From 98e74b55a34be32d0dd402420c5e6170816d3e3e Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 3 Apr 2024 07:54:39 +1000
Subject: [PATCH 001/161] Add support for selecting subsets of data for
 calibration

---
 atomica/calibration.py | 58 ++++++++++++++++++++++++++++++++----------
 atomica/utils.py       |  2 +-
 2 files changed, 45 insertions(+), 15 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index ed827fd6..daabd1c7 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -46,10 +46,17 @@ def _update_parset(parset, y_factors, pars_to_adjust):
             raise NotImplementedError
 
 
-def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project):
-    # y-factors, array of y-factors to apply to specified output_quantities
-    # pars_to_adjust - list of tuples (par_name,pop_name,...) recognized by parset.update()
-    # output_quantities - a tuple like (pop,var,weight,metric) understood by model.get_pop[pop].getVar
+def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project) -> float:
+    """
+    Run the model for a given set of y-factors and return the objective/goodness-of-fit
+
+    :param y_factors: array of y-factors to apply to specified output_quantities
+    :param pars_to_adjust: list of tuples (par_name,pop_name,...) recognized by parset.update()
+    :param output_quantities: a tuple containing (pop, var, weight, metric, start_year, end_year) - start year and end year are inclusive
+    :param parset:
+    :param project:
+    :return: The value of the objective function defined by the output_quantities
+    """
 
     _update_parset(parset, y_factors, pars_to_adjust)
 
@@ -60,7 +67,8 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
 
     objective = 0.0
 
-    for var_label, pop_name, weight, metric in output_quantities:
+    for var_label, pop_name, weight, metric, start_time, end_time in output_quantities:
+
         target = project.data.get_ts(var_label, pop_name)  # This is the TimeSeries with the data for the requested quantity
         if target is None:
             continue
@@ -68,6 +76,12 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
             continue
         var = result.model.get_pop(pop_name).get_variable(var_label)
         data_t, data_v = target.get_arrays()
+        inds = (data_t >= start_time) & (data_t <= end_time)
+        if np.count_nonzero(inds) == 0:
+            # If no time points remain after filtering down to the time points the user requested
+            continue
+        data_t = data_t[inds]
+        data_v = data_v[inds]
 
         # Interpolate the model outputs onto the data times
         # If there is data outside the range when the model was simulated, don't
@@ -114,7 +128,7 @@ def _calc_wape(y_obs, y_fit):
     return abs(y_fit - y_obs) / (y_obs.mean() + calibration_settings["tolerance"])
 
 
-def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities, max_time=60, method="asd", **kwargs) -> ParameterSet:
+def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities, max_time=60, method="asd", time_period=(-np.inf, np.inf), **kwargs) -> ParameterSet:
     """
     Run automated calibration
 
@@ -125,8 +139,13 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                            relevant to the parameter independently, or 'all' which will instead operate
                            on the meta y factor.
     :param output_quantities: list of tuples, (var_label,pop_name,weight,metric), for use in the objective
-                              function. pop_name=None will expand to all pops. pop_name='all' is not supported
+                              function. pop_name=None will expand to all pops. pop_name='all' is not supported. The
+                              output can optionally contain (var_label, pop_name, weight, metric, start_year, end_year)
+                              to select a subset of the data for evaluating the objective. The start year and end year
+                              specified here will take precedence over the time_period argument
     :param max_time: If using ASD, the maximum run time
+    :param time_period: Tuple of start and end years to use for the objective function. Applies to all outputs unless
+                        the output has an explicitly specified start and end year
     :param method: 'asd' or 'pso'. If using 'pso' all upper and lower limits must be finite
     :param kwargs: Dictionary of additional arguments to be passed to the optimization function, e.g. stepsize or pinitial
     :return: A calibrated :class:`ParameterSet`
@@ -145,15 +164,26 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
     pars_to_adjust = p2
 
     # Expand out pop=None in output_quantities
-    o2 = []
+    outputs = []
     for output_tuple in output_quantities:
-        if output_tuple[1] is None:  # If the pop name is None
-            pops = project.data.pops.keys()
-            for pop_name in pops:
-                o2.append((output_tuple[0], pop_name, output_tuple[2], output_tuple[3]))
+        var_label = output_tuple[0]
+        pop_name = output_tuple[1]
+        weight = output_tuple[2]
+        metric = output_tuple[3]
+        if len(output_tuple) == 6:
+            start_time = output_tuple[4]
+            end_time = output_tuple[5]
         else:
-            o2.append(output_tuple)
-    output_quantities = o2
+            start_time = time_period[0]
+            end_time = time_period[1]
+
+        if pop_name is None:  # If the pop name is None
+            for pop in project.data.pops.keys():
+                outputs.append((var_label, pop, weight, metric, start_time, end_time))
+        else:
+            outputs.append((var_label, pop_name, weight, metric, start_time, end_time))
+
+    output_quantities = outputs
 
     args = {
         "project": project,
diff --git a/atomica/utils.py b/atomica/utils.py
index ee342e54..9e7dc3ce 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -489,7 +489,7 @@ def remove_after(self, t_remove) -> None:
         """
         Remove times from start
 
-        :param tval: Remove times up to but not including this time
+        :param tval: Remove times after but not including this time
 
         """
 

From 08bfb32be1de6439a4c16426f8bbd2b8b21bc2df Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 26 Apr 2024 17:30:14 +0200
Subject: [PATCH 002/161] Enable specifying initial values for the y-factors

---
 atomica/calibration.py | 70 +++++++++++++++++++++++++++++++-----------
 1 file changed, 52 insertions(+), 18 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index daabd1c7..ec3444ab 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -79,6 +79,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         inds = (data_t >= start_time) & (data_t <= end_time)
         if np.count_nonzero(inds) == 0:
             # If no time points remain after filtering down to the time points the user requested
+            logger.info(f'No data points remaining after filtering down to requested time period. Skipping...')
             continue
         data_t = data_t[inds]
         data_v = data_v[inds]
@@ -152,15 +153,22 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
     """
 
+
     # Expand out pop=None in pars_to_adjust
     p2 = []
     for par_tuple in pars_to_adjust:
+        if len(par_tuple) == 5:
+            initial_value = par_tuple[4]
+        else:
+            initial_value = None
+
         if par_tuple[1] is None:  # If the pop name is None
             par = parset.pars[par_tuple[0]]
             for pop_name in par.pops:
-                p2.append((par_tuple[0], pop_name, par_tuple[2], par_tuple[3]))
+                p2.append((par_tuple[0], pop_name, par_tuple[2], par_tuple[3], initial_value))
         else:
-            p2.append(par_tuple)
+            p2.append((par_tuple[0], par_tuple[1], par_tuple[2], par_tuple[3], initial_value))
+
     pars_to_adjust = p2
 
     # Expand out pop=None in output_quantities
@@ -185,30 +193,56 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
     output_quantities = outputs
 
-    args = {
-        "project": project,
-        "parset": parset.copy(),
-        "pars_to_adjust": pars_to_adjust,
-        "output_quantities": output_quantities,
-    }
-
     x0 = []
     xmin = []
     xmax = []
+    filtered_pars_to_adjust = []
+    parset = parset.copy()
+
     for i, x in enumerate(pars_to_adjust):
-        par_name, pop_name, scale_min, scale_max = x
+        par_name, pop_name, scale_min, scale_max, initial_value = x
+
         if par_name in parset.pars:
             par = parset.pars[par_name]
-            if pop_name == "all":
-                x0.append(par.meta_y_factor)
-            else:
-                x0.append(par.y_factor[pop_name])
         else:
             tokens = par_name.split("_from_")
             par = parset.transfers[tokens[0]][tokens[1]]
-            x0.append(par.y_factor[pop_name])
-        xmin.append(scale_min)
-        xmax.append(scale_max)
+
+        #if initial_value has not been explicitly set in the tuple: use y_factor in parset
+        if initial_value is None:
+            if pop_name == "all":
+                initial_value = par.meta_y_factor
+            else:
+                initial_value = par.y_factor[pop_name]
+            #if this value is outside the min and max bounds, make it equal to min or max (whichever is closest)
+            #if min == max, this should make the initial value equal to min and max
+            initial_value = np.clip(initial_value, scale_min, scale_max)
+        else:
+            assert (initial_value >= scale_min) and (initial_value <= scale_max), 'Initial value is not consistent with the lower/upper bounds'
+
+        #update y_factors in parset
+        if initial_value is not None: #if statement redundant?
+            if pop_name == 'all':
+                par.meta_y_factor = initial_value
+            else:
+                par.y_factor[pop_name] = initial_value
+
+        if scale_min != scale_max:
+            # Only include the y-factor in the adjustments made in the optimization function if a range
+            # of y-factor values is permitted
+            filtered_pars_to_adjust.append(x)
+            x0.append(initial_value)
+            xmin.append(scale_min)
+            xmax.append(scale_max)
+
+
+
+    args = {
+        "project": project,
+        "parset": parset,
+        "pars_to_adjust": filtered_pars_to_adjust,
+        "output_quantities": output_quantities,
+    }
 
     original_sim_end = project.settings.sim_end
     project.settings.sim_end = min(project.data.tvec[-1], original_sim_end)
@@ -254,7 +288,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
     finally:
         project.settings.sim_end = original_sim_end  # Restore the simulation end year
 
-    _update_parset(args["parset"], x1, pars_to_adjust)
+    _update_parset(args["parset"], x1, args["pars_to_adjust"])
 
     # Log out the commands required for equivalent manual calibration if desired
     for i, x in enumerate(pars_to_adjust):

From 875b2ac75766b9d303b6f0be6291ad821e9daea7 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 31 May 2024 17:15:12 +1000
Subject: [PATCH 003/161] Change *_time to *_year, and permit superfluous
 arguments

---
 atomica/calibration.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index ec3444ab..45790d2a 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -46,10 +46,12 @@ def _update_parset(parset, y_factors, pars_to_adjust):
             raise NotImplementedError
 
 
-def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project) -> float:
+def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project, *args, **kwargs) -> float:
     """
     Run the model for a given set of y-factors and return the objective/goodness-of-fit
 
+    Additional extra arguments will be ignored but will not raise an error.
+
     :param y_factors: array of y-factors to apply to specified output_quantities
     :param pars_to_adjust: list of tuples (par_name,pop_name,...) recognized by parset.update()
     :param output_quantities: a tuple containing (pop, var, weight, metric, start_year, end_year) - start year and end year are inclusive
@@ -67,7 +69,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
 
     objective = 0.0
 
-    for var_label, pop_name, weight, metric, start_time, end_time in output_quantities:
+    for var_label, pop_name, weight, metric, start_year, end_year in output_quantities:
 
         target = project.data.get_ts(var_label, pop_name)  # This is the TimeSeries with the data for the requested quantity
         if target is None:
@@ -76,7 +78,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
             continue
         var = result.model.get_pop(pop_name).get_variable(var_label)
         data_t, data_v = target.get_arrays()
-        inds = (data_t >= start_time) & (data_t <= end_time)
+        inds = (data_t >= start_year) & (data_t <= end_year)
         if np.count_nonzero(inds) == 0:
             # If no time points remain after filtering down to the time points the user requested
             logger.info(f'No data points remaining after filtering down to requested time period. Skipping...')
@@ -135,13 +137,13 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
     :param project: A project instance to provide data and sim settings
     :param parset: A :class:`ParameterSet` instance to calibrate
-    :param pars_to_adjust: list of tuples, (par_name,pop_name,lower_limit,upper_limit)
+    :param pars_to_adjust: list of tuples, `(par_name, pop_name, lower_bound, upper_bound, initial_value)`
                            the pop name can be None, which will be expanded to all populations
                            relevant to the parameter independently, or 'all' which will instead operate
                            on the meta y factor.
     :param output_quantities: list of tuples, (var_label,pop_name,weight,metric), for use in the objective
                               function. pop_name=None will expand to all pops. pop_name='all' is not supported. The
-                              output can optionally contain (var_label, pop_name, weight, metric, start_year, end_year)
+                              output can optionally contain `(var_label, pop_name, weight, metric, start_year, end_year)`
                               to select a subset of the data for evaluating the objective. The start year and end year
                               specified here will take precedence over the time_period argument
     :param max_time: If using ASD, the maximum run time
@@ -179,17 +181,17 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
         weight = output_tuple[2]
         metric = output_tuple[3]
         if len(output_tuple) == 6:
-            start_time = output_tuple[4]
-            end_time = output_tuple[5]
+            start_year = output_tuple[4]
+            end_year = output_tuple[5]
         else:
-            start_time = time_period[0]
-            end_time = time_period[1]
+            start_year = time_period[0]
+            end_year = time_period[1]
 
         if pop_name is None:  # If the pop name is None
             for pop in project.data.pops.keys():
-                outputs.append((var_label, pop, weight, metric, start_time, end_time))
+                outputs.append((var_label, pop, weight, metric, start_year, end_year))
         else:
-            outputs.append((var_label, pop_name, weight, metric, start_time, end_time))
+            outputs.append((var_label, pop_name, weight, metric, start_year, end_year))
 
     output_quantities = outputs
 

From 0b4270fb129ccced0f45cab6b199f9a4fc7ddbb7 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 31 May 2024 18:19:48 +0200
Subject: [PATCH 004/161] Only call ASD if there are pars to adjust left after
 filtering

---
 atomica/calibration.py | 85 ++++++++++++++++++++++--------------------
 1 file changed, 44 insertions(+), 41 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 45790d2a..d4cbf288 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -249,48 +249,51 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
     original_sim_end = project.settings.sim_end
     project.settings.sim_end = min(project.data.tvec[-1], original_sim_end)
 
-    try:
-        if method == "asd":
-            optim_args = {
-                "stepsize": 0.1,
-                "maxiters": 2000,
-                "sinc": 1.5,
-                "sdec": 2.0,
-                "reltol": 1e-3,
-                "abstol": 1e-6,
-                "xmin": xmin,
-                "xmax": xmax,
-            }
-            optim_args.update(kwargs)
-
-            if max_time is not None:
-                optim_args["maxtime"] = max_time
-
-            log_level = logger.getEffectiveLevel()
-            if log_level < logging.WARNING:
-                optim_args["verbose"] = 2
+    if len(filtered_pars_to_adjust) > 0:
+        try:
+            if method == "asd":
+                optim_args = {
+                    "stepsize": 0.1,
+                    "maxiters": 2000,
+                    "sinc": 1.5,
+                    "sdec": 2.0,
+                    "reltol": 1e-3,
+                    "abstol": 1e-6,
+                    "xmin": xmin,
+                    "xmax": xmax,
+                }
+                optim_args.update(kwargs)
+
+                if max_time is not None:
+                    optim_args["maxtime"] = max_time
+
+                log_level = logger.getEffectiveLevel()
+                if log_level < logging.WARNING:
+                    optim_args["verbose"] = 2
+                else:
+                    optim_args["verbose"] = 0
+
+                opt_result = sc.asd(_calculate_objective, x0, args, **optim_args)
+                x1 = opt_result["x"]
+            elif method == "pso":
+                import pyswarm
+
+                optim_args = {"maxiter": 3, "lb": xmin, "ub": xmax, "minstep": 1e-3, "debug": True}
+                if np.any(~np.isfinite(xmin)) or np.any(~np.isfinite(xmax)):
+                    errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every parameter being adjusted needs to have finite bounds)"
+                    raise Exception(errormsg)
+
+                x1, _ = pyswarm.pso(_calculate_objective, kwargs=args, **optim_args)
             else:
-                optim_args["verbose"] = 0
-
-            opt_result = sc.asd(_calculate_objective, x0, args, **optim_args)
-            x1 = opt_result["x"]
-        elif method == "pso":
-            import pyswarm
-
-            optim_args = {"maxiter": 3, "lb": xmin, "ub": xmax, "minstep": 1e-3, "debug": True}
-            if np.any(~np.isfinite(xmin)) or np.any(~np.isfinite(xmax)):
-                errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every parameter being adjusted needs to have finite bounds)"
-                raise Exception(errormsg)
-
-            x1, _ = pyswarm.pso(_calculate_objective, kwargs=args, **optim_args)
-        else:
-            raise Exception("Unrecognized method")
-    except Exception as e:
-        raise e
-    finally:
-        project.settings.sim_end = original_sim_end  # Restore the simulation end year
-
-    _update_parset(args["parset"], x1, args["pars_to_adjust"])
+                raise Exception("Unrecognized method")
+        except Exception as e:
+            raise e
+        finally:
+            project.settings.sim_end = original_sim_end  # Restore the simulation end year
+
+        _update_parset(args["parset"], x1, args["pars_to_adjust"])
+    else:
+        logger.info('No parameters to adjust provided to the optimisation function. Skipping optimisation...')
 
     # Log out the commands required for equivalent manual calibration if desired
     for i, x in enumerate(pars_to_adjust):

From adc6e2ac958c37dad0db2231f24203b0a94c4f0a Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 3 Jun 2024 17:56:59 +1000
Subject: [PATCH 005/161] Add YAML calibration file

---
 atomica/utils.py            |  19 ++
 atomica/yaml_calibration.py | 485 ++++++++++++++++++++++++++++++++++++
 2 files changed, 504 insertions(+)
 create mode 100644 atomica/yaml_calibration.py

diff --git a/atomica/utils.py b/atomica/utils.py
index 9e7dc3ce..c33b7618 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -36,6 +36,7 @@
     "parallel_progress",
     "start_logging",
     "stop_logging",
+    "get_sigfigs_necessary",
 ]
 
 
@@ -982,3 +983,21 @@ def stop_logging() -> None:
             logger.removeHandler(handler)
             # Don't terminate the loop, if by some change there is more than one handler
             # (not supposed to happen though) then we would want to close them all
+
+
+def get_sigfigs_necessary(x, y, min_sigfigs: int = 2) -> int:
+    """
+    Get how many significant figures are necessary to tell the difference berween two numbers
+
+    :param x, y: numbers to compare
+    :param min_sigfigs: minimum number of sigfigs to use if no difference
+    :return: Number of significant figures required
+    """
+    msf = min_sigfigs
+    assert sc.isnumber(x) and sc.isnumber(y), f'Cannot compare sigfigs as {x} and {y} are not both numbers'
+    if x == y or np.isnan(x) or np.isnan(y):
+        return msf
+    else:
+        while sc.sigfig(x=x, sigfigs=msf) == sc.sigfig(x=y, sigfigs=msf):
+            msf += 1
+        return msf
diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
new file mode 100644
index 00000000..4bb7bb13
--- /dev/null
+++ b/atomica/yaml_calibration.py
@@ -0,0 +1,485 @@
+"""
+This file implements YAML calibration, a mechanism of programming multiple automated calibration steps
+(and related operations). Essentially this scheme coordinates repeated calls to at.calibrate with different
+adjustables and measurables in a pre-defined sequence of automated calibration steps.
+"""
+
+import sciris as sc
+from pathlib import Path
+import atomica as at
+from collections import defaultdict, Counter
+import numpy as np
+import yaml
+
+__all__ = ['build', 'run']
+
+def _get_named_nodes():
+    """
+    Return dictionary with all named Node subclasses
+    """
+    return {x._name: x for x in BaseNode.__subclasses__() if x._name is not None}
+
+def build(instructions=None, context=None, name='calibration'):
+    """
+    Construct nodes representing a calibration
+
+    :param instructions: A dictionary of attributes/settings defined for this node OR a string filename
+                         containing a YAML file that can be loaded to provide instructions
+    :param context: A dictionary of attributes/settings inherited from parent nodes
+    :param name: The name to assign this node
+    :param fname: Optionally read the instructions from a file
+    :return: A Node subclass instance, the type of which depends on the instructions
+    """
+
+    if (sc.isstring(instructions) or isinstance(instructions, Path)) and Path(instructions).exists():
+        with open(instructions) as file:
+            instructions =  yaml.load(file, Loader=yaml.FullLoader)
+
+    named_nodes = _get_named_nodes()
+    if isinstance(instructions, dict) and ('adjustables' in instructions or (context is not None and 'adjustables' in context)) and ('measurables' in instructions or (context is not None and 'measurables' in context)):
+        return CalibrationNode(instructions, context, name)
+    elif name in named_nodes:
+        return named_nodes[name](instructions, context, name)
+    else:
+        return Section(instructions, context, name)
+
+def run(node, project, parset, savedir=None, save_intermediate=False, log_output:bool=False,*args, **kwargs):
+    """
+    Run YAML calibration
+
+    This will execute the YAML calibration using the passed-in node (or instructions to build a node), and any associated children
+
+    :param node: Calibration node to execute. If not a node (i.e., a YAML file, or node instructions), it will be converted into a node
+    :param P: Project to which to apply these instructions
+    :param parset: An `at.ParameterSet` instance to calibrate
+    :param savedir: Optionally specify a directory to save the results. Defaults to the current working directory
+    :param save_intermediate: Set whether to save intermediate calibrations (defaults to False)
+    :return new_parset: A calibrated `at.ParameterSet` instance
+    """
+
+    if not isinstance(node, BaseNode):
+        node = build(node)
+
+    parset = sc.dcp(project.parset(parset))
+
+    if savedir is None:
+        savedir = Path('.')
+    else:
+        savedir = Path(savedir)
+
+    nodes = list(node.walk()) # Make a flat list of all nodes to execute in order
+    n_steps = len([x for x in nodes if not isinstance(x[1], Section)])
+    n = 1
+
+    if log_output:
+        at.start_logging(savedir/'calibration_log.txt')
+
+    at.logger.info(f'Starting calibration ({n_steps} steps)')
+
+    for n_reps, node in nodes:
+
+        if isinstance(node, Section):
+            at.logger.info(f'\nSection "{node.name}" (repeat {n_reps} of {node.repeats})')
+        else:
+            at.logger.info(f'\nStep {n} of {n_steps} "{node.name} (repeat {n_reps} of {node.repeats})')
+            parset = node.apply(project, parset, savedir, save_intermediate, *args, **kwargs)
+            n += 1
+
+            if save_intermediate and not isinstance(node, SaveCalibrationNode):
+                output = savedir / f'intermediate_calibration_{n:0{len(str(n_steps))}}_{self.name.replace(" ", "_")}'
+                parset.save_calibration(output)
+                at.logger.info(f'Saved intermediate calibration to {output}')
+
+    at.logger.info(f'\nCalibration completed')
+
+    if log_output:
+        at.stop_logging()
+
+    return parset
+
+
+class BaseNode:
+    """
+    Node base class
+
+    The base node class implements basic node features. Typically there should not be any
+    instances of this class, only instances of subclasses
+    """
+
+    _name = None  # If specified, this key can be used as the name of the step to create a node of this type
+
+    def __init__(self, instructions, context, name):
+        self.name = name
+        self.instructions = sc.dcp(instructions)
+        self.context = context  # Attributes inherited from parent nodes
+        self.children = []
+        self.validate()
+
+    def walk(self):
+        n_reps = 0
+        for repeat in range(self.repeats):
+            n_reps += 1
+            yield (n_reps, self)
+            for child in self.children:
+                yield from child.walk()
+
+    @property
+    def n_steps(self):
+        if type(self) == BaseNode:
+            return self.repeats * sum(child.n_steps for child in self.children)
+        else:
+            return self.repeats
+
+    def __repr__(self):
+        return f'<{self.__class__.__name__} "{self.name}" x{self.repeats}>'
+
+    def __str__(self, indent=0):
+        """
+        Print a tree representation of this node and all children
+
+        :param indent: Recursively increase the indent for child nodes
+        :return:
+        """
+        s =  '\t' * indent + self.__repr__()
+        for child in self.children:
+            s += '\n' + child.__str__(indent=indent + 1)
+        return s
+
+    @property
+    def attributes(self):
+        return sc.mergedicts(self.context, self.instructions)
+
+    def __getitem__(self, item):
+        # Directly index the Node to extract attributes without merging the dictionaries every time
+        if item in self.instructions:
+            return self.instructions[item]
+        elif item in self.context:
+            return self.context[item]
+        else:
+            raise KeyError(item)
+
+    def __setitem__(self, key, value):
+        self.instructions[key] = value
+
+    def __contains__(self, item):
+        return item in self.instructions or item in self.context
+
+    @property
+    def repeats(self):
+        # Although repeats may be part of the context, we only repeat a node if the instructions requested a repeat
+        # i.e., repeats are not inherited
+        if isinstance(self.instructions, dict) and 'repeats' in self.instructions:
+            return self.instructions['repeats']
+        else:
+            return 1
+
+    def validate(self):
+        """
+        Validate/sanitize contents of this node
+
+        If the node isn't valid, an error should be raised
+        """
+        return
+
+    def apply(self, project: at.Project, parset: at.ParameterSet, savedir, *args, **kwargs) -> tuple:
+        """
+        Perform the action associated with this node
+        """
+        return parset
+
+class Section(BaseNode):
+    """
+    A section node is a special kind of node, that contains other nodes
+    """
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.children = self.make_children()
+        self.validate()
+
+    def make_children(self):
+
+        children = []
+
+        # Remove any keys in the instructions that correspond to named nodes
+        # These should be used as instructions for the child node, rather than
+        # forming part of the context that is passed down to all children
+        named_nodes = _get_named_nodes()
+        step_instructions = {k: v for k, v in self.instructions.items() if isinstance(v, dict) or k in named_nodes}
+        for k in step_instructions:
+            del self.instructions[k]
+
+        # Create the child nodes
+        for name, instructions in step_instructions.items():
+            children.append(build(instructions, self.attributes, name))
+
+        return children
+
+class CalibrationNode(BaseNode):
+
+    # Order for list of adjustable parameters and default values
+    adj_defaults = {
+        'lower_bound': 0.1,
+        'upper_bound': 10.0,
+        'initial_value': None,
+    }
+
+    # Order for list of measurable parameters and default values
+    meas_defaults = {
+        'weight': 1.0,
+        'metric': 'fractional',
+        'start_year': -np.inf,
+        'end_year': np.inf,
+    }
+
+    @staticmethod
+    def parse_list(l, defaults):
+        # Routine to parse list of arguments into a dictionary of values
+        d = {}
+        for k, v in zip(list(defaults.keys())[:len(l)], l):
+            d[k] = v
+        return d
+
+    def validate(self):
+        """
+        Pre-parse calibration inputs
+        """
+
+        def process_key(s):
+            # Sanitize key name with optional space separating pop name
+            if ' ' in s:
+                return tuple([x for x in s.split(' ') if x])
+            else:
+                return (s.strip(), None)
+
+        def process_inputs(inputs, defaults):
+            # Process adjustables and measurables, which can be specified in a list representation or nested dict representation
+            # In list representation, the input is a list of lists, where the first item in each list is the quantity (with optional population) and
+            # the remaining items are the supported arguments for the input type, in the order defined by the defaults dictionary.
+            # In a dict representation, the key is the quantity with optional population, and then the value can either be a list (in the order defined by the dictionary)
+            # or a dictionary explicitly naming the inputs.
+            # This function returns a flat dictionary with {(quantity, pop_name):{argument:value}} e.g., {('b_rate','0-5'):{'lower_bound':0.5}}.
+            # In the dict representation, the key can be a comma separated list of quantities with optional values e.g., 'b_rate 0-5, d_rate'. In the list representation,
+            # multiple quantities are not supported (as a comma is already used to separate the arguments)
+            out = {}
+
+            if sc.isstring(inputs):
+                # Support a comma separated string with "quantity pop" specifications of adjustables and measurables
+                # In this case, default values should be used for all other items. Proceed by splitting into a list
+                inputs = inputs.split(',')
+
+            if isinstance(inputs, (tuple, list)):
+                for l in inputs:
+                    l = sc.promotetolist(l)
+                    key, pop_name = process_key(l[0].strip())
+                    d = self.parse_list(l[1:], defaults)
+                    out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+            elif isinstance(inputs, dict):
+                for keys, v in inputs.items():
+                    for key in keys.split(','):
+                        key, pop_name = process_key(key.strip())
+                        if isinstance(v, (tuple, list)):
+                            d = self.parse_list(v, defaults)
+                        else:
+                            d = v.copy()
+                        out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+
+            return out
+
+        self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)
+        self['measurables'] = process_inputs(self['measurables'], self.meas_defaults)
+
+        # Validate adjustables
+        def check_optional_number(key, v, defaults):
+            if key in v and v[key] is not None:
+                if not sc.isnumber(v[key], isnan=False):
+                    raise TypeError(f"Adjustable argument '{key}' needs to be a number or None (defaults to {defaults[key]}). Provided value: {v[key]} ")
+
+        assert len(self['adjustables']) > 0, f'Cannot calibrate with no adjustables for calibration section {self.name}'
+        for (quantity, pop_name), v in self['adjustables'].items():
+            assert 'pop_name' not in v, f'Setting the population name through "pop_name: {v["pop_name"]}" is not supported. Instead, the name of the adjustable quantity should include the population name ("{quantity} {v["pop_name"]}")'
+            assert isinstance(quantity, str), f'Adjustable codename {quantity} needs to be a string'
+            assert pop_name is None or isinstance(pop_name, str), f'Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)'
+            check_optional_number('lower_bound',v, self.adj_defaults)
+            check_optional_number('upper_bound',v, self.adj_defaults)
+            check_optional_number('initial_value',v, self.adj_defaults)
+
+        # Validate measurables
+        assert len(self['measurables']) > 0, f'Cannot calibrate with no measurables for calibration section {self.name}'
+        for (quantity, pop_name), v in self['measurables'].items():
+            assert isinstance(quantity, str), f'Measurable codename {quantity} needs to be a string'
+            assert pop_name is None or isinstance(pop_name, str), f'Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)'
+            assert 'metric' not in v or v['metric'] is None or isinstance(v["metric"], str), f"Measurable metric {v['metric']} needs to be a number or None (defaults to 'fractional')"
+
+            check_optional_number('weight',v, self.meas_defaults)
+            check_optional_number('start_year',v, self.meas_defaults)
+            check_optional_number('end_year',v, self.meas_defaults)
+
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, quiet=False, compare_results=False, **kwargs) -> tuple:
+
+        step_name = self.name
+        attributes = self.attributes
+
+        at.logger.info(f'Calibrating adjustable(s) {[adj[0] for adj in attributes["adjustables"]]} to match measurable(s) {[mea[0] for mea in attributes["measurables"]]}...')
+
+        # Expand adjustables
+        adjustables = []
+        par_names = {x[0] for x in attributes['adjustables']}.intersection(x.name for x in parset.all_pars())
+        pop_names = {x[1] for x in attributes['adjustables']}.intersection({*parset.pop_names} | {'all', None})
+
+        for par_name, pop_name in attributes['adjustables']:
+
+            if par_name not in par_names:
+                at.logger.warning(f"Extra YAML adjustable parameter '{par_name}' does not exist in this project's framework and will be ignored")
+                continue
+            elif pop_name not in pop_names:
+                at.logger.warning(f"Extra YAML adjustable population '{pop_name}' does not exist in this project's databook and will be ignored")
+                continue
+
+            if pop_name is None:
+                pops = parset.pop_names
+            else:
+                pops = sc.promotetolist(pop_name)
+
+            for pop in pops:
+                d = sc.mergedicts(self.adj_defaults, attributes['adjustables'].get((par_name, None), None), attributes['adjustables'].get((par_name, pop), None))
+                adjustables.append((par_name, pop, d['lower_bound'], d['upper_bound'], d['initial_value']))
+
+        # Expand measurables
+        measurables = []
+        par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to support transfer parameters and validate that pars have databook entries in the future
+        pop_names = {x[1] for x in attributes['measurables']}.intersection({*parset.pop_names} | {None})
+
+        for par_name, pop_name in attributes['measurables']:
+
+            if par_name not in par_names:
+                at.logger.warning(f"Extra YAML measurable variable '{par_name}' does not exist in this project's framework and will be ignored")
+                continue
+            elif pop_name not in pop_names:
+                at.logger.warning(f"Extra YAML measurable population '{pop_name}' does not exist in this project's databook and will be ignored")
+                continue
+
+            if pop_name is None:
+                pops = parset.pop_names
+            else:
+                pops = sc.promotetolist(pop_name)
+
+            for pop in pops:
+                d = sc.mergedicts(self.meas_defaults, attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
+                measurables.append((par_name, pop_name, d['weight'], d['metric'], d['start_year'], d['end_year']))
+
+        # Calibration
+        if len(adjustables):
+
+            kwargs = sc.mergedicts(self.attributes, kwargs)
+
+            del kwargs['adjustables'] # supplied via the adjustables variable
+            del kwargs['measurables'] # supplied via the measurables variable
+
+            if 'repeats' in kwargs:
+                del kwargs['repeats']
+
+            if quiet:
+                with at.Quiet(show_warnings=False):
+                    new_cal_parset = at.calibrate(project, parset, adjustables, measurables, **kwargs)
+            else:
+                new_cal_parset = at.calibrate(project, parset, adjustables, measurables, **kwargs)
+        else:
+            new_cal_parset = parset
+
+        at.logger.info(f'Completed "{step_name}"...')
+        made_changes = False
+
+        for par, pop, *_ in adjustables:
+            if pop == 'all':
+                old = parset.pars[par].meta_y_factor
+                new = new_cal_parset.pars[par].meta_y_factor
+            else:
+                old = parset.pars[par].y_factor[pop]
+                new = new_cal_parset.pars[par].y_factor[pop]
+
+            if new != old:
+                at.logger.info(f'...adjusted the y-factor for {par} in {pop} from {old} to {new}')
+                made_changes = True
+            else:
+                at.logger.debug(f'...did NOT adjust the y-factor for {par} in {pop} from {old} to {new}')
+
+        if not made_changes:
+            at.logger.info(f'...made no changes!')
+
+        if compare_results:
+            base_res = project.run_sim(parset=parset)
+            cal_res = project.run_sim(parset=new_cal_parset)
+            for par_name in [par_measure[0] for par_measure in measurables]:
+                base_rms_error = 0
+                cal_rms_error = 0
+                for pop in parset.pars[par_name].ts.keys():
+                    for time_par_ind, time_value in enumerate(parset.pars[par_name].ts[pop].t):
+                        data_time_val = parset.pars[par_name].ts[pop].vals[time_par_ind]
+                        base_res_time_ind = list(base_res.get_variable(par_name, pop)[0].t).index(time_value)
+                        base_time_val = base_res.get_variable(par_name, pop)[0].vals[base_res_time_ind]
+                        cal_res_time_ind = list(cal_res.get_variable(par_name, pop)[0].t).index(time_value)  # probably redundant as they *should* be the same
+                        cal_time_val = cal_res.get_variable(par_name, pop)[0].vals[cal_res_time_ind]
+
+                        base_rms_error += (data_time_val - base_time_val) ** 2
+                        cal_rms_error += (data_time_val - cal_time_val) ** 2
+
+                        sf = at.get_sigfigs_necessary(base_time_val, cal_time_val)
+                        at.logger.info(f'...for parameter {par_name} and population {pop} at time {time_value} the data value was {sc.sigfig(data_time_val, sf)}, the baseline value was {sc.sigfig(base_time_val, sf)}, and the calibrated value was {sc.sigfig(cal_time_val, sf)}.')
+
+                base_rms_error = base_rms_error ** 0.5
+                cal_rms_error = cal_rms_error ** 0.5
+                sf = get_sigfigs_necessary(base_rms_error, cal_rms_error)
+                at.logger.info(f'...RMS error for parameter {par_name} has changed from baseline {sc.sigfig(base_rms_error, sf)} to calibrated {sc.sigfig(cal_rms_error, sf)}')
+
+        return new_cal_parset
+
+
+class InitializationNode(BaseNode):
+    _name = 'set_initialization'
+
+    def __init__(self, instructions, context, name):
+        if not isinstance(instructions, dict):
+            instructions = {'year': instructions}
+        super().__init__(instructions, context, name)
+
+    def validate(self):
+        assert 'year' in self, f'Initialisation year must be specified'
+        assert sc.isnumber(self['year']), f'Reinitialisation year {self["year"]} must be numeric.'
+
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> tuple:
+        new_settings = sc.dcp(project.settings)
+        new_settings.update_time_vector(end=self['year'])
+        res = at.run_model(settings=new_settings, framework=project.framework, parset=parset)
+        parset.set_initialization(res, self['year'])
+        return parset
+
+
+class ClearInitializationNode(BaseNode):
+    _name = 'clear_initialization'
+
+    def __init__(self, instructions, context, name):
+        super().__init__(instructions=None, context=context, name=name)
+
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> tuple:
+        parset.initialization = None
+        return parset
+
+
+class SaveCalibrationNode(BaseNode):
+    """
+    Block in YAML file with "save calibration: <file name>"
+    """
+
+    _name = 'save_calibration'
+
+    def __init__(self, instructions, context, name):
+        if not isinstance(instructions, dict):
+            instructions = {'fname': instructions}
+        super().__init__(instructions, context, name)
+
+    def validate(self):
+        assert self['fname'] is not None, 'A "save calibration" node must have a file name explicitly specified'
+
+    def apply(self, project: at.Project, parset: at.ParameterSet, savedir=None, *args, **kwargs) -> tuple:
+        parset.save_calibration(savedir / self['fname'])
+        return parset

From 134f1ad867f332e8e4302986840ccd615666eb45 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 3 Jun 2024 17:57:11 +1000
Subject: [PATCH 006/161] Improve default handling

---
 atomica/calibration.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 45790d2a..cea7d7b4 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -131,7 +131,7 @@ def _calc_wape(y_obs, y_fit):
     return abs(y_fit - y_obs) / (y_obs.mean() + calibration_settings["tolerance"])
 
 
-def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities, max_time=60, method="asd", time_period=(-np.inf, np.inf), **kwargs) -> ParameterSet:
+def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities, max_time=None, method="asd", time_period=(-np.inf, np.inf), **kwargs) -> ParameterSet:
     """
     Run automated calibration
 
@@ -223,11 +223,10 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             assert (initial_value >= scale_min) and (initial_value <= scale_max), 'Initial value is not consistent with the lower/upper bounds'
 
         #update y_factors in parset
-        if initial_value is not None: #if statement redundant?
-            if pop_name == 'all':
-                par.meta_y_factor = initial_value
-            else:
-                par.y_factor[pop_name] = initial_value
+        if pop_name == 'all':
+            par.meta_y_factor = initial_value
+        else:
+            par.y_factor[pop_name] = initial_value
 
         if scale_min != scale_max:
             # Only include the y-factor in the adjustments made in the optimization function if a range
@@ -260,12 +259,10 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                 "abstol": 1e-6,
                 "xmin": xmin,
                 "xmax": xmax,
+                "maxtime": 60 if max_time is None else max_time,
             }
             optim_args.update(kwargs)
 
-            if max_time is not None:
-                optim_args["maxtime"] = max_time
-
             log_level = logger.getEffectiveLevel()
             if log_level < logging.WARNING:
                 optim_args["verbose"] = 2

From 34a3978d00b6a1a326e0cd4a608544bb2d900c8c Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 3 Jun 2024 17:57:26 +1000
Subject: [PATCH 007/161] Update Project.calibrate() to support YAML

---
 atomica/project.py | 71 ++++++++++++++++++++++++++++++++++++----------
 1 file changed, 56 insertions(+), 15 deletions(-)

diff --git a/atomica/project.py b/atomica/project.py
index e6881957..32877240 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -577,7 +577,18 @@ def run_sampled_sims(self, parset, progset=None, progset_instructions=None, resu
 
         return results
 
-    def calibrate(self, parset=None, adjustables=None, measurables=None, max_time=60, save_to_project=False, new_name=None, default_min_scale=0.0, default_max_scale=2.0, default_weight=1.0, default_metric="fractional", **kwargs) -> ParameterSet:
+    def calibrate(self,
+                  parset=None,
+                  adjustables=None,
+                  measurables=None,
+                  save_to_project=False,
+                  new_name=None,
+                  default_min_scale=0.0,
+                  default_max_scale=2.0,
+                  default_weight=1.0,
+                  default_metric="fractional",
+                  yaml=None,
+                  **kwargs) -> ParameterSet:
         """
         Method to perform automatic calibration.
 
@@ -603,6 +614,23 @@ def calibrate(self, parset=None, adjustables=None, measurables=None, max_time=60
         To calibrate a project-attached parameter set in place, provide its key as the new name argument to this method.
         Current fitting metrics are: "fractional", "meansquare", "wape"
         Note that scaling limits are absolute, not relative.
+
+        Additional keyword arguments will be passed to `at.calibrate` so additional arguments such as `max_time`
+        or `methods` can be provided to this function.
+
+        :param parset:
+        :param adjustables:
+        :param measurables:
+        :param save_to_project:
+        :param new_name:
+        :param default_min_scale:
+        :param default_max_scale:
+        :param default_weight:
+        :param default_metric:
+        :param yaml: Optionally specify a YAML file or YAML calibration dictionary to use for calibration
+        :param kwargs:
+        :return:
+
         """
 
         if parset is None:
@@ -610,23 +638,36 @@ def calibrate(self, parset=None, adjustables=None, measurables=None, max_time=60
         elif not isinstance(parset, ParameterSet):
             parset = self.parsets[parset]
 
+        if yaml is not None:
+            import atomica.yaml_calibration # Avoid circular import
+            assert adjustables is None, "If a YAML file is specified, adjustables should not be set"
+            assert measurables is None, "If a YAML file is specified, measurables should not be set"
+            new_parset = atomica.yaml_calibration.run(yaml, self, parset, **kwargs)
+        else:
+            if adjustables is None:
+                adjustables = list(self.framework.pars.index[~self.framework.pars["calibrate"].isnull()])
+                adjustables += list(self.framework.comps.index[~self.framework.comps["calibrate"].isnull()])
+                adjustables += list(self.framework.characs.index[~self.framework.characs["calibrate"].isnull()])
+
+            if measurables is None:
+                measurables = list(self.framework.comps.index)
+                measurables += list(self.framework.characs.index)
+
+            for index, adjustable in enumerate(adjustables):
+                if sc.isstring(adjustable):  # Assume that a parameter name was passed in if not a tuple.
+                    adjustables[index] = (adjustable, None, default_min_scale, default_max_scale)
+
+            for index, measurable in enumerate(measurables):
+                if sc.isstring(measurable):  # Assume that a parameter name was passed in if not a tuple.
+                    measurables[index] = (measurable, None, default_weight, default_metric)
+
+            new_parset = calibrate(project=self, parset=parset, pars_to_adjust=adjustables, output_quantities=measurables, **kwargs)
+
+
         if new_name is None:
             new_name = parset.name + " (auto-calibrated)"
-        if adjustables is None:
-            adjustables = list(self.framework.pars.index[~self.framework.pars["calibrate"].isnull()])
-            adjustables += list(self.framework.comps.index[~self.framework.comps["calibrate"].isnull()])
-            adjustables += list(self.framework.characs.index[~self.framework.characs["calibrate"].isnull()])
-        if measurables is None:
-            measurables = list(self.framework.comps.index)
-            measurables += list(self.framework.characs.index)
-        for index, adjustable in enumerate(adjustables):
-            if sc.isstring(adjustable):  # Assume that a parameter name was passed in if not a tuple.
-                adjustables[index] = (adjustable, None, default_min_scale, default_max_scale)
-        for index, measurable in enumerate(measurables):
-            if sc.isstring(measurable):  # Assume that a parameter name was passed in if not a tuple.
-                measurables[index] = (measurable, None, default_weight, default_metric)
-        new_parset = calibrate(project=self, parset=parset, pars_to_adjust=adjustables, output_quantities=measurables, max_time=max_time, **kwargs)
         new_parset.name = new_name  # The new parset is a calibrated copy of the old, so change id.
+
         if save_to_project:
             self.parsets.append(new_parset)
 

From d757dbc28d2ba68986bd4867650fdc0038843b63 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 4 Jun 2024 18:55:21 +0200
Subject: [PATCH 008/161] Minor bug fix, type hints

-Change self.name (undefined) to node.name
-Update type hints
-Remove unused imports
---
 atomica/yaml_calibration.py | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 4bb7bb13..5b48d580 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -7,12 +7,16 @@
 import sciris as sc
 from pathlib import Path
 import atomica as at
-from collections import defaultdict, Counter
 import numpy as np
 import yaml
+import time
+from at_tools import get_sigfigs_necessary
 
 __all__ = ['build', 'run']
 
+from atomica import ParameterSet
+
+
 def _get_named_nodes():
     """
     Return dictionary with all named Node subclasses
@@ -74,23 +78,24 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
     if log_output:
         at.start_logging(savedir/'calibration_log.txt')
 
-    at.logger.info(f'Starting calibration ({n_steps} steps)')
+    at.logger.info(f'\nStarting calibration ({n_steps} steps)')
 
     for n_reps, node in nodes:
 
         if isinstance(node, Section):
             at.logger.info(f'\nSection "{node.name}" (repeat {n_reps} of {node.repeats})')
         else:
-            at.logger.info(f'\nStep {n} of {n_steps} "{node.name} (repeat {n_reps} of {node.repeats})')
+            at.logger.info(f'\nStep {n} of {n_steps}: "{node.name}" (repeat {n_reps} of {node.repeats})')
             parset = node.apply(project, parset, savedir, save_intermediate, *args, **kwargs)
             n += 1
 
             if save_intermediate and not isinstance(node, SaveCalibrationNode):
-                output = savedir / f'intermediate_calibration_{n:0{len(str(n_steps))}}_{self.name.replace(" ", "_")}'
+                output = savedir / f'intermediate_calibration_{n:0{len(str(n_steps))}}_{node.name.replace(" ", "_")}'
                 parset.save_calibration(output)
                 at.logger.info(f'Saved intermediate calibration to {output}')
 
-    at.logger.info(f'\nCalibration completed')
+    t = time.process_time()
+    at.logger.info(f'\nCalibration completed. Total time elapsed: {round(t, 2)} seconds ({round(t/60, 2)} minutes)')
 
     if log_output:
         at.stop_logging()
@@ -181,7 +186,7 @@ def validate(self):
         """
         return
 
-    def apply(self, project: at.Project, parset: at.ParameterSet, savedir, *args, **kwargs) -> tuple:
+    def apply(self, project: at.Project, parset: at.ParameterSet, savedir, *args, **kwargs) -> ParameterSet:
         """
         Perform the action associated with this node
         """
@@ -314,7 +319,7 @@ def check_optional_number(key, v, defaults):
             check_optional_number('start_year',v, self.meas_defaults)
             check_optional_number('end_year',v, self.meas_defaults)
 
-    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, quiet=False, compare_results=False, **kwargs) -> tuple:
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, quiet=False, compare_results=False, **kwargs) -> ParameterSet:
 
         step_name = self.name
         attributes = self.attributes
@@ -446,7 +451,7 @@ def validate(self):
         assert 'year' in self, f'Initialisation year must be specified'
         assert sc.isnumber(self['year']), f'Reinitialisation year {self["year"]} must be numeric.'
 
-    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> tuple:
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
         new_settings = sc.dcp(project.settings)
         new_settings.update_time_vector(end=self['year'])
         res = at.run_model(settings=new_settings, framework=project.framework, parset=parset)
@@ -460,7 +465,7 @@ class ClearInitializationNode(BaseNode):
     def __init__(self, instructions, context, name):
         super().__init__(instructions=None, context=context, name=name)
 
-    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> tuple:
+    def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
         parset.initialization = None
         return parset
 
@@ -480,6 +485,6 @@ def __init__(self, instructions, context, name):
     def validate(self):
         assert self['fname'] is not None, 'A "save calibration" node must have a file name explicitly specified'
 
-    def apply(self, project: at.Project, parset: at.ParameterSet, savedir=None, *args, **kwargs) -> tuple:
+    def apply(self, project: at.Project, parset: at.ParameterSet, savedir=None, *args, **kwargs) -> ParameterSet:
         parset.save_calibration(savedir / self['fname'])
         return parset

From 367490259882bfa86e1b31139b541596e6731b4f Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Wed, 5 Jun 2024 16:07:56 +0200
Subject: [PATCH 009/161] Implemented make_constant_parset option for
 reinitialisation node

---
 atomica/yaml_calibration.py | 29 ++++++++++++++++++++++-------
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 5b48d580..ce7e1e51 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -442,19 +442,34 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 class InitializationNode(BaseNode):
     _name = 'set_initialization'
 
-    def __init__(self, instructions, context, name):
-        if not isinstance(instructions, dict):
-            instructions = {'year': instructions}
-        super().__init__(instructions, context, name)
+    def __init__(self, instructions, context, name ):
+        new_instructions = {'constant_parset': False}
+
+        if isinstance(instructions, dict):
+            new_instructions.update(instructions)
+        elif type(instructions) is int:
+            new_instructions.update({'year': instructions})
+        elif isinstance(instructions, (tuple, list)):
+            sc.promotetolist(instructions)
+            new_instructions.update({'year': instructions[0]})
+            if len(instructions) > 1:
+                new_instructions.update({'constant_parset': instructions[1]})
+
+        super().__init__(new_instructions, context, name)
 
     def validate(self):
-        assert 'year' in self, f'Initialisation year must be specified'
-        assert sc.isnumber(self['year']), f'Reinitialisation year {self["year"]} must be numeric.'
+        assert 'year' in self, f'Initialization year must be specified'
+        assert sc.isnumber(self['year']), f'Initialization year {self["year"]} must be numeric.'
+        if 'constant_parset' in self:
+            assert type(self['constant_parset']) == bool, f'Constant parset (optional) {self["constant_parset"]} must be a boolean.'
 
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
+        p2 = sc.dcp(parset)
+        if self.instructions['constant_parset']:
+            p2 = parset.make_constant(year=project.settings.sim_start)
         new_settings = sc.dcp(project.settings)
         new_settings.update_time_vector(end=self['year'])
-        res = at.run_model(settings=new_settings, framework=project.framework, parset=parset)
+        res = at.run_model(settings=new_settings, framework=project.framework, parset=p2)
         parset.set_initialization(res, self['year'])
         return parset
 

From fcae52bcb50af4535a29dbfa04a193091ca9cefc Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 10 Jun 2024 13:54:46 +0200
Subject: [PATCH 010/161] Bug fix: Let max_time update from yaml

Make max_time set in yaml take precedence over max_time in run_calibration argument
---
 atomica/calibration.py      | 2 +-
 atomica/yaml_calibration.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 4d6b920c..f7858ac3 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -260,7 +260,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                     "abstol": 1e-6,
                     "xmin": xmin,
                     "xmax": xmax,
-                    "maxtime": 60 if max_time is None else max_time,
+                    "maxtime": 60 if max_time is None else max_time
                 }
                 optim_args.update(kwargs)
 
diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index ce7e1e51..cb0725b7 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -375,7 +375,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         # Calibration
         if len(adjustables):
 
-            kwargs = sc.mergedicts(self.attributes, kwargs)
+            kwargs = sc.mergedicts(kwargs, self.attributes)
 
             del kwargs['adjustables'] # supplied via the adjustables variable
             del kwargs['measurables'] # supplied via the measurables variable

From b58a5d4c7231e5680b77c82830720745ac3ad703 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 17 Jun 2024 13:46:28 +0200
Subject: [PATCH 011/161] Revert last update

Arguments in calibration function will take precedence over parameters in yaml file
---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index cb0725b7..ce7e1e51 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -375,7 +375,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         # Calibration
         if len(adjustables):
 
-            kwargs = sc.mergedicts(kwargs, self.attributes)
+            kwargs = sc.mergedicts(self.attributes, kwargs)
 
             del kwargs['adjustables'] # supplied via the adjustables variable
             del kwargs['measurables'] # supplied via the measurables variable

From 68553959ec964229c9f857d55d1f5292b2920252 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 17 Jun 2024 16:29:15 +0200
Subject: [PATCH 012/161] Fixed measurables populations not expanding properly

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index ce7e1e51..7d593f46 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -370,7 +370,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
             for pop in pops:
                 d = sc.mergedicts(self.meas_defaults, attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
-                measurables.append((par_name, pop_name, d['weight'], d['metric'], d['start_year'], d['end_year']))
+                measurables.append((par_name, pop, d['weight'], d['metric'], d['start_year'], d['end_year']))
 
         # Calibration
         if len(adjustables):

From 193418e91d12e7a6aff85234e92c6807f4d62f0d Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Sun, 30 Jun 2024 07:06:12 +1000
Subject: [PATCH 013/161] Add missing dependency for deployment

---
 azure-pipelines.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 50fc1d9c..5f41210b 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -115,6 +115,7 @@ jobs:
       inputs:
         versionSpec: '3.12'
     - script: |
+        python -m pip install setuptools
         python -m pip install wheel
         python -m pip install twine
         python setup.py build

From 44c766fb1980de8c0c731a924e1ea05e5aa0e1c6 Mon Sep 17 00:00:00 2001
From: Phillip Luong <iamphillipluong@gmail.com>
Date: Thu, 4 Jul 2024 15:20:52 +1000
Subject: [PATCH 014/161] Implement total population calibration

if the population for a measurable is 'total', then we will use the PlotData object to collect the timeseries for the total value of the measurable, which will then be used to calculate the objective function
---
 atomica/calibration.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index ed827fd6..591d40a3 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -12,6 +12,7 @@
 from .system import logger
 from .parameters import ParameterSet
 import logging
+import atomica
 
 __all__ = ["calibrate"]
 
@@ -43,7 +44,6 @@ def _update_parset(parset, y_factors, pars_to_adjust):
             tokens = par_name.split("_from_")
             par = parset.transfers[tokens[0]][tokens[1]]
             par.y_factor[pop_name] = y_factors[i]
-            raise NotImplementedError
 
 
 def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project):
@@ -66,14 +66,21 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
             continue
         if not target.has_time_data:  # Only use this output quantity if the user entered time-specific data
             continue
-        var = result.model.get_pop(pop_name).get_variable(var_label)
+        if pop_name == 'Total':
+            var = atomica.PlotData(result, outputs=var_label, pops = 'total', project=project)
+        else:
+            var = result.model.get_pop(pop_name).get_variable(var_label)
+
         data_t, data_v = target.get_arrays()
 
         # Interpolate the model outputs onto the data times
         # If there is data outside the range when the model was simulated, don't
         # extrapolate the model outputs
         y = data_v
-        y2 = np.interp(data_t, var[0].t, var[0].vals, left=np.nan, right=np.nan)
+        if pop_name == 'Total':
+            y2 = np.interp(data_t, var.tvals()[0], var.series[0].vals, left=np.nan, right=np.nan)
+        else:
+            y2 = np.interp(data_t, var[0].t, var[0].vals, left=np.nan, right=np.nan)
 
         idx = ~np.isnan(y) & ~np.isnan(y2)
         objective += weight * sum(_calculate_fitscore(y[idx], y2[idx], metric))
@@ -243,7 +250,6 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             tokens = par_name.split("_from_")
             par = args["parset"].transfers[tokens[0]][tokens[1]]
             logger.debug("parset.transfers['{}']['{}'].y_factor['{}']={:.2f}".format(tokens[0], tokens[1], pop_name, par.y_factor[pop_name]))
-            raise NotImplementedError  # Transfers might be handled differently in Atomica
 
     args["parset"].name = "calibrated_" + args["parset"].name
 

From 64721797d7dc042d53b42727c0542a504ef364cf Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 4 Jul 2024 12:52:45 +0200
Subject: [PATCH 015/161] Enable spaces within adjustable/measurable and pop
 labels

Parses '~' in yaml file labels as spaces
---
 atomica/yaml_calibration.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 7d593f46..865043ab 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -252,9 +252,9 @@ def validate(self):
         def process_key(s):
             # Sanitize key name with optional space separating pop name
             if ' ' in s:
-                return tuple([x for x in s.split(' ') if x])
+                return tuple([x.replace('~', ' ') for x in s.split(' ') if x])
             else:
-                return (s.strip(), None)
+                return (s.strip().replace('~', ' '), None)
 
         def process_inputs(inputs, defaults):
             # Process adjustables and measurables, which can be specified in a list representation or nested dict representation

From 6dc0f8dc8ee7d4aefd5a3a81553a7f4425ce51a6 Mon Sep 17 00:00:00 2001
From: Phillip Luong <iamphillipluong@gmail.com>
Date: Fri, 5 Jul 2024 14:25:58 +1000
Subject: [PATCH 016/161] Slight adjustments to tvec

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

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 591d40a3..84f1f86a 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -78,7 +78,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         # extrapolate the model outputs
         y = data_v
         if pop_name == 'Total':
-            y2 = np.interp(data_t, var.tvals()[0], var.series[0].vals, left=np.nan, right=np.nan)
+            y2 = np.interp(data_t, var.series[0].tvec, var.series[0].vals, left=np.nan, right=np.nan)
         else:
             y2 = np.interp(data_t, var[0].t, var[0].vals, left=np.nan, right=np.nan)
 

From 0cd77e105ee9b4965612e33e1a4df1825900004b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 10 Jul 2024 14:17:36 +1000
Subject: [PATCH 017/161] Reduce case sensitivity

---
 atomica/calibration.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 2347221c..7cd6e682 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -34,7 +34,7 @@ def _update_parset(parset, y_factors, pars_to_adjust):
         pop_name = x[1]
 
         if par_name in parset.pars:
-            if pop_name == "all":
+            if pop_name.lower() == "all":
                 par = parset.pars[par_name]
                 par.meta_y_factor = y_factors[i]
             else:
@@ -66,7 +66,8 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
             continue
         if not target.has_time_data:  # Only use this output quantity if the user entered time-specific data
             continue
-        if pop_name == 'Total':
+
+        if pop_name.lower() == 'total':
             var = atomica.PlotData(result, outputs=var_label, pops = 'total', project=project)
         else:
             var = result.model.get_pop(pop_name).get_variable(var_label)
@@ -77,7 +78,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         # If there is data outside the range when the model was simulated, don't
         # extrapolate the model outputs
         y = data_v
-        if pop_name == 'Total':
+        if pop_name.lower() == 'total':
             y2 = np.interp(data_t, var.series[0].tvec, var.series[0].vals, left=np.nan, right=np.nan)
         else:
             y2 = np.interp(data_t, var[0].t, var[0].vals, left=np.nan, right=np.nan)
@@ -179,7 +180,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
         par_name, pop_name, scale_min, scale_max = x
         if par_name in parset.pars:
             par = parset.pars[par_name]
-            if pop_name == "all":
+            if pop_name.lower() == "all":
                 x0.append(par.meta_y_factor)
             else:
                 x0.append(par.y_factor[pop_name])
@@ -244,7 +245,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
         if par_name in parset.pars:
             par = args["parset"].pars[par_name]
 
-            if pop_name == "all":
+            if pop_name.lower() == "all":
                 logger.debug("parset.get_par('{}').meta_y_factor={:.2f}".format(par_name, par.meta_y_factor))
             else:
                 logger.debug("parset.get_par('{}').y_factor['{}']={:.2f}".format(par_name, pop_name, par.y_factor[pop_name]))

From 773dd787a506e884a6bd4e36dbaa29ec0155d26a Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 12 Jul 2024 12:57:02 +1000
Subject: [PATCH 018/161] Update docstring, version and changelog

---
 CHANGELOG.md           | 5 +++++
 atomica/calibration.py | 7 ++++++-
 atomica/version.py     | 4 ++--
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 53b1bd35..9576263c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,11 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.28.6] - 2023-07-12
+
+- Support entering `'total'` as the population name in auto-calibration measurables to calibrate aggregated values across populations in the model to aggregate values entered in the databook under a 'Total' population
+
+
 ## [1.28.5] - 2023-06-28
 
 - Enable automated calibration of transfers and updated documentation to cover this feature
diff --git a/atomica/calibration.py b/atomica/calibration.py
index 7cd6e682..a8066683 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -136,7 +136,12 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                            as the ``pop_name``. For example, to automatically calibrate an aging transfer 'age' from 5-14
                            to 15-64, the tuple would contain ``pars_to_adjust=[('age_from_5-14','15-64',...)]``
     :param output_quantities: list of tuples, (var_label,pop_name,weight,metric), for use in the objective
-                              function. pop_name=None will expand to all pops. pop_name='all' is not supported
+                              function. pop_name=None will expand to all pops. pop_name='all' is not supported. In some cases,
+                              it may be desirable to fit to an aggregated total value across populations. In such cases, the
+                              databook should have an extra row in the TDVE table for a population called "Total". The measurable
+                              can then be given pop_name="Total" which will cause the Atomica model outputs to be aggregated over
+                              all populations, and the aggregate value compared to the "Total" data. The aggregation methods will
+                              be automatically selected depending on units of the quantity (sum for "number" units, average for others).
     :param max_time: If using ASD, the maximum run time
     :param method: 'asd' or 'pso'. If using 'pso' all upper and lower limits must be finite
     :param kwargs: Dictionary of additional arguments to be passed to the optimization function, e.g. stepsize or pinitial
diff --git a/atomica/version.py b/atomica/version.py
index 235b678a..ffdfcfc0 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.28.5"
-versiondate = "2024-06-28"
+version = "1.28.6"
+versiondate = "2024-07-12"
 gitinfo = sc.gitinfo(__file__)

From 75b697a319a22cb0a9c955bbd7049410c8966c02 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 23 Jul 2024 19:39:51 +0200
Subject: [PATCH 019/161] Prevent doubling up of print statements on save

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 865043ab..0bbe672e 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -91,8 +91,8 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
 
             if save_intermediate and not isinstance(node, SaveCalibrationNode):
                 output = savedir / f'intermediate_calibration_{n:0{len(str(n_steps))}}_{node.name.replace(" ", "_")}'
+                at.logger.info(f'Saving intermediate calibration...')
                 parset.save_calibration(output)
-                at.logger.info(f'Saved intermediate calibration to {output}')
 
     t = time.process_time()
     at.logger.info(f'\nCalibration completed. Total time elapsed: {round(t, 2)} seconds ({round(t/60, 2)} minutes)')

From ea5b2c77389c5777bd55adac7e8dd12745161067 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 23 Jul 2024 19:40:32 +0200
Subject: [PATCH 020/161] Prevent doubling up of populations in optimisation
 and print statements

---
 atomica/yaml_calibration.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 0bbe672e..890b7c3c 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -324,10 +324,10 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         step_name = self.name
         attributes = self.attributes
 
-        at.logger.info(f'Calibrating adjustable(s) {[adj[0] for adj in attributes["adjustables"]]} to match measurable(s) {[mea[0] for mea in attributes["measurables"]]}...')
+        at.logger.info(f'Calibrating adjustable(s) {set([adj[0] for adj in attributes["adjustables"]])} to match measurable(s) {set([mea[0] for mea in attributes["measurables"]])}...')
 
         # Expand adjustables
-        adjustables = []
+        adjustables = {}
         par_names = {x[0] for x in attributes['adjustables']}.intersection(x.name for x in parset.all_pars())
         pop_names = {x[1] for x in attributes['adjustables']}.intersection({*parset.pop_names} | {'all', None})
 
@@ -346,8 +346,13 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(self.adj_defaults, attributes['adjustables'].get((par_name, None), None), attributes['adjustables'].get((par_name, pop), None))
-                adjustables.append((par_name, pop, d['lower_bound'], d['upper_bound'], d['initial_value']))
+                d = sc.mergedicts(self.adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
+                adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['initial_value'])
+        # for par_name in attributes['adjustables'].copy():
+        #     # if attributes['adjustables'].get((par_name, None)) is not None:
+        #     attributes['adjustables'].pop((par_name, None), None)
+        adjustables = [(*k, *v) for k,v in adjustables.items()]
+
 
         # Expand measurables
         measurables = []

From c7c6a8b2e2deed9955ba303fbc82084d83d1ed2d Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 23 Jul 2024 19:41:34 +0200
Subject: [PATCH 021/161] Set default value for minval in optim_args

---
 atomica/calibration.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index f7858ac3..53da44f0 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -260,7 +260,8 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                     "abstol": 1e-6,
                     "xmin": xmin,
                     "xmax": xmax,
-                    "maxtime": 60 if max_time is None else max_time
+                    "maxtime": 60 if max_time is None else max_time,
+                    "minval": 0,
                 }
                 optim_args.update(kwargs)
 

From 453295f4c93c52c5d021bcbb27e7ab4e6630c0d4 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 23 Jul 2024 19:43:30 +0200
Subject: [PATCH 022/161] Ability to set verbose from kwargs

---
 atomica/calibration.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 53da44f0..6ba8f700 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -265,11 +265,12 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                 }
                 optim_args.update(kwargs)
 
-                log_level = logger.getEffectiveLevel()
-                if log_level < logging.WARNING:
-                    optim_args["verbose"] = 2
-                else:
-                    optim_args["verbose"] = 0
+                if 'verbose' not in optim_args:
+                    log_level = logger.getEffectiveLevel()
+                    if log_level < logging.WARNING:
+                        optim_args["verbose"] = 2
+                    else:
+                        optim_args["verbose"] = 0
 
                 opt_result = sc.asd(_calculate_objective, x0, args, **optim_args)
                 x1 = opt_result["x"]

From 5f0c05dd74c1a13da3490003977deb575939ef58 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Wed, 24 Jul 2024 14:49:56 +0200
Subject: [PATCH 023/161] Prevent doubling up of measurables populations

---
 atomica/yaml_calibration.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 890b7c3c..bc59e6b9 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -355,7 +355,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
 
         # Expand measurables
-        measurables = []
+        measurables = {}
         par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to support transfer parameters and validate that pars have databook entries in the future
         pop_names = {x[1] for x in attributes['measurables']}.intersection({*parset.pop_names} | {None})
 
@@ -374,8 +374,12 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(self.meas_defaults, attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
-                measurables.append((par_name, pop, d['weight'], d['metric'], d['start_year'], d['end_year']))
+                d = sc.mergedicts(self.meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
+                measurables[(par_name, pop)] = (d['weight'], d['metric'], d['start_year'], d['end_year'])
+        # for par_name in attributes['adjustables'].copy():
+        #     # if attributes['adjustables'].get((par_name, None)) is not None:
+        #     attributes['adjustables'].pop((par_name, None), None)
+        measurables = [(*k, *v) for k,v in measurables.items()]
 
         # Calibration
         if len(adjustables):

From 4779a1ccdff5a7ee9d125c5e3e7c0bd0fcb83484 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Wed, 24 Jul 2024 15:53:13 +0200
Subject: [PATCH 024/161] Refactoring for consistency

-Rename Section to SectionNode
-Consistency in the quotes inside variables (for searchability)
-Docstring formatting
-Delete unused code comments
---
 atomica/yaml_calibration.py | 40 ++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index bc59e6b9..c5e9f0c6 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -45,7 +45,7 @@ def build(instructions=None, context=None, name='calibration'):
     elif name in named_nodes:
         return named_nodes[name](instructions, context, name)
     else:
-        return Section(instructions, context, name)
+        return SectionNode(instructions, context, name)
 
 def run(node, project, parset, savedir=None, save_intermediate=False, log_output:bool=False,*args, **kwargs):
     """
@@ -72,7 +72,7 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
         savedir = Path(savedir)
 
     nodes = list(node.walk()) # Make a flat list of all nodes to execute in order
-    n_steps = len([x for x in nodes if not isinstance(x[1], Section)])
+    n_steps = len([x for x in nodes if not isinstance(x[1], SectionNode)])
     n = 1
 
     if log_output:
@@ -82,7 +82,7 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
 
     for n_reps, node in nodes:
 
-        if isinstance(node, Section):
+        if isinstance(node, SectionNode):
             at.logger.info(f'\nSection "{node.name}" (repeat {n_reps} of {node.repeats})')
         else:
             at.logger.info(f'\nStep {n} of {n_steps}: "{node.name}" (repeat {n_reps} of {node.repeats})')
@@ -192,7 +192,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, savedir, *args, **
         """
         return parset
 
-class Section(BaseNode):
+class SectionNode(BaseNode):
     """
     A section node is a special kind of node, that contains other nodes
     """
@@ -250,21 +250,25 @@ def validate(self):
         """
 
         def process_key(s):
-            # Sanitize key name with optional space separating pop name
+            """
+            Sanitize key name with optional space separating pop name
+            """
             if ' ' in s:
                 return tuple([x.replace('~', ' ') for x in s.split(' ') if x])
             else:
                 return (s.strip().replace('~', ' '), None)
 
         def process_inputs(inputs, defaults):
-            # Process adjustables and measurables, which can be specified in a list representation or nested dict representation
-            # In list representation, the input is a list of lists, where the first item in each list is the quantity (with optional population) and
-            # the remaining items are the supported arguments for the input type, in the order defined by the defaults dictionary.
-            # In a dict representation, the key is the quantity with optional population, and then the value can either be a list (in the order defined by the dictionary)
-            # or a dictionary explicitly naming the inputs.
-            # This function returns a flat dictionary with {(quantity, pop_name):{argument:value}} e.g., {('b_rate','0-5'):{'lower_bound':0.5}}.
-            # In the dict representation, the key can be a comma separated list of quantities with optional values e.g., 'b_rate 0-5, d_rate'. In the list representation,
-            # multiple quantities are not supported (as a comma is already used to separate the arguments)
+            """
+            Process adjustables and measurables, which can be specified in a list representation or nested dict representation
+            In list representation, the input is a list of lists, where the first item in each list is the quantity (with optional population) and
+            the remaining items are the supported arguments for the input type, in the order defined by the defaults dictionary.
+            In a dict representation, the key is the quantity with optional population, and then the value can either be a list (in the order defined by the dictionary)
+            or a dictionary explicitly naming the inputs.
+            This function returns a flat dictionary with {(quantity, pop_name):{argument:value}} e.g., {('b_rate','0-5'):{'lower_bound':0.5}}.
+            In the dict representation, the key can be a comma separated list of quantities with optional values e.g., 'b_rate 0-5, d_rate'. In the list representation,
+            multiple quantities are not supported (as a comma is already used to separate the arguments), but multiple lists (one for each quantity) can be provided.
+            """
             out = {}
 
             if sc.isstring(inputs):
@@ -293,12 +297,12 @@ def process_inputs(inputs, defaults):
         self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)
         self['measurables'] = process_inputs(self['measurables'], self.meas_defaults)
 
-        # Validate adjustables
         def check_optional_number(key, v, defaults):
             if key in v and v[key] is not None:
                 if not sc.isnumber(v[key], isnan=False):
                     raise TypeError(f"Adjustable argument '{key}' needs to be a number or None (defaults to {defaults[key]}). Provided value: {v[key]} ")
 
+        # Validate adjustables
         assert len(self['adjustables']) > 0, f'Cannot calibrate with no adjustables for calibration section {self.name}'
         for (quantity, pop_name), v in self['adjustables'].items():
             assert 'pop_name' not in v, f'Setting the population name through "pop_name: {v["pop_name"]}" is not supported. Instead, the name of the adjustable quantity should include the population name ("{quantity} {v["pop_name"]}")'
@@ -324,7 +328,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         step_name = self.name
         attributes = self.attributes
 
-        at.logger.info(f'Calibrating adjustable(s) {set([adj[0] for adj in attributes["adjustables"]])} to match measurable(s) {set([mea[0] for mea in attributes["measurables"]])}...')
+        at.logger.info(f"Calibrating adjustable(s) {set([adj[0] for adj in attributes['adjustables']])} to match measurable(s) {set([mea[0] for mea in attributes['measurables']])}...")
 
         # Expand adjustables
         adjustables = {}
@@ -348,9 +352,6 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
             for pop in pops:
                 d = sc.mergedicts(self.adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
                 adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['initial_value'])
-        # for par_name in attributes['adjustables'].copy():
-        #     # if attributes['adjustables'].get((par_name, None)) is not None:
-        #     attributes['adjustables'].pop((par_name, None), None)
         adjustables = [(*k, *v) for k,v in adjustables.items()]
 
 
@@ -376,9 +377,6 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
             for pop in pops:
                 d = sc.mergedicts(self.meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
                 measurables[(par_name, pop)] = (d['weight'], d['metric'], d['start_year'], d['end_year'])
-        # for par_name in attributes['adjustables'].copy():
-        #     # if attributes['adjustables'].get((par_name, None)) is not None:
-        #     attributes['adjustables'].pop((par_name, None), None)
         measurables = [(*k, *v) for k,v in measurables.items()]
 
         # Calibration

From 839981ec9bd637b1ccd07befbeca5e4da0c30e75 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:43:30 +1000
Subject: [PATCH 025/161] Enable transfer calibration

---
 atomica/calibration.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 6ba8f700..a17c4565 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -43,7 +43,6 @@ def _update_parset(parset, y_factors, pars_to_adjust):
             tokens = par_name.split("_from_")
             par = parset.transfers[tokens[0]][tokens[1]]
             par.y_factor[pop_name] = y_factors[i]
-            raise NotImplementedError
 
 
 def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project, *args, **kwargs) -> float:
@@ -311,7 +310,6 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             tokens = par_name.split("_from_")
             par = args["parset"].transfers[tokens[0]][tokens[1]]
             logger.debug("parset.transfers['{}']['{}'].y_factor['{}']={:.2f}".format(tokens[0], tokens[1], pop_name, par.y_factor[pop_name]))
-            raise NotImplementedError  # Transfers might be handled differently in Atomica
 
     args["parset"].name = "calibrated_" + args["parset"].name
 

From 08617f7b5e65c6a969d930389668cc0e2d3cf7c8 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:43:57 +1000
Subject: [PATCH 026/161] Support '_from_' transfer naming in
 `ParameterSet.get_par()`

---
 atomica/calibration.py | 33 +++++++++++++--------------------
 atomica/parameters.py  | 12 ++++++++++--
 2 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index a17c4565..19ae89b6 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -20,31 +20,24 @@
 calibration_settings["tolerance"] = 1e-6
 
 
-def _update_parset(parset, y_factors, pars_to_adjust):
-    # Insert updated y-values into the parset
-    # - parset : a ParameterSet object
-    # - y_factors : Array with as many elements as pars_to_adjust
-    # - pars_to_adjust : Array of tuples (par_name,pop_name,...) with special value pop='all' supported to set meta factor
-    #                    Must have as many elements as y_factors. pop=None is not allowed - it must be converted
-    #                    to a full list of pops previously (in perform_autofit)
+def _update_parset(parset, y_factors, pars_to_adjust) -> None:
+    """
 
-    for i, x in enumerate(pars_to_adjust):
-        par_name = x[0]
-        pop_name = x[1]
+    :param parset: a ParameterSet object
+    :param y_factors: Array with as many elements as pars_to_adjust
+    :param pars_to_adjust: Array of tuples (par_name,pop_name,...) with special value pop='all' supported to set meta factor
+                           Must have as many elements as y_factors. pop=None is not allowed - it must be converted
+                           to a full list of pops previously (in perform_autofit)
+    :return: None (parset is modified in-place)
+    """
 
-        if par_name in parset.pars:
-            if pop_name == "all":
-                par = parset.pars[par_name]
-                par.meta_y_factor = y_factors[i]
-            else:
-                parset.pars[par_name].y_factor[pop_name] = y_factors[i]
+    for i, (par_name, pop_name, *_) in enumerate(pars_to_adjust):
+        par = parset.get_par(par_name)
+        if pop_name == "all":
+            par.meta_y_factor = y_factors[i]
         else:
-            # Handle transfers here
-            tokens = par_name.split("_from_")
-            par = parset.transfers[tokens[0]][tokens[1]]
             par.y_factor[pop_name] = y_factors[i]
 
-
 def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project, *args, **kwargs) -> float:
     """
     Run the model for a given set of y-factors and return the objective/goodness-of-fit
diff --git a/atomica/parameters.py b/atomica/parameters.py
index 5f668300..49b177db 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -490,7 +490,10 @@ def get_par(self, name: str, pop: str = None) -> Parameter:
         The parameter values for interactions and transfers are stored keyed by
         the source/from population. Thus, if the quantity name is an interaction
         or transfer, it is also necessary to specify the source population in order
-        to return a :class:`Parameter` instance.
+        to return a :class:`Parameter` instance. However, transfer parameters can
+        also be identified by a parameter name "<transfer_name>_from_<pop>" such
+        that ``ParameterSet.get_par('age','5-14')`` is equivalent to
+        ``ParameterSet.get_par('age_from_5-14')``.
 
         :param name: The code name of a parameter, interaction, or transfer
         :param pop:
@@ -507,7 +510,12 @@ def get_par(self, name: str, pop: str = None) -> Parameter:
             assert not pd.isna(pop), f'"{name}" is an interaction, so the ``pop`` must be specified'
             return self.interactions[name][pop]
         else:
-            raise KeyError(f'Parameter "{name}" not found')
+            for transfer in self.transfers.values():
+                for par in transfer.values():
+                    if par.name == name:
+                        return par
+
+        raise KeyError(f'Parameter "{name}" not found')
 
     def sample(self, constant=True):
         """

From 7dfbecca7e1f309a439d4a442c2ca82cfb9d632f Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:44:15 +1000
Subject: [PATCH 027/161] Clarify warning

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index bc59e6b9..c8775cc4 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -334,7 +334,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         for par_name, pop_name in attributes['adjustables']:
 
             if par_name not in par_names:
-                at.logger.warning(f"Extra YAML adjustable parameter '{par_name}' does not exist in this project's framework and will be ignored")
+                at.logger.warning(f"Extra YAML adjustable parameter '{par_name}' does not exist in this project's framework/parset and will be ignored")
                 continue
             elif pop_name not in pop_names:
                 at.logger.warning(f"Extra YAML adjustable population '{pop_name}' does not exist in this project's databook and will be ignored")

From 9c206786ddb8eae6fc59726a543a20ad38b8473b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:44:50 +1000
Subject: [PATCH 028/161] Check for extra 'population' rows (this is
 specifically used for 'total')

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

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index c8775cc4..ddbce1a4 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -365,8 +365,9 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 at.logger.warning(f"Extra YAML measurable variable '{par_name}' does not exist in this project's framework and will be ignored")
                 continue
             elif pop_name not in pop_names:
-                at.logger.warning(f"Extra YAML measurable population '{pop_name}' does not exist in this project's databook and will be ignored")
-                continue
+                if pop_name not in project.data.tdve[par_name].ts.keys():
+                    at.logger.warning(f"Extra YAML measurable population '{pop_name}' does not exist in this project's databook and will be ignored")
+                    continue
 
             if pop_name is None:
                 pops = parset.pop_names

From d7e05631917a70bdfcd2816366aaa1e11c8b25b1 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:45:29 +1000
Subject: [PATCH 029/161] Support _from_ syntax in yaml calibration

---
 atomica/yaml_calibration.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index ddbce1a4..eb077394 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -405,12 +405,13 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         made_changes = False
 
         for par, pop, *_ in adjustables:
-            if pop == 'all':
-                old = parset.pars[par].meta_y_factor
-                new = new_cal_parset.pars[par].meta_y_factor
+
+            if pop == "all":
+                old = parset.get_par(par).meta_y_factor
+                new = new_cal_parset.get_par(par).meta_y_factor
             else:
-                old = parset.pars[par].y_factor[pop]
-                new = new_cal_parset.pars[par].y_factor[pop]
+                old = parset.get_par(par).y_factor[pop]
+                new = new_cal_parset.get_par(par).y_factor[pop]
 
             if new != old:
                 at.logger.info(f'...adjusted the y-factor for {par} in {pop} from {old} to {new}')

From 4c90bc493e04768d4cc28cb4d5ab8939776fc21c Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:45:36 +1000
Subject: [PATCH 030/161] Remove unused code

---
 atomica/yaml_calibration.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index eb077394..f4e21d42 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -348,9 +348,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
             for pop in pops:
                 d = sc.mergedicts(self.adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
                 adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['initial_value'])
-        # for par_name in attributes['adjustables'].copy():
-        #     # if attributes['adjustables'].get((par_name, None)) is not None:
-        #     attributes['adjustables'].pop((par_name, None), None)
+
         adjustables = [(*k, *v) for k,v in adjustables.items()]
 
 
@@ -377,9 +375,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
             for pop in pops:
                 d = sc.mergedicts(self.meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
                 measurables[(par_name, pop)] = (d['weight'], d['metric'], d['start_year'], d['end_year'])
-        # for par_name in attributes['adjustables'].copy():
-        #     # if attributes['adjustables'].get((par_name, None)) is not None:
-        #     attributes['adjustables'].pop((par_name, None), None)
+
         measurables = [(*k, *v) for k,v in measurables.items()]
 
         # Calibration

From 4d3e6f3be06f1031c953951a9b5e201dab7adad1 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 14:52:05 +1000
Subject: [PATCH 031/161] Update to limit support to 'total' and make it case
 insensitive

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index f4e21d42..bfa69faa 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -363,7 +363,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 at.logger.warning(f"Extra YAML measurable variable '{par_name}' does not exist in this project's framework and will be ignored")
                 continue
             elif pop_name not in pop_names:
-                if pop_name not in project.data.tdve[par_name].ts.keys():
+                if not (pop_name.lower() == 'total' and pop_name.lower() in {x.lower() for x in project.data.tdve[par_name].ts.keys()}):
                     at.logger.warning(f"Extra YAML measurable population '{pop_name}' does not exist in this project's databook and will be ignored")
                     continue
 

From a5e12e4196469968fd68a50d660b1eb1b80745fe Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 15:09:55 +1000
Subject: [PATCH 032/161] Make warning clearer

---
 atomica/data.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/atomica/data.py b/atomica/data.py
index 4422e715..1087055c 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -413,7 +413,9 @@ def from_spreadsheet(spreadsheet, framework):
                                 ts.units = tdve.allowed_units[0]
 
                     if not spec["databook page"]:
-                        logger.warning('A TDVE table for "%s" (%s) was read in and will be used, but the Framework did not mark this quantity as appearing in the databook', tdve.name, code_name)
+                        # Note that if the parameter doesn't have a databook page and the framework is valid, then the parameter must have a function. Therefore,
+                        # if data is also read in, it will not change the simulation outputs and would only be used for calibration/validation
+                        logger.warning('A TDVE table for "%s" (%s) was read in and data will be available for calibration, but the Framework did not mark this quantity as appearing in the databook', tdve.name, code_name)
                     tdve.comment = spec["guidance"]
 
                     if code_name in self.tdve:

From be8a3e146ee7039276aadaab5b85a658191b08a5 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 26 Jul 2024 16:19:34 +1000
Subject: [PATCH 033/161] Possible fix for duplicated initial values

---
 atomica/calibration.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 638b8bb6..8c1e5d97 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -213,15 +213,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
     for i, x in enumerate(pars_to_adjust):
         par_name, pop_name, scale_min, scale_max, initial_value = x
 
-        if par_name in parset.pars:
-            par = parset.pars[par_name]
-            if pop_name.lower() == "all":
-                x0.append(par.meta_y_factor)
-            else:
-                x0.append(par.y_factor[pop_name])
-        else:
-            tokens = par_name.split("_from_")
-            par = parset.transfers[tokens[0]][tokens[1]]
+        par = parset.get_par(par_name)
 
         #if initial_value has not been explicitly set in the tuple: use y_factor in parset
         if initial_value is None:

From 493972f652dd2683494fa7a443a91aa4ce244287 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 19 Aug 2024 22:06:52 +1000
Subject: [PATCH 034/161] Use Atomica's copy of get_sigfigs_necessary()

---
 atomica/yaml_calibration.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index bfa69faa..2929a1c3 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -10,7 +10,6 @@
 import numpy as np
 import yaml
 import time
-from at_tools import get_sigfigs_necessary
 
 __all__ = ['build', 'run']
 
@@ -440,7 +439,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
                 base_rms_error = base_rms_error ** 0.5
                 cal_rms_error = cal_rms_error ** 0.5
-                sf = get_sigfigs_necessary(base_rms_error, cal_rms_error)
+                sf = at.get_sigfigs_necessary(base_rms_error, cal_rms_error)
                 at.logger.info(f'...RMS error for parameter {par_name} has changed from baseline {sc.sigfig(base_rms_error, sf)} to calibrated {sc.sigfig(cal_rms_error, sf)}')
 
         return new_cal_parset

From 07999d000e1e25102d6890534e438755e69c6593 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 19 Aug 2024 22:07:01 +1000
Subject: [PATCH 035/161] Ensure output directory exists

---
 atomica/yaml_calibration.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 2929a1c3..4636dbb7 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -69,6 +69,7 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
         savedir = Path('.')
     else:
         savedir = Path(savedir)
+    savedir.mkdir(exist_ok=True, parents=True)
 
     nodes = list(node.walk()) # Make a flat list of all nodes to execute in order
     n_steps = len([x for x in nodes if not isinstance(x[1], Section)])

From ded6fed1737fcf3f53911f85ab5b678999a9db99 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 20 Aug 2024 16:38:07 +0200
Subject: [PATCH 036/161] Rename start_year, end_year to cal_start, cal_end

---
 atomica/yaml_calibration.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 4c4c4e99..3baeda5f 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -232,8 +232,8 @@ class CalibrationNode(BaseNode):
     meas_defaults = {
         'weight': 1.0,
         'metric': 'fractional',
-        'start_year': -np.inf,
-        'end_year': np.inf,
+        'cal_start': -np.inf,
+        'cal_end': np.inf,
     }
 
     @staticmethod
@@ -320,8 +320,8 @@ def check_optional_number(key, v, defaults):
             assert 'metric' not in v or v['metric'] is None or isinstance(v["metric"], str), f"Measurable metric {v['metric']} needs to be a number or None (defaults to 'fractional')"
 
             check_optional_number('weight',v, self.meas_defaults)
-            check_optional_number('start_year',v, self.meas_defaults)
-            check_optional_number('end_year',v, self.meas_defaults)
+            check_optional_number('cal_start',v, self.meas_defaults)
+            check_optional_number('cal_end',v, self.meas_defaults)
 
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, quiet=False, compare_results=False, **kwargs) -> ParameterSet:
 
@@ -377,7 +377,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
             for pop in pops:
                 d = sc.mergedicts(self.meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
-                measurables[(par_name, pop)] = (d['weight'], d['metric'], d['start_year'], d['end_year'])
+                measurables[(par_name, pop)] = (d['weight'], d['metric'], d['cal_start'], d['cal_end'])
         measurables = [(*k, *v) for k,v in measurables.items()]
 
         # Calibration

From 4fa576552135dae2647158ec3dbdb491df1507a6 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 20 Aug 2024 16:39:05 +0200
Subject: [PATCH 037/161] Rename year to init_year

---
 atomica/yaml_calibration.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 3baeda5f..a9402559 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -457,18 +457,18 @@ def __init__(self, instructions, context, name ):
         if isinstance(instructions, dict):
             new_instructions.update(instructions)
         elif type(instructions) is int:
-            new_instructions.update({'year': instructions})
+            new_instructions.update({'init_year': instructions})
         elif isinstance(instructions, (tuple, list)):
             sc.promotetolist(instructions)
-            new_instructions.update({'year': instructions[0]})
+            new_instructions.update({'init_year': instructions[0]})
             if len(instructions) > 1:
                 new_instructions.update({'constant_parset': instructions[1]})
 
         super().__init__(new_instructions, context, name)
 
     def validate(self):
-        assert 'year' in self, f'Initialization year must be specified'
-        assert sc.isnumber(self['year']), f'Initialization year {self["year"]} must be numeric.'
+        assert 'init_year' in self, f'Initialization year must be specified'
+        assert sc.isnumber(self['init_year']), f'Initialization year {self["init_year"]} must be numeric.'
         if 'constant_parset' in self:
             assert type(self['constant_parset']) == bool, f'Constant parset (optional) {self["constant_parset"]} must be a boolean.'
 
@@ -477,9 +477,9 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **k
         if self.instructions['constant_parset']:
             p2 = parset.make_constant(year=project.settings.sim_start)
         new_settings = sc.dcp(project.settings)
-        new_settings.update_time_vector(end=self['year'])
+        new_settings.update_time_vector(end=self['init_year'])
         res = at.run_model(settings=new_settings, framework=project.framework, parset=p2)
-        parset.set_initialization(res, self['year'])
+        parset.set_initialization(res, self['init_year'])
         return parset
 
 

From b3649b7c94672f4e5ada3e5ba3c937303900e9ad Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 20 Aug 2024 16:45:34 +0200
Subject: [PATCH 038/161] Enable user to set the constant parset year

Enable constant parset year setting from inside initialization node, and also from inherited context
---
 atomica/yaml_calibration.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index a9402559..213cf9e6 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -382,7 +382,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
         # Calibration
         if len(adjustables):
-
+            # note: attributes = instructions + context
             kwargs = sc.mergedicts(self.attributes, kwargs)
 
             del kwargs['adjustables'] # supplied via the adjustables variable
@@ -452,7 +452,7 @@ class InitializationNode(BaseNode):
     _name = 'set_initialization'
 
     def __init__(self, instructions, context, name ):
-        new_instructions = {'constant_parset': False}
+        new_instructions = {}
 
         if isinstance(instructions, dict):
             new_instructions.update(instructions)
@@ -470,12 +470,15 @@ def validate(self):
         assert 'init_year' in self, f'Initialization year must be specified'
         assert sc.isnumber(self['init_year']), f'Initialization year {self["init_year"]} must be numeric.'
         if 'constant_parset' in self:
-            assert type(self['constant_parset']) == bool, f'Constant parset (optional) {self["constant_parset"]} must be a boolean.'
+            assert isinstance(self['constant_parset'], int), f'Constant parset (optional) {self["constant_parset"]} must be numeric (boolean, or int to specify constant parset year).'
 
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
         p2 = sc.dcp(parset)
-        if self.instructions['constant_parset']:
-            p2 = parset.make_constant(year=project.settings.sim_start)
+        if 'constant_parset' in self:
+            if self['constant_parset'] == True:
+                p2 = parset.make_constant(year=project.settings.sim_start)
+            elif type(self['constant_parset']) == int: #constant parset year was provided
+                p2 = parset.make_constant(year=self['constant_parset'])
         new_settings = sc.dcp(project.settings)
         new_settings.update_time_vector(end=self['init_year'])
         res = at.run_model(settings=new_settings, framework=project.framework, parset=p2)

From 0b96e1461a4c7931b3123f0773554264e6d30777 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 20 Aug 2024 16:48:04 +0200
Subject: [PATCH 039/161] Yaml calibration testing

Initial testing step
---
 tests/test_yaml.py                         | 83 ++++++++++++++++++++++
 tests/yaml_tests/config_stepsize_test.yaml | 34 +++++++++
 2 files changed, 117 insertions(+)
 create mode 100644 tests/test_yaml.py
 create mode 100644 tests/yaml_tests/config_stepsize_test.yaml

diff --git a/tests/test_yaml.py b/tests/test_yaml.py
new file mode 100644
index 00000000..b3346ca2
--- /dev/null
+++ b/tests/test_yaml.py
@@ -0,0 +1,83 @@
+# import unittest
+import atomica as at
+import os
+import pytest
+import numpy as np
+import sciris as sc
+import at_tools as att
+
+# class MyTestCase(unittest.TestCase):
+#     def test_something(self):
+#         self.assertEqual(True, False)  # add assertion here
+#
+#
+# if __name__ == '__main__':
+#     unittest.main()
+
+
+# Basic minimum working product test to verify all yaml features run
+
+testdir = at.parent_dir()
+tmpdir = testdir / 'temp'
+yaml_dir = 'yaml_tests'
+
+# List yaml files in directory
+yaml_files = list()
+for f in os.listdir(testdir/yaml_dir):
+    yaml_files.append(f)
+
+
+def run_auto_calibration(proj):
+    """Run an automatic calibration
+
+    :param proj: A Project object
+    :return:
+    """
+
+    proj.calibrate(max_time=10, new_name="auto")
+
+    return
+
+
+# Testing optimizations and calibrations could be expensive
+# Using the parametrize decorator means Pytest will treat
+# each function call as a separate test, which can be run in parallel
+# simply by adding `pytest -n 4` for instance. Or via tox
+# `tox -- -o -n 4` (because the default config is to use n=2 for TravisCI)
+@pytest.mark.parametrize("yaml_fname", yaml_files)
+def test_yaml_calibration(yaml_fname):
+
+    #set these once outside test itself?
+    fw_ver = '2_8_1'
+    db_ver = '2_8'
+    country = 'PAK'
+    dis = 'typh'
+
+    F = at.ProjectFramework(f"framework_bivalent_combined_v{fw_ver}.xlsx")
+    D = at.ProjectData.from_spreadsheet(f"databook_bivalent_{country}_v{db_ver}.xlsx", framework=F)
+    P = at.Project(framework=F, databook=D, do_run=False)
+    P.settings.update_time_vector(start=2000, end=2050, dt=1/52)
+    cal = P.make_parset()
+
+    print(f"\n\nTESTING yaml config {yaml_fname}")
+
+    #import yaml config
+    config_file_path = f'{yaml_dir}/{yaml_fname}'
+    config = att.ProjectConfig.from_yaml(config_file_path) #TODO change this to not rely on atomica tools?
+    #or can import in all the diff ways - make function
+
+    #run calibration with yaml instructions
+    newcal = config.run_calibration(P, parset=cal, quiet=False, savedir=tmpdir, save_intermediate=False, verbose=0, max_time=1)
+    #also run in several diff ways? Or have a separate test for that?
+
+    # Test saving the calibration
+    # save()
+
+    print("Test complete")
+
+
+if __name__ == "__main__":
+
+    np.seterr(all="raise")
+    for f in yaml_files:
+        test_yaml_calibration(f)
diff --git a/tests/yaml_tests/config_stepsize_test.yaml b/tests/yaml_tests/config_stepsize_test.yaml
new file mode 100644
index 00000000..84dc1a56
--- /dev/null
+++ b/tests/yaml_tests/config_stepsize_test.yaml
@@ -0,0 +1,34 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    time_period : [2019,2040]
+    max_time: 1
+
+    Population calibration:
+      max_time: 1
+
+      round 1:
+        stepsize: 0.5
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          stepsize: 0.5
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+#      round 3:
+#        match population sizes 3:
+#          adjustables:
+#            stepsize: 0.5
+#            b_rate,mig_rate: [ 0.1,10 ]
+#          measurables: [ alive ]

From edebd861826b038865bfe36466bad0fb24a34a96 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 21 Aug 2024 16:09:42 +1000
Subject: [PATCH 040/161] Enable adding a custom provenance to default values
 that will populate new databooks

---
 atomica/data.py                               |  12 +++----
 atomica/framework.py                          |  31 ++++++++++++++++--
 .../library/framework_template_advanced.xlsx  | Bin 59446 -> 60662 bytes
 atomica/system.py                             |   2 ++
 4 files changed, 36 insertions(+), 9 deletions(-)

diff --git a/atomica/data.py b/atomica/data.py
index 1087055c..18535a8e 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -22,8 +22,6 @@
 import pandas as pd
 import itertools
 
-_DEFAULT_PROVENANCE = "Framework-supplied default"
-
 __all__ = ["InvalidDatabook", "ProjectData"]
 
 
@@ -303,7 +301,7 @@ class instance (e.g. if creating a new databook).
                         ts = TimeSeries(units=interpop.allowed_units[0])
                         ts.insert(None, spec["default value"])
                         interpop.ts[(from_pop, to_pop)] = ts
-                        interpop.ts_attributes["Provenance"][(from_pop, to_pop)] = _DEFAULT_PROVENANCE
+                        interpop.ts_attributes["Provenance"][(from_pop, to_pop)] = spec["provenance"] if "provenance" in spec else FS.DEFAULT_PROVENANCE
 
         # Finally, insert parameter and characteristic default values
         for df in [framework.comps, framework.characs, framework.pars]:
@@ -315,7 +313,7 @@ class instance (e.g. if creating a new databook).
                     tdve = data.tdve[spec.name]
                     for key, ts in tdve.ts.items():
                         ts.insert(None, spec["default value"])
-                        tdve.ts_attributes["Provenance"][key] = _DEFAULT_PROVENANCE
+                        tdve.ts_attributes["Provenance"][key] = spec["provenance"] if "provenance" in spec else FS.DEFAULT_PROVENANCE
 
         return data
 
@@ -491,8 +489,10 @@ def validate(self, framework) -> bool:
                             for pop in self.pops.keys():
                                 self.tdve[spec_name].ts[pop] = TimeSeries(assumption=spec["default value"], units=units)
                             tdve_page = framework.sheets["databook pages"][0][framework.sheets["databook pages"][0]["datasheet code name"] == spec["databook page"]]["datasheet title"].values[0]
-                            if tdve_page in self.tdve_pages:
-                                self.tdve_pages[tdve_page].append(spec_name)
+                            for existing in self.tdve_pages.keys():
+                                if existing.lower() == tdve_page.lower():
+                                    self.tdve_pages[existing].append(spec_name)
+                                    break
                             else:
                                 self.tdve_pages[tdve_page] = [spec_name]
                     else:
diff --git a/atomica/framework.py b/atomica/framework.py
index 7e686f49..7028bcd0 100644
--- a/atomica/framework.py
+++ b/atomica/framework.py
@@ -627,7 +627,17 @@ def _sanitize_compartments(self) -> None:
             self.sheets["compartments"] = [pd.DataFrame(columns=["code name", "display name"])]
 
         required_columns = ["display name"]
-        defaults = {"is sink": "n", "is source": "n", "is junction": "n", "databook page": None, "default value": None, "databook order": None, "guidance": None, "population type": None}  # Default is for it to be randomly ordered if the databook page is not None
+        defaults = {
+            "is sink": "n",
+            "is source": "n",
+            "is junction": "n",
+            "databook page": None,
+            "default value": None,
+            "population type": None,
+            "databook order": None,  # Default is for it to be randomly ordered if the databook page is not None
+            "guidance": None,
+            "provenance": FS.DEFAULT_PROVENANCE,
+        }
         valid_content = {
             "display name": None,  # Valid content being `None` means that it just cannot be empty
             "is sink": {"y", "n"},
@@ -707,7 +717,16 @@ def _sanitize_characteristics(self) -> None:
             self.sheets["characteristics"] = [pd.DataFrame(columns=["code name", "display name"])]
 
         required_columns = ["display name"]
-        defaults = {"components": None, "denominator": None, "default value": None, "databook page": None, "databook order": None, "guidance": None, "population type": None}
+        defaults = {
+            "components": None,
+            "denominator": None,
+            "default value": None,
+            "databook page": None,
+            "databook order": None,
+            "guidance": None,
+            "population type": None,
+            "provenance": FS.DEFAULT_PROVENANCE,
+        }
         valid_content = {
             "display name": None,
             "components": None,
@@ -801,7 +820,12 @@ def _sanitize_interactions(self) -> None:
             self.sheets["interactions"] = [pd.DataFrame(columns=["code name", "display name", "to population type", "from population type"])]
 
         required_columns = ["display name"]
-        defaults = {"default value": None, "from population type": None, "to population type": None}
+        defaults = {
+            "default value": None,
+            "from population type": None,
+            "to population type": None,
+            "provenance": FS.DEFAULT_PROVENANCE,
+        }
         valid_content = {
             "display name": None,
         }
@@ -843,6 +867,7 @@ def _sanitize_parameters(self) -> None:
             "population type": None,
             "is derivative": "n",
             "timed": "n",
+            "provenance": FS.DEFAULT_PROVENANCE,
         }
         valid_content = {
             "display name": None,
diff --git a/atomica/library/framework_template_advanced.xlsx b/atomica/library/framework_template_advanced.xlsx
index 639bfb54bddd519144064c58098eac526d575247..73cc42986d11480ba79cc3ed11291d9a82790ed6 100644
GIT binary patch
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zb_IgM5pzo<d*J|-$nZ)@<e}%l?V)!o5a?Y@<=>9nVs9PAt$_iJWeAFT!fl?2WeCuW
zYzLnbZ+DxmaDZ!MwNZ2vA_Lg|FQq2|-)0{HpSKB-k!}!_++;vtn+}-Wf}pgg0;gMa
z(7TAY{~dGszl<QzaT;KX1pXeBx&y+BfY?ns;Bo_kqErKrBRBu;8><27n*r$A0t4TW
z%>71SdV>yn*QN5e!_6i@9cjb|$&z;isT*`a*E$3xvKRQZP6xex$3Wi9B9QkMqPuHA
zZW#>$)Eo5B|2!X&&F7!Nv;RtPKp$!B-xo(XetU5zn_R4<6aN}&<NIei@=HZ@<oyW*
Q;zj<b?t?%m)Bo)K5A4Y;3;+NC

diff --git a/atomica/system.py b/atomica/system.py
index c72ab22a..67816a43 100644
--- a/atomica/system.py
+++ b/atomica/system.py
@@ -89,3 +89,5 @@ class FrameworkSettings:
     RESERVED_KEYWORDS += supported_functions.keys()
 
     RESERVED_SYMBOLS = set(":,;/+-*'\" @")  # A code_name in the framework (for characs, comps, pars) cannot contain any of these characters
+
+    DEFAULT_PROVENANCE = "Framework-supplied default"
\ No newline at end of file

From 54e09bd697847f03fabe5b66716b57a2e481a0e7 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 26 Aug 2024 12:27:52 +0200
Subject: [PATCH 041/161] Add yaml tests

---
 tests/yaml_tests/config_caltime_test.yaml  | 39 ++++++++++++++++++++++
 tests/yaml_tests/config_maxtime_test.yaml  | 36 ++++++++++++++++++++
 tests/yaml_tests/config_simtime_test.yaml  | 37 ++++++++++++++++++++
 tests/yaml_tests/config_stepsize_test.yaml |  4 +--
 4 files changed, 113 insertions(+), 3 deletions(-)
 create mode 100644 tests/yaml_tests/config_caltime_test.yaml
 create mode 100644 tests/yaml_tests/config_maxtime_test.yaml
 create mode 100644 tests/yaml_tests/config_simtime_test.yaml

diff --git a/tests/yaml_tests/config_caltime_test.yaml b/tests/yaml_tests/config_caltime_test.yaml
new file mode 100644
index 00000000..ea61d264
--- /dev/null
+++ b/tests/yaml_tests/config_caltime_test.yaml
@@ -0,0 +1,39 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+cal_start: 2000
+cal_end: 2040
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    cal_start: 2000
+    cal_end: 2040
+
+    Population calibration:
+
+      round 1:
+        cal_start: 2000
+        cal_end: 2040
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          cal_start: 2000 #is this meant to work?
+          cal_end: 2040
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 3:
+        match population sizes 3:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables:
+            alive:
+              cal_start: 2000
+              cal_end: 2040
diff --git a/tests/yaml_tests/config_maxtime_test.yaml b/tests/yaml_tests/config_maxtime_test.yaml
new file mode 100644
index 00000000..16f1cc8f
--- /dev/null
+++ b/tests/yaml_tests/config_maxtime_test.yaml
@@ -0,0 +1,36 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+cal_start: 2000
+cal_end: 2040
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    max_time: 1
+
+    Population calibration:
+
+      round 1:
+        max_time: 1
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          max_time: 1
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+#      round 3:
+#        match population sizes 3:
+#          adjustables:
+#            b_rate,mig_rate: [ 0.1,10 ]
+#            b_rate:
+#              cal_start: 2000 #is this meant to work?
+#              cal_end: 2040
+#          measurables: [ alive ]
diff --git a/tests/yaml_tests/config_simtime_test.yaml b/tests/yaml_tests/config_simtime_test.yaml
new file mode 100644
index 00000000..34a372ee
--- /dev/null
+++ b/tests/yaml_tests/config_simtime_test.yaml
@@ -0,0 +1,37 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+sim_start: 2000
+sim_end: 2040
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    sim_start: 2000
+    sim_end: 2040
+
+    Population calibration:
+
+      round 1:
+        sim_start: 2000
+        sim_end: 2040
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          sim_start: 2000 #is this meant to work?
+          sim_end: 2040
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+#      round 3:
+#        match population sizes 3:
+#          adjustables:
+#            stepsize: 0.5
+#            b_rate,mig_rate: [ 0.1,10 ]
+#          measurables: [ alive ]
diff --git a/tests/yaml_tests/config_stepsize_test.yaml b/tests/yaml_tests/config_stepsize_test.yaml
index 84dc1a56..40a1f91b 100644
--- a/tests/yaml_tests/config_stepsize_test.yaml
+++ b/tests/yaml_tests/config_stepsize_test.yaml
@@ -6,11 +6,9 @@ sim_dt: 0.019
   #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
   #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
 calibration:
-    time_period : [2019,2040]
-    max_time: 1
+    stepsize: 1
 
     Population calibration:
-      max_time: 1
 
       round 1:
         stepsize: 0.5

From 02f7e21f1c3b9a25202eecd8cd9ceb43bb2972e0 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 27 Aug 2024 17:19:30 +0200
Subject: [PATCH 042/161] Add yaml tests

---
 tests/yaml_tests/config_bounds_test.yaml      | 13 +++++
 tests/yaml_tests/config_cal_formats.yaml      | 47 +++++++++++++++++++
 .../config_nonexistent_setting_test.yaml      | 31 ++++++++++++
 3 files changed, 91 insertions(+)
 create mode 100644 tests/yaml_tests/config_bounds_test.yaml
 create mode 100644 tests/yaml_tests/config_cal_formats.yaml
 create mode 100644 tests/yaml_tests/config_nonexistent_setting_test.yaml

diff --git a/tests/yaml_tests/config_bounds_test.yaml b/tests/yaml_tests/config_bounds_test.yaml
new file mode 100644
index 00000000..855078f5
--- /dev/null
+++ b/tests/yaml_tests/config_bounds_test.yaml
@@ -0,0 +1,13 @@
+
+calibration:
+    pop cal:
+
+      match population sizes:
+        adjustables:
+          b_rate, mig_rate: [ 0.1, 10 ]
+        measurables:  alive
+
+      turn off epi y-factors:
+        adjustables:
+          typ_active_inf, typ_car, par_active_inf, par_car: [0, 0] #set  y-factor w/o going through cal algorithm
+        measurables:  deaths
\ No newline at end of file
diff --git a/tests/yaml_tests/config_cal_formats.yaml b/tests/yaml_tests/config_cal_formats.yaml
new file mode 100644
index 00000000..4151f3fe
--- /dev/null
+++ b/tests/yaml_tests/config_cal_formats.yaml
@@ -0,0 +1,47 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+
+    Population calibration:
+
+        #dict format
+        match population sizes 1:
+          adjustables:
+            b_rate:
+              lower_bound: 0.1
+              upper_bound: 10
+            mig_rate:
+              starting_y_factor: 1.2
+          measurables:
+            alive:
+              metric: fractional
+
+        #combined format
+        match population sizes 2:
+          version 1:
+            adjustables:
+              b_rate: [ 0.1,10 ]
+              mig_rate: [ 0.1,10 ]
+            measurables:
+              alive: [1.0]
+
+          version 2:
+            adjustables:
+              b_rate, mig_rate: [ 0.1,10 ]
+            measurables:
+              alive: [1.0]
+
+        #list format
+        match population sizes 3:
+          adjustables: [[b_rate, 0.1, 10], [mig_rate, 0.1, 10]]
+          measurables: [alive]
+
+         #string format
+        match population sizes 4:
+          adjustables: [b_rate, mig_rate]
+          measurables: alive
diff --git a/tests/yaml_tests/config_nonexistent_setting_test.yaml b/tests/yaml_tests/config_nonexistent_setting_test.yaml
new file mode 100644
index 00000000..00382e70
--- /dev/null
+++ b/tests/yaml_tests/config_nonexistent_setting_test.yaml
@@ -0,0 +1,31 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    strawberry: 2
+    Population calibration:
+
+      round 1:
+        raddish: 3
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          kiwi: 4
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+#      round 3:
+#        match population sizes 3:
+#          adjustables:
+#            stepsize: 0.5
+#            b_rate,mig_rate: [ 0.1,10 ]
+#          measurables: [ alive ]

From d5457305be4fbdddfe62499adf101a546149a0d8 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 27 Aug 2024 17:21:07 +0200
Subject: [PATCH 043/161] Add repeats test, update simtime test

---
 tests/yaml_tests/config_repeats_test.yaml | 36 +++++++++++++++++++++++
 tests/yaml_tests/config_simtime_test.yaml |  7 -----
 2 files changed, 36 insertions(+), 7 deletions(-)
 create mode 100644 tests/yaml_tests/config_repeats_test.yaml

diff --git a/tests/yaml_tests/config_repeats_test.yaml b/tests/yaml_tests/config_repeats_test.yaml
new file mode 100644
index 00000000..76f59b5e
--- /dev/null
+++ b/tests/yaml_tests/config_repeats_test.yaml
@@ -0,0 +1,36 @@
+---
+#Timestep for the model
+sim_dt: 0.019
+cal_start: 2000
+cal_end: 2040
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    repeats: 1
+
+    Population calibration:
+      repeats: 1
+      round 1:
+        repeats: 1
+        match population sizes 1:
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+      round 2:
+        match population sizes 2:
+          repeats: 1 #should error?
+          adjustables:
+            b_rate,mig_rate: [ 0.1,10 ]
+          measurables: [alive]
+
+#      round 3:
+#        match population sizes 3:
+#          adjustables:
+#            b_rate,mig_rate: [ 0.1,10 ]
+#            b_rate:
+#              cal_start: 2000 #is this meant to work?
+#              cal_end: 2040
+#          measurables: [ alive ]
diff --git a/tests/yaml_tests/config_simtime_test.yaml b/tests/yaml_tests/config_simtime_test.yaml
index 34a372ee..a3f13b1c 100644
--- a/tests/yaml_tests/config_simtime_test.yaml
+++ b/tests/yaml_tests/config_simtime_test.yaml
@@ -28,10 +28,3 @@ calibration:
           adjustables:
             b_rate,mig_rate: [ 0.1,10 ]
           measurables: [alive]
-
-#      round 3:
-#        match population sizes 3:
-#          adjustables:
-#            stepsize: 0.5
-#            b_rate,mig_rate: [ 0.1,10 ]
-#          measurables: [ alive ]

From dfd04a5f3767d5c26864a910f024e18d69903e81 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 27 Aug 2024 17:54:36 +0200
Subject: [PATCH 044/161] Enable calibrating transfers

---
 atomica/yaml_calibration.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 213cf9e6..8cd3d175 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -251,10 +251,12 @@ def validate(self):
 
         def process_key(s):
             """
-            Sanitize key name with optional space separating pop name
+            Sanitize key name with optional commas or spaces separating pop name/s from par name
             """
-            if ' ' in s:
-                return tuple([x.replace('~', ' ') for x in s.split(' ') if x])
+            if ',' in s:
+                return tuple([x.strip().replace('~', ' ') for x in s.split(',') if x])
+            elif ' ' in s:
+                return tuple([x.strip().replace('~', ' ') for x in s.split(' ') if x])
             else:
                 return (s.strip().replace('~', ' '), None)
 

From 2e0eb357a509de4fd9eb1e8c9b44589073de19e0 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 29 Aug 2024 12:40:21 +1000
Subject: [PATCH 045/161] Improve float formatting

---
 atomica/plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 9529ab8d..819dc7ce 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -590,7 +590,7 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             if sc.isstring(t_bins) and t_bins == "all":
                 s.t_labels = ["All"]
             else:
-                s.t_labels = ["%d-%d" % (low, high) for low, high in zip(lower, upper)]
+                s.t_labels = [f"{np.format_float_positional(low,trim='-')}-{np.format_float_positional(high,trim='-')}" for low, high in zip(lower, upper)]
 
         return self
 

From 6a2c09719e571917288f89e21b20a2ea9c4bb95b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 29 Aug 2024 12:40:43 +1000
Subject: [PATCH 046/161] Add validation of time aggregation type

---
 atomica/plotting.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 819dc7ce..f2383a4b 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -203,6 +203,7 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
 
         assert output_aggregation in [None, "sum", "average", "weighted"]
         assert pop_aggregation in [None, "sum", "average", "weighted"]
+        assert time_aggregation in [None, "integrate", "average"]
 
         # First, get all of the pops and outputs requested by flattening the lists
         pops_required = _extract_labels(pops)

From 98d010bd3f39c9f626a86ea3f6a558d1f8ac9564 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 29 Aug 2024 12:40:52 +1000
Subject: [PATCH 047/161] Fix time aggregation validation

---
 atomica/plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index f2383a4b..1aa1708f 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -307,7 +307,7 @@ def placeholder_pop():
                     displayed_annualization_warning = False
                     for dep_label in dep_labels:
                         vars = pop.get_variable(dep_label)
-                        if t_bins is not None and (isinstance(vars[0], Link) or isinstance(vars[0], Parameter)) and time_aggregation == "sum" and not displayed_annualization_warning:
+                        if t_bins is not None and (isinstance(vars[0], Link) or isinstance(vars[0], Parameter)) and time_aggregation == "integrate" and not displayed_annualization_warning:
                             raise Exception("Function includes Parameter/Link so annualized rates are being used. Aggregation should therefore use 'average' rather than 'sum'.")
                         deps[dep_label] = vars
                     par._fcn = fcn

From 44627afcc061f4262a3e4a86e96aab0da4c4e60f Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 29 Aug 2024 12:41:03 +1000
Subject: [PATCH 048/161] Improve comment

---
 atomica/excel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/excel.py b/atomica/excel.py
index 2a0119ce..e864fde5 100644
--- a/atomica/excel.py
+++ b/atomica/excel.py
@@ -403,7 +403,7 @@ def __init__(self, code_name: str, full_name: str, tvec: np.array, from_pops: li
         self.ts = ts if ts is not None else sc.odict()
 
         self.attributes = {}  #: Attributes associated with the table
-        self.ts_attributes = {}  #: Attributes associated with each TimeSeries row
+        self.ts_attributes = {}  #: Attributes associated with each TimeSeries row. This is a dict-of-dicts with `ts_attributes[attribute]={pop:value}` e.g., ts_attributes["Provenance"] = {'popA': 'value', 'popB': 'value'}
         self.ts_attributes["Provenance"] = {}  # Include provenance attribute by default
 
         self.assumption_heading = "Constant"  #: Heading to use for assumption column

From f3faa269e449e133c563bb5160ef0d1ba54e7853 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 29 Aug 2024 17:48:00 +0200
Subject: [PATCH 049/161] Process calibration inputs with parentheses

Adjustables/measurables and populations can now be differentiated in the yaml file by separating the par_name from the pop_name/s with commas, and placing them in parentheses.

Examples:
(b_rate, 0-4): ...
(aging, 0-4, 5-14): ...  --> transfer

The previous notation is still supported:
b_rate 0-4: ...
---
 atomica/yaml_calibration.py | 45 +++++++++++++++++++++++++++++++------
 1 file changed, 38 insertions(+), 7 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 8cd3d175..97267bde 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -10,6 +10,7 @@
 import numpy as np
 import yaml
 import time
+import re
 
 __all__ = ['build', 'run']
 
@@ -249,16 +250,30 @@ def validate(self):
         Pre-parse calibration inputs
         """
 
+        def separate_keys(s):
+            """
+            Separate inputs that kave been defined together as one key in the YAML file
+            """
+            s1 = re.findall(r'\(.*?\)', s)
+            s2 = s
+            for x in s1:
+                s2 = s2.replace(x, '')
+            all_strings = [x for x in s1 if x]
+            s2 = re.sub(r'(,\s)+', ', ', s2).strip(', ').split(',')
+            if s2 != ['']:
+                all_strings += s2
+            return all_strings
+
         def process_key(s):
             """
             Sanitize key name with optional commas or spaces separating pop name/s from par name
             """
-            if ',' in s:
-                return tuple([x.strip().replace('~', ' ') for x in s.split(',') if x])
+            if '(' and ')' in s:
+                return tuple([x.strip() for x in s.strip('() ').split(',') if x])
             elif ' ' in s:
-                return tuple([x.strip().replace('~', ' ') for x in s.split(' ') if x])
+                return tuple([x.strip() for x in s.split(' ') if x])
             else:
-                return (s.strip().replace('~', ' '), None)
+                return (s.strip(), None)
 
         def process_inputs(inputs, defaults):
             """
@@ -281,19 +296,35 @@ def process_inputs(inputs, defaults):
             if isinstance(inputs, (tuple, list)):
                 for l in inputs:
                     l = sc.promotetolist(l)
+                    #TODO enable and test transfers in full list format
                     key, pop_name = process_key(l[0].strip())
                     d = self.parse_list(l[1:], defaults)
                     out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+
             elif isinstance(inputs, dict):
                 for keys, v in inputs.items():
-                    for key in keys.split(','):
-                        key, pop_name = process_key(key.strip())
+
+                    separated_keys = separate_keys(keys)
+                    for key in separated_keys:
+                        #separate par name from pop name/s
+                        keyspops = process_key(key.strip())
+                        if len(keyspops) == 2:
+                            key, pop_name = keyspops
+                        else:
+                            assert len(keyspops) == 3, f'Number of populations must be 1, 2 or None)'
+                            key, pop_name, pop_name2 = keyspops
+
+                        #process values
                         if isinstance(v, (tuple, list)):
                             d = self.parse_list(v, defaults)
                         else:
                             d = v.copy()
-                        out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
 
+                        #add keys and values to outputs dict
+                        if len(keyspops) == 2:
+                            out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+                        else:
+                            out[key, pop_name, pop_name2] = sc.mergedicts(out.get((key, pop_name), {}), d)
             return out
 
         self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)

From f903c2794615f1a78201bd0ac2ec397f5a2437cd Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 30 Aug 2024 09:46:27 +1000
Subject: [PATCH 050/161] Don't create databook spreadsheets unnecessarily

---
 CHANGELOG.md       | 9 ++++++++-
 atomica/project.py | 2 +-
 atomica/version.py | 4 ++--
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9576263c..935876aa 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,11 +2,18 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.28.7] - 2023-08-12
+
+- `Project.load_databook()` will no longer populate `Project.databook` when a `ProjectData` instance is supplied rather than a spreadsheet. The intention of `Project.databook` as opposed to `Project.data.to_spreadsheet()` is that the original databook may contain comments or other content that is not preserved when the databook is loaded into a `ProjectData` instance. Therefore, `Project.databook` serves as a record of the original inputs. However, in previous versions of Atomica, if a `ProjectData` instance was provided rather than a spreadsheet, `ProjectData.to_spreadsheet()` would be used to populate `Project.databook`. For large databooks, this can be computationally expensive and particularly affect the use case of passing in a preloaded databook to improve performance. Since the conversion of the `ProjectData` to a spreadsheet upon loading offers no functional difference to creating the spreadsheet from `Project.data` when required, Atomica no longer performs this conversion upfront.
+
+*Backwards-compatibility notes*
+
+- If accessing `Project.databook`, in some cases this may now be `None` rather than an `sc.Spreadsheet()`. If that occurs, `Project.data.to_spreadsheet()` should be used to produce an equivalent spreadsheet. 
+
 ## [1.28.6] - 2023-07-12
 
 - Support entering `'total'` as the population name in auto-calibration measurables to calibrate aggregated values across populations in the model to aggregate values entered in the databook under a 'Total' population
 
-
 ## [1.28.5] - 2023-06-28
 
 - Enable automated calibration of transfers and updated documentation to cover this feature
diff --git a/atomica/project.py b/atomica/project.py
index e6881957..5049533e 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -260,7 +260,7 @@ def load_databook(self, databook_path, make_default_parset: bool = True, do_run:
         if isinstance(databook_path, ProjectData):
             # If the user passed in a ProjectData instance, use it directly
             data = databook_path
-            databook = data.to_spreadsheet()
+            databook = None
         else:
             if isinstance(databook_path, sc.Spreadsheet):
                 databook = databook_path
diff --git a/atomica/version.py b/atomica/version.py
index ffdfcfc0..9955dee6 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.28.6"
-versiondate = "2024-07-12"
+version = "1.28.7"
+versiondate = "2024-08-30"
 gitinfo = sc.gitinfo(__file__)

From 837203ddc0029cf5f302ab986eb209531fc6fe25 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 30 Aug 2024 15:11:18 +1000
Subject: [PATCH 051/161] Fix calibrating transfers with no to_population
 specified

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

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 8c1e5d97..9306cdea 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -174,7 +174,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             initial_value = None
 
         if par_tuple[1] is None:  # If the pop name is None
-            par = parset.pars[par_tuple[0]]
+            par = parset.get_par(par_tuple[0])
             for pop_name in par.pops:
                 p2.append((par_tuple[0], pop_name, par_tuple[2], par_tuple[3], initial_value))
         else:

From e243d6dcf98b7eaf355e77697863719c51017823 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 30 Aug 2024 13:09:56 +0200
Subject: [PATCH 052/161] Finish enabling transfers in dict form

---
 atomica/yaml_calibration.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 97267bde..de29b8b9 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -312,7 +312,8 @@ def process_inputs(inputs, defaults):
                             key, pop_name = keyspops
                         else:
                             assert len(keyspops) == 3, f'Number of populations must be 1, 2 or None)'
-                            key, pop_name, pop_name2 = keyspops
+                            key = f'{keyspops[0]}_from_{keyspops[1]}'
+                            pop_name = keyspops[2]
 
                         #process values
                         if isinstance(v, (tuple, list)):
@@ -321,10 +322,7 @@ def process_inputs(inputs, defaults):
                             d = v.copy()
 
                         #add keys and values to outputs dict
-                        if len(keyspops) == 2:
-                            out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
-                        else:
-                            out[key, pop_name, pop_name2] = sc.mergedicts(out.get((key, pop_name), {}), d)
+                        out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
             return out
 
         self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)

From bb5f09b8671a1cf6ad8e515058af6fee4e3594f1 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 30 Aug 2024 17:15:34 +0200
Subject: [PATCH 053/161] Fix check_optional_number TypeError

Fixed print statement in TypeError
---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index de29b8b9..98c5e779 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -331,7 +331,7 @@ def process_inputs(inputs, defaults):
         def check_optional_number(key, v, defaults):
             if key in v and v[key] is not None:
                 if not sc.isnumber(v[key], isnan=False):
-                    raise TypeError(f"Adjustable argument '{key}' needs to be a number or None (defaults to {defaults[key]}). Provided value: {v[key]} ")
+                    raise TypeError(f"Adjustable argument {key} needs to be a number or None (defaults to {defaults[key]}). Provided value: {v[key]} ")
 
         # Validate adjustables
         assert len(self['adjustables']) > 0, f'Cannot calibrate with no adjustables for calibration section {self.name}'

From bf48a2ab052eba8c12a6f6be85f33a6c6bc16831 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Tue, 3 Sep 2024 16:04:57 +1000
Subject: [PATCH 054/161] update cal_start and cal_end

---
 atomica/yaml_calibration.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index de29b8b9..75ed91f3 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -366,6 +366,8 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         par_names = {x[0] for x in attributes['adjustables']}.intersection(x.name for x in parset.all_pars())
         pop_names = {x[1] for x in attributes['adjustables']}.intersection({*parset.pop_names} | {'all', None})
 
+        adj_defaults = {k:self.attributes[k] if k in self.attributes else self.adj_defaults[k] for k in self.adj_defaults}
+
         for par_name, pop_name in attributes['adjustables']:
 
             if par_name not in par_names:
@@ -381,7 +383,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(self.adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
+                d = sc.mergedicts(adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
                 adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['initial_value'])
         adjustables = [(*k, *v) for k,v in adjustables.items()]
 
@@ -391,6 +393,8 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
         par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to support transfer parameters and validate that pars have databook entries in the future
         pop_names = {x[1] for x in attributes['measurables']}.intersection({*parset.pop_names} | {None})
 
+        meas_defaults = {k: self.attributes[k] if k in self.attributes else self.meas_defaults[k] for k in self.meas_defaults}
+
         for par_name, pop_name in attributes['measurables']:
 
             if par_name not in par_names:
@@ -407,7 +411,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(self.meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
+                d = sc.mergedicts(meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
                 measurables[(par_name, pop)] = (d['weight'], d['metric'], d['cal_start'], d['cal_end'])
         measurables = [(*k, *v) for k,v in measurables.items()]
 

From 623ef388408c6346a10620166601484f4aa7a449 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Thu, 5 Sep 2024 08:43:25 +1000
Subject: [PATCH 055/161] save a copy of the yaml file

---
 atomica/yaml_calibration.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 75ed91f3..a237d110 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -6,6 +6,7 @@
 
 import sciris as sc
 from pathlib import Path
+import shutil
 import atomica as at
 import numpy as np
 import yaml
@@ -61,17 +62,20 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
     :return new_parset: A calibrated `at.ParameterSet` instance
     """
 
-    if not isinstance(node, BaseNode):
-        node = build(node)
-
-    parset = sc.dcp(project.parset(parset))
-
     if savedir is None:
         savedir = Path('.')
     else:
         savedir = Path(savedir)
     savedir.mkdir(exist_ok=True, parents=True)
 
+    if not isinstance(node, BaseNode):
+        # Save a copy of the yaml-file if saving log output
+        if isinstance(node, Path) and log_output:
+            shutil.copyfile(node, savedir / node.name)
+        node = build(node)
+
+    parset = sc.dcp(project.parset(parset))
+
     nodes = list(node.walk()) # Make a flat list of all nodes to execute in order
     n_steps = len([x for x in nodes if not isinstance(x[1], SectionNode)])
     n = 1

From eac53fd09e15e3834c640958a2ce43362d1c4207 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 5 Sep 2024 16:04:47 +1000
Subject: [PATCH 056/161] Add guard to prevent setting time_period if YAML file
 is in use

---
 atomica/project.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/atomica/project.py b/atomica/project.py
index c024ae51..68925a48 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -642,6 +642,7 @@ def calibrate(self,
             import atomica.yaml_calibration # Avoid circular import
             assert adjustables is None, "If a YAML file is specified, adjustables should not be set"
             assert measurables is None, "If a YAML file is specified, measurables should not be set"
+            assert 'time_period' not in kwargs, "If a YAML file is specified, time_period should not be set - instead, set cal_start and cal_end in the YAML file"
             new_parset = atomica.yaml_calibration.run(yaml, self, parset, **kwargs)
         else:
             if adjustables is None:

From 5d93853f7686249183523f533f9d48c2a87bf5af Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 5 Sep 2024 16:10:37 +1000
Subject: [PATCH 057/161] Clear existing initializations when loading
 calibrations

---
 CHANGELOG.md          | 8 ++++++++
 atomica/parameters.py | 2 ++
 atomica/version.py    | 4 ++--
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 935876aa..b6cb48a7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,14 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+
+version = "1.28.8"
+versiondate = "2024-09-05"
+
+## [1.28.8] - 2023-09-05
+
+- `ParameterSet.load_calibration()` now clears any existing initialization if the calibration being loaded does not contain an initialization. Previously, the absence of an 'initialization' sheet in the calibration would be treated as not making any change to the initialization. This could cause calibrations to become mixed if a calibration without an initialization was loaded after a calibration with an initialization. Now, a missing initialization sheet is treated as meaning 'no initialization' and any existing initialization will be cleared when the calibration is loaded. 
+
 ## [1.28.7] - 2023-08-12
 
 - `Project.load_databook()` will no longer populate `Project.databook` when a `ProjectData` instance is supplied rather than a spreadsheet. The intention of `Project.databook` as opposed to `Project.data.to_spreadsheet()` is that the original databook may contain comments or other content that is not preserved when the databook is loaded into a `ProjectData` instance. Therefore, `Project.databook` serves as a record of the original inputs. However, in previous versions of Atomica, if a `ProjectData` instance was provided rather than a spreadsheet, `ProjectData.to_spreadsheet()` would be used to populate `Project.databook`. For large databooks, this can be computationally expensive and particularly affect the use case of passing in a preloaded databook to improve performance. Since the conversion of the `ProjectData` to a spreadsheet upon loading offers no functional difference to creating the spreadsheet from `Project.data` when required, Atomica no longer performs this conversion upfront.
diff --git a/atomica/parameters.py b/atomica/parameters.py
index 49b177db..630f3e62 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -685,5 +685,7 @@ def load_calibration(self, spreadsheet: sc.Spreadsheet) -> None:
 
         if "Initialization" in excelfile.sheet_names:
             self.initialization = Initialization.from_excel(excelfile)
+        else:
+            self.initialization = None
 
         logger.debug("Loaded calibration from file")
diff --git a/atomica/version.py b/atomica/version.py
index 9955dee6..d2cd1f18 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.28.7"
-versiondate = "2024-08-30"
+version = "1.28.8"
+versiondate = "2024-09-05"
 gitinfo = sc.gitinfo(__file__)

From a6b38d5fa6e4b2f1f14acead6c65de49449ea231 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Sat, 7 Sep 2024 07:38:17 +1000
Subject: [PATCH 058/161] Make initialization slightly more permissive if value
 is 0

---
 atomica/parameters.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/atomica/parameters.py b/atomica/parameters.py
index 630f3e62..bdcf39e4 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -299,7 +299,19 @@ def apply(self, pop, framework=None, parset=None) -> None:
                 if (comp.name, pop.name) not in self.values:
                     comp._vals[:, 0] = 0
                 else:
-                    comp._vals[:, 0] = self.values[(comp.name, pop.name)]
+                    if len(self.values[(comp.name, pop.name)]) != comp._vals.shape[0]:
+                        # If there is a mismatch between the saved initialization duration and the
+                        # duration for the current simulation, if the values were all zero it would probably
+                        # be safe to assume the values can remain zero. If there are fewer time points in the saved
+                        # initialization we *could* insert them, however, there is a risk that the time points are different
+                        # because the step size was changed rather than the duration being changed, so it would not be valid
+                        # to do that. Therefore, if the sizes don't match and any values are nonzero, simply raise an error
+                        if np.all(self.values[(comp.name, pop.name)] == 0):
+                            comp._vals[:, 0] = 0
+                        else:
+                            raise Exception(f'The saved initialization for "{comp.name}" ({pop.name}) has {len(self.values[(comp.name, pop.name)])} time points, but the current parameters lead to a timed compartment duration with {comp._vals.shape[0]} time points. As nonzero values are present, the initialization cannot be applied.')
+                    else:
+                        comp._vals[:, 0] = self.values[(comp.name, pop.name)]
             else:
                 if (comp.name, pop.name) not in self.values:
                     comp.vals[0] = 0

From cb78ee62e7ab580fd80a119fada1b530cdf111b3 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 9 Sep 2024 12:25:26 +1000
Subject: [PATCH 059/161] Fix a numerical precision edge case when calculating
 simulation time vector

---
 CHANGELOG.md       | 8 ++++++--
 atomica/project.py | 7 +++++--
 atomica/version.py | 4 ++--
 3 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b6cb48a7..cea574c7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,9 +2,13 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.29.0] - 2023-09-09
 
-version = "1.28.8"
-versiondate = "2024-09-05"
+- `ProjectSettings` now computes the simulation time vector in a more robust way to reduce edge cases where the reported `sim_dt` doesn't match the input.
+
+*Backwards-compatibility notes*
+
+- In some edge cases, the simulation time points in the output may be different. In those cases, the difference between simulation time points in the model output would not have matched the model input, although the correct time step would have been used to calculate parameter values. In these cases, there may be an extra time point in the model output. Re-running the model should produce results that are close to the original results.
 
 ## [1.28.8] - 2023-09-05
 
diff --git a/atomica/project.py b/atomica/project.py
index 68925a48..2ab71bb0 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -94,8 +94,11 @@ def tvec(self) -> np.ndarray:
         :return: Array of simulation times
 
         """
-
-        return np.linspace(self.sim_start, self.sim_end, int((self.sim_end - self.sim_start) / self.sim_dt) + 1)
+        # The int(np.float32()) helps cases where the numerical precision results in unwanted truncation e.g.,
+        # running a model over the range [2000,2066) with dt=1/12, it calculates 791.9999999999982 time steps
+        # rather than 792, in that case we can convert to a float32 to effectively round it to 792 before
+        # taking the integer. This should generally be safe for the types of values that get used for simulation years
+        return np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1)
 
     def update_time_vector(self, start: float = None, end: float = None, dt: float = None) -> None:
         """
diff --git a/atomica/version.py b/atomica/version.py
index d2cd1f18..62595080 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.28.8"
-versiondate = "2024-09-05"
+version = "1.29.0"
+versiondate = "2024-09-09"
 gitinfo = sc.gitinfo(__file__)

From 14b56c70467fb3124cc9e3e796f11dc45d3a7854 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Thu, 12 Sep 2024 08:09:47 +1000
Subject: [PATCH 060/161] allow for 0 timed compartment durations

---
 atomica/parameters.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/atomica/parameters.py b/atomica/parameters.py
index bdcf39e4..51949121 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -299,7 +299,8 @@ def apply(self, pop, framework=None, parset=None) -> None:
                 if (comp.name, pop.name) not in self.values:
                     comp._vals[:, 0] = 0
                 else:
-                    if len(self.values[(comp.name, pop.name)]) != comp._vals.shape[0]:
+                    vals = self.values[(comp.name, pop.name)]
+                    if (comp._vals.shape[0] > 1 and len(vals) != comp._vals.shape[0]) or (comp._vals.shape[0] == 1 and not np.isscalar(vals)):
                         # If there is a mismatch between the saved initialization duration and the
                         # duration for the current simulation, if the values were all zero it would probably
                         # be safe to assume the values can remain zero. If there are fewer time points in the saved

From ddf30ef496271703a5fbbd44008e4fd1407a4bbe Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 19 Sep 2024 21:16:01 +0200
Subject: [PATCH 061/161] improve process_key()

---
 atomica/yaml_calibration.py | 47 +++++++++++++++++++++++++++----------
 1 file changed, 34 insertions(+), 13 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index eba6e180..1549948b 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -254,21 +254,42 @@ def validate(self):
         Pre-parse calibration inputs
         """
 
-        def separate_keys(s):
+        def separate_keys(keys_str: str) -> list:
             """
-            Separate inputs that kave been defined together as one key in the YAML file
+            Separate inputs that kave been defined together as one key in the YAML file into a list of strnigs.
             """
-            s1 = re.findall(r'\(.*?\)', s)
-            s2 = s
-            for x in s1:
-                s2 = s2.replace(x, '')
-            all_strings = [x for x in s1 if x]
-            s2 = re.sub(r'(,\s)+', ', ', s2).strip(', ').split(',')
-            if s2 != ['']:
-                all_strings += s2
-            return all_strings
-
-        def process_key(s):
+            in_brackets = False
+            brackets_str = ''
+            nobrackets_str = ''
+            separated_keys = []
+
+            for ch in keys_str:
+                if ch == '(':
+                    in_brackets = True
+                    continue
+                elif ch == ')':
+                    in_brackets = False
+                    separated_keys.append(brackets_str)
+                    brackets_str = ''
+                    continue
+
+                if in_brackets:
+                    brackets_str += ch
+                else:
+                    if ch == ',':
+                        if nobrackets_str == ' ' or nobrackets_str == '':
+                            nobrackets_str = ''
+                            continue
+                        else:
+                            separated_keys.append(nobrackets_str)
+                            nobrackets_str = ''
+                    else:
+                        nobrackets_str += ch
+
+            if nobrackets_str != '' and nobrackets_str != ' ':
+                separated_keys.append(nobrackets_str)
+
+            return [x.strip() for x in separated_keys]
             """
             Sanitize key name with optional commas or spaces separating pop name/s from par name
             """

From 9c1e77dc2aa9d8b4ee9f866dc14a25300f2828a3 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 15:34:50 +0200
Subject: [PATCH 062/161] Enable transfers in list format, deprecate pop space
 formatting

-Space between par and pop is deprecated - now pops must be specified in a tuple with the par
-Transfers are now supported in all formats
---
 atomica/yaml_calibration.py | 76 +++++++++++++++++++++++++++++--------
 1 file changed, 60 insertions(+), 16 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 1549948b..f076a8fa 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -245,6 +245,13 @@ class CalibrationNode(BaseNode):
     def parse_list(l, defaults):
         # Routine to parse list of arguments into a dictionary of values
         d = {}
+        #convert number strings back to numerical values
+        for i, e in enumerate(l.copy()):
+            try:
+                l[i] = float(e)  
+            except ValueError:
+                pass
+
         for k, v in zip(list(defaults.keys())[:len(l)], l):
             d[k] = v
         return d
@@ -290,15 +297,39 @@ def separate_keys(keys_str: str) -> list:
                 separated_keys.append(nobrackets_str)
 
             return [x.strip() for x in separated_keys]
+
+        def process_key(key: str) -> tuple:
             """
-            Sanitize key name with optional commas or spaces separating pop name/s from par name
+            Sanitize key name, separating optional pop name/s from par name
+            #TODO add example and further explanation
             """
-            if '(' and ')' in s:
-                return tuple([x.strip() for x in s.strip('() ').split(',') if x])
-            elif ' ' in s:
-                return tuple([x.strip() for x in s.split(' ') if x])
+            if ',' in key:
+                return tuple([x.strip() for x in key.strip('() ').split(',') if x])
             else:
-                return (s.strip(), None)
+                return (key.strip(), None)
+
+        def process_list(l) -> (str,list):
+            """description"""
+            if len(l) == 1:
+                #if the list is already just one string, return that string as key with None pop and vals
+                return (l[0].strip('() '), None), None
+            elif '(' in str(l):
+                # separate out the parenthesis contents as the par/pop/s,
+                # then output the key (par, pop tuple) and value
+
+                # process keys
+                s = str(l).strip("[] ").replace("'", "")
+                s1 = re.findall(r'\(.*?\)', s)
+                key = process_key(s1[0].replace('(', '').replace(')', ''))
+
+                # process values/settings
+                value = s.replace(s1[0], '').strip(', ').split(',')
+                value = [x.strip(', ') for x in value if x]
+                return key, value
+            else:
+                key = process_key(l[0])
+                value = l[1:]
+                return key, value
 
         def process_inputs(inputs, defaults):
             """
@@ -321,33 +352,46 @@ def process_inputs(inputs, defaults):
             if isinstance(inputs, (tuple, list)):
                 for l in inputs:
                     l = sc.promotetolist(l)
-                    #TODO enable and test transfers in full list format
-                    key, pop_name = process_key(l[0].strip())
-                    d = self.parse_list(l[1:], defaults)
-                    out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+                    keyspops, v = process_list(l)
+                    
+                    #process key
+                    if len(keyspops) == 2:
+                        key, pop_name = keyspops
+                    else:
+                        assert len(keyspops) == 3, f'Number of populations must be 0, 1 or 2.'
+                        key = f'{keyspops[0]}_from_{keyspops[1]}'
+                        pop_name = keyspops[2]
+
+                    #process value
+                    if v is None:
+                        value = defaults
+                    else:
+                        value = self.parse_list(v, defaults)
+
+                    out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), value)
 
             elif isinstance(inputs, dict):
                 for keys, v in inputs.items():
 
                     separated_keys = separate_keys(keys)
                     for key in separated_keys:
-                        #separate par name from pop name/s
+                        #separate par name from pop name
                         keyspops = process_key(key.strip())
                         if len(keyspops) == 2:
                             key, pop_name = keyspops
                         else:
-                            assert len(keyspops) == 3, f'Number of populations must be 1, 2 or None)'
+                            assert len(keyspops) == 3, f'Number of populations must be 0, 1 or 2.'
                             key = f'{keyspops[0]}_from_{keyspops[1]}'
                             pop_name = keyspops[2]
 
                         #process values
                         if isinstance(v, (tuple, list)):
-                            d = self.parse_list(v, defaults)
+                            value = self.parse_list(v, defaults)
                         else:
-                            d = v.copy()
+                            value = v.copy()
 
                         #add keys and values to outputs dict
-                        out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), d)
+                        out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), value)
             return out
 
         self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)
@@ -415,7 +459,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
         # Expand measurables
         measurables = {}
-        par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to support transfer parameters and validate that pars have databook entries in the future
+        par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to validate that pars have databook entries in the future
         pop_names = {x[1] for x in attributes['measurables']}.intersection({*parset.pop_names} | {None})
 
         meas_defaults = {k: self.attributes[k] if k in self.attributes else self.meas_defaults[k] for k in self.meas_defaults}

From ba0d0b6cb8f37b5739729daf603f019a813c05bf Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 15:35:16 +0200
Subject: [PATCH 063/161] Enable dict format with empty values

---
 atomica/yaml_calibration.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index f076a8fa..7ae99fcb 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -387,6 +387,8 @@ def process_inputs(inputs, defaults):
                         #process values
                         if isinstance(v, (tuple, list)):
                             value = self.parse_list(v, defaults)
+                        elif v is None:
+                            value = defaults
                         else:
                             value = v.copy()
 

From a437acd0967d81b6d4ebe7e42ff97354ee4cf892 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 16:37:14 +0200
Subject: [PATCH 064/161] Improve testing of whether constant_parset is a
 number (not bool)

---
 atomica/yaml_calibration.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 7ae99fcb..49c6211b 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -581,9 +581,11 @@ def validate(self):
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
         p2 = sc.dcp(parset)
         if 'constant_parset' in self:
-            if self['constant_parset'] == True:
+            if self['constant_parset'] == False:
+                pass
+            elif self['constant_parset'] == True:
                 p2 = parset.make_constant(year=project.settings.sim_start)
-            elif type(self['constant_parset']) == int: #constant parset year was provided
+            elif sc.isnumber(self['constant_parset']): #constant parset year was provided
                 p2 = parset.make_constant(year=self['constant_parset'])
         new_settings = sc.dcp(project.settings)
         new_settings.update_time_vector(end=self['init_year'])

From 5b73362a3ec4bb278740d519bb1bee987edc73ad Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 16:45:27 +0200
Subject: [PATCH 065/161] Rename initial_value to starting_y_factor

---
 atomica/yaml_calibration.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 49c6211b..b1a02dc2 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -230,7 +230,7 @@ class CalibrationNode(BaseNode):
     adj_defaults = {
         'lower_bound': 0.1,
         'upper_bound': 10.0,
-        'initial_value': None,
+        'starting_y_factor': None,
     }
 
     # Order for list of measurable parameters and default values
@@ -412,7 +412,7 @@ def check_optional_number(key, v, defaults):
             assert pop_name is None or isinstance(pop_name, str), f'Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)'
             check_optional_number('lower_bound',v, self.adj_defaults)
             check_optional_number('upper_bound',v, self.adj_defaults)
-            check_optional_number('initial_value',v, self.adj_defaults)
+            check_optional_number('starting_y_factor',v, self.adj_defaults)
 
         # Validate measurables
         assert len(self['measurables']) > 0, f'Cannot calibrate with no measurables for calibration section {self.name}'
@@ -455,7 +455,7 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
             for pop in pops:
                 d = sc.mergedicts(adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
-                adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['initial_value'])
+                adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['starting_y_factor'])
         adjustables = [(*k, *v) for k,v in adjustables.items()]
 
 

From fb0a4ba8c5b3555fbfaa72ebf3294ca8bc936e36 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 19:09:26 +0200
Subject: [PATCH 066/161] Expand docstrings, add type hints

---
 atomica/yaml_calibration.py | 61 +++++++++++++++++++++++++++++--------
 1 file changed, 48 insertions(+), 13 deletions(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index b1a02dc2..9b97f7a1 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -263,7 +263,10 @@ def validate(self):
 
         def separate_keys(keys_str: str) -> list:
             """
-            Separate inputs that kave been defined together as one key in the YAML file into a list of strnigs.
+            Separate inputs that kave been defined together as one key in the YAML file but actually represent multiple
+            parameters.
+            :param str keys_str: Unprocessed input key from the YAML file.
+            :return list : A list of strings, each of which represents a single key.
             """
             in_brackets = False
             brackets_str = ''
@@ -300,16 +303,42 @@ def separate_keys(keys_str: str) -> list:
 
         def process_key(key: str) -> tuple:
             """
-            Sanitize key name, separating optional pop name/s from par name
-            #TODO add example and further explanation
+            Sanitize the key name, separating the parameter codename from the optional population name/s.
+            :par str key: Key representing an adjustable or measurable parameter. It can also contain one or two
+            population names (two in the case of a transfer), separated from the parameter name by a comma.
+            :returns: A tuple of the parameter codename and population name/s. Population defaults to None
+            if not specified.
+
+            EXAMPLES:
+                INPUT: 'b_rate'
+                OUTPUT: (b_rate, None)
+
+                INPUT: 'b_rate, 0-4'
+                OUTPUT: (b_rate, 0-4)
+
+                INPUT: 'aging, 0-4, 5-14'
+                OUTPUT: (aging, 0-4, 5-14)
             """
             if ',' in key:
                 return tuple([x.strip() for x in key.strip('() ').split(',') if x])
             else:
                 return (key.strip(), None)
 
-        def process_list(l) -> (str,list):
-            """description"""
+        def process_list(l: list) -> (tuple,list):
+            """
+            Process list-format inputs and separate them into a key (par_name, pop_name tuple) and a value (list of
+            parameter settings).
+            @param l: List representing the settings for one parameter.
+            @return:    tuple key: Tuple of the parameter codename and the population (default None).
+                        list value: List of calibration settings for this parameter.
+
+            EXAMPLES:
+                INPUT: [b_rate, 0.1, 10]
+                OUTPUT: (b_rate, None) ; [0.1, 10]
+
+                INPUT: [(b_rate, 0-4), 0.1, 10, 1.5]
+                OUTPUT: (b_rate, 0-4) ; [0.1, 10, 1.5]
+            """
             if len(l) == 1:
                 #if the list is already just one string, return that string as key with None pop and vals
                 return (l[0].strip('() '), None), None
@@ -331,16 +360,22 @@ def process_list(l) -> (str,list):
                 value = l[1:]
                 return key, value
 
-        def process_inputs(inputs, defaults):
+        def process_inputs(inputs, defaults: dict) -> dict:
             """
-            Process adjustables and measurables, which can be specified in a list representation or nested dict representation
-            In list representation, the input is a list of lists, where the first item in each list is the quantity (with optional population) and
-            the remaining items are the supported arguments for the input type, in the order defined by the defaults dictionary.
-            In a dict representation, the key is the quantity with optional population, and then the value can either be a list (in the order defined by the dictionary)
-            or a dictionary explicitly naming the inputs.
+            Process adjustables and measurables, which can be specified as a string, list or nested dict representation.
+            * In string representation, only the parameter name is specified, and the default settings are used.
+            * In list representation, the input is a list of lists, where the first item in each list is the parameter
+            (with optional population) and the remaining items are the supported arguments for the input type, in the
+            order defined by the defaults dictionary.
+            *In dict representation, the key is the quantity with optional population, and the value can either be
+            a list (in the order defined by the dictionary) or a dictionary explicitly naming the inputs.
+
             This function returns a flat dictionary with {(quantity, pop_name):{argument:value}} e.g., {('b_rate','0-5'):{'lower_bound':0.5}}.
-            In the dict representation, the key can be a comma separated list of quantities with optional values e.g., 'b_rate 0-5, d_rate'. In the list representation,
-            multiple quantities are not supported (as a comma is already used to separate the arguments), but multiple lists (one for each quantity) can be provided.
+            In the dict representation, the key can be a comma separated list of quantities with optional values e.g.,
+            'b_rate 0-5, d_rate'.
+            In the list representation, multiple quantities are not supported (as a comma is already used to separate
+            the arguments), but multiple lists (one for each quantity) can be provided.
+            #TODO could add examples
             """
             out = {}
 

From b90b9ba9c5e13425b62222309ae24a290b3eaf72 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 19:14:32 +0200
Subject: [PATCH 067/161] Make tuple conversion more explicit

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 9b97f7a1..4c042e68 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -322,7 +322,7 @@ def process_key(key: str) -> tuple:
             if ',' in key:
                 return tuple([x.strip() for x in key.strip('() ').split(',') if x])
             else:
-                return (key.strip(), None)
+                return tuple(key.strip(), None)
 
         def process_list(l: list) -> (tuple,list):
             """

From d84419761a518987591f6a4192cc86e2f3647680 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Mon, 23 Sep 2024 19:48:44 +0200
Subject: [PATCH 068/161] Fix tuple conversion

---
 atomica/yaml_calibration.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 4c042e68..9b97f7a1 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -322,7 +322,7 @@ def process_key(key: str) -> tuple:
             if ',' in key:
                 return tuple([x.strip() for x in key.strip('() ').split(',') if x])
             else:
-                return tuple(key.strip(), None)
+                return (key.strip(), None)
 
         def process_list(l: list) -> (tuple,list):
             """

From 89f2bf804ea09b1fec00c860ddf79f293984ff69 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 24 Sep 2024 09:08:13 +1000
Subject: [PATCH 069/161] Improve robustness for timed compartments with 0
 duration

---
 atomica/parameters.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/atomica/parameters.py b/atomica/parameters.py
index bdcf39e4..ff08e42c 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -299,7 +299,8 @@ def apply(self, pop, framework=None, parset=None) -> None:
                 if (comp.name, pop.name) not in self.values:
                     comp._vals[:, 0] = 0
                 else:
-                    if len(self.values[(comp.name, pop.name)]) != comp._vals.shape[0]:
+                    vals = self.values[(comp.name, pop.name)]
+                    if (comp._vals.shape[0] > 1 and len(vals) != comp._vals.shape[0]) or (comp._vals.shape[0] == 1 and not np.isscalar(vals)):
                         # If there is a mismatch between the saved initialization duration and the
                         # duration for the current simulation, if the values were all zero it would probably
                         # be safe to assume the values can remain zero. If there are fewer time points in the saved
@@ -356,13 +357,13 @@ def from_excel(cls, excelfile: pd.ExcelFile):
         metadata, value_df = atomica.excel.read_dataframes(excelfile.book['Initialization'])
 
         values = {}
-        for k,s in value_df.T.reset_index().T.set_index([0,1]).iterrows():
+        for k, s in value_df.T.reset_index().T.set_index([0, 1]).iterrows():
             v = s.dropna().values
             values[k] = v[0] if len(v) == 1 else v
 
         self = cls(values)
 
-        for k,v in metadata.T.reset_index().T.set_index(0)[1].to_dict().items():
+        for k, v in metadata.T.reset_index().T.set_index(0)[1].to_dict().items():
             setattr(self, k, v)
 
         return self

From 14af4d8476a91abf539b7a00f942a681af8d948d Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 24 Sep 2024 09:08:36 +1000
Subject: [PATCH 070/161] Update migration version package

---
 atomica/migration.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/migration.py b/atomica/migration.py
index 7c893a48..69b98f65 100644
--- a/atomica/migration.py
+++ b/atomica/migration.py
@@ -13,7 +13,7 @@
 
 import sys
 import io
-from distutils.version import LooseVersion
+from packaging.version import Version
 from .system import logger
 from .version import version, gitinfo
 import sciris as sc
@@ -198,7 +198,7 @@ def migrate(obj, registry=migrations, version=version, gitinfo=gitinfo):
         print("Skipping migration")
         return obj  # If migration is disabled then don't make any changes EXCEPT to add in version and gitinfo which may otherwise be hard to catch
 
-    migrations_to_run = sorted(registry[type(obj).__name__], key=lambda m: LooseVersion(m.original_version))
+    migrations_to_run = sorted(registry[type(obj).__name__], key=lambda m: Version(m.original_version))
     if sc.compareversions(obj.version, version) >= 0:
         return obj
     else:

From b89c19f61712fb58c5f60813696eece2516a1bfe Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 24 Sep 2024 18:09:25 +1000
Subject: [PATCH 071/161] Update YAML tests

---
 azure-pipelines.yml                           | 24 +-----
 tests/test_tox_yaml.py                        | 52 ++++++++++++
 tests/test_yaml.py                            | 83 -------------------
 tests/yaml_tests/config_bounds_test.yaml      |  4 +-
 tests/yaml_tests/config_cal_formats.yaml      | 20 ++---
 tests/yaml_tests/config_caltime_test.yaml     | 12 +--
 tests/yaml_tests/config_maxtime_test.yaml     | 12 +--
 .../config_nonexistent_setting_test.yaml      | 12 +--
 tests/yaml_tests/config_repeats_test.yaml     | 12 +--
 tests/yaml_tests/config_simtime_test.yaml     |  8 +-
 tests/yaml_tests/config_stepsize_test.yaml    | 12 +--
 11 files changed, 99 insertions(+), 152 deletions(-)
 create mode 100644 tests/test_tox_yaml.py
 delete mode 100644 tests/test_yaml.py

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 5f41210b..e2bf892e 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -39,34 +39,12 @@ jobs:
         testRunTitle: 'Publish test results for Python $(python.version)'
       condition: succeededOrFailed()
 
-    - task: PublishCodeCoverageResults@1
+    - task: PublishCodeCoverageResults@2
       inputs:
         codeCoverageTool: Cobertura
         summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml'
 #        reportDirectory: '$(System.DefaultWorkingDirectory)/**/htmlcov'
 
-- job: 'flake8'
-  pool:
-    vmImage: 'ubuntu-latest'
-  steps:
-    - task: UsePythonVersion@0
-      displayName: 'Select python version'
-      inputs:
-        versionSpec: '3.12'
-    - script: python -m pip install flake8 flake8-junit-report
-      displayName: 'Install flake8'
-    - script: flake8 atomica tests --exit-zero --output-file flake8.txt
-      # Note - using --exit-zero will cause flake8 'errors' to show up as 'errors' in Azure Pipelines
-      # but the test will have been considered to pass. Therefore, the overall tests suite will still
-      # be considered to be passing as long as tox runs, and flake8 errors are just guidance for style
-      displayName: 'Run flake8'
-    - script: flake8_junit flake8.txt flake8_junit.xml
-      displayName: 'Generate flake8 junit output'
-    - task: PublishTestResults@2
-      inputs:
-        testResultsFiles: '**/flake8_junit.xml'
-        testRunTitle: 'Publish flake8 results'
-
 - job: 'docs'
   condition: and(ne(variables['Build.Reason'], 'PullRequest'), ne(variables['Build.Reason'], 'Schedule')) # Skip if it's a PR build or a scheduled build
   pool:
diff --git a/tests/test_tox_yaml.py b/tests/test_tox_yaml.py
new file mode 100644
index 00000000..0e19195f
--- /dev/null
+++ b/tests/test_tox_yaml.py
@@ -0,0 +1,52 @@
+# import unittest
+import atomica as at
+import os
+import pytest
+import numpy as np
+import sciris as sc
+
+# Basic minimum working product test to verify all yaml features run
+testdir = at.parent_dir()
+tmpdir = testdir / 'temp'
+yaml_dir = testdir/'yaml_tests'
+
+# List yaml files in directory
+yaml_files = list(yaml_dir.glob('*.yaml'))
+
+
+@pytest.fixture()
+def project():
+    P = at.demo('udt_dyn', do_run=False)
+    P.settings.update_time_vector(start=2000, end=2050, dt=1/52)
+    return P
+
+def test_save_intermediate(project):
+    project.calibrate(yaml=yaml_files[0], parset=project.make_parset(), quiet=False, savedir=tmpdir/'yaml_save_test', save_intermediate=True, verbose=0, max_time=0.1)
+
+@pytest.mark.parametrize("yaml_fname", yaml_files, ids=[x.stem for x in yaml_files])
+def test_yaml_calibration(project, yaml_fname):
+
+    # TODO - this demo project has only one population. It would be good
+    # to have a test with multiple populations and where there is a space in the population name
+
+    print(f"\n\nTESTING yaml config {yaml_fname}")
+
+    # run calibration with yaml instructions
+    # config_file_path = yaml_dir/yaml_fname
+    # print(config_file_path)
+    project.calibrate(yaml=yaml_fname, parset=project.make_parset(), quiet=False, savedir=tmpdir, save_intermediate=False, verbose=0, max_time=0.1)
+
+    #or can import in all the diff ways - make function
+    #also run in several diff ways? Or have a separate test for that?
+
+    # Test saving the calibration
+    # save()
+
+    print("Test complete")
+
+
+if __name__ == "__main__":
+
+    np.seterr(all="raise")
+    for f in yaml_files:
+        test_yaml_calibration(f)
diff --git a/tests/test_yaml.py b/tests/test_yaml.py
deleted file mode 100644
index b3346ca2..00000000
--- a/tests/test_yaml.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# import unittest
-import atomica as at
-import os
-import pytest
-import numpy as np
-import sciris as sc
-import at_tools as att
-
-# class MyTestCase(unittest.TestCase):
-#     def test_something(self):
-#         self.assertEqual(True, False)  # add assertion here
-#
-#
-# if __name__ == '__main__':
-#     unittest.main()
-
-
-# Basic minimum working product test to verify all yaml features run
-
-testdir = at.parent_dir()
-tmpdir = testdir / 'temp'
-yaml_dir = 'yaml_tests'
-
-# List yaml files in directory
-yaml_files = list()
-for f in os.listdir(testdir/yaml_dir):
-    yaml_files.append(f)
-
-
-def run_auto_calibration(proj):
-    """Run an automatic calibration
-
-    :param proj: A Project object
-    :return:
-    """
-
-    proj.calibrate(max_time=10, new_name="auto")
-
-    return
-
-
-# Testing optimizations and calibrations could be expensive
-# Using the parametrize decorator means Pytest will treat
-# each function call as a separate test, which can be run in parallel
-# simply by adding `pytest -n 4` for instance. Or via tox
-# `tox -- -o -n 4` (because the default config is to use n=2 for TravisCI)
-@pytest.mark.parametrize("yaml_fname", yaml_files)
-def test_yaml_calibration(yaml_fname):
-
-    #set these once outside test itself?
-    fw_ver = '2_8_1'
-    db_ver = '2_8'
-    country = 'PAK'
-    dis = 'typh'
-
-    F = at.ProjectFramework(f"framework_bivalent_combined_v{fw_ver}.xlsx")
-    D = at.ProjectData.from_spreadsheet(f"databook_bivalent_{country}_v{db_ver}.xlsx", framework=F)
-    P = at.Project(framework=F, databook=D, do_run=False)
-    P.settings.update_time_vector(start=2000, end=2050, dt=1/52)
-    cal = P.make_parset()
-
-    print(f"\n\nTESTING yaml config {yaml_fname}")
-
-    #import yaml config
-    config_file_path = f'{yaml_dir}/{yaml_fname}'
-    config = att.ProjectConfig.from_yaml(config_file_path) #TODO change this to not rely on atomica tools?
-    #or can import in all the diff ways - make function
-
-    #run calibration with yaml instructions
-    newcal = config.run_calibration(P, parset=cal, quiet=False, savedir=tmpdir, save_intermediate=False, verbose=0, max_time=1)
-    #also run in several diff ways? Or have a separate test for that?
-
-    # Test saving the calibration
-    # save()
-
-    print("Test complete")
-
-
-if __name__ == "__main__":
-
-    np.seterr(all="raise")
-    for f in yaml_files:
-        test_yaml_calibration(f)
diff --git a/tests/yaml_tests/config_bounds_test.yaml b/tests/yaml_tests/config_bounds_test.yaml
index 855078f5..13c4a025 100644
--- a/tests/yaml_tests/config_bounds_test.yaml
+++ b/tests/yaml_tests/config_bounds_test.yaml
@@ -4,8 +4,8 @@ calibration:
 
       match population sizes:
         adjustables:
-          b_rate, mig_rate: [ 0.1, 10 ]
-        measurables:  alive
+          b_rate, inf_death: [ 0.1, 10 ]
+        measurables:  all_people
 
       turn off epi y-factors:
         adjustables:
diff --git a/tests/yaml_tests/config_cal_formats.yaml b/tests/yaml_tests/config_cal_formats.yaml
index 4151f3fe..85eb63f0 100644
--- a/tests/yaml_tests/config_cal_formats.yaml
+++ b/tests/yaml_tests/config_cal_formats.yaml
@@ -15,10 +15,10 @@ calibration:
             b_rate:
               lower_bound: 0.1
               upper_bound: 10
-            mig_rate:
+            inf_death:
               starting_y_factor: 1.2
           measurables:
-            alive:
+            all_people:
               metric: fractional
 
         #combined format
@@ -26,22 +26,22 @@ calibration:
           version 1:
             adjustables:
               b_rate: [ 0.1,10 ]
-              mig_rate: [ 0.1,10 ]
+              inf_death: [ 0.1,10 ]
             measurables:
-              alive: [1.0]
+              all_people: [1.0]
 
           version 2:
             adjustables:
-              b_rate, mig_rate: [ 0.1,10 ]
+              b_rate, inf_death: [ 0.1,10 ]
             measurables:
-              alive: [1.0]
+              all_people: [1.0]
 
         #list format
         match population sizes 3:
-          adjustables: [[b_rate, 0.1, 10], [mig_rate, 0.1, 10]]
-          measurables: [alive]
+          adjustables: [[b_rate, 0.1, 10], [inf_death, 0.1, 10]]
+          measurables: [all_people]
 
          #string format
         match population sizes 4:
-          adjustables: [b_rate, mig_rate]
-          measurables: alive
+          adjustables: [b_rate, inf_death]
+          measurables: all_people
diff --git a/tests/yaml_tests/config_caltime_test.yaml b/tests/yaml_tests/config_caltime_test.yaml
index ea61d264..deb40fb0 100644
--- a/tests/yaml_tests/config_caltime_test.yaml
+++ b/tests/yaml_tests/config_caltime_test.yaml
@@ -18,22 +18,22 @@ calibration:
         cal_end: 2040
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           cal_start: 2000 #is this meant to work?
           cal_end: 2040
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 3:
         match population sizes 3:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
+            b_rate,inf_death: [ 0.1,10 ]
           measurables:
-            alive:
+            all_people:
               cal_start: 2000
               cal_end: 2040
diff --git a/tests/yaml_tests/config_maxtime_test.yaml b/tests/yaml_tests/config_maxtime_test.yaml
index 16f1cc8f..95d8f9a8 100644
--- a/tests/yaml_tests/config_maxtime_test.yaml
+++ b/tests/yaml_tests/config_maxtime_test.yaml
@@ -16,21 +16,21 @@ calibration:
         max_time: 1
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           max_time: 1
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
 #      round 3:
 #        match population sizes 3:
 #          adjustables:
-#            b_rate,mig_rate: [ 0.1,10 ]
+#            b_rate,inf_death: [ 0.1,10 ]
 #            b_rate:
 #              cal_start: 2000 #is this meant to work?
 #              cal_end: 2040
-#          measurables: [ alive ]
+#          measurables: [ all_people ]
diff --git a/tests/yaml_tests/config_nonexistent_setting_test.yaml b/tests/yaml_tests/config_nonexistent_setting_test.yaml
index 00382e70..cd7e6b44 100644
--- a/tests/yaml_tests/config_nonexistent_setting_test.yaml
+++ b/tests/yaml_tests/config_nonexistent_setting_test.yaml
@@ -13,19 +13,19 @@ calibration:
         raddish: 3
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           kiwi: 4
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
 #      round 3:
 #        match population sizes 3:
 #          adjustables:
 #            stepsize: 0.5
-#            b_rate,mig_rate: [ 0.1,10 ]
-#          measurables: [ alive ]
+#            b_rate,inf_death: [ 0.1,10 ]
+#          measurables: [ all_people ]
diff --git a/tests/yaml_tests/config_repeats_test.yaml b/tests/yaml_tests/config_repeats_test.yaml
index 76f59b5e..dcd60944 100644
--- a/tests/yaml_tests/config_repeats_test.yaml
+++ b/tests/yaml_tests/config_repeats_test.yaml
@@ -16,21 +16,21 @@ calibration:
         repeats: 1
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           repeats: 1 #should error?
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
 #      round 3:
 #        match population sizes 3:
 #          adjustables:
-#            b_rate,mig_rate: [ 0.1,10 ]
+#            b_rate,inf_death: [ 0.1,10 ]
 #            b_rate:
 #              cal_start: 2000 #is this meant to work?
 #              cal_end: 2040
-#          measurables: [ alive ]
+#          measurables: [ all_people ]
diff --git a/tests/yaml_tests/config_simtime_test.yaml b/tests/yaml_tests/config_simtime_test.yaml
index a3f13b1c..2800f5e4 100644
--- a/tests/yaml_tests/config_simtime_test.yaml
+++ b/tests/yaml_tests/config_simtime_test.yaml
@@ -18,13 +18,13 @@ calibration:
         sim_end: 2040
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           sim_start: 2000 #is this meant to work?
           sim_end: 2040
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
diff --git a/tests/yaml_tests/config_stepsize_test.yaml b/tests/yaml_tests/config_stepsize_test.yaml
index 40a1f91b..d94db8f3 100644
--- a/tests/yaml_tests/config_stepsize_test.yaml
+++ b/tests/yaml_tests/config_stepsize_test.yaml
@@ -14,19 +14,19 @@ calibration:
         stepsize: 0.5
         match population sizes 1:
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
       round 2:
         match population sizes 2:
           stepsize: 0.5
           adjustables:
-            b_rate,mig_rate: [ 0.1,10 ]
-          measurables: [alive]
+            b_rate,inf_death: [ 0.1,10 ]
+          measurables: [all_people]
 
 #      round 3:
 #        match population sizes 3:
 #          adjustables:
 #            stepsize: 0.5
-#            b_rate,mig_rate: [ 0.1,10 ]
-#          measurables: [ alive ]
+#            b_rate,inf_death: [ 0.1,10 ]
+#          measurables: [ all_people ]

From dc2a70237e9e427b6aa65698bc0afacd48e2086c Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 25 Sep 2024 14:54:47 +1000
Subject: [PATCH 072/161] Change aggregation logic for characteristics

---
 atomica/plotting.py | 98 +++++++++++++++++++++++++++++++++++----------
 1 file changed, 77 insertions(+), 21 deletions(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 9529ab8d..84f8bc50 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -139,20 +139,36 @@ class PlotData:
 
     :param output_aggregation: If an output aggregation is requested, combine the outputs listed using one of
 
-          - 'sum' - just add values together
-          - 'average' - unweighted average of quantities
+          - 'sum' - just add values together. If summing together characteristics with a denominator, then
+                    all of the items being included in the sum must have the same denominator. This denominator
+                    will then continue to be tracked for the sum.
+          - 'average' - unweighted average of quantities. If averaging characteristics with a denominator, then
+                        the denominator will be dropped afterwards (so subsequent population aggregation
           - 'weighted' - weighted average where the weight is the
-            compartment size, characteristic value, or link source
-            compartment size (summed over duplicate links). 'weighted'
-            method cannot be used with non-transition parameters and a
-            KeyError will result in that case
-
-    :param pop_aggregation: Same as output_aggregation, except that 'weighted'
-                              uses population sizes. Note that output aggregation is performed
-                              before population aggregation. This also means that population
-                              aggregation can be used to combine already aggregated outputs (e.g.
-                              can first sum 'sus'+'vac' within populations, and then take weighted
-                              average across populations)
+                            - Link source compartment size, or
+                            - Characteristic denominator size, or
+                            - Compartment size
+                         depending on the quantities being aggregated. If one quantity has a denominator, then
+                         all quantities must have denominators. This method cannot be used with non-transition parameters and a
+                         KeyError will result in that case. Note that if using this method to aggregate characteristics, there
+                         should generally not be any overlap in the compartments contributing to the denominators.
+
+         For aggregating characteristics, the behaviour for combining characteristics ``a/b`` and ``c/d`` is as follows:
+
+             - 'sum' will give ``(a+c)/b`` and raise an error if ``b!=d``. The denominator ``b`` can be used for weighted population aggregation.
+             - 'average' will give ``((a/b)+(c/d))/2``. No denominator is tracked for weighted population aggregation, so such aggregation would use the total population size.
+             - 'weighted' will give ``(a+c)/(b+d)``. The denominator ``b+d`` can be used for weighted population aggregation.
+
+        The default is 'average' for dimensionless units, probabilities, rates etc. and 'sum' for everything else, with the exception of characteristics
+        with denominators, where 'sum' is used by default.
+
+    :param pop_aggregation: Same options as output_aggregation, except that 'weighted'
+                            uses population sizes OR characteristic denominator size. Note that output aggregation
+                            is performed before population aggregation. This also means that population
+                            aggregation can be used to combine already aggregated outputs (e.g.
+                            can first sum 'sus'+'vac' within populations, and then take weighted
+                            average across populations). This weighting is principally intended for aggregating
+                            nondimensional quantities (fractions, proportions etc.).
 
