmFRFqhJiyBFIK9zoU9zoL3/g6v3ab723nRswbe+DO88WcMXZ9W3w5tM2HMTBi3M8z9asF2DAeBYFeFtERbbIKdtaxYocS5+ktQvIrf/oKWP9CMrY/QHUtSH83/iNWGZdeI2Gy79L+lDJ7WAcPrFJ61YqvMFqt/aBUzf9K2qrED3RxrCHZ+ctgZWLUzZkTsNmaKtfoXumk6Z46tR9W0vH3aXBtG0TSaanITwRai2nJ+OR36ixFwhDLGJut+Q5XuxCCfKdagKAFNtPrE+W+X5KGxsz6rrqZYY4x2M8UW4QsdkQ3BzjHpkUKwx5n6v40fwut36po8Rw5ZN4T+DujvgFX/gbYdAsEuwBtnLjtrZGwhh9tSgyDs+/nezZOm2hBNtcUP/E7yzdYKYY/c8jZdg/fgXC2mWMlRL3ZbqzphxQiggOJ8XKzlkAyNVrVGtZWKoYH0ioq1psBw92cSEAvMZiRRYNwwRKwOB7narfI1UH4xUzFV+BXOCaJzE+yc113Ee2BXHhTv7pMziday692OlzdBcRHCsV6uLkUilSfj/bid4NhfwBE/gk8egyVPwbKF0Le+4PEZs0PhbYaJQLCrQnJy2VnKiuVL5OhcXoxMItj2q56PnVtiWb9YBwKvgcvMreThY1ctPSFbtE1Q/VFZQ0lEzvrZFWOKttYeLsaEszXhnAA4KTbgZLTjrDpSipWj3Fqxlfexs/92zw2Xfx+/xy9qP0M76lheuDqQ/tc1QXGeUmROoiH9ouNppcCW6FUsdjlF/6dpsOkjWLoQVj6HuvEjhJ7VCJrjo9Q2s/Bxh4lAsKtC8mvs8ptZ7b5yxc+m9P187zbklJPuxDoT9MLLnFJNJcXA7mOnaRqp9LaX6sSK4WdXzPVbzZTZcmSjq/+swqsbxSR13hYoT2On/y3V/bfQJL1UnNdUcVNsid8Yr6jYQpMs0Y/GzsfjHMkkvk6mVTRN86/AEARdkzduJzjgUroHkqzb3E1L/FMmKZ9C5xLYvBSmfdbf8YaBQLCrQnLLill97PS/ldbY2farGj1V9hpKiYrVfPgaeplTqqmkGNid4g2tiyBUb7j9UDO+MYosCUUViHfLYzfaTLGFfOyKKcO2LVCOgGPmsRuiiP1ScQos/spPDr1gZyYadiw3+sHrkcwmKM5dV0xZs3CmDrGm6ZprIytDsaQUFU2KwPidoHmvko4x1ARvdxXiNMVujm0GCkcPgTOPXWk+dtUizED+rOOFyAZOeF+QdUCwRsYa/6sWs7T1g218nLdlrUtYFpnQVFOUxs3UZll97EZZ8IQhqGqau3C3LQfduJEr2PnfN19eNj8MlVUgxxTrS2NXzPHLc/fJmUSr9vVe53M+z7agC58NMcyxOQEURVBsYvSRIHi7q5CJdRNtv9/Z9A5b4ltsMxZPjZ3lDS31Za2GagsG+XIYFcKPmt56pdZzaFWnscs6xQcf59IwnoO0ki1gPtrSnYhituqLW8oTt6oT2zJOjW1xeeyyE4VisU7SK+3n6fw2uAp2ZWjsbG47RQVd6H9zomLz1InVz5HZzvER8OND7cQwxyb8+Nl5YL5DVaz1rt6WbcPsN3E/IlLE/J3W0jyx8glfD3KpUbF2TV/1IHjM1vzgZ0Zs7Udr/w5VuZ9SsWnsAsGuJAwhzhiYBWH0BU+Ad8CRpmnbdDS1G9byh1DaZLgc/99iz+kH+yTdYxvRed1FmFTLVB7kRsUW8LHz0PSZPopFPMqGlSNvZGwBtgb/3Opt2TZMXaiOQ6ccalv26IpHfflkVMLHrlqEGSg3eMJfZKtR9mwwkZ3FlZLXaiixmthiKb2dwce5OIwPwFBpSqoFryTFKUXXEum+maPz2kvBao4vajJsPk/Fj02GUOKV4qMcrIJOvmfcrnkr4vglB09kxjDH8kKl1ayaPOsEv5BA6EYkVDmNXTW/Q8GXoUr53Hafs/1+c+ObrO1bC+R/mWyatxIdYqtIrivLxy4bFZv/glpqdQf8zoGEZV9DKKyOzrDOsGPJjGAXaOyKwmnqqeaBuRy8AiisgRPV4jtaDUhlCirluIkMxW3wK3jZNW9D74/tnFgZKIU0dpaxz82qUky6GENjFy9RY5dWVLP91Tyxrt6WbeMcOOlAGsONtmWPr3wMyD+7KrU0WLUGT5QTFYtPrZsh2A1kys2AJfCiivrC0CyYA0sg2BWF88MhjTL/OgOvJMWBCd8d63NRTLLg8qri5J67UvgW7EpOjVWmIOxY7kdAc/sOZLWexQl2RmRssoQAiqwZVqjqyVHwhlcpISnEUdOPsi3796rHgfwvgF3zNvQv61BTiQTFhfohLItm6bMtAynHvsWfd6iwOnoLQnXPGKsRpz/VaEt1YuCVpDgInHCndM1V+WPTUIy11kPmExxtgXZFPBLl9lduVGzhcdrNJcfYrxjhWBAE8/kvxRy7taQLqu7WbeM4zbHLe5ayum9p/hegRI0dZAeEavrcWa+n2HxR2cGz8LatmXxoXQNJm89MdQm52dc1MKeVhtWfarSlOjHwSlIcBE64U2owgJt50C9KEWNTsVivJ58SoHQTtPv/C2GcIicq1oeA5pbLzvSxK3Ic9GuOjSUV4im78Lc1+NdBINhVNXuN34v2unbbsuc3PJH3ZbKaEor1DzNe7moSZqyzSmtaAU3TCgp6xlo/19MYlZElAUXV6I2lLTnwqgerhinQupSG9SMwWoMnvHzsgnJi7lRaA+UHrUDutnLwExULpV93qeUnCyWDz2+KzX2mlRKtKkZi4o6eOEs39rOxL048paBpGn3xFGu7Y3zU0cvSjf0s2dBPbzxl7psKNHYB5SIKIsdud6xt2QsdT5E757HuU3oQhDEIVJFcB+TmMeoZTPHh+j4+7OilezDpuo+qarYo10IIgmD62m22BFFUk5Br1TAFH+fSsE4URlsOOwNnXWGDQGPnjiSWNmZ6JTf3Q6naJj9Y5yu+TbEl+mMXs5+1LZotCCIjoPkQQq2TecMEXuwErbk2RG1EF+5iSYUNPQmWbOjn/XW9rNw8SFd/0izZCLCma9DU3KUzPnaBYBdQFk5zbGdiIx9tecdz+1Lz2Fm3rzYTn/HiJhWVVZ0DrO4aRFH1JLNrumKs7hwkrWTV6oPJNEs39dMT02dadRF/lfNa6kL6/haBsJq6wjqABR/n0rB+SEetKTZzXcm0ar4X1jJqwaTATiVMksXGdilD6mOX9SX1Y4otNuWKzdRbQoJisPeXMXT7aavqIhAWK9hFZIntx9Yze0IDk1pqaIjKZkCFLAm01IWYNqaWnSc2UheRUFVY2TlASlFNH7tqH3+DWrFVzqyWWcxsnsnS7qXmskXr/s1xs9wLDpejsTNekGpzKtevSWN156CZh2tsQwQB2NiXoCeWYiCZZlJLDYMJhU19usZNlgQmNtfQVBPydZ6ILFEflemPp4GhyTFVDlYNU/BxLg3JprGrnntbSYyPTjKt8vGGPsbWR8zJjSQKo9YEXSrW96qYnjEEKE3ThQypiL2HOjjLaFe+igzGZZeqACh2X6vAWWw+OnM/i2CnFMh/V4iQJNJaF6a1LoyiaqSU3PqxU1trWbZpgGRaZVXnYNYUK1f3OxQIdlWOIAh8bsbn+PUbvzaXPbfuKX75aqPr9oqmsWVAN0+2rQ8X5WeXzjzcNetLK45cCqqqsjy2nI/f+BjRwzTWHUuaKnBZEqiPhGzmpr54KsefKCKL1EVkxE/9XX9EjtBe106jPBY12cyY6HhqQrVlXFnlsQ5823Kd2HIoVduwNRENSUwbU8uGnjjxlMqG3gSCoE92gglBLtZhp1ghxyrYaZpexzmZVhEFgZqQ5ClYmQnQh+gZlEQBVdV8acGK9UiwHrJYocoMoMh0gKZpvgQ002qTVhlMplFUzfQZrYQ5W5/w5H73ZElkWlstyzb1m/lDofpNsYFgtxVw7HbH2gS7/lQfd35w5wi2qPI8/9HzI92EHKolOfFQc9VdV410E4aUtpo2Dpx0IIdPPZzt6+eYy0Oj1McOoDEaojEaomcwxYa+uFlCKZgQ5GLV2BUraImCgIrGis0DpBXNFhggCHp/14QlasMyAvrEW1U1+jJWgaHwsTPaBVp+jV2JwXLlVCmyJnWOJRXWdg+afZbP59UQ7LYMpMyUVM51Q0U0JDGtrY6VmwdMi1G1a/sFrZR6KKOM3t5empqa2Lx5M21tbSPdHFfOfexc3tz45kg3IyBgqyYq1bB721ym1m/P+MboiLVDVVQ+WfIJs3aYhTgMs/94UiGWVqkPS4S2ceHO2feqqrGpP4mYcfEohq6BpJm0FnRTriQKqJrmy++uISKbjvyVpHswRSKt0lYX9vQlTaZVtgymCEmCme7JD5qmu8AAjGuIFKW129QbZ/nKlcycMZ20mvXxa4zKOWZQK6m0SnfGZ1oQBEQh29cN0dCw+ELHkgq98TSyKNBWn9tf05umc8z0Y4bs/Iac0tPTQ2Oju8XOIBDs2DoEu2fXPMulT1860s0ICAgICAgIcDBvyjx+c9hvhuz4xQh2gSl2K+GQKYfw+8N/z7OfPktCSRTeYStBVVU+/fRTJk+e7OljN9RomkZ/qp/1A+vpGOigK941Iu0ICAgICAgol0Cw24o4aPJBHDT5oJFuRkVJpVL861//4rj9jiMU8he9OtTE03E2DG4YVQK0G+l0mueee46DDjoIWR6dQ0FaTfNax2ssXL2QNze+iZYnB2RAQEDAaGB0juYBAWUQlaNMa5w20s0YclKpFEukJezQvEPVCNVDwc5tO3PuLufSGetk0ZpFvLbhNWLp2Ii2SVVVNmzYwPjx40dMU72tEvT9yDGa+37XMbuOdBNMAsEuICBgm6Ctpo35s+Yzf9b8kW5KVlN9cPVoqrcVgr4fOYK+Hx5Gl8gcEBAQEBAQELANEwh2AQEBAQEBAQGjhFEj2P3ud79j+vTpRKNR5s6dyyuvvDLSTQoICAgICAgIGFZGhWB37733smDBAn74wx/yxhtvsMcee3D00UezcePGkW5aQEBAQEBAQMCwMSqCJ2644QYuvPBCzj//fABuvvlmHn30UW677Ta++93v5myfSCRIJLKpLHp6egDo6grylw03qVSKwcFBOjs7A2faYSbo+5Ej6PuRI+j7kSPo+9Lp6+sDsnV287HVC3bJZJLXX3+dK6+80lwmiiJHHHEEL774ous+1157Lddcc03O8lmzZg1ZOwMCAgICAgICyqGvr4+mpqa822z1gt3mzZtRFIXx48fblo8fP56PPvrIdZ8rr7ySBQsWmL+7u7uZNm0aq1evLthhAZWlt7eXKVOmsGbNmoJlUgIqS9D3I0fQ9yNH0PcjR9D3paNpGn19fUycOLHgtlu9YFcKkUiESCS32HNTU1PwsI0QjY2NQd+PEEHfjxxB348cQd+PHEHfl4ZfxdNWHzwxZswYJEliw4YNtuUbNmygvb19hFoVEBAQEBAQEDD8bPWCXTgcZu+992bhwoXmMlVVWbhwIfvvv/8ItiwgICAgICAgYHgZFabYBQsW8KUvfYl99tmHfffdl1/96lcMDAyYUbKFiEQi/PCHP3Q1zwYMLUHfjxxB348cQd+PHEHfjxxB3w8PguYndnYr4KabbuL666+no6ODPffck9/85jfMnTt3pJsVEBAQEBAQEDBsjBrBLiAgICAgICBgW2er97ELCAgICAgICAjQCQS7gICAgICAgIBRQiDYBQQEBAQEBASMEgLBLiAgICAgICBglLDNC3a/+93vmD59OtFolLlz5/LKK6+MdJNGHddeey2f+cxnaGhoYNy4cZx88sl8/PHHtm3i8TiXXHIJbW1t1NfXM3/+/Jyk0wHl84tf/AJBELjsssvMZUHfDx1r167li1/8Im1tbdTU1LDbbrvx2muvmes1TePqq69mwoQJ1NTUcMQRR7BkyZIRbPHoQFEUrrrqKrbbbjtqamrYfvvt+clPfmIroB70feVYvHgxJ5xwAhMnTkQQBB588EHbej993dXVxdlnn01jYyPNzc1ccMEF9Pf3D+NVjB62acHu3nvvZcGCBfzwhz/kjTfeYI899uDoo49m48aNI920UcWzzz7LJZdcwksvvcSTTz5JKpXiqKOOYmBgwNzm8ssv5+GHH+Zvf/sbzz77LOvWrePUU08dwVaPPl599VX+8Ic/sPvu/5+9+46PolobOP6b2ZbeSYMk9CI1gCBVQJBmAVFRUBG9YMOG5eprL9d2LVcRey8oIoqIgiBVeu+9JkAK6T1b5rx/LFlYsumbbBLO9/OJsjOzs89ONrvPnvKcLk7b5bWvHZmZmfTr1w+DwcDChQvZu3cvb731FsHBwY5j3njjDd577z0++ugjNmzYgK+vL8OHD6eoqMiDkTd8r7/+Oh9++CHvv/8++/bt4/XXX+eNN95gxowZjmPktXef/Px8unbtysyZM13ur8y1njhxInv27GHJkiUsWLCAVatWMXXq1Lp6Co2LuIj16tVL3HfffY7bNptNREdHi1dffdWDUTV+qampAhArV64UQgiRlZUlDAaDmDNnjuOYffv2CUCsW7fOU2E2Krm5uaJNmzZiyZIl4vLLLxcPPvigEEJe+9r073//W/Tv37/M/ZqmicjISPHf//7XsS0rK0uYTCbxww8/1EWIjdbo0aPFHXfc4bTtuuuuExMnThRCyGtfmwDx66+/Om5X5lrv3btXAGLTpk2OYxYuXCgURRGnTp2qs9gbi4u2xc5sNrNlyxaGDh3q2KaqKkOHDmXdunUejKzxy87OBiAkJASALVu2YLFYnH4X7du3JzY2Vv4u3OS+++5j9OjRTtcY5LWvTfPnz6dnz57ccMMNhIeHEx8fz6effurYf+zYMZKTk52ufWBgIL1795bXvob69u3L0qVLOXjwIAA7duxg9erVjBw5EpDXvi5V5lqvW7eOoKAgevbs6Thm6NChqKrKhg0b6jzmhq5RLClWHWlpadhsNiIiIpy2R0REsH//fg9F1fhpmsZDDz1Ev3796NSpEwDJyckYjUaCgoKcjo2IiCA5OdkDUTYuP/74I1u3bmXTpk2l9slrX3uOHj3Khx9+yPTp0/m///s/Nm3axAMPPIDRaGTSpEmO6+vqPUhe+5p54oknyMnJoX379uh0Omw2G//5z3+YOHEigLz2dagy1zo5OZnw8HCn/Xq9npCQEPn7qIaLNrGTPOO+++5j9+7drF692tOhXBQSExN58MEHWbJkCV5eXp4O56KiaRo9e/bklVdeASA+Pp7du3fz0UcfMWnSJA9H17j99NNPfP/998yaNYuOHTuyfft2HnroIaKjo+W1lxq9i7YrNiwsDJ1OV2r2X0pKCpGRkR6KqnGbNm0aCxYsYPny5TRr1syxPTIyErPZTFZWltPx8ndRc1u2bCE1NZXu3buj1+vR6/WsXLmS9957D71eT0REhLz2tSQqKopLLrnEaVuHDh1ISEgAcFxf+R7kfo899hhPPPEEN910E507d+bWW2/l4Ycf5tVXXwXkta9LlbnWkZGRpSYtWq1WMjIy5O+jGi7axM5oNNKjRw+WLl3q2KZpGkuXLqVPnz4ejKzxEUIwbdo0fv31V5YtW0aLFi2c9vfo0QODweD0uzhw4AAJCQnyd1FDV1xxBbt27WL79u2On549ezJx4kTHv+W1rx39+vUrVdbn4MGDxMXFAdCiRQsiIyOdrn1OTg4bNmyQ176GCgoKUFXnjzedToemaYC89nWpMte6T58+ZGVlsWXLFscxy5YtQ9M0evfuXecxN3ienr3hST/++KMwmUziq6++Env37hVTp04VQUFBIjk52dOhNSr33HOPCAwMFCtWrBBJSUmOn4KCAscxd999t4iNjRXLli0TmzdvFn369BF9+vTxYNSN1/mzYoWQ1762bNy4Uej1evGf//xHHDp0SHz//ffCx8dHfPfdd45jXnvtNREUFCR+++03sXPnTnHttdeKFi1aiMLCQg9G3vBNmjRJNG3aVCxYsEAcO3ZM/PLLLyIsLEw8/vjjjmPktXef3NxcsW3bNrFt2zYBiLffflts27ZNnDhxQghRuWs9YsQIER8fLzZs2CBWr14t2rRpI26++WZPPaUG7aJO7IQQYsaMGSI2NlYYjUbRq1cvsX79ek+H1OgALn++/PJLxzGFhYXi3nvvFcHBwcLHx0eMHTtWJCUleS7oRuzCxE5e+9rz+++/i06dOgmTySTat28vPvnkE6f9mqaJZ555RkRERAiTySSuuOIKceDAAQ9F23jk5OSIBx98UMTGxgovLy/RsmVL8dRTT4ni4mLHMfLau8/y5ctdvsdPmjRJCFG5a52eni5uvvlm4efnJwICAsTkyZNFbm6uB55Nw6cIcV4pbkmSJEmSJKnBumjH2EmSJEmSJDU2MrGTJEmSJElqJGRiJ0mSJEmS1EjIxE6SJEmSJKmRkImdJEmSJElSIyETO0mSJEmSpEZCJnaSJEmSJEmNhEzsJElqtJ5//nm6detWpfsoisK8efNqJZ6aGjRoEA899JCnw5AkqR6TiZ0kSQ2Coijl/jz//POl7vPoo486rVEpSZLU2Ok9HYAkSVJlJCUlOf49e/Zsnn32WQ4cOODY5ufn5/i3EAKbzYafn5/Tdqk0m82GoiioqvyeL0mNgfxLliSpQYiMjHT8BAYGoiiK4/b+/fvx9/dn4cKF9OjRA5PJxOrVq0t1xW7atIlhw4YRFhZGYGAgl19+OVu3bq1SHIMGDeKBBx7g8ccfJyQkhMjISKfWwuPHj6MoCtu3b3dsy8rKQlEUVqxYAcCKFStQFIW//vqL+Ph4vL29GTJkCKmpqSxcuJAOHToQEBDAhAkTKCgocHp8q9XKtGnTCAwMJCwsjGeeeYbzV4YsLi7m0UcfpWnTpvj6+tK7d2/H4wJ89dVXBAUFMX/+fC655BJMJhMJCQlVugaSJNVfMrGTJKnReOKJJ3jttdfYt28fXbp0KbU/NzeXSZMmsXr1atavX0+bNm0YNWoUubm5VXqcr7/+Gl9fXzZs2MAbb7zBiy++yJIlS6oc7/PPP8/777/P2rVrSUxM5MYbb+R///sfs2bN4o8//mDx4sXMmDGj1GPr9Xo2btzIu+++y9tvv81nn33m2D9t2jTWrVvHjz/+yM6dO7nhhhsYMWIEhw4dchxTUFDA66+/zmeffcaePXsIDw+vcuySJNVPsitWkqRG48UXX2TYsGFl7h8yZIjT7U8++YSgoCBWrlzJVVddVenH6dKlC8899xwAbdq04f3332fp0qXlPrYrL7/8Mv369QPgzjvv5Mknn+TIkSO0bNkSgOuvv57ly5fz73//23GfmJgY3nnnHRRFoV27duzatYt33nmHKVOmkJCQwJdffklCQgLR0dGAfZzhokWL+PLLL3nllVcAsFgsfPDBB3Tt2rVK8UqSVP/JFjtJkhqNnj17lrs/JSWFKVOm0KZNGwIDAwkICCAvL6/KXZEXtgZGRUWRmppa5XjPP09ERAQ+Pj6OpK5k24Xnveyyy1AUxXG7T58+HDp0CJvNxq5du7DZbLRt29YxvtDPz4+VK1dy5MgRx32MRqPLFk1Jkho+2WInSVKj4evrW+7+SZMmkZ6ezrvvvktcXBwmk4k+ffpgNpur9DgGg8HptqIoaJoG4JiEcP64N4vFUuF5FEUp97yVkZeXh06nY8uWLeh0Oqd9508i8fb2dkoOJUlqPGRiJ0nSRWPNmjV88MEHjBo1CoDExETS0tLc+hhNmjQB7LN44+PjAZwmUtTUhg0bnG6XjBXU6XTEx8djs9lITU1lwIABbntMSZIaDpnYSZJ00WjTpg3ffvstPXv2JCcnh8ceewxvb2+3Poa3tzeXXXYZr732Gi1atCA1NZWnn37abedPSEhg+vTp3HXXXWzdupUZM2bw1ltvAdC2bVsmTpzIbbfdxltvvUV8fDxnzpxh6dKldOnShdGjR7stDkmS6ic5xk6SpIvG559/TmZmJt27d+fWW2/lgQceqJUZoV988QVWq5UePXrw0EMP8fLLL7vt3LfddhuFhYX06tWL++67jwcffJCpU6c69n/55ZfcdtttPPLII7Rr144xY8awadMmYmNj3RaDJEn1lyLOHwgiSZIkSZIkNViyxU6SJEmSJKmRkImdJEmSJElSIyETO0mSJEmSpEZCJnaSJEmSJEmNhEzsJEmSJEmSGgmZ2EmSJEmSJDUSMrGTJEmSJElqJGRiJ0mSJEmS1EjIxE6SJEmSJKmRkImdJEmSJElSIyETO0mSJEmSpEZCJnaSJEmSJEmNhEzsJEmSJEmSGgmZ2EmSJEmSJDUSMrGTJEmSJElqJGRiJ0mSJEmS1EjoPR1AfaBpGqdPn8bf3x9FUTwdjiRJkiRJkoMQgtzcXKKjo1HV8tvkZGIHnD59mpiYGE+HIUmSJEmSVKbExESaNWtW7jEysQP8/f0BOHbsGCEhIR6O5uJisVhYvHgxV155JQaDwdPhXFTktfccee09R157z5HXvvpycnKIiYlx5CvlkYkdOLpf/f39CQgI8HA09Yew7AbzRkAB46Uohk5ufwyLxYKPjw8BAQH19g9dCDMUrwLbaVCDwTQYRfXzdFg11hCufWMlr73nyGvvOfLa11xlhovJxE4qRdiSEVkPgGU75+bXaAhDN5Sg91B0kR6Mrm6JooWI7OdAZGG/FhrgBX73g++/5JhMSZIkqV6Rs2KrSQgbmu00wpaCEMLT4biN0PIQGTeDZdfZLdrZH8CyC5ExEaHleSq8OiWKliGyHjqb1IHjOlCEyPsvIm8GmvUkQhR5JkBJkiRJuoBssasiISxY8z/Dmv8VaKkAKLoW6P3uRud9Q8NvwSn8xd7liKtk1Qa2k1A4D3xvqePA6pYQApH7ust9mtCwoiHy3oG8dwATOp/rMfg9iKJrUreBSpIkeZDNZsNisVTqWIvFgl6vp6ioCJvNVsuRNSwGgwGdTueWc8nErgqEsGHOvAeteBnnJz7CdhxL9r8R1iMYAp70XIBuIArnVXxM0TyUBprYCWEFUQyKT/lJuHU/2I6V2mwTGlYufEMqxlbwI7aiZZhCf0LRhaIo3u4NXJIkqR4RQpCcnExWVlaV7hMZGUliYmLDbwSpBUFBQURGRtb42sjErgpshfPQipe62GNP8qz5n6DzGoVq7Fq3gbmTlonr1roSArSMuorGbayWvRTmvo+56A/AhqKG4eVzK95+d7meCKFlltokhHCR1NnZhAWLLZGC1MsAUPWt8fb9FyafCSiKHPEgSVLjUpLUhYeH4+NTwRflszRNIy8vDz8/vwprsV1MhBAUFBSQmmrvBYyKiqrR+WRiVwXWgu84N4DeFR3WglkY61liZ7EcwGY9hKL4YjRdVn5rkj4WzEmU/RxV0MXVRpi1xlK8mpz0W7E/J3tiJrQ0CvPexVy0iICwuajqBbOhddGlzqOVkfBahA3LBddLsx4hP/sJLOaN+AX9TyZ3kiQ1GjabzZHUhYaGVvp+mqZhNpvx8vKSid0FvL3tn8upqamEh4fXqFtWXtkqENYjlJ3wANjQrAfrKpwKWSz7SU+9ivTUwWRlTCUzfSKpSd3Iy32/zAkfis9NlP8cNRSf8bUSb20QopjcjHsAK5RqbdOwWQ9SmPtmqfsp+uZgiOf8PxHhIrHThCiV1JUcDWAu/AVz4fzqhi9JklTvlIyp8/Hx8XAkjUvJ9azsmMWyyMSuKpSKXsQKilI/6ptZrUfJOHMNFssOp+1C5JKX8wp5Oa+6vqPpSjAOwPVLQwXjQDANc3u8tcVctAghyutetlFU8CNCKyy1Rwl4GjBQ3p+JtdwkGEClKP/LSkYrSZLUcMhxcu7lruspE7sq0HtfBZTXPCrQeY+uq3DKlZfzFkIUUrqVyi4/byY22+lS2xVFhxL8IfjeCYrveTt8wfdOlOAPUBT3zNypC1bLXioccSAK0GynSm1WDJ1RQmeBwd61rnPx51JW9+z5R1it+yoZrSRJkiTVjBxjVwU6n9uxFvwIopDS3ZU6FF0UOq9rPBGaE03Lp6jwd8pK6uwUCgvm4ud/f+k9ihHF/zGE3zSwnO1aNrRtkDM9FcWL8ieDlBxocr3Z0BkldDbCehzFdgo17zM08yrK766+4By4Prck1VdCaOQU/EJm7ucUW/aiKAb8vIYREnA3XvVsDLEkSc5ki10VqPpmmEK+A7VkPVk9Jbmxom+JMeQHFNXzYw6Elol9TFl5dGi2pHKPUBRvFGNX+08DTOoAjF5XUlGCq9O3RdWVv6iyom+OYuqHMeQjVK+RZ7fq0FX43UiH0XtUFSKWJM8SQiMp4wGSMx6g2LILsCBEAbmFCziRchU5BXLMqCTVZzKxqyLV2A2v8DUYgmag870Nve9kjCFfYwpbhKovPzmoK4oaRPldxgAaqi6iDqLxLL2hIwbj5ZR9PQTefg9UemyDopgwBb+PKewv9H73YfKZeHZcpavzK4CKl++d1QtekjwgJ/8ncgt+OXvr/JZpG6CRlH4/VtsZD0QmSTV3++23oyiK4yc0NJQRI0awc+dOt5z/+eefp1u3bm45V3XJrthqUBSjfbyd91WeDsUlVfXD5DWK4qI/Kbu1SsPbe1xdhuUxfiEfkJsxGat5I/aXfMmHlcAn4ClMPmOqfE7V0BbV0BYAne8kctMnommnOZfgaaB44R/8Cfqzx0lSQ5CZ9xlll3USgI3s/B8JDSg9jEO6OGmaILPAXInjNHILLFjUYreXOwn2MaKqlfuCPmLECL780j6pLTk5maeffpqrrrqKhIQEt8bkKTKxO8+ZvN8ICr4Zndowux3P5x/wKObiZWfXMS2d3Pn4TkFXT1oYa5uqBhIQOhereS3FhQsQIhedrgUm35vRuahXV1V6QxuCIlZjLlqIpWglAit6Yzwm73Gl6+NJUj0mhI1iy96KjqLIvKOCY6SLSWaBmR4v/+3RGLY8PZRQv8qNZzaZTERGRgIQGRnJE088wYABAzhz5gxNmjQhMTGRRx55hMWLF6OqKgMGDODdd9+lefPmAKxYsYLHH3+cPXv2YDAY6NixI7NmzWL58uW88MILwLkZrl9++SW33367259veWRid54TWa9RoP+WjhGzMOlrVvnZ0/SGNoSE/UJ25sNYrefeqBXFB1+/e/D1f9iD0dU9RVEwmPphMPWrpfMbMXlfi8n72lo5vyTVDRV7q3P541IVxVBH8UhS7crLy+O7776jdevWhIaGYrFYGD58OH369OGff/5Br9fz8ssvO7prVVVlzJgxTJkyhR9++AGz2czGjRtRFIXx48eze/duFi1axN9/2xPdwMDAOn9OMrE7jyYUiqwnOXDmHjpH/trga/QYjJ0JDV+C1bILq/UQiuKD0TQQVfWt+M6SJF10FEXB12sw+UXLKW8Yh6/XFXUZliS51YIFC/Dzs9eczc/PJyoqigULFqCqKrNmzULTND777DOnVregoCBWrFhBz549yc7O5qqrrqJVq1YAdOjQwXFuPz8/9Hq9o0XQE+TkifPsS2oF2Mgz7yTPvN3T4biFoigYjF3w9hmHl/dImdRJklSuEP97KW/ZRJ0uEn+f+jm+WJIqY/DgwWzfvp3t27ezceNGhg8fzsiRIzlx4gQ7duzg8OHD+Pv74+fnh5+fHyEhIRQVFXHkyBFCQkK4/fbbGT58OFdffTXvvvsuSUnlV5ioa7LF7jwr9/fiskv2Azqyi9bib4r3dEiSJEl1ysfrMiKC/0tK5uPYZ3bbzv5foFPDiGnyI6ri5dkgpXol2MfIlqeHVnicpmnk5uXh7+dXK5MnKsvX15fWrVs7bn/22WcEBgby6aefkpeXR48ePfj+++9L3a9JkyaAvQXvgQceYNGiRcyePZunn36aJUuWcNlll9X8ibiBR1vsVq1axdVXX010dDSKojBv3jyn/RdOS1YUhREjRjgdk5GRwcSJEwkICCAoKIg777yTvLy8asWzI7E9GXn2/vCy1lKVJElq7IL8JtAyaj0hAdPw8RqMr9dwIoLfpGXUGkxylrd0AVVVCPUzVeonxMdQ6WOr8lPZGbGuKIqCqqoUFhbSvXt3Dh06RHh4OK1bt3b6OX+8XHx8PE8++SRr166lU6dOzJo1CwCj0YjNVt4Y1drn0cQuPz+frl27MnPmzDKPGTFiBElJSY6fH374wWn/xIkT2bNnD0uWLGHBggWsWrWKqVOnViseTehYvq8vYCPAq0e1ziFJktQYGPTNaBL4b2KafE+zJl8Q5DcBtR4UYJekmiouLiY5OZnk5GT27dvH/fffT15eHldffTUTJ04kLCyMa6+9ln/++Ydjx46xYsUKHnjgAU6ePMmxY8d48sknWbduHSdOnGDx4sUcOnTIMc6uefPmHDt2jO3bt5OWlkZxcXGdPz+PdsWOHDmSkSNHlnvM+dOSL7Rv3z4WLVrEpk2b6NmzJwAzZsxg1KhRvPnmm0RHuy5lUVxc7HSxc3JyHP9etq8f4y89gbfaA4vFUtWnJFVRyTWW17ruyWvvOfLae4689jVnsVgQQqBpGppW+eUVS3rCSu7rCUIIFi1aRFSUvfKFv78/7du3Z/bs2QwcOBCwlzN54oknuO6668jNzaVp06YMGTIEPz8/CgsL2bdvH19//TXp6elERUVx7733MmXKFDRNY+zYscydO5fBgweTlZXF559/XulyJ5qmIYTAYrGg0zkXva/K61UR9aTPUVEUfv31V8aMGePYdvvttzNv3jyMRiPBwcEMGTKEl19+mdDQUAC++OILHnnkETIzMx33sVqteHl5MWfOHMaOHevysZ5//nlHrZnzxTz0E6rJh3+1s9E5pF5cFkmSJEmqV0pmfcbExGA0Vn5sm1Q+s9lMYmIiycnJWK3Oy4IWFBQwYcIEsrOzCQgovz5qvZ48MWLECK677jpatGjBkSNH+L//+z9GjhzJunXr0Ol0JCcnEx4e7nQfvV5PSEgIycnJZZ73ySefZPr06Y7bOTk5xMTEOG4fsIXz71GyK7YuWCwWlixZwrBhwzAYZG2suiSvvefIa+858trXXFFREYmJifj5+eHlVfmJNEIIcnNz8ff3b/DlxGpDUVER3t7eDBw4sNR1Pb9nsSL1OrG76aabHP/u3LkzXbp0oVWrVqxYsYIrrqh+HSWTyYTJVHaF6n8Op5OUYyE2tPGNJ7FoBRzI/oOD2X9QYEvHRx9G+8CraRMwEoMHV9wwGAzyTdZD5LX3nIZ+7fMsKezOmsPhnMVYtHz8DVF0CBxDu8Cr0auVWwXAUxr6tfckm83mmHBQldmtJd2vJfeVnKmqai9R5uK1WZXXaoO6si1btiQsLIzDhw8D9qVAUlNTnY6xWq1kZGTUuDjgl+v2YNYKa3SO+qbQmsm8E1NZf2YGGeYjFNmyyCg+zNrU/zE/4R6KbJX/RiBJ0sUtvfgwv5y4nT2ZcyiyZWITZrLMJ1h35j3+PPkglkb2/ilJDUWDSuxOnjzpGKwI0KdPH7KystiyZYvjmGXLlqFpGr17967RY/246Tgf7buOv0+/Ro6l7G5dsDcvn8jbyB8nn+G7I5OYc/w+dmT8gtmWX6MY3O2flNfIsZzEvpD3+QRZ5mOsSXnTE2FJktTACKHx9+mnsGgFiFLFjAVnivaxJe0zj8QmSRc7j3bF5uXlOVrfAMcU4ZCQEEJCQnjhhRcYN24ckZGRHDlyhMcff5zWrVszfPhwwL6Mx4gRI5gyZQofffQRFouFadOmcdNNN5U5I7ayCot9OHCiBTrdUo7nb+CGuJkEGkufUxM2/k56nYM5f6Og2t/kLJBStJ9tGbMZG/uOy/vVtVzLaRLy15a5X6BxPG8l+ZYz+Bqa1GFkkiQ1NKcKNpFrOV3mfoHG/uzf6Rk2Bb0qixlLUl3yaIvd5s2biY+PJz7evsLD9OnTiY+P59lnn0Wn07Fz506uueYa2rZty5133kmPHj34559/nMbHff/997Rv354rrriCUaNG0b9/fz755JNqxdM0/JTT7R0HuiCwUWzLZVXK+y7vsz1jDgdz7Iv9On9zFeRbM/jj5DP1othxauHeCo8RaJwp2lcH0UiS1JCdKdqPgq7cY6yikGxzYh1FJElSCY+22A0aNKjcpOevv/6q8BwhISGOis811aX1PpK2tHHcPpkSQ1pmKGHB6ZzIX0+e5Qx+57VmacLG9oyfyzyfwEaG+RinCnbQzLebW2KstkrOQJIzlSRJqoiq6Cg9pMPVcfV6fp4kNUoNaoxdbWvV9Cg+Xs7j4o4ktnL8O8t80mlftuU0BbaMcs+poONUwTb3BVlNkd5dUSr4dSvoCPfqVEcRSZLUUDXz6e1ibJ0zH10YgcbYOopIkqQSMrE7j6oTtI497LQtIyfY8W/DhcvpVKKL1b50tue7Yn31YbT0v6LM5E5BpU3ACLz1wS73S5IklQj1akOkd7dyu2O7hEw427InSVJdkondBYIDMp1uZ+cGAeCnb0ITr9ZO+wKM0XjrAimPho1ony5ujbG6+kU8ShOvSwAcCV7J/yO9u9An/EGPxSZJUsNyRdSLBJtaAOe/n9gTuUsCr6Nj0PUei626NGFjZ9ZqPj3yDK/uvYN3DtzP8pQ55FuzPR2a5CHPP/883bp1q9J9FEVh3rx5tRJPZcgBEE4UggOynLZk5gQB0Cvs9lLfPnWKni7B17Eh7StcjTdRUAk0RhPj0712wq0io+rDVTEzOJ63ioPZf5JvPYOfPpy2gVcR59dPjoeRJKnSvPXBjIn9lBN5/3AkdynFthwCjTG0C7yKJl4d6jyePEs2mZY0fHS+hJqqXsfUqlmYdeK/HMjd7KhwkGfNYmnKj6xL+5MprV6iiVezWoj84mXTCkgr+INCyyFUxZtQnxH4GmvvtVPRGPLnnnuO559/3mnbo48+yv33319rMdUG+Ul+nrYBg8nVnMfD5Rf60T3wLi4JGunyPj1CJ5BadJBjeWvOlTsBFBS8dIGMbvoyilJ/GkZVRU9L/yG09B/i6VAk6aJVZC0AqBcz5mtCVfS08B9MC//BHoshvTiZP5K+ZU/2Jsewl2iv5oyImkD7gPhKn2dl6lwO5tprop4/flAgKLTl8u3x13io3Xuo9ej9vCFLL1jM4bRHsIl8FPQIBCezZxDkNZh2Td5Fp/q5/TGTkpIc/549ezbPPvssBw4ccGzz8zv3mEIIbDYbfn5+TtsbAvkKPU+/8Lu4u/PbpbaHKK6TOrDPDhvV9AVGRD9PU59u+OrDCDE2p3eTO5nQ4guCTXLwsCRJ9g+KTRmreGP/Y7yw7z4A3jn4FGvT/m7wCZ6npBcnM+PQk+zN3uw0ljmp6ARfHHuF7ZlrKnUeq2ZhXfqfZY6HtgjB6aIUPjr8Cr+f/pHUoiSXx0mVk1O8mQNn7sUmzn7BwQrYAMgqWsn+M/fVyuNGRkY6fgIDA1EUxXF7//79+Pv7s3DhQnr06IHJZGL16tWlumI3bdrEsGHDCAsLIzAwkMsvv5ytW7eW+Zhms5lp06YRFRWFl5cXcXFxvPrqq7Xy/ErIFrsLRPnFEBlwkOScIse242kFtI8MKPM+iqLSOmAgrQMG1kWIkiQ1QL+f/p7lZxagoKCeHYt2xpzEnJOfkVBwhPExU2W5oSpacPpbimwFaBfM0C1J0Oae/JiOgT0xVLBubYY5mUJbXqntQkCRpseGvbzLgbw9HMrbx98pvzEk/CquiZ4gf2dnaUJDQanU9TiVMxP71EJXM6s1sov+Ibd4B/6mru4Os0JPPPEEb775Ji1btiQ4OJgVK1Y47c/NzWXSpEnMmDEDIQRvvfUWo0aN4tChQ/j7+5c633vvvcf8+fP56aefiI2NJTExkcTE2q3vKBM7F2JDfZwSu4SM+rU0mCRJDcvRvP0sP7MAcD1LfkPGcjoHXkrHwPoxHrchyLNmszdnU7lVB4q1QnZnbyQ+eEAFZ3OdjBRremyOji37MSVJ5LLUBfjrAxkScVVVQ280NKGRY8km15KDVVgABW+dD4GGQEw61yuO2EQ+2UWrKa8OooKO9IKFHknsXnzxRYYNG1bm/iFDnIcxffLJJwQFBbFy5Uquuqr0ayEhIYE2bdrQv39/FEUhLi7O7TFfSHbFutA81LmsyfH0Ag9FIkmVZxM2tmZu4cMj7/PG/lf56vjnHM47JLv56oE1aUtQy3m7VVFZk7a4DiNq+DLNaRWWklLRkW5OqfBcoaYo/PRBTts0AVZUykr6AP5OmY9Vs1Ym3EZHCEG6OY1Mc8bZpA5AUGgrILkoiQKr6wYRIQqpuLi1gk3LdWe4ldazZ89y96ekpDBlyhTatGlDYGAgAQEB5OXlkZCQ4PL422+/ne3bt9OuXTseeOABFi+u/b9z2WLnQlyor9PthHqa2NmEjRP5CZg1M9HeUQQYyu4ulhq3fGs+7xx8k+MFx1BR0dA4nHeI1Wn/0C+0P5Oa3yEHfXvQqcLjpboLz6ehcbroRB1G1PD56HwrPEag4a1WfJxO0dE/7BoWJX/j2GYT5Sd1APm2XBIKjtDSr12Fj9HYFGmFmGxG9PoL31cEoJBWfIamOi90papJBKJT/LCJ0l3f585gw9vQ0v1BV4Kvb/mvl0mTJpGens67775LXFwcJpOJPn36YDabXR7fvXt3jh07xsKFC/n777+58cYbGTp0KD//XPaqVTUlEzsX4kq12NWvrlghBEtTlzP/9AKyLfb6SioqPUO6MzH2ZoKMQZ4NUKpznx37mIQCe2JQkkCU/H9N+mrCvSIYHXW1x+K72JlU191S56toHJjkLNQUSZRXHMlFCeW23HUK6l2p8/VrcjXJRSfYnrUSFfXsGe1JSnksmusP9JrINGexPHUVB3IPoqDQMbADlzcZQICh9BguT9CERpGtiEDKakyw/0byrXkEGJxrvSqKgXC/8STlfkXJhIkLKehp4jvWnSG7zZo1a/jggw8YNWoUAImJiaSlpZV7n4CAAMaPH8/48eO5/vrrGTFiBBkZGYSEhNRKjDKxcyEuxDljP51ViNmqYSz1zcQz5p76ld9P/+G0TUNjc8ZWjuQd5fmOz9abNwCp9iUXJbEre2e5xyxOXsTwiJHoVfkn7wldgy8jsfBomQmIgkp8UJ86jqrhGxE1gS+PlT3DsG/oCAINlfvwVBUd18c8QHzw5WxMX0xCwTGSirPKvY+CQqR306qEXKEtGdt4//DH2ITN8XrZm7OfeacWML3t/XQMrPsagRcqshWW2wJdoqykt2nAvWQVrqDQehzn5E4FNFqGvIhBVztJT021adOGb7/9lp49e5KTk8Njjz2Gt7d3mce//fbbREVFER8fj6qqzJkzh8jISIKCgmotxvqRqdQzsRe02GkCTmbWj+7Y1KIzpZK6EhoameYs/kj6s46jkjxpd/ZulAq7jPJJLHQ9BkSqfb1DBuGj83M5zk5Bxaia6Bta9oBtybUOAd2ZEPsQJtX+waqis8/MRKVf6EiuajqpSudTFIXW/t2Y0Pxx/t3hA8JNUWWOjVRR6RTYo9KJY2WcLkxixuGPsAqr05cAgcCiWXj74AwyzJnlnKFu6CtdzN71+5JeDaRz5M9E+t+CqpxLivyMnWjf5FMi/Me7Icra8fnnn5OZmUn37t259dZbeeCBBwgPDy/zeH9/f9544w169uzJpZdeyvHjx/nzzz9R1dpLv+TXdxcCvQ0E+xjILLA4tp1IL6BlE88XKVydtsYxhsoVDY0VqasYH3ODHFN1kdCE6+6MC9kqeZzkfr56f+5t/QyfHH2NbEuGo9wJ2MeKTWn5b4KM9bOFor7rFtyPjoE92ZW9kQxzCt6qL52Cetc44VIUhUnN7+e9Qy9i0cxO77kqKgGGIK5vNrmm4TtZkrKszMlOAoFVs7A8dRXjml3r1setKqPOhEExUH43tcBH71PmXr0ukJYhzxEX9G/MtmR0ig9GfdkJkrvdfvvt3H777Y7bgwYNcnntn3/+eafVKOLj49m0aZPTMddf77x83vnnmTJlClOmTHFP0JUkE7syxIX6klmQ5bh9op6MsztTXH5fPkCRVkSRrajcPyqp8Wjh27LC2YF6xUC0l3u7jKSqifaO5ekO77EreyOHsvdBItzQbArdw/pgUI2eDq9BM6gmuldY0qTqmvm04NF2r/B3yny2ZK7GKqx4qd5cFjqYoRHX4H/B+LGa2pa5s4JJNoLtmTs8ntgBeOu8KXt2q4JBNeCllt1FWUKneuGtNndnaBc9mdiVoXmoD9sTsxy360vJEz99xa2GOkWHUX5QXDRa+7Uh2qspyUVJLj8UVFT6hvaViX49oFf1xAf3pZPfpfy540+6B/fFoBo8HZZUjnCvKCbE3cVNsVMwa0UYVa9a6w2pTOt7fWl5N6hGggzB5HFhWRKBQTUQboqUxZs9RPbVlSH2wpInGfUjsesbelm53+hUVHqHXCoHyV9EFEXh7lb34aP3KTUeSEGhqXczboi5yUPRSVLjoCoqXjqfWh3i0sa/VYX1Dtv6t3Hctmo2NmfsY0nyRrZm7scmKp7Q4E6+Bj+aescQZAjGR++Lr96PJqYIoryays8gD5JXvgylixTXj67YFn7N6R4Uz7as7aW631RU9Kqeq6NHeyg6yVOivaN5/pKXWJr6N2vTV5NvLSDUGMrlTQZzeZNBmHSylIYk1XfDIq5gY8YWl/uEgHybSqZZx/cnlmBUVOaeWka25Vw9uBBjAPe0Gkf/Jt3qKGJ7K3SgLLFVr8jErgwX1rJLzCjApgl0queblu9pfRdfH/+GNWnrEAgUFASCUGMI97S+i2jvaE+HKHlAkDGYcc1uYFyzGzwdiiRJlVRsM7M8dTtr03ZTbDMT692ehML9TpPkim0GsswmBAqLk7egCSuqWnp8W4Y5h//s+5KnlTvoF1az5bjMmpVVqTtYnbaTQmsxcb5RXBV9GbG+EY5jXE02EEJQaCsmx1KARVjRKzoCDL74yC+XFXLXKkEysSvDhatPWGyCpOxCmgV7fpySUTUwpeWdjGs2lu1ZOzFrZmK8m9EhoL1HZsIW2czkWgrxN3jjpZNj+yRJkirjVMEZHt3+IanFWY4v6CoqBtWHSwKDybakUWRTyTSf+6i2Cis6RSAElDWE7ZMjv9IntHOlPw/yLIXMO7WGP09vIMOcS6DBh2LNTK61wBHXtqzDzD25kjtbjmJ8s8EAFBQUONVw04QguSidXEshimJvZVSAHEsBvnovorzkzO/yFBTYh3wZDDUbdysTuzKE+hrxNerIN58bqHoivaBeJHYlQowhDAkf5LHHP1mQxlfHlrAsZQdWYUOnqAwO78rklsOI8WnisbgkSZLqO6tm4/EdH5NWnAPgGFqjoVGs6dmWmcsrXaYxJ3EZqUVH0M7uVyg7oSuRWpzJvpzjdAyseFmu9OIc7t/yPslFGWdjEJwpthcWVpRzcZWM3/v86J809Q6jbVAEqampAPj4+KAoCunFOeRYXA9byjXngdmG0axSVFRUq3XcGhohBAUFBaSmphIUFIROp6v4TuWQiV0ZFEUhLtSXvUk5jm0n0gvo19q9jyOEYGvmEXZlnUBRFHqGtOaSgJh6P5voWF4y926eSZFmdvzB24TG8pQdrE3by8we99LKX3YJZ5nzOZqXjF7V0z6gKUY5oFiSJGBN2m6SizLK3K+i8N3xJezPPVat82eacyo+CHhz/0+kFmc6jdku7+NHQWHWiaV81HM6gCO504QgrTir3NJLqSj4aSa8vb3r/WecJwQFBREZGVnj88hPmXLEhfpckNi5dwLFifxUntj+DQkFZ9ApKkIIPuEvLgmI4ZWut9LEy701ktzp9X1zKLQVO75FlrChUWQz89q+OXza60EPRed5WeZ8/nfgd/5O3ulIfP313kxsPpBbW1wui0dL0kVuU8Z+dIpa5kxWDcHenGNUd1h3qCmowmOSCtNZn77PaZsC5XbzCgSH806RYy0gKiqK8PBwLBYLOzIP8+HBxeU+nkGo3JTfg4EDB9a4u7GxMRgMNW6pKyETu3JcOM7OnTNjM8153LvpI3KthQBOf9wHck8xbcsnfH3Zg/VyzNrRvGT25pS9PJWG4EDuSQ7lnqKN/8VXFDfPUsTdGz/iZGG60+8111rIR4f/Irkwk393vM6DEUqS5GlWzUZlxsobVQNm7dwqSAIc93OVfClApFcY7f3jKjz3/pzEygXrglWzD1PS6XTodDosesEZrfxWQqPQYbVa8fLykoldLZLNBuW4sOTJCTcWKf41cT05lgKX39ZsQuNkQRp/J+9w2+O50/H8lEodd6ySxzU2cxLXkFiQVuY38XmnNnIg51QdRyVJUn3SLiAGUU5NUgWI9m7C8MheF9S2U9CEPaO7MDFUzv73ntbjKtXVqXPRc1CZeZkhxgCCjc7F8tv4N6vEPaW6IBO7csRekNglZBS4bTryX0lbS3Vjnk9BYXHSNrc8lrt5V7IVsbLHNTa/Jm4o93erU1Tmn9pU5v76TAhBQn46h3NTKLSZPR2OJDVYwyJ6YlQNZa62KoBxzQZwW/MRNPEKchq+IVDQROmP7yivMF7sdBeXhlxSqRi6BrVEr7ju/ivro05BYWyz/qWGk0R4BdMntGOZw0x0ikrHgBaVikuqmWp1xebn5/Paa6+xdOlSUlNT0TTnbx1Hjx51S3Ce1vyCrtgCs40zecWE+3vV+NwlXbBlEQhyKjjGU+KDW+GtM1FoKy7zGC/VSI/gNmXub6yEEI5ZbmWxCY3kwsw6ish9fkvcymeHV5JYYB/w7a0zMCamB/e1vQI/Q83/JiTpYuJn8ObZjpN4dvcXwLnhOCXlRQY06cLVTfuhU1RmdH+IL4/9yd8pm7FoVgBa+8Vwa9yVBBh9yDTnEGoKor1/XJUmJQQa/RgZdSl/nD7/y6hytj4q9uxSKdlqv9kzpB03xAxyeb7p7W7gga0zSCnKcPpyq6IQbPDn4fbXs/nUukrHJ1VPtRK7f/3rX6xcuZJbb72VqKioRju7JTLAC6NexWw9l7ieSC9wS2LXzCeMnOzEMmcQ6RSVWJ+wGj+Ou2lCw6zZGB87kK+OLSnzuJviLsdHf/EVpFQUBT+9d7mJu05RCTL6lrm/Pvr40HI+PLjMqXWh0GZh9vENbE0/zpd9/3VR/r4lqSYuC7uED3tOZ27iSv45sxOzZqW5byRjmw1gWGRPR1dpsNGf6e3Gc0+rMaQWZ+GtMxLuFeyWGO5rM4aUokw2ZhxAh4oNDR06bNhoYgqk0FZMsWYhxiecMc36MyKyF3rVdStfiCmAD3s+zLxTq/nj9HoyzDkEGfwYGdWbsc0G4KvI94i6UK3EbuHChfzxxx/069fP3fHUK6qqEBviw+HUc0u2nEgv4NLmNS+yOLbZZezJLnsCgk1oXNO0d40fx10KrWa+PLKWH49tIr04HwVB66AmZFjPoKKgKgqasNdgur5Zf25vMdTTIXvMyOjuzE1cV+YYO5vQGB4VX8dRVV9CfjofHlwGlB5/oyE4lJvC98fWMaXNoDqPTZIaulZ+0Tze4WYe73Bzhcd6603E6SMqPK4qTDoDr3b9F5szDrIoaRNpxdmEmQIZEXUpPUPaVnkGv7/Bh1ubX8mtza8stc9isbi4h+Ru1UrsgoODCQm5OCpIx5VK7NwzM3ZYZDf+StrG5ozDLlvthkfF0yOklVseq6YKrWYmr/2aPZmnHc3rAoWj2YUo+HB1XDuCTSZCjQFcGdWdaO9QD0fsWROaD2Dh6a0UWIuwlVrPVyE+pAU968nvtjLmJW5BpyjYyhh0oyGYc2KjTOwkqYFSFZVeoe3pFdre06FIblCtyRMvvfQSzz77rGP5i8YsOMD5Es3Zv5dVyYdqPIlCr+p4I/52bmk+CF/9ua7dYKMf97QeyVMdb6w3XdyfH17jlNSVsAmBTSj8mXCUqa1Gc3vLYRd9UgcQ4RXER73uJs43HLCPmSkxJKIzb3Sb1KDq2CXkp6NV8HpPLc7FotnKPUaSJEmqfZVusYuPj3dKNA4fPkxERATNmzcvVY9m69at7ovQg1anHGFB6mbg3DTu1GwLU9f+wK2tevF/XYZXOfnKMRex7swxzDYr7QIjuLvNCCa3vILEgjRURSHWp0mZ4xc8wSY0fjy2qcxZngL7YtHzE3cwsWX96Tr2tJZ+EXzX9yF2Zp3gQM4p9KqOPmFtifJueC3dfnov1HIKqQIYVT36BpSsSpIkNVaVTuzGjBlTi2HUP9nmQu5f/xOa0dtpu1ZkQisy8vWe7TTTRzIo0nnmp6ooRAd5o7ugXLhFs/HWrmV8d2QT5vNaNuJDm/F6z2to7R9Ve0+mBjKLC8g0l98yq1NUDuem1lFEDYeiKHQNbk7X4OaeDqVGrozuzLyTZX9Z0ykqI6M715sWZkmSpItZpRO75557rjbjqHd+PbGDIpsFvC5ohbDpMe/sDMBzO08BpQvNRgZ48dUdl9I+MsCx7f82/878hF2l2r12Zpxi/PKv+G3oFCJ9AqhvvHQVv0QEYFLdV0VcCCGThHrksrCWdAmKYU/2yVLj7FQU9IrKbS37eyg6SZIaEyEExTYrelWHXpW9ANVRrckTmzZtQtM0evd27nrbsGEDOp2Onj17uiU4T9qWkQgoKMZinIr5VEJyThHvLDnIx7far8OezCR+S9jl8libEORYCvn04Fqe6Tai5oG7mZ/Bi56hcWxNTyizO9YmNK6Iqtmg2yxzId/tX8uPR7aTXpRPkMmb61t05c52vWji7VfxCaRaoyoq7/e6lce3zmZ92hF0ioKCglVoBBt9+W+P8bTyD/d0mJIkNWBmm43vDm/m60ObOJmfjYrCoKhW3NWhLz2bxFR4/wNZqaxJOY5NaHQPa0b30KYXbQNBtRK7++67j8cff7xUYnfq1Clef/11NmzY4JbgPEk9O+RdUUHxLUDkV63u2IZjGY6Wp3kndpa72LNNCH4+tp2nu1Z9zF5duKvtQKau+9blPp2i0DGoKT1DK16XsDwTln7H8cJsxyD9zOJCvjiwgV+P7+KnK24jzt89NZuk6gkwePNR79s5kJPEqpQDmDUr7QOiGBjRHkM9GhMqSVLDY7bZ+Nc/s1mbcsyxTUOwMvkIK5KO8Hafa7k6tqPL+6YV5fPQ2nmsSz2BerYBRkPQISic9/tdR3P/hjeuuaaq1c65d+9eunfvXmp7fHw8e/furXFQ9cFl4S0cLVSGuAQUU1GV7p9VYOH42bVlzxTlVTiLttBmochmrV6wtaxveCte6nYtesWe7uoU1VE4s0NgFDN731zjhDS5MLfUzEubEGQWF/DI+vk1OrfkPu0CopjSZhD3tRvKFVEdZVInSVKNfXNoE2tTjiNwrpVpEwINwWMbfiejuPRY72KblVuWf8/GM/aasBrC8bl9MPsMNy39lvQi95Qoa0iq1WJnMplISUmhZcuWTtuTkpLQ66t1ynrnqpjOvLNnGTnmIvArwNR1D0JzTl5mXnYj/SNaO273f305aXnnltnanphJizBfwr387YlPOcmdr95YqfFsnjI2Lp7LI9syL2E7h3NT8dYZGRbdgd5hLWqU1CUX2JffKqtGmk0ItqWf4kBWKu2CZHefJElSYyKE4JtDm8pchQnApmnMPbaTKe0vc9q+MHE/h7LTXN9HCNKLCvjhyDamdby4xgBXq8Xuyiuv5MknnyQ7O9uxLSsri//7v/9j2LBhbgvOk3z1Rj7tOwFfg9FRh0xRBXqdgqIKHusyhKHN2uFl0Dl+4mODnM6xLSELgDHNu5RbKkKnKFzfvFu97IY9X4jJlzva9OOV7mN5putoLmvSssYx78+u3GzaXRnJNXocSZIkqf4ptFk4VVD++tqKAvuzUkptn3d8t6P71RUNwS/HXI9vb8yqldi9+eabJCYmEhcXx+DBgxk8eDAtWrQgOTmZt956q9LnWbVqFVdffTXR0dH2sWjz5jntF0Lw7LPPEhUVhbe3N0OHDuXQoUNOx2RkZDBx4kQCAgIICgrizjvvJC8vD3foHNKUxVfezyOdhhAf0owOgZFc3zyeeUOm8q+2pZdT6xYT5HR7e2IWAJcERTKueVeXLz+dohBk9OFf7fq6JeaGprJdeUad7PKTJElqbPSKrsKpiQoKRhc9WpnFBWVO6jt3TNnrdjdW1UrsmjZtys6dO3njjTe45JJL6NGjB++++y67du0iJqbi2Ssl8vPz6dq1KzNnznS5/4033uC9997jo48+YsOGDfj6+jJ8+HCKis6Nd5s4cSJ79uxhyZIlLFiwgFWrVjF16tTqPC2Xgk0+/KttP34YdAe/XjGVF+JH0z4o0uWx8RckdntP51Bksdese6n7VUxt1w8vnXNZkJ5hsfw0ZDIR3v5ui7khiQ9pWuExOkWhb0Tz2g9GkiRJqlNGnY7+kS3RldP7YxUaQ6PblNoe5xdc7v0UINYvyA1RNizVGtS1atUq+vbtWyqBslqtrFq1ioEDB1bqPCNHjmTkyJEu9wkh+N///sfTTz/NtddeC8A333xDREQE8+bN46abbmLfvn0sWrSITZs2OUqszJgxg1GjRvHmm28SHR3t8tzFxcUUF58bC5eTY28GtlgsNVqkuEOkL+cPpbNqgh0JGXQ/20X7QPsB3NmqN1vTEyjWrLQJCCfOL8Tx2Bcjw9nval5lfMdQURgb14lAnfGivUa1peR6yuta9+S19xx57T2nrGs/tU1vNiUfR++i7U6nKMT5hdAvLK7U/W6I68zfiQdc3q/EzS26NorfdVWegyKqseipTqcjKSmJ8HDnwezp6emEh4djs1V9zUhFUfj1118dK1wcPXqUVq1asW3bNrp16+Y47vLLL6dbt268++67fPHFFzzyyCNkZmY69lutVry8vJgzZw5jx451+VjPP/88L7zwQqnts2bNwsfHp8qxn++17TqSCs+9yMbE2RgcXbN1ZSVJkiRJungVFBQwYcIEsrOzCQgofzGDarXYlbUyQHp6Or6+Vav3VpbkZPtg+YiICKftERERjn3Jycmlkku9Xk9ISIjjGFeefPJJpk+f7ridk5NDTEwMgwcPJjS0ZovYrzHv4act51ajMPtHM2pU1xqdszGzWCwsWbKEoUOHciAvnXnHd5NamEuYly9XxXake9jFW2SytpVc+2HDhpVa71mqXfLae87FdO1TCvJ4bOVCtp5JctreM6Ip/x04gibe7vm8rqyKrn16cT6/Hd/Nwew0vHQ6BkW3pn9Ey3JXoLBqGp8f2MB3h7eQY7b3xHnrDIxt0YkHOw7AS984fsclPYuVUaXE7rrrrgPsrWu33347JpPJsc9ms7Fz50769q3/kwBMJpNT7CUMBkON/9C7x4U4JXY7TuY0+jcPdzAajXSPiKV7RKynQ7nouON1L1WPvPae09ivfZ7ZzIRFP3MyL7tUOal1KSeZ8NfP/DnmNnwMxjqPraxrH2kI4q5OVStNYgCmdRnIlI59OZCVik0I2gY2wdcDz6s2VeW1WqXELjAwELC32Pn7++Pt7e3YZzQaueyyy5gyZUpVTlmmyEj7BIWUlBSioqIc21NSUhxds5GRkaSmOpfLsFqtZGRkOO5f17pdUPLkVFYhqblFhPt7eSQeSZIk6eIz59BuEnKzXM4ZtQnBsexMfj28l4kdutV1aLXCpNPTJdT1uPqLTZUSuy+//BKA5s2b8+ijj7qt29WVFi1aEBkZydKlSx2JXE5ODhs2bOCee+4BoE+fPmRlZbFlyxZ69OgBwLJly1yuY1tX2oT742vUkW8+N85we0IWV3b0TKIpSZIkXXzmHtpd7n4F+PnQnkaT2EnnVKvcyXPPPeeWpC4vL4/t27ezfft2AI4dO8b27dtJSEhAURQeeughXn75ZebPn8+uXbu47bbbiI6Odkyw6NChAyNGjGDKlCls3LiRNWvWMG3aNG666aYyZ8TWNp2q0KVZkNO2knp2kiRJklQX0gsLyq3wJoCMotLLdEkNX7XXsPr555/56aefSEhIwGw2O+3bunVrpc6xefNmBg8e7LhdMqFh0qRJfPXVVzz++OPk5+czdepUsrKy6N+/P4sWLcLL61y35vfff8+0adO44oorUFWVcePG8d5771X3ablFfGwQ646mO26XrEAhSZIkSXUhxj+Q5IK8Umtwl9ApCrH+QXUblFQnqtVi99577zF58mQiIiLYtm0bvXr1IjQ0lKNHj5ZZl86VQYMGIYQo9fPVV18B9kkaL774IsnJyRQVFfH333/Ttm1bp3OEhIQwa9YscnNzyc7O5osvvsDPz686T8ttLlyBYufJLGyaLHkiSZIk1Y0J7buWmdSBfZzdTe271GFEUl2pVmL3wQcf8MknnzBjxgyMRiOPP/44S5Ys4YEHHnBaP/ZideEEinyzjcOp7lnmTJIkSZIqMrplO/pGxaK6KBeloDCgaXNGxJVezUFq+KqV2CUkJDjKmnh7e5ObmwvArbfeyg8//OC+6BqocH8vmgZ5O23blpBZxtGSVPeScnNZfvQoALnnrcIiSVLjYFB1fDn8Om6/pDte562z6qM3cGenHnw2bCy6curDSQ1XtcbYRUZGkpGRQVxcHLGxsaxfv56uXbty7NgxqrGQRaPUsak/p7LOLT782rJNmA3p3NCpE96NuHaSVL+lFRTwzN9/s+TwYYyKwqstWzLkiy+4rlMn/j1wICZ9tYfdSpJUz3jpDTzXZwiP9OjPnvQUFEWhY2h4o6vxJjmr1rv4kCFDmD9/PvHx8UyePJmHH36Yn3/+mc2bNzuKGF/MTufksCHpEBDo2JaVq/DisuXM2b2bWTfeiL+LAsmSVJtyi4u5afZsErKca1uZbTa+2bGDhOxsPhkzxmXXTUNzMjubH3bsZENCIoqi0Dculpu6diHK39/ToUlSnfMzGukdFePpMKQ6Uq3E7pNPPkHTNADuu+8+QkNDWbt2Lddccw133XWXWwNsiB7640/yRTbnJ3bYjGhWE/uTsnl0wTIeGzCg1P2ig7zwMcoWk8ZKCMH6hERm79xNQmYWwT7ejLmkA8PbtcGo09X648/asYMTWVkuB1QLIVh+7BhrExLoHxdX67HUpkUHDvLQgj8RQjgq7u9ISubTjZuZee3VDG7V0sMRSpIk1Z5qZRGqqqKe1zd/0003cdNNN7ktqIZs/5kzbDl9GqFTsFcKKmn9UNAym6MBf2Vo/LVpZan7GnQKr4ztzA095