diff --git a/.gitignore b/.gitignore index 9aa6e8c..001b32a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,7 @@ +typings/* boario/archive.py +archive.py +*.el *.lprof *.prof *.zip @@ -162,3 +165,5 @@ workflow/config_full.json DOC_BUILDING_REMINDER new_docs +pyproject.toml.bak +.vscode/settings.json diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index 474d270..10da363 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -3,3 +3,102 @@ Contributing ################ .. _contributions: + +Contributions are welcome, and they are greatly appreciated! +Every little bit helps, and credit will always be given. +If you are interested in contributing, please read all this section +before doing anything and in case of doubt, `contact the developer`_! + +.. _contact the developer: pro@sjuhel.org + +---------------------- +Types of Contributions +---------------------- + +You can contribute in many ways: + +Report Bugs +=========== + +Before reporting a bug, **Ensure the bug was not already reported** by searching on GitHub under `Issues `_. + +If you are reporting a bug, please use the "Bug report template" `here `_, and include: + +* Your operating system name and version. +* Any details about your local setup that might be helpful in troubleshooting. +* Detailed steps to reproduce the bug. + + +Fix Bugs +======== + +Look through the Github `issues `_ for bugs. +If you want to start working on a bug then please write short message on the issue tracker to prevent duplicate work. + + +Implement Features +================== + +Look through the Github issues for features or add your own request +using the Code feature request template `here `_ +and assign yourself. + +Write Documentation +=================== + +BoARIO could always use more documentation, whether as part of the official documentation website, in docstrings, in Jupyter Notebook examples, +or even on the web in blog posts, articles, and such. + +BoARIO uses `Sphinx `_ for the user manual (that you are currently reading). + + +Submit Feedback +=============== + +The best way to send feedback is to file an issue at `here `_ + +If you are proposing a feature: + +* Explain in detail how it would work. +* Keep the scope as narrow as possible, to make it easier to implement. +* Remember that this is a volunteer-driven project, and that contributions are welcome :) + +----------------------- +Pull Request Guidelines +----------------------- + +To update the documentation, fix bugs or add new features you need to create a Pull Request. +A PR is a change you make to your local copy of the code for us to review and potentially integrate into the code base. + +To create a Pull Request you need to do these steps: + +1. Create a Github account. +2. Fork the repository. +3. Clone your fork locally. +4. Go to the created BoARIO folder with :code:`cd boario`. +5. Create a new branch with :code:`git checkout -b `. +6. Make your changes to the code or documentation. +7. Run :code:`git add .` to add all the changed files to the commit (to see what files will be added you can run :code:`git add . --dry-run`). +8. To commit the added files use :code:`git commit`. (This will open a command line editor to write a commit message. These should have a descriptive 80 line header, followed by an empty line, and then a description of what you did and why. To use your command line text editor of choice use (for example) :code:`export GIT_EDITOR=vim` before running :code:`git commit`). +9. Now you can push your changes to your Github copy of BoARIO by running :code:`git push origin `. +10. If you now go to the webpage for your Github copy of BoARIO you should see a link in the sidebar called "Create Pull Request". +11. Now you need to choose your PR from the menu and click the "Create pull request" button. Be sure to change the pull request target branch to ! + +If you want to create more pull requests, first run :code:`git checkout main` and then start at step 5. with a new branch name. + +Feel free to ask questions about this if you want to contribute to BoARIO :) + +------------------ +Testing Guidelines +------------------ + +To ensure that you do not introduce bugs into BoARIO, you should test your code thouroughly. + +From the base BoARIO folder you call :code:`pytest` to run all the tests, or choose one specific test: + +.. code-block:: console + + $ pytest + $ pytest tests/tests.py::test_log_input + +If you introduce a new feature you should add a new test to the tests directory. See the folder for examples. \ No newline at end of file diff --git a/README.rst b/README.rst index 7c5d0be..e72bd8f 100644 --- a/README.rst +++ b/README.rst @@ -7,6 +7,14 @@ BoARIO BoARIO : The Adaptative Regional Input Output model in python. +.. _`Documentation Website`: https://spjuhel.github.io/BoARIO/boario-what-is.html + +Disclaimer +=========== + +This work is in progress. The code has not been extensively tested. Any results produced with the model should be interpreted with great care. +Do not hesitate to contact the author when using the model ! + What is BoARIO ? ================= @@ -23,18 +31,22 @@ It is still an ongoing project (in parallel of a PhD project). .. _`Hallegatte 2013`: https://doi.org/10.1111/j.1539-6924.2008.01046.x .. _`Pymrio`: https://pymrio.readthedocs.io/en/latest/intro.html -Here is a non-exhaustive chronology of academic works with or about the ARIO model : +You can find most academic literature using ARIO or related models :ref:`here ` -.. image:: https://raw.githubusercontent.com/spjuhel/BoARIO/master/imgs/chronology.svg?sanitize=true - :width: 900 - :alt: ARIO academic work chronology What is ARIO ? =============== -ARIO stands for Adaptive Regional Input-Output. It is an hybrid input-output / agent-based economic model, designed to compute indirect costs from economic shocks. Its first version dates back to 2008 and has originally been developed to assess the indirect costs of natural disasters (Hallegatte 2008). +ARIO stands for Adaptive Regional Input-Output. It is an hybrid input-output / agent-based economic model, +designed to compute indirect costs from economic shocks. Its first version dates back to 2008 and has originally +been developed to assess the indirect costs of natural disasters (Hallegatte 2008). -In ARIO, the economy is modelled as a set of economic sectors and a set of regions. Each economic sector produces its generic product and draws inputs from an inventory. Each sector answers to a total demand consisting of a final demand (household consumption, public spending and private investments) of all regions (local demand and exports) and intermediate demand (through inputs inventory resupply). An initial equilibrium state of the economy is built based on multi-regional input-output tables (MRIO tables). +In ARIO, the economy is modelled as a set of economic sectors and a set of regions. +Each economic sector produces its generic product and draws inputs from an inventory. +Each sector answers to a total demand consisting of a final demand (household consumption, +public spending and private investments) of all regions (local demand and exports) and +intermediate demand (through inputs inventory resupply). An initial equilibrium state of +the economy is built based on multi-regional input-output tables (MRIO tables). Where to get BoARIO ? @@ -92,7 +104,7 @@ This work was supported by the French Environment and Energy Management Agency Development ------------ -* Samuel Juhel +* Samuel Juhel (pro@sjuhel.org) Contributions --------------- @@ -102,4 +114,5 @@ All :ref:`contributions` to the project are welcome ! Acknowledgements ------------------ -I would like to thank Vincent Viguie, Fabio D'Andrea my PhD supervisors as well as Célian Colon and Alessio Ciulo for their inputs during the model implementation. +I would like to thank Vincent Viguie, Fabio D'Andrea my PhD supervisors as well as Célian Colon, Alessio Ciulo and Adrien Delahais +for their inputs during the model implementation. diff --git a/boario/__init__.py b/boario/__init__.py index 4f601ba..7311b8e 100644 --- a/boario/__init__.py +++ b/boario/__init__.py @@ -20,16 +20,11 @@ else: _has_coloredlogs = True -try: - import pygit2 -except: - pass - import pathlib import logging from functools import lru_cache -__version__ = "v0.4.1b0" +__version__ = "v0.5.0a" __author__ = "sjuhel " # __minimum_python_version__ = "3.8" @@ -49,13 +44,13 @@ datefmt="%H:%M:%S", ) - try: + import pygit2 + __git_branch__ = pygit2.Repository(__file__).head.name -except Exception: - pass -else: - logger.info(f"You are using boario from branch {__git_branch__}") + logger.info("You are using boario from branch %s", __git_branch__) +except