     :param project: Optionally provide a :class:`Project` object, which will be used to convert names to labels in the outputs for plotting.
 
@@ -221,9 +237,11 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
             aggregated_outputs = defaultdict(dict)  # Dict with aggregated_outputs[pop_label][aggregated_output_label]
             aggregated_units = dict()  # Dict with aggregated_units[aggregated_output_label]
             aggregated_timescales = dict()
+            aggregated_denominators = defaultdict(dict) # Store aggregated denominators where available at the same dimensionality as outputs
             output_units = dict()
             output_timescales = dict()
             compsize = dict()
+            denomsize = dict() # Characteristic denominator size
             popsize = dict()
             # Defaultdict won't throw key error when checking outputs.
             data_label = defaultdict(str)  # Label used to identify which data to plot, maps output label to data label.
@@ -280,7 +298,8 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
                         output_units[output_label] = vars[0].units
                         output_timescales[output_label] = None
                         data_label[output_label] = vars[0].name
-
+                        if isinstance(vars[0], Characteristic) and vars[0].denominator is not None:
+                            denomsize[output_label] = vars[0].denominator.vals
                     else:
                         raise Exception("Unknown type")
 
@@ -328,14 +347,20 @@ def placeholder_pop():
                             aggregated_outputs[pop_label][output_name] = data_dict[output_name]
                             aggregated_units[output_name] = "unknown"  # Also, we don't know what the units of a function are
                             aggregated_timescales[output_name] = None  # Timescale is lost
+                            aggregated_denominators[pop_label][output_name] = None # Denominators are not tracked
                             continue
 
                         units = list(set([output_units[x] for x in labels]))
                         timescales = list(set([np.nan if isna(output_timescales[x]) else output_timescales[x] for x in labels]))  # Ensure that None and nan don't appear as different timescales
 
+
                         # Set default aggregation method depending on the units of the quantity
                         if output_aggregation is None:
-                            if units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
+                            if labels[0] in denomsize:
+                                # We are aggregating characteristics with denominators, so we want to aggregate with summation
+                                # on the assumption that all of the characteristics being aggregated have the same denominator
+                                output_aggregation = "sum"
+                            elif units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
                                 output_aggregation = "average"
                             else:
                                 output_aggregation = "sum"
@@ -344,7 +369,7 @@ def placeholder_pop():
                             logger.warning("Aggregation for output '%s' is mixing units, this is almost certainly not desired.", output_name)
                             aggregated_units[output_name] = "unknown"
                         else:
-                            if units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE] and output_aggregation == "sum" and len(labels) > 1:  # Dimensionless, like prevalance
+                            if units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE] and output_aggregation == "sum" and len(labels) > 1 and not labels[0] in denomsize:  # Dimensionless, like prevalance
                                 logger.warning("Output '%s' is not in number units, so output aggregation probably should not be 'sum'.", output_name)
                             aggregated_units[output_name] = output_units[labels[0]]
 
@@ -356,16 +381,43 @@ def placeholder_pop():
 
                         if output_aggregation == "sum":
                             aggregated_outputs[pop_label][output_name] = sum(data_dict[x] for x in labels)  # Add together all the outputs
+
+                            # If summing characteristics that have denominators, then all quantities should have denominators, and they should all be equal
+                            # For example, summing SP-TB and SN-TB prevalance where they both have the 'alive' denominator
+                            has_denominator = [x in denomsize for x in labels]
+                            assert all(has_denominator) or not any(has_denominator)  # An output aggregation must either have no quantities with denominators, or only contain quantities with denominators
+                            if all(has_denominator):
+                                for label in labels:
+                                    assert np.all(denomsize[label] == denomsize[labels[0]]), 'When aggregating characteristics with denominators, if summing them (default) then all quantities must have the same denominator. The "weighted" aggregation can be used to combine characteristics with different denominators, on the assumption that their denominators do not overlap.'
+                                aggregated_denominators[pop_label][output_name] = denomsize[labels[0]]
+                            elif not any(has_denominator):
+                                aggregated_denominators[pop_label][output_name] = None
+                            else:
+                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denomsize]} while these do not {[x for x in labels if x not in denomsize]}")
+
                         elif output_aggregation == "average":
-                            aggregated_outputs[pop_label][output_name] = sum(data_dict[x] for x in labels)  # Add together all the outputs
+                            aggregated_outputs[pop_label][output_name] = sum(data_dict[x] for x in labels)
                             aggregated_outputs[pop_label][output_name] /= len(labels)
+                            aggregated_denominators[pop_label][output_name] = None # If taking a direct average then drop the denominator
                         elif output_aggregation == "weighted":
-                            aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * compsize[x] for x in labels)  # Add together all the outputs
-                            aggregated_outputs[pop_label][output_name] /= sum([compsize[x] for x in labels])
+                            has_denominator = [x in denomsize for x in labels]
+                            assert all(has_denominator) or not any(has_denominator)  # An output aggregation must either have no quantities with denominators, or only contain quantities with denominators
+                            if all(has_denominator):
+                                # Take weighted average based on denominator size, and then store the combined denominator further population aggregation
+                                aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * denomsize[x] for x in labels)
+                                aggregated_denominators[pop_label][output_name] = sum([denomsize[x] for x in labels])
+                                aggregated_outputs[pop_label][output_name] /= aggregated_denominators[pop_label][output_name]
+                            elif not any(has_denominator):
+                                aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * compsize[x] for x in labels)
+                                aggregated_outputs[pop_label][output_name] /= sum([compsize[x] for x in labels])
+                                aggregated_denominators[pop_label][output_name] = None  # If taking a direct average then drop the denominator
+                            else:
+                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denomsize]} while these do not {[x for x in labels if x not in denomsize]}")
                     else:
                         aggregated_outputs[pop_label][output] = data_dict[output]
                         aggregated_units[output] = output_units[output]
                         aggregated_timescales[output] = output_timescales[output]
+                        aggregated_denominators[pop_label][output] = denomsize.get(output)
 
             # Now aggregate over populations
             # If we have requested a reduction over populations, this is done for every output present
@@ -390,8 +442,12 @@ def placeholder_pop():
                             vals = sum(aggregated_outputs[x][output_name] for x in pop_labels)  # Add together all the outputs
                             vals /= len(pop_labels)
                         elif pop_aggregation == "weighted":
-                            numerator = sum(aggregated_outputs[x][output_name] * popsize[x] for x in pop_labels)  # Add together all the outputs
-                            denominator = sum([popsize[x] for x in pop_labels])
+                            if aggregated_denominators[pop_labels[0]][output_name] is not None:
+                                weights = {x:aggregated_denominators[x][output_name] for x in pop_labels}
+                            else:
+                                weights = {x:popsize[x] for x in pop_labels}
+                            numerator = sum(aggregated_outputs[x][output_name] * weights[x] for x in pop_labels)  # Add together all the outputs
+                            denominator = sum([weights[x] for x in pop_labels])
                             vals = np.divide(numerator, denominator, out=np.full(numerator.shape, np.nan, dtype=float), where=numerator != 0)
                         else:
                             raise Exception("Unknown pop aggregation method")

From 194d85ae05428019da88eea33277a2666d9ac017 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 25 Sep 2024 15:03:33 +1000
Subject: [PATCH 073/161] Update default for characteristics

---
 atomica/plotting.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 84f8bc50..f8ee3f28 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -427,9 +427,12 @@ def placeholder_pop():
                         pop_name = list(pop.keys())[0]
                         pop_labels = pop[pop_name]
 
-                        # Set population aggregation method depending on
+                        # Set population aggregation method depending on the quantity being aggregated
                         if pop_aggregation is None:
-                            if aggregated_units[output_name] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
+                            if aggregated_denominators[pop_labels[0]][output_name] is not None:
+                                # Outputs with denominators use these for a weighted average by default
+                                pop_aggregation = "weighted"
+                            elif aggregated_units[output_name] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
                                 pop_aggregation = "average"
                             else:
                                 pop_aggregation = "sum"

From 7eb03e6fe83a6f173e03d0ff7863e18f71b38963 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Thu, 3 Oct 2024 16:40:55 +1000
Subject: [PATCH 074/161] add a devtest to check numerical precision of time
 vector

---
 tests/test_time_vector.py | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 tests/test_time_vector.py

diff --git a/tests/test_time_vector.py b/tests/test_time_vector.py
new file mode 100644
index 00000000..f8cc04f4
--- /dev/null
+++ b/tests/test_time_vector.py
@@ -0,0 +1,33 @@
+# Test time vector
+# The problem:
+# In atomica, we want to include the end year: Say we're running a simulation from 2000 to 2050 ->
+# we want to be able to get the results for year 2000, 2001, ... and 2050. Hence, we need to run
+# more time steps after 2050
+
+# # On develop branch: Creates 611 entries -> if taken the integer,the last year doesn't have enough entries
+# On temporal weights: Creates 612 entries -> but 13 in 2038 and 11 in 2048
+
+import atomica as at
+import numpy as np
+
+
+def test_time_vector():
+    # Creating a setting object
+    analysis_years = (2000, 2050)
+    dt = 1 / 12
+    settings = at.ProjectSettings(sim_start=analysis_years[0], sim_end=analysis_years[1] + 1 - dt, sim_dt=dt)  #: Atomica project settings
+    tvec = settings.tvec  #: Simulation/result time points
+
+    assert len(set(np.unique(tvec.astype(int), return_counts=True)[1])) == 1
+    
+    # on temporal weights branch: np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1)
+    #  tvec = np.linspace(2000, 2051-1/12, int(np.float32(((2051-1/12 - 2000) / (1/12)))) + 1)
+
+    # on develop branch: np.linspace(self.sim_start, self.sim_end, int((self.sim_end - self.sim_start) / self.sim_dt) + 1)
+    # develop: tvec = np.linspace(2000, 2051-1/12, int((2051-1/12 - 2000) / (1/12)) + 1)
+    # np.unique(tvec.astype(int), return_counts=True)
+
+
+
+if __name__ == "__main__":
+    test_time_vector()
\ No newline at end of file

From 86950e51675569e59a7a900dc226a87c7629ef44 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Thu, 3 Oct 2024 16:41:25 +1000
Subject: [PATCH 075/161] specify dtype in np.linspace

---
 atomica/project.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/project.py b/atomica/project.py
index 2ab71bb0..b7256485 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -98,7 +98,7 @@ def tvec(self) -> np.ndarray:
         # running a model over the range [2000,2066) with dt=1/12, it calculates 791.9999999999982 time steps
         # rather than 792, in that case we can convert to a float32 to effectively round it to 792 before
         # taking the integer. This should generally be safe for the types of values that get used for simulation years
-        return np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1)
+        return np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1, dtype='float32')
 
     def update_time_vector(self, start: float = None, end: float = None, dt: float = None) -> None:
         """

From e7a8d4d6ff63b1bb65e39178893e48b8d23316b3 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Fri, 4 Oct 2024 15:34:37 +1000
Subject: [PATCH 076/161] clean up dev test

---
 tests/test_time_vector.py | 20 ++++++--------------
 1 file changed, 6 insertions(+), 14 deletions(-)

diff --git a/tests/test_time_vector.py b/tests/test_time_vector.py
index f8cc04f4..27d5c969 100644
--- a/tests/test_time_vector.py
+++ b/tests/test_time_vector.py
@@ -2,32 +2,24 @@
 # The problem:
 # In atomica, we want to include the end year: Say we're running a simulation from 2000 to 2050 ->
 # we want to be able to get the results for year 2000, 2001, ... and 2050. Hence, we need to run
-# more time steps after 2050
+# more time steps after 2050, in particular up to 2051-1/12
 
-# # On develop branch: Creates 611 entries -> if taken the integer,the last year doesn't have enough entries
-# On temporal weights: Creates 612 entries -> but 13 in 2038 and 11 in 2048
+# Currently, at.ProjectSettings.tvec creates 611 entries -> if taken the integer,the last year doesn't have enough entries
 
 import atomica as at
 import numpy as np
+import pandas as pd
 
 
 def test_time_vector():
+    # Test 1
     # Creating a setting object
     analysis_years = (2000, 2050)
     dt = 1 / 12
     settings = at.ProjectSettings(sim_start=analysis_years[0], sim_end=analysis_years[1] + 1 - dt, sim_dt=dt)  #: Atomica project settings
     tvec = settings.tvec  #: Simulation/result time points
-
-    assert len(set(np.unique(tvec.astype(int), return_counts=True)[1])) == 1
-    
-    # on temporal weights branch: np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1)
-    #  tvec = np.linspace(2000, 2051-1/12, int(np.float32(((2051-1/12 - 2000) / (1/12)))) + 1)
-
-    # on develop branch: np.linspace(self.sim_start, self.sim_end, int((self.sim_end - self.sim_start) / self.sim_dt) + 1)
-    # develop: tvec = np.linspace(2000, 2051-1/12, int((2051-1/12 - 2000) / (1/12)) + 1)
-    # np.unique(tvec.astype(int), return_counts=True)
-
+    assert len(set(np.unique(tvec.astype(int), return_counts=True)[1])) == 1  # or pd.Series(tvec).astype(int).value_counts()
 
 
 if __name__ == "__main__":
-    test_time_vector()
\ No newline at end of file
+    test_time_vector()

From ab3e4eacaf4ddcd53a13adff4e66737ab9282952 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Fri, 4 Oct 2024 15:37:39 +1000
Subject: [PATCH 077/161] update assert

---
 tests/test_time_vector.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_time_vector.py b/tests/test_time_vector.py
index 27d5c969..652445cb 100644
--- a/tests/test_time_vector.py
+++ b/tests/test_time_vector.py
@@ -18,7 +18,7 @@ def test_time_vector():
     dt = 1 / 12
     settings = at.ProjectSettings(sim_start=analysis_years[0], sim_end=analysis_years[1] + 1 - dt, sim_dt=dt)  #: Atomica project settings
     tvec = settings.tvec  #: Simulation/result time points
-    assert len(set(np.unique(tvec.astype(int), return_counts=True)[1])) == 1  # or pd.Series(tvec).astype(int).value_counts()
+    assert set(np.unique(tvec.astype(int), return_counts=True)[1]) == {1/dt}  # or pd.Series(tvec).astype(int).value_counts()
 
 
 if __name__ == "__main__":

From 8af5553a3e2468d36662122fe60f11be1a7ed930 Mon Sep 17 00:00:00 2001
From: alina-muellenmeister <alina.muellenmeister@burnet.edu.au>
Date: Fri, 4 Oct 2024 15:38:44 +1000
Subject: [PATCH 078/161] following Kelvin's suggestion: Updating to
 np.arange(), multiply by step size and add add sim start

---
 atomica/project.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/atomica/project.py b/atomica/project.py
index b7256485..2dda2c1a 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -88,17 +88,13 @@ def tvec(self) -> np.ndarray:
         """
         Return simulation time vector
 
-        This method uses `linspace` rather than `arange` to avoid accumulating numerical errors that prevent
-        integer years aligning exactly.
+        This method uses `arange` with a step size of 1, then multiplies with the correct step size and add sim start time.
 
         :return: Array of simulation times
 
         """
-        # The int(np.float32()) helps cases where the numerical precision results in unwanted truncation e.g.,
-        # running a model over the range [2000,2066) with dt=1/12, it calculates 791.9999999999982 time steps
-        # rather than 792, in that case we can convert to a float32 to effectively round it to 792 before
-        # taking the integer. This should generally be safe for the types of values that get used for simulation years
-        return np.linspace(self.sim_start, self.sim_end, int(np.float32(((self.sim_end - self.sim_start) / self.sim_dt))) + 1, dtype='float32')
+
+        return np.arange(round((self.sim_end - self.sim_start) / self.sim_dt) + 1) * self.sim_dt + self.sim_start
 
     def update_time_vector(self, start: float = None, end: float = None, dt: float = None) -> None:
         """

From 10ade850d8559f9097bea2fad1b147e2ddf75504 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 15 Oct 2024 06:25:20 +1000
Subject: [PATCH 079/161] Make timed compartment size check more robust

---
 atomica/parameters.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/atomica/parameters.py b/atomica/parameters.py
index ff08e42c..d3fc6f65 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -299,17 +299,17 @@ def apply(self, pop, framework=None, parset=None) -> None:
                 if (comp.name, pop.name) not in self.values:
                     comp._vals[:, 0] = 0
                 else:
-                    vals = self.values[(comp.name, pop.name)]
-                    if (comp._vals.shape[0] > 1 and len(vals) != comp._vals.shape[0]) or (comp._vals.shape[0] == 1 and not np.isscalar(vals)):
-                        # If there is a mismatch between the saved initialization duration and the
-                        # duration for the current simulation, if the values were all zero it would probably
-                        # be safe to assume the values can remain zero. If there are fewer time points in the saved
-                        # initialization we *could* insert them, however, there is a risk that the time points are different
-                        # because the step size was changed rather than the duration being changed, so it would not be valid
-                        # to do that. Therefore, if the sizes don't match and any values are nonzero, simply raise an error
+                    vals = sc.promotetoarray(self.values[(comp.name, pop.name)])
+                    if comp._vals.shape[0] != vals.shape[0]:
                         if np.all(self.values[(comp.name, pop.name)] == 0):
+                            # If there is a mismatch between the saved initialization duration and the
+                            # duration for the current simulation AND if the values were all zero it is probably
+                            # safe to assume the values can remain zero - this considerably increases flexibility in usage
                             comp._vals[:, 0] = 0
                         else:
+                            # Otherwise, although we could try and guess how to assign the values, there is a risk that the time points
+                            # are different because the step size was changed rather than the duration being changed. Therefore,
+                            # if the sizes don't match and any values are nonzero, simply raise an error
                             raise Exception(f'The saved initialization for "{comp.name}" ({pop.name}) has {len(self.values[(comp.name, pop.name)])} time points, but the current parameters lead to a timed compartment duration with {comp._vals.shape[0]} time points. As nonzero values are present, the initialization cannot be applied.')
                     else:
                         comp._vals[:, 0] = self.values[(comp.name, pop.name)]

From 7acb21375583d61ec6aa714ffd51875eb184eae5 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 16 Oct 2024 10:28:14 +1000
Subject: [PATCH 080/161] Fix array length in time aggregation using Kelvin's
 approach

---
 atomica/plotting.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 1aa1708f..9604fe37 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -546,8 +546,7 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             max_step = 0.5 * min(np.diff(s.tvec))  # Subdivide for trapezoidal integration with at least 2 divisions per timestep. Could be a lot of memory for integrating daily timesteps over a full simulation, but unlikely to be prohibitive
             vals = np.full(lower.shape, fill_value=np.nan)
             for i, (l, u) in enumerate(zip(lower, upper)):
-                n = np.ceil((u - l) / max_step) + 1  # Add 1 so that in most cases, we can use the actual timestep values
-                t2 = np.linspace(l, u, int(n))
+                t2 = np.arange(round((u - l) / max_step) + 1) * max_step + l
                 if interpolation_method == "linear":
                     v2 = np.interp(t2, s.tvec, s.vals, left=np.nan, right=np.nan)  # Return NaN outside bounds - it should never be valid to use extrapolated output values in time aggregation
                     vals[i] = np.trapz(y=v2 / scale, x=t2)  # Note division by timescale here, which annualizes it

From 641bc7cc2fbf85d12d6850beb17fdd24eca06880 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 16 Oct 2024 11:47:04 +1000
Subject: [PATCH 081/161] Use exact time points when within numerical tolerance

---
 atomica/plotting.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 9604fe37..5ad7c0df 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -547,6 +547,16 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             vals = np.full(lower.shape, fill_value=np.nan)
             for i, (l, u) in enumerate(zip(lower, upper)):
                 t2 = np.arange(round((u - l) / max_step) + 1) * max_step + l
+                # Numerical precision issues can cause the wrong bin to be used. This is particularly noticable for 'previous'
+                # e.g., if the interpolated finer bins have 2025.9999999 instead of 2026 causing the 2025 value to be
+                # used instead, this can cause a significant overestimation if the value is changing rapidly. Therefore
+                # we need to perform an extra step of using the exact bin values within some tolerance. The values are on
+                # the order of 0.002 (1 day, in years) so the default np.isclose() should be sufficient. However, we may want to apply this
+                # to linear interpolation as well because if the bin falls numerically *outside* the bounds, it will be extrapolated
+                # as NaN which we wouldn't want
+                nearest = np.searchsorted(s.tvec, t2, side='left')
+                isclose = np.isclose(s.tvec[nearest], t2)
+                t2[isclose] = s.tvec[nearest[isclose]]
                 if interpolation_method == "linear":
                     v2 = np.interp(t2, s.tvec, s.vals, left=np.nan, right=np.nan)  # Return NaN outside bounds - it should never be valid to use extrapolated output values in time aggregation
                     vals[i] = np.trapz(y=v2 / scale, x=t2)  # Note division by timescale here, which annualizes it

From b5d0bc712d15b4a890e0381a84519be12817de9a Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 18 Oct 2024 16:22:29 +1000
Subject: [PATCH 082/161] Add missing test framework

---
 tests/timed_initialization_test_framework.xlsx | Bin 0 -> 37023 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tests/timed_initialization_test_framework.xlsx

diff --git a/tests/timed_initialization_test_framework.xlsx b/tests/timed_initialization_test_framework.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..796297da42684835cdbca268e78ffe57f9faa914
GIT binary patch
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HcmV?d00001


From b842bb681aaa036217ce9b5c86b799648d69f598 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 29 Oct 2024 09:40:15 +1000
Subject: [PATCH 083/161] WIP

---
 CHANGELOG.md              | 12 ++++++++++++
 atomica/plotting.py       | 19 ++++++++++++++++---
 atomica/version.py        |  4 ++--
 tests/test_tox_library.py |  2 +-
 4 files changed, 31 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 935876aa..13195598 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,18 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+
+
+## [1.28.7] - 2023-10-29
+
+- If aggregating characteristics with a denominator, weighted aggregations use the denominator of the quantity rather than the total population size to perform the weighting. This can be useful for quantities that are proportions of things other than the population size e.g., proportion of active infections that are diagnosed
+- If aggregating characteristics with a denominator, weighted aggregation will be used by default (rather than 'average')
+
+*Backwards-compatibility notes*
+
+- Results obtained when aggregating characteristics with a denominator using the 'weighted' method will change. To reproduce the previous results, it is necessary to perform the population size weighting manually by removing the aggregation, then extracting the population sizes, and using those to aggregate the outputs. This is considered to be a rare use case because the updated result is a more useful weighting compared to the previous result.
+- Results obtained when aggregating characteristics with a denominator _without_ explicitly specifying a method will change, because 'weighted' aggregation is now used by default. 
+
 ## [1.28.7] - 2023-08-12
 
 - `Project.load_databook()` will no longer populate `Project.databook` when a `ProjectData` instance is supplied rather than a spreadsheet. The intention of `Project.databook` as opposed to `Project.data.to_spreadsheet()` is that the original databook may contain comments or other content that is not preserved when the databook is loaded into a `ProjectData` instance. Therefore, `Project.databook` serves as a record of the original inputs. However, in previous versions of Atomica, if a `ProjectData` instance was provided rather than a spreadsheet, `ProjectData.to_spreadsheet()` would be used to populate `Project.databook`. For large databooks, this can be computationally expensive and particularly affect the use case of passing in a preloaded databook to improve performance. Since the conversion of the `ProjectData` to a spreadsheet upon loading offers no functional difference to creating the spreadsheet from `Project.data` when required, Atomica no longer performs this conversion upfront.
diff --git a/atomica/plotting.py b/atomica/plotting.py
index f8ee3f28..1f9ce1a1 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -353,24 +353,37 @@ def placeholder_pop():
                         units = list(set([output_units[x] for x in labels]))
                         timescales = list(set([np.nan if isna(output_timescales[x]) else output_timescales[x] for x in labels]))  # Ensure that None and nan don't appear as different timescales
 
+                        # Define default aggregation methods
+                        default_aggregations = {
+                            FS.QUANTITY_TYPE_FRACTION: 'average',
+                            FS.QUANTITY_TYPE_PROPORTION: 'average',
+                            FS.QUANTITY_TYPE_PROBABILITY: 'average',
+                            FS.QUANTITY_TYPE_RATE: 'average',
+                            FS.QUANTITY_TYPE_NUMBER: 'sum',
+                            FS.QUANTITY_TYPE_DURATION: 'sum'
+                        }
 
                         # Set default aggregation method depending on the units of the quantity
                         if output_aggregation is None:
                             if labels[0] in denomsize:
+                                # If aggregating characteristics with denominators, then we want to use
                                 # We are aggregating characteristics with denominators, so we want to aggregate with summation
                                 # on the assumption that all of the characteristics being aggregated have the same denominator
                                 output_aggregation = "sum"
                             elif units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
                                 output_aggregation = "average"
-                            else:
+                            elif units[0] in [FS.QUANTITY_TYPE_NUMBER, FS.QUANTITY_TYPE_DURATION]:
                                 output_aggregation = "sum"
+                            else:
+                                logger.warning(f'Unit quantity "{units[0]}" was not recognized and an output aggregation method was not specified, using "sum" by default')
+
 
                         if len(units) > 1:
                             logger.warning("Aggregation for output '%s' is mixing units, this is almost certainly not desired.", output_name)
                             aggregated_units[output_name] = "unknown"
                         else:
-                            if units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE] and output_aggregation == "sum" and len(labels) > 1 and not labels[0] in denomsize:  # Dimensionless, like prevalance
-                                logger.warning("Output '%s' is not in number units, so output aggregation probably should not be 'sum'.", output_name)
+                            if units[0] in AVG_UNITS and output_aggregation == "sum" and len(labels) > 1 and not labels[0] in denomsize:  # Dimensionless, like prevalance
+                                logger.warning(f"Output '{output_name}' is in '{units[0]}' units, so output aggregation probably should not be 'sum'.")
                             aggregated_units[output_name] = output_units[labels[0]]
 
                         if len(timescales) > 1:
diff --git a/atomica/version.py b/atomica/version.py
index 9955dee6..fd17c0ff 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.28.7"
-versiondate = "2024-08-30"
+version = "1.28.8"
+versiondate = "2024-10-29"
 gitinfo = sc.gitinfo(__file__)
diff --git a/tests/test_tox_library.py b/tests/test_tox_library.py
index 704742dc..f9c78d57 100644
--- a/tests/test_tox_library.py
+++ b/tests/test_tox_library.py
@@ -239,6 +239,6 @@ def test_model(model):
 if __name__ == "__main__":
 
     np.seterr(all="raise")
-    # models = ['tb']
+    models = ['combined']
     for m in models:
         test_model(m)

From 863d8df013339160609dbd6d1264fc40c3d043e7 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 30 Oct 2024 15:37:05 +1000
Subject: [PATCH 084/161] Update default aggregation method and selection of
 default method

---
 CHANGELOG.md                           |   5 +-
 atomica/__init__.py                    |   4 +
 atomica/calibration.py                 |   6 +-
 atomica/migration.py                   |   3 +-
 atomica/model.py                       |   1 -
 atomica/parameters.py                  |   8 +-
 atomica/plotting.py                    | 181 +++++++++++++++----------
 docs/examples/Plot-Units.ipynb         |   2 +-
 docs/examples/Plotting.ipynb           |  95 ++++++++++---
 tests/README.md                        |   2 +-
 tests/test_api_workflow.py             |   2 +-
 tests/test_ensemble_parallel.py        |   2 +-
 tests/test_horizontal_bars.py          |   2 +-
 tests/test_tox_bad_frameworks.py       |   2 +-
 tests/test_tox_calibration.py          |   2 +-
 tests/test_tox_cascade.py              |   2 +-
 tests/test_tox_combined.py             |   4 +-
 tests/test_tox_databooks.py            |   2 +-
 tests/test_tox_derivative.py           |   2 +-
 tests/test_tox_docgen.py               |   2 +-
 tests/test_tox_ensemble_cascade.py     |   2 +-
 tests/test_tox_extra.py                |   2 +-
 tests/test_tox_frameworks.py           |   2 +-
 tests/test_tox_indirect_programs.py    |   2 +-
 tests/test_tox_junctions.py            |   2 +-
 tests/test_tox_library.py              |   4 +-
 tests/test_tox_logging.py              |   2 +-
 tests/test_tox_migration.py            |   2 +-
 tests/test_tox_no_compartments.py      |   2 +-
 tests/test_tox_no_initialization.py    |   2 +-
 tests/test_tox_progbooks.py            |   2 +-
 tests/test_tox_program_calculation.py  |   2 +-
 tests/test_tox_reconciliation.py       |   2 +-
 tests/test_tox_results.py              |   2 +-
 tests/test_tox_save.py                 |   2 +-
 tests/test_tox_seasonal.py             |   2 +-
 tests/test_tox_shortcut_init.py        |   2 +-
 tests/test_tox_stochastic.py           |   2 +-
 tests/test_tox_table_parsing.py        |   2 +-
 tests/test_tox_timed_compartments.py   |   2 +-
 tests/text_tox_timed_initialization.py |  31 +++--
 tests/validate_integration.py          |   2 +-
 42 files changed, 255 insertions(+), 149 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 13195598..fb24bb9e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,12 +2,13 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
-
-
 ## [1.28.7] - 2023-10-29
 
 - If aggregating characteristics with a denominator, weighted aggregations use the denominator of the quantity rather than the total population size to perform the weighting. This can be useful for quantities that are proportions of things other than the population size e.g., proportion of active infections that are diagnosed
 - If aggregating characteristics with a denominator, weighted aggregation will be used by default (rather than 'average')
+- Output aggregation of durations now uses 'sum' instead of 'average' by default
+- If no aggregation method is specified, the aggregation method will now be selected separately for each output
+- Population aggregation of non-number units now uses 'weighted' by default rather than 'average'
 
 *Backwards-compatibility notes*
 
diff --git a/atomica/__init__.py b/atomica/__init__.py
index fa6a254f..0ba03568 100644
--- a/atomica/__init__.py
+++ b/atomica/__init__.py
@@ -95,3 +95,7 @@
 from .scenarios import *
 from .system import *
 from .utils import *
+
+import pathlib
+
+rootdir = pathlib.Path(__file__).parent.parent
diff --git a/atomica/calibration.py b/atomica/calibration.py
index a8066683..d9ba1d63 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -67,8 +67,8 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         if not target.has_time_data:  # Only use this output quantity if the user entered time-specific data
             continue
 
-        if pop_name.lower() == 'total':
-            var = atomica.PlotData(result, outputs=var_label, pops = 'total', project=project)
+        if pop_name.lower() == "total":
+            var = atomica.PlotData(result, outputs=var_label, pops="total", project=project)
         else:
             var = result.model.get_pop(pop_name).get_variable(var_label)
 
@@ -78,7 +78,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         # If there is data outside the range when the model was simulated, don't
         # extrapolate the model outputs
         y = data_v
-        if pop_name.lower() == 'total':
+        if pop_name.lower() == "total":
             y2 = np.interp(data_t, var.series[0].tvec, var.series[0].vals, left=np.nan, right=np.nan)
         else:
             y2 = np.interp(data_t, var[0].t, var[0].vals, left=np.nan, right=np.nan)
diff --git a/atomica/migration.py b/atomica/migration.py
index 7c893a48..ea536eff 100644
--- a/atomica/migration.py
+++ b/atomica/migration.py
@@ -807,9 +807,8 @@ def _convert_framework_columns(framework):
     framework._validate()
     return framework
 
+
 @migration("ParameterSet", "1.28.3", "1.28.4", "Add initialization atttribute")
 def _parset_add_initialization(parset):
     parset.initialization = None
     return parset
-
-
diff --git a/atomica/model.py b/atomica/model.py
index 173de76d..2e67badf 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1848,7 +1848,6 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
 
         """
 
-
         if not self.comps:
             # If this population has no compartments, then nothing needs to be done
             return
diff --git a/atomica/parameters.py b/atomica/parameters.py
index 5f668300..d6bae416 100644
--- a/atomica/parameters.py
+++ b/atomica/parameters.py
@@ -341,16 +341,16 @@ def from_excel(cls, excelfile: pd.ExcelFile):
         """
         # excelfile = spreadsheet.pandas()
 
-        metadata, value_df = atomica.excel.read_dataframes(excelfile.book['Initialization'])
+        metadata, value_df = atomica.excel.read_dataframes(excelfile.book["Initialization"])
 
         values = {}
-        for k,s in value_df.T.reset_index().T.set_index([0,1]).iterrows():
+        for k, s in value_df.T.reset_index().T.set_index([0, 1]).iterrows():
             v = s.dropna().values
             values[k] = v[0] if len(v) == 1 else v
 
         self = cls(values)
 
-        for k,v in metadata.T.reset_index().T.set_index(0)[1].to_dict().items():
+        for k, v in metadata.T.reset_index().T.set_index(0)[1].to_dict().items():
             setattr(self, k, v)
 
         return self
@@ -534,7 +534,7 @@ def make_constant(self, year: float):
         :param year: Year to use for interpolation
         :return: A copy of the ParameterSet with constant parameters
         """
-        ps = self.copy(f'{self.name} (constant)')
+        ps = self.copy(f"{self.name} (constant)")
         for par in ps.all_pars():
             for ts in par.ts.values():
                 ts.insert(None, ts.interpolate(year))
diff --git a/atomica/plotting.py b/atomica/plotting.py
index 1f9ce1a1..be690e5e 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -43,6 +43,28 @@
 settings["dpi"] = 150  # average quality
 settings["transparent"] = False
 
+# Define default aggregation methods for standard units
+DEFAULT_OUTPUT_AGGREGATIONS = {
+    FS.QUANTITY_TYPE_FRACTION: "average",
+    FS.QUANTITY_TYPE_PROPORTION: "average",
+    FS.QUANTITY_TYPE_PROBABILITY: "average",
+    FS.QUANTITY_TYPE_RATE: "average",
+    FS.QUANTITY_TYPE_DURATION: "sum",  # e.g., diagnosis time + treatment time should be summed within a population
+    FS.QUANTITY_TYPE_NUMBER: "sum",
+    "Number of people": "sum",  # This unit is used by compartments and characteristics without denominators
+}
+
+DEFAULT_POP_AGGREGATIONS = {
+    FS.QUANTITY_TYPE_FRACTION: "weighted",
+    FS.QUANTITY_TYPE_PROPORTION: "weighted",
+    FS.QUANTITY_TYPE_PROBABILITY: "weighted",
+    FS.QUANTITY_TYPE_RATE: "weighted",
+    FS.QUANTITY_TYPE_DURATION: "weighted",  # e.g., diagnosis time should be averaged across populations
+    FS.QUANTITY_TYPE_NUMBER: "sum",
+    "Number of people": "sum",  # This unit is used by compartments and characteristics without denominators
+    "": "weighted",  # This unit is used by characteristics with denominators
+}
+
 
 def save_figs(figs, path=".", prefix="", fnames=None, file_format="png") -> None:
     """
@@ -143,7 +165,8 @@ class PlotData:
                     all of the items being included in the sum must have the same denominator. This denominator
                     will then continue to be tracked for the sum.
           - 'average' - unweighted average of quantities. If averaging characteristics with a denominator, then
-                        the denominator will be dropped afterwards (so subsequent population aggregation
+                        the denominator will be dropped afterwards (so subsequent population aggregation will be
+                        performed based on total population size)
           - 'weighted' - weighted average where the weight is the
                             - Link source compartment size, or
                             - Characteristic denominator size, or
@@ -159,16 +182,20 @@ class PlotData:
              - 'average' will give ``((a/b)+(c/d))/2``. No denominator is tracked for weighted population aggregation, so such aggregation would use the total population size.
              - 'weighted' will give ``(a+c)/(b+d)``. The denominator ``b+d`` can be used for weighted population aggregation.
 
-        The default is 'average' for dimensionless units, probabilities, rates etc. and 'sum' for everything else, with the exception of characteristics
-        with denominators, where 'sum' is used by default.
+        The default is 'weighted' for dimensionless units, probabilities, rates, and fractions, and 'sum' for everything else, with the exception of characteristics
+        with denominators, where
+            - 'sum' is used if the characteristics all have the same denominator
+            - 'weighted' is used if the characteristics all have different denominators
+            - 'average' is used otherwise
 
-    :param pop_aggregation: Same options as output_aggregation, except that 'weighted'
-                            uses population sizes OR characteristic denominator size. Note that output aggregation
+    :param pop_aggregation: Same options as output_aggregation, except
+                            - 'weighted' uses population sizes OR characteristic denominator size. Note that output aggregation
                             is performed before population aggregation. This also means that population
                             aggregation can be used to combine already aggregated outputs (e.g.
                             can first sum 'sus'+'vac' within populations, and then take weighted
                             average across populations). This weighting is principally intended for aggregating
                             nondimensional quantities (fractions, proportions etc.).
+                            - By default, quantities in 'duration' units are averaged across populations rather than summed.
 
     :param project: Optionally provide a :class:`Project` object, which will be used to convert names to labels in the outputs for plotting.
 
@@ -237,11 +264,12 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
             aggregated_outputs = defaultdict(dict)  # Dict with aggregated_outputs[pop_label][aggregated_output_label]
             aggregated_units = dict()  # Dict with aggregated_units[aggregated_output_label]
             aggregated_timescales = dict()
-            aggregated_denominators = defaultdict(dict) # Store aggregated denominators where available at the same dimensionality as outputs
+            aggregated_denominators = defaultdict(dict)  # Store aggregated denominators where available at the same dimensionality as outputs
             output_units = dict()
             output_timescales = dict()
             compsize = dict()
-            denomsize = dict() # Characteristic denominator size
+            denoms = sc.odict()  # Record denominators associated with input characteristics
+
             popsize = dict()
             # Defaultdict won't throw key error when checking outputs.
             data_label = defaultdict(str)  # Label used to identify which data to plot, maps output label to data label.
@@ -299,7 +327,7 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
                         output_timescales[output_label] = None
                         data_label[output_label] = vars[0].name
                         if isinstance(vars[0], Characteristic) and vars[0].denominator is not None:
-                            denomsize[output_label] = vars[0].denominator.vals
+                            denoms[output_label] = vars[0].denominator  # Record the denominator for this quantity
                     else:
                         raise Exception("Unknown type")
 
@@ -322,10 +350,9 @@ def placeholder_pop():
                     par = Parameter(pop=placeholder_pop, name=output_label)
                     fcn, dep_labels = parse_function(f_stack_str)
                     deps = {}
-                    displayed_annualization_warning = False
                     for dep_label in dep_labels:
                         vars = pop.get_variable(dep_label)
-                        if t_bins is not None and (isinstance(vars[0], Link) or isinstance(vars[0], Parameter)) and time_aggregation == "sum" and not displayed_annualization_warning:
+                        if t_bins is not None and (isinstance(vars[0], Link) or isinstance(vars[0], Parameter)) and time_aggregation == "sum":
                             raise Exception("Function includes Parameter/Link so annualized rates are being used. Aggregation should therefore use 'average' rather than 'sum'.")
                         deps[dep_label] = vars
                     par._fcn = fcn
@@ -339,7 +366,7 @@ def placeholder_pop():
                 # Third pass, aggregate them according to any aggregations present
                 for output in outputs:  # For each final output
                     if isinstance(output, dict):
-                        output_name = list(output.keys())[0]
+                        output_name = list(output.keys())[0]  # nb. there should be only one item in the output dict at this point
                         labels = output[output_name]
 
                         # If this was a function, aggregation over outputs doesn't apply so just put it straight in.
@@ -347,42 +374,43 @@ def placeholder_pop():
                             aggregated_outputs[pop_label][output_name] = data_dict[output_name]
                             aggregated_units[output_name] = "unknown"  # Also, we don't know what the units of a function are
                             aggregated_timescales[output_name] = None  # Timescale is lost
-                            aggregated_denominators[pop_label][output_name] = None # Denominators are not tracked
+                            aggregated_denominators[pop_label][output_name] = None  # Denominators are not tracked
                             continue
 
                         units = list(set([output_units[x] for x in labels]))
                         timescales = list(set([np.nan if isna(output_timescales[x]) else output_timescales[x] for x in labels]))  # Ensure that None and nan don't appear as different timescales
 
-                        # Define default aggregation methods
-                        default_aggregations = {
-                            FS.QUANTITY_TYPE_FRACTION: 'average',
-                            FS.QUANTITY_TYPE_PROPORTION: 'average',
-                            FS.QUANTITY_TYPE_PROBABILITY: 'average',
-                            FS.QUANTITY_TYPE_RATE: 'average',
-                            FS.QUANTITY_TYPE_NUMBER: 'sum',
-                            FS.QUANTITY_TYPE_DURATION: 'sum'
-                        }
-
-                        # Set default aggregation method depending on the units of the quantity
-                        if output_aggregation is None:
-                            if labels[0] in denomsize:
-                                # If aggregating characteristics with denominators, then we want to use
-                                # We are aggregating characteristics with denominators, so we want to aggregate with summation
-                                # on the assumption that all of the characteristics being aggregated have the same denominator
-                                output_aggregation = "sum"
-                            elif units[0] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
-                                output_aggregation = "average"
-                            elif units[0] in [FS.QUANTITY_TYPE_NUMBER, FS.QUANTITY_TYPE_DURATION]:
-                                output_aggregation = "sum"
+                        # Set default aggregation method depending on the units of the quantity. If the aggregation method has not been specified
+                        # then select a default aggregation method separately for each output
+                        if output_aggregation is not None:
+                            aggregation = output_aggregation
+                        elif denoms:
+                            try:
+                                denominators = set([denoms[x].name for x in labels])
+                            except KeyError as e:
+                                raise Exception("If an aggregation includes characteristics with denominators, then all of the included quantities must be characteristics with denominators") from e
+
+                            if len(denominators) == 1:
+                                # If all characteristics share the same denominator e.g., SP and SN prevalence, then we want to add the characteristics together
+                                aggregation = "sum"
+                            elif len(denominators) == len(labels):
+                                # If all characteristics have different denominators e.g., proportion of SP/SN on treatment aggregated over SP and SN, then use weighted averaging
+                                aggregation = "weighted"
                             else:
-                                logger.warning(f'Unit quantity "{units[0]}" was not recognized and an output aggregation method was not specified, using "sum" by default')
-
+                                # Otherwise if mixing denominators, use a direct average. In general this should not happen because the result is not readily interpretable
+                                aggregation = "average"
+                        elif units[0] in DEFAULT_OUTPUT_AGGREGATIONS:
+                            aggregation = DEFAULT_OUTPUT_AGGREGATIONS[units[0]]
+                        else:
+                            logger.warning(f'Unit quantity "{units[0]}" was not recognized and an output aggregation method was not specified, using "sum" by default')
+                            aggregation = "sum"
 
                         if len(units) > 1:
                             logger.warning("Aggregation for output '%s' is mixing units, this is almost certainly not desired.", output_name)
                             aggregated_units[output_name] = "unknown"
                         else:
-                            if units[0] in AVG_UNITS and output_aggregation == "sum" and len(labels) > 1 and not labels[0] in denomsize:  # Dimensionless, like prevalance
+                            # If the user has requested a sum for a unit that doesn't use summation by default, there's a decent chance that this is a mistake
+                            if aggregation == "sum" and DEFAULT_OUTPUT_AGGREGATIONS.get(units[0], "sum") != "sum" and len(labels) > 1 and not labels[0] in denoms:  # Dimensionless, like prevalance
                                 logger.warning(f"Output '{output_name}' is in '{units[0]}' units, so output aggregation probably should not be 'sum'.")
                             aggregated_units[output_name] = output_units[labels[0]]
 
@@ -392,45 +420,56 @@ def placeholder_pop():
                         else:
                             aggregated_timescales[output_name] = output_timescales[labels[0]]
 
-                        if output_aggregation == "sum":
+                        # Perform additional validation on the denominators
+                        if denoms:
+                            unique_denoms = set([denoms[x].name for x in labels])
+
+                            if len(unique_denoms) > 1 and len(unique_denoms) < len(labels):
+                                logger.warning("When aggregating characteristics with denominators, they should generally either all have the same denominator, or all have different denominators. Partially duplicate denominators is likely not desired.")
+
+                            if len(unique_denoms) > 1:
+                                # Check that the denominators are non-overlapping
+                                comps = [set(result.framework.get_charac_includes(x)) for x in labels]
+                                if sum(len(x) for x in comps) != len(set().union(*comps)):
+                                    logger.warning("When aggregating characteristics with different denominators, generally there should be no overlap in the denominators. However, some compartments are shared between the requested characteristic denominators. Unless this is intentional, this is likely a mistake that should be investigated.")
+
+                        if aggregation == "sum":
                             aggregated_outputs[pop_label][output_name] = sum(data_dict[x] for x in labels)  # Add together all the outputs
 
                             # If summing characteristics that have denominators, then all quantities should have denominators, and they should all be equal
                             # For example, summing SP-TB and SN-TB prevalance where they both have the 'alive' denominator
-                            has_denominator = [x in denomsize for x in labels]
-                            assert all(has_denominator) or not any(has_denominator)  # An output aggregation must either have no quantities with denominators, or only contain quantities with denominators
+                            has_denominator = [x in denoms for x in labels]
                             if all(has_denominator):
-                                for label in labels:
-                                    assert np.all(denomsize[label] == denomsize[labels[0]]), 'When aggregating characteristics with denominators, if summing them (default) then all quantities must have the same denominator. The "weighted" aggregation can be used to combine characteristics with different denominators, on the assumption that their denominators do not overlap.'
-                                aggregated_denominators[pop_label][output_name] = denomsize[labels[0]]
+                                assert len(set([denoms[x].name for x in labels])) == 1, 'When aggregating characteristics with denominators, if summing them then all quantities must have the same denominator. The "weighted" aggregation can be used to combine characteristics with different denominators, on the assumption that their denominators do not overlap.'
+                                aggregated_denominators[pop_label][output_name] = denoms[0].vals
                             elif not any(has_denominator):
                                 aggregated_denominators[pop_label][output_name] = None
                             else:
-                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denomsize]} while these do not {[x for x in labels if x not in denomsize]}")
+                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denoms]} while these do not {[x for x in labels if x not in denoms]}")
 
-                        elif output_aggregation == "average":
+                        elif aggregation == "average":
                             aggregated_outputs[pop_label][output_name] = sum(data_dict[x] for x in labels)
                             aggregated_outputs[pop_label][output_name] /= len(labels)
-                            aggregated_denominators[pop_label][output_name] = None # If taking a direct average then drop the denominator
-                        elif output_aggregation == "weighted":
-                            has_denominator = [x in denomsize for x in labels]
+                            aggregated_denominators[pop_label][output_name] = None  # If taking a direct average then drop the denominator
+                        elif aggregation == "weighted":
+                            has_denominator = [x in denoms for x in labels]
                             assert all(has_denominator) or not any(has_denominator)  # An output aggregation must either have no quantities with denominators, or only contain quantities with denominators
                             if all(has_denominator):
                                 # Take weighted average based on denominator size, and then store the combined denominator further population aggregation
-                                aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * denomsize[x] for x in labels)
-                                aggregated_denominators[pop_label][output_name] = sum([denomsize[x] for x in labels])
+                                aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * denoms[x].vals for x in labels)
+                                aggregated_denominators[pop_label][output_name] = sum([denoms[x].vals for x in labels])
                                 aggregated_outputs[pop_label][output_name] /= aggregated_denominators[pop_label][output_name]
                             elif not any(has_denominator):
                                 aggregated_outputs[pop_label][output_name] = sum(data_dict[x] * compsize[x] for x in labels)
                                 aggregated_outputs[pop_label][output_name] /= sum([compsize[x] for x in labels])
                                 aggregated_denominators[pop_label][output_name] = None  # If taking a direct average then drop the denominator
                             else:
-                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denomsize]} while these do not {[x for x in labels if x not in denomsize]}")
+                                raise Exception(f"When aggregating outputs, if any quantities being aggregated have denominators, then they must all have denominators. These quantities have denominators {[x for x in labels if x in denoms]} while these do not {[x for x in labels if x not in denoms]}")
                     else:
                         aggregated_outputs[pop_label][output] = data_dict[output]
                         aggregated_units[output] = output_units[output]
                         aggregated_timescales[output] = output_timescales[output]
-                        aggregated_denominators[pop_label][output] = denomsize.get(output)
+                        aggregated_denominators[pop_label][output] = denoms[output].vals if output in denoms else None
 
             # Now aggregate over populations
             # If we have requested a reduction over populations, this is done for every output present
@@ -441,32 +480,38 @@ def placeholder_pop():
                         pop_labels = pop[pop_name]
 
                         # Set population aggregation method depending on the quantity being aggregated
-                        if pop_aggregation is None:
-                            if aggregated_denominators[pop_labels[0]][output_name] is not None:
-                                # Outputs with denominators use these for a weighted average by default
-                                pop_aggregation = "weighted"
-                            elif aggregated_units[output_name] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE]:
-                                pop_aggregation = "average"
-                            else:
-                                pop_aggregation = "sum"
+                        if pop_aggregation is not None:
+                            aggregation = pop_aggregation
+                        elif aggregated_denominators[pop_labels[0]][output_name] is not None:
+                            # Outputs with denominators use weighted by default
+                            aggregation = "weighted"
+                        elif aggregated_units[output_name] in DEFAULT_POP_AGGREGATIONS:
+                            aggregation = DEFAULT_POP_AGGREGATIONS[aggregated_units[output_name]]
+                        else:
+                            logger.warning(f'Unit quantity "{aggregated_units[output_name]}" was not recognized and an output aggregation method was not specified, using "sum" by default')
+                            aggregation = "sum"
 
-                        if pop_aggregation == "sum":
-                            if aggregated_units[output_name] in ["", FS.QUANTITY_TYPE_FRACTION, FS.QUANTITY_TYPE_PROPORTION, FS.QUANTITY_TYPE_PROBABILITY, FS.QUANTITY_TYPE_RATE] and len(pop_labels) > 1:
-                                logger.warning("Output '%s' is not in number units, so population aggregation probably should not be 'sum'", output_name)
+                        # Perform aggregation
+                        if aggregation == "sum":
+                            if aggregation == "sum" and DEFAULT_POP_AGGREGATIONS.get(aggregated_units[output_name], "sum") != "sum" and len(pop_labels) > 1:
+                                logger.warning(f"Output '{output_name}' is in '{aggregated_units[output_name]}' units, so output aggregation should probably be '{DEFAULT_POP_AGGREGATIONS.get(aggregated_units[output_name])}', not 'sum'.")
                             vals = sum(aggregated_outputs[x][output_name] for x in pop_labels)  # Add together all the outputs
-                        elif pop_aggregation == "average":
+                        elif aggregation == "average":
+                            if aggregated_denominators[pop_labels[0]][output_name] is not None:
+                                logger.warning(f"Output '{output_name}' has a denominator, so output aggregation should probably be 'weighted' rather than 'average'")
                             vals = sum(aggregated_outputs[x][output_name] for x in pop_labels)  # Add together all the outputs
                             vals /= len(pop_labels)
-                        elif pop_aggregation == "weighted":
+                        elif aggregation == "weighted":
                             if aggregated_denominators[pop_labels[0]][output_name] is not None:
-                                weights = {x:aggregated_denominators[x][output_name] for x in pop_labels}
+                                weights = {x: aggregated_denominators[x][output_name] for x in pop_labels}
                             else:
-                                weights = {x:popsize[x] for x in pop_labels}
+                                weights = {x: popsize[x] for x in pop_labels}
                             numerator = sum(aggregated_outputs[x][output_name] * weights[x] for x in pop_labels)  # Add together all the outputs
                             denominator = sum([weights[x] for x in pop_labels])
                             vals = np.divide(numerator, denominator, out=np.full(numerator.shape, np.nan, dtype=float), where=numerator != 0)
                         else:
-                            raise Exception("Unknown pop aggregation method")
+                            raise Exception(f'Unknown population aggregation method "{aggregation}"')
+
                         self.series.append(Series(tvecs[result_label], vals, result_label, pop_name, output_name, data_label[output_name], units=aggregated_units[output_name], timescale=aggregated_timescales[output_name], data_pop=pop_name))
                     else:
                         vals = aggregated_outputs[pop][output_name]
@@ -2064,7 +2109,7 @@ def _extract_labels(input_arrays) -> set:
     for x in input_arrays:
         if isinstance(x, dict):
             k = list(x.keys())
-            assert len(k) == 1, "Aggregation dict can only have one key"
+            assert len(k) == 1, "Aggregation dict can only have one key, for multiple aggregations, use a list of dicts"
             if sc.isstring(x[k[0]]):
                 continue
             else:
diff --git a/docs/examples/Plot-Units.ipynb b/docs/examples/Plot-Units.ipynb
index 4abc8f50..50ad6dd1 100644
--- a/docs/examples/Plot-Units.ipynb
+++ b/docs/examples/Plot-Units.ipynb
@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "testdir = at.parent_dir()\n",
+    "testdir = at.rootdir/'tests'\n",
     "P = at.Project(framework='unit_demo_framework.xlsx',databook='unit_demo_databook.xlsx')\n",
     "P.load_progbook('unit_demo_progbook.xlsx')\n",
     "res = P.run_sim('default','default',at.ProgramInstructions(start_year=2018))"
diff --git a/docs/examples/Plotting.ipynb b/docs/examples/Plotting.ipynb
index 7c8d3180..a3ff512c 100644
--- a/docs/examples/Plotting.ipynb
+++ b/docs/examples/Plotting.ipynb
@@ -335,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some aggregations do not make sense. For instance, if you aggregate prevalances, they should be averaged, not summed. In cases where the code is able to identify this, a warning will be displayed:"
+    "Some aggregations do not make sense. For instance, if you aggregate prevalances, a weighted average should be performed. In cases where the code is able to identify this, a warning will be displayed:"
    ]
   },
   {
@@ -348,11 +348,21 @@
     "figs = at.plot_series(d)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(par_results,pop_aggregation='average',pops='total',outputs=['ac_prev'])\n",
+    "figs = at.plot_series(d)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you do not specify an aggregation method, the aggregation method will automatically be selected based on the units of the quantity being plotted. Non-dimensional quantities, proportions, fractions, and prevalances will be averaged instead of summed. "
+    "If you do not specify an aggregation method, the aggregation method will automatically be selected based on the units of the quantity being plotted. Non-dimensional quantities, proportions, fractions, and prevalances will use a population-weighted average instead of summation."
    ]
   },
   {
@@ -365,6 +375,57 @@
     "figs = at.plot_series(d)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Characteristic aggregations\n",
+    "\n",
+    "The default aggregation for characteristics with denominators incorporates information about the denominators to select the aggregation method. For output aggregations involving characteristics, if the characteristics share a common denominator, they will be summed. This is useful for summing characteristics like prevalance of mutually exclusive strains e.g., SP and SN prevalence for TB:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(par_results,pops='total',outputs=['sp_prev',\n",
+    "                                                  'sn_prev',\n",
+    "                                                  'ac_prev',\n",
+    "                                                  {'Aggregated':['sp_prev','sn_prev']}])\n",
+    "figs = at.plot_series(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note how the use of summation means that aggregating the SP and SN prevalences returns a value equal to the framework's definition of total prevalence. Alternatively, if aggregating characteristics with different denominators e.g., if there was a characteristic for diagnosed infections as a proportion of all infections, for SP and SN TB separately, a weighted average will be used to combine the characteristics. If neither of these statements are true, a raw average will be used, and a warning will be displayed. If aggregating characteristics that have different denominators, these should generally be no overlap in the compartments for those denominators in order for the weighted average to make sense. If any common compartments are detected, a warning will be displayed. \n",
+    "\n",
+    "For population aggregations of characteristics with denominators, the characteristic denominator will be used to perform weighted averages, rather than the total population size. This is useful when the denominator is something other than the population size. For example, the diagnosis sufficiency is given by people diagnosis divided by all infections. We can calculate this at the population level explicitly by summing the number of people diagnosed and the number of known infections and dividing the totals, and also by using aggregation of a characteristic that uses the number of infections as a denominator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d1 = at.PlotData(par_results, 'known_inf',pops='total')\n",
+    "d2 = at.PlotData(par_results, 'ac_inf'   ,pops='total')\n",
+    "d = at.PlotData(par_results, 'diag_suff' ,pops='total', pop_aggregation='weighted')\n",
+    "figs = at.plot_series(d)\n",
+    "figs[0].axes[0].plot(d1.series[0].tvec, d1.series[0].vals/d2.series[0].vals, 'r--');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the `'weighted'` average method produces the expected result, using the characteristic's denominator (`ac_inf`) as the weighting factor rather than the total population size. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -429,9 +490,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is obviously incorrect because the value is greater than 1. Instead, we need to specify that we are averaging across populations. However, note that because the aggregation is an arbitrary function, the units of the output quantity are unknown. Therefore, a warning is not displayed (because the code does not know what the units are).\n",
-    "\n",
-    "Because the populations have very different sizes, a popsize-weighted average would be most appropriate:"
+    "This is obviously incorrect because the value is greater than 1. Because the aggregation is an arbitrary function, the units of the output quantity are unknown, and Atomica defaults to using summation for the aggregation. We need to instead explicitly specify the aggregation method. As the populations have very different sizes, a popsize-weighted average would be most appropriate."
    ]
   },
   {
@@ -444,6 +503,15 @@
     "figs = at.plot_series(d)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "In general, averaging across populations should use the 'weighted' method rather than 'average'. For quantities that use standard units recognized by Atomica, the 'weighted' method will be selected by default. \n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -486,17 +554,6 @@
     "figs = at.plot_series(d)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Characteristic prevalance units (dimensionless)\n",
-    "d = at.PlotData(par_results,pops='total',output_aggregation='average',pop_aggregation='average',outputs=[{'Test':['lt_prev','spd_prev']}])\n",
-    "figs = at.plot_series(d)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2455,9 +2512,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:atomica311] *",
+   "display_name": "Python [conda env:atomica312]",
    "language": "python",
-   "name": "conda-env-atomica311-py"
+   "name": "conda-env-atomica312-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2469,7 +2526,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tests/README.md b/tests/README.md
index 993869d9..6a6171a9 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -12,6 +12,6 @@ to the `site-packages` directory instead of the repo. Therefore, inside the test
 In practice, it can be useful to simply define the following two variables at the start of a test script:
 
 ```python
-testdir = at.parent_dir()
+testdir = at.rootdir/'tests'
 tmpdir = testdir / "temp"
 ```
\ No newline at end of file
diff --git a/tests/test_api_workflow.py b/tests/test_api_workflow.py
index 0d4ac4e0..07e9366e 100644
--- a/tests/test_api_workflow.py
+++ b/tests/test_api_workflow.py
@@ -11,7 +11,7 @@
 
 test = "sir"  # This test workflow only works with the SIR model
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 F = at.ProjectFramework(at.LIBRARY_PATH / test + "_framework.xlsx")
diff --git a/tests/test_ensemble_parallel.py b/tests/test_ensemble_parallel.py
index 33afdf99..2c31c87a 100644
--- a/tests/test_ensemble_parallel.py
+++ b/tests/test_ensemble_parallel.py
@@ -10,7 +10,7 @@
 if __name__ == "__main__":
 
     # BASIC SETUP
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
     P = at.Project(framework=testdir / "test_uncertainty_framework.xlsx", databook=testdir / "test_uncertainty_databook.xlsx")
     low_uncertainty_progset = at.ProgramSet.from_spreadsheet(testdir / "test_uncertainty_low_progbook.xlsx", project=P)
     high_uncertainty_progset = at.ProgramSet.from_spreadsheet(testdir / "test_uncertainty_high_progbook.xlsx", project=P)
diff --git a/tests/test_horizontal_bars.py b/tests/test_horizontal_bars.py
index fcc85b1c..1559f907 100644
--- a/tests/test_horizontal_bars.py
+++ b/tests/test_horizontal_bars.py
@@ -2,7 +2,7 @@
 from atomica import ProjectFramework, Project
 import os
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 F = ProjectFramework(testdir / "framework_sir_dynamic.xlsx")
diff --git a/tests/test_tox_bad_frameworks.py b/tests/test_tox_bad_frameworks.py
index 1833cd4c..d2205968 100644
--- a/tests/test_tox_bad_frameworks.py
+++ b/tests/test_tox_bad_frameworks.py
@@ -6,7 +6,7 @@
 import atomica as at
 import pytest
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 fdir = testdir / "bad_frameworks"
 
 # List available models based on which framework files exist
diff --git a/tests/test_tox_calibration.py b/tests/test_tox_calibration.py
index 39e2b71b..118c767c 100644
--- a/tests/test_tox_calibration.py
+++ b/tests/test_tox_calibration.py
@@ -4,7 +4,7 @@
 import atomica as at
 import pytest
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_cascade.py b/tests/test_tox_cascade.py
index 5ebbbc91..895b252a 100644
--- a/tests/test_tox_cascade.py
+++ b/tests/test_tox_cascade.py
@@ -5,7 +5,7 @@
 import os
 import pytest
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 try:
diff --git a/tests/test_tox_combined.py b/tests/test_tox_combined.py
index c26536a4..6559e052 100644
--- a/tests/test_tox_combined.py
+++ b/tests/test_tox_combined.py
@@ -2,7 +2,7 @@
 import numpy as np
 import atomica as at
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
@@ -91,7 +91,7 @@ def test_combined_plots():
 
 
 def test_combined_order():
-    testdir = at.parent_dir()  # Must be relative to current file to work with tox
+    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
     P = at.Project(framework=testdir / "test_order_framework.xlsx", databook=testdir / "test_order_databook.xlsx")
     res = P.results[0]
 
diff --git a/tests/test_tox_databooks.py b/tests/test_tox_databooks.py
index 7958424f..c852d58a 100644
--- a/tests/test_tox_databooks.py
+++ b/tests/test_tox_databooks.py
@@ -6,7 +6,7 @@
 from atomica import ProjectFramework, Project, ProjectData
 import sciris as sc
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_derivative.py b/tests/test_tox_derivative.py
index fc9e9ded..b44dbc20 100644
--- a/tests/test_tox_derivative.py
+++ b/tests/test_tox_derivative.py
@@ -8,7 +8,7 @@
 
 def test_derivative():
 
-    testdir = at.parent_dir()  # Must be relative to current file to work with tox
+    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
     F = at.ProjectFramework(testdir / "framework_derivative_test.xlsx")
     D = at.ProjectData.new(F, np.arange(2000, 2010), pops={"mosquitos": "Mosquitos"}, transfers=0)
diff --git a/tests/test_tox_docgen.py b/tests/test_tox_docgen.py
index b5d98ab6..a5bbafd4 100644
--- a/tests/test_tox_docgen.py
+++ b/tests/test_tox_docgen.py
@@ -7,7 +7,7 @@
 import sciris as sc
 import pytest
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 frameworks = list()
 for f in at.LIBRARY_PATH.iterdir():
diff --git a/tests/test_tox_ensemble_cascade.py b/tests/test_tox_ensemble_cascade.py
index a98f2986..a5b09f13 100644
--- a/tests/test_tox_ensemble_cascade.py
+++ b/tests/test_tox_ensemble_cascade.py
@@ -10,7 +10,7 @@
 
 def test_ensemble_cascade():
     # BASIC SETUP
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
 
     P = at.Project(framework=testdir / "test_uncertainty_framework.xlsx", databook=testdir / "test_uncertainty_databook.xlsx")
 
diff --git a/tests/test_tox_extra.py b/tests/test_tox_extra.py
index 7fa4c4ce..5112f6bf 100644
--- a/tests/test_tox_extra.py
+++ b/tests/test_tox_extra.py
@@ -4,7 +4,7 @@
 
 
 def test_extra_data_pop():
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
 
     F_path = testdir / "framework_sir_dynamic.xlsx"
     D_path = testdir / "databook_sir_dynamic_extra.xlsx"
diff --git a/tests/test_tox_frameworks.py b/tests/test_tox_frameworks.py
index 303ee535..eac01c48 100644
--- a/tests/test_tox_frameworks.py
+++ b/tests/test_tox_frameworks.py
@@ -5,7 +5,7 @@
 import pytest
 import pandas as pd
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 frameworks = [
diff --git a/tests/test_tox_indirect_programs.py b/tests/test_tox_indirect_programs.py
index 4949fbd7..29fa5c5e 100644
--- a/tests/test_tox_indirect_programs.py
+++ b/tests/test_tox_indirect_programs.py
@@ -2,7 +2,7 @@
 import numpy as np
 import os
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
 
 def test_indirect_programs():
diff --git a/tests/test_tox_junctions.py b/tests/test_tox_junctions.py
index d28c054c..102f59e6 100644
--- a/tests/test_tox_junctions.py
+++ b/tests/test_tox_junctions.py
@@ -4,7 +4,7 @@
 import atomica as at
 import os
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
 
 def test_junctions():
diff --git a/tests/test_tox_library.py b/tests/test_tox_library.py
index f9c78d57..286bd48b 100644
--- a/tests/test_tox_library.py
+++ b/tests/test_tox_library.py
@@ -8,7 +8,7 @@
 import numpy as np
 import sciris as sc
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 # List available models based on which framework files exist
@@ -239,6 +239,6 @@ def test_model(model):
 if __name__ == "__main__":
 
     np.seterr(all="raise")
-    models = ['combined']
+    models = ["combined"]
     for m in models:
         test_model(m)
diff --git a/tests/test_tox_logging.py b/tests/test_tox_logging.py
index 6a5e8e34..6b585ae2 100644
--- a/tests/test_tox_logging.py
+++ b/tests/test_tox_logging.py
@@ -2,7 +2,7 @@
 
 import atomica as at
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_migration.py b/tests/test_tox_migration.py
index 63393827..2308294e 100644
--- a/tests/test_tox_migration.py
+++ b/tests/test_tox_migration.py
@@ -14,7 +14,7 @@ def test_migration():
 
     at.logger.setLevel("DEBUG")
 
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
     tmpdir = testdir / "temp"
 
     P = at.Project.load(testdir / "migration_test_with_scenarios.prj")
diff --git a/tests/test_tox_no_compartments.py b/tests/test_tox_no_compartments.py
index 765d90e8..0b5e84a4 100644
--- a/tests/test_tox_no_compartments.py
+++ b/tests/test_tox_no_compartments.py
@@ -3,7 +3,7 @@
 import os
 import pytest
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
 
 def test_no_compartments():
diff --git a/tests/test_tox_no_initialization.py b/tests/test_tox_no_initialization.py
index 4583ff8f..22cc8831 100644
--- a/tests/test_tox_no_initialization.py
+++ b/tests/test_tox_no_initialization.py
@@ -4,7 +4,7 @@
 import atomica as at
 import numpy as np
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_progbooks.py b/tests/test_tox_progbooks.py
index 71de7db6..fe480a08 100644
--- a/tests/test_tox_progbooks.py
+++ b/tests/test_tox_progbooks.py
@@ -3,7 +3,7 @@
 import sciris as sc
 import pytest
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_program_calculation.py b/tests/test_tox_program_calculation.py
index 72f2f6a9..463a3be5 100644
--- a/tests/test_tox_program_calculation.py
+++ b/tests/test_tox_program_calculation.py
@@ -2,7 +2,7 @@
 import numpy as np
 import os
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
 
 def test_program_coverage_calculation():
diff --git a/tests/test_tox_reconciliation.py b/tests/test_tox_reconciliation.py
index 5169ca93..f3610f9e 100644
--- a/tests/test_tox_reconciliation.py
+++ b/tests/test_tox_reconciliation.py
@@ -1,7 +1,7 @@
 import pytest
 import atomica as at
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 models = ["tb", "tb_simple", "hypertension_dyn"]
diff --git a/tests/test_tox_results.py b/tests/test_tox_results.py
index 030c368d..ef53678f 100644
--- a/tests/test_tox_results.py
+++ b/tests/test_tox_results.py
@@ -8,7 +8,7 @@
 import pytest
 
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_save.py b/tests/test_tox_save.py
index 5217ce25..748d940a 100644
--- a/tests/test_tox_save.py
+++ b/tests/test_tox_save.py
@@ -1,7 +1,7 @@
 import numpy as np
 import atomica as at
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_seasonal.py b/tests/test_tox_seasonal.py
index 43ee7b65..d80addf9 100644
--- a/tests/test_tox_seasonal.py
+++ b/tests/test_tox_seasonal.py
@@ -7,7 +7,7 @@
 
 
 def test_seasonal():
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
     tmpdir = testdir / "temp"
 
     F_path = testdir / "framework_seasonal_test.xlsx"
diff --git a/tests/test_tox_shortcut_init.py b/tests/test_tox_shortcut_init.py
index 8ded6b58..634bdf49 100644
--- a/tests/test_tox_shortcut_init.py
+++ b/tests/test_tox_shortcut_init.py
@@ -6,7 +6,7 @@
 from numpy.testing import assert_allclose
 import pytest
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_stochastic.py b/tests/test_tox_stochastic.py
index b9b45708..e4ca9225 100644
--- a/tests/test_tox_stochastic.py
+++ b/tests/test_tox_stochastic.py
@@ -6,7 +6,7 @@
 
 def test_stochastic():
 
-    testdir = at.parent_dir()  # Must be relative to current file to work with tox
+    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
     F0 = at.ProjectFramework(testdir / "framework_sir_dynamic.xlsx")
     P0 = at.Project(name="test", framework=F0, do_run=False)
diff --git a/tests/test_tox_table_parsing.py b/tests/test_tox_table_parsing.py
index 77caf269..24e07b7a 100644
--- a/tests/test_tox_table_parsing.py
+++ b/tests/test_tox_table_parsing.py
@@ -5,7 +5,7 @@
 import atomica as at
 import os
 
-testdir = at.parent_dir()  # Must be relative to current file to work with tox
+testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
 
 
 def test_table_parsing():
diff --git a/tests/test_tox_timed_compartments.py b/tests/test_tox_timed_compartments.py
index c8db579f..2574f4f4 100644
--- a/tests/test_tox_timed_compartments.py
+++ b/tests/test_tox_timed_compartments.py
@@ -16,7 +16,7 @@
 #
 #
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/text_tox_timed_initialization.py b/tests/text_tox_timed_initialization.py
index 66782372..91346ba3 100644
--- a/tests/text_tox_timed_initialization.py
+++ b/tests/text_tox_timed_initialization.py
@@ -15,9 +15,10 @@
 # # P.load_databook(at.LIBRARY_PATH+'tb_databook.xlsx')
 # P = at.demo('tb',do_run=True)
 
-testdir = at.parent_dir()
+testdir = at.rootdir / "tests"
 tmpdir = testdir / "temp"
 
+
 def test_timed_initialization():
 
     F = at.ProjectFramework(testdir / "timed_initialization_test_framework.xlsx")
@@ -32,17 +33,16 @@ def set_initialization_basic(F, D, year, y_factor=True):
         ps2 = at.ParameterSet(F, D, "basic")
 
         # Set initial compartment sizes for initialization quantities
-        for qty in itertools.chain(F.characs.index[F.characs['setup weight'] > 0], F.comps.index[F.comps['setup weight'] > 0]):
+        for qty in itertools.chain(F.characs.index[F.characs["setup weight"] > 0], F.comps.index[F.comps["setup weight"] > 0]):
             for pop in D.pops:
                 if y_factor:
                     ps2.pars[qty].meta_y_factor = 1
-                    ps2.pars[qty].y_factor[pop] = res.get_variable(qty, pop)[0].vals[-1]/ps2.pars[qty].ts[pop].interpolate(year)
+                    ps2.pars[qty].y_factor[pop] = res.get_variable(qty, pop)[0].vals[-1] / ps2.pars[qty].ts[pop].interpolate(year)
                 else:
                     ps2.pars[qty].ts[pop].insert(year, res.get_variable(qty, pop)[0].vals[-1])
 
         return ps2
 
-
     P = at.Project(framework=F, databook=D, do_run=False)
     P.settings.sim_dt = 1 / 12
     P.settings.sim_start = 2018
@@ -52,24 +52,25 @@ def set_initialization_basic(F, D, year, y_factor=True):
 
     # Basic steady state initialization
     ps = set_initialization_basic(F, D, 2018)
-    res2 = P.run_sim(ps, result_name='basic')
+    res2 = P.run_sim(ps, result_name="basic")
 
     # Advanced steady state initialization
-    ps2 = at.ParameterSet(F,D)
+    ps2 = at.ParameterSet(F, D)
     ps2.set_initialization(res1, 2021)
-    res3 = P.run_sim(ps2, result_name='advanced')
+    res3 = P.run_sim(ps2, result_name="advanced")
 
-    ps2.pars['alive'].y_factor[0] = 1.1
+    ps2.pars["alive"].y_factor[0] = 1.1
 
-    ps2.save_calibration(tmpdir/'test_cal.xlsx')
-    res3 = P.run_sim(ps2, result_name='advanced')
+    ps2.save_calibration(tmpdir / "test_cal.xlsx")
+    res3 = P.run_sim(ps2, result_name="advanced")
 
-    ps4 = P.make_parset('saved')
-    ps4.load_calibration(tmpdir/'test_cal.xlsx')
-    res4 = P.run_sim(ps4, result_name='loaded')
+    ps4 = P.make_parset("saved")
+    ps4.load_calibration(tmpdir / "test_cal.xlsx")
+    res4 = P.run_sim(ps4, result_name="loaded")
 
     d = at.PlotData([res1, res2, res3, res4], ["sus", "inf", "rec"])
     at.plot_series(d)
 
-if __name__ == '__main__':
-    test_timed_initialization()
\ No newline at end of file
+
+if __name__ == "__main__":
+    test_timed_initialization()
diff --git a/tests/validate_integration.py b/tests/validate_integration.py
index 17dfce03..9df2661c 100644
--- a/tests/validate_integration.py
+++ b/tests/validate_integration.py
@@ -47,7 +47,7 @@ def validate(r1, r2):
 @pytest.mark.parametrize("model", models)
 def test_validate_model(model):
 
-    testdir = at.parent_dir()
+    testdir = at.rootdir / "tests"
     tmpdir = testdir / "temp"
 
     framework_file = at.LIBRARY_PATH / f"{model}_framework.xlsx"

From e45110619d972bbc97504e1a47aaab6c6a2c233b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 30 Oct 2024 15:40:24 +1000
Subject: [PATCH 085/161] Remove flake8 job, change development status

---
 azure-pipelines.yml | 22 ----------------------
 setup.py            |  2 +-
 tox.ini             | 17 -----------------
 3 files changed, 1 insertion(+), 40 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 5f41210b..e0211c8f 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -45,28 +45,6 @@ jobs:
         summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml'
 #        reportDirectory: '$(System.DefaultWorkingDirectory)/**/htmlcov'
 
-- job: 'flake8'
-  pool:
-    vmImage: 'ubuntu-latest'
-  steps:
-    - task: UsePythonVersion@0
-      displayName: 'Select python version'
-      inputs:
-        versionSpec: '3.12'
-    - script: python -m pip install flake8 flake8-junit-report
-      displayName: 'Install flake8'
-    - script: flake8 atomica tests --exit-zero --output-file flake8.txt
-      # Note - using --exit-zero will cause flake8 'errors' to show up as 'errors' in Azure Pipelines
-      # but the test will have been considered to pass. Therefore, the overall tests suite will still
-      # be considered to be passing as long as tox runs, and flake8 errors are just guidance for style
-      displayName: 'Run flake8'
-    - script: flake8_junit flake8.txt flake8_junit.xml
-      displayName: 'Generate flake8 junit output'
-    - task: PublishTestResults@2
-      inputs:
-        testResultsFiles: '**/flake8_junit.xml'
-        testRunTitle: 'Publish flake8 results'
-
 - job: 'docs'
   condition: and(ne(variables['Build.Reason'], 'PullRequest'), ne(variables['Build.Reason'], 'Schedule')) # Skip if it's a PR build or a scheduled build
   pool:
diff --git a/setup.py b/setup.py
index eb71024a..53a5d85c 100644
--- a/setup.py
+++ b/setup.py
@@ -21,7 +21,7 @@
     "Operating System :: OS Independent",
     "Programming Language :: Python",
     "Topic :: Software Development :: Libraries :: Python Modules",
-    "Development Status :: 4 - Beta",
+    "Development Status :: 5 - Production/Stable",
     "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
diff --git a/tox.ini b/tox.ini
index e81a142c..8172f379 100644
--- a/tox.ini
+++ b/tox.ini
@@ -29,20 +29,3 @@ filterwarnings =
     ignore::PendingDeprecationWarning
     ignore::UserWarning:.*openpyxl
 
-[flake8]
-# Use these commands to call autopep8 to tidy up whitespace/trivial formatting
-#
-#    autopep8 --in-place -j 0 --recursive atomica
-#    autopep8 --in-place -j 0 --recursive tests
-#
-# Alternatively, use Black via
-#
-# pip install black
-# black -l 999 .  # Use `-l 999` to more or less ignore line length
-
-exclude = .git,__pycache__,docs,build,dist,junit,.tox
-ignore = E501,F401,E401,E402,C901,F403
-
-# flake8 does not check with E203 properly with list slices
-extend-ignore = E203
-

From f478bab6f729db764eacc69ac4f4f77ac722a1ca Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 30 Oct 2024 15:41:59 +1000
Subject: [PATCH 086/161] Change test result publishing version

---
 azure-pipelines.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e0211c8f..e2bf892e 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -39,7 +39,7 @@ jobs:
         testRunTitle: 'Publish test results for Python $(python.version)'
       condition: succeededOrFailed()
 
-    - task: PublishCodeCoverageResults@1
+    - task: PublishCodeCoverageResults@2
       inputs:
         codeCoverageTool: Cobertura
         summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml'

From b740afabc84bf9452233022174f863a2f0abd4a9 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 30 Oct 2024 15:49:23 +1000
Subject: [PATCH 087/161] Update date

---
 atomica/version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/version.py b/atomica/version.py
index fd17c0ff..23c4754c 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -7,5 +7,5 @@
 import sciris as sc
 
 version = "1.28.8"
-versiondate = "2024-10-29"
+versiondate = "2024-10-30"
 gitinfo = sc.gitinfo(__file__)

From f80c2a29b4474f14a360827aba00ad8babea03ef Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 30 Oct 2024 21:53:06 +1000
Subject: [PATCH 088/161] Revert test path - original implementation was needed
 for tox