TerxsaqaTz6xyIW7DuATlGwCYGqKKw8epz2GzbxzfjrCfHxrvhENTB79+5yZ8npFIU5u3c36MTuSHoGD/7+B5oQTq2SmhBYbDbu/W0+i++YTExQYJnnkCRJasiq3TxUVFTEzp07SU1NdbTelbjmmmtqHFhDtfW0fbFlRRGgLwJr5T+sLTbBqwv3c133ZujUht8dJp3z4bqN/LHvAICjFakkyTqUls7Dv//J1+PH1WoMqXnlz8y2CUHS2YlQtUU7m9DWlm+3bQNwWZhVAJommLV9B/8eNLDWYpDqL7PNxs7TyQBkFhQSHijHO0uNT7USu0WLFnHbbbeRlpZWap+iKNhsNhf3ujjozvvQUgwFiCokdgAZ+WayCy2E+MrBrY1FsdXKl5u3llkF3iYEa04kcCgtnTZhobUWR6iPDydzcsrcr1MUImqhBmRyTi5fbNjC3B17yS0uJtTXh/HdOnN77+4Eebt3DeWVR4+XWvD8fDYhWHnsmEzsLjKaEHy6bhOfr99CobmYlzu04MqPvuSK9m14auggQnx9PB2iJLlNteY633///dxwww0kJSWhaZrTz8Wc1AH0iY051/nqkwGGfFy3H5QtI99c8UFSg7Ev9Qw5FZQUUYC1JxJqNY4bOnUqt7XMJgTXXXKJWx/zSFoG1372Hd9u2u4oq5KeX8BHazdy3RffcyYv362PZxNaxcfIYuEXnRf/WsZbK9aQVVTk2GYTgj/3HuSmb38i57ztktTQVSuxS0lJYfr06URERLg7ngYvNiiIYa1boVMUFFVDF3QSNewgatgB1LAD6MIOMHWEH0deGeX48TM5N5xmFsjErjEpb1xbCUVRsGoVJyU1cUvXrkT5+zu1KpdQFYXLYmIY2Ly5Wx/z0d8WklNUXKoVTROCpOxcnl+01K2Pd2mzZi6fXwmdotA7plmF5xFCYKvl30dNbTxxkntm/0bX12bQ+ZX3uO3bn1l28Iinw6p39qWcYdbWnS732YQgITOLrzdtq+OoJKn2VCuxu/7661mxYoWbQ2k8Xh8+nI5nk15VUVAU0Kv2/1/RuhWPDuiPTlUcP8G+zuM8ZItd49I2LAyTvvxZr5oQxEdH1WocQd7ezB4/nt4xzpNzVEVhbIcOfDpmjFsLlu46ncye5NQyu0ZtQrD04FFSct23Kstt3buV2xWrCcHE+K5l7t+blMojc/+ky3/eo+NL73LljC/4Zv1WzPWsJ+L7Tdu59Zs5rDx0jCKLFbPNxuYTJ7ln9nzeXrba0+HVKz/v2F3umGVNCH7ctqsOI5Kk2lWtMXbvv/8+N9xwA//88w+dO3fGcEHB3QceeMAtwTVUAV5e/HTTeJYcPsIve/eQll9As8AAbuzcmf5xcaW6w0J8jCRmnCtm3NgSu5zCIuZt38vG4ycRQtAjrhlj4y8huJZngdYXfiYjN3TuxKztO1223ukUhTZhYbWe2AFE+fvz7fXXczQjgx2nTsHRoyyZPJmoQPfPEt2TnFrhMZoQHEhNI8LfPWP7ukZF8fSQQby8bAU6VXF0u+oUBU0IXrpyKO2aNHF535WHjnHfj/MRCMf9EjOyefWvlSw9cJRPJ47BWA8KOB8+k85Li5YDOCWxJf/+eM0mejePoV/Lhju72Z1OZ+dU2P1+Ji+/1if2SFJdqda71A8//MDixYvx8vJixYoVKOdPGFCUiz6xAzDodIxq15ZR7dpWeGzwBRMlGlNit/nESe767jcKzOee07KDR5mxfC3v33wNvWKjPRhd3Xns8v7sTk5he1IyJYVwwN5aFuztzfvXjnb6O6ptLUNCiPH358+jRwnzqZ2B45WtzefuGn639+hOl8hIvty8hfWJJ1GA/s3jmNQjnq5RrpPnfLOZh3/+A5umOY2ILfn3xhOJfLF2C3cP7O3WWKvjhy07Uc+WzHFFpyp8u2m7TOzOCvbxdkryXfEzGWVSJzUa1UrsnnrqKV544QWeeOIJp3p2UvVcOAM2s5Ekdik5eUz5dh7FVqvz9BEBRVYr98z6jd/vnuip8OqUr9HI9zffwNxde/hhxy5OZmcT5OXF2E6XMLFbV0I9PCvvYHIaP2/ZxYm0LAK8vRjZuS2Xt2tRo67Zfi3trdPljTH0Mxnp1tSebOUVFfP7jv1sPXEaVYHeLWMZ1aUdXoaqv011bxpN96aV/9KwYNd+CsyWMvcLAd9t2s6U/pc6ronFZiMjrxAvo55AN8/uLc/WxNPlz/zVBDtOJtVZPPXd1R3b8/OOPWXu1ykKYzu7d9KQJHlStRI7s9nM+PHjZVLnJiE+F7TYNZLJE7M376TYai1jpQOw2TR+2rKbits0GweTXs+E+K5MKGeMlyfMWLqOz1ZvcbRqqIrCHzv30zE6nE9vv46ganaZR/j7MaZzB+bt2ldmcje5V3e8DHo2HE1k2vfzKSg2oygKCvD7jv28vfgfPpk0lkuia3ei1r6kM+hVtdwJLGl5BWQWFOJtMPDxig38tHEXOUX2mb7d46KZenkvdKpKak4eYf6+XNYqBkMtrChirGC8JlArj9tQXRYXQ9/msaw/kVjqdahTFHxNRu7o3cND0UmS+1UrM5s0aRKzZ892dywXrQu7YhtLi92yA0fLba2xCcGKA0frMCLJlW/XbgXOlQEp+Z3tTzrD9B//qNG5nx9xBQPOdgmWDGAv+f/1XTtyb//eJGZkcfc3v1JgNtuLCJ+3FFhWQRF3fDGXrIJCl+d3l8okS2Bvpbv1k5/44p8tjqQOYNuJ09z99TymfPkLT81dzF1f/crg1z7l9+373B7r4DYty+021KkKQ9rKZdNKKIrCB9dfw8gObVE4txYQQIvQYL6/5UaaBgZ4KjxJcrtqtdjZbDbeeOMN/vrrL7p06VJq8sTbb7/tluAuFhd2xWYUlN0l1JCYrRXPJLTUs9mGnpCYnsWinQfJKSwmJiSQEV3acvRMBqsPHMdi0+gUE8GgDi3d3gqjVTCg3CYE648msi8plQ5R4dV6DC+Dnk/Gj2Fz4inm7dpHRn4BkQH+jOvakU5R9la4WRt2YNU0XH0H0IQgt9jM3C17uHNAz2rFUBmXt2nBNxvKLnmhKgqdm0bw08ZdHExJc/7CIlxXqszIL+TfPy0C4OpuHdwW6w3xnfhkzUYKLaVbw+2Ji8Itl3Zz2+M1Bj5GA++MGcWjg/vzz6GjcOoEn990HZfGxdTp2FZJqgvVSux27dpFfHw8ALt373baJ/9Iqi7Yp3G22HVrFsmJjMwyBy3rVIVOTSOA+l0vrLZYbDZe+nUZv2zajaIoqKqC1abx4q9LEdivj4K9vl2Yvw/v3XoNXePcN3M2ISOrwmN0isKqA8erndiB/T3h0thmXBrrun7c33sOlzuwXQjBkj2HajWx69MylnYRYRw+k+4yFk0IpvS9lGfmLimV1FXkjT9XMbJzO/Q69wxdCfX14dMJY5n6wzzyi81OE3F0qsL/xl1F6ya1t4JJQ9Y0MIBxXTvy56kTxDeNkp9XUqNUrcRu+fLl7o7jotZYJ0/c3Ksrv2zfW+Z+mya4sWcXUnZvr7ug6pFXf1vBL5t3I7AnL5rN/hFd8kFtTzDstzLyCrnzs7n88tAtxIYGueXxK9NaqihKucdtO3aK71dvZ8vRU+hUhf7tmzOhfzxto8IqHUex1eqWY2pCVRQ+mTiWO76Zy5G0DEd5FEVREAievPJyusdGk11Y9RUK0vMK2HA0kX5t3DdLtUdMU5bdfye/7tzL2qMJaEKje0xTbojvRBM/X7c9jiRJDY9bijLl5OSwbNky2rdvT/v27d1xyotKyAUFinOLrZitGkZ9w56c0rlpJNOH9uPtv9egO688Q8lMyXsG9qZHbDR/XoSJXXJWLnM27nLZ/eiKJgRmq5VvV2/jqWsHuyWGmOBADlVwjFXTzraqlvbF8k2888dqp1IS8zbt4deNe3h94khGdGtXqTguiQ5nzeETlWjZrV0R/n78ds+tLD9wlCX7D1FgttCmSRhju13C8eRMPvt7Y7XPnZbr3qXTAAK9vbi9d3du793d7eeWJKnhqlZid+ONNzJw4ECmTZtGYWEhPXv25Pjx4wgh+PHHHxk3bpy742zULuyKBcgqMBMeUHclFGrL1AG96BAZzpdrt9gLFCPoEduU2/t2Z0i7VlgsjWM8YVUt3XsEUcU1hG2aYMG2fW5L7LyM9i8U9oH4pWNRFYVwf18GtG1eat/mIyd554/VjrjOjxHgiVkL6RQTSbPQigsf39y7G6sOHi9zv00T3NSrbmYS61WVYR1aM6xDawCOpmRwz0e/kpCWhV5VUQwgLhyBXwnhAe4pwNyY2DSNDQcTOZWRTaCPF/07tMDHZKj4jpIklataid2qVat46qmnAPj1118RQpCVlcXXX3/Nyy+/LBO7Kgr0NqAoOLXeZDSSxA5gQJvmDGjTHHH2CcpxLfaabeUVmS1LebXWqqtdVBN2nEp1/H7A3kpm0ut5d8LVLmvZfffPtnKLvgoBc9bv5OHRAyp8/IFtm3Nz7678sGGH099BScvu/Vf0oWMdtNhdKDu/iDs+mENWvn1GrlXTUCwgjNjz4Eq8jBWgSYAfvVpWvD7txeSffcd4YfbfpGafW07O22jgrit7M3lIT/keIUk1UK3ELjs7m5CQEAAWLVrEuHHj8PHxYfTo0Tz22GNuDfBioNepBHobyDpvNmxjWn2ihHyzPqd5WHCFyxxdSAG3ja8738e3jmHOtr3M3riTU5k5+BgNXN2tA5P6di/z8bYcPVlu/JoQbD56slKPrygKT181mK4xUXy9Ziv7kuxLkXWNieKOAT25okOrcu+fU1DE/A17+XvHIQqKLXRoFs71/bvQOS6y3PtZbRrLdx7mtw17SMnOIyLInzG9OzKocyv0OpVfNuwmI6/A6QuXomFP7gyAAPVs6RZXM4wV7Mfc2qsbWw6fIq5JEBHB/qWOs1htrNh1hENJaZgMegZ3bkXLyMY7+WHDoQTu//Q3py8SAIVmC/9bsBqbJpgyrJeHopOkhq9aiV1MTAzr1q0jJCSERYsW8eOPPwKQmZmJl1fjaGWqayE+RqfELjP/4uyivFgMvqQlgT5e5BQUValD9qY+7u+S9DIauKN/T+7oX/lZp5VJ0lWl8mNEFUXhmm4duKZbB8xWG4pSuSK7h5PS+NeMn8nKL3QkYIeT0pi3YQ9Thvdm2ui+Lu9XUGzm3g9/ZdvR046WwcOn0/lnzzF6tG7G+3eN4c9t+12OgVRtIGwgdBDob2Jsr46E+fny4/odJGRkO44LNnihmuG9eavPPkfof0kLnrh+MBEB9pVGNh8+yZPf/kVmXiF6VUUTgvfmr6FbyyiigwM5k51HWKAvV13agb4dmjsSyYbsnd//QZQzEOHjxesZ378LAXW4mockNSbVGp3/0EMPMXHiRJo1a0Z0dDSDBg0C7F20nTt3dmd8F41S68U2ktUnJNeMej3/ueFKe5mTSiVJCt2bN+WGXp3qILqK9W0X5yg0XIoA1QpFuWZem72MzYdOlmqdKY9Rr6tUUmex2rj3w1/tyfF5py9pSfz0rw0s2nrA5X1f+WkZO47Zl90qKV9S8v9tR07x+tzl5BWV/TeoYE/w/BQjj44YyO39e7Dwkcn8cM9NvDvhKibGdyE3s4js/HOzaIWAtfuOc8tbP5CSZe+CfOTz3x3HWDXN0fK342gSC7fsZ/PhkyzZdpBpH83jrvfnUljcsL/wnTiTyd7E1HInDZmtNpbuPFx3QUlSI1OtxO7ee+9l3bp1fPHFF6xevdqxtFjLli15+eWX3RrgxeLCCRQZeTKxa+wGX9KKL6deT/fm59Y01akKXWOjiA4+Vwk/wMvEnYN68sm/rsOod8tE9hq7dUC8y1VFFCvoikC1wNFT6fy8eidT/jeHKf+bQ25hsYszVd+ynYdJycors0tYVRS+Wrq51Pa0nHz+2Ly/zFVRNCH4feM+YkODyk5esf+uWp/XZaooCl1jougeG83cNbtc3semCXIKivh88UbHY5W3OkvJfQC2HD7Jf2YvLffY+i4jt6DCY3SqUqnjJElyrdqfEj179qRnT+eum9GjR9c4oIvVhSVPMmWL3UWhZ8tmfH33jZzJzSenoIjwAD/8vU0IITiVmYPFZqNpcECNEzqzxYrVpuFtMrhlrGPHmEheuGEYz81ZYp8Eogl7Undeg5Imzi3JsO3oaf79+R98MO26Gj92iQ0HE9CpKrYy1nfVhGBfYioFxWZ8TOe+OG09cqoSyZR9xY+1B0+Uc4zgxr5dSm1fsGlfuS1SNk2waOt+ug1s7jw27+w/y/rtaELw5+b9PHBtf8IDG+Ys20gXYwwvZNMEkcFyiS9Jqq5Kf1pMnz6dl156CV9fX6ZPn17usXJJsaoL8TU53W6MkyeksjXx96WJ/7nCsoqi0Cyk4lIhFVm39wRfLtrE5gOJADQNC2TCFfHccHnNx+qN7dWJrs2j+WntDjYdOcnJk5mYcV3MWNME6/ad4MDJM7Rr1qTGjw1UevLJhcdV9n7tosK4qkcHFmxxvd7r6O7tGXhJi1LbT2fmoqqKo+B0ZWIqUVHKrQnBpoOJjL7UfUuU1aWo4AB6tm5WbnLtYzIwpHP5E2YkSSpbpRO7bdu2OWqObdtW9pqKUvXIFjvJ3X5euYNXZi1zGnB/Oi2bN2evYOO+BF65c0SNH6NleAhPjBnM6fQcRj/7ebnH6lSFFTuPuC2x69Yimnnr95S5XwHiwoPx83Ie5tCleSSuK/edd18FurSIZmi3tnRoFs43K7aQcrY0R3igH7dd3p1bBsa7bP0M9vWq0pjCStMEigbLNh/CpOi4vFsrDHr3rh9cFx69ZiCTZszGYtVcJnePjbkcb6OsZydJ1VXpxO78ZcTkkmLuV2qMnWyxk2rgdHoOr/1g/zs9v7uv5F8rdx7ljw37cNfHZ7Gl4iW/FEWp1HGVNaJHO96et4q8IrPLBEEAtwzuXir5ahoayOWdWvLP3mMuW850qsKgzq0c3Ya3Xd6diQO6kZyVC0BkkL/L2n7n4mrPhwvXl7lfpyr0O9vSp6oK2Jx/P6VSRSFQLaCcbQxdve0IK7ccJtDXi+cmD2dgt4bVunVJTARfTbuR/8xdxu6EFMf2yGB/HrqqP6O6y9WLJKkmqjRw54477qjwGEVR+Pzz8r+5S6U11vViJc/49Z9dlNcspSjwy6qdjO9S8+5egKiQALyNBgrLKaBstWm0ia78GrIV8TYaeHfqtdzz4S9YrDZHklZSvuSaXpcwro/rWfrP3TyMO96bw/GUDMA5oYoLD+bZm4Y6Ha9TVZpWsmu8eXgwYy/rxLz1u0tdfkUBVVW5c1hPjuzYTExYIEdSsuyxl/H7KknqSuIreZ45+UU8+sF8Pn70BuLbNqwCyB1jI5n18ASOpqRzMi2bQF9vOsdGNopyLpLkaVVK7L766ivi4uKIj4+vna6Gi5gsdyK506FTaS6L5pYQAo6lZADuSey8jHrG9O3I7FU7XBfrVcDf28QV3Vq75fFKdG/VlF+evI3Z/+xg8baDFFmstI0OY/yAbgzp0qrMiSIh/j58/8jNzFu/m1/W7SYtJ58mgX6M7dOJsZd1dJpsUR1PjR+Cl1HPT6t3YtM0FEVBCEFEkD//uWUE7ZqGc2QHfHTv9XywZ+BkVQAAOdhJREFUaD2/b9xLscXeJKfXqdhsmj3H0wSq62GL9mRUwMfz1/HRozfUKF5PaRkRSsuIxluMWZI8oUqJ3T333MMPP/zAsWPHmDx5MrfccotjBQqpZkIu6IotsmgUmm14GxveGBrJ80wGvaPlqizGStSKq4p7rurLpoOJHE3KcHpcnaqgKAqv3TEao8H95VqahgYyfcxApo8ZWKX7+XoZmTioOxMHdXd7TAadjieuH8yU4b1ZufsohcVmWkSEclm7WFRVcYxXDvAx8cz4oUy/diCn0rMxGfQE+3kzd80ufl6zi5TUHMD1rF84u8LH/kQycgoIOVv0uLE7lHCGn5duZ+eh0+h1Kv27tWTs4C6Eh1Q841aSLgZVqmM3c+ZMkpKSePzxx/n999+JiYnhxhtv5K+//pIteDV0YYsdyFY7qfoGdWtVblKnUxUGdGnp1sf09zbx5fTx3DmiF0G+3oB9DNngrq355tGb6NMhrtR9NE1gsZbRJNUIhPr7cF2fTkwc1J2+HeLK7Gr09TLStmkT4sKDCfDxYvKwS/nj+Tu4ZVA8el3Fb9N5bq4RWF/9+NdWbnn6W35fuZujJ9M5eOIMX83fyPWPf8nmvQmeDk+S6oUqf302mUzcfPPN3HzzzZw4cYKvvvqKe++9F6vVyp49e/Dza5j1lTwtwEtfalH1zHwzTYO8PRiV1FBdEd+aD8ICSM7ILTVBoCS1GD+4G0d3lS7gWxN+3ibuvaovd4/qQ0GxGZNR73IViZ0HT/Ht75tYs/0omiZoFhHEDVfGM25oV/QNcKZnbWnWJAibrewWOwCDTiUs0LfcYxqDrfsTeef7FYBzuRhNCMwWK4++8xvz3v4XQf7yPVOqfcdPp7Ng5R6SzmQT6O/NlX3b07Vt03qxJnq1Vp5w3FlVHWNHbLbG+627LiiKImfGSm5jNOj56OHriQ61j6HTqcrZLlH7vjfuuor2MeG19viqquDnbXKZ1C1Zt5+7X5zN2rNJHcCplCz+9+1yHnt7HtZG3IJXVcN7tys30dWpCiMu64CPV83GBDYEsxZuLXMlECGgyGzl91W76zgq6WIjhGDGrJXc9NhX/PDnZpZtPMRvy3Zy94uzmf7fXyiqB8v+VTmxKy4u5ocffmDYsGG0bduWXbt28f7775OQkCBb62pI1rKT3KlpWCBzX5jEm3dfzTV9OzKiV3seueFy/np9CoPdPImhsrJyC3jxo0X2L4MXlGERwPqdx/l5yXaPxFYf+ft48djNg4HSZVB0qkKQvzd3X9u37gPzgE17E8otLi2EYPMe2R0r1a6f/trG93/YezpsmnB6L9uw6wSvfrbYk+EBVeyKvffee/nxxx+JiYnhjjvu4IcffiAszH3lCy52ssVOcje9TmVIfGuGxHsmkbvQH6v2YC2Z8emKgJ/+2sr4EaXrz12srru8CwG+Xnz821qOJdnLs+hUhSE92vDgDZcTcbFMGqjEOO7yO60lqWasNo2v528oc7+mCRav28/dNw4gqonnlsWrUmL30UcfERsbS8uWLVm5ciUrV650edwvv/ziluCef/55XnjhBadt7dq1Y//+/QAUFRXxyCOP8OOPP1JcXMzw4cP54IMPiIiIcMvj1zVZy05q7A6eOFPuqg8COH0mh2KzFS+TXH2gxNCebbmiRxsSUrLILyomOjTwohtL1q1dMzbtOVFmq52qKMS3a+q4nXAqgyWr95GTW0RkeADDB15CSFDjH4so1Z6Dx1PJyC4o/yABa3ccZdzQbnUSkytVSuxuu+22Ov8W3bFjR/7++2/Hbf15i6E//PDD/PHHH8yZM4fAwECmTZvGddddx5o1a+o0RneRteykxs6o16FUsJ6XApWaCXqxURSFuMhgt5xL0wQHj6aQV1BMs8ggIsPdU8+wNt08vDvrdx13uU8BdDqVay/vjNVq4/UPF7NwxR5HqR1NE3z07SrumjiAG0bH12ncUuNhruQKO2aLZ8cJV7lAcV3T6/VERkaW2p6dnc3nn3/OrFmzGDJkCABffvklHTp0YP369Vx22WV1HWqNXVjLTnbFSp5ks2nsPXiarJxCIpoE0KZFeI2/2PXv3pLfV5Y9wF1VFXp1ipMzY2vRXyv28ums1aScyXFs69k1jof+NYS4ZvW3WPBlXZoz9bq+fPLLWqcKAo46ifdfTWiQL29+soRFK+1rCNuPOfstQsAH367C38/koWcg1bW09DyOJpzBaNDToW0UJmPN6mjGRYeUql5xIU0I2sS6Zz3s6nJ/tVA3O3ToENHR0Xh5edGnTx9effVVYmNj2bJlCxaLhaFDzy390759e2JjY1m3bl25iV1xcTHFxefqPuXk2N/gLBaLo3CoJwR4OX+YpecVezSeulDy/Br783Q3IQTbdieycOluTidn4u/nzeD+7Rnctw3GSi6gXt61X7nuIJ9/v5oz6XmObc1jQph2xxA6to+udty9OsXSqlkIp1OzynxznDiqhyOmnNwilv6zj5OnM/H2MtL/sja0a1XzBNPTPPW6/33xTmZ+bR9CYzScaxXdvT+R+5/+gfdeGk/TyKA6jakixxLS2LLjBBarjY4tI5j5+HXMW7GTvUdT0OtVLuvSgrGDOtMsIoiUM9ksWrELg77sFt/Z8zdywxXh8j3HA+rqdZ+ekc/ML5ezfssxR41dXx8j11/dgxuv6Vntpev8vA0M69OWlZsOuXz/UlWFqLBAurSJdPtzrMr5FFGPKwsvXLiQvLw82rVrR1JSEi+88AKnTp1i9+7d/P7770yePNkpQQPo1asXgwcP5vXXXy/zvK7G7gHMmjULHx/PVW/ffEbh28Pnkrsob8ET3WTpB0mSJEm6mBUUFDBhwgSys7MJCCh/Yka9brEbOXKk499dunShd+/exMXF8dNPP+HtXf2Bw08++STTp0933M7JySEmJobBgwcTGuq5rgj/Q2l8e3ir47ZVZ2LUqEEei6cuWCwWlixZwrBhwzAY5GD5yvjh1418+9N6l8PUdKpC29YRvP3CjRWex9W1N5utTLjnM/LLGN+pKAptWjTh3f/cVJOngNVqY93O46zdfpRis5WWMWGM6t+RkED7F6uN247z3OvzXd5XVRW6d47lpSevrVEMnuSJ1/2fS3cz48vl5U4uVVWY/eEU/P286iSmsthsGtOf+Ykjx8+UahlRVQWTUc+Hb0wkItz5A+6Xhdv49MfVaOVMjzXqFe65oaV8z/GAunjdfzFrDXP/2FruWtkfvjGB5jHVr+iRk1/EnL+28fuq3eTmFWE06Bh6WTvGj+hBs4igap+33MfMyan4oLPqdWJ3oaCgINq2bcvhw4cZNmwYZrOZrKwsgoKCHMekpKS4HJN3PpPJhMlUepyFwWDw6B96kwvWeswssKDX6xt8t1NlePraNxQWi42f5m+l2FL2J9eOPac5eiKddq3L/zsocf61X7PpKJnZReUev/tAMqdTcmo0HstgMDCkd3uG9G7vcv83P23AahVlLou2dvMxjiVk0LZVw5wBX6IuX/fJaXnYbPaSDeXJzi0mJNizJVQ2bD3M7gPJZe63WDTm/rmdB6dc4bQ9LqYJRcXlPz9FsXfTyvccz6mtay+EYP7iXRQVl93TpVMV/lqxn2l3DK7244QGGbh7/EDuunEAxRYrRr2+2t27lVWV69Wgpp7l5eVx5MgRoqKi6NGjBwaDgaVLlzr2HzhwgISEBPr06ePBKKsv+IICxVZNkFtc8Swc6eJxPDGNnNzyEy9VVdiy80S1zp+WnlepLxJn0nKrdf7KyMjMZ9/BpArXul217lCtxdAYBfp7l9uKUcLTrXUAy/7ZX+4HpU0TLF6+t9T2S7vE0STUD7WM17BOVegT7941kqX6w2K1kZtX/vujJgSpZyrf+lUeRVHwMhpqPamrqnqd2D366KOsXLmS48ePs3btWsaOHYtOp+Pmm28mMDCQO++8k+nTp7N8+XK2bNnC5MmT6dOnT4OcEQul69iBrGUnOavMB7P9uOqdPzjIl8oMuw2uxXpglVmSR1EUiorl30ZVDOnXrtz9qqIQ3ymGsBDPryCUk1tY4Wu9oKC41DadTuWZB0ah0ymlPmx1qkKAvzd33zLArbFK9YdBr8PLVH5HpKqqBAV6bix9XajXid3Jkye5+eabadeuHTfeeCOhoaGsX7+eJk3sU4nfeecdrrrqKsaNG8fAgQOJjIx0W3FkT/A26DBdMJtLljyRzhcXE4q3d/nrgmqaoFOH6s1c7XtpS7y9ym7yVxSFFrFhtIyrvRVnQkP8yo0B7N2JNRkjczEKD/Pn+qu6u9ynKPaff03oX8dRudY0KrjMdWFLRJRRe697p1g+fGUCfbq3pKThzqDXMWJwRz7/761EeHBFAKl2KYrCiCGdyn3t2GwaVw66pA6jqnv1eozdjz/+WO5+Ly8vZs6cycyZM+sootqlKAohvkaSzhvjJNeLlc7nZTJw7fCuzP5ts8uWNZ2qENsslK6XNKvW+b29jEy9dSDvfrq01D5FsReCve+OQbU67tNk1DN6WBd+KWMAtKKAyWRgyADX4/Okst076XIMeh2zf9+M1aqhKgqaEIQG+/LEtBF06dC04pPUgauu7Myvf24rc7+iKIwZ2a3M/e1bRfL6k2PJyy8mr6CYoABvx0omssxJ43bz2F78vWofBYXmUu8fiqLQ99KWdGxX/ZJNDUG9TuwuRsE+zoldRr58E5Kc3TmxPweOpLBtVwKKojgSPFVRCAjw5uUnrq1R4jXuqu6oqsKn3/1DXv657q6wED8euedKesW3qPFzqMjkm/uyafsxEk9lOr05q6oCAv7voZH4VNByKZWm06ncfdtAbh5zKWs2H6GgwEyzqCAu7dYcnRtX+0hOymLrluPYbBrtO0TTpm3lJvKUaNMyghuu6cGc+VtK7VNVhdYtwhkzqluF5/HzNeHnKwsSX0yiIgJ5/9WbefGtBRw9kebYrqoKI4d04qG7hjb6CYkysatn5HqxUkVMRj1vPX89S1bu47dF2zmVnIW/nxfDB3fk2uFd3TJ+ZOyoeEYN7czGbcfIzikkskkA8Z1j3frhXx5/Py8+eH0i381Zz/y/dpBfYEYBenSJ47bxfejasXotkpJdYIA3o4Z0cvt58/OLeev1P1i1cr/T9rbtonj6uTFEN638kmjT7hxMVEQgs+ZuJC3DXijbZNIzemhnpt46UK4lLJWpZVwTvnz3dvYeTOLw0VSMRj2XxjevF+NH64JM7OoZuV6sVBl6vY6RV3Ri5BXu/3AuYTLqGdC7Ta2dvyL+fl7cM3kQU24dQHZuId4mIz4+spWuvrLZNP7v8dns23uq1L4jh5N5aNo3fPz5nQSX8eFaVGRh2eJd/P3XbrKzCmjaLITR18Yz+9OpJJzKwGq1EdM0RLbUSpWiKAod20U3+m5XV2RiV8+E+Dh/C5UtdtLFTq/XERp8cXzTbsg2rj/Cnt0nXe6z2QRZWQXM+3ULk++8vNT+tDO5PHr/t5xKzEBRQAg4mZDOutUH6X95e556caxcP1iSKqlez4q9GJVqsZOJnSRJDcDfS3aXW89L0wR/Ldzpct/Lz8wl6XQmgGNljJKxlWtW7ef7r1a7N1hJasRkYlfPXDjGrjEndkIICmVXsyQ1CpkZ+eXXnhOC7PR8Thw9g+281S8OHUhiz66TaDbX9xUC5s3ZhFkWa5ekSpGJXT0T7NP4x9iZiy3M/uofJo56mwkj3wLgxcdns2tb9VZLkCSp5mw2jRNHUzl2KAVzJYpEXygyKhCdzkWLnRAoZhtqoRUtr5ip4z9g0rXvMv+njQgh2L7leIWV+/Pyijh2NLXKMUnSxUiOsatnQhv4rFghBAd2n2L1sr0UFZiJadGEK0Z3wc/fG7AndU9O+5Y92xMRQmAw2r9b7Nh0jE2rD/PEy+MYNLyzJ5+CJF1UNE1j3g/r+fnbtaSfsS8V5+NrYvT1Pbl16mBMZ4tFa5rGhlUHWfjrFpJOZhIU7MvQq7oyaERnTF4GRozqyuJFu5xPLgRqkRWEvQZiiTMpOcz870KST2cR1MSz69JKUmMjE7t65sIxdlmFFmyaqLAKe32Qn1vEi4/OZvvGo/ayGApoNo1P31nM9OeuZcioLvwyaz17diSWKq6raQIh4M3n59H9slYENPIlXySpPhBCMOPVBfz5i3O9uIL8YuZ+u5YDu0/xysxbAXjpkdls+Ocgqk5BswkSlTR2bjnOz9+u5b+f3k7nLjEMvuISVizb6xgnp1i1Uknd+eZ+v45HXxxb4fJh3t4G4lo0qenTlaSLguyKrWcuHGMnBGQX1v8ixUIIXnx0Njs3HwPs3To2q4YQYDFbef3puWxee5j5szciynkTt1o1/l6wo67ClqSL2t4diaWSuhKaJti55Th/L9jB1x8sY+PqQ/btZ8fClXw5O3kijVefnIuiKDzxf9cw4dZ+jrI0ilUrM6kDe8Hkg7tP0rptRJk1EhVF4aoxPfCqYJk5SZLsZGJXzwT5lH7zaggTKA7sOcX2jUfL/OatKgrff7yC9LTccs+jqnDscEpthChJ0gX+/HVzuUWnFUXht9kb+H32JpdL2IE90du+8SjHj6Si06tMvvNy5sx7kDf/NxG13LTO/gUw6WQmz758PcEhvk4rApSMu+vWI47bpwyq+pOTpIuU7IqtZ0x6HX4mPXnnzQBrCOvFrl66F51OdZrtdj5NE+zdmQiuBlc7URxjeiTpQpqmsXXNYZbO30bGmVyaRAUybEx3uvRq2eiXCaoNJ4+nl/k3C/ZWudMJGRRX0GugKPZxss1bhQP2tXy7xcfh62ciL7eozPupqoKfvzdRTYP5+JupLPx9G0sW7iQnu5DoswWKBw/tKGvYSVIVyMSuHgr2NTgldg2hxa6o0FL2QJrzdOoWy96diWW27NlsGn0ub+fm6KTGoKjQzIvTvmPbusOoOhXNpqHTqSz9bRt9h17CE2/ehMEo39Kqwj/Q22m9YVe8fAwVJnagnCtAd57BIzrz5y9byv3CN+jKjgAEBHgzfmJfxk/sW+n4JUkqTXbF1kMhF5Y8ySsu48j6I65lE7RyvvkD+PiZmPCvgWV31+oUWrWNJL5Xy9oIUWrgZr40nx0bjgA4XmslCcO6pfv46p3FHoutoRo0vHO5SZ2qKgwd3RWjqfyEWQhBx/i4UtvHTeiD0aR3Wc5EVRXaXhLNpf08t2ydJDVGMrGrZ06fSCftgnpNH/9vCX//6nqAc30xZGSXcltLVFVh1HU96HFZax59fgx6vYqiKKjndc3GtQzn5fcmoqryZSk5yziTy7L528r8UiCEYMGP68nPK7vbTypt4NBLiGkehupinJ2qKvj6ezHulr6MHNujzFpzqk6lQ5dmtG4fVWpfVLNg/vvRJMIiAgD7ZAnH2LlLW/Cf924pd4yfJElVJ/st6pGUkxk8fMMM8lrHQmyEY3tOsZW3Hp9NblYBYycP8GCEZfP192L6c2N4/amfURTF6QNYVRViWjRhwr/sa0QOu6obl/Zrw+L520g4Zk9in3njRi7t27bOkzqb1UbC4RQ0m6BZqyaYvOQC4/XRjg1HKiyJYS62snfrCS4dKLvyK8toMvD6R7fz8uOz2bszEVWnoKBgs2lERAfx3Js3E9okgDseGMqRA0ns3paAoiqOme2KohAW7s//vX5jmY/RpkM0X/36AFvXH+HA3tMYDDou7deGlm0iyryPJEnVJxO7euTrd/4iL6cI3QVV321G+2SCL974kyvGdCcg2NcT4VVo8MjOBIf68v2nK9m5+Thg734dPa4nN985EF9/L8exQcG+3DipPxaLhT///JPuvVvVaVKnaRpzP1nOL5+uICs9DwBvXxOjb+nLLQ+PkAlePVPeAH+n46yVO046J7SJP+98+S8O7DnFlvWH0WwaHTrHEN+7peNv0svbyGsfT2LFwt38OXczSacyCQz2YdjV3Rg5tofT37YrOp3Kpf3ayG5XSaoDMrGrJwryilj15w77gPALEzuTPbGz2TSW/76da2/r54kQK6Vbr5Z069WSvNxCigotBAX7ojfUrxltQgj+9+/ZLJmz0Wl7YX4xv3y6ggPbE/jPt3fLgfj1SJuOTSs8RlEUWl0SXQfRNE7tOjalXTnX2WDQM+yabgy7plvdBSVJUpXJwQ31RGZarqO1QVfsPAu2KMAXgf1bb+qpDA9EV3V+/t6EhQfUu6QOYNeGI6WSuhKaJti14QhLf9lcx1FJ5YlrHUHnni1cjgUD+zivPld0oElkYB1HJkmSVL/IxK6e8D9vCS3vTOcivlZfLwpDA9A0rd52wzYki35cX2aCAPaWnz+/X1OHEUmV8ehrNxAc5ldqEL+iKkQ2Deb+58Z4JjBJkqR6RCZ29URAsC/d+7dB1SmYsvMx5uQ77c+OCUcTgstHd/NMgI3IqaOp5ZZmEUJw+kR6HUYkVUZ4dBAzf7mfm+8eTFhEAAajjojoICY9MIx3f7qXoFA/T4coSZLkcXIQUT1y64PD2bn+KELRCExI5UynFo59uc3CmdwhjMiYEA9G2Dj4B/s6zexzpaLB4JJnBAb7csu0odwybainQ5EkqYE5dSSFxT+sJfVkBoGhfgy5vjdt45s7HWO1WFn753a2LNuLpmm0796Cwdf3xqcBfSbIxK4ead8tlhc/v4O3Hp9N8UnnxM5mMtBxfHcPRld/nNh/mh2r9qMJjY69W9OmW/NSxwgh2LfxCGdOZRAY5k/nvm3RnV2WaNA13dmycn+Z51dVhSuu61lb4UuSJEl1SAjBZ8/PZe7MxWeH4QgURWHex0vpf3V37nxuHOYiC8VFZl6e/BGpiRno9PYOzSU/rOWzF+by7Df3ED+wg2efSCXJxK6eie/bhq9X/h9bVx/kseXHOV5kc+ybvzOZoR1LFwG9WGSl5fL6vz5h28p99nVBFRCaoF2PFjz5xV1ExoYBsHnpbmY+9j1Jx8447hscHsCUl25kyI2XMXB0N376cCmnjp0p1SWr6lT8Ar256tb+dfrcJMldhBAU5BZhMOoxynWXJYlfPvybuTPtK9Oce88XIASr521m9byzk+V0KiVLTp9fOqmooJhnb57BByueJaZNZB1GXj1yjF09pNOpXHp5eyZf2d5p+5K9KeSft4asu2SmZvP96/O5+7JnmNT5MV64+T22LttT7lJDdc1cZOGJa95kx+oDgP3Dq6Qr9dD2Ezw26g1ys/LZunwPz9z4LsnH05zun5mawxt3fcaSH9Zg9DLw2qx7ad/NvgSSqlMckymi40J5Y/Y0QsID6vDZSVLNWcxW5ry7kNsueZRxze7lmoipPHntf9m+cq+nQ5Mkj7GYrcz+38LSO4SA84fjKPZtrj72hCbQrBq/fbqs1uJ0J9liV4+N7hLFiwv2Yjv74iu02FiyN4Ux8RXX9KqswztO8MTVb1CQU+io7J92MoN1f2xj9J2Dmfb2rfbWMQ8xF5nZtfoA6xft4Pi+Uy6P0WwaZ05n8OObC1i/aCdAmUnpJ0/9xKBxvQkJD+CtuQ9wcGcC21cfxGbT6NCjBV37tPbo85Wk6rCYrTxz/TvsWLnv3GtfwI5V+9m2Yi/TZ97BlbfUz1VrJKk2Hdx2nJyMPOeNFyZ1ABW879tsGv/M38K0Nya4OUL3k4ldPRbmZ2JAmzBWHDjXpThv+6lyE7szJ9PZv/EwKAod+7QlJDKozGNLPgwKcouclmsqqfL/x+fLad0tjpGTLq/5k6kiIQRz313ID6//Rl5WARgMoCqlki4hBFhtYLPx89t/oJjKXzEiNzOfzX/vps+obgC07RJL2y6xtfU03M5cbEGnUx3jBSUJYP4nS52TurM0m4YQgnfu+YwdK/YQHhPGkJv7EtNWFnJ2t8K8Ipb/uJa96w+i6lTih3Si35hLMZrqvjs8PSmT4gIzYU2DMdbTVXQObTvOvJl/sXXpbjRNo8uADoy590o69m3rdJzVYmXzkp2kncogqEkg3Yd2ZseKPaz6eQMFeYU0axPFyDsG06yN62FKxUXm0hvL6oyqILkrLjQjhKj3X/5lYlfPXdst2imxW7kvlenX/pcxtw2k37U9HR/wORl5vDftc1bP2+ToolR1KoPH92Xau7fj4+9d6txr5m8hMyW7zMdWFPhlxl+MuG1gnb+Qv335F75/Zd55weA6qTNbcLSdVzLGjOSscvdnpmaTsO8kBqOBNj1aenwFCk3TWPjFcn59byEJ+0+jKNBtcCdueOQqeg7r4tHYpPph/kd/l0rqhBCgaWCzoQFLZ61GVRVmvforI24fxAPv33HRf0E4ujOB3z9ewu41B9DpVHqN7MZVU4cSfna8blmO7U7k1xkLWTNvExazlcjmTUg+fobiIjOqTkUB/vp6JcHhgVx+/WXk5xTg7e/NpcO7cmxXAou/WUlWag5hzUIYdecQht8+CC8fU42fz7rft/Ddy3M5tO04AN5+Xoy4YzC3PTMO3/NqpR7blcAfny3l2O5EvP28GHBdLwbd2BeTd/lJoNViZd3vW9i5ah8IQecBHeh7bU/0hqq9Ry77cS3v3PU5qk5xjGVb89smVs3dwNTXJjDuwZEArPx5PTMf/orsM+dqu5ZUNFD1KppVQ9Wr/PzOH9z6zDhueeq6Uo8V1zbaRRUEV/2tZ//j6nPGYkWYLRTk5DHK91YuHdGNGx+5ik792pc+Tz2giPo0kMpDcnJyCAwMJC0tjdDQUE+H4yQ1NZvL/rsSTXfuDThi+WYCtx2g66BLeGneYwA8dPnznNh70uVkgHY9W/Hm30+X+uOb8fA3LPp6ZYXra845PgP/kNqpEVayVuyoUaMwGOzfbNNPZ3JL24ecn4tBD6rqlNwJq9XeWldCUSpssQN47vv76DMqvtT2zNRsPpz+Navmrkc7e00Cwvy58dFruP7h0XW6lm0JTdN4bdJMVsxeh6Kcy2FVnYpm05j23mSuuXtYtc7t6tpLdcOd195cZOGa8KmltgubvSXbFUVRGHPfcO5+89YaPXZDVHLtlRRv3r//K3Q61fEeqOpUdAYdL8x9pMwvTev/2MoLN7wDCMf9FFUp1ZIjhHDkDyVjeEvVzzx7eMvOcbz599P4BVW/AP2CT5fy3n2fl0piVJ1KTLto/rfyeXwDffj2pbl8+9JcdHr78y45PiKuCW8sfoqoFuEuz39sdyJPXf0qaScz0J1dUchmsREaHczLv/2bVi6qE1yo5Np/dudvmPMtZR73zvJnyUrN5oXx75TduubCo5/dzTAXQw5evO0D1v+189z11wQuB9NdULheCIEoLHL+nMF+TYUmeOTTu7jytoGVD7AGSvKU7OxsAgLKHwMuJ0/Ucx9O+wLfwyedtmW3i0Mz6Nmx7hDvPz6LP75dxZEDp7GqKppB7/RjVVX2bD3G3z9vJL/Y6vRjAWx6HVoFPwVmW6n7uvOn2IbT7YU/rLXHdf5zUVWEQe+IyaZT0RTF+Ri9Dpuq2PeV8Vy8w/xp3799qRgy0nJ4eOCzrPr5XFIHkJOWy2dPfM9Hj3xTx795u2Wz1rBi9jrA+X2o5A1q5oNfcfpIiidCa3CO7DjOe/d+ysOXP8tTo19h0ZfLKS4s9nRYNaYz6EqtxiGEKDOpK9n/+8dLyEnPLfOYxu7jx74D4Tz7UbNpWIutPH/9Wy5b9nMy8nh5wrtoNtu5+ymUm9SVnNdlUXRh/zm+J5GZD31d7eeSmZrNzAe/sp9SK90dn3jgND+9+TvLZ6/l25fmAueed8nxZ06l8/Q1b6BppePMSc/l8WEvkpGUZb+vxYbNYn99ZaZk89iwl8hMySozvrRT6Xzx