ModuleNotFoundError: + logger.info("Unable to tell git branch as pygit2 was not found.") @lru_cache(10) diff --git a/boario/event.py b/boario/event.py index f95b1bb..9c7a37a 100644 --- a/boario/event.py +++ b/boario/event.py @@ -14,15 +14,23 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . from __future__ import annotations -import abc -from typing import Callable, Optional, Union, List, Tuple -from numpy.typing import ArrayLike +from abc import ABC, abstractmethod +from typing import Callable, Optional, Union, List, Tuple, get_origin, get_args +import numpy.typing as npt import numpy as np import pandas as pd -from boario import logger import math import inspect from functools import partial +import warnings + +from boario import logger +from boario.utils.recovery_functions import ( + concave_recovery, + convexe_recovery, + linear_recovery, + convexe_recovery_scaled, +) __all__ = [ "Event", @@ -36,160 +44,33 @@ "RegionsList", ] -Impact = Union[int, float, list, dict, np.ndarray, pd.DataFrame, pd.Series] -IndustriesList = Union[List[Tuple[str, str]], pd.MultiIndex, np.ndarray] -SectorsList = Union[List[str], pd.Index, np.ndarray] -RegionsList = Union[List[str], pd.Index, np.ndarray] - - -def linear_recovery( - elapsed_temporal_unit: int, init_kapital_destroyed: np.ndarray, recovery_time: int -): - r"""Linear Kapital recovery function - - Kapital is entirely recovered when `recovery_time` has passed since event - started recovering - - Parameters - ---------- - - init_kapital_destroyed : float - Initial kapital destroyed - - elapsed_temporal_unit : int - Elapsed time since event started recovering - - recovery_time : int - Total time it takes the event to fully recover - - """ - - return init_kapital_destroyed * (1 - (elapsed_temporal_unit / recovery_time)) - - -def convexe_recovery( - elapsed_temporal_unit: int, init_kapital_destroyed: np.ndarray, recovery_time: int -): - r"""Convexe Kapital recovery function - - Kapital is recovered with characteristic time `recovery_time`. (This doesn't mean Kapital is fully recovered after this time !) - This function models a recovery similar as the one happening in the rebuilding case, for the same characteristic time. - - Parameters - ---------- - - init_kapital_destroyed : float - Initial kapital destroyed - - elapsed_temporal_unit : int - Elapsed time since event started recovering - - recovery_time : int - Total time it takes the event to fully recover - - """ - - return init_kapital_destroyed * (1 - (1 / recovery_time)) ** elapsed_temporal_unit +VectorImpact = Union[list, dict, np.ndarray, pd.DataFrame, pd.Series] +ScalarImpact = Union[int, float, np.integer] +Impact = Union[VectorImpact, ScalarImpact] +IndustriesList = Union[List[Tuple[str, str]], pd.MultiIndex] +SectorsList = Union[List[str], pd.Index, str] +RegionsList = Union[List[str], pd.Index, str] +FinalCatList = Union[List[str], pd.Index, str] +REBUILDING_FINALDEMAND_CAT_REGEX = ( + r"(?i)(?=.*household)(?=.*final)(?!.*NPISH|profit).*|HFCE" +) -def convexe_recovery_scaled( - elapsed_temporal_unit: int, - init_kapital_destroyed: np.ndarray, - recovery_time: int, - scaling_factor: float = 4, -): - r"""Convexe Kapital recovery function (scaled to match other recovery duration) +LOW_DEMAND_THRESH = 10 - Kapital is mostly recovered (>95% by default for most cases) when `recovery_time` has passed since event - started recovering. - Parameters - ---------- - - init_kapital_destroyed : float - Initial kapital destroyed - - elapsed_temporal_unit : int - Elapsed time since event started recovering - - recovery_time : int - Total time it takes the event to fully recover - - scaling_factor: float - Used to scale the exponent in the function so that kapital is mostly rebuilt after `recovery_time`. A value of 4 insure >95% of kapital is recovered for a reasonable range of `recovery_time` values. - - """ - - return init_kapital_destroyed * (1 - (1 / recovery_time)) ** ( - scaling_factor * elapsed_temporal_unit - ) - - -def concave_recovery( - elapsed_temporal_unit: int, - init_kapital_destroyed: np.ndarray, - recovery_time: int, - steep_factor: float = 0.000001, - half_recovery_time: Optional[int] = None, -): - r"""Concave (s-shaped) Kapital recovery function - - Kapital is mostly (>95% in most cases) recovered when `recovery_time` has passed since event started recovering. - - Parameters - ---------- - - init_kapital_destroyed : float - Initial kapital destroyed - - elapsed_temporal_unit : int - Elapsed time since event started recovering - - recovery_time : int - Total time it takes the event to fully recover - - steep_factor: float - This coefficient governs the slope of the central part of the s-shape, smaller values lead to a steeper slope. As such it also affect the percentage of kapital rebuilt after `recovery_time` has elapsed. A value of 0.000001 should insure 95% of the kapital is rebuild for a reasonable range of recovery duration. - - half_recovery_time : int - This can by use to change the time the inflexion point of the s-shape curve is attained. By default it is set to half the recovery duration. - - """ - - if half_recovery_time is None: - tau_h = 2 - else: - tau_h = recovery_time / half_recovery_time - exponent = (np.log(recovery_time) - np.log(steep_factor)) / ( - np.log(recovery_time) - np.log(tau_h) - ) - return (init_kapital_destroyed * recovery_time) / ( - recovery_time + steep_factor * (elapsed_temporal_unit**exponent) - ) - - -class Event(metaclass=abc.ABCMeta): +class Event(ABC): # Class Attributes - __required_class_attributes = [ - "possible_sectors", - "possible_regions", - "temporal_unit_range", - "z_shape", - "y_shape", - "x_shape", - "regions_idx", - "sectors_idx", - "monetary_factor", - "sectors_gva_shares", - "Z_distrib", - "mrio_name", - ] - possible_sectors: SectorsList = pd.Index([]) + + possible_sectors: pd.Index = pd.Index([]) r"""List of sectors present in the MRIO used by the model""" - possible_regions: RegionsList = pd.Index([]) + possible_regions: pd.Index = pd.Index([]) r"""List of regions present in the MRIO used by the model""" + possible_final_demand_cat: pd.Index = pd.Index([]) + r"""List of final demand categories present in the MRIO used by the model""" + temporal_unit_range: int = 0 r"""Maximum temporal unit simulated""" @@ -202,37 +83,38 @@ class Event(metaclass=abc.ABCMeta): x_shape: tuple[int, int] = (0, 0) r"""Shape of the x (production) vector in the model""" - regions_idx: np.ndarray = np.array([]) + regions_idx: npt.NDArray = np.array([]) r"""lexicographic region indexes""" - sectors_idx: np.ndarray = np.array([]) + sectors_idx: npt.NDArray = np.array([]) r"""lexicographic sector indexes""" - monetary_factor: int = 0 + model_monetary_factor: int = 1 r"""Amount of unitary currency used in the MRIO (e.g. 1000000 if in € millions)""" - sectors_gva_shares: np.ndarray = np.array([]) + gva_df: pd.Series = pd.Series([], dtype="float64") + r"""GVA per (region,sector)""" + + sectors_gva_shares: npt.NDArray = np.array([]) r"""Fraction of total (regional) GVA for each sectors""" - Z_distrib: np.ndarray = np.array([]) + Z_distrib: npt.NDArray = np.array([]) r"""Normalized intermediate consumption matrix""" + Y_distrib: npt.NDArray = np.array([]) + r"""Normalized final consumption matrix""" + mrio_name: str = "" r"""MRIO identification""" + @abstractmethod def __init__( self, - impact: Impact, - aff_regions: Optional[RegionsList] = None, - aff_sectors: Optional[SectorsList] = None, - aff_industries: Optional[IndustriesList] = None, - impact_industries_distrib=None, - impact_regional_distrib=None, - impact_sectoral_distrib_type="custom", - impact_sectoral_distrib=None, - name="Unnamed", - occurrence=1, - duration=1, + *, + impact: pd.Series, + name: str | None = "Unnamed", + occurrence: int = 1, + duration: int = 1, ) -> None: r"""Create an event shocking the model from a dictionary. @@ -252,206 +134,25 @@ def __init__( FIXME: Add docs. """ - - self._aff_sectors_idx = None - self._aff_sectors = None - self._aff_regions_idx = None - self._aff_regions = None - logger.debug("Initializing new Event") + logger.info("Initializing new Event") logger.debug("Checking required Class attributes are defined") - for v in Event.__required_class_attributes: - if Event.