---
 docs/examples/Plot-Units.ipynb         | 2 +-
 tests/README.md                        | 2 +-
 tests/test_api_workflow.py             | 2 +-
 tests/test_ensemble_parallel.py        | 2 +-
 tests/test_horizontal_bars.py          | 2 +-
 tests/test_tox_bad_frameworks.py       | 2 +-
 tests/test_tox_calibration.py          | 2 +-
 tests/test_tox_cascade.py              | 2 +-
 tests/test_tox_combined.py             | 4 ++--
 tests/test_tox_databooks.py            | 2 +-
 tests/test_tox_derivative.py           | 2 +-
 tests/test_tox_docgen.py               | 2 +-
 tests/test_tox_ensemble_cascade.py     | 2 +-
 tests/test_tox_extra.py                | 2 +-
 tests/test_tox_frameworks.py           | 2 +-
 tests/test_tox_indirect_programs.py    | 2 +-
 tests/test_tox_junctions.py            | 2 +-
 tests/test_tox_library.py              | 2 +-
 tests/test_tox_logging.py              | 2 +-
 tests/test_tox_migration.py            | 2 +-
 tests/test_tox_no_compartments.py      | 2 +-
 tests/test_tox_no_initialization.py    | 2 +-
 tests/test_tox_progbooks.py            | 2 +-
 tests/test_tox_program_calculation.py  | 2 +-
 tests/test_tox_reconciliation.py       | 2 +-
 tests/test_tox_results.py              | 2 +-
 tests/test_tox_save.py                 | 2 +-
 tests/test_tox_seasonal.py             | 2 +-
 tests/test_tox_shortcut_init.py        | 2 +-
 tests/test_tox_stochastic.py           | 2 +-
 tests/test_tox_table_parsing.py        | 2 +-
 tests/test_tox_timed_compartments.py   | 2 +-
 tests/text_tox_timed_initialization.py | 2 +-
 tests/validate_integration.py          | 2 +-
 34 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/docs/examples/Plot-Units.ipynb b/docs/examples/Plot-Units.ipynb
index 50ad6dd1..4abc8f50 100644
--- a/docs/examples/Plot-Units.ipynb
+++ b/docs/examples/Plot-Units.ipynb
@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "testdir = at.rootdir/'tests'\n",
+    "testdir = at.parent_dir()\n",
     "P = at.Project(framework='unit_demo_framework.xlsx',databook='unit_demo_databook.xlsx')\n",
     "P.load_progbook('unit_demo_progbook.xlsx')\n",
     "res = P.run_sim('default','default',at.ProgramInstructions(start_year=2018))"
diff --git a/tests/README.md b/tests/README.md
index 6a6171a9..993869d9 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -12,6 +12,6 @@ to the `site-packages` directory instead of the repo. Therefore, inside the test
 In practice, it can be useful to simply define the following two variables at the start of a test script:
 
 ```python
-testdir = at.rootdir/'tests'
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 ```
\ No newline at end of file
diff --git a/tests/test_api_workflow.py b/tests/test_api_workflow.py
index 07e9366e..0d4ac4e0 100644
--- a/tests/test_api_workflow.py
+++ b/tests/test_api_workflow.py
@@ -11,7 +11,7 @@
 
 test = "sir"  # This test workflow only works with the SIR model
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 F = at.ProjectFramework(at.LIBRARY_PATH / test + "_framework.xlsx")
diff --git a/tests/test_ensemble_parallel.py b/tests/test_ensemble_parallel.py
index 2c31c87a..33afdf99 100644
--- a/tests/test_ensemble_parallel.py
+++ b/tests/test_ensemble_parallel.py
@@ -10,7 +10,7 @@
 if __name__ == "__main__":
 
     # BASIC SETUP
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
     P = at.Project(framework=testdir / "test_uncertainty_framework.xlsx", databook=testdir / "test_uncertainty_databook.xlsx")
     low_uncertainty_progset = at.ProgramSet.from_spreadsheet(testdir / "test_uncertainty_low_progbook.xlsx", project=P)
     high_uncertainty_progset = at.ProgramSet.from_spreadsheet(testdir / "test_uncertainty_high_progbook.xlsx", project=P)
diff --git a/tests/test_horizontal_bars.py b/tests/test_horizontal_bars.py
index 1559f907..fcc85b1c 100644
--- a/tests/test_horizontal_bars.py
+++ b/tests/test_horizontal_bars.py
@@ -2,7 +2,7 @@
 from atomica import ProjectFramework, Project
 import os
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 F = ProjectFramework(testdir / "framework_sir_dynamic.xlsx")
diff --git a/tests/test_tox_bad_frameworks.py b/tests/test_tox_bad_frameworks.py
index d2205968..1833cd4c 100644
--- a/tests/test_tox_bad_frameworks.py
+++ b/tests/test_tox_bad_frameworks.py
@@ -6,7 +6,7 @@
 import atomica as at
 import pytest
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 fdir = testdir / "bad_frameworks"
 
 # List available models based on which framework files exist
diff --git a/tests/test_tox_calibration.py b/tests/test_tox_calibration.py
index 118c767c..39e2b71b 100644
--- a/tests/test_tox_calibration.py
+++ b/tests/test_tox_calibration.py
@@ -4,7 +4,7 @@
 import atomica as at
 import pytest
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_cascade.py b/tests/test_tox_cascade.py
index 895b252a..5ebbbc91 100644
--- a/tests/test_tox_cascade.py
+++ b/tests/test_tox_cascade.py
@@ -5,7 +5,7 @@
 import os
 import pytest
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 try:
diff --git a/tests/test_tox_combined.py b/tests/test_tox_combined.py
index 6559e052..c26536a4 100644
--- a/tests/test_tox_combined.py
+++ b/tests/test_tox_combined.py
@@ -2,7 +2,7 @@
 import numpy as np
 import atomica as at
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
@@ -91,7 +91,7 @@ def test_combined_plots():
 
 
 def test_combined_order():
-    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+    testdir = at.parent_dir()  # Must be relative to current file to work with tox
     P = at.Project(framework=testdir / "test_order_framework.xlsx", databook=testdir / "test_order_databook.xlsx")
     res = P.results[0]
 
diff --git a/tests/test_tox_databooks.py b/tests/test_tox_databooks.py
index c852d58a..7958424f 100644
--- a/tests/test_tox_databooks.py
+++ b/tests/test_tox_databooks.py
@@ -6,7 +6,7 @@
 from atomica import ProjectFramework, Project, ProjectData
 import sciris as sc
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_derivative.py b/tests/test_tox_derivative.py
index b44dbc20..fc9e9ded 100644
--- a/tests/test_tox_derivative.py
+++ b/tests/test_tox_derivative.py
@@ -8,7 +8,7 @@
 
 def test_derivative():
 
-    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+    testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
     F = at.ProjectFramework(testdir / "framework_derivative_test.xlsx")
     D = at.ProjectData.new(F, np.arange(2000, 2010), pops={"mosquitos": "Mosquitos"}, transfers=0)
diff --git a/tests/test_tox_docgen.py b/tests/test_tox_docgen.py
index a5bbafd4..b5d98ab6 100644
--- a/tests/test_tox_docgen.py
+++ b/tests/test_tox_docgen.py
@@ -7,7 +7,7 @@
 import sciris as sc
 import pytest
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 frameworks = list()
 for f in at.LIBRARY_PATH.iterdir():
diff --git a/tests/test_tox_ensemble_cascade.py b/tests/test_tox_ensemble_cascade.py
index a5b09f13..a98f2986 100644
--- a/tests/test_tox_ensemble_cascade.py
+++ b/tests/test_tox_ensemble_cascade.py
@@ -10,7 +10,7 @@
 
 def test_ensemble_cascade():
     # BASIC SETUP
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
 
     P = at.Project(framework=testdir / "test_uncertainty_framework.xlsx", databook=testdir / "test_uncertainty_databook.xlsx")
 
diff --git a/tests/test_tox_extra.py b/tests/test_tox_extra.py
index 5112f6bf..7fa4c4ce 100644
--- a/tests/test_tox_extra.py
+++ b/tests/test_tox_extra.py
@@ -4,7 +4,7 @@
 
 
 def test_extra_data_pop():
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
 
     F_path = testdir / "framework_sir_dynamic.xlsx"
     D_path = testdir / "databook_sir_dynamic_extra.xlsx"
diff --git a/tests/test_tox_frameworks.py b/tests/test_tox_frameworks.py
index eac01c48..303ee535 100644
--- a/tests/test_tox_frameworks.py
+++ b/tests/test_tox_frameworks.py
@@ -5,7 +5,7 @@
 import pytest
 import pandas as pd
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 frameworks = [
diff --git a/tests/test_tox_indirect_programs.py b/tests/test_tox_indirect_programs.py
index 29fa5c5e..4949fbd7 100644
--- a/tests/test_tox_indirect_programs.py
+++ b/tests/test_tox_indirect_programs.py
@@ -2,7 +2,7 @@
 import numpy as np
 import os
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
 
 def test_indirect_programs():
diff --git a/tests/test_tox_junctions.py b/tests/test_tox_junctions.py
index 102f59e6..d28c054c 100644
--- a/tests/test_tox_junctions.py
+++ b/tests/test_tox_junctions.py
@@ -4,7 +4,7 @@
 import atomica as at
 import os
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
 
 def test_junctions():
diff --git a/tests/test_tox_library.py b/tests/test_tox_library.py
index 286bd48b..110dd246 100644
--- a/tests/test_tox_library.py
+++ b/tests/test_tox_library.py
@@ -8,7 +8,7 @@
 import numpy as np
 import sciris as sc
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 # List available models based on which framework files exist
diff --git a/tests/test_tox_logging.py b/tests/test_tox_logging.py
index 6b585ae2..6a5e8e34 100644
--- a/tests/test_tox_logging.py
+++ b/tests/test_tox_logging.py
@@ -2,7 +2,7 @@
 
 import atomica as at
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_migration.py b/tests/test_tox_migration.py
index 2308294e..63393827 100644
--- a/tests/test_tox_migration.py
+++ b/tests/test_tox_migration.py
@@ -14,7 +14,7 @@ def test_migration():
 
     at.logger.setLevel("DEBUG")
 
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
     tmpdir = testdir / "temp"
 
     P = at.Project.load(testdir / "migration_test_with_scenarios.prj")
diff --git a/tests/test_tox_no_compartments.py b/tests/test_tox_no_compartments.py
index 0b5e84a4..765d90e8 100644
--- a/tests/test_tox_no_compartments.py
+++ b/tests/test_tox_no_compartments.py
@@ -3,7 +3,7 @@
 import os
 import pytest
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
 
 def test_no_compartments():
diff --git a/tests/test_tox_no_initialization.py b/tests/test_tox_no_initialization.py
index 22cc8831..4583ff8f 100644
--- a/tests/test_tox_no_initialization.py
+++ b/tests/test_tox_no_initialization.py
@@ -4,7 +4,7 @@
 import atomica as at
 import numpy as np
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_progbooks.py b/tests/test_tox_progbooks.py
index fe480a08..71de7db6 100644
--- a/tests/test_tox_progbooks.py
+++ b/tests/test_tox_progbooks.py
@@ -3,7 +3,7 @@
 import sciris as sc
 import pytest
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_program_calculation.py b/tests/test_tox_program_calculation.py
index 463a3be5..72f2f6a9 100644
--- a/tests/test_tox_program_calculation.py
+++ b/tests/test_tox_program_calculation.py
@@ -2,7 +2,7 @@
 import numpy as np
 import os
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
 
 def test_program_coverage_calculation():
diff --git a/tests/test_tox_reconciliation.py b/tests/test_tox_reconciliation.py
index f3610f9e..5169ca93 100644
--- a/tests/test_tox_reconciliation.py
+++ b/tests/test_tox_reconciliation.py
@@ -1,7 +1,7 @@
 import pytest
 import atomica as at
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 models = ["tb", "tb_simple", "hypertension_dyn"]
diff --git a/tests/test_tox_results.py b/tests/test_tox_results.py
index ef53678f..030c368d 100644
--- a/tests/test_tox_results.py
+++ b/tests/test_tox_results.py
@@ -8,7 +8,7 @@
 import pytest
 
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_save.py b/tests/test_tox_save.py
index 748d940a..5217ce25 100644
--- a/tests/test_tox_save.py
+++ b/tests/test_tox_save.py
@@ -1,7 +1,7 @@
 import numpy as np
 import atomica as at
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_seasonal.py b/tests/test_tox_seasonal.py
index d80addf9..43ee7b65 100644
--- a/tests/test_tox_seasonal.py
+++ b/tests/test_tox_seasonal.py
@@ -7,7 +7,7 @@
 
 
 def test_seasonal():
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
     tmpdir = testdir / "temp"
 
     F_path = testdir / "framework_seasonal_test.xlsx"
diff --git a/tests/test_tox_shortcut_init.py b/tests/test_tox_shortcut_init.py
index 634bdf49..8ded6b58 100644
--- a/tests/test_tox_shortcut_init.py
+++ b/tests/test_tox_shortcut_init.py
@@ -6,7 +6,7 @@
 from numpy.testing import assert_allclose
 import pytest
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/test_tox_stochastic.py b/tests/test_tox_stochastic.py
index e4ca9225..b9b45708 100644
--- a/tests/test_tox_stochastic.py
+++ b/tests/test_tox_stochastic.py
@@ -6,7 +6,7 @@
 
 def test_stochastic():
 
-    testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+    testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
     F0 = at.ProjectFramework(testdir / "framework_sir_dynamic.xlsx")
     P0 = at.Project(name="test", framework=F0, do_run=False)
diff --git a/tests/test_tox_table_parsing.py b/tests/test_tox_table_parsing.py
index 24e07b7a..77caf269 100644
--- a/tests/test_tox_table_parsing.py
+++ b/tests/test_tox_table_parsing.py
@@ -5,7 +5,7 @@
 import atomica as at
 import os
 
-testdir = at.rootdir / "tests"  # Must be relative to current file to work with tox
+testdir = at.parent_dir()  # Must be relative to current file to work with tox
 
 
 def test_table_parsing():
diff --git a/tests/test_tox_timed_compartments.py b/tests/test_tox_timed_compartments.py
index 2574f4f4..c8db579f 100644
--- a/tests/test_tox_timed_compartments.py
+++ b/tests/test_tox_timed_compartments.py
@@ -16,7 +16,7 @@
 #
 #
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/text_tox_timed_initialization.py b/tests/text_tox_timed_initialization.py
index 91346ba3..80e0ff48 100644
--- a/tests/text_tox_timed_initialization.py
+++ b/tests/text_tox_timed_initialization.py
@@ -15,7 +15,7 @@
 # # P.load_databook(at.LIBRARY_PATH+'tb_databook.xlsx')
 # P = at.demo('tb',do_run=True)
 
-testdir = at.rootdir / "tests"
+testdir = at.parent_dir()
 tmpdir = testdir / "temp"
 
 
diff --git a/tests/validate_integration.py b/tests/validate_integration.py
index 9df2661c..17dfce03 100644
--- a/tests/validate_integration.py
+++ b/tests/validate_integration.py
@@ -47,7 +47,7 @@ def validate(r1, r2):
 @pytest.mark.parametrize("model", models)
 def test_validate_model(model):
 
-    testdir = at.rootdir / "tests"
+    testdir = at.parent_dir()
     tmpdir = testdir / "temp"
 
     framework_file = at.LIBRARY_PATH / f"{model}_framework.xlsx"

From 2b4cfcbdf0a0e89aa5282894b579becaeaa1cfa5 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 31 Oct 2024 15:23:22 +1000
Subject: [PATCH 089/161] Handle out of bounds bins in time aggregation more
 explicitly without throwing an error

---
 atomica/plotting.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 1d5c2b9c..6a3e3673 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -619,12 +619,16 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             interpolation_method = "linear"
 
         if not hasattr(t_bins, "__len__"):
-            # If a scalar bin is provided, then it is
+            # If a scalar bin is provided, then it is assumed to correspond to the bin width, in which case we need
+            # to create uniformly spaced bins up to the last complete bin.
             if t_bins > (self.series[0].tvec[-1] - self.series[0].tvec[0]):
                 # If bin width is greater than the sim duration, treat it the same as aggregating over all times
                 t_bins = "all"
             else:
                 if not (self.series[0].tvec[-1] - self.series[0].tvec[0]) % t_bins:
+                    # If the simulation ends in a multiple of the t_bins, then we can have a final bin extending up to the last
+                    # simulation year. If we just use tvec[-1] in np.arange then that final bin will be excluded. So we add
+                    # t_bins on to that final bin to ensure it's included.
                     upper = self.series[0].tvec[-1] + t_bins
                 else:
                     upper = self.series[0].tvec[-1]
@@ -663,6 +667,12 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             max_step = 0.5 * min(np.diff(s.tvec))  # Subdivide for trapezoidal integration with at least 2 divisions per timestep. Could be a lot of memory for integrating daily timesteps over a full simulation, but unlikely to be prohibitive
             vals = np.full(lower.shape, fill_value=np.nan)
             for i, (l, u) in enumerate(zip(lower, upper)):
+
+                # For bins that partially extend out of bounds, return NaN as the value immediately
+                if l < s.tvec[0] or u > s.tvec[-1]:
+                    vals[i] = np.nan
+                    continue
+
                 t2 = np.arange(round((u - l) / max_step) + 1) * max_step + l
                 # Numerical precision issues can cause the wrong bin to be used. This is particularly noticable for 'previous'
                 # e.g., if the interpolated finer bins have 2025.9999999 instead of 2026 causing the 2025 value to be

From f0246eca410c43d013d76c9a9015ab870c43cb38 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 4 Nov 2024 15:06:23 +1000
Subject: [PATCH 090/161] Improve time aggregation to no longer interpolate
 unless necessary

---
 CHANGELOG.md        |  4 +++-
 atomica/plotting.py | 48 ++++++++++++++++++++++++++++-----------------
 2 files changed, 33 insertions(+), 19 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0d999d3f..aae5da61 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,12 +7,14 @@ This file records changes to the codebase grouped by version release. Unreleased
 - `ProjectSettings` now computes the simulation time vector in a more robust way to reduce edge cases where the reported `sim_dt` doesn't match the input.
 - `ParameterSet.load_calibration()` now clears any existing initialization if the calibration being loaded does not contain an initialization. Previously, the absence of an 'initialization' sheet in the calibration would be treated as not making any change to the initialization. This could cause calibrations to become mixed if a calibration without an initialization was loaded after a calibration with an initialization. Now, a missing initialization sheet is treated as meaning 'no initialization' and any existing initialization will be cleared when the calibration is loaded. 
 - `Project.load_databook()` will no longer populate `Project.databook` when a `ProjectData` instance is supplied rather than a spreadsheet. The intention of `Project.databook` as opposed to `Project.data.to_spreadsheet()` is that the original databook may contain comments or other content that is not preserved when the databook is loaded into a `ProjectData` instance. Therefore, `Project.databook` serves as a record of the original inputs. However, in previous versions of Atomica, if a `ProjectData` instance was provided rather than a spreadsheet, `ProjectData.to_spreadsheet()` would be used to populate `Project.databook`. For large databooks, this can be computationally expensive and particularly affect the use case of passing in a preloaded databook to improve performance. Since the conversion of the `ProjectData` to a spreadsheet upon loading offers no functional difference to creating the spreadsheet from `Project.data` when required, Atomica no longer performs this conversion upfront.
+- `PlotData.time_aggregate()` now only interpolates if necessary, using simulated time points as much as possible.
 
 *Backwards-compatibility notes*
 
 - In some edge cases, the simulation time points in the output may be different. In those cases, the difference between simulation time points in the model output would not have matched the model input, although the correct time step would have been used to calculate parameter values. In these cases, there may be an extra time point in the model output. Re-running the model should produce results that are close to the original results.
 - If accessing `Project.databook`, in some cases this may now be `None` rather than an `sc.Spreadsheet()`. If that occurs, `Project.data.to_spreadsheet()` should be used to produce an equivalent spreadsheet. 
-
+- Time aggregation of `PlotData` may produce slightly different results due to the more accurate selection of time points in this version.
+- 
 ## [1.28.7] - 2024-10-29
 
 - If aggregating characteristics with a denominator, weighted aggregations use the denominator of the quantity rather than the total population size to perform the weighting. This can be useful for quantities that are proportions of things other than the population size e.g., proportion of active infections that are diagnosed
diff --git a/atomica/plotting.py b/atomica/plotting.py
index 6a3e3673..826fc997 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -661,34 +661,46 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
             else:
                 scale = 1.0
 
-            # We interpolate in time-aggregation because the time bins are independent of the step size. In contrast,
-            # accumulation preserves the same time bins, so we don't need the interpolation step and instead go straight
-            # to summation or trapezoidal integration
-            max_step = 0.5 * min(np.diff(s.tvec))  # Subdivide for trapezoidal integration with at least 2 divisions per timestep. Could be a lot of memory for integrating daily timesteps over a full simulation, but unlikely to be prohibitive
             vals = np.full(lower.shape, fill_value=np.nan)
-            for i, (l, u) in enumerate(zip(lower, upper)):
+            lower_idx = np.searchsorted(s.tvec, lower, side='left') # Time index for bin start
+            upper_idx = np.searchsorted(s.tvec, upper, side='right') # Time index for bin end
+
+            for i, (l, u, l_idx, u_idx) in enumerate(zip(lower, upper, lower_idx, upper_idx)):
 
                 # For bins that partially extend out of bounds, return NaN as the value immediately
                 if l < s.tvec[0] or u > s.tvec[-1]:
                     vals[i] = np.nan
                     continue
+                elif l == u:
+                    vals[i] = 0
+                    continue
+
+                # The bins will consist of the actual simulation time points, plus
+                # partial bins that are interpolated before and after if the requested
+                # bins don't line up with the simulation timepoints
+                idx = np.arange(l_idx,u_idx)
+                t2 = s.tvec[idx]
+                interpolate = False
+                if t2[0] > l or t2[-1] < u:
+                    interpolate = True
+                    t2 = list(t2)
+                    if t2[0] > l:
+                        t2.insert(0,l)
+                    if t2[-1] < u:
+                        t2.append(u)
+                    t2 = np.array(t2, dtype=float)
 
-                t2 = np.arange(round((u - l) / max_step) + 1) * max_step + l
-                # Numerical precision issues can cause the wrong bin to be used. This is particularly noticable for 'previous'
-                # e.g., if the interpolated finer bins have 2025.9999999 instead of 2026 causing the 2025 value to be
-                # used instead, this can cause a significant overestimation if the value is changing rapidly. Therefore
-                # we need to perform an extra step of using the exact bin values within some tolerance. The values are on
-                # the order of 0.002 (1 day, in years) so the default np.isclose() should be sufficient. However, we may want to apply this
-                # to linear interpolation as well because if the bin falls numerically *outside* the bounds, it will be extrapolated
-                # as NaN which we wouldn't want
-                nearest = np.searchsorted(s.tvec, t2, side='left')
-                isclose = np.isclose(s.tvec[nearest], t2)
-                t2[isclose] = s.tvec[nearest[isclose]]
                 if interpolation_method == "linear":
-                    v2 = np.interp(t2, s.tvec, s.vals, left=np.nan, right=np.nan)  # Return NaN outside bounds - it should never be valid to use extrapolated output values in time aggregation
+                    if interpolate:
+                        v2 = np.interp(t2, s.tvec, s.vals, left=np.nan, right=np.nan)  # Return NaN outside bounds - it should never be valid to use extrapolated output values in time aggregation
+                    else:
+                        v2 = s.vals[idx]
                     vals[i] = np.trapz(y=v2 / scale, x=t2)  # Note division by timescale here, which annualizes it
                 elif interpolation_method == "previous":
-                    v2 = scipy.interpolate.interp1d(s.tvec, s.vals, kind="previous", copy=False, assume_sorted=True, bounds_error=False, fill_value=(np.nan, np.nan))(t2)
+                    if interpolate:
+                        v2 = scipy.interpolate.interp1d(s.tvec, s.vals, kind="previous", copy=False, assume_sorted=True, bounds_error=False, fill_value=(np.nan, np.nan))(t2)
+                    else:
+                        v2 = s.vals[idx]
                     vals[i] = sum(v2[:-1] / scale * np.diff(t2))
 
             s.tvec = (lower + upper) / 2.0

From adaf5eb775315def10e082087e188e784ca855b4 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 13 Nov 2024 13:25:22 +1000
Subject: [PATCH 091/161] Tweak text

---
 docs/examples/Plotting.ipynb | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/docs/examples/Plotting.ipynb b/docs/examples/Plotting.ipynb
index a3ff512c..dd4c50b0 100644
--- a/docs/examples/Plotting.ipynb
+++ b/docs/examples/Plotting.ipynb
@@ -1593,19 +1593,29 @@
     "- `np.arange(2005,2040,5) = array([2005, 2010, 2015, 2020, 2025, 2030, 2035])` so the last bin is correct\n",
     "- `np.arange(2005,2045,5) = array([2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040])` so the final bin is out of bounds\n",
     "\n",
-    "If you simply specify the bin size, e.g. `t_bins = 5` then the middle example will automatically be used. \n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "In general, it is easiest to only specify the bin width, as long as you are happy for the bins to start from the first simulation year\n",
-    "</div>\n",
-    "\n",
-    "Finally, note that because time aggregation is implemented by `PlotData`, you can apply time aggregation to `plot_series` as well as `plot_bars` e.g. if you want to plot actual annual values."
+    "If you simply specify the bin size, e.g. `t_bins = 5` then the middle example will automatically be used. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData([par_results,scen_results],pops=['0-4','5-14'],outputs=['sus','vac'],t_bins=5)\n",
+    "d.series[0].t_labels"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "<div class=\"alert alert-info\">\n",
+    "In general, it is easiest to only specify the bin width, as long as you are happy for the bins to start from the first simulation year\n",
+    "</div>\n",
+    "\n",
+    "Finally, note that because time aggregation is implemented by `PlotData`, you can apply time aggregation to `plot_series` as well as `plot_bars` e.g. if you want to plot actual annual values.\n",
+    "\n",
     "So, to make our bar graph, we first select the results, pops, and outputs using the `PlotData` constructor, then perform any necessary time aggregation, and finally render the plot using `plotBars`:"
    ]
   },

From 4aeeb94f369a6fdef8eea39eabd7093c3fd0ff61 Mon Sep 17 00:00:00 2001
From: Rowanmh <rowanmh@gmail.com>
Date: Thu, 6 Feb 2025 19:42:48 +1100
Subject: [PATCH 092/161] Add code for a 'Databook default all' column in the
 framework

If set for a parameter, then when creating a new databook it will just fill in an 'All' population instead of each population row.
---
 atomica/data.py  | 8 ++++++--
 atomica/excel.py | 4 +++-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/atomica/data.py b/atomica/data.py
index 18535a8e..d8531d7d 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -261,13 +261,15 @@ class instance (e.g. if creating a new databook).
                     pop_type = spec.get("population type")
                     databook_order = spec.get("databook order")
                     full_name = spec["display name"]
+                    default_all = spec.get("databook default all")
+                    allowed_units = [framework.get_databook_units(full_name)]
 
                     if pd.isna(databook_order):
                         order = np.inf
                     else:
                         order = databook_order
                     pages[databook_page].append((spec.name, order))
-                    data.tdve[spec.name] = TimeDependentValuesEntry(full_name, data.tvec, allowed_units=[framework.get_databook_units(full_name)], comment=spec["guidance"], pop_type=pop_type)
+                    data.tdve[spec.name] = TimeDependentValuesEntry(full_name, data.tvec, allowed_units=allowed_units, comment=spec["guidance"], pop_type=pop_type, default_all=default_all)
                     data.tdve[spec.name].write_units = True
                     data.tdve[spec.name].write_uncertainty = True
                     if obj_type == "pars":
@@ -276,6 +278,8 @@ class instance (e.g. if creating a new databook).
                             data.tdve[spec.name].tvec = []  # If parameter is timed, don't show any years
                             data.tdve[spec.name].write_uncertainty = False  # Don't show uncertainty for timed parameters. In theory users could manually add the column and sample over it, but because the duration is rounded to the timestep, it's likely to have confusing stepped effects
                     data.tdve[spec.name].pop_type = pop_type
+                    if default_all:
+                        data.tdve[spec.name].ts['All'] = TimeSeries(units=allowed_units[0])
 
         # Now convert pages to full names and sort them into the correct order
         for _, spec in framework.sheets["databook pages"][0].iterrows():
@@ -654,7 +658,7 @@ def add_pop(self, code_name: str, full_name: str, pop_type: str = None) -> None:
             # Since TDVEs in databooks must have the unit set in the framework, all ts objects must share the same units
             # And, there is only supposed to be one type of unit allowed for TDVE tables (if the unit is empty, it will be 'N.A.')
             # so can just pick the first of the allowed units
-            if tdve.pop_type == pop_type:
+            if tdve.pop_type == pop_type and not (hasattr(tdve, 'default_all') and tdve.default_all):
                 tdve.ts[code_name] = TimeSeries(units=tdve.allowed_units[0])
 
     def rename_pop(self, existing_code_name: str, new_code_name: str, new_full_name: str) -> None:
diff --git a/atomica/excel.py b/atomica/excel.py
index e864fde5..9501b184 100644
--- a/atomica/excel.py
+++ b/atomica/excel.py
@@ -884,7 +884,7 @@ class TimeDependentValuesEntry:
 
     """
 
-    def __init__(self, name, tvec: np.array = None, ts=None, allowed_units: list = None, comment: str = None, pop_type: str = None):
+    def __init__(self, name, tvec: np.array = None, ts=None, allowed_units: list = None, comment: str = None, pop_type: str = None, default_all: bool = False):
 
         if ts is None:
             ts = sc.odict()
@@ -906,6 +906,8 @@ def __init__(self, name, tvec: np.array = None, ts=None, allowed_units: list = N
         self.write_units = None  #: Write a column for units (if None, units will be written if any of the TimeSeries have units)
         self.write_uncertainty = None  #: Write a column for uncertainty (if None, uncertainty will be written if any of the TimeSeries have uncertainty)
         self.write_assumption = None  #: Write a column for assumption/constant (if None, assumption will be written if any of the TimeSeries have an assumption)
+        
+        self.default_all = default_all #If the TDVE should by default only have the 'All' population in the databook e.g. the same value for all populations unless exceptions are manually entered
 
     def __repr__(self):
         output = sc.prepr(self)

From fdb66fea89d3b8eaf9408bbda14a54472b2e48c9 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 20:31:48 +0100
Subject: [PATCH 093/161] Add hierarchical YAML test

---
 .../config_hierarchical_syntax_test.yaml      | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 tests/yaml_tests/config_hierarchical_syntax_test.yaml

diff --git a/tests/yaml_tests/config_hierarchical_syntax_test.yaml b/tests/yaml_tests/config_hierarchical_syntax_test.yaml
new file mode 100644
index 00000000..9dcc1ba3
--- /dev/null
+++ b/tests/yaml_tests/config_hierarchical_syntax_test.yaml
@@ -0,0 +1,23 @@
+calibration:
+    pop cal:
+      repeats: 1
+
+      match population sizes:
+        adjustables:
+          #general, then pop-specific
+          b_rate, mig_rate: [ 0.1, 10 ]
+          (b_rate, 0-4):
+            initial_value: 2
+            upper_bound: 10
+            lower_bound: 0.1
+
+          typ_active_inf, typ_car, par_active_inf, par_car: [0, 0]
+        measurables:  alive
+
+
+      other formats, hierarchical tests:
+        adjustables: #[ [ b_rate, 0.1, 10 ], [ mig_rate ] ]
+          b_rate, mig_rate: [ 0.1, 10 ]
+          (b_rate, 0-4), mig_rate: [ 0.5, 20 ]
+          (b_rate, 0-4):
+            initial_value: 1.2
\ No newline at end of file

From 999d1396dcf5ca85814b0e7ad14d37350f80239f Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:26:50 +0100
Subject: [PATCH 094/161] Add stripped tutorial notebooks

---
 docs/general/YAML_calibration.ipynb           | 491 ++++++++++++
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 722 ++++++++++++++++++
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 create mode 100644 docs/general/YAML_calibration.ipynb
 create mode 100644 docs/tutorial/T7/T7_YAML_autocalibration.ipynb

diff --git a/docs/general/YAML_calibration.ipynb b/docs/general/YAML_calibration.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# YAML calibration documentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "This page contains additional information about features and functionality supported by the YAML calibration system\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## YAML internal structure\n",
+    "\n",
+    "The native representation of the YAML calibration is as a graph of 'nodes' where each node corresponds to either an action or a section containing other nodes. Each node has associated with it a collection of attributes/settings defined for that node (`instructions`), and a collection of attributes inherited from all parent nodes (`context`). The `atomica.yaml_calibration.build()` function takes in the content of the YAML file and loads it into a tree of `at.yaml_calibration.BaseNode` instances with a root node called 'calibration' at the top level. For example, consider the following YAML file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "print(open('T7_YAML_3_repeats.yaml').read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "This file would be loaded into the following tree of nodes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica.yaml_calibration \n",
+    "nodes = at.yaml_calibration.build('T7_YAML_3_repeats.yaml') \n",
+    "print(nodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "Section nodes have a `children` attribute that in turn contains others nodes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes.children[0].children[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "The nodes corresponding to actions each have their own type, with methods that implement the action performed by the node:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes.children[0].children[0].children[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "The context for a node consists of all settings defined in the node's parents. For example, the 'Match population sizes' node inherits the 'repeats' context from the parent 'calibration' node:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes.children[0].children[0].children[0].context"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "The 'instructions' for the 'Match population sizes' node contains all of the settings that are defined within the node itself - in this case, the adjustables and measurables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes.children[0].children[0].children[0].instructions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "The YAML file is executed by sequentially traversing the tree of nodes, and calling the `apply()` method on each node in turn. The order of execution can be obtained using the `walk()` method e.g., "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(nodes.walk())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "This returns a flat list of tuples, where the first item corresponds to the number of times the node has been repeated (which is used when printing progress during execution) and the second item is the node itself. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## `CalibrationNode` functionality\n",
+    "\n",
+    "### Adjustables and measurables settings\n",
+    "\n",
+    "A calibration node contains adjustables and measurables. Each adjustable and measurable in turn has its own settings. Each adjustable has:\n",
+    "\n",
+    "- `adj_label` (required): Adjustable codename (can be found in the framework)\n",
+    "- `pop_name`: Population to calibrate (default: all populations)\n",
+    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
+    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 1)\n",
+    "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable’s current `y_factor` in the parset)\n",
+    "\n",
+    "\n",
+    "Each measurable has:\n",
+    "\n",
+    "- `meas_label` (required): Measurable codename (can be found in the framework)\n",
+    "- `pop_name`: Population to use for calibration (default: all populations)\n",
+    "- `weight`: Weight for a particular population (default: weight = 1. This implies that, by default, all populations are weighted equally regardless of size. See the [section on setting weights](#measurable-weights \"Measurable weights\") for further details)\n",
+    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
+    "- `cal_start`: Starting year that the calibration will be evaluated for (default: `sim_start`)\n",
+    "- `cal_end`: End year that the calibration will be evaluated for (default: `sim_end`)\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "Note that <code>sim_start</code> and <code>sim_end</code> are governed by the project settings and are not set as part of the YAML calibration routine. \n",
+    "</div>\n",
+    "\n",
+    "When creating a calibration node in the YAML file, it is possible to create the adjustables and measurables using their labels only, using the default values for all other settings. Alternatively, users can specify some or all of the settings. The YAML calibration framework for Atomica supports four ways of setting adjustables and measurables, so as to give users a high level of flexibility. These are: \n",
+    "\n",
+    "- String format\n",
+    "- List format \n",
+    "- Dictionary format\n",
+    "- Combined format \n",
+    "\n",
+    "In general, we can only use one notation within any particular block of adjustables or measurables (with the exception of **combined format**). However, notation does not necessarily have to be consistent between different calibration blocks, or even between the adjustables and the measurables of the same calibration block. \n",
+    "\n",
+    "Below, we will describe each notation and how to use them in a YAML calibration file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "#### String format\n",
+    "\n",
+    "The simplest notation is string format, where only parameter names are passed to the adjustables and measurables, like so: \n",
+    "\n",
+    "    Calibration: \n",
+    "        match population sizes:\n",
+    "            adjustables: births, mig_rate\n",
+    "            measurables: alive\n",
+    "\n",
+    "Multiple parameters can be provided, separated by commas. When we use string notation, the optimisation algorithm will perform an autocalibration run using the default settings for adjustables and measurables. If we want to the optimisation algorithm to use specific settings, rather than just the defaults, it is necessary to use one of the other formats."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "#### Dictionary format\n",
+    "\n",
+    "Dictionary format allows us to explicitly set calibration settings for each adjustable and measurable. We do this by writing the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so: \n",
+    "\n",
+    "    Calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \tb_rate: \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            \tmig_rate: \n",
+    "                    lower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            measurables: \n",
+    "                alive:\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "\n",
+    "We can also specify the same settings for multiple adjustables or measurables at once, by placing them together, separated by commas:  \n",
+    "\n",
+    "    Calibration: \n",
+    "        match population sizes:\n",
+    "            adjustables: \n",
+    "            \tbirths, mig_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            measurables: alive       \n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20",
+   "metadata": {},
+   "source": [
+    "#### List format\n",
+    "\n",
+    "In list format, as the name suggests, we specify the adjustables and measurables settings in a list. It can be useful as a shorthand of dictionary form, since the labels for each setting don't need to be explicitly written. Instead, we simply write the value of each setting, following the same order as in the [Adjustables and Measurables Settings](#Adjustables-and-measurables-settings) section.\n",
+    "\n",
+    "To use list format, place the parameter name and ordered settings values in a list (that is, in square brackets, separated by commas) after the adjustables and/or measurables keyword. The general structure and order to follow are shown below. \n",
+    "\n",
+    "    adjustables: [adj_label, lower_bound, upper_bound, starting_y_factor]\n",
+    "    measurables: [meas_label, weight, metric, start_year, end_year]\n",
+    "\n",
+    "Although this might seem like a lot of information for each adjustable and measurable, it is not necessary to include each item every time we use list format – only up to the point where the last setting we want to change is. For example, if we just want to set the `b_rate` adjustable’s `lower bound` to, say, 0.2, we only need to list the `par_label` and `lower_bound` values in order. Any subsequent settings will retain their default values.\n",
+    "\n",
+    "    adjustables: [b_rate, 0.2]\n",
+    "    measurables: [alive]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "However, if we wanted to set values that are at the end of the list order, we need to explicitly specify the default values of all the previous settings. For example, to set the `starting_y_factor` to 1.2 and the `end_year` to 2020, assuming the simulation start year `sim_start` was 2000, we would write:\n",
+    "\n",
+    "    adjustables: [b_rate, 0.1, 10, 1.2]\n",
+    "    measurables: [alive, 1.0, fractional, 2000, 2020]\n",
+    "\n",
+    "We can also specify settings for multiple adjustables/measurables at once, by writing a list for each adjustable or measurable, and placing them together in a list of lists. The first example from the previous section on Dictionary format would be written as\n",
+    "\n",
+    "    adjustables: [[b_rate, 0.1, 10, 1.2], [mig_rate, 0.5, 20]]\n",
+    "    measurables: [alive]\n",
+    "\n",
+    "In this particular case, it might be most practical to use the dictionary format for the `mig_rate`, while the `b_rate` is more concise in list format. List notation may also become convoluted and hard to read if there are parameters to calibrate in the same block. In case like these, we can use the combined format instead, described below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "#### Combined format\n",
+    "\n",
+    "Combined format uses dictionary keys, while the values are in list form. This has two main benefits: Firstly, it separates out the parameters in a clear and organised way, which avoids ending up with a dense list of lists containing long series of numbers. Secondly, it allows us to use both the list and dictionary formats under one same adjustables or measurables block.\n",
+    "\n",
+    "    calibration: \n",
+    "        match population sizes:\n",
+    "            adjustables: \n",
+    "                    b_rate: \t\t\n",
+    "            \t\t\tstarting_y_factor: \t1.2 \t------> dictionary format\n",
+    "            \t\tmig_rate: [0.5, 20]\t            ------> combined format\n",
+    "            measurables: alive       \n",
+    "\n",
+    "In the above example, the `b_rate` adjustable settings are in dictionary format, while the `mig_rate` is now in combined format. When using the combined format, the list of settings is defined in the same order as in the [Adjustables and Measurables Settings](#Adjustables-and-measurables-settings) section. In other words, the order is the same as when using the list format, except we don't specify the first entry (corresponding to the parameter code name) inside the list, as it is already specified before the colon. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23",
+   "metadata": {},
+   "source": [
+    "### Calibrating populations\n",
+    "\n",
+    "The YAML calibration framework allows us to indicate specific populations to calibrate. This can be useful if we wish to calibrate some populations separately, or use different calibration settings for different populations. By default, if only the code name of the adjustable or measurable is provided, a separate copy will be created for every population. To specify the population in any format (except for string format, which does not support populations), the `par_name` and `pop_name` must be placed in a tuple, i.e. in round brackets and separated by a comma, like so: `(births, 0-4)`. Calibrating populations with spaces in the `pop_label` is supported, and follows the same syntax: `(births, 0-4 HIV+)`. The following are examples of this feature’s usage in all supported formats: \n",
+    "\n",
+    "Dictionary format:\n",
+    "\n",
+    "    adjustables:\n",
+    "        (births, 0-4), mig_rate: \n",
+    "                lower_bound: 0.5\n",
+    "                upper_bound: 20\n",
+    "    measurables: \n",
+    "        (alive, 0-4):\n",
+    "            weight: 0.1\n",
+    "\n",
+    "List format:\n",
+    "\n",
+    "    adjustables: [ [(births, 0-4), 0.5, 20], [mig_rate, 0.5, 20] ]\n",
+    "    measurables: [(alive, 0-4), 1.0]\n",
+    "\n",
+    "Combined format:\n",
+    "\n",
+    "    adjustables:\n",
+    "        (births, 0-4), mig_rate: [0.5, 20] \n",
+    "    measurables: \n",
+    "        (alive, 0-4): [1.0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24",
+   "metadata": {},
+   "source": [
+    "#### Meta Y-factors\n",
+    "\n",
+    "For each parameter, the meta Y-factor is applied to all populations. To calibrate the meta Y-factor and apply the same changes to every population, set the population name to `all` using the syntax above e.g., `(births, all)` would set the meta Y-factor for the `births` parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "#### Overriding population specific settings\n",
+    "\n",
+    "For each adjustable that has been created, the population-specific settings will take precedence over non-population-specific settings. Recall that if no population is specified, this is equivalent to defined adjustables and measurables for each population separately. For example:\n",
+    "\n",
+    "    adjustables:\n",
+    "        births:\n",
+    "            lower_bound: 0.5\n",
+    "            upper_bound: 20\n",
+    "        (births, 0-4) \n",
+    "            upper_bound: 10\n",
+    "\n",
+    "In this case, `births` will be adjusted in every population with a lower bound of 0.5 and upper bound of 20, _except_ for the 0-4 population, in which the lower bound will be 0.5 but the upper bound will be 10."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26",
+   "metadata": {},
+   "source": [
+    "### Calibrating transfers and interactions\n",
+    "\n",
+    "In the case of transfers and interactions, there are two populations involved: the ‘to’ population, and the ‘from' population. The approach is the same as with regular populations, except that we now have two population names in the tuple instead of one: \n",
+    "\n",
+    "    adjustables:\n",
+    "        (aging, 5-14, 15-64): \n",
+    "                lower_bound: 0.5\n",
+    "            \tupper_bound: 20\n",
+    "    measurables: alive\n",
+    "\n",
+    "And in list form:\n",
+    "\n",
+    "\tadjustables: [(aging, 5-14, 15-64), 0.1, 10]\n",
+    "    measurables: alive\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "### Calibrating to total population data\n",
+    "\n",
+    "Usually, our data is structured like below, with each parameter containing several populations, such as age groups. \n",
+    "\n",
+    "![](databook_pops.png \"Example of a databook with multiple populations\")\n",
+    "\n",
+    "In the YAML file, we could write \n",
+    "\n",
+    "    calibration\n",
+    "        adjustables: b_rate\n",
+    "        measurables: alive\n",
+    "\n",
+    "Since no population has been specified, all populations will be calibrated. \n",
+    "\n",
+    "However, for some parameters, our source data might not be broken down into populations or age groups. In that case, the above YAML file will not work, since there is no data available at the individual population level. What we can do in that situation is add an extra row to the databook with a population called `Total` (which is a reserved keyword in Atomica), and explicitly set the population name to `Total` in the YAML file. If it was our 'alive' data that was not broken down by populations, the databook would look like so\n",
+    "\n",
+    "![](databook_pops_Total.png \"Example of a databook with a Total population\")\n",
+    "\n",
+    "And we would adjust the previous YAML file like so.\n",
+    "\n",
+    "\tcalibration\n",
+    "        adjustables: b_rate\n",
+    "        measurables: (alive, Total)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28",
+   "metadata": {},
+   "source": [
+    "## Measurable Weights \n",
+    "\n",
+    "In one calibration block, we can include several measurables at the same time, or multiple populations of the same measurable. But say we trusted the data from one measurable’s data source more than another, or wanted to prioritise fitting a particular population – how would we indicate this to the optimisation algorithm? \n",
+    "\n",
+    "In the measurable settings, we can set weights for this purpose. The default value for the `weight` setting is 1.0 which is used for all measurables and populations, which corresponds to giving them each an equal weight, regardless of size. That might be desirable if, for example, we have a key population that is smaller than the other populations – if they were weighted proportionally to size, the small key population might be effectively ignored in the optimisation. However, there might be cases where we want to do things differently. For example, we could give a key population a higher weight, or we could weight different age bins according to their size. Another reason to use measurable weights could simply be that we trust one data source more than another. \n",
+    "\n",
+    "In the following example, we set the `0-4 HIV+` population of the `alive` measurable to have double the weight than the `0-4` population. \n",
+    "\n",
+    "    calibration: \n",
+    "        match population sizes:\n",
+    "            adjustables: \n",
+    "            \tb_rate: \n",
+    "                        lower_bound: 0.1\n",
+    "                        upper_bound: 10\n",
+    "            measurables: \n",
+    "                (alive, 0-4 HIV+):        \n",
+    "                    \tweight: 2\n",
+    "                (alive, 0-4):        \n",
+    "        \t\t\t\tweight: 1\n",
+    "\n",
+    "Note that the important factor is the proportion between the different weights, not the weight values themselves. That is to say, if we instead set the above weight values to `4` and `2` respectively, the result would be the same. \n"
+   ]
+  }
+ ],
+ "metadata": {
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+   "display_name": "Python [conda env:atomica312]",
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diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
new file mode 100644
index 00000000..348fc980
--- /dev/null
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -0,0 +1,722 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# T7 - YAML calibration\n",
+    "\n",
+    "As we saw in the earlier calibration tutorial, most autocalibrations involve multiple steps, since the optimization algorithm often gets stuck in local minima if we try to optimize too many parameters at once. We could run each of these steps ourselves through separate calls to `P.calibrate()` – however, there is significant value in being able to explicitly frame the overall calibration process as an algorithm, as this makes it easier to modify the calibration steps and apply the calibration algorithm across a collection of projects. This is implemented in Atomica through the 'YAML calibration' feature, in which the calibration steps are specified in a file which Atomica can then read and use to execute the calibration.\n",
+    "\n",
+    "## YAML files\n",
+    "\n",
+    "The calibration algorithm files used by Atomica are written in YAML. YAML is a plain-text, human-readable data serialization language used to make configuration files. Essentially, a YAML file can be read into Python variables (dictionaries, lists, strings) which in turn can be used as arguments to Python functions. Here is an example of how variables can be specified in a YAML file:\n",
+    "\n",
+    "```\n",
+    " foo: a string\n",
+    " bar: 1\n",
+    " baz: [a,b,c]\n",
+    " list:\n",
+    "   - i\n",
+    "   - j\n",
+    " nested:\n",
+    "   x: 1\n",
+    "   y: 2\n",
+    " ```\n",
+    "\n",
+    "When parsed into Python, this becomes\n",
+    "\n",
+    "```\n",
+    "{'foo': 'a string',\n",
+    " 'bar': 1,\n",
+    " 'baz': ['a', 'b', 'c'],\n",
+    " 'list': ['i', 'j'],\n",
+    " 'nested': {'x': 1, 'y': 2}}\n",
+    "```\n",
+    "\n",
+    "Using YAML files provides a simple way to define a calibration algorithm in a format that is easy to work with and that Atomica can directly execute. This can cut down the time we spend manually calibrating, or even running autocalibrations. It allows us to conduct reproducible calibration runs, and is also highly scalable, since it allows us to apply the same calibration algorithm in multiple countries or settings.\n",
+    "\n",
+    "The following tutorial outlines how to use the YAML framework that has been developed for Atomica calibration. Specifically, it will cover how to write a YAML configuration file with calibration instructions for Atomica, and how to use this file to execute a calibration. Bear in mind that YAML calibration is not intended to be a standalone tool that will perfectly calibrate any model – rather, it is one part of the calibration toolbox. It can be used to reduce the time spent on calibration by autocalibrating Atomica models to a reasonable level, but additional tweaking may be required to obtain a consistently high calibration quality across all parameters, populations and/or countries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Basic calibration example\n",
+    "\n",
+    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `assets/T7`. \n",
+    "\n",
+    "_TODO: Update paths when moved to Atomica repository_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "F = at.ProjectFramework('T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet('T7_databook.xlsx', framework=F)\n",
+    "P = at.Project(framework=F,databook=D, do_run=False)\n",
+    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "In the example above, no calibration has been loaded, so all of the calibration Y-factors are equal to 1, and the model is uncalibrated. We can run a simulation and plot it, to see what our model looks like before calibration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal = P.make_parset()\n",
+    "res = P.run_sim(parset=cal, result_name = 'Uncalibrated')\n",
+    "d = at.PlotData(res, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(10,7)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "Two issues with these simulation outputs immediately stand out. First, for the variables and years in which data is available, the model output doesn't match the data very well at all. Second, there is a large sudden change in the values of several variables right at the start of the simulation. In the absence of associated changes in interventions or disease transmission, disease burden is typically much more stable over time, so we would not expect to see such sudden changes at the start of the simulation.\n",
+    "\n",
+    "Tutorial 2 covers some of the detail around how to approach calibration. Although the parameters used for calibration vary from model to model, as a general rule, calibration proceeds by\n",
+    "\n",
+    "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
+    "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
+    "\n",
+    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them, and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use a YAML file to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Minimal YAML file\n",
+    "\n",
+    "To illustrate how YAML calibration works, let’s start with a simple example of what a YAML file could look like:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tadjustables: b_rate, mig_rate\n",
+    "    \tmeasurables: alive\n",
+    "\n",
+    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "\n",
+    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g. [T7_YAML_1_minimal.yaml](./<`T7_YAML_1_minimal`>.<yaml>)), the calibration can be run using\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()`, simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
+    "\n",
+    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Sections\n",
+    "\n",
+    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modify the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file\n",
+    "\n",
+    "- _actions_ which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains 'adjustables' and 'measurables'. Other examples of actions are detailed below\n",
+    "- _sections_ which are containers for actions. Sections can contain attributes such as how many times to repeat the contents of the section, or can define settings that are applied to any actions within the section\n",
+    "\n",
+    "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    Match population sizes:\n",
+    "        adjustables: b_rate, mig_rate\n",
+    "        measurables: alive\n",
+    "    Match deaths:\n",
+    "        adjustables: d_rate\n",
+    "        measurables: deaths\n",
+    "```\n",
+    "\n",
+    "To organize the YAML file further, we could group these into an additional section. Both of these actions affect the overall population calibration, so we could logically group them as follows:\n",
+    "\n",
+    "```\n",
+    "calibration: \n",
+    "    Population calibration:\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "            measurables: alive\n",
+    "        Match deaths:\n",
+    "            adjustables: d_rate\n",
+    "            measurables: deaths\n",
+    "```\n",
+    "\n",
+    "The overall structure of this YAML file is thus:\n",
+    "\n",
+    "1. A top-level section titled `calibration` which has one sub-section (`Population calibration`)\n",
+    "2. A sub-section called `Population calibration` which in turn contains two actions\n",
+    "3. An action called `Match population sizes` corresponding to the original calibration step for adjusting the birth rate and migration rate\n",
+    "4. An action called `Match deaths` corresponding to the new step of adjusting the death rate\n",
+    "\n",
+    "An action is differentiated from a section in two possible ways:\n",
+    "\n",
+    "- By contents e.g., if it contains `adjustables` and `measurables` then it is interpreted as a calibration action rather than a section\n",
+    "- By the title i.e., if the name corresponds to the name of a supported operation, as described below.\n",
+    " \n",
+    "Aside for the names of supported operations, sections can freely named."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "### Repeating a section\n",
+    "\n",
+    "The *repeats* keyword can be used to loop over any part of the calibration multiple times. We do this by writing `repeats: n` inside a particular section, where *n* is the number of times we would like to loop over that section. All subsections contained in it will also be looped over *n* times. \n",
+    "\n",
+    "    calibration: \n",
+    "        repeats: 2\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                repeats: 2\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "\n",
+    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
+    "\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "\n",
+    "In this way, it is possible for even a very compact YAML file to correspond to a large number of individual autocalibration steps. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## Sections vs Actions\n",
+    "\n",
+    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in a [later section](#specifying-calibration-settings-in-outer-sections)). \n",
+    "\n",
+    "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block it is, or it contains no sub-sections.\n",
+    "</div>\n",
+    "\n",
+    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading the calibration, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica.yaml_calibration \n",
+    "calibration_tree = at.yaml_calibration.build('T7_YAML_3_repeats.yaml') \n",
+    "print(calibration_tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## Types of Functional Blocks/Actions\n",
+    "\n",
+    "YAML calibration files can contain the following types of action blocks: \n",
+    "\n",
+    "- Calibration block\n",
+    "- Initialization block\n",
+    "- Clear intialization block\n",
+    "- Saving block"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "### Calibration blocks\n",
+    "\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action block. Calibration blocks are defined by the fact that they contain two keywords called `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making a call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "\n",
+    "#### Adjustable and measurable settings\n",
+    "\n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default settings for adjustables and measurables. For more flexibility, we can specify further settings for the optimisation. The settings for the adjustables and measurables directly map to the options supported by `P.calibrate()`. \n",
+    "\n",
+    "Each adjustable has:\n",
+    "\n",
+    "- `adj_label` (required): Adjustable codename (can be found in the framework)\n",
+    "- `pop_name`: Population to calibrate (default: all populations)\n",
+    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
+    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 1)\n",
+    "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable’s current `y_factor` in the parset)\n",
+    "\n",
+    "Each measurable has:\n",
+    "\n",
+    "- `meas_label` (required): Measurable codename (can be found in the framework)\n",
+    "- `pop_name`: Population to use for calibration (default: all populations)\n",
+    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights]((./YAML_documentation_page.ipynb#measurable-weights \"Measurable weights\") for further details.\n",
+    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
+    "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
+    "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
+    "\n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan) which are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-calibration-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per-measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
+    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so: \n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \tb_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \tmig_rate: \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                alive:\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "                 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Specifying Populations\n",
+    "\n",
+    "In some cases, you may want to only set adjustables or evaluation measurables for a specific population, or you may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so: \n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4): \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \t(mig_rate, 5-14): \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "       \n",
+    "Note that we can specify the same settings for more than one adjustable/measurable at once by placing several parameter names before the colon, separated by commas – this is applicable to any set of adjustables/measurables, not only different populations of the same parameter. For example, we could write: \n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4), mig_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\n",
+    "                    starting_y_factor: 1.2\t\t \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "               \tcal_end: 2040\n",
+    "\n",
+    "Finally, this same syntax can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on calibrating in these cases, see [Calibrating transfers and interactions](./YAML_documentation_page.ipynb#calibrating-transfers-and-interactions \"Calibrating transfern detail by this point> TODO: Update link \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Setting initialisations\n",
+    "\n",
+    "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
+    "\n",
+    "- The initial conditions are underdetermined, for example, if there are two strains of a disease with different levels of infectiousness, but data is only available on the total prevalence. In the absence of any other information, Atomica will automatically split up the total prevalence equally between the two strains, when in fact maybe one or the other strain may be dominant, which would change the overall transmission\n",
+    "- The model parameters may not give rise to an equilibrium solution that matches the data, if data sources have been mixed, combined across different years, collected in different ways or different definitions, or because the simplified dynamics in the model don't capture all of the processes in the real world. \n",
+    "\n",
+    "Regardless of the cause, there can often be an initial transience period at the beginning of the simulation, where we can observe abrupt spikes in some parameters until the system reaches equilibrium. \n",
+    "\n",
+    "<img src=\"assets/fig_pre_init.png\" alt=\"Pre-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "In some models, this can be treated as a 'burn-in' period and the initial part of the simulation can simply be discarded. However, there is a risk that being too far from the correct initialization results in contamination of the estimates of parameters during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of the transient on the calibration. \n",
+    "\n",
+    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, or might limit the amount of data used for the calibraiton.\n",
+    "\n",
+    "An alternative approach is to override the initialisation for our calibration. Initialisations work by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are mostly constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will avoid the initial transient in the model. \n",
+    "\n",
+    "\n",
+    "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](./YAML_documentation_page.ipynb \"Documentation\").\n",
+    "\n",
+    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialisation`. Under this title, we can specify further settings: \n",
+    "\n",
+    "- `init_year` (required): The year to use to take the compartment sizes from. The simulation will be run up to this year\n",
+    "- `constant_parset` (default: False): Whether to use a constant parset for the initialisation, and which year to use in `parset.make_constant()`. It can be a Boolean (True/False) or numerical value (representing the year from which to draw the constant values for the parset, defaults to the same year as `sim_start`).\n",
+    "\n",
+    "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialisation:\n",
+    "        init_year: 2030\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using the parameter values from the `sim_start` year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialisation:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: True\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using the parameter values from a specific year:\n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialisation:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: 2005\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "### Clearing initialisations\n",
+    "\n",
+    "If we have previously set an initialisation in our calibration algorithm, and then set another initialisation later in the YAML file, it uses information from the previous initialisation to calculate the next, since the new simulation will start from the initial compartment sizes calculated in the previous initialization.\n",
+    "\n",
+    "Sometimes we might want to calculate a new initialisation from scratch, without using the previous initialisation as a starting point. This could be useful if we have done some calibration steps between the previous initialisation and now, in which case the y-factors will have changed, and we might be better off using a different starting point. \n",
+    "\n",
+    "To do this in the YAML file, we can add a section titled `clear_initialisation`, followed by a boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. For example:\n",
+    "\n",
+    "    calibration:\n",
+    "        set_initialisation 1:\n",
+    "    \t\tinit_year: 2030\n",
+    "\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "        \tmeasurables: alive\n",
+    "        \n",
+    "    \tclear_initialisation: True\n",
+    "        \n",
+    "        set_initialisation 2:\n",
+    "    \t\tinit_year: 2030"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "### Saving calibrations\n",
+    "\n",
+    "Throughout a YAML calibration, we might wish to save the calibration state at specific points in the calibration process. For example, if our YAML file has a population section and an epidemiological section, we might want to save the calibration after the population calibration section so we can see the progress made up until that point, or otherwise isolate the effect that different parts of the algorithm are having on the calibration. To save a calibration, we simply make a section titled `save_calibration`. Under the title, we can indicate the filename we wish to save the calibration to, either by providing it directly, or by using the keyword `fname` - both examples are shown below:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tPopulation section: \n",
+    "    \t\t[…]\n",
+    "    \n",
+    "    \tsave_calibration: \n",
+    "    \t\tfname: pop_calibration.xlsx\n",
+    "    \n",
+    "    \tEpidemiology section: \n",
+    "    \t\t[…]\n",
+    "\n",
+    "    \tsave_calibration: epi_calibration.xlsx\n",
+    "        \n",
+    "Note that when we save a calibration, if initial compartment sizes have been explicitly specified by using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus include the initial compartment sizes. \n",
+    "\n",
+    "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, you can use the `save_intermediate_calibrations` option when running the calibration e.g.\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
+    "\n",
+    "For more information on intermediate calibrations, see the relevant [documentation section](./YAML_documentation_page.ipynb#saving-intermediate-calibrations \"Saving intermediate calibrations\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Specifying settings in outer sections\n",
+    "\n",
+    "We saw previously that sections (i.e. YAML blocks that don't correspond to actions) don't directly modify the calibration, and are mainly used to structure the YAML file. However, it is possible to determine calibration settings inside a section, such that they are passed down to any subsections or action blocks contained within. Another way to think of this is that an action block will inherit any settings that are defined in any of its parent sections.\n",
+    "\n",
+    "This can simplify the process of writing YAML files where we want to override the default settings in several action blocks, as it allows us to specify those settings once in a parent section, rather than repeatedly in every action block. Additionally, this feature is hierarchical, so settings that are specified further in (e.g. in the action block itself) will always override those set in a section that is further out, allowing for more flexibility.\n",
+    "\n",
+    "Some settings that this feature is commonly used for include: \n",
+    "\n",
+    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for \n",
+    "- `stepsize`: Initial stepsize, i.e. how much the y-factors will be incremented/decreased by in the optimisation algorithm.\n",
+    "\n",
+    "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
+    "\n",
+    "- `cal_start` and `cal_end`: These can be used to change the calibration timespan for multiple calibration steps at the same time.\n",
+    "- `metric`: Used for when the measurables should use a different non-standard metric for assessing calibration quality. \n",
+    "\n",
+    "We will now provide some examples of how this functionality can be used.\n",
+    "\n",
+    "### Inheritance\n",
+    "\n",
+    "Settings are automatically inherited by all sub-sections. To set a `max_time` of 120 for every calibration step in the entire calibration, we would write the following. This will result in every calibration step being limited to 120 seconds. \n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Settings hierarchy\n",
+    "\n",
+    "Inherited settings can be overwritten inside sub-sections (in both sections and actions). Say we wanted all calibration blocks to run for a `max_time` of 120 seconds, except for `Match_deaths`, which we want to run for only 60 seconds. In that case, we can override the setting determined in the parent section by specifying the updated value inside `Match_deaths`:\n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                max_time_ 60\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Adjustables/measurables settings\n",
+    "\n",
+    "Finally, here is an example of how to use the adjustables and measurables settings outside of a calibration block: \n",
+    "\n",
+    "    calibration:\n",
+    "        Population calibration:\n",
+    "            upper_bound: 2\n",
+    "            lower_bound: .5\n",
+    "\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables alive\n",
+    "\n",
+    "            Match deaths:\n",
+    "                    adjustables: d_rate\n",
+    "                    measurab\n",
+    "\n",
+    "        Epidemiological calibration:\n",
+    "            [...]\n",
+    "\n",
+    "In this case, the upper and lower bounds have been updated to 2 and 0.5 respectively, for all the adjustables in the `Population calibration` section (`b_rate`, `mig_rate` and `d_rate`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "## Running the YAML calibration\n",
+    "\n",
+    "Now that we have finished writing our YAML calibration file, we can proceed to running the calibration. Having loaded a project, `P.calibrate` can then be called directly, passing in the name of the YAML file:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "This function supports several additional optional arguments:\n",
+    "\n",
+    "- `savedir` (default: current working directory) - any saved calibrations and logs will be saved into this folder\n",
+    "- `save_intermediate` (default: `False`) - if `True`, this will save all intermediate calibrations\n",
+    "- `max_time` (optional) - override the default\n",
+    "- `log_output` (default: `False`) - if `True`, this will save a text file into the output directory containing all console output (e.g., objective function values)\n",
+    "\n",
+    "The YAML file can also be supplied as a dictionary, which would normally be obtained via\n",
+    "\n",
+    "    import yaml\n",
+    "    calibration_yaml =  yaml.load(file, Loader=yaml.FullLoader)\n",
+    "    \n",
+    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programatically prior to running the calibration. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## Exercise: worked example\n",
+    "\n",
+    "Now that we understand what all of the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution. \n",
+    "\n",
+    "_**Question 1.** We want to do a basic population calibration, where we calibrate the death rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
+    "\n",
+    "**Hint**: Open the framework and look at the `Compartments`, `Parameters` and `Characteristics` pages. The `Code Names` and `Display Names` show us how we have to write the parameter names in the YAML file, and what quantities they correspond to. Those with a value in the `Databook Page` column have data values supplied, and can therefore be used as measurables.\n",
+    "\n",
+    "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
+    "\n",
+    "| Framework Sheet | Code Name | Display Name | Databook Page |\n",
+    "| :---------------- | :------: | :------: | ----: |\n",
+    "|COMPARTMENTS | <td colspan=3> |\n",
+    "| |birth\t\t|Birth    \t\t\t\t\t\t|none|\n",
+    "| |death   \t|Death    \t\t\t\t\t\t |none|\n",
+    "| PARAMETERS | <td colspan=3> |\n",
+    "| |b_rate\t\t|Birth rate     \t\t\t\t|demographic|\n",
+    "| |deaths\t\t|All-cause deaths    \t\t\t|demographic|\n",
+    "| |d_rate\t\t|Background mortality rate    \t|demographic|\n",
+    "| |mig_rate\t|Migration Rate    \t\t\t\t|demographic|\n",
+    "| CHARACTERISTICS | <td colspan=3> |\n",
+    "| |alive\t|\tTotal population\t\t| demographic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20",
+   "metadata": {},
+   "source": [
+    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the the YAML file?_\n",
+    "\n",
+    "**Hint**: Use the `repeats` keyword.\n",
+    "\n",
+    "_**Question 3.** Say that, for this particular project, we are only interested in calibrating results from 2005 to 2040. How would we specify this in the YAML file to reduce calibration time?_\n",
+    "\n",
+    "**Hint**: Using the `cal_start` and `cal_end` keywords\n",
+    "\n",
+    "_**Question 4.** When calibrating the birth rate, it only really makes sense to calibrate the 0-4 population. Modify the YAML file to reflect this._\n",
+    "\n",
+    "**Hint**: We place the parameter code name and population name in a tuple.\n",
+    "\n",
+    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set its starting y_factor to 1.2 to speed up the optimisation._\n",
+    "\n",
+    "**Hint**: Use `starting_y_factor`.\n",
+    "\n",
+    "_**Question 6.** We want to avoid the presence of transients in our calibrated simulation. Let’s initialize the calibration in order to eliminate any jumps._\n",
+    "\n",
+    "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
+    "\n",
+    "_**Question 7.** We also want to clear the previous initialisation every time we make a new one, instead of using information from the previous initialisation. How can we update the YAML file to reflect this?_\n",
+    "\n",
+    "**Hint**: Use `clear_initialisation`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
+    "\n",
+    "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
+    "\n",
+    "**Hint**: Use `save_calibration` and specify a `fname`.\n",
+    "\n",
+    "All solutions to the worked example are in the `Worked_example` folder. _TODO: Update paths when moved to Atomica repository_ The calibration obtained after running the last YAML file in this exercise yields the following simulation result: \n",
+    "\n",
+    "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "## Further resources\n",
+    "\n",
+    "### Epidemiological calibration\n",
+    "\n",
+    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration can be found at _TODO: Update paths when moved to Atomica repository_\n",
+    "\n",
+    "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
+    "\n",
+    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly inmpact the population calibration results. \n",
+    "\n",
+    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030. \n",
+    "\n",
+    "After the population calibration, the typhoid y-factors get set t\n",
+    "#TODO: Deal with issue: epi y-factors get reset to 1 in every pass, which means a) all the epi calibration loops except the first one don't achieve anything,  b) if we remove those loops, there's no going back to the pop calibration after the epi calibration to make sure it still fits well after calibrating epi \n",
+    "\n",
+    "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
+    "\n",
+    "At the end of the typhoid calibration, we set an initialisation in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice. \n",
+    "\n",
+    "The resulting calibration from running this YAML file is like so:\n",
+    "\n",
+    "<img src=\"assets/T7_plot_4_complete.png\" alt=\"Complete calibration result\" style=\"width: 800px;\"/>\n",
+    "\n",
+    "### Documentation\n",
+    "\n",
+    "For more information on using Atomica's YAML calibration functionality, see [the documentation](./YAML_documentation_page.ipynb#yaml-calibration-documentation-page \"YAML documentation page\").\n"
+   ]
+  }
+ ],
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From 0de5d3e1a2fe53ad20d9c6477de129d32df9c3b0 Mon Sep 17 00:00:00 2001
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Date: Thu, 13 Feb 2025 21:33:06 +0100
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From 99f83245f4ba9d5d9ab73ebe0e55c25c95b26ffd Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:34:08 +0100
Subject: [PATCH 096/161] Add framework and databook

---
 docs/tutorial/T7/assets/T7_databook.xlsx  | Bin 0 -> 84343 bytes
 docs/tutorial/T7/assets/T7_framework.xlsx | Bin 0 -> 36452 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/tutorial/T7/assets/T7_databook.xlsx
 create mode 100644 docs/tutorial/T7/assets/T7_framework.xlsx

diff --git a/docs/tutorial/T7/assets/T7_databook.xlsx b/docs/tutorial/T7/assets/T7_databook.xlsx
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HcmV?d00001


From 3c7f70e9727d3d75f2fa0f86b88ad1a7bed623ac Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:36:01 +0100
Subject: [PATCH 097/161] Add calibration scripts

---
 .../T7/calibrations/run_model_autocals.py     | 80 +++++++++++++++++++
 .../T7/calibrations/run_tutorial_autocals.py  | 80 +++++++++++++++++++
 2 files changed, 160 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/run_model_autocals.py
 create mode 100644 docs/tutorial/T7/calibrations/run_tutorial_autocals.py

diff --git a/docs/tutorial/T7/calibrations/run_model_autocals.py b/docs/tutorial/T7/calibrations/run_model_autocals.py
new file mode 100644
index 00000000..f4baac2c
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/run_model_autocals.py
@@ -0,0 +1,80 @@
+# This file produces a calibrations of the whole typhoid model.
+# Output files will be saved locally in the outputs folder.
+# Desirable results must be manually moved to the calibrations folder so that they can be plotted by the
+# Plot_final_typhoid_calibration.ipynb notebook. This is to avoid crowding the final results folder (calibrations) when
+# trying out new ways of calibrating the model.
+
+
+import atomica as at
+import atomica.yaml_calibration
+import os
+import time
+import shutil
+import winsound
+
+print(os.getcwd())
+#run settings
+yaml_dir = '../calibrations'
+yaml_fname = 'typ_calibration_instructions'
+yaml_path = f'{yaml_dir}/{yaml_fname}.yaml'
+testing = False  #<------------
+savecals = True
+dis_model = 'typh'
+out_path = 'outputs'
+
+#make project   ------------------------------------------------
+print(os.getcwd())
+inputs = '../assets'
+F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')
+D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)
+
+P = at.Project(framework=F, databook=D, do_run=False)
+P.settings.update_time_vector(start=2000, end=2050, dt=1 / 52)
+cal = P.make_parset()
+# starting calibration (if needed)
+# cal.load_calibration(out_path + "/PAK_typh_YAML_autocalibrations_2024-04-25_1941_reinitialising/PAK_typh_YAML-autocalibration_2024-04-25_1959.xlsx")
+# print('Existing calibration loaded')
+
+# new folder to put calibrations in
+date = time.strftime("%Y-%m-%d_%H%M")
+calspath = f'{out_path}/cal_{yaml_fname}'
+if testing:
+    calspath += '_TESTING'
+    savecals = False
+if savecals:
+    #save everything in folder
+    try:
+        os.makedirs(calspath)
+    except FileExistsError:
+        calspath += f'_{date}'
+        os.makedirs(f'{calspath}')
+
+    # save yaml file for reference
+    new_fname = f'{yaml_fname}_{date}.yaml'
+    shutil.copy(yaml_path, calspath)
+    shutil.move(f'{calspath}/{yaml_fname}.yaml', f'{calspath}/{new_fname}.yaml') #renaming file
+
+    #save logfile
+    at.start_logging(f'{calspath}/logfile_{date}.txt')
+
+    # save current runfile for reference
+    runfile_name = f'{os.path.basename(__file__)}_{date}'
+    shutil.copy(__file__, f'{calspath}/{runfile_name}')
+
+# display tree
+cal_tree = at.yaml_calibration.build(yaml_path)
+print(cal_tree)
+
+#run calibration w yaml instructions # <--------------------------
+newcal = P.calibrate(parset = cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
+
+#save final calibration
+date = time.strftime("%Y-%m-%d_%H%M")
+newcal.save_calibration(f'{calspath}/typ_calibration.xlsx')
+at.stop_logging()
+
+
+#Notification - calibration finished
+winsound.Beep(frequency = 2500, duration = 200)
+winsound.Beep(frequency = 2750, duration = 200)
+winsound.Beep(frequency = 3050, duration = 200)
diff --git a/docs/tutorial/T7/calibrations/run_tutorial_autocals.py b/docs/tutorial/T7/calibrations/run_tutorial_autocals.py
new file mode 100644
index 00000000..8a8bd7c7
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/run_tutorial_autocals.py
@@ -0,0 +1,80 @@
+# This file produces calibrations of each YAML file shown in T7: YAML autocalibration tutorial.
+# Output files will be saved locally in the outputs folder.
+# Desirable results must be manually moved to the calibrations folder so that they can be plotted by the
+# YAML_tutorial_plots_final.ipynb notebook. This is to avoid crowding the final results folder (calibrations) when
+# re-running the calibrations or trying out new versions of the model.
+
+import sys
+in_path = '../inputs'
+sys.path.append(in_path)
+import atomica as at
+import atomica.yaml_calibration
+import os
+from os.path import isfile
+import time
+import shutil
+import winsound
+
+yaml_dir = os.getcwd()
+fnames = [f for f in os.listdir(yaml_dir) if isfile(f"{yaml_dir}/{f}")]
+yaml_files = [f for f in fnames if ".yaml" in f and "T7_YAML" in f]
+yaml_files += 'WE_8'
+
+testing = False  #<------------
+savecals = True
+out_path = 'outputs'
+
+#make project   ------------------------------------------------
+inputs = '../assets'
+F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')
+D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)
+
+P = at.Project(framework=F, databook=D, do_run=False)
+P.settings.update_time_vector(start=2000, end=2050, dt=1 / 52)
+cal = P.make_parset()
+# starting calibration (if needed)
+# cal.load_calibration(out_path + "/PAK_typh_YAML_autocalibrations_2024-04-25_1941_reinitialising/PAK_typh_YAML-autocalibration_2024-04-25_1959.xlsx")
+# print('Existing calibration loaded')
+
+for yaml_fname in yaml_files:
+    yaml_path = f'{yaml_dir}/{yaml_fname}'
+
+    # new folder to put calibrations in
+    date = time.strftime("%Y-%m-%d_%H%M")
+    calspath = f'{out_path}/cals_{yaml_fname[8]}'
+    if savecals:
+        #save everything in folder
+        try:
+            os.makedirs(calspath)
+        except FileExistsError:
+            calspath += f'_{date}'
+            os.makedirs(f'{calspath}')
+
+        # save yaml file for reference
+        new_fname = f'{yaml_fname}_{date}.yaml'
+        shutil.copy(yaml_path, calspath)
+        shutil.move(f'{calspath}/{yaml_fname}', f'{calspath}/{new_fname}') #renaming file
+
+        #save logfile
+        at.start_logging(f'{calspath}/logfile_{date}.txt')
+
+        # save current runfile for reference
+        runfile_name = f'{os.path.basename(__file__)}_{date}'
+        shutil.copy(__file__, f'{calspath}/{runfile_name}')
+
+    # display tree
+    cal_tree = at.yaml_calibration.build(yaml_path)
+    print(cal_tree)
+
+    #run calibration w yaml instructions # <--------------------------
+    newcal = P.calibrate(parset = cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
+
+    #save final calibration
+    newcal.save_calibration(f'{calspath}/cal_{yaml_fname[8]}')  #newcal.save_calibration(f'{calspath}/cal_{yaml_fname}')
+    at.stop_logging()
+
+
+#Notification - calibration finished
+winsound.Beep(frequency = 2500, duration = 200)
+winsound.Beep(frequency = 2750, duration = 200)
+winsound.Beep(frequency = 3050, duration = 200)

From 63220e9c1fae97f73b2635c4704ba05aa4ebb12d Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:38:25 +0100
Subject: [PATCH 098/161] Add YAML files shown in tutorial

---
 .../T7/calibrations/T7_YAML_1_minimal.yaml      |  7 +++++++
 .../T7/calibrations/T7_YAML_2_sections.yaml     | 12 ++++++++++++
 .../T7/calibrations/T7_YAML_3_repeats.yaml      | 11 +++++++++++
 .../tutorial/T7/calibrations/T7_YAML_4_cal.yaml | 16 ++++++++++++++++
 .../T7/calibrations/T7_YAML_5_cal_pop.yaml      | 17 +++++++++++++++++
 .../T7_YAML_6_cal_multiple_params.yaml          | 17 +++++++++++++++++
 .../T7_YAML_7_transience_initialisation.yaml    | 14 ++++++++++++++
 .../calibrations/T7_YAML_8_clearing_init.yaml   | 14 ++++++++++++++
 .../calibrations/T7_YAML_9_outer_settings.yaml  | 15 +++++++++++++++
 .../T7_YAML_9_outer_settings_maxtime.yaml       | 12 ++++++++++++
 10 files changed, 135 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_1_minimal.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_2_sections.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_3_repeats.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_4_cal.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_5_cal_pop.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_6_cal_multiple_params.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings.yaml
 create mode 100644 docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings_maxtime.yaml

diff --git a/docs/tutorial/T7/calibrations/T7_YAML_1_minimal.yaml b/docs/tutorial/T7/calibrations/T7_YAML_1_minimal.yaml
new file mode 100644
index 00000000..dac077d3
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_1_minimal.yaml
@@ -0,0 +1,7 @@
+
+calibration:
+    adjustables: b_rate, mig_rate
+    measurables: alive
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_2_sections.yaml b/docs/tutorial/T7/calibrations/T7_YAML_2_sections.yaml
new file mode 100644
index 00000000..227d32d5
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_2_sections.yaml
@@ -0,0 +1,12 @@
+
+calibration:
+
+  Population calibration:
+
+    Match population sizes:
+      adjustables: b_rate, mig_rate
+      measurables: alive
+
+    Match deaths:
+      adjustables: d_rate
+      measurables: deaths
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_3_repeats.yaml b/docs/tutorial/T7/calibrations/T7_YAML_3_repeats.yaml
new file mode 100644
index 00000000..f5b526f0
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_3_repeats.yaml
@@ -0,0 +1,11 @@
+
+calibration:
+    repeats: 2
+    Population calibration:
+        Match population sizes:
+            repeats: 5
+            adjustables: b_rate, mig_rate
+            measurables: alive
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_4_cal.yaml b/docs/tutorial/T7/calibrations/T7_YAML_4_cal.yaml
new file mode 100644
index 00000000..5fca7b66
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_4_cal.yaml
@@ -0,0 +1,16 @@
+
+calibration:
+    Match population sizes:
+        adjustables:
+            b_rate:
+                lower_bound: 0.5
+                upper_bound: 20
+            mig_rate:
+                starting_y_factor: 1.2
+        measurables:
+            alive:
+                cal_start: 2000
+                cal_end: 2040
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_5_cal_pop.yaml b/docs/tutorial/T7/calibrations/T7_YAML_5_cal_pop.yaml
new file mode 100644
index 00000000..caef4bbc
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_5_cal_pop.yaml
@@ -0,0 +1,17 @@
+
+calibration:
+    Match population sizes:
+        adjustables:
+            (b_rate, 0-4):
+                lower_bound: 0.5
+                upper_bound: 20
+            (mig_rate, 5-14):
+                starting_y_factor: 1.2
+        measurables:
+            (alive, 0-4), (alive, 5-14):
+                cal_start: 2000
+                cal_end: 2040
+
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_6_cal_multiple_params.yaml b/docs/tutorial/T7/calibrations/T7_YAML_6_cal_multiple_params.yaml
new file mode 100644
index 00000000..c97d9248
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_6_cal_multiple_params.yaml
@@ -0,0 +1,17 @@
+
+calibration:
+    Match population sizes:
+        adjustables:
+            b_rate, mig_rate:
+                lower_bound: 0.5
+                upper_bound: 20
+                starting_y_factor: 1.2
+        measurables:
+            alive:
+                cal_start: 2000
+                cal_end: 2040
+
+
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml b/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
new file mode 100644
index 00000000..d0d4e37e
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
@@ -0,0 +1,14 @@
+
+
+
+
+
+    set_initialisation:
+        init_year: 2010
+        constant_parset: True
+
+
+
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml b/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
new file mode 100644
index 00000000..6dd34940
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
@@ -0,0 +1,14 @@
+
+calibration:
+    clear_initialisation: True
+
+
+
+
+
+    save_calibration:
+        fname: Pop_calibration.xlsx
+
+
+
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings.yaml b/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings.yaml
new file mode 100644
index 00000000..b6e3622c
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings.yaml
@@ -0,0 +1,15 @@
+
+
+calibration:
+    Population calibration:
+        upper_bound: 2
+        lower_bound: 0.5
+
+        Match population sizes:
+            adjustables: b_rate, mig_rate
+            measurables: alive
+
+        Match deaths:
+                adjustables: d_rate
+                measurables: deaths
+
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings_maxtime.yaml b/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings_maxtime.yaml
new file mode 100644
index 00000000..f742fedc
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/T7_YAML_9_outer_settings_maxtime.yaml
@@ -0,0 +1,12 @@
+
+calibration:
+    Population calibration:
+        max_time: 120
+        Match population sizes:
+            adjustables: b_rate, mig_rate
+            measurables: alive
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
+
+

From f2d4ea750e5f1b60de703597f6e013d9bf2c2b24 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:39:28 +0100
Subject: [PATCH 099/161] Add typhoid YAML file and calibration output

---
 .../T7/calibrations/typ_calibration.xlsx      | Bin 0 -> 10520 bytes
 .../typ_calibration_instructions.yaml         |  55 ++++++++++++++++++
 2 files changed, 55 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/typ_calibration.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/typ_calibration_instructions.yaml