1Cymdn3EZYzn0+lV5n2wmI///X3FT/w8igI/vvGby/HV975+MyERgedWHCqrc6fkuZecw2ItldTBeUMc7v6EtHq4zKfsiq3HUk6cYc1vm/Bv3pTcdnGO7UXRTTh8/3gADgIcM8O9N5Z7rrt3ZMKOv5w3mkLhjmsrjKP3/1ZXNfQq0vP4xgtmG909rtYeLf4VVzObBPqh/dHn5KPPzceQm49afO4b5eebT5E6azPB4XW7FukfC/aQ3qez62+X2Ftenvh0NT2GVr1L1qbZOJSocGT5EXRq4+2SC/MzcmbBev545WenVoqNC7fx3Utz+O/S54hqEeHpMKtNp1O5dHhXNi0+r0WiEh0xVouNDQu3u2zhuBjo9Cqu2oyEEFiKrfz5+bJSXXtLvl2Fpcha6vKWl9RVhmbTWPHTWqa+MbFa7zFLvl2FcJGQnX/+3z9eQpOYUBRFcZn8aFZ7Arh58U56jejmtG/hF8vIychzmZBpNo2CnAL++HQptzxd+n370NajPDb0BQpzi9AZK36fsVk1ti3fTc6ZnAqPPZ8QcPJgEqkJaUTENXHaFxYVzIy/n2LOjL9Y9N0/FOQWoehA2C54PgKwaXD2i5Iwl92qCPYEddGXK7jl6dJdwJ4kE7t6zD6OAXxPnEYtLELzbjiVrxseBaufD1Y/H6CJyyO+2pkC1HHrWPMY+085VgGr/j5YzQfQsfDkkWretyHxw3TbVfgdSsD3YALG9CwU7JONnhr9Kp/tftsjXe3uMn76KDb+teNsyYZK3kmBovyi2gyrXitvCIrQBNuW7S6V2B3YdKRq17iK8RzacpReI0sPE6nIyYNJKKoKWtmttHlZBfaJaOXQGXRsXryjVGK3cs66clvZNE2wcs66UomdzWrj2WtfpzC3CM2moaOSXyBrcH0tZtclwYLDA5j60g3864VxFOYXo9OpvP/Id/z94zp7yStVxWqxYfIxcv9bt9Lv6njGhNxZbiiaJji07Vj1g60lcowd9XeM3eJvVvHmvz4CIHVwT7Li23k4IklqJITzGBtFVSs798aNIbh3dp0QF3ZxVfzWrqpq2d1Sjdj5dTDLpCilu7g1Uav1Pav7+3BnXIqioFzwvLWyxqQ53xNVd+GQAEq1JNrH9EF5r8+yWhUrVjqGyhAlEydQnN4HyltT3PGIZ6/XsEsi+PjW2luxqCpj7GSLXT3WsU8bx79D1+6gKDKUoqjyZ2tJklQJiuI0+01Qqd5Ldwfh/sd0+kCu+AOugs/XRky54Fq5Vir3u+B1427V/n24MS6XfwuVPL/LXPmClnABZ0f3l30+UfKY1VBRvl62s92v59+/Eq34Jder+o/rfjKxq8eatomi55Vd2Lp0NxRbiP3hL6zeJsTZEgWqTqXvNT2Z/NJ4Xrj+bU4dTi418FVVVVp0juXpWQ+g93DZDlesFgvLli9nyODB6M+bHahpGvM/XMKCT5ZSXHiuDlGzNlFMfe1mWnS2157btXo/7z/4NQW5hfa1/YS9Dl+HXq154P3JlZplJoTg4+fmsPzv3Zh9vLEE+GL190VcMEOq88AOBIbW7YoU+dkFbF9RxiogioLRZKDHsM7odFUfI6cJjeTkZCIjI1GVhtsNWZ6CgmJW702SwxgkSbpoyK5Y6m9XLNhnHD0y5EVOHU5GYB+UWzI9vU2PFryx6Cl8A33ISc/l3Wmfs2beZkcSoOpUBt3Yh/vfm+yyjl19UFHZh8K8IrYt201BXhHN2kTRrmfLUt1XxYVm/vl1I8d2JWL0MnDZ6O6069mySnEkHUvl3p7/pjC/uHTJGFUhfmhnXlnwpEcKU677fQv/ueU9LEVWRz0/zabRpFkory18kph21Ss2ezGUO9E0jZub38tJ1UBemzjy28RiDah+SQlJkiRXhl0Swae31Y+uWJnYUb8TO4CC3EL++noli79eSUZyFk1iQhl15xCGTuxfqqp4amI6+zYcQlEUOvZtS2hUsIeirpz6lFwc3XmC/0x4l8QDp8/VpUJh0E19eejDKXj7eq7VJycjj8XfrOTg5qPojXouHd6V/mN71ah4cn269rVp9hu/8fmT39vHVQGWIH/E2ZqOiqLgE+DN64ufwVCHKwRYrVb++ecfBgwYgF5f+y3plmIL+dkF+Ph7YfSueSHchuz8a39izymWz17Lke3HUXUqnfu3Y9CNfYkso5ZbiaL8YjYu3MaOlXsxF1uJ69CUAdf1IiKuCcUFxSg6FZvVxuKvVrJy7gYKsgtQVIXO/dvTskssh7cfJ/tMLqHRwfS79lI69mvrtsk7CftPsX35HizFFqJbRdBjWBenzwnNprHhz60sn72OpGOpGL2MXHplZ4bc3I/wWNcTx8Des7H6140s+XYVqSfsRfObxIQy9JaBDBjX22X8mqbx/HX/5UxiGppNoDfpuf614fz8xF9Yz657PvHpcQy8vk+5j3v6SDKZKTn4B/sS26Gp4wt2Xa0CkZ2Wy9r5m0jYdxq9QUfnAR2Iv6KT0/uvn0lPTIhPOWepGZnYVVF9T+was/qWXAgh2L1mP4e3HcfoZeDS4d0qrELfUNW3a19brBYrz497kw0LtjgVb9XpVfRGA68teopO/TvUaUwXy7Wvj+r62muaRn52ASYfk0eWF3M3IQRZZ3JACILCAytMrE4dTuLRIS+Qdiodo7eBqd+P57PbfqIo18y1943gvvfuqPdLdNUHcvKEJFWToih07t+BznX8QS/VHr1Bzwu/PMbS7//ht5mLSNh3Ei8fE4Nu6sfYB0YR3SrS0yFKjZiqqvgH187KPZ6gKEqVau01bR3F53ve4e9vV/HPPHux9QHXXcaoO4fW+Reqi0WjSexmzpzJf//7X5KTk+natSszZsygV69eng5LkqR6QKfXceWkQVw5aZCnQ5Gki46PvzfX3DuckVOG8Oeff/LwJ3fLlupa1Cimws2ePZvp06fz3HPPsXXrVrp27crw4cNJTU31dGiSJEmSJEl1plEkdm+//TZTpkxh8uTJXHLJJXz00Uf4+PjwxRdfeDo0SZIkSZKkOtPgu2LNZjNbtmzhySefdGxTVZWhQ4eybt06l/cpLi6muPjc4t/Z2dkAZGTUv8V8GzuLxUJBQQHp6emyab6OyWvvOfLae4689p4jr3315ebmAlRqRY4Gn9ilpaVhs9mIiHBexDsiIoL9+/e7vM+rr77KCy+8UGp727ZtayVGSZIkSZKkmsrNzSUwsPzJKw0+sauOJ598kunTpztuZ2VlERcXR0JCQoUXTHKvnJwcYmJiSExMrHAKt+Re8tp7jrz2niOvvefIa199Qghyc3OJjq64IH2DT+zCwsLQ6XSkpKQ4bU9JSSEy0nUZA5PJhMlUukhnYGCgfLF5SEBAgLz2HiKvvefIa+858tp7jrz21VPZhqcGP3nCaDTSo0cPli5d6timaRpLly6lT5+yq1lLkiRJkiQ1Ng2+xQ5g+vTpTJo0iZ49e9KrVy/+97//kZ+fz+TJkz0dmiRJkiRJUp1pFInd+PHjOXPmDM8++yzJycl069aNRYsWlZpQURaTycRzzz3nsntWql3y2nuOvPaeI6+958hr7zny2tcNuVasJEmSJElSI9Hgx9hJkiRJkiRJdjKxkyRJkiRJaiRkYidJkiRJktRIyMROkiRJkiSpkbjoE7uZM2fSvHlzvLy86N27Nxs3bvR0SI3Oq6++yqWXXoq/vz/h4eGMGTOGAwcOOB1TVFTEfffdR2hoKH5+fowbN65U0Wmp5l577TUUReGhhx5ybJPXvvacOnWKW265hdDQULy9vencuTObN2927BdC8OyzzxIVFYW3tzdDhw7l0KFDHoy4cbDZbDzzzDO0aNECb29vWrVqxUsvveS0zqa89u6zatUqrr76aqKjo1EUhXnz5jntr8y1zsjIYOLEiQQEBBAUFMSdd95JXl5eHT6LxuOiTuxmz57N9OnTee6559i6dStdu3Zl+PDhpKamejq0RmXlypXcd999rF+/niVLlmCxWLjyyivJz893HPPwww/z+++/M2fOHFauXMnp06e57rrrPBh147Np0yY+/vhjunTp4rRdXvvakZmZSb9+/TAYDCxcuJC9e/fy1ltvERwc7DjmjTfe4L333uOjjz5iw4YN+Pr6Mnz4cIqKijwYecP3+uuv8+GHH/L++++zb98+Xn/9dd544w1mzJjhOEZee/fJz8+na9euzJw50+X+ylzriRMnsmfPHpYsWcKCBQtYtWoVU6dOraun0LiIi1ivXr3Efffd57hts9lEdHS0ePXVVz0YVeOXmpoqALFy5UohhBBZWVnCYDCIOXPmOI7Zt2+fAMS6des8FWajkpubK9q0aSOWLFkiLr/8cvHggw8KIeS1r03//ve/Rf/+/cvcr2maiIyMFP/9738d27KysoTJZBI//PBDXYTYaI0ePVrccccdTtuuu+46MXHiRCGEvPa1CRC//vqr43ZlrvXevXsFIDZt2uQ4ZuHChUJRFHHq1Kk6i72xuGhb7MxmM1u2bGHo0KGObaqqMnToUNatW+fByBq/7OxsAEJCQgDYsmULFovF6XfRvn17YmNj5e/CTe677z5Gjx7tdI1BXvvaNH/+fHr27MkNN9xAeHg48fHxfPrpp479x44dIzk52enaBwYG0rt3b3nta6hv374sXbqUgwcPArBjxw5Wr17NyJEjAXnt61JlrvW6desICgqiZ8+ejmOGDh2Kqqps2PD/7d15SBTvHwfw97Tbbh65W1i7XYbB2m1tSrVIFEUQFVRQUUkuEdklapfZLdFhQadREZVGJdYf3dGFF9ixmgfZZdK1/qFtVqKhVO4+3z++fAc3+/XT0jbH9wsGduZ5Zp6Pn8H148w8u7Y/HnNbp4hvnvgVlZWVcDqdjb6dwmAw4Pnz5x6KSvlcLhdiY2MRFhaGIUOGAAAqKiqg0Wig1+vd+hoMBlRUVHggSmVJS0tDQUEB8vLyGrUx963n1atXOHLkCFauXIn169cjLy8P0dHR0Gg0sFqtcn5/9B7E3P+e+Ph4VFdXY8CAAVCpVHA6ndi+fTvCw8MBgLn/g5qS64qKCnTv3t2tXa1Wo2vXrjwfv6DdFnbkGcuXL8fjx4+Rk5Pj6VDahbKyMsTExODOnTvo1KmTp8NpV1wuF0JDQ7Fjxw4AgNlsxuPHj3H06FFYrVYPR6ds58+fx9mzZ5GamorBgwejqKgIsbGx6NmzJ3NPitdub8X6+/tDpVI1mv337t07GI1GD0WlbFFRUbh27RoyMzPRu3dvebvRaMTXr19RVVXl1p/n4vfl5+fD4XBgxIgRUKvVUKvVyM7OxsGDB6FWq2EwGJj7VtKjRw8MGjTIbdvAgQNht9sBQM4v34Na3po1axAfH485c+Zg6NChmD9/PlasWIGdO3cCYO7/pKbk2mg0Npq0WF9fj48fP/J8/IJ2W9hpNBqEhIQgPT1d3uZyuZCeng6LxeLByJRHCIGoqChcvHgRGRkZCAwMdGsPCQlBx44d3c5FSUkJ7HY7z8VvmjBhAoqLi1FUVCQvoaGhCA8Pl18z960jLCys0cf6vHjxAn379gUABAYGwmg0uuW+uroaNpuNuf9NtbW16NDB/c+bSqWCy+UCwNz/SU3JtcViQVVVFfLz8+U+GRkZcLlcGDVq1B+Puc3z9OwNT0pLSxNarVakpKSIp0+fisjISKHX60VFRYWnQ1OUpUuXCp1OJ7KyskR5ebm81NbWyn2WLFkiAgICREZGhnj48KGwWCzCYrF4MGrlajgrVgjmvrXk5uYKtVottm/fLkpLS8XZs2eFt7e3OHPmjNwnMTFR6PV6cfnyZfHo0SMxbdo0ERgYKOrq6jwYedtntVpFr169xLVr18Tr16/FhQsXhL+/v4iLi5P7MPctp6amRhQWForCwkIBQOzdu1cUFhaKt2/fCiGalutJkyYJs9ksbDabyMnJESaTScydO9dTP1Kb1q4LOyGESEpKEgEBAUKj0YiRI0eKBw8eeDokxQHwwyU5OVnuU1dXJ5YtWya6dOkivL29xYwZM0R5ebnnglaw7ws75r71XL16VQwZMkRotVoxYMAAcezYMbd2l8slNm3aJAwGg9BqtWLChAmipKTEQ9EqR3V1tYiJiREBAQGiU6dOol+/fmLDhg3iy5cvch/mvuVkZmb+8D3earUKIZqW6w8fPoi5c+cKX19f4efnJxYsWCBqamo88NO0fZIQDT6Km4iIiIjarHb7jB0RERGR0rCwIyIiIlIIFnZERERECsHCjoiIiEghWNgRERERKQQLOyIiIiKFYGFHREREpBAs7IiIiIgUgoUdESlWQkIChg8f3qx9JEnCpUuXWiWe3zVu3DjExsZ6Ogwi+ouxsCOiNkGSpJ8uCQkJjfZZvXq125ePExEpndrTARARNUV5ebn8+ty5c9i8eTNKSkrkbb6+vvJrIQScTid8fX3dtlNjTqcTkiShQwf+n0+kBPxNJqI2wWg0yotOp4MkSfL68+fP0blzZ9y4cQMhISHQarXIyclpdCs2Ly8PEydOhL+/P3Q6HcaOHYuCgoJmxTFu3DhER0cjLi4OXbt2hdFodLta+ObNG0iShKKiInlbVVUVJElCVlYWACArKwuSJOHWrVswm83w8vLC+PHj4XA4cOPGDQwcOBB+fn6YN28eamtr3cavr69HVFQUdDod/P39sWnTJjT8yu8vX75g9erV6NWrF3x8fDBq1Ch5XABISUmBXq/HlStXMGjQIGi1Wtjt9mblgIj+XizsiEgx4uPjkZiYiGfPniE4OLhRe01NDaxWK3JycvDgwQOYTCZMnjwZNTU1zRrn1KlT8PHxgc1mw+7du7F161bcuXOn2fEmJCTg0KFDuHfvHsrKyjB79mzs378fqampuH79Om7fvo2kpKRGY6vVauTm5uLAgQPYu3cvjh8/LrdHRUXh/v37SEtLw6NHjzBr1ixMmjQJpaWlcp/a2lrs2rULx48fx5MnT9C9e/dmx05EfyfeiiUixdi6dSsmTpz4P9vHjx/vtn7s2DHo9XpkZ2dj6tSpTR4nODgYW7ZsAQCYTCYcOnQI6enpPx37R7Zt24awsDAAwMKFC7Fu3Tq8fPkS/fr1AwDMnDkTmZmZWLt2rbxPnz59sG/fPkiShP79+6O4uBj79u3DokWLYLfbkZycDLvdjp49ewL49znDmzdvIjk5GTt27AAAfPv2DYcPH8awYcOaFS8R/f14xY6IFCM0NPSn7e/evcOiRYtgMpmg0+ng5+eHz58/N/tW5PdXA3v06AGHw9HseBsex2AwwNvbWy7q/tv2/XFHjx4NSZLkdYvFgtLSUjidThQXF8PpdCIoKEh+vtDX1xfZ2dl4+fKlvI9Go/nhFU0iavt4xY6IFMPHx+en7VarFR8+fMCBAwfQt29faLVaWCwWfP36tVnjdOzY0W1dkiS4XC4AkCchNHzu7du3b//3OJIk/fS4TfH582eoVCrk5+dDpVK5tTWcROLl5eVWHBKRcrCwI6J24+7duzh8+DAmT54MACgrK0NlZWWLjtGtWzcA/87iNZvNAOA2keJ32Ww2t/X/nhVUqVQwm81wOp1wOBwYM2ZMi41JRG0HCzsiajdMJhNOnz6N0NBQVFdXY82aNfDy8mrRMby8vDB69GgkJiYiMDAQDocDGzdubLHj2+12rFy5EosXL0ZBQQGSkpKwZ88eAEBQUBDCw8MRERGBPXv2wGw24/3790hPT0dwcDCmTJnSYnEQ0d+Jz9gRUbtx4sQJfPr0CSNGjMD8+fMRHR3dKjNCT548ifr6eoSEhCA2Nhbbtm1rsWNHRESgrq4OI0eOxPLlyxETE4PIyEi5PTk5GREREVi1ahX69++P6dOnIy8vDwEBAS0WAxH9vSTR8EEQIiIiImqzeMWOiIiISCFY2BEREREpBAs7IiIiIoVgYUdERESkECzsiIiIiBSChR0RERGRQrCwIyIiIlIIFnZERERECsHCjoiIiEghWNgRERERKQQLOyIiIiKF+AcSbmiKaRQHdAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module randomnet to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n",
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module sir to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n",
-      "/Users/dklein/GIT/starsim/starsim/results.py:226: RuntimeWarning: \n",
-      "You are adding a result from module sim to module MultiSim; check that this is intentional.\n",
-      "  ss.warn(warnmsg)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Figure(933.333x700)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 933.333x700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/dklein/GIT/starsim/starsim/calibration.py:409: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
-      "  plt.legend()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Confirm\n",
     "sc.printcyan('\\nConfirming fit...')\n",
-    "calib.confirm_fit()\n",
-    "\n",
-    "calib.plot_sims()\n",
-    "calib.plot_trend()"
+    "calib.confirm_fit(n_runs=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can view some plots of the results. Blue is before calibration using the `guess` values whereas orange is after."
    ]
   },
   {
@@ -576,7 +294,18 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "calib.plot_sims()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calib.plot_trend()"
+   ]
   }
  ],
  "metadata": {
diff --git a/starsim/calibration.py b/starsim/calibration.py
index 0638a0b9..21ceef1d 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -243,13 +243,12 @@ def make_study(self):
         output = op.create_study(storage=self.run_args.storage, study_name=self.run_args.study_name)
         return output
 