__dict__[v] is None: - raise AttributeError( - "Required Event Class attribute {} is not set yet so instantiating an Event isn't possible".format( - v - ) - ) - - self.name: str = name - r"""An identifying name for the event (for convenience mostly)""" - - self.occurrence = occurrence - self.duration = duration - self.impact_df: pd.Series = pd.Series( - 0, - dtype="float64", - index=pd.MultiIndex.from_product( - [self.possible_regions, self.possible_sectors], - names=["region", "sector"], - ), - ) - r"""A pandas Series with all possible industries as index, holding the impact vector of the event. The impact is defined for each sectors in each region.""" - - ################## DATAFRAME INIT ################# - # CASE VECTOR 1 (everything is there and regrouped) (only df creation) - if isinstance(impact, pd.Series): - logger.debug("Given impact is a pandas Series") - self.impact_df.loc[impact.index] = impact - if self.name == "Unnamed" and not impact.name is None: - self.name = str(impact.name) - elif isinstance(impact, dict): - logger.debug("Given impact is a dict, converting it to pandas Series") - impact = pd.Series(impact) - self.impact_df.loc[impact.index] = impact - elif isinstance(impact, pd.DataFrame): - logger.debug("Given Impact is a pandas DataFrame, squeezing it to a Series") - impact = impact.squeeze() - if not isinstance(impact, pd.Series): - raise ValueError( - "The given impact DataFrame is not a Series after being squeezed" - ) - self.impact_df.loc[impact.index] = impact - # CASE VECTOR 2 (everything is there but not regrouped) AND CASE SCALAR (Only df creation) - elif ( - isinstance(impact, (int, float, list, np.ndarray)) - and aff_industries is not None - ): - logger.debug( - f"Given Impact is a {type(impact)} and list of impacted industries given. Proceeding." - ) - self.impact_df.loc[aff_industries] = impact - elif ( - isinstance(impact, (int, float, list, np.ndarray)) - and aff_regions is not None - and aff_sectors is not None - ): - logger.debug( - f"Given Impact is a {type(impact)} and lists of impacted regions and sectors given. Proceeding." - ) - if isinstance(aff_regions, str): - aff_regions = [aff_regions] - if isinstance(aff_sectors, str): - aff_sectors = [aff_sectors] - - self.impact_df.loc[ - pd.MultiIndex.from_product([aff_regions, aff_sectors]) - ] = impact - else: - raise ValueError("Invalid input format. Could not initiate pandas Series.") - # Check for <0 values and remove 0. - if (self.impact_df < 0).any(): - logger.warning( - "Found negative values in impact vector. This should raise concern" + if np.size(self.possible_regions) == 0 or np.size(self.possible_sectors) == 0: + raise AttributeError( + "It appears that no model has been instantiated as some class attributes are not initialized (possible_regions, possible_sectors). Events require to instantiate a model and a simulation context before they can be instantiated" ) - # SORT DF - # at this point impact_df is built, and can be sorted. Note that if impact was a scalar, impact_df contains copies of this scalar. - logger.debug("Sorting Impact Series") - self.impact_df = self.impact_df.sort_index() - - # Init self.impact_sectoral_distrib_type, - self.impact_sectoral_distrib_type = impact_sectoral_distrib_type - ################################################# - - # SET INDEXES ATTR - # note that the following also sets aff_regions and aff_sectors - assert isinstance(self.impact_df.index, pd.MultiIndex) - # Only look for industries where impact is greater than 0 - self.aff_industries = self.impact_df.loc[self.impact_df > 0].index - - logger.debug( - f"Impact df at the moment:\n {self.impact_df.loc[self.aff_industries]}" - ) - - ###### SCALAR DISTRIBUTION ###################### - # if impact_industries_distrib is given, set it. We assume impact is scalar ! - # CASE SCALAR + INDUS DISTRIB - if impact_industries_distrib is not None and not isinstance( - impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray) - ): - logger.debug("Impact is Scalar and impact_industries_distrib was given") - self.impact_industries_distrib = np.array(impact_industries_distrib) - self.impact_df.loc[self.aff_industries] = ( - self.impact_df.loc[self.aff_industries] * self.impact_industries_distrib - ) - # if impact_reg_dis and sec_dis are give, deduce the rest. We also assume impact is scalar ! - # CASE SCALAR + REGION and SECTOR DISTRIB - elif ( - impact_regional_distrib is not None - and impact_sectoral_distrib is not None - and not isinstance( - impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray) - ) - ): - logger.debug( - "Impact is Scalar and impact_regional_distrib and impact_sectoral_distrib were given" - ) - if len(impact_regional_distrib) != len(self.aff_regions) or len( - impact_sectoral_distrib - ) != len(self.aff_sectors): - raise