diff --git a/docs/tutorial/T7/calibrations/typ_calibration.xlsx b/docs/tutorial/T7/calibrations/typ_calibration.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..db1b00f89211f171d7f9df5fb3dc72ffe0d063d5
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literal 0
HcmV?d00001

diff --git a/docs/tutorial/T7/calibrations/typ_calibration_instructions.yaml b/docs/tutorial/T7/calibrations/typ_calibration_instructions.yaml
new file mode 100644
index 00000000..9fc49bdc
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/typ_calibration_instructions.yaml
@@ -0,0 +1,55 @@
+---
+#Timestep for the model
+#sim_dt: 0.019
+
+#Ordered instructions for how to calibrate the model
+  #adj order: (adjustable var_label, populations to adjust for (default all), minimum value (default 0.1), maximum value (default 10), initial y_factor value (optional, default is parset value))
+  #meas order: #(measurable var_label, populations to measure for (default all), weight (default 1.0), metric (default "fractional"), start_year (optional), end_year (optional))
+calibration:
+    repeats: 3
+    max_time: 120
+
+    Population calibration:              #--------------------------------------------------
+      repeats: 5
+
+      match population sizes:
+        adjustables:
+          (b_rate, 0-4), mig_rate: [ 0.1, 10 ]
+          (b_rate, 0-4):
+            initial_value: 1.2
+        measurables: alive
+
+      match all-cause deaths:
+        adjustables:  d_rate
+        measurables:  deaths
+
+      pop initialization:
+        clear_initialization: True
+        set_initialization:
+          init_year: 2010
+          constant_parset: True
+
+
+    Epidemiology calibration:       # -----------------------------------------------------
+      repeats: 10
+
+      Typhoid calibration:
+
+        match typhoid incidence:
+          adjustables:
+            typ_susceptibility: [0.01, 100 ]
+          measurables: [ typ_incidence ]
+
+        match typhoid prevalence:
+          adjustables: [ [ typ_gen_dur, 0.5, 2 ], [ typ_infx, 0.01, 100 ] ]
+          measurables: [ typ_prev ]
+
+        match typhoid deaths:
+          adjustables: [ typ_d_rate ]
+          measurables: [ typ_num_deaths ]
+
+        reinitialization typhoid:
+          clear_initialization: True
+          set_initialization:
+            init_year: 2010
+            constant_parset: True

From eda9576049fea7428924099363ab315c5d2ba6e4 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:39:56 +0100
Subject: [PATCH 100/161] Add worked example solutions

---
 docs/tutorial/T7/calibrations/WE_1.yaml | 13 +++++++++++
 docs/tutorial/T7/calibrations/WE_2.yaml |  9 ++++++++
 docs/tutorial/T7/calibrations/WE_3.yaml |  9 ++++++++
 docs/tutorial/T7/calibrations/WE_4.yaml |  9 ++++++++
 docs/tutorial/T7/calibrations/WE_5.yaml | 13 +++++++++++
 docs/tutorial/T7/calibrations/WE_6.yaml | 16 ++++++++++++++
 docs/tutorial/T7/calibrations/WE_7.yaml | 18 +++++++++++++++
 docs/tutorial/T7/calibrations/WE_8.yaml | 29 +++++++++++++++++++++++++
 8 files changed, 116 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/WE_1.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_2.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_3.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_4.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_5.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_6.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_7.yaml
 create mode 100644 docs/tutorial/T7/calibrations/WE_8.yaml

diff --git a/docs/tutorial/T7/calibrations/WE_1.yaml b/docs/tutorial/T7/calibrations/WE_1.yaml
new file mode 100644
index 00000000..d99f3331
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_1.yaml
@@ -0,0 +1,13 @@
+
+
+calibration:
+    Population calibration:
+        Match population sizes:
+            adjustables: b_rate, mig_rate
+            measurables: alive
+
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
+
+        
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_2.yaml b/docs/tutorial/T7/calibrations/WE_2.yaml
new file mode 100644
index 00000000..d2aa7e29
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_2.yaml
@@ -0,0 +1,9 @@
+calibration:
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables:
+                b_rate, mig_rate:
+                    lower_bound: 0.1
+                    upper_bound: 10
+            measurables: alive
diff --git a/docs/tutorial/T7/calibrations/WE_3.yaml b/docs/tutorial/T7/calibrations/WE_3.yaml
new file mode 100644
index 00000000..fa87a1c3
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_3.yaml
@@ -0,0 +1,9 @@
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables: b_rate, mig_rate
+            measurables: alive
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_4.yaml b/docs/tutorial/T7/calibrations/WE_4.yaml
new file mode 100644
index 00000000..77560b42
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_4.yaml
@@ -0,0 +1,9 @@
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables: (b_rate, 0-4), mig_rate
+            measurables: alive
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_5.yaml b/docs/tutorial/T7/calibrations/WE_5.yaml
new file mode 100644
index 00000000..3cdce1e6
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_5.yaml
@@ -0,0 +1,13 @@
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables:
+              (b_rate, 0-4):
+                starting_y_factor: 1.2
+              mig_rate:
+                lower_bound: 0.1
+            measurables: alive
diff --git a/docs/tutorial/T7/calibrations/WE_6.yaml b/docs/tutorial/T7/calibrations/WE_6.yaml
new file mode 100644
index 00000000..70669139
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_6.yaml
@@ -0,0 +1,16 @@
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables:
+              (b_rate, 0-4):
+                starting_y_factor: 1.2
+              mig_rate:
+                lower_bound: 0.1
+            measurables: alive
+
+        set_initialization:
+            init_year: 2025
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_7.yaml b/docs/tutorial/T7/calibrations/WE_7.yaml
new file mode 100644
index 00000000..1a3c6dc2
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_7.yaml
@@ -0,0 +1,18 @@
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+        Match population sizes:
+            adjustables:
+              (b_rate, 0-4):
+                starting_y_factor: 1.2
+              mig_rate:
+                lower_bound: 0.1
+            measurables: alive
+
+        clear_initialization: True
+
+        set_initialization:
+            init_year: 2025
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_8.yaml b/docs/tutorial/T7/calibrations/WE_8.yaml
new file mode 100644
index 00000000..4256b51b
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/WE_8.yaml
@@ -0,0 +1,29 @@
+
+
+calibration:
+    cal_start: 2005
+    cal_end: 2040
+
+    Population calibration:
+        repeats: 10
+
+        Match population sizes:
+            adjustables:
+                (b_rate, 0-4):
+                    starting_y_factor: 1.2
+                mig_rate:
+                    lower_bound: 0.1
+            measurables: alive
+
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
+
+
+        clear_initialization: True
+
+        set_initialization:
+            init_year: 2010
+
+    save_calibration:
+        fname: Pop_calibration.xlsx
\ No newline at end of file

From cc7ab6f04ecf3bd93210bb24c53821c94605d837 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:40:58 +0100
Subject: [PATCH 101/161] Add calibration outputs of YAML files shown in the
 tutorial

And also of the final worked example solution
---
 docs/tutorial/T7/calibrations/cal_1.xlsx   | Bin 0 -> 6921 bytes
 docs/tutorial/T7/calibrations/cal_3.xlsx   | Bin 0 -> 6982 bytes
 docs/tutorial/T7/calibrations/cal_4.xlsx   | Bin 0 -> 6936 bytes
 docs/tutorial/T7/calibrations/cal_5.xlsx   | Bin 0 -> 6926 bytes
 docs/tutorial/T7/calibrations/cal_6.xlsx   | Bin 0 -> 6937 bytes
 docs/tutorial/T7/calibrations/cal_7.xlsx   | Bin 0 -> 6917 bytes
 docs/tutorial/T7/calibrations/cal_8.xlsx   | Bin 0 -> 6917 bytes
 docs/tutorial/T7/calibrations/cal_9.xlsx   | Bin 0 -> 6938 bytes
 docs/tutorial/T7/calibrations/cal_WE8.xlsx | Bin 0 -> 13828 bytes
 9 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/tutorial/T7/calibrations/cal_1.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_3.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_4.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_5.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_6.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_7.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_8.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_9.xlsx
 create mode 100644 docs/tutorial/T7/calibrations/cal_WE8.xlsx

diff --git a/docs/tutorial/T7/calibrations/cal_1.xlsx b/docs/tutorial/T7/calibrations/cal_1.xlsx
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diff --git a/docs/tutorial/T7/calibrations/cal_3.xlsx b/docs/tutorial/T7/calibrations/cal_3.xlsx
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literal 0
HcmV?d00001


From 5d8b66b1c8f9a4e0ac01b5e48a4f0e6a85739719 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:42:12 +0100
Subject: [PATCH 102/161] Add stripped plotting notebooks

---
 .../Plot_YAML_tutorial_plots.ipynb            | 516 ++++++++++++++++++
 .../Plot_final_typhoid_calibration.ipynb      | 202 +++++++
 2 files changed, 718 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/Plot_YAML_tutorial_plots.ipynb
 create mode 100644 docs/tutorial/T7/calibrations/Plot_final_typhoid_calibration.ipynb

diff --git a/docs/tutorial/T7/calibrations/Plot_YAML_tutorial_plots.ipynb b/docs/tutorial/T7/calibrations/Plot_YAML_tutorial_plots.ipynb
new file mode 100644
index 00000000..b8995e53
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/Plot_YAML_tutorial_plots.ipynb
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Calibration plots for the YAML files shown in the YAML tutorial\n",
+    "\n",
+    "This plots the calibration reslts from `run_tutorial_autocals` (which includes the final worked example as well as the YAML examples in the tutorial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Make project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "import os\n",
+    "from os.path import isfile\n",
+    "import re\n",
+    "import sciris as sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = '../assets'\n",
+    "F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)\n",
+    "P = at.Project(framework=F,databook=D)\n",
+    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Pre-calibraton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal0 = P.make_parset()\n",
+    "res0 = P.run_sim(parset=cal0, result_name = 'Uncalibrated')\n",
+    "d = at.PlotData(res0, outputs=['alive','deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(8,4)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal0 = P.make_parset()\n",
+    "res0 = P.run_sim(parset=cal0, result_name = 'Uncalibrated')\n",
+    "d = at.PlotData(res0, outputs=['alive', 'typ_incidence', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(8,6)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "# Import calibrations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calibration files to import\n",
+    "calpaths = ['cal_1', '', 'cal_3', 'cal_4', 'cal_5', 'cal_6', 'cal_7', 'cal_8', 'cal_9', 'cal_WE8']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resdict = sc.odict()\n",
+    "for i, file in enumerate(calpaths):\n",
+    "        print(i+1)\n",
+    "        print(file)\n",
+    "        print(f'res_{i+1}')\n",
+    "        if file == '':\n",
+    "            continue\n",
+    "        cal = P.make_parset()    \n",
+    "        cal = cal.load_calibration(f'{file}.xlsx')\n",
+    "        resdict[f'res_{i+1}'] = P.run_sim(parset=cal, result_name =  file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "# Plot calibrations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "## 1 - Minimal yaml file (shown in tutorial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specific calibration\n",
+    "file = 'cal_1.xlsx'\n",
+    "cal1 = P.make_parset()    \n",
+    "cal1 = cal1.load_calibration(file)\n",
+    "res1 = P.run_sim(parset=cal1, result_name = 'Simple calibration')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "d = at.PlotData([res0, res1], outputs='alive', project=P, pops='65+')\n",
+    "at.plot_series(d, axis='results', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(res1, outputs=['alive','typ_incidence', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(12,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(res1, outputs=['alive', 'deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(12,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 3 - Repeats (not shown at the moment)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(resdict['res_3'], outputs=['alive','deaths', 'typ_incidence', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(10,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(resdict['res_3'], outputs=['alive','typ_incidence', 'typ_prev', 'typ_num_deaths', ], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(10,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4 - cal dict format  (not shown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_4'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5 - calibrate specific pops (not shown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_5'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 6 - cal with mult params (not shown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_6'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 7 - init (showing a different example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_7'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 8 - clear init (showing a different example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_8'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31",
+   "metadata": {},
+   "source": [
+    "# cal 9 - outer settings (not shown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plot total pop: cals vs no calibration\n",
+    "d = at.PlotData(resdict['res_9'], outputs='alive', project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### add -cal with outer params (not shown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #plot total pop: cals vs no calibration\n",
+    "# d = at.PlotData(resdict['res_9'], outputs='alive', project=P)\n",
+    "# at.plot_series(d, axis='pops', data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35",
+   "metadata": {},
+   "source": [
+    "# WE_8: Worked example calibration (shown in tutorial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(resdict['res_10'], outputs=['alive', 'deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(12,8)\n",
+    "fig.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "AtomicaTest",
+   "language": "python",
+   "name": "atomicatest"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorial/T7/calibrations/Plot_final_typhoid_calibration.ipynb b/docs/tutorial/T7/calibrations/Plot_final_typhoid_calibration.ipynb
new file mode 100644
index 00000000..6c4ca419
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/Plot_final_typhoid_calibration.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Calibration plots for the pop + epi calibration\n",
+    "\n",
+    "This plots the results from `run_model_autocals.py` (whole calibration)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "import os\n",
+    "from os.path import isfile\n",
+    "import re\n",
+    "import sciris as sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load typhoid project\n",
+    "inputs = '../assets'\n",
+    "F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)\n",
+    "P = at.Project(framework=F, databook=D, do_run=False)\n",
+    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "# Plot uncalibrated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal0 = P.make_parset()\n",
+    "res0 = P.run_sim(parset=cal0, result_name =  'Uncalibrated')\n",
+    "d = at.PlotData(res0, outputs=['alive', 'deaths'], project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data, n_cols=2, legend_mode='none');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(res0, outputs=['alive','typ_incidence', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(12,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "# Load in calibrations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calfile= \"typ_calibration.xlsx\"\n",
+    "print(calfile)\n",
+    "cal = P.make_parset()   \n",
+    "cal = cal.load_calibration(f'{calfile}')\n",
+    "res1 = P.run_sim(parset=cal, result_name =  'Complete YAML calibration')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "# Plot calibrations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(res1, outputs=['alive', 'deaths'], project=P)\n",
+    "at.plot_series(d, axis='pops', data=P.data, n_cols=2, legend_mode='none');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = at.PlotData(res1, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(12,8)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#display deaths\n",
+    "d = at.PlotData(res1, outputs='d_rate:flow', project=P) \n",
+    "for s in d.series:\n",
+    "    s.data_label = 'deaths'\n",
+    "at.plot_series(d,axis='pops',data=P.data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#display other typh vars\n",
+    "d = at.PlotData(res1, outputs=['life_exp'], project=P)\n",
+    "at.plot_series(d,axis='pops',data=P.data);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "AtomicaTest",
+   "language": "python",
+   "name": "atomicatest"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 17fbfc73be3bc4f17caaf2f71bd337c147c37c6b Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 13 Feb 2025 21:42:28 +0100
Subject: [PATCH 103/161] Add README

---
 docs/tutorial/T7/calibrations/README.txt | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 docs/tutorial/T7/calibrations/README.txt

diff --git a/docs/tutorial/T7/calibrations/README.txt b/docs/tutorial/T7/calibrations/README.txt
new file mode 100644
index 00000000..db78fa2e
--- /dev/null
+++ b/docs/tutorial/T7/calibrations/README.txt
@@ -0,0 +1,10 @@
+Contains example YAML files and scripts to run calibrations with them
+
+- `T7_YAML_*` files correspond to the examples of YAML calibrations used in Tutorial 7
+- `WE_*.yaml` are the solutions to the 'worked example' section in Tutorial 7
+- `typ_calibration_instructions.yaml` corresponds to a full finished YAML calibration for the model
+
+This folder also contains
+
+- `*.py` files to run the YAML files and produce `*.xlsx` calibration outputs
+- `*.ipynb` notebooks to generate plots using those calibration outputs
\ No newline at end of file

From 764b7d9df270e3e025ad646cd8e744d3e0fbc5bc Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 14 Feb 2025 00:11:39 +0100
Subject: [PATCH 104/161] Update tutorial text

---
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 56 ++++++++++---------
 1 file changed, 30 insertions(+), 26 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index 348fc980..0f2fbf22 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -101,7 +101,7 @@
     "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
     "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
     "\n",
-    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them, and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use a YAML file to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
+    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them (or whether some should be adjusted simultaneously), and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use YAML files to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
    ]
   },
   {
@@ -121,15 +121,18 @@
     "\n",
     "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
     "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    calibrated_parset = P.calibrate(parset = cal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
     "\n",
     "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g. [T7_YAML_1_minimal.yaml](./<`T7_YAML_1_minimal`>.<yaml>)), the calibration can be run using\n",
     "\n",
     "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
     "\n",
-    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()`, simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
+    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()` – simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
     "\n",
-    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>"
+    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>\n",
+    "<img src=\"assets/T7_plot_1_simple_compare.png\" alt=\"Simple calibration comparison\" style=\"width: 500px;\"/>\n",
+    "\n",
+    "As you can see, these plots don't look very different to the uncalibrated simulation results. If we plot the uncalibrated simulation in the same plot as our simple calibration, we can see that there has been a slight change, but not nearly enough to be able to consider this a good calibration. Next, we will illustrate the YAML calibration features that we can use to improve on this initial result. "
    ]
   },
   {
@@ -139,10 +142,10 @@
    "source": [
     "## Sections\n",
     "\n",
-    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modify the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file\n",
+    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modifies the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file:\n",
     "\n",
-    "- _actions_ which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains 'adjustables' and 'measurables'. Other examples of actions are detailed below\n",
-    "- _sections_ which are containers for actions. Sections can contain attributes such as how many times to repeat the contents of the section, or can define settings that are applied to any actions within the section\n",
+    "- _actions_, which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains `adjustables` and `measurables`. Other examples of actions are detailed below.\n",
+    "- _sections_, which are containers for actions. Sections can contain attributes, such as how many times to repeat the contents of the section, or they can define settings that are applied to any relevant actions within that section.\n",
     "\n",
     "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
     "\n",
@@ -172,17 +175,18 @@
     "\n",
     "The overall structure of this YAML file is thus:\n",
     "\n",
-    "1. A top-level section titled `calibration` which has one sub-section (`Population calibration`)\n",
-    "2. A sub-section called `Population calibration` which in turn contains two actions\n",
-    "3. An action called `Match population sizes` corresponding to the original calibration step for adjusting the birth rate and migration rate\n",
-    "4. An action called `Match deaths` corresponding to the new step of adjusting the death rate\n",
+    "1. A top-level section titled `calibration`, which has one sub-section (`Population calibration`)\n",
+    "2. A sub-section called `Population calibration`, which in turn contains two actions\n",
+    "3. An action called `Match population sizes`, corresponding to the original calibration step we used to adjust the birth rate and migration rate\n",
+    "4. An action called `Match deaths`, corresponding to the new step of adjusting the death rate\n",
     "\n",
     "An action is differentiated from a section in two possible ways:\n",
     "\n",
-    "- By contents e.g., if it contains `adjustables` and `measurables` then it is interpreted as a calibration action rather than a section\n",
-    "- By the title i.e., if the name corresponds to the name of a supported operation, as described below.\n",
-    " \n",
-    "Aside for the names of supported operations, sections can freely named."
+    "- By its contents (e.g. if it contains `adjustables` and `measurables`, then it will be interpreted as a calibration action rather than a section)\n",
+    "- By the title (if the name corresponds to the name of a supported operation, as described below)\n",
+    "\n",
+    "Additionally, actions can never contain any sub-sections, whereas sections do. \n",
+    "Apart from the names of supported operations, sections can be freely named."
    ]
   },
   {
@@ -224,15 +228,15 @@
    "source": [
     "## Sections vs Actions\n",
     "\n",
-    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in a [later section](#specifying-calibration-settings-in-outer-sections)). \n",
+    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-calibration-settings-in-outer-sections)). \n",
     "\n",
     "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
     "\n",
     "<div class=\"alert alert-info\">\n",
-    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block it is, or it contains no sub-sections.\n",
+    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block they are, and they don't contain any sub-sections.\n",
     "</div>\n",
     "\n",
-    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading the calibration, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
+    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading in the YAML file, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
    ]
   },
   {
@@ -252,9 +256,9 @@
    "id": "11",
    "metadata": {},
    "source": [
-    "## Types of Functional Blocks/Actions\n",
+    "## Types of Action Blocks\n",
     "\n",
-    "YAML calibration files can contain the following types of action blocks: \n",
+    "YAML calibration files can contain the following types of actions, or action blocks: \n",
     "\n",
     "- Calibration block\n",
     "- Initialization block\n",
@@ -269,30 +273,30 @@
    "source": [
     "### Calibration blocks\n",
     "\n",
-    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action block. Calibration blocks are defined by the fact that they contain two keywords called `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making a call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making a call to `P.calibrate()` with the same adjustables and measurables.\n",
     "\n",
     "#### Adjustable and measurable settings\n",
     "\n",
-    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default settings for adjustables and measurables. For more flexibility, we can specify further settings for the optimisation. The settings for the adjustables and measurables directly map to the options supported by `P.calibrate()`. \n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default settings for adjustables and measurables. For more flexibility, we can customise the settings to be used for the optimisation. The settings for the adjustables and measurables directly map to the options supported by `P.calibrate()`. \n",
     "\n",
     "Each adjustable has:\n",
     "\n",
-    "- `adj_label` (required): Adjustable codename (can be found in the framework)\n",
+    "- `adj_label` (required): Adjustable parameter codename (can be found in the framework)\n",
     "- `pop_name`: Population to calibrate (default: all populations)\n",
     "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
-    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 1)\n",
+    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
     "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable’s current `y_factor` in the parset)\n",
     "\n",
     "Each measurable has:\n",
     "\n",
-    "- `meas_label` (required): Measurable codename (can be found in the framework)\n",
+    "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
     "- `pop_name`: Population to use for calibration (default: all populations)\n",
     "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights]((./YAML_documentation_page.ipynb#measurable-weights \"Measurable weights\") for further details.\n",
     "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
     "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
     "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
     "\n",
-    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan) which are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-calibration-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per-measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-calibration-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
     "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so: \n",
     "\n",
     "    calibration: \n",

From 95666ff8993ddb2ccb39b5910ffb739798a7158b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 13:01:20 +1000
Subject: [PATCH 105/161] Remove extra calculations

---
 docs/tutorial/T7/assets/T7_databook.xlsx | Bin 84343 -> 75643 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

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From 9c9b456f76db261d810373945e43939acc46282d Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 14:14:46 +1000
Subject: [PATCH 106/161] Resolve todos and fix links

---
 docs/general/YAML_calibration.ipynb           |  7 ++--
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 40 ++++++++-----------
 docs/tutorial/index.rst                       |  1 +
 3 files changed, 22 insertions(+), 26 deletions(-)

diff --git a/docs/general/YAML_calibration.ipynb b/docs/general/YAML_calibration.ipynb
index bd8c7dab..027215e2 100644
--- a/docs/general/YAML_calibration.ipynb
+++ b/docs/general/YAML_calibration.ipynb
@@ -23,7 +23,7 @@
    "source": [
     "## YAML internal structure\n",
     "\n",
-    "The native representation of the YAML calibration is as a graph of 'nodes' where each node corresponds to either an action or a section containing other nodes. Each node has associated with it a collection of attributes/settings defined for that node (`instructions`), and a collection of attributes inherited from all parent nodes (`context`). The `atomica.yaml_calibration.build()` function takes in the content of the YAML file and loads it into a tree of `at.yaml_calibration.BaseNode` instances with a root node called 'calibration' at the top level. For example, consider the following YAML file:"
+    "The native representation of the YAML calibration is as a graph of 'nodes' where each node corresponds to either an action or a section containing other nodes. Each node has associated with it a collection of attributes/settings defined for that node (`instructions`), and a collection of attributes inherited from all parent nodes (`context`). The `atomica.yaml_calibration.build()` function takes in the content of the YAML file and loads it into a tree of `at.yaml_calibration.BaseNode` instances with a root node called 'calibration' at the top level. For example, consider the following YAML file from Tutorial 7:"
    ]
   },
   {
@@ -34,7 +34,8 @@
    "outputs": [],
    "source": [
     "import atomica as at\n",
-    "print(open('T7_YAML_3_repeats.yaml').read())"
+    "yaml_file = at.rootdir/'docs'/'tutorial'/'T7'/'calibrations'/'T7_YAML_3_repeats.yaml'\n",
+    "print(open(yaml_file).read())"
    ]
   },
   {
@@ -53,7 +54,7 @@
    "outputs": [],
    "source": [
     "import atomica.yaml_calibration \n",
-    "nodes = at.yaml_calibration.build('T7_YAML_3_repeats.yaml') \n",
+    "nodes = at.yaml_calibration.build(yaml_file) \n",
     "print(nodes)"
    ]
   },
diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index 0f2fbf22..ff2b517e 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -47,9 +47,7 @@
    "source": [
     "## Basic calibration example\n",
     "\n",
-    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `assets/T7`. \n",
-    "\n",
-    "_TODO: Update paths when moved to Atomica repository_"
+    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `assets/T7`. "
    ]
   },
   {
@@ -60,8 +58,8 @@
    "outputs": [],
    "source": [
     "import atomica as at\n",
-    "F = at.ProjectFramework('T7_framework.xlsx')\n",
-    "D = at.ProjectData.from_spreadsheet('T7_databook.xlsx', framework=F)\n",
+    "F = at.ProjectFramework('assets/T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet('assets/T7_databook.xlsx', framework=F)\n",
     "P = at.Project(framework=F,databook=D, do_run=False)\n",
     "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
    ]
@@ -123,7 +121,7 @@
     "\n",
     "    calibrated_parset = P.calibrate(parset = cal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
     "\n",
-    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g. [T7_YAML_1_minimal.yaml](./<`T7_YAML_1_minimal`>.<yaml>)), the calibration can be run using\n",
+    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g., `T7_YAML_1_minimal.yaml`), the calibration can be run using\n",
     "\n",
     "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
     "\n",
@@ -228,7 +226,7 @@
    "source": [
     "## Sections vs Actions\n",
     "\n",
-    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-calibration-settings-in-outer-sections)). \n",
+    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-settings-in-outer-sections). \n",
     "\n",
     "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
     "\n",
@@ -247,7 +245,7 @@
    "outputs": [],
    "source": [
     "import atomica.yaml_calibration \n",
-    "calibration_tree = at.yaml_calibration.build('T7_YAML_3_repeats.yaml') \n",
+    "calibration_tree = at.yaml_calibration.build('calibrations/T7_YAML_3_repeats.yaml') \n",
     "print(calibration_tree)"
    ]
   },
@@ -291,12 +289,12 @@
     "\n",
     "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
     "- `pop_name`: Population to use for calibration (default: all populations)\n",
-    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights]((./YAML_documentation_page.ipynb#measurable-weights \"Measurable weights\") for further details.\n",
+    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](/general/YAML_calibration.html#Measurable-Weights) for further details.\n",
     "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
     "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
     "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
     "\n",
-    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-calibration-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
     "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so: \n",
     "\n",
     "    calibration: \n",
@@ -318,7 +316,6 @@
    "cell_type": "markdown",
    "id": "13",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -356,7 +353,7 @@
     "                \tcal_start: 2000\n",
     "               \tcal_end: 2040\n",
     "\n",
-    "Finally, this same syntax can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on calibrating in these cases, see [Calibrating transfers and interactions](./YAML_documentation_page.ipynb#calibrating-transfers-and-interactions \"Calibrating transfern detail by this point> TODO: Update link \n"
+    "Finally, this same syntax can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on calibrating in these cases, see [Calibrating transfers and interactions](/general/YAML_calibration.html#Calibrating-transfers-and-interactions)."
    ]
   },
   {
@@ -389,7 +386,7 @@
     "\n",
     "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
     "\n",
-    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](./YAML_documentation_page.ipynb \"Documentation\").\n",
+    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](/general/YAML_calibration.html).\n",
     "\n",
     "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialisation`. Under this title, we can specify further settings: \n",
     "\n",
@@ -478,7 +475,7 @@
     "\n",
     "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
     "\n",
-    "For more information on intermediate calibrations, see the relevant [documentation section](./YAML_documentation_page.ipynb#saving-intermediate-calibrations \"Saving intermediate calibrations\")."
+    "For more information on intermediate calibrations, see the relevant [documentation section](/general/YAML_calibration.html#saving-intermediate-calibrations \"Saving intermediate calibrations\")."
    ]
   },
   {
@@ -650,7 +647,7 @@
     "\n",
     "**Hint**: Use `save_calibration` and specify a `fname`.\n",
     "\n",
-    "All solutions to the worked example are in the `Worked_example` folder. _TODO: Update paths when moved to Atomica repository_ The calibration obtained after running the last YAML file in this exercise yields the following simulation result: \n",
+    "All solutions to the worked example are in the `Worked_example` folder in code repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result: \n",
     "\n",
     "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
    ]
@@ -664,7 +661,7 @@
     "\n",
     "### Epidemiological calibration\n",
     "\n",
-    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration can be found at _TODO: Update paths when moved to Atomica repository_\n",
+    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration can be found in the `Worked_example` folder in code repository, in the `typ_calibration_instructions.yaml` file. \n",
     "\n",
     "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
     "\n",
@@ -672,9 +669,6 @@
     "\n",
     "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030. \n",
     "\n",
-    "After the population calibration, the typhoid y-factors get set t\n",
-    "#TODO: Deal with issue: epi y-factors get reset to 1 in every pass, which means a) all the epi calibration loops except the first one don't achieve anything,  b) if we remove those loops, there's no going back to the pop calibration after the epi calibration to make sure it still fits well after calibrating epi \n",
-    "\n",
     "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
     "\n",
     "At the end of the typhoid calibration, we set an initialisation in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice. \n",
@@ -685,15 +679,15 @@
     "\n",
     "### Documentation\n",
     "\n",
-    "For more information on using Atomica's YAML calibration functionality, see [the documentation](./YAML_documentation_page.ipynb#yaml-calibration-documentation-page \"YAML documentation page\").\n"
+    "For additional information on YAML calibration functionality, see [the documentation](/general/YAML_calibration.html).\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:atomica312]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-atomica312-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -705,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/tutorial/index.rst b/docs/tutorial/index.rst
index 8cf8a11c..ec817ec7 100644
--- a/docs/tutorial/index.rst
+++ b/docs/tutorial/index.rst
@@ -12,3 +12,4 @@ These tutorials walk through getting starting with building models in Atomica
     T4-Characteristics
     T5-Junctions
     T6-Programs
+    T7/T7_YAML_autocalibration  

From 6963e52be452aa68f477b5ebb7b431a0a8931962 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 19:02:02 +1000
Subject: [PATCH 107/161] Switch to non-sciris separate legend routine and
 update for matplotlib 3.10

---
 atomica/plotting.py | 48 +++++++++++++++++++++++++++++++++++++++------
 1 file changed, 42 insertions(+), 6 deletions(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 826fc997..3bd51d3a 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -1237,6 +1237,44 @@ def interpolate(self, new_tvec):
         return np.interp(sc.promotetoarray(new_tvec), self.tvec, self.vals, left=np.nan, right=np.nan)
 
 
+
+def separatelegend(ax=None, handles=None, labels=None, reverse=False, legendsettings=None):
+    ''' Allows the legend of a figure to be rendered in a separate window instead '''
+
+    # Handle settings
+    if ax is None:
+        fig, ax = plt.subplots()
+    else:
+        fig = ax.figure
+    fig.set_size_inches(4.0,4.8)
+
+    l_settings = sc.mergedicts({'loc': 'center', 'bbox_to_anchor': None, 'frameon': False}, legendsettings)
+
+    # Get handles and labels
+    if handles is None and labels is None:
+        handles, labels = ax.get_legend_handles_labels()
+    elif handles is None:
+        handles, _ = ax.get_legend_handles_labels()
+    elif labels is None:
+        labels = [h.get_label() for h in handles]
+    assert len(handles) == len(labels), f"Number of handles ({len(handles)}) and labels ({len(labels)}) must match"
+
+    # Set up new plot
+    ax.set_position([-0.05,-0.05,1.1,1.1])
+    ax.set_axis_off()
+
+    # Reverse order, e.g. for stacked plots
+    if reverse: # pragma: no cover
+        handles = handles[::-1]
+        labels   = labels[::-1]
+
+    # Plot the new legend
+    ax.legend(handles=handles, labels=labels, **l_settings)
+
+    return fig
+
+
+
 def plot_bars(plotdata, stack_pops=None, stack_outputs=None, outer=None, legend_mode=None, show_all_labels=False, orientation="vertical") -> list:
     """
     Produce a bar plot
@@ -1583,7 +1621,7 @@ def process_input_stacks(input_stacks, available_items):
     if legend_mode == "together":
         _render_legend(ax, plot_type="bar", handles=legend_patches)
     elif legend_mode == "separate":
-        figs.append(sc.separatelegend(handles=legend_patches, reverse=True))
+        figs.append(separatelegend(handles=legend_patches, reverse=True))
 
     return figs
 
@@ -1760,14 +1798,12 @@ def _prepare_figures(dim1, dim2, n_cols):
 
     if legend_mode == "separate":
         reverse_legend = True if plot_type in ["stacked", "proportion"] else False
+        handles, labels = ax.get_legend_handles_labels()
 
         if not subplots:
-            # Replace the last figure with a legend figure
-            plt.close(figs[-1])  # TODO - update Sciris to allow passing in an existing figure
-            figs[-1] = sc.separatelegend(ax, reverse=reverse_legend)
+            separatelegend(axes[-1], handles=handles, labels=labels, reverse=reverse_legend)
         else:
             legend_ax = axes[-1]
-            handles, labels = ax.get_legend_handles_labels()
             legend_ax.set_axis_off()  # Hide axis lines
             if reverse_legend:  # pragma: no cover
                 handles = handles[::-1]
@@ -1884,7 +1920,7 @@ def plot_legend(entries: dict, plot_type=None, fig=None, legendsettings: dict =
             raise Exception(f'Unknown plot type "{p_type}"')
 
     if fig is None:  # Draw in a new figure
-        fig = sc.separatelegend(handles=h, legendsettings=legendsettings)
+        fig = separatelegend(handles=h, legendsettings=legendsettings)
     else:
         existing_legend = fig.findobj(Legend)
         if existing_legend and existing_legend[0].parent is fig:  # If existing legend and this is a separate legend fig

From 3147dd5babeccfda706a0be18d3cf997df9bdbdc Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 19:02:18 +1000
Subject: [PATCH 108/161] Increment version

---
 CHANGELOG.md       | 7 ++++++-
 atomica/version.py | 4 ++--
 docs/conf.py       | 2 +-
 3 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index aae5da61..471965e1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,12 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
-## [1.XX.0] - 2024
+## [1.30.0] - 2025-02-17
+
+- Automatic calibration can now selectively weight parts of the time series to select or prioritise a subset of time points.
+- Added YAML-based calibration support to Atomica, covered in Tutorial 7 in the online documentation.
+
+## [1.29.0] - 2024-10-30
 
 - `ProjectSettings` now computes the simulation time vector in a more robust way to reduce edge cases where the reported `sim_dt` doesn't match the input.
 - `ParameterSet.load_calibration()` now clears any existing initialization if the calibration being loaded does not contain an initialization. Previously, the absence of an 'initialization' sheet in the calibration would be treated as not making any change to the initialization. This could cause calibrations to become mixed if a calibration without an initialization was loaded after a calibration with an initialization. Now, a missing initialization sheet is treated as meaning 'no initialization' and any existing initialization will be cleared when the calibration is loaded. 
diff --git a/atomica/version.py b/atomica/version.py
index 4091ba3f..91aaedea 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.29.0"
-versiondate = "2024-10-30"
+version = "1.30.0"
+versiondate = "2025-02-17"
 gitinfo = sc.gitinfo(__file__)
diff --git a/docs/conf.py b/docs/conf.py
index edda0d44..699d1c5b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -24,7 +24,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Atomica"
-copyright = "2023, Atomica Team"
+copyright = "2025, Atomica Team"
 author = "Atomica Team"
 
 import atomica

From 16cce415ebab27c0d4f4b22eeeb81f4222bf8238 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 19:02:51 +1000
Subject: [PATCH 109/161] Apply auto-formatting

---
 atomica/calibration.py                        |  22 +-
 atomica/plotting.py                           |  22 +-
 atomica/project.py                            |  19 +-
 atomica/system.py                             |   2 +-
 atomica/utils.py                              |   2 +-
 atomica/yaml_calibration.py                   | 246 +++++++++---------
 .../T7/calibrations/run_model_autocals.py     |  60 ++---
 .../T7/calibrations/run_tutorial_autocals.py  |  55 ++--
 tests/test_time_vector.py                     |   2 +-
 tests/test_tox_yaml.py                        |  18 +-
 10 files changed, 220 insertions(+), 228 deletions(-)

diff --git a/atomica/calibration.py b/atomica/calibration.py
index 4d873e90..c100a8b5 100644
--- a/atomica/calibration.py
+++ b/atomica/calibration.py
@@ -39,6 +39,7 @@ def _update_parset(parset, y_factors, pars_to_adjust) -> None:
         else:
             par.y_factor[pop_name] = y_factors[i]
 
+
 def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, project, *args, **kwargs) -> float:
     """
     Run the model for a given set of y-factors and return the objective/goodness-of-fit
@@ -79,7 +80,7 @@ def _calculate_objective(y_factors, pars_to_adjust, output_quantities, parset, p
         inds = (data_t >= start_year) & (data_t <= end_year)
         if np.count_nonzero(inds) == 0:
             # If no time points remain after filtering down to the time points the user requested
-            logger.info(f'No data points remaining after filtering down to requested time period. Skipping...')
+            logger.info(f"No data points remaining after filtering down to requested time period. Skipping...")
             continue
         data_t = data_t[inds]
         data_v = data_v[inds]
@@ -164,7 +165,6 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
     """
 
-
     # Expand out pop=None in pars_to_adjust
     p2 = []
     for par_tuple in pars_to_adjust:
@@ -215,20 +215,20 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
         par = parset.get_par(par_name)
 
-        #if initial_value has not been explicitly set in the tuple: use y_factor in parset
+        # if initial_value has not been explicitly set in the tuple: use y_factor in parset
         if initial_value is None:
             if pop_name == "all":
                 initial_value = par.meta_y_factor
             else:
                 initial_value = par.y_factor[pop_name]
-            #if this value is outside the min and max bounds, make it equal to min or max (whichever is closest)
-            #if min == max, this should make the initial value equal to min and max
+            # if this value is outside the min and max bounds, make it equal to min or max (whichever is closest)
+            # if min == max, this should make the initial value equal to min and max
             initial_value = np.clip(initial_value, scale_min, scale_max)
         else:
-            assert (initial_value >= scale_min) and (initial_value <= scale_max), 'Initial value is not consistent with the lower/upper bounds'
+            assert (initial_value >= scale_min) and (initial_value <= scale_max), "Initial value is not consistent with the lower/upper bounds"
 
-        #update y_factors in parset
-        if pop_name == 'all':
+        # update y_factors in parset
+        if pop_name == "all":
             par.meta_y_factor = initial_value
         else:
             par.y_factor[pop_name] = initial_value
@@ -241,8 +241,6 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
             xmin.append(scale_min)
             xmax.append(scale_max)
 
-
-
     args = {
         "project": project,
         "parset": parset,
@@ -270,7 +268,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
                 }
                 optim_args.update(kwargs)
 
-                if 'verbose' not in optim_args:
+                if "verbose" not in optim_args:
                     log_level = logger.getEffectiveLevel()
                     if log_level < logging.WARNING:
                         optim_args["verbose"] = 2
@@ -297,7 +295,7 @@ def calibrate(project, parset: ParameterSet, pars_to_adjust, output_quantities,
 
         _update_parset(args["parset"], x1, args["pars_to_adjust"])
     else:
-        logger.info('No parameters to adjust provided to the optimisation function. Skipping optimisation...')
+        logger.info("No parameters to adjust provided to the optimisation function. Skipping optimisation...")
 
     # Log out the commands required for equivalent manual calibration if desired
     for i, x in enumerate(pars_to_adjust):
diff --git a/atomica/plotting.py b/atomica/plotting.py
index 3bd51d3a..d85d34e8 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -662,8 +662,8 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
                 scale = 1.0
 
             vals = np.full(lower.shape, fill_value=np.nan)
-            lower_idx = np.searchsorted(s.tvec, lower, side='left') # Time index for bin start
-            upper_idx = np.searchsorted(s.tvec, upper, side='right') # Time index for bin end
+            lower_idx = np.searchsorted(s.tvec, lower, side="left")  # Time index for bin start
+            upper_idx = np.searchsorted(s.tvec, upper, side="right")  # Time index for bin end
 
             for i, (l, u, l_idx, u_idx) in enumerate(zip(lower, upper, lower_idx, upper_idx)):
 
@@ -678,14 +678,14 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
                 # The bins will consist of the actual simulation time points, plus
                 # partial bins that are interpolated before and after if the requested
                 # bins don't line up with the simulation timepoints
-                idx = np.arange(l_idx,u_idx)
+                idx = np.arange(l_idx, u_idx)
                 t2 = s.tvec[idx]
                 interpolate = False
                 if t2[0] > l or t2[-1] < u:
                     interpolate = True
                     t2 = list(t2)
                     if t2[0] > l:
-                        t2.insert(0,l)
+                        t2.insert(0, l)
                     if t2[-1] < u:
                         t2.append(u)
                     t2 = np.array(t2, dtype=float)
@@ -1237,18 +1237,17 @@ def interpolate(self, new_tvec):
         return np.interp(sc.promotetoarray(new_tvec), self.tvec, self.vals, left=np.nan, right=np.nan)
 
 
-
 def separatelegend(ax=None, handles=None, labels=None, reverse=False, legendsettings=None):
-    ''' Allows the legend of a figure to be rendered in a separate window instead '''
+    """Allows the legend of a figure to be rendered in a separate window instead"""
 
     # Handle settings
     if ax is None:
         fig, ax = plt.subplots()
     else:
         fig = ax.figure
-    fig.set_size_inches(4.0,4.8)
+    fig.set_size_inches(4.0, 4.8)
 
-    l_settings = sc.mergedicts({'loc': 'center', 'bbox_to_anchor': None, 'frameon': False}, legendsettings)
+    l_settings = sc.mergedicts({"loc": "center", "bbox_to_anchor": None, "frameon": False}, legendsettings)
 
     # Get handles and labels
     if handles is None and labels is None:
@@ -1260,13 +1259,13 @@ def separatelegend(ax=None, handles=None, labels=None, reverse=False, legendsett
     assert len(handles) == len(labels), f"Number of handles ({len(handles)}) and labels ({len(labels)}) must match"
 
     # Set up new plot
-    ax.set_position([-0.05,-0.05,1.1,1.1])
+    ax.set_position([-0.05, -0.05, 1.1, 1.1])
     ax.set_axis_off()
 
     # Reverse order, e.g. for stacked plots
-    if reverse: # pragma: no cover
+    if reverse:  # pragma: no cover
         handles = handles[::-1]
-        labels   = labels[::-1]
+        labels = labels[::-1]
 
     # Plot the new legend
     ax.legend(handles=handles, labels=labels, **l_settings)
@@ -1274,7 +1273,6 @@ def separatelegend(ax=None, handles=None, labels=None, reverse=False, legendsett
     return fig
 
 
-
 def plot_bars(plotdata, stack_pops=None, stack_outputs=None, outer=None, legend_mode=None, show_all_labels=False, orientation="vertical") -> list:
     """
     Produce a bar plot
diff --git a/atomica/project.py b/atomica/project.py
index 2dda2c1a..a7911dd4 100644
--- a/atomica/project.py
+++ b/atomica/project.py
@@ -576,18 +576,7 @@ def run_sampled_sims(self, parset, progset=None, progset_instructions=None, resu
 
         return results
 
-    def calibrate(self,
-                  parset=None,
-                  adjustables=None,
-                  measurables=None,
-                  save_to_project=False,
-                  new_name=None,
-                  default_min_scale=0.0,
-                  default_max_scale=2.0,
-                  default_weight=1.0,
-                  default_metric="fractional",
-                  yaml=None,
-                  **kwargs) -> ParameterSet:
+    def calibrate(self, parset=None, adjustables=None, measurables=None, save_to_project=False, new_name=None, default_min_scale=0.0, default_max_scale=2.0, default_weight=1.0, default_metric="fractional", yaml=None, **kwargs) -> ParameterSet:
         """
         Method to perform automatic calibration.
 
@@ -638,10 +627,11 @@ def calibrate(self,
             parset = self.parsets[parset]
 
         if yaml is not None:
-            import atomica.yaml_calibration # Avoid circular import
+            import atomica.yaml_calibration  # Avoid circular import
+
             assert adjustables is None, "If a YAML file is specified, adjustables should not be set"
             assert measurables is None, "If a YAML file is specified, measurables should not be set"
-            assert 'time_period' not in kwargs, "If a YAML file is specified, time_period should not be set - instead, set cal_start and cal_end in the YAML file"
+            assert "time_period" not in kwargs, "If a YAML file is specified, time_period should not be set - instead, set cal_start and cal_end in the YAML file"
             new_parset = atomica.yaml_calibration.run(yaml, self, parset, **kwargs)
         else:
             if adjustables is None:
@@ -663,7 +653,6 @@ def calibrate(self,
 
             new_parset = calibrate(project=self, parset=parset, pars_to_adjust=adjustables, output_quantities=measurables, **kwargs)
 
-
         if new_name is None:
             new_name = parset.name + " (auto-calibrated)"
         new_parset.name = new_name  # The new parset is a calibrated copy of the old, so change id.
diff --git a/atomica/system.py b/atomica/system.py
index 67816a43..9e3c149c 100644
--- a/atomica/system.py
+++ b/atomica/system.py
@@ -90,4 +90,4 @@ class FrameworkSettings:
 
     RESERVED_SYMBOLS = set(":,;/+-*'\" @")  # A code_name in the framework (for characs, comps, pars) cannot contain any of these characters
 
-    DEFAULT_PROVENANCE = "Framework-supplied default"
\ No newline at end of file
+    DEFAULT_PROVENANCE = "Framework-supplied default"
diff --git a/atomica/utils.py b/atomica/utils.py
index c33b7618..eb7bb3d8 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -994,7 +994,7 @@ def get_sigfigs_necessary(x, y, min_sigfigs: int = 2) -> int:
     :return: Number of significant figures required
     """
     msf = min_sigfigs
-    assert sc.isnumber(x) and sc.isnumber(y), f'Cannot compare sigfigs as {x} and {y} are not both numbers'
+    assert sc.isnumber(x) and sc.isnumber(y), f"Cannot compare sigfigs as {x} and {y} are not both numbers"
     if x == y or np.isnan(x) or np.isnan(y):
         return msf
     else:
diff --git a/atomica/yaml_calibration.py b/atomica/yaml_calibration.py
index 9b97f7a1..25ebd4ef 100644
--- a/atomica/yaml_calibration.py
+++ b/atomica/yaml_calibration.py
@@ -13,7 +13,7 @@
 import time
 import re
 
-__all__ = ['build', 'run']
+__all__ = ["build", "run"]
 
 from atomica import ParameterSet
 
@@ -24,7 +24,8 @@ def _get_named_nodes():
     """
     return {x._name: x for x in BaseNode.__subclasses__() if x._name is not None}
 
-def build(instructions=None, context=None, name='calibration'):
+
+def build(instructions=None, context=None, name="calibration"):
     """
     Construct nodes representing a calibration
 
@@ -38,17 +39,18 @@ def build(instructions=None, context=None, name='calibration'):
 
     if (sc.isstring(instructions) or isinstance(instructions, Path)) and Path(instructions).exists():
         with open(instructions) as file:
-            instructions =  yaml.load(file, Loader=yaml.FullLoader)
+            instructions = yaml.load(file, Loader=yaml.FullLoader)
 
     named_nodes = _get_named_nodes()
-    if isinstance(instructions, dict) and ('adjustables' in instructions or (context is not None and 'adjustables' in context)) and ('measurables' in instructions or (context is not None and 'measurables' in context)):
+    if isinstance(instructions, dict) and ("adjustables" in instructions or (context is not None and "adjustables" in context)) and ("measurables" in instructions or (context is not None and "measurables" in context)):
         return CalibrationNode(instructions, context, name)
     elif name in named_nodes:
         return named_nodes[name](instructions, context, name)
     else:
         return SectionNode(instructions, context, name)
 
-def run(node, project, parset, savedir=None, save_intermediate=False, log_output:bool=False,*args, **kwargs):
+
+def run(node, project, parset, savedir=None, save_intermediate=False, log_output: bool = False, *args, **kwargs):
     """
     Run YAML calibration
 
@@ -63,7 +65,7 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
     """
 
     if savedir is None:
-        savedir = Path('.')
+        savedir = Path(".")
     else:
         savedir = Path(savedir)
     savedir.mkdir(exist_ok=True, parents=True)
@@ -76,14 +78,14 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
 
     parset = sc.dcp(project.parset(parset))
 
-    nodes = list(node.walk()) # Make a flat list of all nodes to execute in order
+    nodes = list(node.walk())  # Make a flat list of all nodes to execute in order
     n_steps = len([x for x in nodes if not isinstance(x[1], SectionNode)])
     n = 1
 
     if log_output:
-        at.start_logging(savedir/'calibration_log.txt')
+        at.start_logging(savedir / "calibration_log.txt")
 
-    at.logger.info(f'\nStarting calibration ({n_steps} steps)')
+    at.logger.info(f"\nStarting calibration ({n_steps} steps)")
 
     for n_reps, node in nodes:
 
@@ -96,11 +98,11 @@ def run(node, project, parset, savedir=None, save_intermediate=False, log_output
 
             if save_intermediate and not isinstance(node, SaveCalibrationNode):
                 output = savedir / f'intermediate_calibration_{n:0{len(str(n_steps))}}_{node.name.replace(" ", "_")}'
-                at.logger.info(f'Saving intermediate calibration...')
+                at.logger.info(f"Saving intermediate calibration...")
                 parset.save_calibration(output)
 
     t = time.process_time()
-    at.logger.info(f'\nCalibration completed. Total time elapsed: {round(t, 2)} seconds ({round(t/60, 2)} minutes)')
+    at.logger.info(f"\nCalibration completed. Total time elapsed: {round(t, 2)} seconds ({round(t/60, 2)} minutes)")
 
     if log_output:
         at.stop_logging()
@@ -150,9 +152,9 @@ def __str__(self, indent=0):
         :param indent: Recursively increase the indent for child nodes
         :return:
         """
-        s =  '\t' * indent + self.__repr__()
+        s = "\t" * indent + self.__repr__()
         for child in self.children:
-            s += '\n' + child.__str__(indent=indent + 1)
+            s += "\n" + child.__str__(indent=indent + 1)
         return s
 
     @property
@@ -178,8 +180,8 @@ def __contains__(self, item):
     def repeats(self):
         # Although repeats may be part of the context, we only repeat a node if the instructions requested a repeat
         # i.e., repeats are not inherited
-        if isinstance(self.instructions, dict) and 'repeats' in self.instructions:
-            return self.instructions['repeats']
+        if isinstance(self.instructions, dict) and "repeats" in self.instructions:
+            return self.instructions["repeats"]
         else:
             return 1
 
@@ -197,10 +199,12 @@ def apply(self, project: at.Project, parset: at.ParameterSet, savedir, *args, **
         """
         return parset
 
+
 class SectionNode(BaseNode):
     """
     A section node is a special kind of node, that contains other nodes
     """
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.children = self.make_children()
@@ -224,35 +228,36 @@ def make_children(self):
 
         return children
 
+
 class CalibrationNode(BaseNode):
 
     # Order for list of adjustable parameters and default values
     adj_defaults = {
-        'lower_bound': 0.1,
-        'upper_bound': 10.0,
-        'starting_y_factor': None,
+        "lower_bound": 0.1,
+        "upper_bound": 10.0,
+        "starting_y_factor": None,
     }
 
     # Order for list of measurable parameters and default values
     meas_defaults = {
-        'weight': 1.0,
-        'metric': 'fractional',
-        'cal_start': -np.inf,
-        'cal_end': np.inf,
+        "weight": 1.0,
+        "metric": "fractional",
+        "cal_start": -np.inf,
+        "cal_end": np.inf,
     }
 
     @staticmethod
     def parse_list(l, defaults):
         # Routine to parse list of arguments into a dictionary of values
         d = {}
-        #convert number strings back to numerical values
+        # convert number strings back to numerical values
         for i, e in enumerate(l.copy()):
             try:
-                l[i] = float(e)  
+                l[i] = float(e)
             except ValueError:
                 pass
 
-        for k, v in zip(list(defaults.keys())[:len(l)], l):
+        for k, v in zip(list(defaults.keys())[: len(l)], l):
             d[k] = v
         return d
 
@@ -269,34 +274,34 @@ def separate_keys(keys_str: str) -> list:
             :return list : A list of strings, each of which represents a single key.
             """
             in_brackets = False
-            brackets_str = ''
-            nobrackets_str = ''
+            brackets_str = ""
+            nobrackets_str = ""
             separated_keys = []
 
             for ch in keys_str:
-                if ch == '(':
+                if ch == "(":
                     in_brackets = True
                     continue
-                elif ch == ')':
+                elif ch == ")":
                     in_brackets = False
                     separated_keys.append(brackets_str)
-                    brackets_str = ''
+                    brackets_str = ""
                     continue
 
                 if in_brackets:
                     brackets_str += ch
                 else:
-                    if ch == ',':
-                        if nobrackets_str == ' ' or nobrackets_str == '':
-                            nobrackets_str = ''
+                    if ch == ",":
+                        if nobrackets_str == " " or nobrackets_str == "":
+                            nobrackets_str = ""
                             continue
                         else:
                             separated_keys.append(nobrackets_str)
-                            nobrackets_str = ''
+                            nobrackets_str = ""
                     else:
                         nobrackets_str += ch
 
-            if nobrackets_str != '' and nobrackets_str != ' ':
+            if nobrackets_str != "" and nobrackets_str != " ":
                 separated_keys.append(nobrackets_str)
 
             return [x.strip() for x in separated_keys]
@@ -319,12 +324,12 @@ def process_key(key: str) -> tuple:
                 INPUT: 'aging, 0-4, 5-14'
                 OUTPUT: (aging, 0-4, 5-14)
             """
-            if ',' in key:
-                return tuple([x.strip() for x in key.strip('() ').split(',') if x])
+            if "," in key:
+                return tuple([x.strip() for x in key.strip("() ").split(",") if x])
             else:
                 return (key.strip(), None)
 
-        def process_list(l: list) -> (tuple,list):
+        def process_list(l: list) -> (tuple, list):
             """
             Process list-format inputs and separate them into a key (par_name, pop_name tuple) and a value (list of
             parameter settings).
@@ -340,20 +345,20 @@ def process_list(l: list) -> (tuple,list):
                 OUTPUT: (b_rate, 0-4) ; [0.1, 10, 1.5]
             """
             if len(l) == 1:
-                #if the list is already just one string, return that string as key with None pop and vals
-                return (l[0].strip('() '), None), None
-            elif '(' in str(l):
+                # if the list is already just one string, return that string as key with None pop and vals
+                return (l[0].strip("() "), None), None
+            elif "(" in str(l):
                 # separate out the parenthesis contents as the par/pop/s,
                 # then output the key (par, pop tuple) and value
 
                 # process keys
                 s = str(l).strip("[] ").replace("'", "")
-                s1 = re.findall(r'\(.*?\)', s)
-                key = process_key(s1[0].replace('(', '').replace(')', ''))
+                s1 = re.findall(r"\(.*?\)", s)
+                key = process_key(s1[0].replace("(", "").replace(")", ""))
 
                 # process values/settings
-                value = s.replace(s1[0], '').strip(', ').split(',')
-                value = [x.strip(', ') for x in value if x]
+                value = s.replace(s1[0], "").strip(", ").split(",")
+                value = [x.strip(", ") for x in value if x]
                 return key, value
             else:
                 key = process_key(l[0])
@@ -382,22 +387,22 @@ def process_inputs(inputs, defaults: dict) -> dict:
             if sc.isstring(inputs):
                 # Support a comma separated string with "quantity pop" specifications of adjustables and measurables
                 # In this case, default values should be used for all other items. Proceed by splitting into a list
-                inputs = inputs.split(',')
+                inputs = inputs.split(",")
 
             if isinstance(inputs, (tuple, list)):
                 for l in inputs:
                     l = sc.promotetolist(l)
                     keyspops, v = process_list(l)
-                    
-                    #process key
+
+                    # process key
                     if len(keyspops) == 2:
                         key, pop_name = keyspops
                     else:
-                        assert len(keyspops) == 3, f'Number of populations must be 0, 1 or 2.'
-                        key = f'{keyspops[0]}_from_{keyspops[1]}'
+                        assert len(keyspops) == 3, f"Number of populations must be 0, 1 or 2."
+                        key = f"{keyspops[0]}_from_{keyspops[1]}"
                         pop_name = keyspops[2]
 
-                    #process value
+                    # process value
                     if v is None:
                         value = defaults
                     else:
@@ -410,16 +415,16 @@ def process_inputs(inputs, defaults: dict) -> dict:
 
                     separated_keys = separate_keys(keys)
                     for key in separated_keys:
-                        #separate par name from pop name
+                        # separate par name from pop name
                         keyspops = process_key(key.strip())
                         if len(keyspops) == 2:
                             key, pop_name = keyspops
                         else:
-                            assert len(keyspops) == 3, f'Number of populations must be 0, 1 or 2.'
-                            key = f'{keyspops[0]}_from_{keyspops[1]}'
+                            assert len(keyspops) == 3, f"Number of populations must be 0, 1 or 2."
+                            key = f"{keyspops[0]}_from_{keyspops[1]}"
                             pop_name = keyspops[2]
 
-                        #process values
+                        # process values
                         if isinstance(v, (tuple, list)):
                             value = self.parse_list(v, defaults)
                         elif v is None:
@@ -427,12 +432,12 @@ def process_inputs(inputs, defaults: dict) -> dict:
                         else:
                             value = v.copy()
 
-                        #add keys and values to outputs dict
+                        # add keys and values to outputs dict
                         out[key, pop_name] = sc.mergedicts(out.get((key, pop_name), {}), value)
             return out
 
-        self['adjustables'] = process_inputs(self['adjustables'], self.adj_defaults)
-        self['measurables'] = process_inputs(self['measurables'], self.meas_defaults)
+        self["adjustables"] = process_inputs(self["adjustables"], self.adj_defaults)
+        self["measurables"] = process_inputs(self["measurables"], self.meas_defaults)
 
         def check_optional_number(key, v, defaults):
             if key in v and v[key] is not None:
@@ -440,25 +445,25 @@ def check_optional_number(key, v, defaults):
                     raise TypeError(f"Adjustable argument {key} needs to be a number or None (defaults to {defaults[key]}). Provided value: {v[key]} ")
 
         # Validate adjustables
-        assert len(self['adjustables']) > 0, f'Cannot calibrate with no adjustables for calibration section {self.name}'
-        for (quantity, pop_name), v in self['adjustables'].items():
-            assert 'pop_name' not in v, f'Setting the population name through "pop_name: {v["pop_name"]}" is not supported. Instead, the name of the adjustable quantity should include the population name ("{quantity} {v["pop_name"]}")'
-            assert isinstance(quantity, str), f'Adjustable codename {quantity} needs to be a string'
-            assert pop_name is None or isinstance(pop_name, str), f'Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)'
-            check_optional_number('lower_bound',v, self.adj_defaults)
-            check_optional_number('upper_bound',v, self.adj_defaults)
-            check_optional_number('starting_y_factor',v, self.adj_defaults)
+        assert len(self["adjustables"]) > 0, f"Cannot calibrate with no adjustables for calibration section {self.name}"
+        for (quantity, pop_name), v in self["adjustables"].items():
+            assert "pop_name" not in v, f'Setting the population name through "pop_name: {v["pop_name"]}" is not supported. Instead, the name of the adjustable quantity should include the population name ("{quantity} {v["pop_name"]}")'
+            assert isinstance(quantity, str), f"Adjustable codename {quantity} needs to be a string"
+            assert pop_name is None or isinstance(pop_name, str), f"Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)"
+            check_optional_number("lower_bound", v, self.adj_defaults)
+            check_optional_number("upper_bound", v, self.adj_defaults)
+            check_optional_number("starting_y_factor", v, self.adj_defaults)
 
         # Validate measurables
-        assert len(self['measurables']) > 0, f'Cannot calibrate with no measurables for calibration section {self.name}'
-        for (quantity, pop_name), v in self['measurables'].items():
-            assert isinstance(quantity, str), f'Measurable codename {quantity} needs to be a string'
-            assert pop_name is None or isinstance(pop_name, str), f'Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)'
-            assert 'metric' not in v or v['metric'] is None or isinstance(v["metric"], str), f"Measurable metric {v['metric']} needs to be a number or None (defaults to 'fractional')"
+        assert len(self["measurables"]) > 0, f"Cannot calibrate with no measurables for calibration section {self.name}"
+        for (quantity, pop_name), v in self["measurables"].items():
+            assert isinstance(quantity, str), f"Measurable codename {quantity} needs to be a string"
+            assert pop_name is None or isinstance(pop_name, str), f"Adjustable population {pop_name} needs to be a string or None (defaults to all populations for that parameter)"
+            assert "metric" not in v or v["metric"] is None or isinstance(v["metric"], str), f"Measurable metric {v['metric']} needs to be a number or None (defaults to 'fractional')"
 
-            check_optional_number('weight',v, self.meas_defaults)
-            check_optional_number('cal_start',v, self.meas_defaults)
-            check_optional_number('cal_end',v, self.meas_defaults)
+            check_optional_number("weight", v, self.meas_defaults)
+            check_optional_number("cal_start", v, self.meas_defaults)
+            check_optional_number("cal_end", v, self.meas_defaults)
 
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, quiet=False, compare_results=False, **kwargs) -> ParameterSet:
 
@@ -469,12 +474,12 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
 
         # Expand adjustables
         adjustables = {}
-        par_names = {x[0] for x in attributes['adjustables']}.intersection(x.name for x in parset.all_pars())
-        pop_names = {x[1] for x in attributes['adjustables']}.intersection({*parset.pop_names} | {'all', None})
+        par_names = {x[0] for x in attributes["adjustables"]}.intersection(x.name for x in parset.all_pars())
+        pop_names = {x[1] for x in attributes["adjustables"]}.intersection({*parset.pop_names} | {"all", None})
 
-        adj_defaults = {k:self.attributes[k] if k in self.attributes else self.adj_defaults[k] for k in self.adj_defaults}
+        adj_defaults = {k: self.attributes[k] if k in self.attributes else self.adj_defaults[k] for k in self.adj_defaults}
 
-        for par_name, pop_name in attributes['adjustables']:
+        for par_name, pop_name in attributes["adjustables"]:
 
             if par_name not in par_names:
                 at.logger.warning(f"Extra YAML adjustable parameter '{par_name}' does not exist in this project's framework/parset and will be ignored")
@@ -489,25 +494,24 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(adj_defaults,  attributes['adjustables'].get((par_name, None), None),  attributes['adjustables'].get((par_name, pop), None))
-                adjustables[(par_name, pop)] = (d['lower_bound'], d['upper_bound'], d['starting_y_factor'])
-        adjustables = [(*k, *v) for k,v in adjustables.items()]
-
+                d = sc.mergedicts(adj_defaults, attributes["adjustables"].get((par_name, None), None), attributes["adjustables"].get((par_name, pop), None))
+                adjustables[(par_name, pop)] = (d["lower_bound"], d["upper_bound"], d["starting_y_factor"])
+        adjustables = [(*k, *v) for k, v in adjustables.items()]
 
         # Expand measurables
         measurables = {}
-        par_names = {x[0] for x in attributes['measurables']}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to validate that pars have databook entries in the future
-        pop_names = {x[1] for x in attributes['measurables']}.intersection({*parset.pop_names} | {None})
+        par_names = {x[0] for x in attributes["measurables"]}.intersection(x.name for x in parset.all_pars())  # TODO: This is probably OK for now but will need to validate that pars have databook entries in the future
+        pop_names = {x[1] for x in attributes["measurables"]}.intersection({*parset.pop_names} | {None})
 
         meas_defaults = {k: self.attributes[k] if k in self.attributes else self.meas_defaults[k] for k in self.meas_defaults}
 
-        for par_name, pop_name in attributes['measurables']:
+        for par_name, pop_name in attributes["measurables"]:
 
             if par_name not in par_names:
                 at.logger.warning(f"Extra YAML measurable variable '{par_name}' does not exist in this project's framework and will be ignored")
                 continue
             elif pop_name not in pop_names:
-                if not (pop_name.lower() == 'total' and pop_name.lower() in {x.lower() for x in project.data.tdve[par_name].ts.keys()}):
+                if not (pop_name.lower() == "total" and pop_name.lower() in {x.lower() for x in project.data.tdve[par_name].ts.keys()}):
                     at.logger.warning(f"Extra YAML measurable population '{pop_name}' does not exist in this project's databook and will be ignored")
                     continue
 
@@ -517,20 +521,20 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 pops = sc.promotetolist(pop_name)
 
             for pop in pops:
-                d = sc.mergedicts(meas_defaults,  attributes['measurables'].get((par_name, None), None), attributes['measurables'].get((par_name, pop), None))
-                measurables[(par_name, pop)] = (d['weight'], d['metric'], d['cal_start'], d['cal_end'])
-        measurables = [(*k, *v) for k,v in measurables.items()]
+                d = sc.mergedicts(meas_defaults, attributes["measurables"].get((par_name, None), None), attributes["measurables"].get((par_name, pop), None))
+                measurables[(par_name, pop)] = (d["weight"], d["metric"], d["cal_start"], d["cal_end"])
+        measurables = [(*k, *v) for k, v in measurables.items()]
 
         # Calibration
         if len(adjustables):
             # note: attributes = instructions + context
             kwargs = sc.mergedicts(self.attributes, kwargs)
 
-            del kwargs['adjustables'] # supplied via the adjustables variable
-            del kwargs['measurables'] # supplied via the measurables variable
+            del kwargs["adjustables"]  # supplied via the adjustables variable
+            del kwargs["measurables"]  # supplied via the measurables variable
 
-            if 'repeats' in kwargs:
-                del kwargs['repeats']
+            if "repeats" in kwargs:
+                del kwargs["repeats"]
 
             if quiet:
                 with at.Quiet(show_warnings=False):
@@ -553,13 +557,13 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                 new = new_cal_parset.get_par(par).y_factor[pop]
 
             if new != old:
-                at.logger.info(f'...adjusted the y-factor for {par} in {pop} from {old} to {new}')
+                at.logger.info(f"...adjusted the y-factor for {par} in {pop} from {old} to {new}")
                 made_changes = True
             else:
-                at.logger.debug(f'...did NOT adjust the y-factor for {par} in {pop} from {old} to {new}')
+                at.logger.debug(f"...did NOT adjust the y-factor for {par} in {pop} from {old} to {new}")
 
         if not made_changes:
-            at.logger.info(f'...made no changes!')
+            at.logger.info(f"...made no changes!")
 
         if compare_results:
             base_res = project.run_sim(parset=parset)
@@ -579,58 +583,58 @@ def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, qui
                         cal_rms_error += (data_time_val - cal_time_val) ** 2
 
                         sf = at.get_sigfigs_necessary(base_time_val, cal_time_val)
-                        at.logger.info(f'...for parameter {par_name} and population {pop} at time {time_value} the data value was {sc.sigfig(data_time_val, sf)}, the baseline value was {sc.sigfig(base_time_val, sf)}, and the calibrated value was {sc.sigfig(cal_time_val, sf)}.')
+                        at.logger.info(f"...for parameter {par_name} and population {pop} at time {time_value} the data value was {sc.sigfig(data_time_val, sf)}, the baseline value was {sc.sigfig(base_time_val, sf)}, and the calibrated value was {sc.sigfig(cal_time_val, sf)}.")
 
-                base_rms_error = base_rms_error ** 0.5
-                cal_rms_error = cal_rms_error ** 0.5
+                base_rms_error = base_rms_error**0.5
+                cal_rms_error = cal_rms_error**0.5
                 sf = at.get_sigfigs_necessary(base_rms_error, cal_rms_error)
-                at.logger.info(f'...RMS error for parameter {par_name} has changed from baseline {sc.sigfig(base_rms_error, sf)} to calibrated {sc.sigfig(cal_rms_error, sf)}')
+                at.logger.info(f"...RMS error for parameter {par_name} has changed from baseline {sc.sigfig(base_rms_error, sf)} to calibrated {sc.sigfig(cal_rms_error, sf)}")
 
         return new_cal_parset
 
 
 class InitializationNode(BaseNode):
-    _name = 'set_initialization'
+    _name = "set_initialization"
 
-    def __init__(self, instructions, context, name ):
+    def __init__(self, instructions, context, name):
         new_instructions = {}
 
         if isinstance(instructions, dict):
             new_instructions.update(instructions)
         elif type(instructions) is int:
-            new_instructions.update({'init_year': instructions})
+            new_instructions.update({"init_year": instructions})
         elif isinstance(instructions, (tuple, list)):
             sc.promotetolist(instructions)
-            new_instructions.update({'init_year': instructions[0]})
+            new_instructions.update({"init_year": instructions[0]})
             if len(instructions) > 1:
-                new_instructions.update({'constant_parset': instructions[1]})
+                new_instructions.update({"constant_parset": instructions[1]})
 
         super().__init__(new_instructions, context, name)
 
     def validate(self):
-        assert 'init_year' in self, f'Initialization year must be specified'
-        assert sc.isnumber(self['init_year']), f'Initialization year {self["init_year"]} must be numeric.'
-        if 'constant_parset' in self:
-            assert isinstance(self['constant_parset'], int), f'Constant parset (optional) {self["constant_parset"]} must be numeric (boolean, or int to specify constant parset year).'
+        assert "init_year" in self, f"Initialization year must be specified"
+        assert sc.isnumber(self["init_year"]), f'Initialization year {self["init_year"]} must be numeric.'
+        if "constant_parset" in self:
+            assert isinstance(self["constant_parset"], int), f'Constant parset (optional) {self["constant_parset"]} must be numeric (boolean, or int to specify constant parset year).'
 
     def apply(self, project: at.Project, parset: at.ParameterSet, n: int, *args, **kwargs) -> ParameterSet:
         p2 = sc.dcp(parset)
-        if 'constant_parset' in self:
-            if self['constant_parset'] == False:
+        if "constant_parset" in self:
+            if self["constant_parset"] == False:
                 pass
-            elif self['constant_parset'] == True:
+            elif self["constant_parset"] == True:
                 p2 = parset.make_constant(year=project.settings.sim_start)
-            elif sc.isnumber(self['constant_parset']): #constant parset year was provided
-                p2 = parset.make_constant(year=self['constant_parset'])
+            elif sc.isnumber(self["constant_parset"]):  # constant parset year was provided
+                p2 = parset.make_constant(year=self["constant_parset"])
         new_settings = sc.dcp(project.settings)
-        new_settings.update_time_vector(end=self['init_year'])
+        new_settings.update_time_vector(end=self["init_year"])
         res = at.run_model(settings=new_settings, framework=project.framework, parset=p2)
-        parset.set_initialization(res, self['init_year'])
+        parset.set_initialization(res, self["init_year"])
         return parset
 
 
 class ClearInitializationNode(BaseNode):
-    _name = 'clear_initialization'
+    _name = "clear_initialization"
 
     def __init__(self, instructions, context, name):
         super().__init__(instructions=None, context=context, name=name)
@@ -645,16 +649,16 @@ class SaveCalibrationNode(BaseNode):
     Block in YAML file with "save calibration: <file name>"
     """
 
-    _name = 'save_calibration'
+    _name = "save_calibration"
 
     def __init__(self, instructions, context, name):
         if not isinstance(instructions, dict):
-            instructions = {'fname': instructions}
+            instructions = {"fname": instructions}
         super().__init__(instructions, context, name)
 
     def validate(self):
-        assert self['fname'] is not None, 'A "save calibration" node must have a file name explicitly specified'
+        assert self["fname"] is not None, 'A "save calibration" node must have a file name explicitly specified'
 
     def apply(self, project: at.Project, parset: at.ParameterSet, savedir=None, *args, **kwargs) -> ParameterSet:
-        parset.save_calibration(savedir / self['fname'])
+        parset.save_calibration(savedir / self["fname"])
         return parset
diff --git a/docs/tutorial/T7/calibrations/run_model_autocals.py b/docs/tutorial/T7/calibrations/run_model_autocals.py
index f4baac2c..0dc315d6 100644
--- a/docs/tutorial/T7/calibrations/run_model_autocals.py
+++ b/docs/tutorial/T7/calibrations/run_model_autocals.py
@@ -13,20 +13,20 @@
 import winsound
 
 print(os.getcwd())
-#run settings
-yaml_dir = '../calibrations'
-yaml_fname = 'typ_calibration_instructions'
-yaml_path = f'{yaml_dir}/{yaml_fname}.yaml'
-testing = False  #<------------
+# run settings
+yaml_dir = "../calibrations"
+yaml_fname = "typ_calibration_instructions"
+yaml_path = f"{yaml_dir}/{yaml_fname}.yaml"
+testing = False  # <------------
 savecals = True
-dis_model = 'typh'
-out_path = 'outputs'
+dis_model = "typh"
+out_path = "outputs"
 
-#make project   ------------------------------------------------
+# make project   ------------------------------------------------
 print(os.getcwd())
-inputs = '../assets'
-F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')
-D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)
+inputs = "../assets"
+F = at.ProjectFramework(f"{inputs}/T7_framework.xlsx")
+D = at.ProjectData.from_spreadsheet(f"{inputs}/T7_databook.xlsx", framework=F)
 
 P = at.Project(framework=F, databook=D, do_run=False)
 P.settings.update_time_vector(start=2000, end=2050, dt=1 / 52)
@@ -37,44 +37,44 @@
 
 # new folder to put calibrations in
 date = time.strftime("%Y-%m-%d_%H%M")
-calspath = f'{out_path}/cal_{yaml_fname}'
+calspath = f"{out_path}/cal_{yaml_fname}"
 if testing:
-    calspath += '_TESTING'
+    calspath += "_TESTING"
     savecals = False
 if savecals:
-    #save everything in folder
+    # save everything in folder
     try:
         os.makedirs(calspath)
     except FileExistsError:
-        calspath += f'_{date}'
-        os.makedirs(f'{calspath}')
+        calspath += f"_{date}"
+        os.makedirs(f"{calspath}")
 
     # save yaml file for reference
-    new_fname = f'{yaml_fname}_{date}.yaml'
+    new_fname = f"{yaml_fname}_{date}.yaml"
     shutil.copy(yaml_path, calspath)
-    shutil.move(f'{calspath}/{yaml_fname}.yaml', f'{calspath}/{new_fname}.yaml') #renaming file
+    shutil.move(f"{calspath}/{yaml_fname}.yaml", f"{calspath}/{new_fname}.yaml")  # renaming file
 
-    #save logfile
-    at.start_logging(f'{calspath}/logfile_{date}.txt')
+    # save logfile
+    at.start_logging(f"{calspath}/logfile_{date}.txt")
 
     # save current runfile for reference
-    runfile_name = f'{os.path.basename(__file__)}_{date}'
-    shutil.copy(__file__, f'{calspath}/{runfile_name}')
+    runfile_name = f"{os.path.basename(__file__)}_{date}"
+    shutil.copy(__file__, f"{calspath}/{runfile_name}")
 
 # display tree
 cal_tree = at.yaml_calibration.build(yaml_path)
 print(cal_tree)
 
-#run calibration w yaml instructions # <--------------------------
-newcal = P.calibrate(parset = cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
+# run calibration w yaml instructions # <--------------------------
+newcal = P.calibrate(parset=cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
 
-#save final calibration
+# save final calibration
 date = time.strftime("%Y-%m-%d_%H%M")
-newcal.save_calibration(f'{calspath}/typ_calibration.xlsx')
+newcal.save_calibration(f"{calspath}/typ_calibration.xlsx")
 at.stop_logging()
 
 
-#Notification - calibration finished
-winsound.Beep(frequency = 2500, duration = 200)
-winsound.Beep(frequency = 2750, duration = 200)
-winsound.Beep(frequency = 3050, duration = 200)
+# Notification - calibration finished
+winsound.Beep(frequency=2500, duration=200)
+winsound.Beep(frequency=2750, duration=200)
+winsound.Beep(frequency=3050, duration=200)
diff --git a/docs/tutorial/T7/calibrations/run_tutorial_autocals.py b/docs/tutorial/T7/calibrations/run_tutorial_autocals.py
index 8a8bd7c7..5510fe43 100644
--- a/docs/tutorial/T7/calibrations/run_tutorial_autocals.py
+++ b/docs/tutorial/T7/calibrations/run_tutorial_autocals.py
@@ -5,7 +5,8 @@
 # re-running the calibrations or trying out new versions of the model.
 
 import sys
-in_path = '../inputs'
+
+in_path = "../inputs"
 sys.path.append(in_path)
 import atomica as at
 import atomica.yaml_calibration
@@ -18,16 +19,16 @@
 yaml_dir = os.getcwd()
 fnames = [f for f in os.listdir(yaml_dir) if isfile(f"{yaml_dir}/{f}")]
 yaml_files = [f for f in fnames if ".yaml" in f and "T7_YAML" in f]
-yaml_files += 'WE_8'
+yaml_files += "WE_8"
 
-testing = False  #<------------
+testing = False  # <------------
 savecals = True
-out_path = 'outputs'
+out_path = "outputs"
 
-#make project   ------------------------------------------------
-inputs = '../assets'
-F = at.ProjectFramework(f'{inputs}/T7_framework.xlsx')
-D = at.ProjectData.from_spreadsheet(f'{inputs}/T7_databook.xlsx', framework=F)
+# make project   ------------------------------------------------
+inputs = "../assets"
+F = at.ProjectFramework(f"{inputs}/T7_framework.xlsx")
+D = at.ProjectData.from_spreadsheet(f"{inputs}/T7_databook.xlsx", framework=F)
 
 P = at.Project(framework=F, databook=D, do_run=False)
 P.settings.update_time_vector(start=2000, end=2050, dt=1 / 52)
@@ -37,44 +38,44 @@
 # print('Existing calibration loaded')
 
 for yaml_fname in yaml_files:
-    yaml_path = f'{yaml_dir}/{yaml_fname}'
+    yaml_path = f"{yaml_dir}/{yaml_fname}"
 
     # new folder to put calibrations in
     date = time.strftime("%Y-%m-%d_%H%M")
-    calspath = f'{out_path}/cals_{yaml_fname[8]}'
+    calspath = f"{out_path}/cals_{yaml_fname[8]}"
     if savecals:
-        #save everything in folder
+        # save everything in folder
         try:
             os.makedirs(calspath)
         except FileExistsError:
-            calspath += f'_{date}'
-            os.makedirs(f'{calspath}')
+            calspath += f"_{date}"
+            os.makedirs(f"{calspath}")
 
         # save yaml file for reference
-        new_fname = f'{yaml_fname}_{date}.yaml'
+        new_fname = f"{yaml_fname}_{date}.yaml"
         shutil.copy(yaml_path, calspath)
-        shutil.move(f'{calspath}/{yaml_fname}', f'{calspath}/{new_fname}') #renaming file
+        shutil.move(f"{calspath}/{yaml_fname}", f"{calspath}/{new_fname}")  # renaming file
 
-        #save logfile
-        at.start_logging(f'{calspath}/logfile_{date}.txt')
+        # save logfile
+        at.start_logging(f"{calspath}/logfile_{date}.txt")
 
         # save current runfile for reference
-        runfile_name = f'{os.path.basename(__file__)}_{date}'
-        shutil.copy(__file__, f'{calspath}/{runfile_name}')
+        runfile_name = f"{os.path.basename(__file__)}_{date}"
+        shutil.copy(__file__, f"{calspath}/{runfile_name}")
 
     # display tree
     cal_tree = at.yaml_calibration.build(yaml_path)
     print(cal_tree)
 
-    #run calibration w yaml instructions # <--------------------------
-    newcal = P.calibrate(parset = cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
+    # run calibration w yaml instructions # <--------------------------
+    newcal = P.calibrate(parset=cal, yaml=yaml_path, quiet=False, savedir=calspath, save_intermediate=False, verbose=0)
 
-    #save final calibration
-    newcal.save_calibration(f'{calspath}/cal_{yaml_fname[8]}')  #newcal.save_calibration(f'{calspath}/cal_{yaml_fname}')
+    # save final calibration
+    newcal.save_calibration(f"{calspath}/cal_{yaml_fname[8]}")  # newcal.save_calibration(f'{calspath}/cal_{yaml_fname}')
     at.stop_logging()
 
 
-#Notification - calibration finished
-winsound.Beep(frequency = 2500, duration = 200)
-winsound.Beep(frequency = 2750, duration = 200)
-winsound.Beep(frequency = 3050, duration = 200)
+# Notification - calibration finished
+winsound.Beep(frequency=2500, duration=200)
+winsound.Beep(frequency=2750, duration=200)
+winsound.Beep(frequency=3050, duration=200)
diff --git a/tests/test_time_vector.py b/tests/test_time_vector.py
index 652445cb..5fc3fae2 100644
--- a/tests/test_time_vector.py
+++ b/tests/test_time_vector.py
@@ -18,7 +18,7 @@ def test_time_vector():
     dt = 1 / 12
     settings = at.ProjectSettings(sim_start=analysis_years[0], sim_end=analysis_years[1] + 1 - dt, sim_dt=dt)  #: Atomica project settings
     tvec = settings.tvec  #: Simulation/result time points
-    assert set(np.unique(tvec.astype(int), return_counts=True)[1]) == {1/dt}  # or pd.Series(tvec).astype(int).value_counts()
+    assert set(np.unique(tvec.astype(int), return_counts=True)[1]) == {1 / dt}  # or pd.Series(tvec).astype(int).value_counts()
 
 
 if __name__ == "__main__":
diff --git a/tests/test_tox_yaml.py b/tests/test_tox_yaml.py
index 0e19195f..53c90bf9 100644
--- a/tests/test_tox_yaml.py
+++ b/tests/test_tox_yaml.py
@@ -7,21 +7,23 @@
 
 # Basic minimum working product test to verify all yaml features run
 testdir = at.parent_dir()
-tmpdir = testdir / 'temp'
-yaml_dir = testdir/'yaml_tests'
+tmpdir = testdir / "temp"
+yaml_dir = testdir / "yaml_tests"
 
 # List yaml files in directory
-yaml_files = list(yaml_dir.glob('*.yaml'))
+yaml_files = list(yaml_dir.glob("*.yaml"))
 
 
 @pytest.fixture()
 def project():
-    P = at.demo('udt_dyn', do_run=False)
-    P.settings.update_time_vector(start=2000, end=2050, dt=1/52)
+    P = at.demo("udt_dyn", do_run=False)
+    P.settings.update_time_vector(start=2000, end=2050, dt=1 / 52)
     return P
 
+
 def test_save_intermediate(project):
-    project.calibrate(yaml=yaml_files[0], parset=project.make_parset(), quiet=False, savedir=tmpdir/'yaml_save_test', save_intermediate=True, verbose=0, max_time=0.1)
+    project.calibrate(yaml=yaml_files[0], parset=project.make_parset(), quiet=False, savedir=tmpdir / "yaml_save_test", save_intermediate=True, verbose=0, max_time=0.1)
+
 
 @pytest.mark.parametrize("yaml_fname", yaml_files, ids=[x.stem for x in yaml_files])
 def test_yaml_calibration(project, yaml_fname):
@@ -36,8 +38,8 @@ def test_yaml_calibration(project, yaml_fname):
     # print(config_file_path)
     project.calibrate(yaml=yaml_fname, parset=project.make_parset(), quiet=False, savedir=tmpdir, save_intermediate=False, verbose=0, max_time=0.1)
 
-    #or can import in all the diff ways - make function
-    #also run in several diff ways? Or have a separate test for that?
+    # or can import in all the diff ways - make function
+    # also run in several diff ways? Or have a separate test for that?
 
     # Test saving the calibration
     # save()

From 760d17539269c054ca8324ed74e52c77f07a0912 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 19:43:57 +1000
Subject: [PATCH 110/161] Replace with patched sciris function

---
 atomica/plotting.py | 78 +++++++++++++++++++++++++++++++--------------
 1 file changed, 54 insertions(+), 24 deletions(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index d85d34e8..2f1567e8 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -1237,42 +1237,70 @@ def interpolate(self, new_tvec):
         return np.interp(sc.promotetoarray(new_tvec), self.tvec, self.vals, left=np.nan, right=np.nan)
 
 
-def separatelegend(ax=None, handles=None, labels=None, reverse=False, legendsettings=None):
-    """Allows the legend of a figure to be rendered in a separate window instead"""
+# Temporary copy of function from Sciris to remove after Sciris update
+def _get_legend_handles(ax, handles, labels):
+    """
+    Construct handle and label list, from one of:
 
-    # Handle settings
-    if ax is None:
-        fig, ax = plt.subplots()
-    else:
-        fig = ax.figure
-    fig.set_size_inches(4.0, 4.8)