-    def calibrate(self, calib_pars=None, confirm_fit=False, load=False, tidyup=True, **kwargs):
+    def calibrate(self, calib_pars=None, load=False, tidyup=True, **kwargs):
         """
         Perform calibration.
 
         Args:
             calib_pars (dict): if supplied, overwrite stored calib_pars
-            confirm_fit (bool): if True, run simulations with parameters from before and after calibration
             load (bool): whether to load existing trials from the database (if rerunning the same calibration)
             tidyup (bool): whether to delete temporary files from trial runs
             verbose (bool): whether to print output from each trial
@@ -299,13 +298,9 @@ def calibrate(self, calib_pars=None, confirm_fit=False, load=False, tidyup=True,
         if not self.run_args.keep_db:
             self.remove_db()
 
-        # Optionally compute the sims before and after the fit
-        if confirm_fit:
-            self.confirm_fit()
-
         return self
 
-    def confirm_fit(self):
+    def confirm_fit(self, n_runs=25):
         """ Run before and after simulations to validate the fit """
 
         if self.verbose: print('\nConfirming fit...')
@@ -318,8 +313,6 @@ def confirm_fit(self):
         for parname, spec in after_pars.items():
             spec['value'] = self.best_pars[parname]
 
-
-        n_runs = 25
         before_sim = self.build_fn(self.sim, calib_pars=before_pars, **self.build_kwargs)
         before_sim.label = 'Before calibration'
         self.before_msim = ss.MultiSim(before_sim, n_runs=n_runs)
@@ -400,13 +393,21 @@ def plot_sims(self, **kwargs):
         if self.before_msim is None:
             self.confirm_fit()
 
+        # Turn off jupyter mode so we can receive the figure handles
+        jup = ss.options.jupyter if 'jupyter' in ss.options else sc.isjupyter()
+        ss.options.jupyter = False
+
         self.before_msim.reduce()
-        fig = self.before_msim.plot()#, label='Before calibration')
+        fig_before = self.before_msim.plot()
+        fig_before.suptitle('Before calibration')
 
         self.after_msim.reduce()
-        self.after_msim.plot(fig=fig)#, label='After calibration')
-        fig.legend()
-        return fig
+        fig_after = self.after_msim.plot(fig=fig_before)
+        fig_after.suptitle('After calibration')
+
+        ss.options.jupyter = jup
+
+        return fig_before, fig_after
 
     def plot_trend(self, best_thresh=None, fig_kw=None):
         """

From f696cc9e6afab96afae5b99abe844e29e8c7c6c9 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 21:41:13 -0500
Subject: [PATCH 22/28] move ax to devtests

---
 tests/{test_axbo.py => devtests/devtest_axbo.py}                | 0
 .../{test_axbo_service.py => devtests/devtest_axbo_service.py}  | 0
 tests/test_calibration.py                                       | 2 +-
 3 files changed, 1 insertion(+), 1 deletion(-)
 rename tests/{test_axbo.py => devtests/devtest_axbo.py} (100%)
 rename tests/{test_axbo_service.py => devtests/devtest_axbo_service.py} (100%)

diff --git a/tests/test_axbo.py b/tests/devtests/devtest_axbo.py
similarity index 100%
rename from tests/test_axbo.py
rename to tests/devtests/devtest_axbo.py
diff --git a/tests/test_axbo_service.py b/tests/devtests/devtest_axbo_service.py
similarity index 100%
rename from tests/test_axbo_service.py
rename to tests/devtests/devtest_axbo_service.py
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 43259449..0222adcd 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -121,7 +121,7 @@ def test_calibration(do_plot=False):
 
         components = [infectious],
 
-        total_trials = 1_000,
+        total_trials = 20,
         n_workers = None, # None indicates to use all available CPUs
         die = True,
         debug = debug,

From 206106db07f5919a480479486e1794e9d353cc54 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 21:47:27 -0500
Subject: [PATCH 23/28] start tidying test_calibration

---
 tests/test_calibration.py | 20 ++------------------
 1 file changed, 2 insertions(+), 18 deletions(-)

diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 0222adcd..c3a3b89a 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -24,11 +24,10 @@ def make_sim():
 
     sim = ss.Sim(
         n_agents = n_agents,
-        start = sc.date('1990-01-01'),
+        start = sc.date('2020-01-01'),
         dur = 40,
         dt = 1,
         unit = 'day',
-        #total_pop = 10000,
         diseases = sir,
         networks = random,
     )
@@ -75,21 +74,6 @@ def test_calibration(do_plot=False):
     # Make the sim and data
     sim = make_sim()
 
-    '''
-    prevalence = ss.CalibComponent(
-        name = 'hiv.prevalence',
-
-        # By default, automate these based on name
-        real_data = data['hiv.prevalence'],
-        sim_data_fn = lambda sim: pd.Series(sim.results.hiv.prevalence, index=sim.results.hiv.timevec),
-
-        conform = ss.eConform.PREVALENT,
-        likelihood = ss.eLikelihood.POISSON,
-
-        weight = 1,
-    )
-    '''
-
     infectious = ss.CalibComponent(
         name = 'Infectious',
 
@@ -98,7 +82,7 @@ def test_calibration(do_plot=False):
         real_data = pd.DataFrame({
             'n': [200, 197, 195], # Number of individuals sampled
             'x': [30, 30, 10],    # Number of individuals found to be infectious
-        }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates
+        }, index=pd.Index([ss.date(d) for d in ['2020-01-12', '2020-01-25', '2020-02-02']], name='t')), # On these dates
         
         sim_data_fn = lambda sim: pd.DataFrame({
             'n': sim.results.n_alive,

From f75d6ff5ad27669d0795b4d5ecb9bb88af17a4cb Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 22:07:23 -0500
Subject: [PATCH 24/28] refactor to use strings and actual and expected

---
 starsim/calibration.py    | 159 ++++++++++++++++++--------------------
 tests/test_calibration.py |  12 +--
 2 files changed, 80 insertions(+), 91 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 21ceef1d..5b2a699c 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -9,11 +9,10 @@
 import sciris as sc
 import starsim as ss
 import matplotlib.pyplot as plt
-from scipy.special import gammaln as gln
-from enum import Enum
+from scipy.special import gammaln
 
 
-__all__ = ['Calibration', 'CalibComponent', 'eConform', 'eLikelihood']
+__all__ = ['Calibration', 'CalibComponent']
 
 
 class Calibration(sc.prettyobj):
@@ -446,15 +445,6 @@ def plot_trend(self, best_thresh=None, fig_kw=None):
         sc.figlayout()
         return fig
 
-#############
-
-class eConform(Enum):
-    PREVALENT = 0
-    INCIDENT = 1
-
-class eLikelihood(Enum):
-    BETA_BINOMIAL = 0
-    GAMMA_POISSON = 1
 
 class CalibComponent(sc.prettyobj):
     """
@@ -464,125 +454,124 @@ class CalibComponent(sc.prettyobj):
     observed data.
 
     Args:
-        name (str) : the of this component. Importantly,
-            sim_extract_fn is None, the code will attempt to use the name, like
+        name (str) : the name of this component. Importantly, if
+            extract_fn is None, the code will attempt to use the name, like
             "hiv.prevalence" to automatically extract data from the simulation.
         data (df) : pandas Series containing calibration data. The index should be the time in either floating point years or datetime.
-        mode (eMode): To handle misaligned timepoints between observed data and simulation output, it's important to know if the data are incident (like new cases) or prevalent (like the number infected).
-            If eMode.PREVALENT, simulation outputs will be interpolated to observed timepoints.
-            If eMode.INCIDENT, ...
+        mode (str/func): To handle misaligned timepoints between observed data and simulation output, it's important to know if the data are incident (like new cases) or prevalent (like the number infected).
+            If 'prevalent', simulation outputs will be interpolated to observed timepoints.
+            If 'incident', outputs will be interpolated to cumulative incidence.
     """
-    def __init__(self, name, real_data, sim_data_fn, conform, nll_fn, weight=1):
+    def __init__(self, name, expected, extract_fn, conform, nll_fn, weight=1):
         self.name = name
-        self.real_data = real_data
-        self.sim_data_fn = sim_data_fn
+        self.expected = expected
+        self.extract_fn = extract_fn
         self.weight = weight
 
-        if isinstance(nll_fn, eLikelihood):
-            if nll_fn == eLikelihood.BETA_BINOMIAL:
-                self.nll_fn = self.beta_binomial
-            elif nll_fn == eLikelihood.GAMMA_POISSON:
-                self.nll_fn = self.gamma_poisson
+        if isinstance(nll_fn, str):
+            if nll_fn == 'beta':
+                self.nll_fn = self.nll_beta
+            elif nll_fn == 'gamma':
+                self.nll_fn = self.nll_gamma
+            else:
+                errormsg = f'The nll_fn (negative log-likelihood function) argument must be "beta" or "gamma", not {conform}.'
+                raise ValueError(errormsg)
         else:
             if not callable(conform):
-                msg = f'The nll_fn argument must be an eLikelihood or callable function, not {type(nll_fn)}.'
+                msg = f'The nll_fn (negative log-likelihood function) argument must be a string or a callable function, not {type(nll_fn)}.'
                 raise Exception(msg)
             self.nll_fn = nll_fn
 
-        if isinstance(conform, eConform):
-            if conform == eConform.INCIDENT:
+        if isinstance(conform, str):
+            if conform == 'incident':
                 self.conform = self.linear_accum
-            elif conform == eConform.PREVALENT:
+            elif conform == 'prevalent':
                 self.conform = self.linear_interp
+            else:
+                errormsg = f'The conform argument must be "prevalent" or "incident", not {conform}.'
+                raise ValueError(errormsg)
         else:
             if not callable(conform):
-                msg = f'The conform argument must be an eConform or callable function, not {type(conform)}.'
-                raise Exception(msg)
+                errormsg = f'The conform argument must be a string or a callable function, not {type(conform)}.'
+                raise TypeError(errormsg)
             self.conform = conform
 
         pass
 
     @staticmethod
-    def beta_binomial(real_data, sim_data):
-        # For the beta-binomial log likelihood, we begin with a Beta(1,1) prior
-        # and subsequently observe sim_data['x'] successes (positives) in sim_data['n'] trials (total observations).
-        # The result is a Beta(sim_data['x']+1, sim_data['n']-sim_data['x']+1) posterior.
-        # We then compare this to the real data, which has real_data['x'] successes (positives) in real_data['n'] trials (total observations).
-        # To do so, we use a beta-binomial likelihood:
-        # p(x|n, x, a, b) = (n choose x) B(x+a, n-x+b) / B(a, b)
-        # where
-        #   x=real_data['x']
-        #   n=real_data['n']
-        #   a=sim_data['x']+1
-        #   b=sim_data['n']-sim_data['x']+1 
-        # and B is the beta function, B(x, y) = Gamma(x)Gamma(y)/Gamma(x+y)
-
-        # We compute the log of p(x|n, x, a, b), noting that gln is the log of the gamma function
-        logL = gln(real_data['n'] + 1) - gln(real_data['x'] + 1) - gln(real_data['n'] - real_data['x'] + 1)
-        logL += gln(real_data['x'] + sim_data['x'] + 1) + gln(real_data['n'] - real_data['x'] + sim_data['n'] - sim_data['x'] + 1) - gln(real_data['n'] + sim_data['n'] + 2)
-        logL += gln(sim_data['n'] + 2) - gln(sim_data['x'] + 1) - gln(sim_data['n'] - sim_data['x'] + 1)
-
+    def nll_beta(expected, actual):
+        """
+        For the beta-binomial negative log-likelihood, we begin with a Beta(1,1) prior
+        and subsequently observe actual['x'] successes (positives) in actual['n'] trials (total observations).
+        The result is a Beta(actual['x']+1, actual['n']-actual['x']+1) posterior.
+        We then compare this to the real data, which has expected['x'] successes (positives) in expected['n'] trials (total observations).
+        To do so, we use a beta-binomial likelihood:
+        p(x|n, x, a, b) = (n choose x) B(x+a, n-x+b) / B(a, b)
+        where
+          x=expected['x']
+          n=expected['n']
+          a=actual['x']+1
+          b=actual['n']-actual['x']+1 
+        and B is the beta function, B(x, y) = Gamma(x)Gamma(y)/Gamma(x+y)
+
+        We compute the log of p(x|n, x, a, b), noting that gammaln is the log of the gamma function
+        """
+        e_n, e_x = expected['n'], expected['x']
+        a_n, a_x = actual['n'], actual['x']
+        logL = gammaln(e_n + 1) - gammaln(e_x + 1) - gammaln(e_n - e_x + 1)
+        logL += gammaln(e_x + a_x + 1) + gammaln(e_n - e_x + a_n - a_x + 1) - gammaln(e_n + a_n + 2)
+        logL += gammaln(a_n + 2) - gammaln(a_x + 1) - gammaln(a_n - a_x + 1)
         return -logL
 
     @staticmethod
-    def gamma_poisson(real_data, sim_data):
-        # Also called negative binomial, but parameterized differently
-        # The gamma-poisson likelihood is a Poisson likelihood with a gamma-distributed rate parameter
-        #
-
-        logL = gammaln(real_data['x'] + sim_data['x'] + 1) \
-            - gammaln(real_data['x'] + 1) \
-            - gammaln(sim_data['x'] + 1)
-
-        logL += (real_data['x'] + 1) * np.log(real_data['n'])
-
-        logL += (sim_data['x'] + 1) * np.log(sim_data['n'])
-
-        logL -= (real_data['x'] + sim_data['x'] + 1) \
-                  * np.log(real_data['n'] + sim_data['n'])
-
+    def nll_gamma(expected, actual):
+        """
+        Also called negative binomial, but parameterized differently
+        The gamma-poisson likelihood is a Poisson likelihood with a gamma-distributed rate parameter
+        """
+        e_n, e_x = expected['n'], expected['x']
+        a_n, a_x = actual['n'], actual['x']
+        logL = gammaln(e_x + a_x + 1) - gammaln(e_x + 1) - gammaln(e_x + 1)
+        logL += (e_x + 1) * np.log(e_n)
+        logL += (a_x + 1) * np.log(a_n)
+        logL -= (e_x + a_x + 1) * np.log(e_n + a_n)
         return -logL
 
     @staticmethod
-    def linear_interp(real_data, sim_data):
+    def linear_interp(expected, actual):
         """
         Simply interpolate
         Use for prevalent data like prevalence
         """
-        t = real_data.index
-        #sim_t = np.array([sc.datetoyear(t.date()) for t in sim_data.index if isinstance(t, dt.date)])
-
-        conformed = pd.DataFrame(index=real_data.index)
-        for k in sim_data:
-            conformed[k] = np.interp(x=t, xp=sim_data.index, fp=sim_data[k])
+        t = expected.index
+        conformed = pd.DataFrame(index=expected.index)
+        for k in actual:
+            conformed[k] = np.interp(x=t, xp=actual.index, fp=actual[k])
 
         return conformed
 
     @staticmethod
-    def linear_accum(real_data, sim_data):
+    def linear_accum(expected, actual):
         """
         Interpolate in the accumulation, then difference.
         Use for incident data like incidence or new_deaths
         """
-        t = real_data.index
+        t = expected.index
         t_step = np.diff(t)
         assert np.all(t_step == t_step[0])
         ti = np.append(t, t[-1] + t_step) # Add one more because later we'll diff
 
-        sim_t = np.array([sc.datetoyear(t) for t in sim_data.index if isinstance(t, dt.date)])
+        sim_t = np.array([sc.datetoyear(t) for t in actual.index if isinstance(t, dt.date)])
 
-        sdi = np.interp(x=ti, xp=sim_t, fp=sim_data.cumsum())
+        sdi = np.interp(x=ti, xp=sim_t, fp=actual.cumsum())
         df = pd.Series(sdi.diff(), index=t)
         return df
 
     def eval(self, sim):
-        # Compute and return the negative log likelihood
-
-        sim_data = self.sim_data_fn(sim) # Extract
-        sim_data = self.conform(self.real_data, sim_data) # Conform
-
-        self.nll = self.nll_fn(self.real_data, sim_data) # Negative log likelihood
-
+        """ Compute and return the negative log likelihood """
+        actual = self.extract_fn(sim) # Extract
+        actual = self.conform(self.expected, actual) # Conform
+        self.nll = self.nll_fn(self.expected, actual) # Negative log likelihood
         return self.weight * np.sum(self.nll)
 
     def __call__(self, sim):
@@ -592,4 +581,4 @@ def __repr__(self):
         return f'Calibration component with name {self.name}'
 
     def plot(self):
-        pass
\ No newline at end of file
+        NotImplementedError
\ No newline at end of file
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index c3a3b89a..927cc524 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -77,20 +77,20 @@ def test_calibration(do_plot=False):
     infectious = ss.CalibComponent(
         name = 'Infectious',
 
-        # "real_data" actually from a simulation with pars
+        # "expected" actually from a simulation with pars
         #   beta=0.075, init_prev=0.02, n_contacts=4
-        real_data = pd.DataFrame({
+        expected = pd.DataFrame({
             'n': [200, 197, 195], # Number of individuals sampled
             'x': [30, 30, 10],    # Number of individuals found to be infectious
         }, index=pd.Index([ss.date(d) for d in ['2020-01-12', '2020-01-25', '2020-02-02']], name='t')), # On these dates
         
-        sim_data_fn = lambda sim: pd.DataFrame({
+        extract_fn = lambda sim: pd.DataFrame({
             'n': sim.results.n_alive,
             'x': sim.results.sir.n_infected,
         }, index=pd.Index(sim.results.timevec, name='t')),
 
-        conform = ss.eConform.PREVALENT,
-        nll_fn = ss.eLikelihood.BETA_BINOMIAL,
+        conform = 'prevalent',
+        nll_fn = 'beta',
 
         weight = 1,
     )
@@ -135,7 +135,7 @@ def test_calibration(do_plot=False):
 #%% Run as a script
 if __name__ == '__main__':
 
-    # Useful for generating fake "real_data"
+    # Useful for generating fake "expected" data
     if False:
         sim = make_sim()
         pars = {

From 3c286b75e3412f3b4c85bd4036df7916037dc796 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 22:12:14 -0500
Subject: [PATCH 25/28] update results warnings

---
 starsim/calibration.py | 2 +-
 starsim/results.py     | 5 +----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/starsim/calibration.py b/starsim/calibration.py
index 5b2a699c..acb86053 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -299,7 +299,7 @@ def calibrate(self, calib_pars=None, load=False, tidyup=True, **kwargs):
 
         return self
 
-    def confirm_fit(self, n_runs=25):
+    def confirm_fit(self, n_runs=5):
         """ Run before and after simulations to validate the fit """
 
         if self.verbose: print('\nConfirming fit...')
diff --git a/starsim/results.py b/starsim/results.py
index 23c4e2bf..01b38991 100644
--- a/starsim/results.py
+++ b/starsim/results.py
@@ -217,13 +217,10 @@ def append(self, arg, key=None):
             result = arg
 
         if not isinstance(result, Result):
-            warnmsg = f'You are adding a result of type {type(result)} to Results, which is inadvisable.'
+            warnmsg = f'You are adding a result of type {type(result)} to Results, which is inadvisable; if you intended to add it, use results[key] = value instead'
             ss.warn(warnmsg)
 
         if result.module != self._module:
-            if result.module:
-                warnmsg = f'You are adding a result from module {result.module} to module {self._module}; check that this is intentional.'
-                ss.warn(warnmsg)
             result.module = self._module
 
         super().append(result, key=key)

From d10b86a1b5b28b36193708bf31a63e6e612ff148 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 22:28:25 -0500
Subject: [PATCH 26/28] more calibration refactoring

---
 docs/tutorials/tut_calibration.ipynb | 36 +++++++++++-----------
 starsim/calibration.py               | 46 ++++++++++------------------
 tests/test_calibration.py            | 14 +++------
 3 files changed, 39 insertions(+), 57 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 50cc7d17..2fe3d561 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -35,12 +35,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We begin with a few imports and default settings"
+    "We begin with a few imports and default settings:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,32 +168,32 @@
     "\n",
     "As an alternative to directly specifying the evaluation function, you can use `CalibComponent`s. Each component includes real data, for example from a survey, that is compared against simulation data from the model. Several components and be used at the same time, for example one for disease prevalence and another for treatment coverage. Each component computes a likelihood of the data given the input parameters, as assess via simulation. Components are combined assuming independence.\n",
     "\n",
-    "When defining a `CalibComponent`, we give it a `name` and pass in `real_data`. The required data fields depend on the likelihood function. Importantly, the functional form of the negative log likelihood, or nll, is defined by the `nll_fn`. The value for `nll_fn` can be any value of the `eLikelihood` enumeration, like `BETA_BINOMIAL`, or a negative log likelihood function of your own creation. If designing your own function for `nll_fn`, it should take two arguments: `real_data` and `sim_data`. For a Beta binomial, the data must define `n` and `x`, where `n` is the number of individuals that were sampled and `x` is the number that were found, e.g. identified as positive.\n",
+    "When defining a `CalibComponent`, we give it a `name` and pass in `expected` (the real data to be calibrated to). The required data fields depend on the likelihood function. Importantly, the functional form of the negative log likelihood, or nll, is defined by the `nll_fn`. The value for `nll_fn` can be `'beta'`, `'gamma'`, or a negative log likelihood function of your own creation. If designing your own function for `nll_fn`, it should take two arguments: `expected` and `actual`. For a beta binomial, the data must define `n` and `x`, where `n` is the number of individuals who were sampled and `x` is the number that were found, e.g. identified as positive.\n",
     "\n",
     "Output from the simulation is obtained via a function. The function takes a completed `sim` object as input and returns a dictionary with fields as required for the evaluation function of your choice. In the example below, we use an in-line lambda function to extract `n` and `x` from the simulation, as required by the Beta binomial component.\n",
     "\n",
     "Each component has a `weight`. The final goodness of fit is a weighted sum of negative log likelihoods.\n",
     "\n",
-    "Finally, the `conform` argument describes how the simulation output is adjusted to align with the real data. For example, if the real data is a prevalence measurement, choosing `ss.eConform.PREVALENT` will interpolate the simulation output at the time points of the real data. Choosing `ss.eConform.INCIDENT`, the simulation output will be aggregated between time points of the real data."
+    "Finally, the `conform` argument describes how the simulation output is adjusted to align with the real data. For example, if the real data is a prevalence measurement, choosing `'prevalent'` will interpolate the simulation output at the time points of the real data. Choosing `'incident'`, the simulation output will be aggregated between time points of the real data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "infectious = ss.CalibComponent(\n",
     "    name = 'Infectious',\n",
     "\n",
-    "    # For this example, the \"real_data\" comes from a simulation with pars\n",
+    "    # For this example, the \"expected\" comes from a simulation with pars\n",
     "    #   beta=0.075, init_prev=0.02, n_contacts=4\n",
-    "    real_data = pd.DataFrame({\n",
+    "    expected = pd.DataFrame({\n",
     "        'n': [200, 197, 195], # Number of individuals sampled\n",
     "        'x': [30, 30, 10],    # Number of individuals found to be infectious\n",
     "    }, index=pd.Index([ss.date(d) for d in ['1990-01-12', '1990-01-25', '1990-02-02']], name='t')), # On these dates\n",
     "\n",
-    "    sim_data_fn = lambda sim: pd.DataFrame({\n",
+    "    extract_fn = lambda sim: pd.DataFrame({\n",
     "        'n': sim.results.n_alive, # Number of individuals sampled\n",
     "        'x': sim.results.sir.n_infected, # Number of individuals found to be infectious\n",
     "    }, index=pd.Index(sim.results.timevec, name='t')), # Index is time\n",
@@ -209,7 +209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can bring all the pieces together. We make a single base simulation and create an instance of a Starsim Calibration object. This object requires a few arguments, like the `calib_pars` and `sim`. We also pass in the function that modifies the base `sim`, here our `build_sim` function. No additional `build_kwargs` are required in this example.\n",
+    "Finally, we can bring all the pieces together. We make a single base simulation and create an instance of a Starsim Calibration object. This object requires a few arguments, like the `calib_pars` and `sim`. We also pass in the function that modifies the base `sim`, here our `build_sim` function. No additional `build_kw` are required in this example.\n",
     "\n",
     "We also pass in a list of `components`. Instead of using this \"component-based\" system, a user could simply provide an `eval_fn`, which takes in a completed sim an any `eval_kwargs` and returns a \"goodness of fit\" score to be maximized.\n",
     "\n",
@@ -233,7 +233,7 @@
     "    sim = sim,\n",
     "\n",
     "    build_fn = build_sim, # Use default builder, Calibration.translate_pars\n",
-    "    build_kwargs = None,\n",
+    "    build_kw = None,\n",
     "\n",
     "    components = [infectious],\n",
     "\n",
@@ -245,7 +245,7 @@
     "\n",
     "# Perform the calibration\n",
     "sc.printcyan('\\nPeforming calibration...')\n",
-    "calib.calibrate(confirm_fit=False);"
+    "calib.calibrate();"
    ]
   },
   {
@@ -268,7 +268,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the calibration is complete, we can compare the `guess` values to the best values found by calling `confirm_fit`."
+    "Once the calibration is complete, we can compare the `guess` values to the best values found by calling `check_fit`."
    ]
   },
   {
@@ -279,7 +279,7 @@
    "source": [
     "# Confirm\n",
     "sc.printcyan('\\nConfirming fit...')\n",
-    "calib.confirm_fit(n_runs=5)"
+    "calib.check_fit(n_runs=5)"
    ]
   },
   {
@@ -310,7 +310,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py312",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -324,7 +324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/starsim/calibration.py b/starsim/calibration.py
index acb86053..b7340b82 100644
--- a/starsim/calibration.py
+++ b/starsim/calibration.py
@@ -25,24 +25,18 @@ class Calibration(sc.prettyobj):
         calib_pars   (dict) : a dictionary of the parameters to calibrate of the format dict(key1=dict(low=1, high=2, guess=1.5, **kwargs), key2=...), where kwargs can include "suggest_type" to choose the suggest method of the trial (e.g. suggest_float) and args passed to the trial suggest function like "log" and "step"
         n_workers    (int)  : the number of parallel workers (if None, will use all available CPUs)
         total_trials (int)  : the total number of trials to run, each worker will run approximately n_trials = total_trial / n_workers
-
         reseed       (bool) : whether to generate new random seeds for each trial
-
         build_fn  (callable): function that takes a sim object and calib_pars dictionary and returns a modified sim
-        build_kwargs  (dict): a dictionary of options that are passed to build_fn to aid in modifying the base simulation. The API is self.build_fn(sim, calib_pars=calib_pars, **self.build_kwargs), where sim is a copy of the base simulation to be modified with calib_pars
-
-        components (list of CalibComponent objects): CalibComponents independently assess pseudo-likelihood as part of evaluating the quality of input parameters
-
+        build_kw  (dict): a dictionary of options that are passed to build_fn to aid in modifying the base simulation. The API is self.build_fn(sim, calib_pars=calib_pars, **self.build_kw), where sim is a copy of the base simulation to be modified with calib_pars
+        components (list): CalibComponents independently assess pseudo-likelihood as part of evaluating the quality of input parameters
         eval_fn  (callable): Function mapping a sim to a float (e.g. negative log likelihood) to be maximized. If None, the default will use CalibComponents.
         eval_kwargs  (dict): Additional keyword arguments to pass to the eval_fn
-
         label        (str)  : a label for this calibration object
         study_name   (str)  : name of the optuna study
         db_name      (str)  : the name of the database file (default: 'starsim_calibration.db')
         keep_db      (bool) : whether to keep the database after calibration (default: false)
         storage      (str)  : the location of the database (default: sqlite)
         sampler (BaseSampler): the sampler used by optuna, like optuna.samplers.TPESampler
-
         die          (bool) : whether to stop if an exception is encountered (default: false)
         debug        (bool) : if True, do not run in parallel
         verbose      (bool) : whether to print details of the calibration
@@ -50,11 +44,8 @@ class Calibration(sc.prettyobj):
     Returns:
         A Calibration object
     """
-    def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
-                 reseed=True,
-                 build_fn=None, build_kwargs=None, eval_fn=None, eval_kwargs=None,
-                 components=None,
-
+    def __init__(self, sim, calib_pars, n_workers=None, total_trials=None, reseed=True,
+                 build_fn=None, build_kw=None, eval_fn=None, eval_kwargs=None, components=None,
                  label=None, study_name=None, db_name=None, keep_db=None, storage=None,
                  sampler=None, die=False, debug=False, verbose=True):
 
@@ -67,7 +58,7 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         if storage      is None: storage        = f'sqlite:///{db_name}'
         
         self.build_fn       = build_fn or self.translate_pars
-        self.build_kwargs   = build_kwargs or dict()
+        self.build_kw   = build_kw or dict()
         self.eval_fn        = eval_fn or self._eval_fit
         self.eval_kwargs    = eval_kwargs or dict()
         self.components     = components
@@ -88,13 +79,8 @@ def __init__(self, sim, calib_pars, n_workers=None, total_trials=None,
         self.before_msim = None
         self.after_msim  = None
 
-        # Temporarily store a filename
-        self.tmp_filename = 'tmp_calibration_%05i.obj'
-
-        # Initialize sim
-        #if not self.sim.initialized:
-        #    self.sim.init()
-
+        # Temporarily store a filename for storing intermediate results
+        self.tmp_filename = 'tmp_calibration_%06i.obj'
         return
 
     def run_sim(self, calib_pars=None, label=None):
@@ -102,7 +88,7 @@ def run_sim(self, calib_pars=None, label=None):
         sim = sc.dcp(self.sim)
         if label: sim.label = label
 
-        sim = self.build_fn(sim, calib_pars=calib_pars, **self.build_kwargs)
+        sim = self.build_fn(sim, calib_pars=calib_pars, **self.build_kw)
 
         # Run the sim
         try:
@@ -176,10 +162,10 @@ def _sample_from_trial(self, pardict=None, trial=None):
         return pars
 
     def _eval_fit(self, sim, **kwargs):
+        """ Evaluate the fit by evaluating the negative log likelihood """
         nll = 0 # Negative log likelihood
-        for c in self.components:
-            nll += c(sim)
-
+        for component in sc.tolist(self.components):
+            nll += component(sim)
         return nll
 
     def run_trial(self, trial):
@@ -299,10 +285,10 @@ def calibrate(self, calib_pars=None, load=False, tidyup=True, **kwargs):
 
         return self
 
-    def confirm_fit(self, n_runs=5):
+    def check_fit(self, n_runs=5):
         """ Run before and after simulations to validate the fit """
 
-        if self.verbose: print('\nConfirming fit...')
+        if self.verbose: print('\nChecking fit...')
 
         before_pars = sc.dcp(self.calib_pars)
         for spec in before_pars.values():
@@ -312,13 +298,13 @@ def confirm_fit(self, n_runs=5):
         for parname, spec in after_pars.items():
             spec['value'] = self.best_pars[parname]
 
-        before_sim = self.build_fn(self.sim, calib_pars=before_pars, **self.build_kwargs)
+        before_sim = self.build_fn(self.sim, calib_pars=before_pars, **self.build_kw)
         before_sim.label = 'Before calibration'
         self.before_msim = ss.MultiSim(before_sim, n_runs=n_runs)
         self.before_msim.run()
         self.before_fits = np.array([self.eval_fn(sim, **self.eval_kwargs) for sim in self.before_msim.sims])
 
-        after_sim = self.build_fn(self.sim, calib_pars=after_pars, **self.build_kwargs)
+        after_sim = self.build_fn(self.sim, calib_pars=after_pars, **self.build_kw)
         after_sim.label = 'Before calibration'
         self.after_msim = ss.MultiSim(after_sim, n_runs=n_runs)
         self.after_msim.run()
@@ -390,7 +376,7 @@ def plot_sims(self, **kwargs):
             kwargs (dict): passed to MultiSim.plot()
         """
         if self.before_msim is None:
-            self.confirm_fit()
+            self.check_fit()
 
         # Turn off jupyter mode so we can receive the figure handles
         jup = ss.options.jupyter if 'jupyter' in ss.options else sc.isjupyter()
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index 927cc524..ea339d94 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -99,12 +99,8 @@ def test_calibration(do_plot=False):
     calib = ss.Calibration(
         calib_pars = calib_pars,
         sim = sim,
-
         build_fn = build_sim, # Use default builder, Calibration.translate_pars
-        build_kwargs = None,
-
-        components = [infectious],
-
+        components = infectious,
         total_trials = 20,
         n_workers = None, # None indicates to use all available CPUs
         die = True,
@@ -113,11 +109,11 @@ def test_calibration(do_plot=False):
 
     # Perform the calibration
     sc.printcyan('\nPeforming calibration...')
-    calib.calibrate(confirm_fit=False)
+    calib.calibrate()
 
-    # Confirm
-    sc.printcyan('\nConfirming fit...')
-    calib.confirm_fit()
+    # Check
+    sc.printcyan('\nChecking fit...')
+    calib.check_fit()
     print(f'Fit with original pars: {calib.before_fits}')
     print(f'Fit with best-fit pars: {calib.after_fits}')
     if calib.after_fits.mean() <= calib.before_fits.mean():

From 7527939913404d5d708be42e4263e8cbaa349e91 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 22:29:48 -0500
Subject: [PATCH 27/28] change hyperlinks

---
 docs/tutorials/tut_calibration.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 2fe3d561..6ac642f1 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -97,7 +97,7 @@
     "\n",
     "Each parameter entry should have range defined by `low` and `high` as well as a `guess` values. The `guess` value is not used by Optuna, rather only for a check after calibration completes to see if the new parameters are better than the `guess` values.\n",
     "\n",
-    "You'll notice there are a few other parameters that can be specified. For example, the data type of the parameter appears in `suggest_type`. Possible values are listed in the Optuna documentation, and include suggest_float (https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_float) for float values and suggest_int (https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_int) for integer types.\n",
+    "You'll notice there are a few other parameters that can be specified. For example, the data type of the parameter appears in `suggest_type`. Possible values are listed in the Optuna documentation, and include [suggest_float](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_float) for float values and [suggest_int](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html#optuna.trial.Trial.suggest_int) for integer types.\n",
     "\n",
     "To make things easier for the search algorithm, it's helpful to indicate how outputs are expected to change with inputs. For example, increasing `beta` from 0.01 to 0.02 should double disease transmission, but increasing from 0.11 to 0.12 will have a small effect. Thus, we indicate that this parameter should be calibrated with `log=True`."
    ]

From 82616a33cf65e46a126d8ec6daf961d3381aafaa Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Sun, 17 Nov 2024 22:30:40 -0500
Subject: [PATCH 28/28] fix syntax

---
 docs/tutorials/tut_calibration.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorials/tut_calibration.ipynb b/docs/tutorials/tut_calibration.ipynb
index 6ac642f1..fc2ccdf5 100644
--- a/docs/tutorials/tut_calibration.ipynb
+++ b/docs/tutorials/tut_calibration.ipynb
@@ -198,8 +198,8 @@
     "        'x': sim.results.sir.n_infected, # Number of individuals found to be infectious\n",
     "    }, index=pd.Index(sim.results.timevec, name='t')), # Index is time\n",
     "\n",
-    "    conform = ss.eConform.PREVALENT,\n",
-    "    nll_fn = ss.eLikelihood.BETA_BINOMIAL,\n",
+    "    conform = 'prevalent',\n",
+    "    nll_fn = 'beta',\n",
     "\n",
     "    weight = 1, # Not required if only one component\n",
     ")"