+         - A list of handles and a list of labels
+         - A list of handles, where each handle contains the label
+         - An axis object, containing the objects that should appear in the legend
+         - A figure object, from which the first axis will be used
+    """
+    if handles is None:
+        if ax is None:
+            ax = plt.gca()
+        elif isinstance(ax, plt.Figure): # Allows an argument of a figure instead of an axes # pragma: no cover
+            ax = ax.axes[-1]
+        handles, labels = ax.get_legend_handles_labels()
+    else: # pragma: no cover
+        if labels is None:
+            labels = [h.get_label() for h in handles]
+        else:
+            assert len(handles) == len(labels), f"Number of handles ({len(handles)}) and labels ({len(labels)}) must match"
+    return ax, handles, labels
 
-    l_settings = sc.mergedicts({"loc": "center", "bbox_to_anchor": None, "frameon": False}, legendsettings)
+# Temporary copy of function from Sciris to remove after Sciris update
+def separatelegend(ax=None, handles=None, labels=None, reverse=False, figsettings=None, legendsettings=None):
+    """ Allows the legend of a figure to be rendered in a separate window instead """
+
+    # Handle settings
+    f_settings = sc.mergedicts({'figsize':(4.0,4.8)}, figsettings) # (6.4,4.8) is the default, so make it a bit narrower
+    l_settings = sc.mergedicts({'loc': 'center', 'bbox_to_anchor': None, 'frameon': False}, legendsettings)
 
     # Get handles and labels
-    if handles is None and labels is None:
-        handles, labels = ax.get_legend_handles_labels()
-    elif handles is None:
-        handles, _ = ax.get_legend_handles_labels()
-    elif labels is None:
-        labels = [h.get_label() for h in handles]
-    assert len(handles) == len(labels), f"Number of handles ({len(handles)}) and labels ({len(labels)}) must match"
+    _, handles, labels = _get_legend_handles(ax, handles, labels)
 
     # Set up new plot
-    ax.set_position([-0.05, -0.05, 1.1, 1.1])
-    ax.set_axis_off()
+    fig = plt.figure(**f_settings)
+    ax = fig.add_subplot(111)
+    ax.set_position([-0.05,-0.05,1.1,1.1]) # This cuts off the axis labels, ha-ha
+    ax.set_axis_off()  # Hide axis lines
+
+    # A legend renders the line/patch based on the object handle. However, an object
+    # can only appear in one figure. Thus, if the legend is in a different figure, the
+    # object cannot be shown in both the original figure and in the legend. Thus we need
+    # to copy the handles, and use the copies to render the legend
+    handles2 = []
+    for h in handles:
+        h2 = sc.cp(h)
+        h2.axes = None
+        h2._parent_figure = None
+        h2.figure = None
+        handles2.append(h2)
 
     # Reverse order, e.g. for stacked plots
-    if reverse:  # pragma: no cover
-        handles = handles[::-1]
-        labels = labels[::-1]
+    if reverse: # pragma: no cover
+        handles2 = handles2[::-1]
+        labels   = labels[::-1]
 
     # Plot the new legend
-    ax.legend(handles=handles, labels=labels, **l_settings)
+    ax.legend(handles=handles2, labels=labels, **l_settings)
 
     return fig
 
 
+
 def plot_bars(plotdata, stack_pops=None, stack_outputs=None, outer=None, legend_mode=None, show_all_labels=False, orientation="vertical") -> list:
     """
     Produce a bar plot
@@ -1796,12 +1824,14 @@ def _prepare_figures(dim1, dim2, n_cols):
 
     if legend_mode == "separate":
         reverse_legend = True if plot_type in ["stacked", "proportion"] else False
-        handles, labels = ax.get_legend_handles_labels()
 
         if not subplots:
-            separatelegend(axes[-1], handles=handles, labels=labels, reverse=reverse_legend)
+            # Replace the last figure with a legend figure
+            plt.close(figs[-1])  # TODO - update Sciris to allow passing in an existing figure
+            figs[-1] = separatelegend(ax, reverse=reverse_legend)
         else:
             legend_ax = axes[-1]
+            handles, labels = ax.get_legend_handles_labels()
             legend_ax.set_axis_off()  # Hide axis lines
             if reverse_legend:  # pragma: no cover
                 handles = handles[::-1]

From f8091fdb1349fa4b2a3700dbacb246d939a86af3 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 21:15:17 +1000
Subject: [PATCH 111/161] Update python version in tox.ini

---
 tox.ini | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tox.ini b/tox.ini
index 8172f379..e3648053 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,5 +1,5 @@
 [tox]
-envlist = py311
+envlist = py312
 
 [testenv]
 description = Run basic usage tests

From 6af1b2042c9a5ee0c5ae22665c6a60db19a7a712 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 17 Feb 2025 21:16:03 +1000
Subject: [PATCH 112/161] Update how path is identified

---
 docs/general/YAML_calibration.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/general/YAML_calibration.ipynb b/docs/general/YAML_calibration.ipynb
index 027215e2..adf79647 100644
--- a/docs/general/YAML_calibration.ipynb
+++ b/docs/general/YAML_calibration.ipynb
@@ -34,7 +34,8 @@
    "outputs": [],
    "source": [
     "import atomica as at\n",
-    "yaml_file = at.rootdir/'docs'/'tutorial'/'T7'/'calibrations'/'T7_YAML_3_repeats.yaml'\n",
+    "import pathlib\n",
+    "yaml_file = pathlib.Path.cwd()/'..'/'tutorial'/'T7'/'calibrations'/'T7_YAML_3_repeats.yaml'\n",
     "print(open(yaml_file).read())"
    ]
   },

From 4d4fa1713924f5bc5d767f0b73f8687d56af311a Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:03:22 +1000
Subject: [PATCH 113/161] Add test

---
 tests/sir_framework_default_all.xlsx | Bin 0 -> 27090 bytes
 tests/test_tox_databooks.py          |  18 +++++++++++++++++-
 2 files changed, 17 insertions(+), 1 deletion(-)
 create mode 100644 tests/sir_framework_default_all.xlsx

diff --git a/tests/sir_framework_default_all.xlsx b/tests/sir_framework_default_all.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..99b7b42940ba3e133959ba8709ee237e3ea7e964
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z3HC+gAcnNUNqVqislRsOKO$QZDTo1@aLTVP`v%JOQ$R!tVp1TS@&Hz5d;{fr$qSK!
z7#;?v#FO7hxgPT)QV`RW;FNBv8!6Yu4MYlJwh5f_6y_?r;fStn4u}-Q-~l*=mE}gt
zbqoO_1+i)yPPu#gnu1uvjR-(2q=N(Ixvv56vN}X5Vu27`D#iP!6tP?gk$_lD0Vn7R
zToeASwgM4^Xmf{y(1iaDbZvS^1R?r_;UGM*e*;~+h7m!CmP$B?6Z~(WYl9^s2+@)T
z2MI`BgZ@8*8X^$U%moLkO8*xi+}ec*M6^M{ftE7=1qe4qAp#NoLvWy<?0*5m-9(5$
zMB@z{m>_oz{LO}gs7CZXz|~rczo~D${~<2!!__64zo~D$?;);gz}0*Dzo~D$-yzPj
z;OY$HKh^)7b0Hr0?-Log7N#@-0RCfIgDC%f$oWq>p4mU-e+@u2RncIsC;)&K_U(Z6
KlxG&d{{0`UMU7(s

literal 0
HcmV?d00001

diff --git a/tests/test_tox_databooks.py b/tests/test_tox_databooks.py
index 7958424f..0952a698 100644
--- a/tests/test_tox_databooks.py
+++ b/tests/test_tox_databooks.py
@@ -180,10 +180,26 @@ def test_databook_all():
     d = at.PlotData(res, outputs=["sus", "ch_prev", "ch_all"], project=P)
     at.plot_series(d, axis="pops", data=P.data)
 
+def test_databook_default_all():
+    F = at.ProjectFramework(testdir/'sir_framework_default_all.xlsx')
+    D = at.ProjectData.new(F, np.arange(2000,2005), pops=4, transfers=1)
+    D.save(tmpdir / "databook_default_all_test.xlsx")  # Test saving it back
+
+    D.add_pop('new','new')
+    assert list(D.tdve['sus'].ts.keys()) == ['pop_0','pop_1','pop_2','pop_3','new']
+    assert list(D.tdve['ch_prev'].ts.keys()) == ['All']
+
+    D.tdve['contacts'].ts['pop_0'] = D.tdve['contacts'].ts['All']
+    del D.tdve['contacts'].ts['All']
+    D.add_pop('new2','new2')
+    assert list(D.tdve['ch_prev'].ts.keys()) == ['All']
+    assert list(D.tdve['contacts'].ts.keys()) == ['pop_0','new2']
+    D.save(tmpdir / "databook_default_all_test_2.xlsx")  # Test saving it back
 
 if __name__ == "__main__":
     # test_mixed_years_2()
     # test_mixed_years_1()
     # test_databooks()
     # test_databook_comments()
-    test_databook_all()
+    # test_databook_all()
+    test_databook_default_all()

From e16cdfaee63b167c6894b91e2b017eb77cc4ba66 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:03:53 +1000
Subject: [PATCH 114/161] Fix migrations

---
 atomica/data.py             | 11 +++++++++-
 atomica/migration.py        | 41 +++++++++++++++++++++++++++++++++++++
 tests/test_tox_migration.py |  5 +++++
 3 files changed, 56 insertions(+), 1 deletion(-)

diff --git a/atomica/data.py b/atomica/data.py
index d8531d7d..6737645b 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -58,11 +58,20 @@ def __init__(self, framework):
         self.tdve_pages = sc.odict()  #: This is an odict mapping worksheet name to an (ordered) list of TDVE code names appearing on that sheet
 
         # Internal storage used with methods while writing
-        self._pop_types = list(framework.pop_types.keys())  # : Store set of valid population types from framework
+        self._pop_types = list(framework.pop_types.keys())  #: Store set of valid population types from framework
         self._formats = None  #: Temporary storage for the Excel formatting while writing a databook
         self._book = None  #: Temporary storage for the workbook while writing a databook
         self._references = None  #: Temporary storage for cell references while writing a databook
 
+
+    def __setstate__(self, d):
+        from .migration import migrate
+
+        self.__dict__ = d
+        projectdata = migrate(self)
+        self.__dict__ = projectdata.__dict__
+
+
     def tables(self):
         """
         Return iterator over all TDVE and TDC tables
diff --git a/atomica/migration.py b/atomica/migration.py
index 10abed30..fdc351f9 100644
--- a/atomica/migration.py
+++ b/atomica/migration.py
@@ -812,3 +812,44 @@ def _convert_framework_columns(framework):
 def _parset_add_initialization(parset):
     parset.initialization = None
     return parset
+
+
+@migration("ProjectData", "1.30.0", "1.31.0", "Add pop types attribute and default_all")
+def _projectdata_add_types_default(D):
+
+    if not hasattr(D,'_pop_types'):
+        # We have to check for existence because
+        # Are any population types present?
+        pop_types = list(x['type'] for x in D.pops.values() if 'type' in x)
+        if not pop_types:
+            pop_types = [FS.DEFAULT_POP_TYPE]
+
+        for pop, spec in D.pops.items():
+            if 'type' not in spec:
+                spec['type'] = pop_types[0]
+        D._pop_types = list({x['type']:None for x in D.pops.values()}.keys()) # Using a dictionary here allows for order-preserving unique
+
+    for tdve in D.tdve.values():
+
+        # Fix the '_add_pop_type' migration which added tdve.type instead of tdve.pop_type
+        if hasattr(tdve, 'type'):
+            tdve.pop_type = tdve.type
+            delattr(tdve, 'type')
+        elif not hasattr(tdve, 'pop_type'):
+            tdve.pop_type = D._pop_types[0] # The majority of the time, if the pop_type is missing, the default needs to be added
+
+        # Also add the default_all attribute
+        tdve.default_all = False
+
+        # Some old TDVE tables have a lowercase 'n.a.' instead of the correct default value
+        for ts in tdve.ts.values():
+            if ts.units == FS.DEFAULT_SYMBOL_INAPPLICABLE.lower():
+                ts.units = FS.DEFAULT_SYMBOL_INAPPLICABLE
+
+        # A TDVE should always have some allowed units, if the allowed units have not been populated, then draw
+        # them from the ts entries. There are some older saved files that may have no allowed units even though the ts
+        # entries themselves have units.
+        if not hasattr(tdve, 'allowed_units') or not tdve.allowed_units:
+            tdve.allowed_units = list({x.units:None for x in tdve.ts.values()}.keys())
+
+    return D
diff --git a/tests/test_tox_migration.py b/tests/test_tox_migration.py
index 63393827..d3219bc4 100644
--- a/tests/test_tox_migration.py
+++ b/tests/test_tox_migration.py
@@ -33,6 +33,11 @@ def test_migration():
     P.databook.save(tmpdir / "migration_test_databook_save")  # Save original databook
     P.data.save(tmpdir / "migration_test_data_save")  # Re-convert data to spreadsheet and save
 
+    # Test databook operations
+    P.data.add_pop('test_pop','test_pop') # Requires migration
+    P.data.add_transfer('test_transfer','test_transfer')
+    P.data.add_interaction('test_interaction','test_interaction')
+
 
 if __name__ == "__main__":
     test_migration()

From 5d26e8954451ea1d28a3abe9e2039b86829b5a9e Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:06:09 +1000
Subject: [PATCH 115/161] Increment version, update logic for manually edited
 tables

---
 CHANGELOG.md       |  4 ++++
 atomica/data.py    | 17 ++++++++++-------
 atomica/excel.py   |  4 ++--
 atomica/version.py |  2 +-
 4 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 471965e1..c1f60ebb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,10 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.0] - 2025-02-18
+
+- Framework definitions of compartments, characteristics, and parameters support a new column 'databook default all'. If set to 'y', then when a databook is produced, the data entry table will only contain a record for 'All' instead of having population-specific rows. Further manual editing of the tables is supported as normal. 
+
 ## [1.30.0] - 2025-02-17
 
 - Automatic calibration can now selectively weight parts of the time series to select or prioritise a subset of time points.
diff --git a/atomica/data.py b/atomica/data.py
index 6737645b..8f7b5eb9 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -63,7 +63,6 @@ def __init__(self, framework):
         self._book = None  #: Temporary storage for the workbook while writing a databook
         self._references = None  #: Temporary storage for cell references while writing a databook
 
-
     def __setstate__(self, d):
         from .migration import migrate
 
@@ -71,7 +70,6 @@ def __setstate__(self, d):
         projectdata = migrate(self)
         self.__dict__ = projectdata.__dict__
 
-
     def tables(self):
         """
         Return iterator over all TDVE and TDC tables
@@ -270,7 +268,7 @@ class instance (e.g. if creating a new databook).
                     pop_type = spec.get("population type")
                     databook_order = spec.get("databook order")
                     full_name = spec["display name"]
-                    default_all = spec.get("databook default all")
+                    default_all = spec["databook default all"] == "y"
                     allowed_units = [framework.get_databook_units(full_name)]
 
                     if pd.isna(databook_order):
@@ -287,8 +285,10 @@ class instance (e.g. if creating a new databook).
                             data.tdve[spec.name].tvec = []  # If parameter is timed, don't show any years
                             data.tdve[spec.name].write_uncertainty = False  # Don't show uncertainty for timed parameters. In theory users could manually add the column and sample over it, but because the duration is rounded to the timestep, it's likely to have confusing stepped effects
                     data.tdve[spec.name].pop_type = pop_type
+
                     if default_all:
-                        data.tdve[spec.name].ts['All'] = TimeSeries(units=allowed_units[0])
+                        # add_pop normally adds TDVE rows, but it won't operate on any TDVEs that default to 'All' so we need to add the 'All' rows here
+                        data.tdve[spec.name].ts["All"] = TimeSeries(units=allowed_units[0])
 
         # Now convert pages to full names and sort them into the correct order
         for _, spec in framework.sheets["databook pages"][0].iterrows():
@@ -640,7 +640,7 @@ def add_pop(self, code_name: str, full_name: str, pop_type: str = None) -> None:
 
         :param code_name: The code name for the new population
         :param full_name: The full name/label for the new population
-        :param pop_type: String with the population type code name
+        :param pop_type: String with the population type code name (optional) - default is the type of the first population
 
         """
 
@@ -666,8 +666,11 @@ def add_pop(self, code_name: str, full_name: str, pop_type: str = None) -> None:
         for tdve in self.tdve.values():
             # Since TDVEs in databooks must have the unit set in the framework, all ts objects must share the same units
             # And, there is only supposed to be one type of unit allowed for TDVE tables (if the unit is empty, it will be 'N.A.')
-            # so can just pick the first of the allowed units
-            if tdve.pop_type == pop_type and not (hasattr(tdve, 'default_all') and tdve.default_all):
+            # so can just pick the first of the allowed units. We will add the population row if the pop type matches and if
+            # the TDVE is either not a 'default_all' or if the user has removed the 'All' row from the TDVE despite it being default_all
+            if tdve.pop_type != pop_type or (tdve.default_all and ("All" in tdve.ts or "all" in tdve.ts)):
+                continue
+            else:
                 tdve.ts[code_name] = TimeSeries(units=tdve.allowed_units[0])
 
     def rename_pop(self, existing_code_name: str, new_code_name: str, new_full_name: str) -> None:
diff --git a/atomica/excel.py b/atomica/excel.py
index 9501b184..304490ab 100644
--- a/atomica/excel.py
+++ b/atomica/excel.py
@@ -906,8 +906,8 @@ def __init__(self, name, tvec: np.array = None, ts=None, allowed_units: list = N
         self.write_units = None  #: Write a column for units (if None, units will be written if any of the TimeSeries have units)
         self.write_uncertainty = None  #: Write a column for uncertainty (if None, uncertainty will be written if any of the TimeSeries have uncertainty)
         self.write_assumption = None  #: Write a column for assumption/constant (if None, assumption will be written if any of the TimeSeries have an assumption)
-        
-        self.default_all = default_all #If the TDVE should by default only have the 'All' population in the databook e.g. the same value for all populations unless exceptions are manually entered
+
+        self.default_all = default_all  #: Record whether the framework specifies that this TDVE should default to having an 'All' row instead of population-specific rows (the user can manually modify further)
 
     def __repr__(self):
         output = sc.prepr(self)
diff --git a/atomica/version.py b/atomica/version.py
index 91aaedea..6d57766a 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -6,6 +6,6 @@
 
 import sciris as sc
 
-version = "1.30.0"
+version = "1.31.0"
 versiondate = "2025-02-17"
 gitinfo = sc.gitinfo(__file__)

From d8d59260b03f49f44366b45924068d467a13d630 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:06:25 +1000
Subject: [PATCH 116/161] Update formatting

---
 atomica/framework.py        |  6 ++++++
 atomica/migration.py        | 22 +++++++++++-----------
 atomica/plotting.py         | 18 +++++++++---------
 tests/test_tox_databooks.py | 22 ++++++++++++----------
 tests/test_tox_migration.py |  6 +++---
 5 files changed, 41 insertions(+), 33 deletions(-)

diff --git a/atomica/framework.py b/atomica/framework.py
index 7028bcd0..76d13de1 100644
--- a/atomica/framework.py
+++ b/atomica/framework.py
@@ -632,6 +632,7 @@ def _sanitize_compartments(self) -> None:
             "is source": "n",
             "is junction": "n",
             "databook page": None,
+            "databook default all": "n",
             "default value": None,
             "population type": None,
             "databook order": None,  # Default is for it to be randomly ordered if the databook page is not None
@@ -643,6 +644,7 @@ def _sanitize_compartments(self) -> None:
             "is sink": {"y", "n"},
             "is source": {"y", "n"},
             "is junction": {"y", "n"},
+            "databook default all": {"y", "n"},
         }
         numeric_columns = ["databook order", "default value"]
 
@@ -723,6 +725,7 @@ def _sanitize_characteristics(self) -> None:
             "default value": None,
             "databook page": None,
             "databook order": None,
+            "databook default all": "n",
             "guidance": None,
             "population type": None,
             "provenance": FS.DEFAULT_PROVENANCE,
@@ -730,6 +733,7 @@ def _sanitize_characteristics(self) -> None:
         valid_content = {
             "display name": None,
             "components": None,
+            "databook default all": {"y", "n"},
         }
         numeric_columns = ["databook order", "default value"]
 
@@ -861,6 +865,7 @@ def _sanitize_parameters(self) -> None:
             "function": None,
             "databook page": None,
             "databook order": None,
+            "databook default all": "n",
             "targetable": "n",
             "guidance": None,
             "timescale": None,
@@ -874,6 +879,7 @@ def _sanitize_parameters(self) -> None:
             "targetable": {"y", "n"},
             "is derivative": {"y", "n"},
             "timed": {"y", "n"},
+            "databook default all": {"y", "n"},
         }
         numeric_columns = ["databook order", "default value", "minimum value", "maximum value", "timescale"]
 
diff --git a/atomica/migration.py b/atomica/migration.py
index fdc351f9..724855a7 100644
--- a/atomica/migration.py
+++ b/atomica/migration.py
@@ -817,26 +817,26 @@ def _parset_add_initialization(parset):
 @migration("ProjectData", "1.30.0", "1.31.0", "Add pop types attribute and default_all")
 def _projectdata_add_types_default(D):
 
-    if not hasattr(D,'_pop_types'):
+    if not hasattr(D, "_pop_types"):
         # We have to check for existence because
         # Are any population types present?
-        pop_types = list(x['type'] for x in D.pops.values() if 'type' in x)
+        pop_types = list(x["type"] for x in D.pops.values() if "type" in x)
         if not pop_types:
             pop_types = [FS.DEFAULT_POP_TYPE]
 
         for pop, spec in D.pops.items():
-            if 'type' not in spec:
-                spec['type'] = pop_types[0]
-        D._pop_types = list({x['type']:None for x in D.pops.values()}.keys()) # Using a dictionary here allows for order-preserving unique
+            if "type" not in spec:
+                spec["type"] = pop_types[0]
+        D._pop_types = list({x["type"]: None for x in D.pops.values()}.keys())  # Using a dictionary here allows for order-preserving unique
 
     for tdve in D.tdve.values():
 
         # Fix the '_add_pop_type' migration which added tdve.type instead of tdve.pop_type
-        if hasattr(tdve, 'type'):
+        if hasattr(tdve, "type"):
             tdve.pop_type = tdve.type
-            delattr(tdve, 'type')
-        elif not hasattr(tdve, 'pop_type'):
-            tdve.pop_type = D._pop_types[0] # The majority of the time, if the pop_type is missing, the default needs to be added
+            delattr(tdve, "type")
+        elif not hasattr(tdve, "pop_type"):
+            tdve.pop_type = D._pop_types[0]  # The majority of the time, if the pop_type is missing, the default needs to be added
 
         # Also add the default_all attribute
         tdve.default_all = False
@@ -849,7 +849,7 @@ def _projectdata_add_types_default(D):
         # A TDVE should always have some allowed units, if the allowed units have not been populated, then draw
         # them from the ts entries. There are some older saved files that may have no allowed units even though the ts
         # entries themselves have units.
-        if not hasattr(tdve, 'allowed_units') or not tdve.allowed_units:
-            tdve.allowed_units = list({x.units:None for x in tdve.ts.values()}.keys())
+        if not hasattr(tdve, "allowed_units") or not tdve.allowed_units:
+            tdve.allowed_units = list({x.units: None for x in tdve.ts.values()}.keys())
 
     return D
diff --git a/atomica/plotting.py b/atomica/plotting.py
index 2f1567e8..1931b996 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -1250,23 +1250,24 @@ def _get_legend_handles(ax, handles, labels):
     if handles is None:
         if ax is None:
             ax = plt.gca()
-        elif isinstance(ax, plt.Figure): # Allows an argument of a figure instead of an axes # pragma: no cover
+        elif isinstance(ax, plt.Figure):  # Allows an argument of a figure instead of an axes # pragma: no cover
             ax = ax.axes[-1]
         handles, labels = ax.get_legend_handles_labels()
-    else: # pragma: no cover
+    else:  # pragma: no cover
         if labels is None:
             labels = [h.get_label() for h in handles]
         else:
             assert len(handles) == len(labels), f"Number of handles ({len(handles)}) and labels ({len(labels)}) must match"
     return ax, handles, labels
 
+
 # Temporary copy of function from Sciris to remove after Sciris update
 def separatelegend(ax=None, handles=None, labels=None, reverse=False, figsettings=None, legendsettings=None):
-    """ Allows the legend of a figure to be rendered in a separate window instead """
+    """Allows the legend of a figure to be rendered in a separate window instead"""
 
     # Handle settings
-    f_settings = sc.mergedicts({'figsize':(4.0,4.8)}, figsettings) # (6.4,4.8) is the default, so make it a bit narrower
-    l_settings = sc.mergedicts({'loc': 'center', 'bbox_to_anchor': None, 'frameon': False}, legendsettings)
+    f_settings = sc.mergedicts({"figsize": (4.0, 4.8)}, figsettings)  # (6.4,4.8) is the default, so make it a bit narrower
+    l_settings = sc.mergedicts({"loc": "center", "bbox_to_anchor": None, "frameon": False}, legendsettings)
 
     # Get handles and labels
     _, handles, labels = _get_legend_handles(ax, handles, labels)
@@ -1274,7 +1275,7 @@ def separatelegend(ax=None, handles=None, labels=None, reverse=False, figsetting
     # Set up new plot
     fig = plt.figure(**f_settings)
     ax = fig.add_subplot(111)
-    ax.set_position([-0.05,-0.05,1.1,1.1]) # This cuts off the axis labels, ha-ha
+    ax.set_position([-0.05, -0.05, 1.1, 1.1])  # This cuts off the axis labels, ha-ha
     ax.set_axis_off()  # Hide axis lines
 
     # A legend renders the line/patch based on the object handle. However, an object
@@ -1290,9 +1291,9 @@ def separatelegend(ax=None, handles=None, labels=None, reverse=False, figsetting
         handles2.append(h2)
 
     # Reverse order, e.g. for stacked plots
-    if reverse: # pragma: no cover
+    if reverse:  # pragma: no cover
         handles2 = handles2[::-1]
-        labels   = labels[::-1]
+        labels = labels[::-1]
 
     # Plot the new legend
     ax.legend(handles=handles2, labels=labels, **l_settings)
@@ -1300,7 +1301,6 @@ def separatelegend(ax=None, handles=None, labels=None, reverse=False, figsetting
     return fig
 
 
-
 def plot_bars(plotdata, stack_pops=None, stack_outputs=None, outer=None, legend_mode=None, show_all_labels=False, orientation="vertical") -> list:
     """
     Produce a bar plot
diff --git a/tests/test_tox_databooks.py b/tests/test_tox_databooks.py
index 0952a698..03110d42 100644
--- a/tests/test_tox_databooks.py
+++ b/tests/test_tox_databooks.py
@@ -180,22 +180,24 @@ def test_databook_all():
     d = at.PlotData(res, outputs=["sus", "ch_prev", "ch_all"], project=P)
     at.plot_series(d, axis="pops", data=P.data)
 
+
 def test_databook_default_all():
-    F = at.ProjectFramework(testdir/'sir_framework_default_all.xlsx')
-    D = at.ProjectData.new(F, np.arange(2000,2005), pops=4, transfers=1)
+    F = at.ProjectFramework(testdir / "sir_framework_default_all.xlsx")
+    D = at.ProjectData.new(F, np.arange(2000, 2005), pops=4, transfers=1)
     D.save(tmpdir / "databook_default_all_test.xlsx")  # Test saving it back
 
-    D.add_pop('new','new')
-    assert list(D.tdve['sus'].ts.keys()) == ['pop_0','pop_1','pop_2','pop_3','new']
-    assert list(D.tdve['ch_prev'].ts.keys()) == ['All']
+    D.add_pop("new", "new")
+    assert list(D.tdve["sus"].ts.keys()) == ["pop_0", "pop_1", "pop_2", "pop_3", "new"]
+    assert list(D.tdve["ch_prev"].ts.keys()) == ["All"]
 
-    D.tdve['contacts'].ts['pop_0'] = D.tdve['contacts'].ts['All']
-    del D.tdve['contacts'].ts['All']
-    D.add_pop('new2','new2')
-    assert list(D.tdve['ch_prev'].ts.keys()) == ['All']
-    assert list(D.tdve['contacts'].ts.keys()) == ['pop_0','new2']
+    D.tdve["contacts"].ts["pop_0"] = D.tdve["contacts"].ts["All"]
+    del D.tdve["contacts"].ts["All"]
+    D.add_pop("new2", "new2")
+    assert list(D.tdve["ch_prev"].ts.keys()) == ["All"]
+    assert list(D.tdve["contacts"].ts.keys()) == ["pop_0", "new2"]
     D.save(tmpdir / "databook_default_all_test_2.xlsx")  # Test saving it back
 
+
 if __name__ == "__main__":
     # test_mixed_years_2()
     # test_mixed_years_1()
diff --git a/tests/test_tox_migration.py b/tests/test_tox_migration.py
index d3219bc4..76c63e4e 100644
--- a/tests/test_tox_migration.py
+++ b/tests/test_tox_migration.py
@@ -34,9 +34,9 @@ def test_migration():
     P.data.save(tmpdir / "migration_test_data_save")  # Re-convert data to spreadsheet and save
 
     # Test databook operations
-    P.data.add_pop('test_pop','test_pop') # Requires migration
-    P.data.add_transfer('test_transfer','test_transfer')
-    P.data.add_interaction('test_interaction','test_interaction')
+    P.data.add_pop("test_pop", "test_pop")  # Requires migration
+    P.data.add_transfer("test_transfer", "test_transfer")
+    P.data.add_interaction("test_interaction", "test_interaction")
 
 
 if __name__ == "__main__":

From 23ac4244a5224e6b808e07e543f815d3b8ef7c47 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:09:35 +1000
Subject: [PATCH 117/161] Update PyPI url

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 2316c6cb..abea6312 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
-# Atomica
+1# Atomica
 
 [![Build Status](https://dev.azure.com/AtomicaTeam/Atomica/_apis/build/status/atomicateam.atomica?branchName=master)](https://dev.azure.com/AtomicaTeam/Atomica/_build/latest?definitionId=1&branchName=master)
 
-[![PyPi version](https://badgen.net/pypi/v/atomica/)](https://pypi.com/project/atomica)
+[![PyPi version](https://badgen.net/pypi/v/atomica/)](https://pypi.org/project/atomica)
 
 Atomica is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
 

From aef5b474ed4c0719c0efd253c33cc5328878e970 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 11:33:33 +1000
Subject: [PATCH 118/161] Add version attribute

---
 atomica/data.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/atomica/data.py b/atomica/data.py
index 8f7b5eb9..60763755 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -21,6 +21,7 @@
 from collections import defaultdict
 import pandas as pd
 import itertools
+from .version import version, gitinfo
 
 __all__ = ["InvalidDatabook", "ProjectData"]
 
@@ -63,6 +64,9 @@ def __init__(self, framework):
         self._book = None  #: Temporary storage for the workbook while writing a databook
         self._references = None  #: Temporary storage for cell references while writing a databook
 
+        self.version = version  #: Current Atomica version
+        self.gitinfo = sc.dcp(gitinfo)  #: Atomica Git version information, if being run in a Git repository
+
     def __setstate__(self, d):
         from .migration import migrate
 

From 13b1117a8c2d49eb031d0ff4992a1eba1cb50327 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 18 Feb 2025 12:51:32 +1000
Subject: [PATCH 119/161] Fix typo

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index abea6312..59573fd3 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-1# Atomica
+# Atomica
 
 [![Build Status](https://dev.azure.com/AtomicaTeam/Atomica/_apis/build/status/atomicateam.atomica?branchName=master)](https://dev.azure.com/AtomicaTeam/Atomica/_build/latest?definitionId=1&branchName=master)
 

From ffa16e730313041394e13fa6780f0197eb966c9d Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 20 Feb 2025 14:03:38 +0100
Subject: [PATCH 120/161] Add deaths calibration to all worked example steps

---
 docs/tutorial/T7/calibrations/WE_1.yaml | 3 +--
 docs/tutorial/T7/calibrations/WE_2.yaml | 5 +++++
 docs/tutorial/T7/calibrations/WE_3.yaml | 7 ++++++-
 docs/tutorial/T7/calibrations/WE_4.yaml | 7 ++++++-
 docs/tutorial/T7/calibrations/WE_5.yaml | 5 +++++
 docs/tutorial/T7/calibrations/WE_6.yaml | 5 +++++
 docs/tutorial/T7/calibrations/WE_7.yaml | 5 +++++
 docs/tutorial/T7/calibrations/WE_8.yaml | 1 -
 8 files changed, 33 insertions(+), 5 deletions(-)

diff --git a/docs/tutorial/T7/calibrations/WE_1.yaml b/docs/tutorial/T7/calibrations/WE_1.yaml
index d99f3331..e93ca5e4 100644
--- a/docs/tutorial/T7/calibrations/WE_1.yaml
+++ b/docs/tutorial/T7/calibrations/WE_1.yaml
@@ -1,7 +1,6 @@
-
-
 calibration:
     Population calibration:
+
         Match population sizes:
             adjustables: b_rate, mig_rate
             measurables: alive
diff --git a/docs/tutorial/T7/calibrations/WE_2.yaml b/docs/tutorial/T7/calibrations/WE_2.yaml
index d2aa7e29..00af8391 100644
--- a/docs/tutorial/T7/calibrations/WE_2.yaml
+++ b/docs/tutorial/T7/calibrations/WE_2.yaml
@@ -1,9 +1,14 @@
 calibration:
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables:
                 b_rate, mig_rate:
                     lower_bound: 0.1
                     upper_bound: 10
             measurables: alive
+
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
diff --git a/docs/tutorial/T7/calibrations/WE_3.yaml b/docs/tutorial/T7/calibrations/WE_3.yaml
index fa87a1c3..6e90a45e 100644
--- a/docs/tutorial/T7/calibrations/WE_3.yaml
+++ b/docs/tutorial/T7/calibrations/WE_3.yaml
@@ -4,6 +4,11 @@ calibration:
 
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables: b_rate, mig_rate
-            measurables: alive
\ No newline at end of file
+            measurables: alive
+
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_4.yaml b/docs/tutorial/T7/calibrations/WE_4.yaml
index 77560b42..a422925a 100644
--- a/docs/tutorial/T7/calibrations/WE_4.yaml
+++ b/docs/tutorial/T7/calibrations/WE_4.yaml
@@ -4,6 +4,11 @@ calibration:
 
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables: (b_rate, 0-4), mig_rate
-            measurables: alive
\ No newline at end of file
+            measurables: alive
+
+        Match deaths:
+            adjustables: d_rate
+            measurables: deaths
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_5.yaml b/docs/tutorial/T7/calibrations/WE_5.yaml
index 3cdce1e6..da9fa653 100644
--- a/docs/tutorial/T7/calibrations/WE_5.yaml
+++ b/docs/tutorial/T7/calibrations/WE_5.yaml
@@ -4,6 +4,7 @@ calibration:
 
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables:
               (b_rate, 0-4):
@@ -11,3 +12,7 @@ calibration:
               mig_rate:
                 lower_bound: 0.1
             measurables: alive
+
+        Match deaths:
+          adjustables: d_rate
+          measurables: deaths
diff --git a/docs/tutorial/T7/calibrations/WE_6.yaml b/docs/tutorial/T7/calibrations/WE_6.yaml
index 70669139..02a6554c 100644
--- a/docs/tutorial/T7/calibrations/WE_6.yaml
+++ b/docs/tutorial/T7/calibrations/WE_6.yaml
@@ -4,6 +4,7 @@ calibration:
 
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables:
               (b_rate, 0-4):
@@ -12,5 +13,9 @@ calibration:
                 lower_bound: 0.1
             measurables: alive
 
+        Match deaths:
+          adjustables: d_rate
+          measurables: deaths
+
         set_initialization:
             init_year: 2025
\ No newline at end of file
diff --git a/docs/tutorial/T7/calibrations/WE_7.yaml b/docs/tutorial/T7/calibrations/WE_7.yaml
index 1a3c6dc2..1628e563 100644
--- a/docs/tutorial/T7/calibrations/WE_7.yaml
+++ b/docs/tutorial/T7/calibrations/WE_7.yaml
@@ -4,6 +4,7 @@ calibration:
 
     Population calibration:
         repeats: 10
+
         Match population sizes:
             adjustables:
               (b_rate, 0-4):
@@ -12,6 +13,10 @@ calibration:
                 lower_bound: 0.1
             measurables: alive
 
+        Match deaths:
+          adjustables: d_rate
+          measurables: deaths
+
         clear_initialization: True
 
         set_initialization:
diff --git a/docs/tutorial/T7/calibrations/WE_8.yaml b/docs/tutorial/T7/calibrations/WE_8.yaml
index 4256b51b..849d5750 100644
--- a/docs/tutorial/T7/calibrations/WE_8.yaml
+++ b/docs/tutorial/T7/calibrations/WE_8.yaml
@@ -19,7 +19,6 @@ calibration:
             adjustables: d_rate
             measurables: deaths
 
-
         clear_initialization: True
 
         set_initialization:

From 0e789fc8422fafed2be16983993592373c7d4e5f Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 20 Feb 2025 14:08:55 +0100
Subject: [PATCH 121/161] Change initialisation spelling to initialization in
 YAML files

---
 .../T7/calibrations/T7_YAML_7_transience_initialisation.yaml    | 2 +-
 docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml b/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
index d0d4e37e..508c0749 100644
--- a/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
+++ b/docs/tutorial/T7/calibrations/T7_YAML_7_transience_initialisation.yaml
@@ -3,7 +3,7 @@
 
 
 
-    set_initialisation:
+    set_initialization:
         init_year: 2010
         constant_parset: True
 
diff --git a/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml b/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
index 6dd34940..314f0069 100644
--- a/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
+++ b/docs/tutorial/T7/calibrations/T7_YAML_8_clearing_init.yaml
@@ -1,6 +1,6 @@
 
 calibration:
-    clear_initialisation: True
+    clear_initialization: True
 
 
 

From e30d0848f75f28a926fbc592fb07ad7aa20509d1 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Fri, 21 Feb 2025 18:35:36 +0100
Subject: [PATCH 122/161] Update T7 notebook

-Update text, fix typos
-Fix broken links and paths
-Fix worked example table
-Consistent spelling of 'initialization'
---
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 115 +++++++++---------
 1 file changed, 57 insertions(+), 58 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index ff2b517e..321f7b87 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -47,7 +47,7 @@
    "source": [
     "## Basic calibration example\n",
     "\n",
-    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `assets/T7`. "
+    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `T7/assets`."
    ]
   },
   {
@@ -260,7 +260,7 @@
     "\n",
     "- Calibration block\n",
     "- Initialization block\n",
-    "- Clear intialization block\n",
+    "- Intialization clearing block\n",
     "- Saving block"
    ]
   },
@@ -271,11 +271,11 @@
    "source": [
     "### Calibration blocks\n",
     "\n",
-    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making a call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
     "\n",
     "#### Adjustable and measurable settings\n",
     "\n",
-    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default settings for adjustables and measurables. For more flexibility, we can customise the settings to be used for the optimisation. The settings for the adjustables and measurables directly map to the options supported by `P.calibrate()`. \n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
     "\n",
     "Each adjustable has:\n",
     "\n",
@@ -283,21 +283,21 @@
     "- `pop_name`: Population to calibrate (default: all populations)\n",
     "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
     "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
-    "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable’s current `y_factor` in the parset)\n",
+    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
     "\n",
     "Each measurable has:\n",
     "\n",
     "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
     "- `pop_name`: Population to use for calibration (default: all populations)\n",
-    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](/general/YAML_calibration.html#Measurable-Weights) for further details.\n",
+    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
     "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
     "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
     "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
     "\n",
-    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections). The `cal_start` and `cal_end` years can be set per measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration. \n",
-    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so: \n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections \"Specifying settings in outer sections\"). The `cal_start` and `cal_end` years can be set independently for each measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration.\n",
+    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so:\n",
     "\n",
-    "    calibration: \n",
+    "    calibration:\n",
     "        Match population sizes:\n",
     "            adjustables: \n",
     "            \tb_rate: \n",
@@ -316,6 +316,7 @@
    "cell_type": "markdown",
    "id": "13",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -324,7 +325,7 @@
    "source": [
     "#### Specifying Populations\n",
     "\n",
-    "In some cases, you may want to only set adjustables or evaluation measurables for a specific population, or you may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so: \n",
+    "In some cases, you may want to only set adjustables or measurables for a specific population, or you may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so:\n",
     "\n",
     "    calibration: \n",
     "        Match population sizes:\n",
@@ -353,7 +354,7 @@
     "                \tcal_start: 2000\n",
     "               \tcal_end: 2040\n",
     "\n",
-    "Finally, this same syntax can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on calibrating in these cases, see [Calibrating transfers and interactions](/general/YAML_calibration.html#Calibrating-transfers-and-interactions)."
+    "Finally, the same syntax we use to calibrate populations can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on how to calibrate in these cases, see the documentation on [Calibrating transfers and interactions](https://atomica.tools/docs/master/general/YAML_calibration.html#Calibrating-transfers-and-interactions \"Calibrating transfers and interactions\")."
    ]
   },
   {
@@ -366,7 +367,7 @@
     "tags": []
    },
    "source": [
-    "### Setting initialisations\n",
+    "### Setting initializations\n",
     "\n",
     "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
     "\n",
@@ -379,25 +380,25 @@
     "\n",
     "In some models, this can be treated as a 'burn-in' period and the initial part of the simulation can simply be discarded. However, there is a risk that being too far from the correct initialization results in contamination of the estimates of parameters during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of the transient on the calibration. \n",
     "\n",
-    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, or might limit the amount of data used for the calibraiton.\n",
+    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, or might limit the amount of data used for the calibration.\n",
     "\n",
-    "An alternative approach is to override the initialisation for our calibration. Initialisations work by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are mostly constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will avoid the initial transient in the model. \n",
+    "An alternative approach is to override the initialization for our calibration. initializations work by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are mostly constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will avoid the initial transient in the model.\n",
     "\n",
     "\n",
     "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
     "\n",
-    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](/general/YAML_calibration.html).\n",
+    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"Documentation\").\n",
     "\n",
-    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialisation`. Under this title, we can specify further settings: \n",
+    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialization`. Under this title, we can specify further settings:\n",
     "\n",
     "- `init_year` (required): The year to use to take the compartment sizes from. The simulation will be run up to this year\n",
-    "- `constant_parset` (default: False): Whether to use a constant parset for the initialisation, and which year to use in `parset.make_constant()`. It can be a Boolean (True/False) or numerical value (representing the year from which to draw the constant values for the parset, defaults to the same year as `sim_start`).\n",
+    "- `constant_parset` (default: False): Whether to use a constant parset for the initialization, and which year to use in `parset.make_constant()`. It can be a Boolean (True/False) or numerical value (representing the year from which to draw the constant values for the parset, defaults to the same year as `sim_start`).\n",
     "\n",
     "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
     "\n",
     "```\n",
     "calibration:\n",
-    "    set_initialisation:\n",
+    "    set_initialization:\n",
     "        init_year: 2030\n",
     "```\n",
     "\n",
@@ -405,7 +406,7 @@
     "\n",
     "```\n",
     "calibration:\n",
-    "    set_initialisation:\n",
+    "    set_initialization:\n",
     "        init_year: 2030\n",
     "        constant_parset: True\n",
     "```\n",
@@ -415,7 +416,7 @@
     "\n",
     "```\n",
     "calibration:\n",
-    "    set_initialisation:\n",
+    "    set_initialization:\n",
     "        init_year: 2030\n",
     "        constant_parset: 2005\n",
     "```\n"
@@ -426,25 +427,25 @@
    "id": "15",
    "metadata": {},
    "source": [
-    "### Clearing initialisations\n",
+    "### Clearing initializations\n",
     "\n",
-    "If we have previously set an initialisation in our calibration algorithm, and then set another initialisation later in the YAML file, it uses information from the previous initialisation to calculate the next, since the new simulation will start from the initial compartment sizes calculated in the previous initialization.\n",
+    "If we have previously set an initialization in our calibration algorithm, and then set another initialization later in the YAML file, it uses information from the previous initialization to calculate the next, since the new simulation will start from the initial compartment sizes calculated in the previous initialization.\n",
     "\n",
-    "Sometimes we might want to calculate a new initialisation from scratch, without using the previous initialisation as a starting point. This could be useful if we have done some calibration steps between the previous initialisation and now, in which case the y-factors will have changed, and we might be better off using a different starting point. \n",
+    "Sometimes we might want to calculate a new initialization from scratch, without using the previous initialization as a starting point. This could be useful if we have done some calibration steps between the previous initialization and now, in which case the y-factors will have changed, and we might be better off using a different starting point.\n",
     "\n",
-    "To do this in the YAML file, we can add a section titled `clear_initialisation`, followed by a boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. For example:\n",
+    "To do this in the YAML file, we can add a section titled `clear_initialization`, followed by a boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. For example:\n",
     "\n",
     "    calibration:\n",
-    "        set_initialisation 1:\n",
+    "        set_initialization 1:\n",
     "    \t\tinit_year: 2030\n",
     "\n",
     "        Match population sizes:\n",
     "            adjustables: b_rate, mig_rate\n",
     "        \tmeasurables: alive\n",
     "        \n",
-    "    \tclear_initialisation: True\n",
+    "    \tclear_initialization: True\n",
     "        \n",
-    "        set_initialisation 2:\n",
+    "        set_initialization 2:\n",
     "    \t\tinit_year: 2030"
    ]
   },
@@ -469,13 +470,11 @@
     "\n",
     "    \tsave_calibration: epi_calibration.xlsx\n",
     "        \n",
-    "Note that when we save a calibration, if initial compartment sizes have been explicitly specified by using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus include the initial compartment sizes. \n",
+    "Note that when we save a calibration, if initial compartment sizes have been explicitly specified by using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus include the initial compartment sizes.\n",
     "\n",
     "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, you can use the `save_intermediate_calibrations` option when running the calibration e.g.\n",
     "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
-    "\n",
-    "For more information on intermediate calibrations, see the relevant [documentation section](/general/YAML_calibration.html#saving-intermediate-calibrations \"Saving intermediate calibrations\")."
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)"
    ]
   },
   {
@@ -492,7 +491,7 @@
     "Some settings that this feature is commonly used for include: \n",
     "\n",
     "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for \n",
-    "- `stepsize`: Initial stepsize, i.e. how much the y-factors will be incremented/decreased by in the optimisation algorithm.\n",
+    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by in the optimisation algorithm.\n",
     "\n",
     "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
     "\n",
@@ -538,7 +537,7 @@
     "\n",
     "### Adjustables/measurables settings\n",
     "\n",
-    "Finally, here is an example of how to use the adjustables and measurables settings outside of a calibration block: \n",
+    "Finally, here is an example of how to use the adjustables and measurables settings outside of a calibration block:\n",
     "\n",
     "    calibration:\n",
     "        Population calibration:\n",
@@ -582,7 +581,7 @@
     "    import yaml\n",
     "    calibration_yaml =  yaml.load(file, Loader=yaml.FullLoader)\n",
     "    \n",
-    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programatically prior to running the calibration. "
+    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programmatically prior to running the calibration."
    ]
   },
   {
@@ -592,7 +591,7 @@
    "source": [
     "## Exercise: worked example\n",
     "\n",
-    "Now that we understand what all of the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution. \n",
+    "Now that we understand what all the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution.\n",
     "\n",
     "_**Question 1.** We want to do a basic population calibration, where we calibrate the death rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
     "\n",
@@ -600,18 +599,18 @@
     "\n",
     "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
     "\n",
-    "| Framework Sheet | Code Name | Display Name | Databook Page |\n",
-    "| :---------------- | :------: | :------: | ----: |\n",
-    "|COMPARTMENTS | <td colspan=3> |\n",
-    "| |birth\t\t|Birth    \t\t\t\t\t\t|none|\n",
-    "| |death   \t|Death    \t\t\t\t\t\t |none|\n",
-    "| PARAMETERS | <td colspan=3> |\n",
-    "| |b_rate\t\t|Birth rate     \t\t\t\t|demographic|\n",
-    "| |deaths\t\t|All-cause deaths    \t\t\t|demographic|\n",
-    "| |d_rate\t\t|Background mortality rate    \t|demographic|\n",
-    "| |mig_rate\t|Migration Rate    \t\t\t\t|demographic|\n",
+    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
+    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
+    "| COMPARTMENTS    | <td colspan=3> |\n",
+    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
+    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
+    "| PARAMETERS      | <td colspan=3> |\n",
+    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
+    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
+    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
+    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
     "| CHARACTERISTICS | <td colspan=3> |\n",
-    "| |alive\t|\tTotal population\t\t| demographic"
+    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
    ]
   },
   {
@@ -619,7 +618,7 @@
    "id": "20",
    "metadata": {},
    "source": [
-    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the the YAML file?_\n",
+    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the YAML file?_\n",
     "\n",
     "**Hint**: Use the `repeats` keyword.\n",
     "\n",
@@ -639,15 +638,15 @@
     "\n",
     "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
     "\n",
-    "_**Question 7.** We also want to clear the previous initialisation every time we make a new one, instead of using information from the previous initialisation. How can we update the YAML file to reflect this?_\n",
+    "_**Question 7.** We also want to clear the previous initialization every time we make a new one, instead of using information from the previous initialization. How can we update the YAML file to reflect this?_\n",
     "\n",
-    "**Hint**: Use `clear_initialisation`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
+    "**Hint**: Use `clear_initialization`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
     "\n",
     "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
     "\n",
     "**Hint**: Use `save_calibration` and specify a `fname`.\n",
     "\n",
-    "All solutions to the worked example are in the `Worked_example` folder in code repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result: \n",
+    "All solutions to the worked example are in the `docs/tutorial/T7/calibrations` folder of the Atomica repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result:\n",
     "\n",
     "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
    ]
@@ -661,17 +660,17 @@
     "\n",
     "### Epidemiological calibration\n",
     "\n",
-    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration can be found in the `Worked_example` folder in code repository, in the `typ_calibration_instructions.yaml` file. \n",
+    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration instructions (`typ_calibration_instructions.yaml`) can be found in the Atomica code repository, at `docs/tutorial/T7/calibrations`.\n",
     "\n",
     "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
     "\n",
-    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly inmpact the population calibration results. \n",
+    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly impact the population calibration results.\n",
     "\n",
-    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030. \n",
+    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030.\n",
     "\n",
     "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
     "\n",
-    "At the end of the typhoid calibration, we set an initialisation in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice. \n",
+    "At the end of the typhoid calibration, we set an initialization in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice.\n",
     "\n",
     "The resulting calibration from running this YAML file is like so:\n",
     "\n",
@@ -679,15 +678,15 @@
     "\n",
     "### Documentation\n",
     "\n",
-    "For additional information on YAML calibration functionality, see [the documentation](/general/YAML_calibration.html).\n"
+    "For more information on using Atomica's YAML calibration functionality, see [the documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"YAML documentation page\").\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:atomica312]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-atomica312-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -699,7 +698,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.13"
   },
   "toc": {
    "base_numbering": 1,

From a12466e162741b86095dfa9e6bb7143755a3de33 Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Tue, 4 Mar 2025 17:41:25 +0100
Subject: [PATCH 123/161] Update YAML tutorial notebooks

Update notebook text, fix broken links
---
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 1451 +++++++++--------
 1 file changed, 732 insertions(+), 719 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index 321f7b87..d6d3d2d0 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -1,719 +1,732 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0",
-   "metadata": {},
-   "source": [
-    "# T7 - YAML calibration\n",
-    "\n",
-    "As we saw in the earlier calibration tutorial, most autocalibrations involve multiple steps, since the optimization algorithm often gets stuck in local minima if we try to optimize too many parameters at once. We could run each of these steps ourselves through separate calls to `P.calibrate()` – however, there is significant value in being able to explicitly frame the overall calibration process as an algorithm, as this makes it easier to modify the calibration steps and apply the calibration algorithm across a collection of projects. This is implemented in Atomica through the 'YAML calibration' feature, in which the calibration steps are specified in a file which Atomica can then read and use to execute the calibration.\n",
-    "\n",
-    "## YAML files\n",
-    "\n",
-    "The calibration algorithm files used by Atomica are written in YAML. YAML is a plain-text, human-readable data serialization language used to make configuration files. Essentially, a YAML file can be read into Python variables (dictionaries, lists, strings) which in turn can be used as arguments to Python functions. Here is an example of how variables can be specified in a YAML file:\n",
-    "\n",
-    "```\n",
-    " foo: a string\n",
-    " bar: 1\n",
-    " baz: [a,b,c]\n",
-    " list:\n",
-    "   - i\n",
-    "   - j\n",
-    " nested:\n",
-    "   x: 1\n",
-    "   y: 2\n",
-    " ```\n",
-    "\n",
-    "When parsed into Python, this becomes\n",
-    "\n",
-    "```\n",
-    "{'foo': 'a string',\n",
-    " 'bar': 1,\n",
-    " 'baz': ['a', 'b', 'c'],\n",
-    " 'list': ['i', 'j'],\n",
-    " 'nested': {'x': 1, 'y': 2}}\n",
-    "```\n",
-    "\n",
-    "Using YAML files provides a simple way to define a calibration algorithm in a format that is easy to work with and that Atomica can directly execute. This can cut down the time we spend manually calibrating, or even running autocalibrations. It allows us to conduct reproducible calibration runs, and is also highly scalable, since it allows us to apply the same calibration algorithm in multiple countries or settings.\n",
-    "\n",
-    "The following tutorial outlines how to use the YAML framework that has been developed for Atomica calibration. Specifically, it will cover how to write a YAML configuration file with calibration instructions for Atomica, and how to use this file to execute a calibration. Bear in mind that YAML calibration is not intended to be a standalone tool that will perfectly calibrate any model – rather, it is one part of the calibration toolbox. It can be used to reduce the time spent on calibration by autocalibrating Atomica models to a reasonable level, but additional tweaking may be required to obtain a consistently high calibration quality across all parameters, populations and/or countries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1",
-   "metadata": {},
-   "source": [
-    "## Basic calibration example\n",
-    "\n",
-    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `T7/assets`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import atomica as at\n",
-    "F = at.ProjectFramework('assets/T7_framework.xlsx')\n",
-    "D = at.ProjectData.from_spreadsheet('assets/T7_databook.xlsx', framework=F)\n",
-    "P = at.Project(framework=F,databook=D, do_run=False)\n",
-    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3",
-   "metadata": {},
-   "source": [
-    "In the example above, no calibration has been loaded, so all of the calibration Y-factors are equal to 1, and the model is uncalibrated. We can run a simulation and plot it, to see what our model looks like before calibration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cal = P.make_parset()\n",
-    "res = P.run_sim(parset=cal, result_name = 'Uncalibrated')\n",
-    "d = at.PlotData(res, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
-    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
-    "fig.set_size_inches(10,7)\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5",
-   "metadata": {},
-   "source": [
-    "Two issues with these simulation outputs immediately stand out. First, for the variables and years in which data is available, the model output doesn't match the data very well at all. Second, there is a large sudden change in the values of several variables right at the start of the simulation. In the absence of associated changes in interventions or disease transmission, disease burden is typically much more stable over time, so we would not expect to see such sudden changes at the start of the simulation.\n",
-    "\n",
-    "Tutorial 2 covers some of the detail around how to approach calibration. Although the parameters used for calibration vary from model to model, as a general rule, calibration proceeds by\n",
-    "\n",
-    "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
-    "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
-    "\n",
-    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them (or whether some should be adjusted simultaneously), and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use YAML files to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## Minimal YAML file\n",
-    "\n",
-    "To illustrate how YAML calibration works, let’s start with a simple example of what a YAML file could look like:\n",
-    "\n",
-    "    calibration:\n",
-    "    \tadjustables: b_rate, mig_rate\n",
-    "    \tmeasurables: alive\n",
-    "\n",
-    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
-    "\n",
-    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g., `T7_YAML_1_minimal.yaml`), the calibration can be run using\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
-    "\n",
-    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()` – simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
-    "\n",
-    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>\n",
-    "<img src=\"assets/T7_plot_1_simple_compare.png\" alt=\"Simple calibration comparison\" style=\"width: 500px;\"/>\n",
-    "\n",
-    "As you can see, these plots don't look very different to the uncalibrated simulation results. If we plot the uncalibrated simulation in the same plot as our simple calibration, we can see that there has been a slight change, but not nearly enough to be able to consider this a good calibration. Next, we will illustrate the YAML calibration features that we can use to improve on this initial result. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7",
-   "metadata": {},
-   "source": [
-    "## Sections\n",
-    "\n",
-    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modifies the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file:\n",
-    "\n",
-    "- _actions_, which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains `adjustables` and `measurables`. Other examples of actions are detailed below.\n",
-    "- _sections_, which are containers for actions. Sections can contain attributes, such as how many times to repeat the contents of the section, or they can define settings that are applied to any relevant actions within that section.\n",
-    "\n",
-    "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
-    "\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    Match population sizes:\n",
-    "        adjustables: b_rate, mig_rate\n",
-    "        measurables: alive\n",
-    "    Match deaths:\n",
-    "        adjustables: d_rate\n",
-    "        measurables: deaths\n",
-    "```\n",
-    "\n",
-    "To organize the YAML file further, we could group these into an additional section. Both of these actions affect the overall population calibration, so we could logically group them as follows:\n",
-    "\n",
-    "```\n",
-    "calibration: \n",
-    "    Population calibration:\n",
-    "        Match population sizes:\n",
-    "            adjustables: b_rate, mig_rate\n",
-    "            measurables: alive\n",
-    "        Match deaths:\n",
-    "            adjustables: d_rate\n",
-    "            measurables: deaths\n",
-    "```\n",
-    "\n",
-    "The overall structure of this YAML file is thus:\n",
-    "\n",
-    "1. A top-level section titled `calibration`, which has one sub-section (`Population calibration`)\n",
-    "2. A sub-section called `Population calibration`, which in turn contains two actions\n",
-    "3. An action called `Match population sizes`, corresponding to the original calibration step we used to adjust the birth rate and migration rate\n",
-    "4. An action called `Match deaths`, corresponding to the new step of adjusting the death rate\n",
-    "\n",
-    "An action is differentiated from a section in two possible ways:\n",
-    "\n",
-    "- By its contents (e.g. if it contains `adjustables` and `measurables`, then it will be interpreted as a calibration action rather than a section)\n",
-    "- By the title (if the name corresponds to the name of a supported operation, as described below)\n",
-    "\n",
-    "Additionally, actions can never contain any sub-sections, whereas sections do. \n",
-    "Apart from the names of supported operations, sections can be freely named."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8",
-   "metadata": {},
-   "source": [
-    "### Repeating a section\n",
-    "\n",
-    "The *repeats* keyword can be used to loop over any part of the calibration multiple times. We do this by writing `repeats: n` inside a particular section, where *n* is the number of times we would like to loop over that section. All subsections contained in it will also be looped over *n* times. \n",
-    "\n",
-    "    calibration: \n",
-    "        repeats: 2\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                repeats: 2\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "\n",
-    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
-    "\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
-    "\n",
-    "In this way, it is possible for even a very compact YAML file to correspond to a large number of individual autocalibration steps. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9",
-   "metadata": {},
-   "source": [
-    "## Sections vs Actions\n",
-    "\n",
-    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-settings-in-outer-sections). \n",
-    "\n",
-    "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block they are, and they don't contain any sub-sections.\n",
-    "</div>\n",
-    "\n",
-    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading in the YAML file, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import atomica.yaml_calibration \n",
-    "calibration_tree = at.yaml_calibration.build('calibrations/T7_YAML_3_repeats.yaml') \n",
-    "print(calibration_tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11",
-   "metadata": {},
-   "source": [
-    "## Types of Action Blocks\n",
-    "\n",
-    "YAML calibration files can contain the following types of actions, or action blocks: \n",
-    "\n",
-    "- Calibration block\n",
-    "- Initialization block\n",
-    "- Intialization clearing block\n",
-    "- Saving block"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12",
-   "metadata": {},
-   "source": [
-    "### Calibration blocks\n",
-    "\n",
-    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
-    "\n",
-    "#### Adjustable and measurable settings\n",
-    "\n",
-    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
-    "\n",
-    "Each adjustable has:\n",
-    "\n",
-    "- `adj_label` (required): Adjustable parameter codename (can be found in the framework)\n",
-    "- `pop_name`: Population to calibrate (default: all populations)\n",
-    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
-    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
-    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
-    "\n",
-    "Each measurable has:\n",
-    "\n",
-    "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
-    "- `pop_name`: Population to use for calibration (default: all populations)\n",
-    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
-    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
-    "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
-    "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
-    "\n",
-    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections \"Specifying settings in outer sections\"). The `cal_start` and `cal_end` years can be set independently for each measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration.\n",
-    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so:\n",
-    "\n",
-    "    calibration:\n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \tb_rate: \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\t\t\n",
-    "            \tmig_rate: \n",
-    "            \t\tstarting_y_factor: 1.2 \n",
-    "            measurables: \n",
-    "                alive:\n",
-    "                \tcal_start: 2000\n",
-    "                \tcal_end: 2040\n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "#### Specifying Populations\n",
-    "\n",
-    "In some cases, you may want to only set adjustables or measurables for a specific population, or you may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so:\n",
-    "\n",
-    "    calibration: \n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \t(b_rate, 0-4): \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\t\t\n",
-    "            \t(mig_rate, 5-14): \n",
-    "            \t\tstarting_y_factor: 1.2 \n",
-    "            measurables: \n",
-    "                (alive, 0-4), (alive, 5-14):\n",
-    "                \tcal_start: 2000\n",
-    "                \tcal_end: 2040\n",
-    "       \n",
-    "Note that we can specify the same settings for more than one adjustable/measurable at once by placing several parameter names before the colon, separated by commas – this is applicable to any set of adjustables/measurables, not only different populations of the same parameter. For example, we could write: \n",
-    "\n",
-    "    calibration: \n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \t(b_rate, 0-4), mig_rate: \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\n",
-    "                    starting_y_factor: 1.2\t\t \n",
-    "            measurables: \n",
-    "                (alive, 0-4), (alive, 5-14):\n",
-    "                \tcal_start: 2000\n",
-    "               \tcal_end: 2040\n",
-    "\n",
-    "Finally, the same syntax we use to calibrate populations can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on how to calibrate in these cases, see the documentation on [Calibrating transfers and interactions](https://atomica.tools/docs/master/general/YAML_calibration.html#Calibrating-transfers-and-interactions \"Calibrating transfers and interactions\")."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14",
-   "metadata": {
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "### Setting initializations\n",
-    "\n",
-    "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
-    "\n",
-    "- The initial conditions are underdetermined, for example, if there are two strains of a disease with different levels of infectiousness, but data is only available on the total prevalence. In the absence of any other information, Atomica will automatically split up the total prevalence equally between the two strains, when in fact maybe one or the other strain may be dominant, which would change the overall transmission\n",
-    "- The model parameters may not give rise to an equilibrium solution that matches the data, if data sources have been mixed, combined across different years, collected in different ways or different definitions, or because the simplified dynamics in the model don't capture all of the processes in the real world. \n",
-    "\n",
-    "Regardless of the cause, there can often be an initial transience period at the beginning of the simulation, where we can observe abrupt spikes in some parameters until the system reaches equilibrium. \n",
-    "\n",
-    "<img src=\"assets/fig_pre_init.png\" alt=\"Pre-initialization plot\" style=\"width: 400px;\"/>\n",
-    "\n",
-    "In some models, this can be treated as a 'burn-in' period and the initial part of the simulation can simply be discarded. However, there is a risk that being too far from the correct initialization results in contamination of the estimates of parameters during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of the transient on the calibration. \n",
-    "\n",
-    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, or might limit the amount of data used for the calibration.\n",
-    "\n",
-    "An alternative approach is to override the initialization for our calibration. initializations work by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are mostly constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will avoid the initial transient in the model.\n",
-    "\n",
-    "\n",
-    "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
-    "\n",
-    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient will still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases an initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"Documentation\").\n",
-    "\n",
-    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialization`. Under this title, we can specify further settings:\n",
-    "\n",
-    "- `init_year` (required): The year to use to take the compartment sizes from. The simulation will be run up to this year\n",
-    "- `constant_parset` (default: False): Whether to use a constant parset for the initialization, and which year to use in `parset.make_constant()`. It can be a Boolean (True/False) or numerical value (representing the year from which to draw the constant values for the parset, defaults to the same year as `sim_start`).\n",
-    "\n",
-    "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "```\n",
-    "\n",
-    "Setting the initialization year with a constant parset, using the parameter values from the `sim_start` year:\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "        constant_parset: True\n",
-    "```\n",
-    "\n",
-    "Setting the initialization year with a constant parset, using the parameter values from a specific year:\n",
-    "\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "        constant_parset: 2005\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15",
-   "metadata": {},
-   "source": [
-    "### Clearing initializations\n",
-    "\n",
-    "If we have previously set an initialization in our calibration algorithm, and then set another initialization later in the YAML file, it uses information from the previous initialization to calculate the next, since the new simulation will start from the initial compartment sizes calculated in the previous initialization.\n",
-    "\n",
-    "Sometimes we might want to calculate a new initialization from scratch, without using the previous initialization as a starting point. This could be useful if we have done some calibration steps between the previous initialization and now, in which case the y-factors will have changed, and we might be better off using a different starting point.\n",
-    "\n",
-    "To do this in the YAML file, we can add a section titled `clear_initialization`, followed by a boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. For example:\n",
-    "\n",
-    "    calibration:\n",
-    "        set_initialization 1:\n",
-    "    \t\tinit_year: 2030\n",
-    "\n",
-    "        Match population sizes:\n",
-    "            adjustables: b_rate, mig_rate\n",
-    "        \tmeasurables: alive\n",
-    "        \n",
-    "    \tclear_initialization: True\n",
-    "        \n",
-    "        set_initialization 2:\n",
-    "    \t\tinit_year: 2030"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16",
-   "metadata": {},
-   "source": [
-    "### Saving calibrations\n",
-    "\n",
-    "Throughout a YAML calibration, we might wish to save the calibration state at specific points in the calibration process. For example, if our YAML file has a population section and an epidemiological section, we might want to save the calibration after the population calibration section so we can see the progress made up until that point, or otherwise isolate the effect that different parts of the algorithm are having on the calibration. To save a calibration, we simply make a section titled `save_calibration`. Under the title, we can indicate the filename we wish to save the calibration to, either by providing it directly, or by using the keyword `fname` - both examples are shown below:\n",
-    "\n",
-    "    calibration:\n",
-    "    \tPopulation section: \n",
-    "    \t\t[…]\n",
-    "    \n",
-    "    \tsave_calibration: \n",
-    "    \t\tfname: pop_calibration.xlsx\n",
-    "    \n",
-    "    \tEpidemiology section: \n",
-    "    \t\t[…]\n",
-    "\n",
-    "    \tsave_calibration: epi_calibration.xlsx\n",
-    "        \n",
-    "Note that when we save a calibration, if initial compartment sizes have been explicitly specified by using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus include the initial compartment sizes.\n",
-    "\n",
-    "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, you can use the `save_intermediate_calibrations` option when running the calibration e.g.\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17",
-   "metadata": {},
-   "source": [
-    "## Specifying settings in outer sections\n",
-    "\n",
-    "We saw previously that sections (i.e. YAML blocks that don't correspond to actions) don't directly modify the calibration, and are mainly used to structure the YAML file. However, it is possible to determine calibration settings inside a section, such that they are passed down to any subsections or action blocks contained within. Another way to think of this is that an action block will inherit any settings that are defined in any of its parent sections.\n",
-    "\n",
-    "This can simplify the process of writing YAML files where we want to override the default settings in several action blocks, as it allows us to specify those settings once in a parent section, rather than repeatedly in every action block. Additionally, this feature is hierarchical, so settings that are specified further in (e.g. in the action block itself) will always override those set in a section that is further out, allowing for more flexibility.\n",
-    "\n",
-    "Some settings that this feature is commonly used for include: \n",
-    "\n",
-    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for \n",
-    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by in the optimisation algorithm.\n",
-    "\n",
-    "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
-    "\n",
-    "- `cal_start` and `cal_end`: These can be used to change the calibration timespan for multiple calibration steps at the same time.\n",
-    "- `metric`: Used for when the measurables should use a different non-standard metric for assessing calibration quality. \n",
-    "\n",
-    "We will now provide some examples of how this functionality can be used.\n",
-    "\n",
-    "### Inheritance\n",
-    "\n",
-    "Settings are automatically inherited by all sub-sections. To set a `max_time` of 120 for every calibration step in the entire calibration, we would write the following. This will result in every calibration step being limited to 120 seconds. \n",
-    "\n",
-    "    calibration:\n",
-    "        max_time: 120\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "        \n",
-    "        Epidemiological calibration\n",
-    "            [...]\n",
-    "\n",
-    "### Settings hierarchy\n",
-    "\n",
-    "Inherited settings can be overwritten inside sub-sections (in both sections and actions). Say we wanted all calibration blocks to run for a `max_time` of 120 seconds, except for `Match_deaths`, which we want to run for only 60 seconds. In that case, we can override the setting determined in the parent section by specifying the updated value inside `Match_deaths`:\n",
-    "\n",
-    "    calibration:\n",
-    "        max_time: 120\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                max_time_ 60\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "        \n",
-    "        Epidemiological calibration\n",
-    "            [...]\n",
-    "\n",
-    "### Adjustables/measurables settings\n",
-    "\n",
-    "Finally, here is an example of how to use the adjustables and measurables settings outside of a calibration block:\n",
-    "\n",
-    "    calibration:\n",
-    "        Population calibration:\n",
-    "            upper_bound: 2\n",
-    "            lower_bound: .5\n",
-    "\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables alive\n",
-    "\n",
-    "            Match deaths:\n",
-    "                    adjustables: d_rate\n",
-    "                    measurab\n",
-    "\n",
-    "        Epidemiological calibration:\n",
-    "            [...]\n",
-    "\n",
-    "In this case, the upper and lower bounds have been updated to 2 and 0.5 respectively, for all the adjustables in the `Population calibration` section (`b_rate`, `mig_rate` and `d_rate`)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18",
-   "metadata": {},
-   "source": [
-    "## Running the YAML calibration\n",
-    "\n",
-    "Now that we have finished writing our YAML calibration file, we can proceed to running the calibration. Having loaded a project, `P.calibrate` can then be called directly, passing in the name of the YAML file:\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
-    "\n",
-    "This function supports several additional optional arguments:\n",
-    "\n",
-    "- `savedir` (default: current working directory) - any saved calibrations and logs will be saved into this folder\n",
-    "- `save_intermediate` (default: `False`) - if `True`, this will save all intermediate calibrations\n",
-    "- `max_time` (optional) - override the default\n",
-    "- `log_output` (default: `False`) - if `True`, this will save a text file into the output directory containing all console output (e.g., objective function values)\n",
-    "\n",
-    "The YAML file can also be supplied as a dictionary, which would normally be obtained via\n",
-    "\n",
-    "    import yaml\n",
-    "    calibration_yaml =  yaml.load(file, Loader=yaml.FullLoader)\n",
-    "    \n",
-    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programmatically prior to running the calibration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19",
-   "metadata": {},
-   "source": [
-    "## Exercise: worked example\n",
-    "\n",
-    "Now that we understand what all the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution.\n",
-    "\n",
-    "_**Question 1.** We want to do a basic population calibration, where we calibrate the death rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
-    "\n",
-    "**Hint**: Open the framework and look at the `Compartments`, `Parameters` and `Characteristics` pages. The `Code Names` and `Display Names` show us how we have to write the parameter names in the YAML file, and what quantities they correspond to. Those with a value in the `Databook Page` column have data values supplied, and can therefore be used as measurables.\n",
-    "\n",
-    "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
-    "\n",
-    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
-    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
-    "| COMPARTMENTS    | <td colspan=3> |\n",
-    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
-    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
-    "| PARAMETERS      | <td colspan=3> |\n",
-    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
-    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
-    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
-    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
-    "| CHARACTERISTICS | <td colspan=3> |\n",
-    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20",
-   "metadata": {},
-   "source": [
-    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the YAML file?_\n",
-    "\n",
-    "**Hint**: Use the `repeats` keyword.\n",
-    "\n",
-    "_**Question 3.** Say that, for this particular project, we are only interested in calibrating results from 2005 to 2040. How would we specify this in the YAML file to reduce calibration time?_\n",
-    "\n",
-    "**Hint**: Using the `cal_start` and `cal_end` keywords\n",
-    "\n",
-    "_**Question 4.** When calibrating the birth rate, it only really makes sense to calibrate the 0-4 population. Modify the YAML file to reflect this._\n",
-    "\n",
-    "**Hint**: We place the parameter code name and population name in a tuple.\n",
-    "\n",
-    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set its starting y_factor to 1.2 to speed up the optimisation._\n",
-    "\n",
-    "**Hint**: Use `starting_y_factor`.\n",
-    "\n",
-    "_**Question 6.** We want to avoid the presence of transients in our calibrated simulation. Let’s initialize the calibration in order to eliminate any jumps._\n",
-    "\n",
-    "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
-    "\n",
-    "_**Question 7.** We also want to clear the previous initialization every time we make a new one, instead of using information from the previous initialization. How can we update the YAML file to reflect this?_\n",
-    "\n",
-    "**Hint**: Use `clear_initialization`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
-    "\n",
-    "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
-    "\n",
-    "**Hint**: Use `save_calibration` and specify a `fname`.\n",
-    "\n",
-    "All solutions to the worked example are in the `docs/tutorial/T7/calibrations` folder of the Atomica repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result:\n",
-    "\n",
-    "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21",
-   "metadata": {},
-   "source": [
-    "## Further resources\n",
-    "\n",
-    "### Epidemiological calibration\n",
-    "\n",
-    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration instructions (`typ_calibration_instructions.yaml`) can be found in the Atomica code repository, at `docs/tutorial/T7/calibrations`.\n",
-    "\n",
-    "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
-    "\n",
-    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly impact the population calibration results.\n",
-    "\n",
-    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030.\n",
-    "\n",
-    "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
-    "\n",
-    "At the end of the typhoid calibration, we set an initialization in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice.\n",
-    "\n",
-    "The resulting calibration from running this YAML file is like so:\n",
-    "\n",
-    "<img src=\"assets/T7_plot_4_complete.png\" alt=\"Complete calibration result\" style=\"width: 800px;\"/>\n",
-    "\n",
-    "### Documentation\n",
-    "\n",
-    "For more information on using Atomica's YAML calibration functionality, see [the documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"YAML documentation page\").\n"
-   ]
-  }
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# T7 - YAML calibration\n",
+    "\n",
+    "As we saw in the earlier calibration tutorial, most autocalibrations involve multiple steps, since the optimization algorithm often gets stuck in local minima if we try to optimize too many parameters at once. We could run each of these steps ourselves through separate calls to `P.calibrate()` – however, there is significant value in being able to explicitly frame the overall calibration process as an algorithm, as this makes it easier to modify the calibration steps and apply the calibration algorithm across a collection of projects. This is implemented in Atomica through the 'YAML calibration' feature, in which the calibration steps are specified in a file which Atomica can then read and use to execute the calibration.\n",
+    "\n",
+    "## YAML files\n",
+    "\n",
+    "The calibration algorithm files used by Atomica are written in YAML. YAML is a plain-text, human-readable data serialization language used to make configuration files. Essentially, a YAML file can be read into Python variables (dictionaries, lists, strings) which in turn can be used as arguments to Python functions. Here is an example of how variables can be specified in a YAML file:\n",
+    "\n",
+    "```\n",
+    " foo: a string\n",
+    " bar: 1\n",
+    " baz: [a,b,c]\n",
+    " list:\n",
+    "   - i\n",
+    "   - j\n",
+    " nested:\n",
+    "   x: 1\n",
+    "   y: 2\n",
+    " ```\n",
+    "\n",
+    "When parsed into Python, this becomes\n",
+    "\n",
+    "```\n",
+    "{'foo': 'a string',\n",
+    " 'bar': 1,\n",
+    " 'baz': ['a', 'b', 'c'],\n",
+    " 'list': ['i', 'j'],\n",
+    " 'nested': {'x': 1, 'y': 2}}\n",
+    "```\n",
+    "\n",
+    "Using YAML files provides a simple way to define a calibration algorithm in a format that is easy to work with and that Atomica can directly execute. This can cut down the time we spend manually calibrating, or even running autocalibrations. It allows us to conduct reproducible calibration runs, and is also highly scalable, since it allows us to apply the same calibration algorithm in multiple countries or settings.\n",
+    "\n",
+    "The following tutorial outlines how to use the YAML framework that has been developed for Atomica calibration. Specifically, it will cover how to write a YAML configuration file with calibration instructions for Atomica, and how to use this file to execute a calibration. Bear in mind that YAML calibration is not intended to be a standalone tool that will perfectly calibrate any model – rather, it is one part of the calibration toolbox. It can be used to reduce the time spent on calibration by autocalibrating Atomica models to a reasonable level, but additional tweaking may be required to obtain a consistently high calibration quality across all parameters, populations and/or countries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Basic calibration example\n",
+    "\n",
+    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `T7/assets`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "F = at.ProjectFramework('assets/T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet('assets/T7_databook.xlsx', framework=F)\n",
+    "P = at.Project(framework=F,databook=D, do_run=False)\n",
+    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "In the example above, no calibration has been loaded, so all of the calibration Y-factors are equal to 1, and the model is uncalibrated. We can run a simulation and plot it, to see what our model looks like before calibration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cal = P.make_parset()\n",
+    "res = P.run_sim(parset=cal, result_name = 'Uncalibrated')\n",
+    "d = at.PlotData(res, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(10,7)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "Two issues with these simulation outputs immediately stand out. First, for the variables and years in which data is available, the model output doesn't match the data very well at all. Second, there is a large sudden change in the values of several variables right at the start of the simulation. In the absence of associated changes in interventions or disease transmission, disease burden is typically much more stable over time, so we would not expect to see such sudden changes at the start of the simulation.\n",
+    "\n",
+    "Tutorial 2 covers some of the detail around how to approach calibration. Although the parameters used for calibration vary from model to model, as a general rule, calibration proceeds by\n",
+    "\n",
+    "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
+    "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
+    "\n",
+    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them (or whether some should be adjusted simultaneously), and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use YAML files to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Minimal YAML file\n",
+    "\n",
+    "To illustrate how YAML calibration works, let’s start with a simple example of what a YAML file could look like:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tadjustables: b_rate, mig_rate\n",
+    "    \tmeasurables: alive\n",
+    "\n",
+    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
+    "\n",
+    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g., `T7_YAML_1_minimal.yaml`), the calibration can be run using\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()` – simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
+    "\n",
+    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>\n",
+    "<img src=\"assets/T7_plot_1_simple_compare.png\" alt=\"Simple calibration comparison\" style=\"width: 500px;\"/>\n",
+    "\n",
+    "As you can see, these plots don't look very different to the uncalibrated simulation results. If we plot the uncalibrated simulation in the same plot as our simple calibration, we can see that there has been a slight change, but not nearly enough to be able to consider this a good calibration. Next, we will illustrate the YAML calibration features that we can use to improve on this initial result. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Sections\n",
+    "\n",
+    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modifies the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file:\n",
+    "\n",
+    "- _actions_, which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains `adjustables` and `measurables`. Other examples of actions are detailed below.\n",
+    "- _sections_, which are containers for actions. Sections can contain attributes, such as how many times to repeat the contents of the section, or they can define settings that are applied to any relevant actions within that section.\n",
+    "\n",
+    "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    Match population sizes:\n",
+    "        adjustables: b_rate, mig_rate\n",
+    "        measurables: alive\n",
+    "    Match deaths:\n",
+    "        adjustables: d_rate\n",
+    "        measurables: deaths\n",
+    "```\n",
+    "\n",
+    "To organize the YAML file further, we could group these into an additional section. Both of these actions affect the overall population calibration, so we could logically group them as follows:\n",
+    "\n",
+    "```\n",
+    "calibration: \n",
+    "    Population calibration:\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "            measurables: alive\n",
+    "        Match deaths:\n",
+    "            adjustables: d_rate\n",
+    "            measurables: deaths\n",
+    "```\n",
+    "\n",
+    "The overall structure of this YAML file is thus:\n",
+    "\n",
+    "1. A top-level section titled `calibration`, which has one sub-section (`Population calibration`)\n",
+    "2. A sub-section called `Population calibration`, which in turn contains two actions\n",
+    "3. An action called `Match population sizes`, corresponding to the original calibration step we used to adjust the birth rate and migration rate\n",
+    "4. An action called `Match deaths`, corresponding to the new step of adjusting the death rate\n",
+    "\n",
+    "An action is differentiated from a section in two possible ways:\n",
+    "\n",
+    "- By its contents (e.g. if it contains `adjustables` and `measurables`, then it will be interpreted as a calibration action rather than a section)\n",
+    "- By the title (if the name corresponds to the name of a supported operation, as described below)\n",
+    "\n",
+    "Additionally, actions can never contain any sub-sections, whereas sections do. \n",
+    "Apart from the names of supported operations, sections can be freely named."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "### Repeating a section\n",
+    "\n",
+    "The *repeats* keyword can be used to loop over any part of the calibration multiple times. We do this by writing `repeats: n` inside a particular section, where *n* is the number of times we would like to loop over that section. All subsections contained in it will also be looped over *n* times. \n",
+    "\n",
+    "    calibration: \n",
+    "        repeats: 2\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                repeats: 2\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "\n",
+    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
+    "\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "\n",
+    "In this way, it is possible for even a very compact YAML file to correspond to a large number of individual autocalibration steps. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## Sections vs Actions\n",
+    "\n",
+    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-settings-in-outer-sections). \n",
+    "\n",
+    "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block they are, and they don't contain any sub-sections.\n",
+    "</div>\n",
+    "\n",
+    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading in the YAML file, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica.yaml_calibration \n",
+    "calibration_tree = at.yaml_calibration.build('calibrations/T7_YAML_3_repeats.yaml') \n",
+    "print(calibration_tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## Types of Action Blocks\n",
+    "\n",
+    "YAML calibration files can contain the following types of actions, or action blocks: \n",
+    "\n",
+    "- Calibration block\n",
+    "- Initialization block\n",
+    "- Intialization clearing block\n",
+    "- Saving block"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "### Calibration blocks\n",
+    "\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "\n",
+    "#### Adjustable and measurable settings\n",
+    "\n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
+    "\n",
+    "Each adjustable has:\n",
+    "\n",
+    "- `adj_label` (required): Adjustable parameter codename (can be found in the framework)\n",
+    "- `pop_name`: Population to calibrate (default: all populations)\n",
+    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
+    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
+    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
+    "\n",
+    "Each measurable has:\n",
+    "\n",
+    "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
+    "- `pop_name`: Population to use for calibration (default: all populations)\n",
+    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
+    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
+    "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
+    "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
+    "\n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections \"Specifying settings in outer sections\"). The `cal_start` and `cal_end` years can be set independently for each measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration.\n",
+    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so:\n",
+    "\n",
+    "    calibration:\n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \tb_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \tmig_rate: \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                alive:\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "                 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Specifying Populations\n",
+    "\n",
+    "In some cases, we may only want to set adjustables or measurables for a specific population, or we may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so:\n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4): \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \t(mig_rate, 5-14): \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "       \n",
+    "Note that we can specify the same settings for more than one adjustable/measurable at once by placing several parameter names before the colon, separated by commas – this is applicable to any set of adjustables/measurables, not only different populations of the same parameter. For example, we could write: \n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4), mig_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\n",
+    "                    starting_y_factor: 1.2\t\t \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "               \tcal_end: 2040\n",
+    "\n",
+    "Finally, the same syntax we use to calibrate populations can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on how to calibrate in these cases, see the documentation on [Calibrating transfers and interactions](https://atomica.tools/docs/master/general/YAML_calibration.html#Calibrating-transfers-and-interactions \"Calibrating transfers and interactions\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Setting initializations\n",
+    "\n",
+    "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
+    "\n",
+    "- The initial conditions are underdetermined, for example, if there are two strains of a disease with different levels of infectiousness, but data is only available on the total prevalence. In the absence of any other information, Atomica will automatically split up the total prevalence equally between the two strains, when in fact maybe one or the other strain may be dominant, which would change the overall transmission.\n",
+    "- The model parameters may not give rise to an equilibrium solution that matches the data. This could be due to data sources being mixed, combined across different years, collected in different ways or with different definitions, or because the simplified dynamics in the model simply don't capture all the processes in the real world.\n",
+    "\n",
+    "Regardless of the cause, there can often be an initial transience period at the beginning of the simulation, where we can observe abrupt spikes in some parameters until the system reaches equilibrium. \n",
+    "\n",
+    "<img src=\"assets/fig_pre_init.png\" alt=\"Pre-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "In some models, this can be treated as a 'burn-in' period, and the initial part of the simulation is simply discarded. However, there is a risk that being too far from the correct initialization can contaminate the parameter estimates during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of transients on the calibration.\n",
+    "\n",
+    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, and it might limit the amount of data that can be used for calibration.\n",
+    "\n",
+    "An alternative approach is to override the initial values of our simulation and replace them with new initialization values. Setting an initialization works by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are approximately constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will prevent the appearance of an initial transient in the model.\n",
+    "\n",
+    "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient can still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases a small initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation on this topic](https://atomica.tools/docs/master/general/Compartment-Initialization.html \"Initializing compartments\").\n",
+    "\n",
+    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialization`. Under this title, we can specify further settings:\n",
+    "\n",
+    "- `init_year` (required): The future year to take the compartment sizes from. The simulation will be run up to this year.\n",
+    "- `constant_parset` (default: False): Whether to make the parameters constant while the initialization is being calculated, and which year to extract these constant parameter values from, i.e. which year to use in `parset.make_constant()`. It can be a numerical value (representing the year from which to draw the constant values for the parset) or Boolean (if `True`, the default year `sim_start` will be used).\n",
+    "\n",
+    "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using the parameter values from the default `sim_start` year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: True\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using constant parameter values from a specific year:\n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: 2005\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "### Clearing initializations\n",
+    "\n",
+    "If we have previously set an initialization in our YAML calibration algorithm, and we then set another initialization later in the YAML file, it uses information from the previous initialization to calculate the next, since the new simulation will start from the initial compartment sizes calculated in that previous initialization.\n",
+    "\n",
+    "Sometimes we might wish to calculate a new initialization from scratch, without using our previous initializations as a starting point. This could be useful if we have done some calibration steps between the previous initialization and now, in which case the y-factors will have changed, which in turn could have caused the equilibrium values to vary.\n",
+    "\n",
+    "To do this in the YAML file, we can make a section titled `clear_initialization`, followed by a Boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. The most simple example is as follows:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    clear_initialization: True\n",
+    "```\n",
+    "\n",
+    "Below is a more practical example of how we would use the `clear_initialization` feature in a real YAML file, where we first set an initialization, perform some calibration steps, and then clear our previous initialization before making a new one.\n",
+    "\n",
+    "    calibration:\n",
+    "        First initialization:\n",
+    "            set_initialization:\n",
+    "                init_year: 2030\n",
+    "\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "        \tmeasurables: alive\n",
+    "        \n",
+    "    \tclear_initialization: True\n",
+    "\n",
+    "        Second initialization:\n",
+    "            set_initialization:\n",
+    "                init_year: 2030\n",
+    "\n",
+    "\n",
+    "Note that we need to wrap `set_initialization` in an outer section, since YAML does not allow duplicate keys (i.e. identical section/action titles at the same level)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "### Saving calibrations\n",
+    "\n",
+    "Throughout a YAML calibration, we might wish to save the calibration state at specific points in the process. For example, if our YAML file has a population section and an epidemiological section, we might want to save the calibration after the population calibration section so we can see the progress made up to that point, or otherwise identify the effect that different parts of the algorithm are having on the calibration. To save a calibration, we simply make a section titled `save_calibration`. Under the title, we indicate the filename we wish to save the calibration to, either by providing it directly, or by using the keyword `fname` - both examples are shown below:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tPopulation section: \n",
+    "    \t\t[…]\n",
+    "    \n",
+    "    \tsave_calibration: \n",
+    "    \t\tfname: pop_calibration.xlsx\n",
+    "    \n",
+    "    \tEpidemiology section: \n",
+    "    \t\t[…]\n",
+    "\n",
+    "    \tsave_calibration: epi_calibration.xlsx\n",
+    "        \n",
+    "Note that when we save a calibration, if the initial compartment sizes have been explicitly specified using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus also set the initial compartment sizes to those values.\n",
+    "\n",
+    "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, we can use the `save_intermediate_calibrations` option when loading in and running the calibration, e.g.\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
+    "\n",
+    "More options for running the calibration can be found [here](#running-the-yaml-calibration \"Running the YAML calibration\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Specifying settings in outer sections\n",
+    "\n",
+    "We saw previously that sections (i.e. YAML blocks that don't correspond to actions) don't directly modify the calibration, and are mainly used to structure the YAML file. However, it is possible to determine calibration settings inside a section, such that they are passed down to any subsections or action blocks contained within. Another way to think of this is that an action block will inherit any settings that are defined in any of its parent sections.\n",
+    "\n",
+    "This can simplify the process of writing YAML files where we want to override the default settings in several action blocks, as it allows us to specify those settings once in a parent section, rather than repeatedly in every action block. Additionally, this feature is hierarchical, so settings that are specified further in (e.g. in the action block itself) will always override those set in a section that is further out, allowing for more flexibility.\n",
+    "\n",
+    "Some settings that this feature is commonly used for include: \n",
+    "\n",
+    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for (default: 60 seconds)\n",
+    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by the optimisation algorithm (default: 0.1)\n",
+    "\n",
+    "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
+    "\n",
+    "- `cal_start` and `cal_end`: These can be used to change the calibration timespan for multiple calibration steps at the same time.\n",
+    "- `metric`: To change the metric used to assess calibration quality, i.e. how well the simulation fits the measurables data.\n",
+    "\n",
+    "We will now provide some examples of how this functionality can be used.\n",
+    "\n",
+    "### Inheritance\n",
+    "\n",
+    "Settings are automatically inherited by all subsections. To set a `max_time` of 120 for every calibration step in the entire calibration, we would write the following. This will result in every calibration step running for at most 120 seconds, rather than the default 60 seconds.\n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Settings hierarchy\n",
+    "\n",
+    "Inherited settings can be overwritten inside sub-sections (in both sections and actions). Say we wanted all calibration blocks to run for a `max_time` of 120 seconds, except for `Match_deaths`, which we want to run for only 60 seconds. In that case, we can override the setting determined in the parent section by specifying the updated value inside `Match_deaths`:\n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                max_time_ 60\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Adjustables/measurables settings\n",
+    "\n",
+    "Finally, here is an example of how to use the adjustables and measurables settings outside a calibration block:\n",
+    "\n",
+    "    calibration:\n",
+    "        Population calibration:\n",
+    "            upper_bound: 2\n",
+    "            lower_bound: .5\n",
+    "\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables alive\n",
+    "\n",
+    "            Match deaths:\n",
+    "                    adjustables: d_rate\n",
+    "                    measurab\n",
+    "\n",
+    "        Epidemiological calibration:\n",
+    "            [...]\n",
+    "\n",
+    "In this case, the upper and lower bounds have been updated to 2 and 0.5 respectively, for all the adjustables in the `Population calibration` section (`b_rate`, `mig_rate` and `d_rate`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "## Running the YAML calibration\n",
+    "\n",
+    "Now that we have finished writing our YAML calibration file, we can proceed to running the calibration. Having loaded a project, `P.calibrate` can then be called directly, passing in the name of the YAML file:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "This function supports several additional optional arguments:\n",
+    "\n",
+    "- `savedir` (default: current working directory) - any saved calibrations and logs will be saved into this folder\n",
+    "- `save_intermediate` (default: `False`) - if `True`, this will save all intermediate calibrations\n",
+    "- `max_time` (optional) - override the default\n",
+    "- `log_output` (default: `False`) - if `True`, this will save a text file into the output directory containing all console output (e.g., objective function values)\n",
+    "\n",
+    "The YAML file can also be supplied as a dictionary, which would normally be obtained via\n",
+    "\n",
+    "    import yaml\n",
+    "    calibration_yaml =  yaml.load(file, Loader=yaml.FullLoader)\n",
+    "    \n",
+    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programmatically prior to running the calibration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## Exercise: worked example\n",
+    "\n",
+    "Now that we understand what all the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution.\n",
+    "\n",
+    "_**Question 1.** We want to do a basic population calibration, where we calibrate the death rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
+    "\n",
+    "**Hint**: Open the framework and look at the `Compartments`, `Parameters` and `Characteristics` pages. The `Code Names` and `Display Names` show us how we have to write the parameter names in the YAML file, and what quantities they correspond to. Those with a value in the `Databook Page` column have data values supplied, and can therefore be used as measurables.\n",
+    "\n",
+    "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
+    "\n",
+    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
+    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
+    "| COMPARTMENTS    | <td colspan=3> |\n",
+    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
+    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
+    "| PARAMETERS      | <td colspan=3> |\n",
+    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
+    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
+    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
+    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
+    "| CHARACTERISTICS | <td colspan=3> |\n",
+    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20",
+   "metadata": {},
+   "source": [
+    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the YAML file?_\n",
+    "\n",
+    "**Hint**: Use the `repeats` keyword.\n",
+    "\n",
+    "_**Question 3.** Say that, for this particular project, we are only interested in calibrating results from 2005 to 2040. How would we specify this in the YAML file to reduce calibration time?_\n",
+    "\n",
+    "**Hint**: Using the `cal_start` and `cal_end` keywords\n",
+    "\n",
+    "_**Question 4.** When calibrating the birth rate, it only really makes sense to calibrate the 0-4 population. Modify the YAML file to reflect this._\n",
+    "\n",
+    "**Hint**: We place the parameter code name and population name in a tuple.\n",
+    "\n",
+    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set its starting y_factor to 1.2 to speed up the optimisation._\n",
+    "\n",
+    "**Hint**: Use `starting_y_factor`.\n",
+    "\n",
+    "_**Question 6.** We want to avoid the presence of transients in our calibrated simulation. Let’s initialize the calibration in order to eliminate any jumps._\n",
+    "\n",
+    "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
+    "\n",
+    "_**Question 7.** We also want to clear the previous initialization every time we make a new one, instead of using information from the previous initialization. How can we update the YAML file to reflect this?_\n",
+    "\n",
+    "**Hint**: Use `clear_initialization`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
+    "\n",
+    "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
+    "\n",
+    "**Hint**: Use `save_calibration` and specify a `fname`.\n",
+    "\n",
+    "All solutions to the worked example are in the `docs/tutorial/T7/calibrations` folder of the Atomica repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result:\n",
+    "\n",
+    "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "## Further resources\n",
+    "\n",
+    "### Epidemiological calibration\n",
+    "\n",
+    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration instructions (`typ_calibration_instructions.yaml`) can be found in the Atomica code repository, at `docs/tutorial/T7/calibrations`.\n",
+    "\n",
+    "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
+    "\n",
+    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly impact the population calibration results.\n",
+    "\n",
+    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030.\n",
+    "\n",
+    "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
+    "\n",
+    "At the end of the typhoid calibration, we set an initialization in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice.\n",
+    "\n",
+    "The resulting calibration from running this YAML file is like so:\n",
+    "\n",
+    "<img src=\"assets/T7_plot_4_complete.png\" alt=\"Complete calibration result\" style=\"width: 800px;\"/>\n",
+    "\n",
+    "### Documentation\n",
+    "\n",
+    "For more information on using Atomica's YAML calibration functionality, see [the documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"YAML documentation page\").\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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+   },
+   "file_extension": ".py",
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+   "nbconvert_exporter": "python",
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+   "version": "3.10.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
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+   "title_cell": "Table of Contents",
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+}

From 966a7d39212d67f41e90ceb37853e2622bb37e3b Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 6 Mar 2025 20:09:46 +0100
Subject: [PATCH 124/161] YAML tutorial edits

-Added description of plots and how to "turn off" disease components in the YAML file
-Formatting, spelling edits
---
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 1470 +++++++++--------
 1 file changed, 738 insertions(+), 732 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index d6d3d2d0..e142307a 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -1,732 +1,738 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0",
-   "metadata": {},
-   "source": [
-    "# T7 - YAML calibration\n",
-    "\n",
-    "As we saw in the earlier calibration tutorial, most autocalibrations involve multiple steps, since the optimization algorithm often gets stuck in local minima if we try to optimize too many parameters at once. We could run each of these steps ourselves through separate calls to `P.calibrate()` – however, there is significant value in being able to explicitly frame the overall calibration process as an algorithm, as this makes it easier to modify the calibration steps and apply the calibration algorithm across a collection of projects. This is implemented in Atomica through the 'YAML calibration' feature, in which the calibration steps are specified in a file which Atomica can then read and use to execute the calibration.\n",
-    "\n",
-    "## YAML files\n",
-    "\n",
-    "The calibration algorithm files used by Atomica are written in YAML. YAML is a plain-text, human-readable data serialization language used to make configuration files. Essentially, a YAML file can be read into Python variables (dictionaries, lists, strings) which in turn can be used as arguments to Python functions. Here is an example of how variables can be specified in a YAML file:\n",
-    "\n",
-    "```\n",
-    " foo: a string\n",
-    " bar: 1\n",
-    " baz: [a,b,c]\n",
-    " list:\n",
-    "   - i\n",
-    "   - j\n",
-    " nested:\n",
-    "   x: 1\n",
-    "   y: 2\n",
-    " ```\n",
-    "\n",
-    "When parsed into Python, this becomes\n",
-    "\n",
-    "```\n",
-    "{'foo': 'a string',\n",
-    " 'bar': 1,\n",
-    " 'baz': ['a', 'b', 'c'],\n",
-    " 'list': ['i', 'j'],\n",
-    " 'nested': {'x': 1, 'y': 2}}\n",
-    "```\n",
-    "\n",
-    "Using YAML files provides a simple way to define a calibration algorithm in a format that is easy to work with and that Atomica can directly execute. This can cut down the time we spend manually calibrating, or even running autocalibrations. It allows us to conduct reproducible calibration runs, and is also highly scalable, since it allows us to apply the same calibration algorithm in multiple countries or settings.\n",
-    "\n",
-    "The following tutorial outlines how to use the YAML framework that has been developed for Atomica calibration. Specifically, it will cover how to write a YAML configuration file with calibration instructions for Atomica, and how to use this file to execute a calibration. Bear in mind that YAML calibration is not intended to be a standalone tool that will perfectly calibrate any model – rather, it is one part of the calibration toolbox. It can be used to reduce the time spent on calibration by autocalibrating Atomica models to a reasonable level, but additional tweaking may be required to obtain a consistently high calibration quality across all parameters, populations and/or countries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1",
-   "metadata": {},
-   "source": [
-    "## Basic calibration example\n",
-    "\n",
-    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `T7/assets`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import atomica as at\n",
-    "F = at.ProjectFramework('assets/T7_framework.xlsx')\n",
-    "D = at.ProjectData.from_spreadsheet('assets/T7_databook.xlsx', framework=F)\n",
-    "P = at.Project(framework=F,databook=D, do_run=False)\n",
-    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3",
-   "metadata": {},
-   "source": [
-    "In the example above, no calibration has been loaded, so all of the calibration Y-factors are equal to 1, and the model is uncalibrated. We can run a simulation and plot it, to see what our model looks like before calibration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cal = P.make_parset()\n",
-    "res = P.run_sim(parset=cal, result_name = 'Uncalibrated')\n",
-    "d = at.PlotData(res, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
-    "fig = at.plot_series(d,axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
-    "fig.set_size_inches(10,7)\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5",
-   "metadata": {},
-   "source": [
-    "Two issues with these simulation outputs immediately stand out. First, for the variables and years in which data is available, the model output doesn't match the data very well at all. Second, there is a large sudden change in the values of several variables right at the start of the simulation. In the absence of associated changes in interventions or disease transmission, disease burden is typically much more stable over time, so we would not expect to see such sudden changes at the start of the simulation.\n",
-    "\n",
-    "Tutorial 2 covers some of the detail around how to approach calibration. Although the parameters used for calibration vary from model to model, as a general rule, calibration proceeds by\n",
-    "\n",
-    "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
-    "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
-    "\n",
-    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them (or whether some should be adjusted simultaneously), and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use YAML files to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
-   "source": [
-    "## Minimal YAML file\n",
-    "\n",
-    "To illustrate how YAML calibration works, let’s start with a simple example of what a YAML file could look like:\n",
-    "\n",
-    "    calibration:\n",
-    "    \tadjustables: b_rate, mig_rate\n",
-    "    \tmeasurables: alive\n",
-    "\n",
-    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
-    "\n",
-    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g., `T7_YAML_1_minimal.yaml`), the calibration can be run using\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
-    "\n",
-    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()` – simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
-    "\n",
-    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>\n",
-    "<img src=\"assets/T7_plot_1_simple_compare.png\" alt=\"Simple calibration comparison\" style=\"width: 500px;\"/>\n",
-    "\n",
-    "As you can see, these plots don't look very different to the uncalibrated simulation results. If we plot the uncalibrated simulation in the same plot as our simple calibration, we can see that there has been a slight change, but not nearly enough to be able to consider this a good calibration. Next, we will illustrate the YAML calibration features that we can use to improve on this initial result. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7",
-   "metadata": {},
-   "source": [
-    "## Sections\n",
-    "\n",
-    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modifies the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file:\n",
-    "\n",
-    "- _actions_, which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains `adjustables` and `measurables`. Other examples of actions are detailed below.\n",
-    "- _sections_, which are containers for actions. Sections can contain attributes, such as how many times to repeat the contents of the section, or they can define settings that are applied to any relevant actions within that section.\n",
-    "\n",
-    "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
-    "\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    Match population sizes:\n",
-    "        adjustables: b_rate, mig_rate\n",
-    "        measurables: alive\n",
-    "    Match deaths:\n",
-    "        adjustables: d_rate\n",
-    "        measurables: deaths\n",
-    "```\n",
-    "\n",
-    "To organize the YAML file further, we could group these into an additional section. Both of these actions affect the overall population calibration, so we could logically group them as follows:\n",
-    "\n",
-    "```\n",
-    "calibration: \n",
-    "    Population calibration:\n",
-    "        Match population sizes:\n",
-    "            adjustables: b_rate, mig_rate\n",
-    "            measurables: alive\n",
-    "        Match deaths:\n",
-    "            adjustables: d_rate\n",
-    "            measurables: deaths\n",
-    "```\n",
-    "\n",
-    "The overall structure of this YAML file is thus:\n",
-    "\n",
-    "1. A top-level section titled `calibration`, which has one sub-section (`Population calibration`)\n",
-    "2. A sub-section called `Population calibration`, which in turn contains two actions\n",
-    "3. An action called `Match population sizes`, corresponding to the original calibration step we used to adjust the birth rate and migration rate\n",
-    "4. An action called `Match deaths`, corresponding to the new step of adjusting the death rate\n",
-    "\n",
-    "An action is differentiated from a section in two possible ways:\n",
-    "\n",
-    "- By its contents (e.g. if it contains `adjustables` and `measurables`, then it will be interpreted as a calibration action rather than a section)\n",
-    "- By the title (if the name corresponds to the name of a supported operation, as described below)\n",
-    "\n",
-    "Additionally, actions can never contain any sub-sections, whereas sections do. \n",
-    "Apart from the names of supported operations, sections can be freely named."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8",
-   "metadata": {},
-   "source": [
-    "### Repeating a section\n",
-    "\n",
-    "The *repeats* keyword can be used to loop over any part of the calibration multiple times. We do this by writing `repeats: n` inside a particular section, where *n* is the number of times we would like to loop over that section. All subsections contained in it will also be looped over *n* times. \n",
-    "\n",
-    "    calibration: \n",
-    "        repeats: 2\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                repeats: 2\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "\n",
-    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
-    "\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
-    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
-    "\n",
-    "In this way, it is possible for even a very compact YAML file to correspond to a large number of individual autocalibration steps. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9",
-   "metadata": {},
-   "source": [
-    "## Sections vs Actions\n",
-    "\n",
-    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-settings-in-outer-sections). \n",
-    "\n",
-    "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
-    "\n",
-    "<div class=\"alert alert-info\">\n",
-    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block they are, and they don't contain any sub-sections.\n",
-    "</div>\n",
-    "\n",
-    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading in the YAML file, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import atomica.yaml_calibration \n",
-    "calibration_tree = at.yaml_calibration.build('calibrations/T7_YAML_3_repeats.yaml') \n",
-    "print(calibration_tree)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11",
-   "metadata": {},
-   "source": [
-    "## Types of Action Blocks\n",
-    "\n",
-    "YAML calibration files can contain the following types of actions, or action blocks: \n",
-    "\n",
-    "- Calibration block\n",
-    "- Initialization block\n",
-    "- Intialization clearing block\n",
-    "- Saving block"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12",
-   "metadata": {},
-   "source": [
-    "### Calibration blocks\n",
-    "\n",
-    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
-    "\n",
-    "#### Adjustable and measurable settings\n",
-    "\n",
-    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
-    "\n",
-    "Each adjustable has:\n",
-    "\n",
-    "- `adj_label` (required): Adjustable parameter codename (can be found in the framework)\n",
-    "- `pop_name`: Population to calibrate (default: all populations)\n",
-    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
-    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
-    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
-    "\n",
-    "Each measurable has:\n",
-    "\n",
-    "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
-    "- `pop_name`: Population to use for calibration (default: all populations)\n",
-    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
-    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
-    "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
-    "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
-    "\n",
-    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections \"Specifying settings in outer sections\"). The `cal_start` and `cal_end` years can be set independently for each measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration.\n",
-    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so:\n",
-    "\n",
-    "    calibration:\n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \tb_rate: \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\t\t\n",
-    "            \tmig_rate: \n",
-    "            \t\tstarting_y_factor: 1.2 \n",
-    "            measurables: \n",
-    "                alive:\n",
-    "                \tcal_start: 2000\n",
-    "                \tcal_end: 2040\n",
-    "                 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "#### Specifying Populations\n",
-    "\n",
-    "In some cases, we may only want to set adjustables or measurables for a specific population, or we may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so:\n",
-    "\n",
-    "    calibration: \n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \t(b_rate, 0-4): \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\t\t\n",
-    "            \t(mig_rate, 5-14): \n",
-    "            \t\tstarting_y_factor: 1.2 \n",
-    "            measurables: \n",
-    "                (alive, 0-4), (alive, 5-14):\n",
-    "                \tcal_start: 2000\n",
-    "                \tcal_end: 2040\n",
-    "       \n",
-    "Note that we can specify the same settings for more than one adjustable/measurable at once by placing several parameter names before the colon, separated by commas – this is applicable to any set of adjustables/measurables, not only different populations of the same parameter. For example, we could write: \n",
-    "\n",
-    "    calibration: \n",
-    "        Match population sizes:\n",
-    "            adjustables: \n",
-    "            \t(b_rate, 0-4), mig_rate: \n",
-    "            \t\tlower_bound: 0.5\n",
-    "            \t\tupper_bound: 20\n",
-    "                    starting_y_factor: 1.2\t\t \n",
-    "            measurables: \n",
-    "                (alive, 0-4), (alive, 5-14):\n",
-    "                \tcal_start: 2000\n",
-    "               \tcal_end: 2040\n",
-    "\n",
-    "Finally, the same syntax we use to calibrate populations can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on how to calibrate in these cases, see the documentation on [Calibrating transfers and interactions](https://atomica.tools/docs/master/general/YAML_calibration.html#Calibrating-transfers-and-interactions \"Calibrating transfers and interactions\")."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14",
-   "metadata": {
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "source": [
-    "### Setting initializations\n",
-    "\n",
-    "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
-    "\n",
-    "- The initial conditions are underdetermined, for example, if there are two strains of a disease with different levels of infectiousness, but data is only available on the total prevalence. In the absence of any other information, Atomica will automatically split up the total prevalence equally between the two strains, when in fact maybe one or the other strain may be dominant, which would change the overall transmission.\n",
-    "- The model parameters may not give rise to an equilibrium solution that matches the data. This could be due to data sources being mixed, combined across different years, collected in different ways or with different definitions, or because the simplified dynamics in the model simply don't capture all the processes in the real world.\n",
-    "\n",
-    "Regardless of the cause, there can often be an initial transience period at the beginning of the simulation, where we can observe abrupt spikes in some parameters until the system reaches equilibrium. \n",
-    "\n",
-    "<img src=\"assets/fig_pre_init.png\" alt=\"Pre-initialization plot\" style=\"width: 400px;\"/>\n",
-    "\n",
-    "In some models, this can be treated as a 'burn-in' period, and the initial part of the simulation is simply discarded. However, there is a risk that being too far from the correct initialization can contaminate the parameter estimates during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of transients on the calibration.\n",
-    "\n",
-    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, and it might limit the amount of data that can be used for calibration.\n",
-    "\n",
-    "An alternative approach is to override the initial values of our simulation and replace them with new initialization values. Setting an initialization works by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are approximately constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will prevent the appearance of an initial transient in the model.\n",
-    "\n",
-    "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
-    "\n",
-    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient can still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases a small initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation on this topic](https://atomica.tools/docs/master/general/Compartment-Initialization.html \"Initializing compartments\").\n",
-    "\n",
-    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialization`. Under this title, we can specify further settings:\n",
-    "\n",
-    "- `init_year` (required): The future year to take the compartment sizes from. The simulation will be run up to this year.\n",
-    "- `constant_parset` (default: False): Whether to make the parameters constant while the initialization is being calculated, and which year to extract these constant parameter values from, i.e. which year to use in `parset.make_constant()`. It can be a numerical value (representing the year from which to draw the constant values for the parset) or Boolean (if `True`, the default year `sim_start` will be used).\n",
-    "\n",
-    "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "```\n",
-    "\n",
-    "Setting the initialization year with a constant parset, using the parameter values from the default `sim_start` year:\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "        constant_parset: True\n",
-    "```\n",
-    "\n",
-    "Setting the initialization year with a constant parset, using constant parameter values from a specific year:\n",
-    "\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    set_initialization:\n",
-    "        init_year: 2030\n",
-    "        constant_parset: 2005\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15",
-   "metadata": {},
-   "source": [
-    "### Clearing initializations\n",
-    "\n",
-    "If we have previously set an initialization in our YAML calibration algorithm, and we then set another initialization later in the YAML file, it uses information from the previous initialization to calculate the next, since the new simulation will start from the initial compartment sizes calculated in that previous initialization.\n",
-    "\n",
-    "Sometimes we might wish to calculate a new initialization from scratch, without using our previous initializations as a starting point. This could be useful if we have done some calibration steps between the previous initialization and now, in which case the y-factors will have changed, which in turn could have caused the equilibrium values to vary.\n",
-    "\n",
-    "To do this in the YAML file, we can make a section titled `clear_initialization`, followed by a Boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. The most simple example is as follows:\n",
-    "\n",
-    "```\n",
-    "calibration:\n",
-    "    clear_initialization: True\n",
-    "```\n",
-    "\n",
-    "Below is a more practical example of how we would use the `clear_initialization` feature in a real YAML file, where we first set an initialization, perform some calibration steps, and then clear our previous initialization before making a new one.\n",
-    "\n",
-    "    calibration:\n",
-    "        First initialization:\n",
-    "            set_initialization:\n",
-    "                init_year: 2030\n",
-    "\n",
-    "        Match population sizes:\n",
-    "            adjustables: b_rate, mig_rate\n",
-    "        \tmeasurables: alive\n",
-    "        \n",
-    "    \tclear_initialization: True\n",
-    "\n",
-    "        Second initialization:\n",
-    "            set_initialization:\n",
-    "                init_year: 2030\n",
-    "\n",
-    "\n",
-    "Note that we need to wrap `set_initialization` in an outer section, since YAML does not allow duplicate keys (i.e. identical section/action titles at the same level)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16",
-   "metadata": {},
-   "source": [
-    "### Saving calibrations\n",
-    "\n",
-    "Throughout a YAML calibration, we might wish to save the calibration state at specific points in the process. For example, if our YAML file has a population section and an epidemiological section, we might want to save the calibration after the population calibration section so we can see the progress made up to that point, or otherwise identify the effect that different parts of the algorithm are having on the calibration. To save a calibration, we simply make a section titled `save_calibration`. Under the title, we indicate the filename we wish to save the calibration to, either by providing it directly, or by using the keyword `fname` - both examples are shown below:\n",
-    "\n",
-    "    calibration:\n",
-    "    \tPopulation section: \n",
-    "    \t\t[…]\n",
-    "    \n",
-    "    \tsave_calibration: \n",
-    "    \t\tfname: pop_calibration.xlsx\n",
-    "    \n",
-    "    \tEpidemiology section: \n",
-    "    \t\t[…]\n",
-    "\n",
-    "    \tsave_calibration: epi_calibration.xlsx\n",
-    "        \n",
-    "Note that when we save a calibration, if the initial compartment sizes have been explicitly specified using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus also set the initial compartment sizes to those values.\n",
-    "\n",
-    "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, we can use the `save_intermediate_calibrations` option when loading in and running the calibration, e.g.\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
-    "\n",
-    "More options for running the calibration can be found [here](#running-the-yaml-calibration \"Running the YAML calibration\")."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17",
-   "metadata": {},
-   "source": [
-    "## Specifying settings in outer sections\n",
-    "\n",
-    "We saw previously that sections (i.e. YAML blocks that don't correspond to actions) don't directly modify the calibration, and are mainly used to structure the YAML file. However, it is possible to determine calibration settings inside a section, such that they are passed down to any subsections or action blocks contained within. Another way to think of this is that an action block will inherit any settings that are defined in any of its parent sections.\n",
-    "\n",
-    "This can simplify the process of writing YAML files where we want to override the default settings in several action blocks, as it allows us to specify those settings once in a parent section, rather than repeatedly in every action block. Additionally, this feature is hierarchical, so settings that are specified further in (e.g. in the action block itself) will always override those set in a section that is further out, allowing for more flexibility.\n",
-    "\n",
-    "Some settings that this feature is commonly used for include: \n",
-    "\n",
-    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for (default: 60 seconds)\n",
-    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by the optimisation algorithm (default: 0.1)\n",
-    "\n",
-    "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
-    "\n",
-    "- `cal_start` and `cal_end`: These can be used to change the calibration timespan for multiple calibration steps at the same time.\n",
-    "- `metric`: To change the metric used to assess calibration quality, i.e. how well the simulation fits the measurables data.\n",
-    "\n",
-    "We will now provide some examples of how this functionality can be used.\n",
-    "\n",
-    "### Inheritance\n",
-    "\n",
-    "Settings are automatically inherited by all subsections. To set a `max_time` of 120 for every calibration step in the entire calibration, we would write the following. This will result in every calibration step running for at most 120 seconds, rather than the default 60 seconds.\n",
-    "\n",
-    "    calibration:\n",
-    "        max_time: 120\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "        \n",
-    "        Epidemiological calibration\n",
-    "            [...]\n",
-    "\n",
-    "### Settings hierarchy\n",
-    "\n",
-    "Inherited settings can be overwritten inside sub-sections (in both sections and actions). Say we wanted all calibration blocks to run for a `max_time` of 120 seconds, except for `Match_deaths`, which we want to run for only 60 seconds. In that case, we can override the setting determined in the parent section by specifying the updated value inside `Match_deaths`:\n",
-    "\n",
-    "    calibration:\n",
-    "        max_time: 120\n",
-    "        Population calibration:\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables: alive\n",
-    "            Match deaths:\n",
-    "                max_time_ 60\n",
-    "                adjustables: d_rate\n",
-    "                measurables: deaths\n",
-    "        \n",
-    "        Epidemiological calibration\n",
-    "            [...]\n",
-    "\n",
-    "### Adjustables/measurables settings\n",
-    "\n",
-    "Finally, here is an example of how to use the adjustables and measurables settings outside a calibration block:\n",
-    "\n",
-    "    calibration:\n",
-    "        Population calibration:\n",
-    "            upper_bound: 2\n",
-    "            lower_bound: .5\n",
-    "\n",
-    "            Match population sizes:\n",
-    "                adjustables: b_rate, mig_rate\n",
-    "                measurables alive\n",
-    "\n",
-    "            Match deaths:\n",
-    "                    adjustables: d_rate\n",
-    "                    measurab\n",
-    "\n",
-    "        Epidemiological calibration:\n",
-    "            [...]\n",
-    "\n",
-    "In this case, the upper and lower bounds have been updated to 2 and 0.5 respectively, for all the adjustables in the `Population calibration` section (`b_rate`, `mig_rate` and `d_rate`)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18",
-   "metadata": {},
-   "source": [
-    "## Running the YAML calibration\n",
-    "\n",
-    "Now that we have finished writing our YAML calibration file, we can proceed to running the calibration. Having loaded a project, `P.calibrate` can then be called directly, passing in the name of the YAML file:\n",
-    "\n",
-    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml')\n",
-    "\n",
-    "This function supports several additional optional arguments:\n",
-    "\n",
-    "- `savedir` (default: current working directory) - any saved calibrations and logs will be saved into this folder\n",
-    "- `save_intermediate` (default: `False`) - if `True`, this will save all intermediate calibrations\n",
-    "- `max_time` (optional) - override the default\n",
-    "- `log_output` (default: `False`) - if `True`, this will save a text file into the output directory containing all console output (e.g., objective function values)\n",
-    "\n",
-    "The YAML file can also be supplied as a dictionary, which would normally be obtained via\n",
-    "\n",
-    "    import yaml\n",
-    "    calibration_yaml =  yaml.load(file, Loader=yaml.FullLoader)\n",
-    "    \n",
-    "The `calibration_yaml` variable above can be passed to `P.calibrate(..., yaml=calibration_yaml)`. This enables changes to be made to the YAML content programmatically prior to running the calibration."
-   ]
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-   "cell_type": "markdown",
-   "id": "19",
-   "metadata": {},
-   "source": [
-    "## Exercise: worked example\n",
-    "\n",
-    "Now that we understand what all the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution.\n",
-    "\n",
-    "_**Question 1.** We want to do a basic population calibration, where we calibrate the death rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
-    "\n",
-    "**Hint**: Open the framework and look at the `Compartments`, `Parameters` and `Characteristics` pages. The `Code Names` and `Display Names` show us how we have to write the parameter names in the YAML file, and what quantities they correspond to. Those with a value in the `Databook Page` column have data values supplied, and can therefore be used as measurables.\n",
-    "\n",
-    "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
-    "\n",
-    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
-    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
-    "| COMPARTMENTS    | <td colspan=3> |\n",
-    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
-    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
-    "| PARAMETERS      | <td colspan=3> |\n",
-    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
-    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
-    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
-    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
-    "| CHARACTERISTICS | <td colspan=3> |\n",
-    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
-   ]
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-    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the YAML file?_\n",
-    "\n",
-    "**Hint**: Use the `repeats` keyword.\n",
-    "\n",
-    "_**Question 3.** Say that, for this particular project, we are only interested in calibrating results from 2005 to 2040. How would we specify this in the YAML file to reduce calibration time?_\n",
-    "\n",
-    "**Hint**: Using the `cal_start` and `cal_end` keywords\n",
-    "\n",
-    "_**Question 4.** When calibrating the birth rate, it only really makes sense to calibrate the 0-4 population. Modify the YAML file to reflect this._\n",
-    "\n",
-    "**Hint**: We place the parameter code name and population name in a tuple.\n",
-    "\n",
-    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set its starting y_factor to 1.2 to speed up the optimisation._\n",
-    "\n",
-    "**Hint**: Use `starting_y_factor`.\n",
-    "\n",
-    "_**Question 6.** We want to avoid the presence of transients in our calibrated simulation. Let’s initialize the calibration in order to eliminate any jumps._\n",
-    "\n",
-    "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
-    "\n",
-    "_**Question 7.** We also want to clear the previous initialization every time we make a new one, instead of using information from the previous initialization. How can we update the YAML file to reflect this?_\n",
-    "\n",
-    "**Hint**: Use `clear_initialization`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
-    "\n",
-    "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
-    "\n",
-    "**Hint**: Use `save_calibration` and specify a `fname`.\n",
-    "\n",
-    "All solutions to the worked example are in the `docs/tutorial/T7/calibrations` folder of the Atomica repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result:\n",
-    "\n",
-    "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>"
-   ]
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-    "## Further resources\n",
-    "\n",
-    "### Epidemiological calibration\n",
-    "\n",
-    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births and deaths to match population size data, without considering disease burden. This same functionality applies to epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration instructions (`typ_calibration_instructions.yaml`) can be found in the Atomica code repository, at `docs/tutorial/T7/calibrations`.\n",
-    "\n",
-    "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. \n",
-    "\n",
-    "At the beginning of the calibration, we also set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero for the first pass of the population calibration. This essentially \"turns off\" of the typhoid disease until it gets reactivated in the `reset epi y-factors` step, giving the population an opportunity to be reasonably calibrated without interference from the disease components. This can be useful to do since, before calibrating the disease, the magnitude of the disease parameters could be large enough to significantly impact the population calibration results.\n",
-    "\n",
-    "We then calibrate the population following the same principles as illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030.\n",
-    "\n",
-    "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
-    "\n",
-    "At the end of the typhoid calibration, we set an initialization in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice.\n",
-    "\n",
-    "The resulting calibration from running this YAML file is like so:\n",
-    "\n",
-    "<img src=\"assets/T7_plot_4_complete.png\" alt=\"Complete calibration result\" style=\"width: 800px;\"/>\n",
-    "\n",
-    "### Documentation\n",
-    "\n",
-    "For more information on using Atomica's YAML calibration functionality, see [the documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"YAML documentation page\").\n"
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+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# T7 - YAML calibration\n",
+    "\n",
+    "As we saw in the earlier calibration tutorial, most autocalibrations involve multiple steps, since the optimization algorithm often gets stuck in local minima if we try to optimize too many parameters at once. We could run each of these steps ourselves through separate calls to `P.calibrate()` – however, there is significant value in being able to explicitly frame the overall calibration process as an algorithm, as this makes it easier to modify the calibration steps and apply the calibration algorithm across a collection of projects. This is implemented in Atomica through the 'YAML calibration' feature, in which the calibration steps are specified in a file which Atomica can then read and use to execute the calibration.\n",
+    "\n",
+    "## YAML files\n",
+    "\n",
+    "The calibration algorithm files used by Atomica are written in YAML. YAML is a plain-text, human-readable data serialization language used to make configuration files. Essentially, a YAML file can be read into Python variables (dictionaries, lists, strings) which in turn can be used as arguments to Python functions. Here is an example of how variables can be specified in a YAML file:\n",
+    "\n",
+    "```\n",
+    " foo: a string\n",
+    " bar: 1\n",
+    " baz: [a,b,c]\n",
+    " list:\n",
+    "   - i\n",
+    "   - j\n",
+    " nested:\n",
+    "   x: 1\n",
+    "   y: 2\n",
+    " ```\n",
+    "\n",
+    "When parsed into Python, this becomes\n",
+    "\n",
+    "```\n",
+    "{'foo': 'a string',\n",
+    " 'bar': 1,\n",
+    " 'baz': ['a', 'b', 'c'],\n",
+    " 'list': ['i', 'j'],\n",
+    " 'nested': {'x': 1, 'y': 2}}\n",
+    "```\n",
+    "\n",
+    "Using YAML files provides a simple way to define a calibration algorithm in a format that is easy to work with and that Atomica can directly execute. This can cut down the time we spend manually calibrating, or even running autocalibrations. It allows us to conduct reproducible calibration runs, and is also highly scalable, since it allows us to apply the same calibration algorithm in multiple countries or settings.\n",
+    "\n",
+    "The following tutorial outlines how to use the YAML framework that has been developed for Atomica calibration. Specifically, it will cover how to write a YAML configuration file with calibration instructions for Atomica, and how to use this file to execute a calibration. Bear in mind that YAML calibration is not intended to be a standalone tool that will perfectly calibrate any model – rather, it is one part of the calibration toolbox. It can be used to reduce the time spent on calibration by autocalibrating Atomica models to a reasonable level, but additional tweaking may be required to obtain a consistently high calibration quality across all parameters, populations and/or countries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "## Basic calibration example\n",
+    "\n",
+    "In this tutorial, we will work with a simple version of a typhoid model. This model captures typhoid infections, as well as asymptomatic carriers and vaccination. Firstly, we need to create an Atomica `Project` by loading in the Framework and Databook files, just like we did in [the first Atomica tutorial](https://atomica.tools/docs/master/tutorial/T1-Defining-a-model.html). The Framework and Databook for this project can be found in the Atomica repository under `T7/assets`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica as at\n",
+    "F = at.ProjectFramework('assets/T7_framework.xlsx')\n",
+    "D = at.ProjectData.from_spreadsheet('assets/T7_databook.xlsx', framework=F)\n",
+    "P = at.Project(framework=F, databook=D, do_run=False)\n",
+    "P.settings.update_time_vector(start=2000, end=2040, dt=1/52)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "In the example above, no calibration has been loaded, so all the calibration Y-factors are equal to 1, and the model is uncalibrated. We can run a simulation and plot it, to see what our model looks like before calibration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uncal = P.make_parset()\n",
+    "res = P.run_sim(parset=uncal, result_name='Uncalibrated')\n",
+    "d = at.PlotData(res, outputs=['alive','deaths', 'typ_prev', 'typ_num_deaths'], project=P)\n",
+    "fig = at.plot_series(d, axis='pops', data=P.data, n_cols=2, legend_mode='none')[0]\n",
+    "fig.set_size_inches(10,7)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "Two issues with these simulation outputs immediately stand out. First, for the variables and years in which data is available, the model output doesn't match the data very well at all. Second, there is a large sudden change in the values of several variables right at the start of the simulation. In the absence of associated changes in interventions or disease transmission, disease burden is typically much more stable over time, so we would not expect to see such sudden changes at the start of the simulation.\n",
+    "\n",
+    "Tutorial 2 covers some of the detail around how to approach calibration. Although the parameters used for calibration vary from model to model, as a general rule, calibration proceeds by\n",
+    "\n",
+    "1. Demographic calibration: Adjusting birth and background death rates to match quantities like the total population size.\n",
+    "2. Epidemiological calibration: Adjusting parameters such as force of infection, diagnosis rate and mortality rate, to match quantities like incidence, prevalence and deaths.\n",
+    "\n",
+    "Each of these steps involves calibrating multiple parameters, which may be adjusted sequentially and repeatedly. They may also be interspersed with adjusting the model's initialization, to help minimize sudden transients at the start of the simulation. The purpose of the YAML file is to specify the sequence of steps to follow in carrying out a calibration run, in terms of which parameters to adjust, the order in which to adjust them (or whether some should be adjusted simultaneously), and how to assess the quality of the calibration at each step of the process. In this tutorial, we will use YAML files to specify a sequence of steps to calibrate the simple typhoid model shown above, starting with a minimal example and then introducing key features provided by Atomica's YAML calibration system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Minimal YAML file\n",
+    "\n",
+    "To illustrate how YAML calibration works, let’s start with a simple example of what a YAML file could look like:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tadjustables: b_rate, mig_rate\n",
+    "    \tmeasurables: alive\n",
+    "\n",
+    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = uncal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
+    "\n",
+    "Running the YAML calibration is very similar to performing a standalone auto-calibration. After saving the YAML file to disk (e.g., `T7_YAML_1_minimal.yaml`), the calibration can be run using\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = uncal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "As the YAML calibration framework allows us to specify the adjustables and measurables, it is not necessary to provide them to `Project.calibrate()` – simply providing the YAML file is sufficient. However, the YAML file can contain multiple calibration commands, and therefore a single call to `Project.calibrate()` with a YAML file might be equivalent to multiple explicit calibration steps. The resulting simulation after running this simple calibration is like so: \n",
+    "\n",
+    "<img src=\"assets/T7_plot_2_simple.png\" alt=\"Simple calibration result\" style=\"width: 800px;\"/>\n",
+    "<img src=\"assets/T7_plot_1_simple_compare.png\" alt=\"Simple calibration comparison\" style=\"width: 450px;\"/>\n",
+    "\n",
+    "As you can see, these plots don't look very different to the uncalibrated simulation results. If we plot the uncalibrated simulation in the same plot as our simple calibration, we can see that there has been a slight change, but not nearly enough to be able to consider this a good calibration. Next, we will illustrate the YAML calibration features that we can use to improve on this initial result. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Sections\n",
+    "\n",
+    "At the most basic level, a YAML calibration file defines a sequence of individual steps, where each step incrementally modifies the calibration. The YAML file therefore defines an overall _algorithm_ for performing an automatic calibration. This algorithm is defined using two structures in the YAML file:\n",
+    "\n",
+    "- _actions_, which are associated with particular operations, like running a gradient-descent calibration step with a particular set of parameters and data. A calibration action contains `adjustables` and `measurables`. Other examples of actions are detailed below.\n",
+    "- _sections_, which are containers for actions. Sections can contain attributes, such as how many times to repeat the contents of the section, or they can define settings that are applied to any relevant actions within that section.\n",
+    "\n",
+    "The original YAML file above consisted of a single calibration action. If we wanted to extend the algorithm by adding a step to calibrate the death rate, we could update our YAML file to the following: \n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    Match population sizes:\n",
+    "        adjustables: b_rate, mig_rate\n",
+    "        measurables: alive\n",
+    "    Match deaths:\n",
+    "        adjustables: d_rate\n",
+    "        measurables: deaths\n",
+    "```\n",
+    "\n",
+    "To organize the YAML file further, we could group these into an additional section. Both of these actions affect the overall population calibration, so we could logically group them as follows:\n",
+    "\n",
+    "```\n",
+    "calibration: \n",
+    "    Population calibration:\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "            measurables: alive\n",
+    "        Match deaths:\n",
+    "            adjustables: d_rate\n",
+    "            measurables: deaths\n",
+    "```\n",
+    "\n",
+    "The overall structure of this YAML file is thus:\n",
+    "\n",
+    "1. A top-level section titled `calibration`, which has one sub-section (`Population calibration`)\n",
+    "2. A sub-section called `Population calibration`, which in turn contains two actions\n",
+    "3. An action called `Match population sizes`, corresponding to the original calibration step we used to adjust the birth rate and migration rate\n",
+    "4. An action called `Match deaths`, corresponding to the new step of adjusting the death rate\n",
+    "\n",
+    "An action is differentiated from a section in two possible ways:\n",
+    "\n",
+    "- By its contents (e.g. if it contains `adjustables` and `measurables`, then it will be interpreted as a calibration action rather than a section)\n",
+    "- By the title (if the name corresponds to the name of a supported operation, as described below)\n",
+    "\n",
+    "Additionally, actions can never contain any sub-sections, whereas sections always do.\n",
+    "Apart from the names of supported operations, sections can be freely named."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "### Repeating a section\n",
+    "\n",
+    "The *repeats* keyword can be used to loop over any part of the calibration multiple times. We do this by writing `repeats: n` inside a particular section, where *n* is the number of times we would like to loop over that section. All subsections contained in it will also be looped over *n* times. \n",
+    "\n",
+    "    calibration: \n",
+    "        repeats: 2\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                repeats: 2\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "\n",
+    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
+    "\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
+    "    parset = P.calibrate(parset = parset, adjustables = [d_rate], measurables=deaths)\n",
+    "\n",
+    "In this way, it is possible for even a very compact YAML file to correspond to a large number of individual autocalibration steps. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## Sections vs Actions\n",
+    "\n",
+    "As shown above, sections can help us to structure the calibration in a way that is practical and intuitive. They can be used to group blocks of YAML code that are conceptually related, that we want to repeat together several times, or that we want to apply similar settings to (we will cover which settings are supported in [the corresponding section](#specifying-settings-in-outer-sections)).\n",
+    "\n",
+    "Importantly though, _sections_ do not modify the calibration itself – they are merely wrappers for the innermost blocks that actually correspond to specific actions. It is in these _actions_ that operations are performed on the calibration, such as modifying the calibration or saving it.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "We can tell action blocks apart from sections because action blocks contain keywords indicating what kind of block they are, and they don't contain any sub-sections.\n",
+    "</div>\n",
+    "\n",
+    "It is possible to load and inspect a YAML file in Atomica without executing it. This can help confirm that the YAML file has been parsed correctly. After loading in the YAML file, it can be printed to show a summary of the sections and actions that are present, how they are nested, and how many times they are repeated: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atomica.yaml_calibration \n",
+    "calibration_tree = at.yaml_calibration.build('calibrations/T7_YAML_3_repeats.yaml') \n",
+    "print(calibration_tree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## Types of Action Blocks\n",
+    "\n",
+    "YAML calibration files can contain the following types of actions, or action blocks: \n",
+    "\n",
+    "- Calibration block\n",
+    "- Initialization block\n",
+    "- Intialization clearing block\n",
+    "- Saving block"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "### Calibration blocks\n",
+    "\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "\n",
+    "#### Adjustable and measurable settings\n",
+    "\n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
+    "\n",
+    "Each adjustable has:\n",
+    "\n",
+    "- `adj_label` (required): Adjustable parameter codename (can be found in the framework)\n",
+    "- `pop_name`: Population to calibrate (default: all populations)\n",
+    "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
+    "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
+    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
+    "\n",
+    "Each measurable has:\n",
+    "\n",
+    "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
+    "- `pop_name`: Population to use for calibration (default: all populations)\n",
+    "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
+    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
+    "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
+    "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
+    "\n",
+    "Note that `sim_start` and `sim_end` are the start and end years that the simulation will run for (the simulation timespan). These are distinct from `cal_start` and `cal_end`, which specify the time period for which we want to calibrate the model, i.e. a subset of the simulation timespan. For more information, see the [section on outer settings](#specifying-settings-in-outer-sections \"Specifying settings in outer sections\"). The `cal_start` and `cal_end` years can be set independently for each measurable, so it is possible to prioritize different years for different variables or for different steps of the calibration.\n",
+    "To specify these adjustables and measurables settings in the YAML file, we simply write the setting names and their values under the relevant parameter name. Each adjustable and measurable is placed on a new line, and their respective settings are also specified on separate indented lines, like so:\n",
+    "\n",
+    "    calibration:\n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \tb_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \tmig_rate: \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                alive:\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "                 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Specifying Populations\n",
+    "\n",
+    "In some cases, we may only want to set adjustables or measurables for a specific population, or we may wish to use different settings for one population compared to another. A population can optionally be specified after the parameter name, as the second element of a tuple. Thus, if we only wish to calibrate some populations, we can rewrite our previous YAML file like so:\n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4): \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\t\t\n",
+    "            \t(mig_rate, 5-14): \n",
+    "            \t\tstarting_y_factor: 1.2 \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "                \tcal_end: 2040\n",
+    "       \n",
+    "Note that we can specify the same settings for more than one adjustable/measurable at once by placing several parameter names before the colon, separated by commas – this is applicable to any set of adjustables/measurables, not only different populations of the same parameter. For example, we could write: \n",
+    "\n",
+    "    calibration: \n",
+    "        Match population sizes:\n",
+    "            adjustables: \n",
+    "            \t(b_rate, 0-4), mig_rate: \n",
+    "            \t\tlower_bound: 0.5\n",
+    "            \t\tupper_bound: 20\n",
+    "                    starting_y_factor: 1.2\t\t \n",
+    "            measurables: \n",
+    "                (alive, 0-4), (alive, 5-14):\n",
+    "                \tcal_start: 2000\n",
+    "               \tcal_end: 2040\n",
+    "\n",
+    "Finally, the same syntax we use to calibrate populations can be used to calibrate transfers and interactions, but in such cases the tuple should have three elements - the parameter name, the _from_ population and the _to_ population. For more information on how to calibrate in these cases, see the documentation on [Calibrating transfers and interactions](https://atomica.tools/docs/master/general/YAML_calibration.html#Calibrating-transfers-and-interactions \"Calibrating transfers and interactions\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Setting initializations\n",
+    "\n",
+    "In some cases, the model may exhibit an unrealistically large transient at the start of the simulation. This can occur if the initial compartment sizes calculated by Atomica are very different to the equilibrium compartment sizes associated with the model's parameters. Two common reasons for this are:\n",
+    "\n",
+    "- The initial conditions are underdetermined, for example, if there are two strains of a disease with different levels of infectiousness, but data is only available on the total prevalence. In the absence of any other information, Atomica will automatically split up the total prevalence equally between the two strains, when in fact maybe one or the other strain may be dominant, which would change the overall transmission.\n",
+    "- The model parameters may not give rise to an equilibrium solution that matches the data. This could be due to data sources being mixed, combined across different years, collected in different ways or with different definitions, or because the simplified dynamics in the model simply don't capture all the processes in the real world.\n",
+    "\n",
+    "Regardless of the cause, there can often be an initial transience period at the beginning of the simulation, where we can observe abrupt spikes in some parameters until the system reaches equilibrium. \n",
+    "\n",
+    "<img src=\"assets/fig_pre_init.png\" alt=\"Pre-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "In some models, this can be treated as a 'burn-in' period, and the initial part of the simulation is simply discarded. However, there is a risk that being too far from the correct initialization can contaminate the parameter estimates during the calibration period. For example, if the model is initialized with the incorrect prevalence, the calibration applied to the force of infection in order to match the observed incidence would be impacted, which might subsequently affect the model's sensitivity to interventions that change the prevalence later on. Therefore, we wish to minimize the effect of transients on the calibration.\n",
+    "\n",
+    "We can sometimes achieve this by setting the calibration start year `cal_start` to be a few years after the simulation start year `sim_start`, so that the simulation has a few years to reach equilibrium before the calibration process itself begins. However, this extends the duration of the simulation, and it might limit the amount of data that can be used for calibration.\n",
+    "\n",
+    "An alternative approach is to override the initial values of our simulation and replace them with new initialization values. Setting an initialization works by running a normal Atomica simulation for a few years (past the initial transient), taking the compartment sizes of that future year, and setting the initial compartment sizes to those stabilized values. In cases where the model parameters are approximately constant, these future compartment sizes will be roughly equivalent to what the initial compartment sizes should be, which will prevent the appearance of an initial transient in the model.\n",
+    "\n",
+    "<img src=\"assets/fig_post_init.png\" alt=\"Post-initialization plot\" style=\"width: 400px;\"/>\n",
+    "\n",
+    "If the model parameters are not roughly constant, the equilibrium that the model converges to in the future might not correspond to the equilibrium solution for the model's initial parameter values. In that case, an initial transient can still occur. To address this, we can remove any time variation in the model's parameter values using the `ParameterSet.make_constant()` method. This will return a copy of the parset in which all parameters are constant over time, thus ensuring that the future compartment sizes are computed based on the same parameter values as the initial simulation year. This often provides a suitable solution, although changes to the total population size due to births and deaths can still take place, so in some cases a small initial transient may still be present. In such cases, repeatedly setting initialization based on a shorter simulation can help minimize the discrepancy. For more information on Atomica initializations, see the [documentation on this topic](https://atomica.tools/docs/master/general/Compartment-Initialization.html \"Initializing compartments\").\n",
+    "\n",
+    "In the YAML file, we indicate that we want to set a new initialization by making a YAML block with the title `set_initialization`. Under this title, we can specify further settings:\n",
+    "\n",
+    "- `init_year` (required): The future year to take the compartment sizes from. The simulation will be run up to this year.\n",
+    "- `constant_parset` (default: False): Whether to make the parameters constant while the initialization is being calculated, and which year to extract these constant parameter values from, i.e. which year to use in `parset.make_constant()`. It can be a numerical value (representing the year from which to draw the constant values for the parset) or Boolean (if `True`, the default year `sim_start` will be used).\n",
+    "\n",
+    "There are thus several valid ways to set an initialization. For example, only setting the initialization year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using the parameter values from the default `sim_start` year:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: True\n",
+    "```\n",
+    "\n",
+    "Setting the initialization year with a constant parset, using constant parameter values from a specific year:\n",
+    "\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    set_initialization:\n",
+    "        init_year: 2030\n",
+    "        constant_parset: 2005\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "### Clearing initializations\n",
+    "\n",
+    "If we have previously set an initialization in our YAML calibration algorithm, and we then set another initialization later in the YAML file, it uses information from the previous initialization to calculate the next, since the new simulation will start from the initial compartment sizes calculated in that previous initialization.\n",
+    "\n",
+    "Sometimes we might wish to calculate a new initialization from scratch, without using our previous initializations as a starting point. This could be useful if we have done some calibration steps between the previous initialization and now, in which case the y-factors will have changed, which in turn could have caused the equilibrium values to vary.\n",
+    "\n",
+    "To do this in the YAML file, we can make a section titled `clear_initialization`, followed by a Boolean value. If it is set to `True`, any existing initialization will be cleared; if `False`, nothing will happen. The most simple example is as follows:\n",
+    "\n",
+    "```\n",
+    "calibration:\n",
+    "    clear_initialization: True\n",
+    "```\n",
+    "\n",
+    "Below is a more practical example of how we would use the `clear_initialization` feature in a real YAML file, where we first set an initialization, perform some calibration steps, and then clear our previous initialization before making a new one.\n",
+    "\n",
+    "    calibration:\n",
+    "        First initialization:\n",
+    "            set_initialization:\n",
+    "                init_year: 2030\n",
+    "\n",
+    "        Match population sizes:\n",
+    "            adjustables: b_rate, mig_rate\n",
+    "        \tmeasurables: alive\n",
+    "        \n",
+    "    \tclear_initialization: True\n",
+    "\n",
+    "        Second initialization:\n",
+    "            set_initialization:\n",
+    "                init_year: 2030\n",
+    "\n",
+    "\n",
+    "Note that we need to wrap `set_initialization` in an outer section, since YAML does not allow duplicate keys (i.e. identical section/action titles at the same level)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "### Saving calibrations\n",
+    "\n",
+    "Throughout a YAML calibration, we might wish to save the calibration state at specific points in the process. For example, if our YAML file has a population section and an epidemiological section, we might want to save the calibration after the population calibration section so we can see the progress made up to that point, or otherwise identify the effect that different parts of the algorithm are having on the calibration. To save a calibration, we simply make a section titled `save_calibration`. Under the title, we indicate the filename we wish to save the calibration to, either by providing it directly, or by using the keyword `fname` - both examples are shown below:\n",
+    "\n",
+    "    calibration:\n",
+    "    \tPopulation section: \n",
+    "    \t\t[…]\n",
+    "    \n",
+    "    \tsave_calibration: \n",
+    "    \t\tfname: pop_calibration.xlsx\n",
+    "    \n",
+    "    \tEpidemiology section: \n",
+    "    \t\t[…]\n",
+    "\n",
+    "    \tsave_calibration: epi_calibration.xlsx\n",
+    "        \n",
+    "Note that when we save a calibration, if the initial compartment sizes have been explicitly specified using `set_initialization`, these compartment sizes will be saved along with the y-factors in the same Excel file. Loading the calibration from this file will thus also set the initial compartment sizes to those values.\n",
+    "\n",
+    "Another option that can be useful for debugging is to save the calibration state at every intermediate step of the YAML file. In that case, we can use the `save_intermediate_calibrations` option when loading in and running the calibration, e.g.\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset = cal, yaml='T7_YAML_1_minimal.yaml', save_intermediate=True)\n",
+    "\n",
+    "More options for running the calibration can be found [here](#running-the-yaml-calibration \"Running the YAML calibration\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Specifying settings in outer sections\n",
+    "\n",
+    "We saw previously that sections (i.e. YAML blocks that don't correspond to actions) don't directly modify the calibration, and are mainly used to structure the YAML file. However, it is possible to determine calibration settings inside a section, such that they are passed down to any subsections or action blocks contained within. Another way to think of this is that an action block will inherit any settings that are defined in any of its parent sections.\n",
+    "\n",
+    "This can simplify the process of writing YAML files where we want to override the default settings in several action blocks, as it allows us to specify those settings once in a parent section, rather than repeatedly in every action block. Additionally, this feature is hierarchical, so settings that are specified further in (e.g. in the action block itself) will always override those set in a section that is further out, allowing for more flexibility.\n",
+    "\n",
+    "Some settings that this feature is commonly used for include: \n",
+    "\n",
+    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for (default: 60 seconds)\n",
+    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by the optimisation algorithm (default: 0.1)\n",
+    "\n",
+    "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
+    "\n",
+    "- `cal_start` and `cal_end`: These can be used to change the calibration timespan for multiple calibration steps at the same time.\n",
+    "- `metric`: To change the metric used to assess calibration quality, i.e. how well the simulation fits the measurables data.\n",
+    "\n",
+    "We will now provide some examples of how this functionality can be used.\n",
+    "\n",
+    "### Inheritance\n",
+    "\n",
+    "Settings are automatically inherited by all subsections. To set a `max_time` of 120 for every calibration step in the entire calibration, we would write the following. This will result in every calibration step running for at most 120 seconds, rather than the default 60 seconds.\n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Settings hierarchy\n",
+    "\n",
+    "Inherited settings can be overwritten inside subsections (in both sections and actions). Say we wanted all calibration blocks to run for a `max_time` of 120 seconds, except for `Match_deaths`, which we want to run for only 60 seconds. In that case, we can override the setting determined in the parent section by specifying the updated value inside `Match_deaths` subsection (which happens to be an action block):\n",
+    "\n",
+    "    calibration:\n",
+    "        max_time: 120\n",
+    "        Population calibration:\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "            Match deaths:\n",
+    "                max_time: 60\n",
+    "                adjustables: d_rate\n",
+    "                measurables: deaths\n",
+    "        \n",
+    "        Epidemiological calibration\n",
+    "            [...]\n",
+    "\n",
+    "### Outer adjustables/measurables settings\n",
+    "\n",
+    "Finally, here is an example of how to use the adjustables and measurables settings outside a calibration block:\n",
+    "\n",
+    "    calibration:\n",
+    "        Population calibration:\n",
+    "            upper_bound: 2\n",
+    "            lower_bound: 0.5\n",
+    "\n",
+    "            Match population sizes:\n",
+    "                adjustables: b_rate, mig_rate\n",
+    "                measurables: alive\n",
+    "\n",
+    "            Match deaths:\n",
+    "                    adjustables: d_rate\n",
+    "                    measurables: deaths\n",
+    "\n",
+    "        Epidemiological calibration:\n",
+    "            [...]\n",
+    "\n",
+    "In this case, the upper and lower bounds have been updated to 2 and 0.5 respectively, for all the adjustables in the `Population calibration` section (that is, `b_rate`, `mig_rate` and `d_rate`). The upper and lower bounds in the Epidemiological section will remain unchanged, using the default values of 0.1 and 10."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "## Running the YAML calibration\n",
+    "\n",
+    "Now that we have finished writing our YAML calibration file, we can proceed to running the calibration. Having loaded a project, `P.calibrate()` can then be called directly, passing in the uncalibrated parset and the name of the YAML file:\n",
+    "\n",
+    "    calibrated_parset = P.calibrate(parset=uncal, yaml='T7_YAML_1_minimal.yaml')\n",
+    "\n",
+    "This function also supports several optional arguments:\n",
+    "\n",
+    "- `savedir`: Any saved calibrations and logs will be saved into this folder (default: current working directory).\n",
+    "- `save_intermediate`: If `True`, this will save a calibration file at every step (default: `False`).\n",
+    "- `max_time`: Maximum amount of time each calibration step will run for (default: 60 seconds).\n",
+    "- `log_output`: If `True`, this will save a text file into the output directory containing all console output, e.g. objective function values (default: `False`).\n",
+    "\n",
+    "The YAML file can also be supplied as a dictionary, which would normally be obtained via\n",
+    "\n",
+    "    import yaml\n",
+    "    yaml_instructions = yaml.load(file, Loader=yaml.FullLoader)\n",
+    "    \n",
+    "The `yaml_instructions` variable above can be passed to `P.calibrate(..., yaml=yaml_instructions)`. This enables changes to be made to the YAML content programmatically prior to running the calibration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## Exercise: Worked Example\n",
+    "\n",
+    "Now that we understand what all the working parts of a YAML file are, let's put it all together. For each question, write a YAML file to calibrate the model as described. Each question will consist of incremental additions to the previous solution.\n",
+    "\n",
+    "_**Question 1.** We want to do a basic population calibration, where we calibrate the birth rate and migration rate to match the data corresponding to the total number of people alive. We also want to calibrate the death rate. What should this YAML file look like?_\n",
+    "\n",
+    "**Hint**: Open the framework and look at the `Compartments`, `Parameters` and `Characteristics` pages. The `Code Names` and `Display Names` show us how we have to write the parameter names in the YAML file, and what quantities they correspond to. Those with a value in the `Databook Page` column have data values supplied, and can therefore be used as measurables.\n",
+    "\n",
+    "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
+    "\n",
+    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
+    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
+    "| COMPARTMENTS    | <td colspan=3> |\n",
+    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
+    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
+    "| PARAMETERS      | <td colspan=3> |\n",
+    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
+    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
+    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
+    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
+    "| CHARACTERISTICS | <td colspan=3> |\n",
+    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20",
+   "metadata": {},
+   "source": [
+    "_**Question 2.** A single calibration run may not be enough to get good results, so let's loop over our simple population calibration ten times. How would we make that change to the YAML file?_\n",
+    "\n",
+    "**Hint**: Use the `repeats` keyword.\n",
+    "\n",
+    "_**Question 3.** Say that, for this particular project, we are only interested in calibrating results from 2005 to 2040. How would we specify this in the YAML file to reduce calibration time?_\n",
+    "\n",
+    "**Hint**: Using the `cal_start` and `cal_end` keywords.\n",
+    "\n",
+    "_**Question 4.** When calibrating the birth rate, it only really makes sense to calibrate the 0-4 population. Modify the YAML file to reflect this._\n",
+    "\n",
+    "**Hint**: We place the parameter code name and population name in a tuple.\n",
+    "\n",
+    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set the starting y_factor for this parameter to 1.2 to speed up the optimisation._\n",
+    "\n",
+    "**Hint**: Use `starting_y_factor`.\n",
+    "\n",
+    "_**Question 6.** We want to avoid the presence of transients in our calibrated simulation. Let’s initialize the calibration in order to eliminate any jumps._\n",
+    "\n",
+    "**Hint**: Use `set_initialization` after the calibration blocks, and make sure the initialization gets re-calculated in every loop! \n",
+    "\n",
+    "_**Question 7.** We also want to clear the previous initialization every time we make a new one, instead of using information from the previous initialization. How can we update the YAML file to reflect this?_\n",
+    "\n",
+    "**Hint**: Use `clear_initialization`, and make sure the initialization gets cleared and re-calculated in every loop!\n",
+    "\n",
+    "_**Question 8.** We are almost ready to run the YAML calibration! Now, what instructions do we need to add to automatically save our population calibration once it is done?_\n",
+    "\n",
+    "**Hint**: Use `save_calibration` and specify a `fname`.\n",
+    "\n",
+    "All solutions to the worked example can be found in the `docs/tutorial/T7/calibrations` folder of the Atomica repository. The calibration obtained after running the last YAML file in this exercise yields the following simulation result:\n",
+    "\n",
+    "<img src=\"assets/T7_plot_3_worked_example.png\" alt=\"Worked example calibration result\" style=\"width: 800px;\"/>\n",
+    "\n",
+    "The above calibration result is an improvement on the simple example shown earlier in the tutorial - while the simulation result for the 15-64 age group (in green) may not look as close to the data, the remaining populations have an excellent fit, leading to a very good overall population calibration. Additionally, all-cause deaths are now calibrated. Note that we do not expect the calibrated simulation to match the data points exactly, but to follow the same overall trends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "## Further resources\n",
+    "\n",
+    "### Epidemiological calibration\n",
+    "\n",
+    "In this tutorial we have demonstrated the key functionality of Atomica's YAML calibration system applied to a population calibration, focussing on adjusting births, migration and deaths to match population size data, without considering disease burden. The same functionality can be used for epidemiological calibration, just with different model parameters and data. A complete YAML file of the population and epidemiological calibration instructions can be found in the Atomica code repository, at `docs/tutorial/T7/calibrations/typ_calibration_instructions.yaml`.\n",
+    "\n",
+    "In this YAML file, we have set the `max_time` to 120, to give the calibration a bit more time to reach an optimal solution in each calibration step. We then calibrate the population following the same principles illustrated previously, repeating the population calibration ten times, and setting a new initialization at the end of each loop, with `constant_parset=True` and the `init_year` set to 2030.\n",
+    "\n",
+    "In the epidemiological calibration section, the typhoid incidence, prevalence, and typhoid deaths are calibrated. Since we don't have a lot of information on the order of magnitude of the susceptibility and infectiousness, the lower and upper bounds are expanded to leave more room for variation. However, we don't want the disease duration `typ_gen_dur` to vary a lot, since we have a pretty clear idea of its magnitude and don't want it to vary too much between calibrations and settings, so we set stricter bounds. \n",
+    "\n",
+    "At the end of the typhoid calibration, we set an initialization in the same way we did for the population calibration. We then repeat the epidemiological calibration ten times, and finally, repeat the whole YAML calibration process (except silencing the epi y-factors) twice.\n",
+    "\n",
+    "The resulting calibration from running this YAML file is like so:\n",
+    "\n",
+    "<img src=\"assets/T7_plot_4_complete.png\" alt=\"Complete calibration result\" style=\"width: 800px;\"/>\n",
+    "\n",
+    "As you can see, this is an improvement on the previous calibrations shown, since the epidemiological parameters (like typhoid prevalence and typhoid deaths) are now also calibrated to match the data.\n",
+    "\n",
+    "__Alternative approach__\n",
+    "\n",
+    "If the epidemiological parameters have a very large impact on the population parameters (for example, if the uncalibrated disease prevalence or disease mortality is much larger than the data would indicate), this can make it hard to properly calibrate the population in the first instance. We might observe that the population y-factors relating to births and migration increase (or, in the case of deaths, decrease) by a lot, to compensate for the large amount of people dying from disease.\n",
+    "\n",
+    "To prevent this behaviour, we can do the following: At the beginning of the YAML file, we set the parameters relating to the typhoid disease (`typ_active_inf` and `typ_car`) to zero. This essentially \"turns off\" of the typhoid disease, giving us the opportunity to reasonably calibrate the population without interference from the disease components. Then, just after the population calibration section, we \"reactivate\" the disease by setting the `typ_active_inf` and `typ_car` y-factors back to 1, before proceeding to the epidemiological calibration. Looping over this whole algorithm a couple of times (except for the first step where we \"turn off\" the disease) allows the population to be fine-tuned while taking the epidemiological parameters into account.\n",
+    "\n",
+    "### Documentation\n",
+    "\n",
+    "For more information on using Atomica's YAML calibration functionality, see [the documentation](https://atomica.tools/docs/master/general/YAML_calibration.html \"YAML documentation page\").\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
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From 87a0d75448c46d72e12cb3aa810e48b33e03594a Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 6 Mar 2025 20:17:40 +0100
Subject: [PATCH 125/161] Update YAML documentation

-Add png files of databook screenshots
-Update links
-Formatting and spelling
---
 docs/general/YAML_calibration.ipynb  |  14 +++++++-------
 docs/general/databook_pops.png       | Bin 0 -> 45849 bytes
 docs/general/databook_pops_Total.png | Bin 0 -> 33878 bytes
 3 files changed, 7 insertions(+), 7 deletions(-)
 create mode 100644 docs/general/databook_pops.png
 create mode 100644 docs/general/databook_pops_Total.png

diff --git a/docs/general/YAML_calibration.ipynb b/docs/general/YAML_calibration.ipynb
index adf79647..0029dfa3 100644
--- a/docs/general/YAML_calibration.ipynb
+++ b/docs/general/YAML_calibration.ipynb
@@ -172,7 +172,7 @@
     "- `pop_name`: Population to calibrate (default: all populations)\n",
     "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
     "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 1)\n",
-    "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable’s current `y_factor` in the parset)\n",
+    "- `starting_y_factor`: Y-factor value the autocalibration will start from when running the optimisation algorithm (default: the adjustable's current `y_factor` in the parset)\n",
     "\n",
     "\n",
     "Each measurable has:\n",
@@ -265,7 +265,7 @@
     "    adjustables: [adj_label, lower_bound, upper_bound, starting_y_factor]\n",
     "    measurables: [meas_label, weight, metric, start_year, end_year]\n",
     "\n",
-    "Although this might seem like a lot of information for each adjustable and measurable, it is not necessary to include each item every time we use list format – only up to the point where the last setting we want to change is. For example, if we just want to set the `b_rate` adjustable’s `lower bound` to, say, 0.2, we only need to list the `par_label` and `lower_bound` values in order. Any subsequent settings will retain their default values.\n",
+    "Although this might seem like a lot of information for each adjustable and measurable, it is not necessary to include each item every time we use list format – only up to the point where the last setting we want to change is. For example, if we just want to set the `b_rate` adjustable's `lower bound` to, say, 0.2, we only need to list the `par_label` and `lower_bound` values in order. Any subsequent settings will retain their default values.\n",
     "\n",
     "    adjustables: [b_rate, 0.2]\n",
     "    measurables: [alive]"
@@ -377,7 +377,7 @@
    "source": [
     "### Calibrating transfers and interactions\n",
     "\n",
-    "In the case of transfers and interactions, there are two populations involved: the ‘to’ population, and the ‘from' population. The approach is the same as with regular populations, except that we now have two population names in the tuple instead of one: \n",
+    "In the case of transfers and interactions, there are two populations involved: the _to_ population, and the _from_ population. The approach is the same as with regular populations, except that we now have two population names in the tuple instead of one:\n",
     "\n",
     "    adjustables:\n",
     "        (aging, 5-14, 15-64): \n",
@@ -401,7 +401,7 @@
     "\n",
     "Usually, our data is structured like below, with each parameter containing several populations, such as age groups. \n",
     "\n",
-    "![](databook_pops.png \"Example of a databook with multiple populations\")\n",
+    "<img src=\"databook_pops.png\" alt=\"Example of a databook with multiple populations\" style=\"width: 800px;\"/>\n",
     "\n",
     "In the YAML file, we could write \n",
     "\n",
@@ -411,9 +411,9 @@
     "\n",
     "Since no population has been specified, all populations will be calibrated. \n",
     "\n",
-    "However, for some parameters, our source data might not be broken down into populations or age groups. In that case, the above YAML file will not work, since there is no data available at the individual population level. What we can do in that situation is add an extra row to the databook with a population called `Total` (which is a reserved keyword in Atomica), and explicitly set the population name to `Total` in the YAML file. If it was our 'alive' data that was not broken down by populations, the databook would look like so\n",
+    "However, for some parameters, our source data might not be broken down into populations or age groups. In that case, the above YAML file will not work, since there is no data available at the individual population level. What we can do in that situation is to add an extra row to the databook with a population called `Total` (which is a reserved keyword in Atomica), and explicitly set the population name to `Total` in the YAML file. If it was our 'alive' data that was not broken down by populations, the databook would look like so\n",
     "\n",
-    "![](databook_pops_Total.png \"Example of a databook with a Total population\")\n",
+    "<img src=\"databook_pops_Total.png\" alt=\"Example of a databook with a Total population\" style=\"width: 800px;\"/>\n",
     "\n",
     "And we would adjust the previous YAML file like so.\n",
     "\n",
@@ -429,7 +429,7 @@
    "source": [
     "## Measurable Weights \n",
     "\n",
-    "In one calibration block, we can include several measurables at the same time, or multiple populations of the same measurable. But say we trusted the data from one measurable’s data source more than another, or wanted to prioritise fitting a particular population – how would we indicate this to the optimisation algorithm? \n",
+    "In one calibration block, we can include several measurables at the same time, or multiple populations of the same measurable. But say we trusted the data from one measurable's data source more than another, or wanted to prioritise fitting a particular population – how would we indicate this to the optimisation algorithm?\n",
     "\n",
     "In the measurable settings, we can set weights for this purpose. The default value for the `weight` setting is 1.0 which is used for all measurables and populations, which corresponds to giving them each an equal weight, regardless of size. That might be desirable if, for example, we have a key population that is smaller than the other populations – if they were weighted proportionally to size, the small key population might be effectively ignored in the optimisation. However, there might be cases where we want to do things differently. For example, we could give a key population a higher weight, or we could weight different age bins according to their size. Another reason to use measurable weights could simply be that we trust one data source more than another. \n",
     "\n",
diff --git a/docs/general/databook_pops.png b/docs/general/databook_pops.png
new file mode 100644
index 0000000000000000000000000000000000000000..be026587cb66082785c2452030ecc1f318199f16
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literal 0
HcmV?d00001


From 262823680c76b9f166746e231fafc903b40a909b Mon Sep 17 00:00:00 2001
From: Eloisa Perez-Bennetts <49991958+epbennetts@users.noreply.github.com>
Date: Thu, 6 Mar 2025 20:24:12 +0100
Subject: [PATCH 126/161] Consistent spelling in T7

Update spelling of 'optimization' for consistency
---
 docs/tutorial/T7/T7_YAML_autocalibration.ipynb | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index e142307a..2a3f3610 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -117,7 +117,7 @@
     "    \tadjustables: b_rate, mig_rate\n",
     "    \tmeasurables: alive\n",
     "\n",
-    "The above YAML file represents a single call to the autocalibration optimisation function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
+    "The above YAML file represents a single call to the autocalibration optimization function, where the `b_rate` and `mig_rate` y-factors are adjusted to match `alive`. It is equivalent to running a calibration with the following command:\n",
     "\n",
     "    calibrated_parset = P.calibrate(parset = uncal, adjustables = ['b_rate', 'mig_rate'], measurables = 'alive')\n",
     "\n",
@@ -207,7 +207,7 @@
     "                adjustables: d_rate\n",
     "                measurables: deaths\n",
     "\n",
-    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimisation algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to \n",
+    "In the above example, we have set `repeats: 2` inside the `calibration` section, so the entire YAML calibration will be repeated twice. Then, the `Match population sizes` section also has `repeats: 2`, so the calibration step defined in that section will also be repeated twice each time. In total, there will be four calls to the optimization algorithm to match `alive`, and two to match `deaths, so the YAML file above would be equivalent to\n",
     "\n",
     "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
     "    parset = P.calibrate(parset = parset, adjustables = [b_rate, mig_rate], measurables=alive)\n",
@@ -271,11 +271,11 @@
    "source": [
     "### Calibration blocks\n",
     "\n",
-    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimisation algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimisation run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
+    "In all of the YAML examples shown above, we have worked with 'calibration blocks', which are the main type of action. Calibration blocks are defined by the fact that they contain the keywords `adjustables` and `measurables`. Under `adjustables`, we list the parameters for Atomica’s optimization algorithm to adjust, and under `measurables`, we list the parameters to calibrate to. Each calibration block provides instructions for one optimization run, and is equivalent to making one call to `P.calibrate()` with the same adjustables and measurables.\n",
     "\n",
     "#### Adjustable and measurable settings\n",
     "\n",
-    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimisation algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimisation, which directly map to the options supported by `P.calibrate()`.\n",
+    "So far, we have only specified the names of the adjustables and measurables, with no further information – in that case, the optimization algorithm will use the default calibration settings for adjustables and measurables. For more flexibility, we can customise the settings that will be used for the optimization, which directly map to the options supported by `P.calibrate()`.\n",
     "\n",
     "Each adjustable has:\n",
     "\n",
@@ -283,14 +283,14 @@
     "- `pop_name`: Population to calibrate (default: all populations)\n",
     "- `lower_bound`: Lowest value the y-factor will be allowed to take (default: 0.1)\n",
     "- `upper_bound`: Highest value the y-factor will be allowed to take (default: 10)\n",
-    "- `starting_y_factor`: Y-factor value the optimisation algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
+    "- `starting_y_factor`: Y-factor value the optimization algorithm will start from (default: the adjustable's current `y_factor` in the parset)\n",
     "\n",
     "Each measurable has:\n",
     "\n",
     "- `meas_label` (required): Measurable parameter codename (can be found in the framework)\n",
     "- `pop_name`: Population to use for calibration (default: all populations)\n",
     "- `weight`: Weight for a particular population (default: 1). By default, all populations are weighted equally regardless of size. See [the documentation on weights](https://atomica.tools/docs/master/general/YAML_calibration.html#Measurable-Weights \"Measurable weights\") for further details.\n",
-    "- `metric`: Metric to be used by the optimisation algorithm (default: fractional)\n",
+    "- `metric`: Metric to be used by the optimization algorithm (default: fractional)\n",
     "- `cal_start`: Starting year that the calibration will be evaluated from (default: `sim_start`)\n",
     "- `cal_end`: End year until which the calibration will be evaluated (default: `sim_end`)\n",
     "\n",
@@ -503,8 +503,8 @@
     "\n",
     "Some settings that this feature is commonly used for include: \n",
     "\n",
-    "- `max_time`: Maximum amount of time each call to the optimisation algorithm will run for (default: 60 seconds)\n",
-    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by the optimisation algorithm (default: 0.1)\n",
+    "- `max_time`: Maximum amount of time each call to the optimization algorithm will run for (default: 60 seconds)\n",
+    "- `stepsize`: Initial step size, i.e. how much the y-factors will be increased/decreased by the optimization algorithm (default: 0.1)\n",
     "\n",
     "Additionally, any adjustable or measurable setting can also be set outside of its calibration block, although some settings lend themselves more to this feature than others. For example, changing the measurable `weight` for an entire calibration block has no effect, as what matters is the proportion between the weights of different measurables in the same block. The following settings can be useful to set in parent sections:\n",
     "\n",
@@ -643,7 +643,7 @@
     "\n",
     "**Hint**: We place the parameter code name and population name in a tuple.\n",
     "\n",
-    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set the starting y_factor for this parameter to 1.2 to speed up the optimisation._\n",
+    "_**Question 5.** Say we know that our data source underestimates the birth rate. Let’s set the starting y_factor for this parameter to 1.2 to speed up the optimization._\n",
     "\n",
     "**Hint**: Use `starting_y_factor`.\n",
     "\n",

From 616dcd07005d3641b5879363c8c71ce31789e7f9 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 10 Mar 2025 10:58:28 +1000
Subject: [PATCH 127/161] Fix table

---
 .../tutorial/T7/T7_YAML_autocalibration.ipynb | 71 +++++++++++++++----
 1 file changed, 56 insertions(+), 15 deletions(-)

diff --git a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
index 2a3f3610..f557fd60 100644
--- a/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
+++ b/docs/tutorial/T7/T7_YAML_autocalibration.ipynb
@@ -316,7 +316,6 @@
    "cell_type": "markdown",
    "id": "13",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -612,18 +611,55 @@
     "\n",
     "**Hint 2**: Some of the parameter `Code Names` and `Display Names` in the databook are as follows: \n",
     "\n",
-    "| Framework Sheet |   Code Name    |          Display Name          | Databook Page |\n",
-    "|:----------------|:--------------:|:------------------------------:|--------------:|\n",
-    "| COMPARTMENTS    | <td colspan=3> |\n",
-    "|                 |    birth\t\t     |        Birth    \t\t\t\t\t\t         |          none |\n",
-    "|                 |   death   \t    |        Death    \t\t\t\t\t\t         |          none |\n",
-    "| PARAMETERS      | <td colspan=3> |\n",
-    "|                 |    b_rate\t\t    |      Birth rate     \t\t\t\t       |   demographic |\n",
-    "|                 |    deaths\t\t    |    All-cause deaths    \t\t\t     |   demographic |\n",
-    "|                 |    d_rate\t\t    | Background mortality rate    \t |   demographic |\n",
-    "|                 |   mig_rate\t    |     Migration Rate    \t\t\t\t     |   demographic |\n",
-    "| CHARACTERISTICS | <td colspan=3> |\n",
-    "|                 |     alive\t     |      \tTotal population\t\t       |   demographic |"
+    "<table class=\"docutils align-default\">  <thead>\n",
+    "    <tr>\n",
+    "      <th>Framework Sheet</th>\n",
+    "      <th>Code Name</th>\n",
+    "      <th>Display Name</th>\n",
+    "      <th>Databook Page</th>\n",
+    "    </tr>\n",
+    "  </thead>\n",
+    "  <tbody>\n",
+    "    <tr>\n",
+    "      <td rowspan=\"2\">Compartments</td>\n",
+    "      <td>birth</td>\n",
+    "      <td>Birth</td>\n",
+    "      <td>none</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td>death</td>\n",
+    "      <td>Death</td>\n",
+    "      <td>none</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td rowspan=\"4\">Parameters</td>\n",
+    "      <td>b_rate</td>\n",
+    "      <td>Birth rate</td>\n",
+    "      <td>demographic</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td>deaths</td>\n",
+    "      <td>All-cause deaths</td>\n",
+    "      <td>demographic</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td>d_rate</td>\n",
+    "      <td>Background mortality rate</td>\n",
+    "      <td>demographic</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td>mig_rate</td>\n",
+    "      <td>Migration Rate</td>\n",
+    "      <td>demographic</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "      <td rowspan=\"1\">Characteristics</td>\n",
+    "      <td>alive</td>\n",
+    "      <td>Total population</td>\n",
+    "      <td>demographic</td>\n",
+    "    </tr>\n",
+    "  </tbody>\n",
+    "</table>"
    ]
   },
   {
@@ -717,7 +753,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   },
   "toc": {
    "base_numbering": 1,
@@ -728,7 +764,12 @@
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
    "toc_cell": false,
-   "toc_position": {},
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "343.4px"
+   },
    "toc_section_display": true,
    "toc_window_display": true
   }

From 9483b7b867525fbb71e6c9e685b2575c0e39a89a Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Fri, 30 May 2025 15:44:05 -0400
Subject: [PATCH 128/161] replace setup.py with pyproject.toml

---
 docs/index.rst  | 20 ++++++++++++++++-
 pyproject.toml  | 56 ++++++++++++++++++++++++++++++++++++++++++++++
 setup.py        | 59 -------------------------------------------------
 tests/simple.py | 10 +++++++++
 4 files changed, 85 insertions(+), 60 deletions(-)
 create mode 100644 pyproject.toml
 delete mode 100644 setup.py
 create mode 100644 tests/simple.py

diff --git a/docs/index.rst b/docs/index.rst
index 16beee87..4cadb6a6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -5,7 +5,25 @@
     :align: center
 
 
-Atomica is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
+[Atomica](https://https://github.com/atomicateam/atomica) is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
+
+To install via PyPI:
+
+.. code-block:: python
+
+    pip install atomica
+
+You can run a plot an Atomica demo with:
+
+.. code-block:: python
+
+    import atomica as at
+    import matplotlib.pyplot as plt
+
+    P = at.demo("sir")
+    d = at.PlotData(P.results[0], project=P)
+    figs = at.plot_series(d)
+    plt.show()
 
 .. toctree::
     :maxdepth: 1
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..d6aaafd7
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,56 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "atomica"
+dynamic = ["version"]
+description = "Toolbox for compartment-based dynamic systems with costing and optimization"
+readme = "README.md"
+authors = [
+    { name="Atomica Team", email="info@atomica.tools" },
+]
+license = { text="MIT" }
+keywords = ["dynamic", "compartment", "optimization", "disease"]
+classifiers = [
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Development Status :: 5 - Production/Stable",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13"
+]
+requires-python = ">=3.7"
+
+dependencies = [
+    "matplotlib",
+    "numpy>=1.10.1,<2",
+    "scipy>=1.6",
+    "pandas",
+    "xlsxwriter",
+    "openpyxl",
+    "pyswarm",
+    "hyperopt",
+    "sciris",
+    "tqdm",
+]
+
+[project.urls]
+Homepage = "https://atomica.tools"
+
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+where = ["."]
+
+[tool.setuptools.dynamic]
+version = {attr = "atomica.version.__version__"}
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 53a5d85c..00000000
--- a/setup.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from setuptools import setup, find_packages
-import os
-
-cwd = os.path.abspath(os.path.dirname(__file__))
-
-# Read version
-with open(os.path.join(cwd, "atomica", "version.py"), "r") as f:
-    version = [x.split("=")[1].replace('"', "").strip() for x in f if x.startswith("version =")][0]
-
-# Read README.md for description
-with open(os.path.join(cwd, "README.md"), "r") as f:
-    long_description = f.read()
-
-CLASSIFIERS = [
-    "Environment :: Console",
-    "Intended Audience :: Science/Research",
-    "License :: OSI Approved :: MIT License",
-    "Operating System :: OS Independent",
-    "Programming Language :: Python",
-    "Topic :: Software Development :: Libraries :: Python Modules",
-    "Development Status :: 5 - Production/Stable",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.12",
-]
-
-setup(
-    name="atomica",
-    version=version,
-    author="Atomica Team",
-    author_email="info@atomica.tools",
-    description="Toolbox for compartment-based dynamic systems with costing and optimization",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://atomica.tools",
-    keywords=["dynamic", "compartment", "optimization", "disease"],
-    platforms=["OS Independent"],
-    classifiers=CLASSIFIERS,
-    packages=find_packages(),
-    include_package_data=True,
-    install_requires=[
-        "matplotlib",
-        "numpy>=1.10.1,<2",
-        "scipy>=1.6",
-        "pandas",
-        "xlsxwriter",
-        "openpyxl",
-        "pyswarm",
-        "hyperopt",
-        "sciris",
-        "tqdm",
-    ],
-)
diff --git a/tests/simple.py b/tests/simple.py
new file mode 100644
index 00000000..26d6a963
--- /dev/null
+++ b/tests/simple.py
@@ -0,0 +1,10 @@
+"""
+Minimal example of running Atomica.
+"""
+import atomica as at
+import matplotlib.pyplot as plt
+
+P = at.demo("sir")
+d = at.PlotData(P.results[0], project=P)
+figs = at.plot_series(d)
+plt.show()
\ No newline at end of file

From 8b6fc9fa612d164d8c22a303bd7c9128906d2700 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Fri, 30 May 2025 15:50:02 -0400
Subject: [PATCH 129/161] update version

---
 CHANGELOG.md        | 4 ++++
 atomica/__init__.py | 9 ++++++---
 atomica/version.py  | 8 ++------
 pyproject.toml      | 2 +-
 4 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c1f60ebb..5c07437c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,10 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.1] - 2025-05-30
+
+- Switched from `setup.py` to `pyproject.toml`-based installation.
+
 ## [1.31.0] - 2025-02-18
 
 - Framework definitions of compartments, characteristics, and parameters support a new column 'databook default all'. If set to 'y', then when a databook is produced, the data entry table will only contain a record for 'All' instead of having population-specific rows. Further manual editing of the tables is supported as normal. 
diff --git a/atomica/__init__.py b/atomica/__init__.py
index 0ba03568..55484117 100644
--- a/atomica/__init__.py
+++ b/atomica/__init__.py
@@ -56,12 +56,15 @@
     logger.addHandler(warning_handler)
     logger.setLevel("INFO")  # Set the overall log level
 
-from .version import version as __version__, versiondate as __versiondate__, gitinfo as __gitinfo__
 
-# Check scipy version
-import scipy
+# Get version information
 import sciris as sc
+__gitinfo__ = sc.gitinfo(__file__)
+from .version import version as __version__, versiondate as __versiondate__
+
 
+# Check scipy version
+import scipy
 if sc.compareversions(scipy.__version__, "1.2.1") < 0:
     raise Exception(f"Atomica requires Scipy 1.2.1 or later - installed version is {scipy.__version__}")
 
diff --git a/atomica/version.py b/atomica/version.py
index 6d57766a..751f4d57 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -3,9 +3,5 @@
 
 Standard location for module version number and date.
 """
-
-import sciris as sc
-
-version = "1.31.0"
-versiondate = "2025-02-17"
-gitinfo = sc.gitinfo(__file__)
+version = "1.31.1"
+versiondate = "2025-05-30"
diff --git a/pyproject.toml b/pyproject.toml
index d6aaafd7..70aed4f0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,4 +53,4 @@ include-package-data = true
 where = ["."]
 
 [tool.setuptools.dynamic]
-version = {attr = "atomica.version.__version__"}
\ No newline at end of file
+version = {attr = "atomica.version.version"}
\ No newline at end of file

From 2e9ed306816850166e9e5256ca95e953838bd407 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Fri, 30 May 2025 15:52:31 -0400
Subject: [PATCH 130/161] update order

---
 atomica/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/__init__.py b/atomica/__init__.py
index 55484117..e751eb87 100644
--- a/atomica/__init__.py
+++ b/atomica/__init__.py
@@ -58,9 +58,9 @@
 
 
 # Get version information
+from .version import version as __version__, versiondate as __versiondate__
 import sciris as sc
 __gitinfo__ = sc.gitinfo(__file__)
-from .version import version as __version__, versiondate as __versiondate__
 
 
 # Check scipy version

From 708dc0e7721fed17e4b9f4da9a13dec425d20d41 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Fri, 30 May 2025 15:56:32 -0400
Subject: [PATCH 131/161] add gitinfo back to version module

---
 atomica/__init__.py | 1 +
 docs/index.rst      | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/atomica/__init__.py b/atomica/__init__.py
index e751eb87..a1f7b482 100644
--- a/atomica/__init__.py
+++ b/atomica/__init__.py
@@ -61,6 +61,7 @@
 from .version import version as __version__, versiondate as __versiondate__
 import sciris as sc
 __gitinfo__ = sc.gitinfo(__file__)
+version.gitinfo = __gitinfo__ # Add back (so version.py does not require imports)
 
 
 # Check scipy version
diff --git a/docs/index.rst b/docs/index.rst
index 4cadb6a6..8fb55856 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -13,7 +13,7 @@ To install via PyPI:
 
     pip install atomica
 
-You can run a plot an Atomica demo with:
+You can run and plot an Atomica demo with:
 
 .. code-block:: python
 

From 784a1c94a809f1ac95c4da3b29b23d4748a0da2c Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Fri, 30 May 2025 16:00:46 -0400
Subject: [PATCH 132/161] update license date

---
 LICENSE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index 43382f75..c75ac9ae 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Atomica Development Team
+Copyright (c) 2017 - 2025 Atomica Development Team
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

From 256fde2aa9c2fc7af8f816c20ec86462684a3e1f Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 2 Jun 2025 10:21:49 +1000
Subject: [PATCH 133/161] Lift numpy version restriction

---
 atomica/programs.py | 2 +-
 pyproject.toml      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/programs.py b/atomica/programs.py
index f3f60f1f..02640a22 100644
--- a/atomica/programs.py
+++ b/atomica/programs.py
@@ -1552,7 +1552,7 @@ def get_outcome(self, prop_covered):
         # RANDOM CALCULATION
         elif self.cov_interaction == "random":
             # Outcome += c1(1-c2)* delta_out1 + c2(1-c1)*delta_out2 + c1c2* max(delta_out1,delta_out2)
-            combination_coverage = np.product(self.combinations * cov + (self.combinations ^ 1) * (1 - cov), axis=1)
+            combination_coverage = np.prod(self.combinations * cov + (self.combinations ^ 1) * (1 - cov), axis=1)
             outcome += np.sum(combination_coverage.ravel() * self._combination_outcomes.ravel())
         else:
             raise Exception('Unknown reachability type "%s"', self.cov_interaction)
diff --git a/pyproject.toml b/pyproject.toml
index 70aed4f0..af0cf1e5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ requires-python = ">=3.7"
 
 dependencies = [
     "matplotlib",
-    "numpy>=1.10.1,<2",
+    "numpy>=1.10.1",
     "scipy>=1.6",
     "pandas",
     "xlsxwriter",

From 46a40d95359e2d325222cb0f7c077c91b6fa6469 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 2 Jun 2025 10:26:21 +1000
Subject: [PATCH 134/161] Fix RST link syntax

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index 8fb55856..3397fb48 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -5,7 +5,7 @@
     :align: center
 
 
-[Atomica](https://https://github.com/atomicateam/atomica) is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
+`Atomica <https://github.com/atomicateam/atomica>`_ is a simulation engine for compartmental models. It can be used to simulate disease epidemics, health care cascades, and many other things.
 
 To install via PyPI:
 

From 5146fe62483e7343cd257bb384f767d6946de2d6 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 18 Jun 2025 10:02:06 +1000
Subject: [PATCH 135/161] Fix comment

---
 atomica/excel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/excel.py b/atomica/excel.py
index 304490ab..c40f46dc 100644
--- a/atomica/excel.py
+++ b/atomica/excel.py
@@ -403,7 +403,7 @@ def __init__(self, code_name: str, full_name: str, tvec: np.array, from_pops: li
         self.ts = ts if ts is not None else sc.odict()
 
         self.attributes = {}  #: Attributes associated with the table
-        self.ts_attributes = {}  #: Attributes associated with each TimeSeries row. This is a dict-of-dicts with `ts_attributes[attribute]={pop:value}` e.g., ts_attributes["Provenance"] = {'popA': 'value', 'popB': 'value'}
+        self.ts_attributes = {}  #: Attributes associated with each TimeSeries row. This is a dict-of-dicts with `ts_attributes[attribute]={(from_pop, to_pop):value}` e.g., `ts_attributes["Provenance"] = {('0-4', '5-14'): 'value'}`
         self.ts_attributes["Provenance"] = {}  # Include provenance attribute by default
 
         self.assumption_heading = "Constant"  #: Heading to use for assumption column

From 9192dc10ff3864c0dda228c6c75f091421c0d1c4 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 18 Jun 2025 10:02:30 +1000
Subject: [PATCH 136/161] Adjust precision

---
 tests/test_tox_timed_compartments.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_tox_timed_compartments.py b/tests/test_tox_timed_compartments.py
index c8db579f..1cd6e68a 100644
--- a/tests/test_tox_timed_compartments.py
+++ b/tests/test_tox_timed_compartments.py
@@ -55,6 +55,7 @@ def test_zero_duration():
     P = get_project()
     ps = P.parsets[0].copy()
     ps.pars["nat_rec"].ts[0].insert(None, 0)  # Sub-timestep size
+    P.settings.sim_dt = 1/24  # One of the tests had a numerical precision issue on just one platform, hard to reproduce but seeing if this avoids it
     res2 = P.run_sim(ps)
     d = at.PlotData(res2, [":inf", "inf:", "inf"])
     at.plot_series(d)

From ca9a4fdfd4de3be000beb3323de2d16abb17465a Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 18 Jun 2025 15:01:14 +1000
Subject: [PATCH 137/161] Update build/deploy commands

---
 azure-pipelines.yml | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e2bf892e..c9cbc47a 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -93,12 +93,9 @@ jobs:
       inputs:
         versionSpec: '3.12'
     - script: |
-        python -m pip install setuptools
-        python -m pip install wheel
+        python -m pip install build
         python -m pip install twine
-        python setup.py build
-        python setup.py sdist
-        python setup.py bdist_wheel
+        python -m build
       displayName: 'Build Atomica'
     - task: TwineAuthenticate@1
       displayName: 'Twine Authenticate'
@@ -106,4 +103,5 @@ jobs:
         pythonUploadServiceConnection: pypi_atomica
     - script: |
         python -m twine upload -r "pypi_atomica" --config-file $(PYPIRC_PATH) dist/*
-      displayName: 'Upload to PyPI'
\ No newline at end of file
+      displayName: 'Upload to PyPI'
+

From 51545defb8452692f4d0485bb66148147126285c Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Thu, 26 Jun 2025 09:13:24 +1000
Subject: [PATCH 138/161] Fix issue with duplicate databook sheet names

---
 CHANGELOG.md       |  4 ++++
 atomica/data.py    | 18 +++++++-----------
 atomica/version.py |  4 ++--
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5c07437c..70fd0a4c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,10 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.2] - 2025-06-26
+
+- Fix bug in creating databook if framework 'Databook pages' sheet contains multiple code names mapping to the same full name.
+
 ## [1.31.1] - 2025-05-30
 
 - Switched from `setup.py` to `pyproject.toml`-based installation.
diff --git a/atomica/data.py b/atomica/data.py
index 60763755..8b1f28c5 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -56,7 +56,7 @@ def __init__(self, framework):
         self.interpops = list()  #: This stores a list of :class:`TimeDependentConnections` instances for interactions
         self.tvec = None  #: This is the data's tvec used when instantiating new tables. Not _guaranteed_ to be the same for every TDVE/TDC table
         self.tdve = sc.odict()  #: This is an odict storing :class:`TimeDependentValuesEntry` instances keyed by the code name of the TDVE
-        self.tdve_pages = sc.odict()  #: This is an odict mapping worksheet name to an (ordered) list of TDVE code names appearing on that sheet
+        self.tdve_pages = sc.odict(defaultdict=list)  #: This is an odict mapping worksheet name to an (ordered) list of TDVE code names appearing on that sheet
 
         # Internal storage used with methods while writing
         self._pop_types = list(framework.pop_types.keys())  #: Store set of valid population types from framework
@@ -264,7 +264,7 @@ class instance (e.g. if creating a new databook).
         data = ProjectData(framework=framework)
         data.tvec = sc.promotetoarray(tvec)
         pages = defaultdict(list)  # This will store {sheet_name:(code_name,databook_order)} which will then get sorted further
-
+        page_names = framework.sheets["databook pages"][0].set_index('datasheet code name')['datasheet title'].to_dict()
         for obj_type, df in zip(["comps", "characs", "pars"], [framework.comps, framework.characs, framework.pars]):
             for _, spec in df.iterrows():
                 databook_page = spec.get("databook page")
@@ -279,7 +279,7 @@ class instance (e.g. if creating a new databook).
                         order = np.inf
                     else:
                         order = databook_order
-                    pages[databook_page].append((spec.name, order))
+                    pages[page_names[databook_page]].append((spec.name, order))
                     data.tdve[spec.name] = TimeDependentValuesEntry(full_name, data.tvec, allowed_units=allowed_units, comment=spec["guidance"], pop_type=pop_type, default_all=default_all)
                     data.tdve[spec.name].write_units = True
                     data.tdve[spec.name].write_uncertainty = True
@@ -294,14 +294,10 @@ class instance (e.g. if creating a new databook).
                         # add_pop normally adds TDVE rows, but it won't operate on any TDVEs that default to 'All' so we need to add the 'All' rows here
                         data.tdve[spec.name].ts["All"] = TimeSeries(units=allowed_units[0])
 
-        # Now convert pages to full names and sort them into the correct order
-        for _, spec in framework.sheets["databook pages"][0].iterrows():
-
-            if spec["datasheet code name"] in pages:
-                pages[spec["datasheet code name"]].sort(key=lambda x: x[1])
-                data.tdve_pages[spec["datasheet title"]] = [x[0] for x in pages[spec["datasheet code name"]]]
-            else:
-                data.tdve_pages[spec["datasheet title"]] = list()
+        # Now sort them into the correct order
+        for page, tables in pages.items():
+            tables.sort(key=lambda x: x[1])
+            data.tdve_pages[page] = [x[0] for x in tables]
 
         # Now, proceed to add pops, transfers, and interactions
         for code_name, spec in new_pops.items():
diff --git a/atomica/version.py b/atomica/version.py
index 751f4d57..26dfd0e0 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -3,5 +3,5 @@
 
 Standard location for module version number and date.
 """
-version = "1.31.1"
-versiondate = "2025-05-30"
+version = "1.31.2"
+versiondate = "2025-06-26"

From 8fe80dd2645a84f30684f6531172ebacce6dac65 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 27 Jun 2025 17:21:39 +1000
Subject: [PATCH 139/161] Proposed solution

---
 atomica/model.py               |  52 +++++++++++++++++-------
 tests/framework_init_zero.xlsx | Bin 0 -> 31751 bytes
 tests/test_tox_zero_init.py    |  70 +++++++++++++++++++++++++++++++++
 3 files changed, 107 insertions(+), 15 deletions(-)
 create mode 100644 tests/framework_init_zero.xlsx
 create mode 100644 tests/test_tox_zero_init.py

diff --git a/atomica/model.py b/atomica/model.py
index 2e67badf..3f3e05d4 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1861,27 +1861,48 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
         # values for the compartments by solving the set of characteristics simultaneously
 
         # Build up the comps and characs containing the setup values in the databook - the `b` in `x=A*b`
-        characs_to_use = framework.characs.index[(framework.characs["setup weight"] > 0) & (framework.characs["population type"] == self.type)]
-        comps_to_use = framework.comps.index[(framework.comps["setup weight"] > 0) & (framework.comps["population type"] == self.type)]
-        b_objs = [self.charac_lookup[x] for x in characs_to_use] + [self.comp_lookup[x] for x in comps_to_use]
+        characs_to_use = set(framework.characs.index[(framework.characs["setup weight"] > 0) & (framework.characs["population type"] == self.type)])
+        comps_to_use = set(framework.comps.index[(framework.comps["setup weight"] > 0) & (framework.comps["population type"] == self.type)])
+
+        b_vals = {}
+        b_objs = {}
+
+        # First add all of the compartments that will be used from the databook
+        for comp in comps_to_use:
+            b_objs[comp] = self.comp_lookup[comp]
+            par = parset.pars[comp]
+            b_vals[comp] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+
+        # Then add all of the characteristics, and additional zero compartments
+        for charac in characs_to_use:
+            obj = self.charac_lookup[charac]
+            b_objs[charac] = obj
+            par = parset.pars[charac]
+            b_vals[charac] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+            if b_vals[charac] == 0:
+                for comp in obj.get_included_comps():
+                    if comp.name in b_vals and b_vals[comp.name] != 0:
+                        raise BadInitialization(f'Compartment {comp.name} was explicitly specified as having a non-zero value, but characteristic {charac} has a zero value - input data not consistent')
+                    else:
+                        b_vals[comp.name] = 0
+                        b_objs[comp.name] = comp
+            else:
+                if obj.denominator is not None:
+                    denom_par = parset.pars[obj.denominator.name]
+                    b_vals[charac] *= denom_par.interpolate(t_init, pop_name=self.name)[0] * denom_par.y_factor[self.name] * denom_par.meta_y_factor
+
 
         # Build up the comps corresponding to the `x` values in `x=A*b` i.e. the compartments being solved for
         comps = [c for c in self.comps if not (isinstance(c, SourceCompartment) or isinstance(c, SinkCompartment))]
-        charac_indices = {c.name: i for i, c in enumerate(b_objs)}  # Make lookup dict for characteristic indices
+        b_indices = {c.name: i for i, c in enumerate(b_objs.values())}  # Make lookup dict for characteristic indices
         comp_indices = {c.name: i for i, c in enumerate(comps)}  # Make lookup dict for compartment indices
 
-        b = np.zeros((len(b_objs), 1))
+        b = np.fromiter(b_vals.values(),dtype=float).reshape(-1,1)
         A = np.zeros((len(b_objs), len(comps)))
 
-        # Construct the characteristic value vector (b) and the includes matrix (A)
-        for i, obj in enumerate(b_objs):
-            # Look up the characteristic value
-            par = parset.pars[obj.name]
-            b[i] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+        # Fill out the includes matrix (A)
+        for i, obj in enumerate(b_objs.values()):
             if isinstance(obj, Characteristic):
-                if obj.denominator is not None:
-                    denom_par = parset.pars[obj.denominator.name]
-                    b[i] *= denom_par.interpolate(t_init, pop_name=self.name)[0] * denom_par.y_factor[self.name] * denom_par.meta_y_factor
                 for inc in obj.get_included_comps():
                     A[i, comp_indices[inc.name]] = 1.0
             else:
@@ -1889,6 +1910,7 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
 
         # Solve the linear system (nb. lstsq returns the minimum norm solution
         x = np.linalg.lstsq(A, b.ravel(), rcond=None)[0].reshape(-1, 1)
+
         proposed = np.matmul(A, x)
         residual = np.sum((proposed.ravel() - b.ravel()) ** 2)
 
@@ -1922,8 +1944,8 @@ def report_characteristic(charac, n_indent=0):
             """
 
             msg = ""
-            if charac.name in charac_indices:
-                msg += n_indent * "\t" + "Characteristic '{0}': Target value = {1}\n".format(charac.name, b[charac_indices[charac.name]])
+            if charac.name in b_indices:
+                msg += n_indent * "\t" + "Characteristic '{0}': Target value = {1}\n".format(charac.name, b[b_indices[charac.name]])
             else:
                 msg += n_indent * "\t" + "Characteristic '{0}' not in databook: Target value = N/A (0.0)\n".format(charac.name)
 
diff --git a/tests/framework_init_zero.xlsx b/tests/framework_init_zero.xlsx
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diff --git a/tests/test_tox_zero_init.py b/tests/test_tox_zero_init.py
new file mode 100644
index 00000000..8ded6b58
--- /dev/null
+++ b/tests/test_tox_zero_init.py
@@ -0,0 +1,70 @@
+# Test frameworks with no compartments/characteristics to ensure that
+# they are initialized as 0 correctly
+
+import atomica as at
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+
+testdir = at.parent_dir()
+tmpdir = testdir / "temp"
+
+
+def _run_framework(fname):
+    F = at.ProjectFramework(testdir / fname)
+    D = at.ProjectData.new(F, [2020], 1, 0)
+    P = at.Project(framework=F, databook=D, do_run=False)
+    P.settings.update_time_vector(dt=1)
+    res = P.run_sim()
+    return res
+
+
+def test_shortcut_init_1():
+    res = _run_framework("test_shortcut_init_framework_1.xlsx")
+    print(res)
+
+    # With no shortcut initialization for infected, 40 people get assigned to it, which flow downstream to R and D
+    assert_allclose(res.get_variable("s")[0].vals[0], 40)
+    assert_allclose(res.get_variable("i")[0].vals[0], 0)
+    assert_allclose(res.get_variable("r")[0].vals[0], 60)
+    assert_allclose(res.get_variable("d")[0].vals[0], 20)
+
+
+def test_shortcut_init_2():
+    res = _run_framework("test_shortcut_init_framework_2.xlsx")
+
+    # With 0 shortcut initialization for infected, 0 people get assigned to it, so we have an even split
+    assert_allclose(res.get_variable("s")[0].vals[0], 60)
+    assert_allclose(res.get_variable("i")[0].vals[0], 0)
+    assert_allclose(res.get_variable("r")[0].vals[0], 60)
+    assert_allclose(res.get_variable("d")[0].vals[0], 0)
+
+
+def test_shortcut_init_3():
+    # With a nonzero shortcut initialization, an error should occur
+    with pytest.raises(at.InvalidFramework):
+        _run_framework("test_shortcut_init_framework_3.xlsx")
+
+
+def test_shortcut_init_4():
+    res = _run_framework("test_shortcut_init_framework_4.xlsx")
+
+    # With 0 shortcut initialization for infected, 0 people get assigned to it, so we have an even split
+    assert_allclose(res.get_variable("s")[0].vals[0], 60)
+    assert_allclose(res.get_variable("i")[0].vals[0], 0)
+    assert_allclose(res.get_variable("r")[0].vals[0], 60)
+    assert_allclose(res.get_variable("d")[0].vals[0], 0)
+
+
+def test_shortcut_init_5():
+    # With a nonzero shortcut initialization, an error should occur
+    with pytest.raises(at.InvalidFramework):
+        _run_framework("test_shortcut_init_framework_5.xlsx")
+
+
+if __name__ == "__main__":
+    test_shortcut_init_1()
+    test_shortcut_init_2()
+    test_shortcut_init_3()
+    test_shortcut_init_4()
+    test_shortcut_init_5()

From 865a5e18b2bb05c63bbafff3b0b24a7e58f60ceb Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Fri, 27 Jun 2025 17:22:02 +1000
Subject: [PATCH 140/161] Add a test (although overall suite doesn't pass yet)

---
 tests/framework_init_zero.xlsx | Bin 31751 -> 31855 bytes
 tests/test_tox_zero_init.py    |  57 +++------------------------------
 2 files changed, 5 insertions(+), 52 deletions(-)

diff --git a/tests/framework_init_zero.xlsx b/tests/framework_init_zero.xlsx
index 5521fa1ea5281d593bf47416b256f210a8d3e927..b7409836d6f3b795a09450883a8f9649009201b9 100644
GIT binary patch
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diff --git a/tests/test_tox_zero_init.py b/tests/test_tox_zero_init.py
index 8ded6b58..c9ca9956 100644
--- a/tests/test_tox_zero_init.py
+++ b/tests/test_tox_zero_init.py
@@ -1,5 +1,4 @@
-# Test frameworks with no compartments/characteristics to ensure that
-# they are initialized as 0 correctly
+# Test frameworks where characteristics with zero values get extra initialization conditions for constituent compartments
 
 import atomica as at
 import numpy as np
@@ -10,61 +9,15 @@
 tmpdir = testdir / "temp"
 
 
-def _run_framework(fname):
-    F = at.ProjectFramework(testdir / fname)
+def test_zero_initialization():
+    F = at.ProjectFramework(testdir / "framework_init_zero.xlsx")
     D = at.ProjectData.new(F, [2020], 1, 0)
     P = at.Project(framework=F, databook=D, do_run=False)
-    P.settings.update_time_vector(dt=1)
+    P.settings.update_time_vector(start=2020 ,end=2021, dt=1)
     res = P.run_sim()
     return res
 
 
-def test_shortcut_init_1():
-    res = _run_framework("test_shortcut_init_framework_1.xlsx")
-    print(res)
-
-    # With no shortcut initialization for infected, 40 people get assigned to it, which flow downstream to R and D
-    assert_allclose(res.get_variable("s")[0].vals[0], 40)
-    assert_allclose(res.get_variable("i")[0].vals[0], 0)
-    assert_allclose(res.get_variable("r")[0].vals[0], 60)
-    assert_allclose(res.get_variable("d")[0].vals[0], 20)
-
-
-def test_shortcut_init_2():
-    res = _run_framework("test_shortcut_init_framework_2.xlsx")
-
-    # With 0 shortcut initialization for infected, 0 people get assigned to it, so we have an even split
-    assert_allclose(res.get_variable("s")[0].vals[0], 60)
-    assert_allclose(res.get_variable("i")[0].vals[0], 0)
-    assert_allclose(res.get_variable("r")[0].vals[0], 60)
-    assert_allclose(res.get_variable("d")[0].vals[0], 0)
-
-
-def test_shortcut_init_3():
-    # With a nonzero shortcut initialization, an error should occur
-    with pytest.raises(at.InvalidFramework):
-        _run_framework("test_shortcut_init_framework_3.xlsx")
-
-
-def test_shortcut_init_4():
-    res = _run_framework("test_shortcut_init_framework_4.xlsx")
-
-    # With 0 shortcut initialization for infected, 0 people get assigned to it, so we have an even split
-    assert_allclose(res.get_variable("s")[0].vals[0], 60)
-    assert_allclose(res.get_variable("i")[0].vals[0], 0)
-    assert_allclose(res.get_variable("r")[0].vals[0], 60)
-    assert_allclose(res.get_variable("d")[0].vals[0], 0)
-
-
-def test_shortcut_init_5():
-    # With a nonzero shortcut initialization, an error should occur
-    with pytest.raises(at.InvalidFramework):
-        _run_framework("test_shortcut_init_framework_5.xlsx")
-
 
 if __name__ == "__main__":
-    test_shortcut_init_1()
-    test_shortcut_init_2()
-    test_shortcut_init_3()
-    test_shortcut_init_4()
-    test_shortcut_init_5()
+    test_zero_initialization()

From f634cc1c1bbea1bc3339179b2a232372aee72ba3 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Sat, 28 Jun 2025 01:58:22 +1000
Subject: [PATCH 141/161] Fix error message

---
 atomica/model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/model.py b/atomica/model.py
index 3f3e05d4..78edddca 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1920,10 +1920,10 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
         characteristic_tolerence_failed = False
 
         # Print warning for characteristics that are not well matched by the compartment size solution
-        for i in range(0, len(b_objs)):
+        for i, obj in enumerate(b_objs.values()):
             if abs(proposed[i] - b[i]) > model_settings["tolerance"]:
                 characteristic_tolerence_failed = True
-                error_msg += "Characteristic '{0}' '{1}' - Requested {2}, Calculated {3}\n".format(self.name, b_objs[i].name, b[i], proposed[i])
+                error_msg += "Characteristic '{0}' '{1}' - Requested {2}, Calculated {3}\n".format(self.name, obj.name, b[i], proposed[i])
 
         # Print expanded diagnostic for negative compartments showing parent characteristics
         def report_characteristic(charac, n_indent=0):

From d23d05a847b3b61d8d46f3b07ad98bee8f4b53bb Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Sat, 28 Jun 2025 03:35:58 +1000
Subject: [PATCH 142/161] Improve message

---
 atomica/model.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/atomica/model.py b/atomica/model.py
index 78edddca..aadab773 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1880,6 +1880,7 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
             par = parset.pars[charac]
             b_vals[charac] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
             if b_vals[charac] == 0:
+                logger.debug(f'Characteristic {charac} has a zero value, adding extra zero compartments')
                 for comp in obj.get_included_comps():
                     if comp.name in b_vals and b_vals[comp.name] != 0:
                         raise BadInitialization(f'Compartment {comp.name} was explicitly specified as having a non-zero value, but characteristic {charac} has a zero value - input data not consistent')
@@ -1923,7 +1924,7 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
         for i, obj in enumerate(b_objs.values()):
             if abs(proposed[i] - b[i]) > model_settings["tolerance"]:
                 characteristic_tolerence_failed = True
-                error_msg += "Characteristic '{0}' '{1}' - Requested {2}, Calculated {3}\n".format(self.name, obj.name, b[i], proposed[i])
+                error_msg += f"{obj.__class__.__name__} '{obj.name}' ({self.name})- Requested {b[i]}, Calculated {proposed[i]}\n"
 
         # Print expanded diagnostic for negative compartments showing parent characteristics
         def report_characteristic(charac, n_indent=0):

From e083bdcf14e4f7857de0a4a61f9b34dc0da13276 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 30 Jun 2025 15:49:38 +1000
Subject: [PATCH 143/161] Improve comment

---
 atomica/data.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/atomica/data.py b/atomica/data.py
index 8b1f28c5..2ecf0713 100644
--- a/atomica/data.py
+++ b/atomica/data.py
@@ -170,7 +170,12 @@ def get_ts(self, name: str, key=None):
         is therefore encountered on the :class:`Result` and plotting side.
 
         If retrieving values for a comp/charac/par and the databook contains an entry for 'all' rather
-        than specific populations, then the 'all' time series will be returned regardless of the key
+        than specific populations, then the 'all' time series will be returned if the key does not match
+        any population names.
+
+        Note that the TDVE can contain time series for population names that don't correspond to actual populations.
+        For example, users could add an extra row for 'Total', or anything else. `get_ts()` will retrieve these
+        rows without an error, so population names are not validated against the populations in the databook.
         """
 
         # Exit immediately if the name is not specified

From c7d96f2cda5e51f72d3e3b05fd9b08aeed821412 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 8 Jul 2025 07:39:06 +1000
Subject: [PATCH 144/161] Resolve numpy deprecation warning (plus it's a
 fraction faster too)

---
 atomica/model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/atomica/model.py b/atomica/model.py
index aadab773..2c4f66b4 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -602,7 +602,7 @@ def balance(self, ti: int) -> None:
             if self.duration_group:
                 link._vals[:, ti] = flow
             else:
-                link.vals[ti] = flow
+                link.vals[ti] = flow[0]
 
     def initial_flush(self) -> None:
         """
@@ -1986,7 +1986,7 @@ def report_characteristic(charac, n_indent=0):
 
         # Otherwise, insert the values
         for i, c in enumerate(comps):
-            c[0] = max(0.0, x[i])
+            c[0] = max(0.0, x[i,0])
 
 
 class Model:
@@ -2662,7 +2662,7 @@ def update_pars(self) -> None:
 
                 for par, val in zip(pars, par_vals):
                     if par.skip_function is None or (self.t[ti] < par.skip_function[0]) or (self.t[ti] > par.skip_function[1]):  # Careful - note how the < here matches >= in Parameter.update()
-                        par[ti] = par.scale_factor * val
+                        par[ti] = par.scale_factor * val[0]
 
             # Restrict the parameter's value if a limiting range was defined
             for par in pars:

From 3a177cb19985dab7f4b2b49681078028f791d3ab Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 9 Jul 2025 06:27:19 +1000
Subject: [PATCH 145/161] Add function to clear TimeSeries objects

---
 atomica/utils.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/atomica/utils.py b/atomica/utils.py
index eb7bb3d8..eb1db16b 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -624,6 +624,22 @@ def sample(self, constant=True):
 
         return new
 
+    def clear(self) -> None:
+        """
+        Clear the TimeSeries
+
+        This method resets TimeSeries but keeps the units present, so it is useful when
+        wanting to replace the values associated with a particular TimeSeries instance.
+
+        :return: None
+        """
+
+        self.t = []
+        self.vals = []
+        self.assumption = None
+        self.sigma = None
+        self._sampled = False
+        return None
 
 def evaluate_plot_string(plot_string: str):
     """

From 62207d5c4abdba39a5ac2fc21d8231d84b75ed23 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 14 Jul 2025 15:47:20 +1000
Subject: [PATCH 146/161] Add separate initialization tolerance

---
 CHANGELOG.md     | 11 +++++++++++
 atomica/model.py | 11 ++++++-----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 70fd0a4c..9cec3373 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,17 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.3] - 2025-XX-XX
+
+- Changed initialization method to increase prioritization for characteristics with zero value. If a characteristic has a zero value, this will now automatically add extra constraints that all constitutent compartments should also be zero. In practice, this means that it is less likely that frameworks will cause negative initial popsizes, so some frameworks that previously did not work will now initialize correctly.
+- Added `at.TimeSeries.clear()` to reset a time series while preserving the units. This function can be useful when updating the value of databook quantities programatically. 
+- Added a separate numerical tolerance used for initialization (`at.model.model_settings['initialization_tolerance']`) which permits more approximate initializations while still maintaining the same numerical tolerance for the rest of the integration.
+
+- *Backwards-compatibility notes*
+
+- Some initializations might show numerical (e.g., `1e-10`) differences in their values due to the new algorithm. In a small number of cases (depending on the framework), it is possible that the updated initialization method could result in a slightly different initialization.  
+- The default initialization tolerance is now `1e-3` instead of `1e-6`, some models that previously raised a `BadInitialization` error will now run without error. Users should note that if it's necessary to guarantee an exact initialization, this tolerance should be reduced. 
+
 ## [1.31.2] - 2025-06-26
 
 - Fix bug in creating databook if framework 'Databook pages' sheet contains multiple code names mapping to the same full name.
diff --git a/atomica/model.py b/atomica/model.py
index 2c4f66b4..2bad544b 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -26,6 +26,7 @@
 
 model_settings = dict()
 model_settings["tolerance"] = 1e-6
+model_settings["initialization_tolerance"] = 1e-3
 
 __all__ = [
     "BadInitialization",
@@ -1922,7 +1923,7 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
 
         # Print warning for characteristics that are not well matched by the compartment size solution
         for i, obj in enumerate(b_objs.values()):
-            if abs(proposed[i] - b[i]) > model_settings["tolerance"]:
+            if abs(proposed[i] - b[i]) > model_settings["initialization_tolerance"]:
                 characteristic_tolerence_failed = True
                 error_msg += f"{obj.__class__.__name__} '{obj.name}' ({self.name})- Requested {b[i]}, Calculated {proposed[i]}\n"
 
@@ -1960,7 +1961,7 @@ def report_characteristic(charac, n_indent=0):
             return msg
 
         for i in range(0, len(comps)):
-            if x[i] < -model_settings["tolerance"]:
+            if x[i] < -model_settings["initialization_tolerance"]:
                 error_msg += "Compartment %s %s - Calculated %f\n" % (self.name, comps[i].name, x[i])
                 for charac in b_objs:
                     try:
@@ -1970,10 +1971,10 @@ def report_characteristic(charac, n_indent=0):
                         if comps[i] == charac:
                             error_msg += report_characteristic(charac)
 
-        if residual > model_settings["tolerance"]:
+        if residual > model_settings["initialization_tolerance"]:
             # Halt for an unsatisfactory overall solution
-            raise BadInitialization("Global residual was %g which is unacceptably large (should be < %g)\n%s" % (residual, model_settings["tolerance"], error_msg))
-        elif np.any(np.less(x, -model_settings["tolerance"])):
+            raise BadInitialization("Global residual was %g which is unacceptably large (should be < %g)\n%s" % (residual, model_settings["initialization_tolerance"], error_msg))
+        elif np.any(np.less(x, -model_settings["initialization_tolerance"])):
             # Halt for any negative popsizes
             raise BadInitialization(f"Negative initial popsizes:\n{error_msg}")
         elif characteristic_tolerence_failed:

From fa31a9df96bec097c7dd097e3ff38b77f4919f17 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 14 Jul 2025 15:47:33 +1000
Subject: [PATCH 147/161] Improve error message

---
 atomica/plotting.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 1931b996..49b4563e 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -288,7 +288,10 @@ def __init__(self, results, outputs=None, pops=None, output_aggregation=None, po
                         vars = pop.get_variable(output_label)
                     except NotFoundError as e:
                         in_pops = [x.name for x in result.model.pops if output_label in x]
-                        message = f'Variable "{output_label}" was requested in population "{pop.name}" but it is only defined in these populations: {in_pops}'
+                        if not in_pops:
+                            message = f'Variable "{output_label}" was requested in population "{pop.name}" but it is not defined in any populations'
+                        else:
+                            message = f'Variable "{output_label}" was requested in population "{pop.name}" but it is only defined in these populations: {in_pops}'
                         raise NotFoundError(message) from e
 
                     if vars[0].vals is None:

From 450375724370240842baa5ed3a9ba4b38520c773 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 14 Jul 2025 15:47:46 +1000
Subject: [PATCH 148/161] Switch to more recent numpy function

---
 atomica/plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/plotting.py b/atomica/plotting.py
index 49b4563e..5749feab 100644
--- a/atomica/plotting.py
+++ b/atomica/plotting.py
@@ -698,7 +698,7 @@ def time_aggregate(self, t_bins, time_aggregation=None, interpolation_method=Non
                         v2 = np.interp(t2, s.tvec, s.vals, left=np.nan, right=np.nan)  # Return NaN outside bounds - it should never be valid to use extrapolated output values in time aggregation
                     else:
                         v2 = s.vals[idx]
-                    vals[i] = np.trapz(y=v2 / scale, x=t2)  # Note division by timescale here, which annualizes it
+                    vals[i] = np.trapezoid(y=v2 / scale, x=t2)  # Note division by timescale here, which annualizes it
                 elif interpolation_method == "previous":
                     if interpolate:
                         v2 = scipy.interpolate.interp1d(s.tvec, s.vals, kind="previous", copy=False, assume_sorted=True, bounds_error=False, fill_value=(np.nan, np.nan))(t2)

From 5a85b7f6d17a85888626498412dc361d6024f463 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 21 Jul 2025 16:57:11 +1000
Subject: [PATCH 149/161] Add additional logging level

---
 CHANGELOG.md        |  1 +
 atomica/__init__.py | 15 +++++++++------
 atomica/utils.py    |  2 +-
 3 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9cec3373..78d6e3fe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,7 @@ This file records changes to the codebase grouped by version release. Unreleased
 - Changed initialization method to increase prioritization for characteristics with zero value. If a characteristic has a zero value, this will now automatically add extra constraints that all constitutent compartments should also be zero. In practice, this means that it is less likely that frameworks will cause negative initial popsizes, so some frameworks that previously did not work will now initialize correctly.
 - Added `at.TimeSeries.clear()` to reset a time series while preserving the units. This function can be useful when updating the value of databook quantities programatically. 
 - Added a separate numerical tolerance used for initialization (`at.model.model_settings['initialization_tolerance']`) which permits more approximate initializations while still maintaining the same numerical tolerance for the rest of the integration.
+- Added another logging level (`at.VERBOSE`) which enables more targeted additional output
 
 - *Backwards-compatibility notes*
 
diff --git a/atomica/__init__.py b/atomica/__init__.py
index a1f7b482..efcf58d0 100644
--- a/atomica/__init__.py
+++ b/atomica/__init__.py
@@ -26,7 +26,10 @@
 import sys
 import logging
 
+VERBOSE = 15
+logging.addLevelName(VERBOSE, "VERBOSE")
 logger = logging.getLogger("atomica")
+logging.Logger.verbose = lambda self, msg, *args, **kwargs: self.log(VERBOSE, msg, *args, **kwargs)
 
 if not logger.handlers:
     # Only add handlers if they don't already exist in the module-level logger
@@ -34,16 +37,16 @@
     # prior to importing Atomica, and the user's custom logger won't be overwritten as long as it has
     # at least one handler already added. The use case was originally to suppress messages on import, but since
     # importing is silent now, it doesn't matter so much.
-    debug_handler = logging.StreamHandler(sys.stdout)  # info_handler will handle all messages below WARNING sending them to STDOUT
-    info_handler = logging.StreamHandler(sys.stdout)  # info_handler will handle all messages below WARNING sending them to STDOUT
+    debug_handler = logging.StreamHandler(sys.stdout)  # handle all messages below WARNING sending them to STDOUT
+    info_handler = logging.StreamHandler(sys.stdout)  # handle all messages below WARNING sending them to STDOUT
     warning_handler = logging.StreamHandler(sys.stderr)  # warning_handler will send all messages at or above WARNING to STDERR
 
-    debug_handler.setLevel(0)  # Handle all lower levels - the output should be filtered further by setting the logger level, not the handler level
-    info_handler.setLevel(logging.INFO)  # Handle all lower levels - the output should be filtered further by setting the logger level, not the handler level
+    debug_handler.setLevel(0)  # Handle all levels - the output is then filtered further by setting the logger level, not the handler level
+    info_handler.setLevel(logging.INFO)  # Handle levels INFO or higher
     warning_handler.setLevel(logging.WARNING)
 
-    debug_handler.addFilter(type("ThresholdFilter", (object,), {"filter": lambda x, logRecord: logRecord.levelno < logging.INFO})())  # Display anything INFO or higher
-    info_handler.addFilter(type("ThresholdFilter", (object,), {"filter": lambda x, logRecord: logRecord.levelno < logging.WARNING})())  # Don't display WARNING or higher
+    debug_handler.addFilter(type("ThresholdFilter", (object,), {"filter": lambda x, logRecord: logRecord.levelno < logging.INFO})())  # Display only messages below the INFO level
+    info_handler.addFilter(type("ThresholdFilter", (object,), {"filter": lambda x, logRecord: logRecord.levelno < logging.WARNING})())  # Display only messages below WARNING (the info_handler level already rejects anything below INFO)
 
     warning_formatter = logging.Formatter("%(levelname)s {%(filename)s:%(lineno)d} - %(message)s")
     warning_handler.setFormatter(warning_formatter)
diff --git a/atomica/utils.py b/atomica/utils.py
index eb1db16b..21a1a345 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -1003,7 +1003,7 @@ def stop_logging() -> None:
 
 def get_sigfigs_necessary(x, y, min_sigfigs: int = 2) -> int:
     """
-    Get how many significant figures are necessary to tell the difference berween two numbers
+    Get how many significant figures are necessary to tell the difference between two numbers
 
     :param x, y: numbers to compare
     :param min_sigfigs: minimum number of sigfigs to use if no difference

From 16efa1eb6e245b8023ebd8a51fd572c97c4d607b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 11 Aug 2025 11:33:06 +1000
Subject: [PATCH 150/161] Update initialization method to enforce zero
 compartments by omitting them from the initialization

---
 CHANGELOG.md                         |  4 +--
 atomica/model.py                     | 43 +++++++++++++++++++---------
 atomica/version.py                   |  4 +--
 tests/test_tox_timed_compartments.py | 21 ++++----------
 4 files changed, 38 insertions(+), 34 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 78d6e3fe..f73acaec 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,9 +2,9 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
-## [1.31.3] - 2025-XX-XX
+## [1.31.3] - 2025-08-11
 
-- Changed initialization method to increase prioritization for characteristics with zero value. If a characteristic has a zero value, this will now automatically add extra constraints that all constitutent compartments should also be zero. In practice, this means that it is less likely that frameworks will cause negative initial popsizes, so some frameworks that previously did not work will now initialize correctly.
+- Changed initialization method to increase prioritization for characteristics with zero value. If a characteristic has a zero value, this will now automatically force included compartments to have a zero value. In practice, this means that it is less likely that frameworks will cause negative initial popsizes, so some frameworks that previously did not work will now initialize correctly.
 - Added `at.TimeSeries.clear()` to reset a time series while preserving the units. This function can be useful when updating the value of databook quantities programatically. 
 - Added a separate numerical tolerance used for initialization (`at.model.model_settings['initialization_tolerance']`) which permits more approximate initializations while still maintaining the same numerical tolerance for the rest of the integration.
 - Added another logging level (`at.VERBOSE`) which enables more targeted additional output
diff --git a/atomica/model.py b/atomica/model.py
index 2bad544b..3a354ff9 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1869,33 +1869,41 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
         b_objs = {}
 
         # First add all of the compartments that will be used from the databook
+        zero_compartments = set() # Keep track of compartments that will be initialized as zero
         for comp in comps_to_use:
-            b_objs[comp] = self.comp_lookup[comp]
             par = parset.pars[comp]
-            b_vals[comp] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+            val = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+            obj = self.comp_lookup[comp]
+            if val == 0:
+                zero_compartments.add(obj)
+            else:
+                b_objs[comp] = obj
+                b_vals[comp] = val
 
-        # Then add all of the characteristics, and additional zero compartments
+        # Then add all of the characteristics
         for charac in characs_to_use:
-            obj = self.charac_lookup[charac]
-            b_objs[charac] = obj
             par = parset.pars[charac]
-            b_vals[charac] = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
-            if b_vals[charac] == 0:
-                logger.debug(f'Characteristic {charac} has a zero value, adding extra zero compartments')
+            val = par.interpolate(t_init, pop_name=self.name)[0] * par.y_factor[self.name] * par.meta_y_factor
+            obj = self.charac_lookup[charac]
+            if val == 0:
+                # If the characteristic is zero, then all of the compartments included in it must also be zero
                 for comp in obj.get_included_comps():
-                    if comp.name in b_vals and b_vals[comp.name] != 0:
+                    if comp.name in b_vals:
+                        # If a separate databook entry just for this compartment says the compartment should be non-zero, then we have two essentially equally
+                        # direct specifications for the compartment. In that case, we should raise an error as the user has explicitly specified contradictory values
                         raise BadInitialization(f'Compartment {comp.name} was explicitly specified as having a non-zero value, but characteristic {charac} has a zero value - input data not consistent')
                     else:
-                        b_vals[comp.name] = 0
-                        b_objs[comp.name] = comp
+                        zero_compartments.add(comp)
             else:
+                b_objs[charac] = obj
+                b_vals[charac] = val
                 if obj.denominator is not None:
                     denom_par = parset.pars[obj.denominator.name]
                     b_vals[charac] *= denom_par.interpolate(t_init, pop_name=self.name)[0] * denom_par.y_factor[self.name] * denom_par.meta_y_factor
 
 
         # Build up the comps corresponding to the `x` values in `x=A*b` i.e. the compartments being solved for
-        comps = [c for c in self.comps if not (isinstance(c, SourceCompartment) or isinstance(c, SinkCompartment))]
+        comps = [c for c in self.comps if not (isinstance(c, SourceCompartment) or isinstance(c, SinkCompartment) or c in zero_compartments)]
         b_indices = {c.name: i for i, c in enumerate(b_objs.values())}  # Make lookup dict for characteristic indices
         comp_indices = {c.name: i for i, c in enumerate(comps)}  # Make lookup dict for compartment indices
 
@@ -1906,7 +1914,8 @@ def initialize_compartments(self, parset: ParameterSet, framework, t_init: float
         for i, obj in enumerate(b_objs.values()):
             if isinstance(obj, Characteristic):
                 for inc in obj.get_included_comps():
-                    A[i, comp_indices[inc.name]] = 1.0
+                    if inc not in zero_compartments:
+                        A[i, comp_indices[inc.name]] = 1.0
             else:
                 A[i, comp_indices[obj.name]] = 1.0
 
@@ -1956,6 +1965,8 @@ def report_characteristic(charac, n_indent=0):
                 for inc in charac.includes:
                     if isinstance(inc, Characteristic):
                         msg += report_characteristic(inc, n_indent)
+                    elif inc in zero_compartments:
+                        msg += n_indent * "\t" + "Compartment %s: Preassigned value = 0.0\n" % (inc.name)
                     else:
                         msg += n_indent * "\t" + "Compartment %s: Computed value = %f\n" % (inc.name, x[comp_indices[inc.name]])
             return msg
@@ -1985,7 +1996,11 @@ def report_characteristic(charac, n_indent=0):
             # (but it exists as a fallback to ensure that any inconsistencies result in the error being raised)
             raise BadInitialization(f"Initialization error\n{error_msg}")
 
-        # Otherwise, insert the values
+        # Initialize any compartments that were specified as zero
+        for c in zero_compartments:
+            c[0] = 0.0
+
+        # Insert the calculated initial values
         for i, c in enumerate(comps):
             c[0] = max(0.0, x[i,0])
 
diff --git a/atomica/version.py b/atomica/version.py
index 26dfd0e0..560edd03 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -3,5 +3,5 @@
 
 Standard location for module version number and date.
 """
-version = "1.31.2"
-versiondate = "2025-06-26"
+version = "1.31.3"
+versiondate = "2025-08-11"
diff --git a/tests/test_tox_timed_compartments.py b/tests/test_tox_timed_compartments.py
index 1cd6e68a..cf306ba1 100644
--- a/tests/test_tox_timed_compartments.py
+++ b/tests/test_tox_timed_compartments.py
@@ -2,19 +2,8 @@
 import sciris as sc
 import os
 import pytest
-import sys
-
-# # P = at.Project(framework='dummy_framework.xlsx',databook='dummy_databook.xlsx')
-# # # P.run_sim()
-# # P = at.demo('tb',do_run=True)
-# # P.results[0].plot()
-# # P = at.Project.load('asdf2.prj')
-#
-# # P = at.Project(framework=at.LIBRARY_PATH+'tb_framework.xlsx')
-# # P.load_databook(at.LIBRARY_PATH+'tb_databook.xlsx')
-# P = at.demo('tb',do_run=True)
-#
-#
+import numpy as np
+
 
 testdir = at.parent_dir()
 tmpdir = testdir / "temp"
@@ -61,9 +50,9 @@ def test_zero_duration():
     at.plot_series(d)
 
     pop = res2.model.pops[0]
-    assert pop.get_variable("foi")[0].vals[0] == 24  # Flow into the compartment
-    assert pop.get_variable("inf")[0].vals[1] == 24 * res2.dt  # Compartment contents should now equal the inflow
-    assert pop.get_variable("inf")[0].vals[2] == 24 * res2.dt  # Same again, contents equals the inflow because it was flushed entirely
+    assert np.isclose(pop.get_variable("foi")[0].vals[0], 24)  # Flow into the compartment
+    assert np.isclose(pop.get_variable("inf")[0].vals[1], 24 * res2.dt)  # Compartment contents should now equal the inflow
+    assert np.isclose(pop.get_variable("inf")[0].vals[2], 24 * res2.dt)  # Same again, contents equals the inflow because it was flushed entirely
 
     # Check formally that total inflows equal total outflows
     assert pop.get_variable("inf")[0].vals[0] == sum(link.vals[0] for link in pop.get_variable("inf")[0].outlinks)

From 41297ebd0dfbc3d1780127f6fdf01da31f050e51 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 11 Aug 2025 13:57:50 +1000
Subject: [PATCH 151/161] Switch pipeline to main

---
 azure-pipelines.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index c9cbc47a..5a918b58 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -3,7 +3,7 @@ schedules:
   displayName: Nightly build
   branches:
     include:
-    - master
+    - main
     - develop
 
 jobs:
@@ -84,7 +84,7 @@ jobs:
 
 - job: 'deploy'
   dependsOn: 'tox'
-  condition: and(ne(variables['Build.Reason'], 'Schedule'),and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/master')))
+  condition: and(ne(variables['Build.Reason'], 'Schedule'),and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main')))
   pool:
     vmImage: 'ubuntu-latest'
   steps:

From 9af6d365b0c02ba9ccf837a826cbf5b7849a59f4 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Mon, 11 Aug 2025 14:37:28 +1000
Subject: [PATCH 152/161] Allow numerical differences

---
 tests/test_tox_frameworks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_tox_frameworks.py b/tests/test_tox_frameworks.py
index 303ee535..ccded5d8 100644
--- a/tests/test_tox_frameworks.py
+++ b/tests/test_tox_frameworks.py
@@ -56,7 +56,7 @@ def test_framework_single_char():
     P.settings = P1.settings
     r2 = P.run_sim()
 
-    test_equal = lambda var1, var2: np.testing.assert_array_equal(r1.get_variable(var1)[0].vals, r2.get_variable(var2)[0].vals)
+    test_equal = lambda var1, var2: np.testing.assert_allclose(r1.get_variable(var1)[0].vals, r2.get_variable(var2)[0].vals)
 
     test_equal("sus", "s")
     test_equal("inf", "i")

From 623feb8735b92e5786257346a5f6c6e03f8a95ad Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 28 Jan 2026 10:08:44 +1100
Subject: [PATCH 153/161] Changes for tests to pass with Pandas 3

---
 atomica/framework.py      |  6 +++---
 atomica/model.py          |  2 +-
 atomica/optimization.py   |  2 +-
 atomica/reconciliation.py | 14 +++++++++-----
 atomica/utils.py          |  9 ++++++++-
 azure-pipelines.yml       |  4 ++--
 tox.ini                   |  2 +-
 7 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/atomica/framework.py b/atomica/framework.py
index 76d13de1..cd378003 100644
--- a/atomica/framework.py
+++ b/atomica/framework.py
@@ -667,9 +667,9 @@ def _sanitize_compartments(self) -> None:
             self.comps["setup weight"] = ((~self.comps["databook page"].isna() | ~self.comps["default value"].isna()) & (self.comps["is source"] == "n") & (self.comps["is sink"] == "n")).astype(float)
         else:
             fill_ones = self.comps["setup weight"].isna() & (~self.comps["databook page"].isna() | ~self.comps["default value"].isna()) & (self.comps["is source"] == "n") & (self.comps["is sink"] == "n")
+            self.comps["setup weight"] = self.comps["setup weight"].astype(float)
             self.comps.loc[fill_ones, "setup weight"] = 1
             self.comps.loc[self.comps["setup weight"].isna(), "setup weight"] = 0
-            self.comps["setup weight"] = self.comps["setup weight"].astype(float)
 
         if "calibrate" not in self.comps:
             # If calibration column is not present, then it calibrate if in the databook
@@ -750,9 +750,9 @@ def _sanitize_characteristics(self) -> None:
             self.characs["setup weight"] = (~self.characs["databook page"].isna() | ~self.characs["default value"].isna()).astype(float)
         else:
             fill_ones = self.characs["setup weight"].isna() & (~self.characs["databook page"].isna() | ~self.characs["default value"].isna())
+            self.characs["setup weight"] = self.characs["setup weight"].astype(float)
             self.characs.loc[fill_ones, "setup weight"] = 1
             self.characs.loc[self.characs["setup weight"].isna(), "setup weight"] = 0
-            self.characs["setup weight"] = self.characs["setup weight"].astype(float)
 
         if "calibrate" not in self.characs:
             # If calibration column is not present, then it calibrate if in the databook
@@ -903,7 +903,7 @@ def _sanitize_parameters(self) -> None:
         # If framework has units that case-insensitively match the standard units, then correct the case
         if not self.pars.empty:
             lower_idx = self.pars["format"].str.lower().isin(FS.STANDARD_UNITS)
-            self.pars["format"][lower_idx] = self.pars["format"][lower_idx].str.lower()
+            self.pars.loc[lower_idx, "format"] = self.pars.loc[lower_idx, "format"].str.lower()
 
     def _process_transitions(self) -> None:
         """
diff --git a/atomica/model.py b/atomica/model.py
index 3a354ff9..59b2d145 100644
--- a/atomica/model.py
+++ b/atomica/model.py
@@ -1973,7 +1973,7 @@ def report_characteristic(charac, n_indent=0):
 
         for i in range(0, len(comps)):
             if x[i] < -model_settings["initialization_tolerance"]:
-                error_msg += "Compartment %s %s - Calculated %f\n" % (self.name, comps[i].name, x[i])
+                error_msg += "Compartment %s %s - Calculated %f\n" % (self.name, comps[i].name, x[i,0])
                 for charac in b_objs:
                     try:
                         if comps[i] in charac.get_included_comps():
diff --git a/atomica/optimization.py b/atomica/optimization.py
index f43319d0..556e597e 100644
--- a/atomica/optimization.py
+++ b/atomica/optimization.py
@@ -998,7 +998,7 @@ def get_hard_constraint(self, optimization, instructions: ProgramInstructions) -
 
             # Lastly, apply the budget factor
             if len(self.budget_factor) == 1:
-                hard_constraints["initial_total_spend"][t] = total_spend * self.budget_factor
+                hard_constraints["initial_total_spend"][t] = total_spend * self.budget_factor.item()
             else:
                 hard_constraints["initial_total_spend"][t] = total_spend * self.budget_factor[idx]
 
diff --git a/atomica/reconciliation.py b/atomica/reconciliation.py
index e87f6e8e..e27627ea 100644
--- a/atomica/reconciliation.py
+++ b/atomica/reconciliation.py
@@ -161,7 +161,7 @@ def _objective(x, mapping, progset, eval_years, target_vals, num_eligible, dt):
     return obj
 
 
-def _convert_to_single_year(progset, reconciliation_year):
+def _convert_to_single_year(progset, reconciliation_year: float):
     # Take in a progset
     # Return a progset with values only in the reconciliation year
     # This is then what actually gets reconciled
@@ -242,11 +242,15 @@ def reconcile(project, parset, progset, reconciliation_year: float, max_time=10,
 
     logger.warning("Reconcilation when parameter is in number units not fully tested")
 
-    reconciliation_year = sc.promotetoarray(reconciliation_year)
-    assert len(reconciliation_year) == 1, "Reconciliation year must be a scalar"
+    # Convert reconciliation year to a scalar
+    if hasattr(reconciliation_year, "__len__"):
+        if len(reconciliation_year) == 1:
+            reconciliation_year = reconciliation_year[0]
+        else:
+            raise ValueError("Reconciliation year must be a scalar")
 
     if eval_range is None:
-        eval_range = [reconciliation_year[0], reconciliation_year[0] + project.settings.sim_dt]
+        eval_range = [reconciliation_year, reconciliation_year + project.settings.sim_dt]
 
     # Do a prerun to get the baseline values and coverage denominator
     parset_results = project.run_sim(parset=parset, progset=progset, store_results=False)
@@ -255,7 +259,7 @@ def reconcile(project, parset, progset, reconciliation_year: float, max_time=10,
     target_vals, num_eligible = _extract_targets(parset_results, progset, ti, eval_pars)
 
     # Prepare ASD inputs
-    new_progset = _convert_to_single_year(progset, reconciliation_year[0])
+    new_progset = _convert_to_single_year(progset, reconciliation_year)
     bounds = _prepare_bounds(new_progset, unit_cost_bounds, baseline_bounds, capacity_bounds, outcome_bounds)  # Convert reconcile() inputs into full detailed bounds
     x0, xmin, xmax, mapping = _prepare_asd_inputs(new_progset, bounds)  # Assemble ASD variables
 
diff --git a/atomica/utils.py b/atomica/utils.py
index 21a1a345..335e0f72 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -377,8 +377,15 @@ def insert(self, t, v) -> None:
         :param v: Value to insert. If ``None``, this function will return immediately without doing anything
 
         """
+        # TODO - could potentially incorporate iterability check above for greater efficiency
+        # TODO - add handling series inputs here too?
+
+        # Convert 1-element lists/arrays to scalars
+        if hasattr(t, "__len__") and len(t) == 1:
+            t = t[0]
+        if hasattr(v, "__len__") and len(v) == 1:
+            v = v[0]
 
-        # Check if inputs are iterable
         iterable_input = True
         try:
             assert len(t) == len(v), "Cannot insert non-matching lengths or types of time and values %s and %s" % (t, v)
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 5a918b58..ed2b192b 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -9,8 +9,8 @@ schedules:
 jobs:
 - job: 'tox'
   variables:
-    python.version: '3.12'
-    TOXENV: 'py312'
+    python.version: '3.14'
+    TOXENV: 'py314'
   strategy:
     matrix:
       linux:
diff --git a/tox.ini b/tox.ini
index e3648053..889520bb 100644
--- a/tox.ini
+++ b/tox.ini
@@ -12,7 +12,7 @@ commands =
     pytest --nbsmoke-run {toxinidir} {posargs}
 
 [pytest]
-addopts = -ra -v -n 2 --junitxml=junit/test-results.xml --color=no --cov=atomica --cov-report=xml --cov-report=html
+addopts = -ra -v -n 2 --junitxml=junit/test-results.xml --color=no --cov=atomica --cov-report=xml --cov-report=html --log-level=DEBUG
 junit_family=xunit1
 nbsmoke_cell_timeout = 600
 console_output_style = count

From b739bfdb5df6f583da93dd3c18ded6bd43d21553 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 28 Jan 2026 10:53:04 +1100
Subject: [PATCH 154/161] Fix deprecated ast.Str

This class has now been replaced by ast.Constant encompassing all constant values. For simplicity, this check has been relaxed to accept any constant as this should offer an equivalent level of parsing safety (otherwise we could additionally check that the value was a string)
---
 atomica/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/utils.py b/atomica/utils.py
index 335e0f72..c20dfd7c 100644
--- a/atomica/utils.py
+++ b/atomica/utils.py
@@ -680,7 +680,7 @@ def evaluate_plot_string(plot_string: str):
         fcn_ast = ast.parse(plot_string, mode="eval")
         for node in ast.walk(fcn_ast):
             if not (node is fcn_ast):
-                assert isinstance(node, ast.Dict) or isinstance(node, ast.Str) or isinstance(node, ast.List) or isinstance(node, ast.Load), "Only allowed to initialize lists and dicts of strings here"
+                assert isinstance(node, ast.Dict) or isinstance(node, ast.List) or isinstance(node, ast.Load) or isinstance(node, ast.Constant), "Only allowed to initialize lists and dicts of strings here"
         compiled_code = compile(fcn_ast, filename="<ast>", mode="eval")
         return eval(compiled_code)
     else:

From 3ebab7391efd805c2c663070f042c4d1a7ff36cd Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Wed, 28 Jan 2026 10:55:42 +1100
Subject: [PATCH 155/161] Increment version

---
 CHANGELOG.md       | 4 ++++
 atomica/version.py | 4 ++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f73acaec..bfec4729 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,10 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.4] - 2026-01-28
+
+- Updated internal functions for Python 3.14 and Pandas 3.0.0
+
 ## [1.31.3] - 2025-08-11
 
 - Changed initialization method to increase prioritization for characteristics with zero value. If a characteristic has a zero value, this will now automatically force included compartments to have a zero value. In practice, this means that it is less likely that frameworks will cause negative initial popsizes, so some frameworks that previously did not work will now initialize correctly.
diff --git a/atomica/version.py b/atomica/version.py
index 560edd03..3277ea6b 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -3,5 +3,5 @@
 
 Standard location for module version number and date.
 """
-version = "1.31.3"
-versiondate = "2025-08-11"
+version = "1.31.4"
+versiondate = "2026-01-28"

From 684ebb42077c59088660f1d2bdc42edd778191a8 Mon Sep 17 00:00:00 2001
From: Cliff Kerr <questionably@gmail.com>
Date: Mon, 25 May 2026 08:55:01 -0400
Subject: [PATCH 156/161] Update pyswarm syntax and version

---
 atomica/optimization.py | 2 +-
 pyproject.toml          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/atomica/optimization.py b/atomica/optimization.py
index 556e597e..a31aa34d 100644
--- a/atomica/optimization.py
+++ b/atomica/optimization.py
@@ -1452,7 +1452,7 @@ def optimize(project, optimization, parset: ParameterSet, progset: ProgramSet, i
             errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every Adjustable needs to have finite bounds)"
             raise Exception(errormsg)
 
-        x_opt, _ = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args)
+        x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args)
 
     elif optimization.method == "hyperopt":
 
diff --git a/pyproject.toml b/pyproject.toml
index af0cf1e5..9a444bc0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "pandas",
     "xlsxwriter",
     "openpyxl",
-    "pyswarm",
+    "pyswarm>=1.0",
     "hyperopt",
     "sciris",
     "tqdm",

From 22aef7353c282c1cf35e7aa87088191e04c8dacf Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 26 May 2026 09:44:10 +1000
Subject: [PATCH 157/161] Fix pyswarm.pso() call and update pyproject.toml

---
 atomica/optimization.py | 2 +-
 pyproject.toml          | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/atomica/optimization.py b/atomica/optimization.py
index a31aa34d..0c6235f8 100644
--- a/atomica/optimization.py
+++ b/atomica/optimization.py
@@ -1452,7 +1452,7 @@ def optimize(project, optimization, parset: ParameterSet, progset: ProgramSet, i
             errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every Adjustable needs to have finite bounds)"
             raise Exception(errormsg)
 
-        x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args)
+        x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args).x
 
     elif optimization.method == "hyperopt":
 
diff --git a/pyproject.toml b/pyproject.toml
index 9a444bc0..e70c3c0f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,7 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13"
 ]
-requires-python = ">=3.7"
+requires-python = ">=3.10"
 
 dependencies = [
     "matplotlib",
@@ -37,7 +37,7 @@ dependencies = [
     "pandas",
     "xlsxwriter",
     "openpyxl",
-    "pyswarm>=1.0",
+    "pyswarm",
     "hyperopt",
     "sciris",
     "tqdm",
@@ -53,4 +53,4 @@ include-package-data = true
 where = ["."]
 
 [tool.setuptools.dynamic]
-version = {attr = "atomica.version.version"}
\ No newline at end of file
+version = {attr = "atomica.version.version"}

From 836f4815d888fe12b3957737517f7404f5da4c5b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 26 May 2026 09:44:18 +1000
Subject: [PATCH 158/161] Increment version

---
 CHANGELOG.md       | 5 +++++
 atomica/version.py | 4 ++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bfec4729..44ecf4d9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,11 @@
 
 This file records changes to the codebase grouped by version release. Unreleased changes are generally only present during development (relevant parts of the changelog can be written and saved in that section before a version number has been assigned)
 
+## [1.31.5] - 2026-05-26
+
+- Update optimization function for compatibility with latest `pyswarm` release
+- Update minimum Python version to 3.10 as previous versions are deprecated
+
 ## [1.31.4] - 2026-01-28
 
 - Updated internal functions for Python 3.14 and Pandas 3.0.0
diff --git a/atomica/version.py b/atomica/version.py
index 3277ea6b..fa7668d7 100644
--- a/atomica/version.py
+++ b/atomica/version.py
@@ -3,5 +3,5 @@
 
 Standard location for module version number and date.
 """
-version = "1.31.4"
-versiondate = "2026-01-28"
+version = "1.31.5"
+versiondate = "2026-05-26"

From 4ce4337077111057f3f1dc64b74ff17473a9acb3 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 26 May 2026 09:56:07 +1000
Subject: [PATCH 159/161] Force pyswarm 1.0.0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index e70c3c0f..723895bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "pandas",
     "xlsxwriter",
     "openpyxl",
-    "pyswarm",
+    "pyswarm>=1.0.0",
     "hyperopt",
     "sciris",
     "tqdm",

From e02a0546c73189ad7d3337c4d39dfd36f04caf3b Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 26 May 2026 10:10:56 +1000
Subject: [PATCH 160/161] Maintain compatibility as Mac OS is installing a
 different version

---
 atomica/optimization.py | 7 ++++++-
 pyproject.toml          | 2 +-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/atomica/optimization.py b/atomica/optimization.py
index 0c6235f8..f872612c 100644
--- a/atomica/optimization.py
+++ b/atomica/optimization.py
@@ -1452,7 +1452,12 @@ def optimize(project, optimization, parset: ParameterSet, progset: ProgramSet, i
             errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every Adjustable needs to have finite bounds)"
             raise Exception(errormsg)
 
-        x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args).x
+        if sc.compareversions(pyswarm, ">1.0.0"):
+            x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args).x
+        else:
+            # On Mac OS, Pyswarm 1.0.0 is not installing yet. This can be revisited and hopefully
+            # removed once the incompatibility is resolved (possibly by switching to more recent UV pipeline)
+            x_opt, _ = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args)
 
     elif optimization.method == "hyperopt":
 
diff --git a/pyproject.toml b/pyproject.toml
index 723895bb..e70c3c0f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,7 +37,7 @@ dependencies = [
     "pandas",
     "xlsxwriter",
     "openpyxl",
-    "pyswarm>=1.0.0",
+    "pyswarm",
     "hyperopt",
     "sciris",
     "tqdm",

From b365b318f770f1cc0f58b2f4b7413a029b6db877 Mon Sep 17 00:00:00 2001
From: Romesh Abeysuriya <romesh.abey@gmail.com>
Date: Tue, 26 May 2026 10:23:29 +1000
Subject: [PATCH 161/161] Fix typo

---
 atomica/optimization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/atomica/optimization.py b/atomica/optimization.py
index f872612c..7620a87f 100644
--- a/atomica/optimization.py
+++ b/atomica/optimization.py
@@ -1452,7 +1452,7 @@ def optimize(project, optimization, parset: ParameterSet, progset: ProgramSet, i
             errormsg = "PSO optimization requires finite upper and lower bounds to specify the search domain (i.e. every Adjustable needs to have finite bounds)"
             raise Exception(errormsg)
 
-        if sc.compareversions(pyswarm, ">1.0.0"):
+        if sc.compareversions(pyswarm, ">=1.0.0"):
             x_opt = pyswarm.pso(_objective_fcn, kwargs=args, **optim_args).x
         else:
             # On Mac OS, Pyswarm 1.0.0 is not installing yet. This can be revisited and hopefully