ValueError( - "Lengths of `impact_regional_distrib` and/or `impact_sectoral_distrib` are incompatible with `aff_regions` and/or `aff_sectors`." - ) - else: - self.impact_regional_distrib = np.array(impact_regional_distrib) - self.impact_sectoral_distrib = np.array(impact_sectoral_distrib) - self.impact_industries_distrib = ( - self.impact_regional_distrib[:, np.newaxis] - * self.impact_sectoral_distrib - ).flatten() - self.impact_df.loc[self.aff_industries] = ( - self.impact_df.loc[self.aff_industries] - * self.impact_industries_distrib - ) - # CASE SCALAR + 'gdp' distrib - elif ( - impact_regional_distrib is not None - and impact_sectoral_distrib_type is not None - and impact_sectoral_distrib_type == "gdp" - and not isinstance( - impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray) + if self.temporal_unit_range == 0: + raise AttributeError( + "It appears that no simulation context has been instantiated as some class attributes are not initialized (temporal_unit_range). Events require to instantiate a model and a simulation context before they can be instantiated" ) - ): - logger.debug("Impact is Scalar and impact_sectoral_distrib_type is 'gdp'") - - self.impact_regional_distrib = np.array(impact_regional_distrib) - shares = self.sectors_gva_shares.reshape( - (len(self.possible_regions), len(self.possible_sectors)) - ) - self.impact_sectoral_distrib = ( - shares[self._aff_regions_idx][:, self._aff_sectors_idx] - / shares[self._aff_regions_idx][:, self._aff_sectors_idx].sum(axis=1)[ - :, np.newaxis - ] - ) - self.impact_industries_distrib = ( - self.impact_regional_distrib[:, np.newaxis] - * self.impact_sectoral_distrib - ).flatten() - self.impact_df.loc[self.aff_industries] = ( - self.impact_df.loc[self.aff_industries] * self.impact_industries_distrib - ) - self.impact_sectoral_distrib_type = "gdp" - # CASE SCALAR + NO DISTRIB + list of industries - # if neither was given, we use default values. Again impact should be scalar here ! - elif isinstance(aff_industries, (list, np.ndarray)) and not isinstance( - impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray) - ): - logger.debug( - "Impact is Scalar and no distribution was given but a list of affected industries was given" - ) - self._default_distribute_impact_from_industries_list() - self.impact_sectoral_distrib_type = "default (shared equally between affected regions and then affected sectors)" - # CASE SCALAR + NO DISTRIB + list of region + list of sectors - elif ( - aff_regions is not None - and aff_sectors is not None - and not isinstance( - impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray) - ) - ): - logger.debug( - "Impact is Scalar and no distribution was given but lists of regions and sectors affected were given" - ) - self._default_distribute_impact_from_industries_list() - self.impact_sectoral_distrib_type = "default (shared equally between affected regions and then affected sectors)" - elif not isinstance(impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray)): - raise ValueError(f"Invalid input format: Could not compute impact") + self.name: str = name if name else "unnamed" + r"""An identifying name for the event (for convenience mostly)""" - self._finish_init() - ################################################## + self.occurrence = occurrence + self.duration = duration + self.impact_df = impact self.happened: bool = False r"""States if the event happened""" @@ -468,7 +169,6 @@ def __init__( "impact": self.total_impact, "impact_industries_distrib": list(self.impact_industries_distrib), "impact_regional_distrib": list(self.impact_regional_distrib), - "impact_sectoral_distrib_type": self.impact_sectoral_distrib_type, "globals_vars": { "possible_sectors": list(self.possible_sectors), "possible_regions": list(self.possible_regions), @@ -476,53 +176,358 @@ def __init__( "z_shape": self.z_shape, "y_shape": self.y_shape, "x_shape": self.x_shape, - "monetary_factor": self.monetary_factor, + "model_monetary_factor": self.model_monetary_factor, "mrio_used": self.mrio_name, }, } r"""Store relevant information about the event""" - def _default_distribute_impact_from_industries_list(self): - # at this point, impact should still be scalar. - logger.debug("Using default impact distribution to industries") - logger.debug( - f"Impact df at the moment:\n {self.impact_df.loc[self.aff_industries]}" - ) - self.impact_regional_distrib = np.full( - len(self.aff_regions), 1 / len(self.aff_regions) + @classmethod + @abstractmethod + def _instantiate( + cls, + impact: pd.Series, + *, + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + ): + return cls(impact=impact, occurrence=occurrence, duration=duration, name=name) + + @classmethod + def from_series( + cls, + impact: pd.Series, + *, + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + **kwarg, + ) -> Event: + """Create an event from an impact pd.Series. + + Args: + impact (pd.Series): A vector definition of the impact per (region,sector) + occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1. + duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1. + name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None. + + Raises: + ValueError: Raised if impact is empty or contains negative values. + + Returns: + Event: an Event object (or one of its subclass). + """ + if impact.size == 0: + raise ValueError( + "Empty impact Series at init, did you not set the impact correctly ?" + ) + impact = impact[impact != 0] + if np.less_equal(impact, 0).any(): + logger.debug(f"Impact has negative values:\n{impact}\n{impact[impact<0]}") + raise ValueError("Impact has negative values") + return cls._instantiate( + impact=impact, + occurrence=occurrence, + duration=duration, + name=name, + **kwarg, ) - logger.debug( - f"self.impact_regional_distrib: {list(self.impact_regional_distrib)}" - ) - logger.debug(f"len aff_regions: {len(self.aff_regions)}") - self.impact_df.loc[self.aff_industries] = ( - self.impact_df.loc[self.aff_industries] * 1 / len(self.aff_regions) - ) - impact_sec_vec = np.array( - [ - 1 / len(self.aff_industries.to_series().loc[reg]) - for reg in self.aff_regions - ] + @classmethod + def from_dataframe( + cls, + impact: pd.DataFrame, + *, + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + **kwarg, + ) -> Event: + """Convenience function for DataFrames. See :meth:`~boario.event.Event.from_series` + + Raises: + ValueError: If impact cannot be squeezed to a Series + + Returns: + Event: an Event object (or one of its subclass). + """ + impact = impact.squeeze() + if not isinstance(impact, pd.Series): + raise ValueError("Could not squeeze impact dataframe to a serie.") + + return cls.from_series( + impact=impact, + occurrence=occurrence, + duration=duration, + name=name, + **kwarg, ) - self.impact_df.loc[self.aff_industries] = ( - self.impact_df.loc[self.aff_industries] * impact_sec_vec + + @classmethod + def distribute_impact_by_gva(cls, impact_vec: pd.Series) -> pd.Series: + """Distribute a vector of impact by the GVA of affected industries. + + Each values of the given impact are mutliplied by the share of the GVA + the industry has over the GVA of all affected industries. + + Args: + impact_vec (pd.Series): The impact values to be reweigthed. + Current use-case assumes all values are the total impact. + + Returns: + pd.Series: The impact where each value was multiplied by the share + of GVA of each affected industry (over total GVA affected). + """ + gva = cls.gva_df.loc[impact_vec.index] + gva = gva.transform(lambda x: x / sum(x)) + return impact_vec * gva + + @classmethod + def distribute_impact_equally(cls, impact_vec: pd.Series) -> pd.Series: + """Distribute an impact equally between all affected regions. + + Assume impact is given as a vector with all value being the + total impact to distribute. + + Args: + impact_vec (pd.Series): The impact to distribute. + + Returns: + pd.Series: The impact vector equally distributed among affected industries. + """ + dfg = impact_vec.groupby("region") + return dfg.transform(lambda x: x / (dfg.ngroups * x.count())) + + @classmethod + def from_scalar_industries( + cls, + impact: ScalarImpact, + *, + industries: IndustriesList, + impact_industries_distrib: Optional[npt.ArrayLike] = None, + gva_distrib: bool = False, + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + **kwarg, + ) -> Event: + """Creates an Event from a scalar and a list of industries affected. + + The scalar impact is distributed evenly by default. Otherwise it can be distributed proportionnaly to the GVA of + affected industries, or to a custom distribution. + + Args: + impact (ScalarImpact): The scalar impact. + industries (IndustriesList): The list of industries affected by the impact + impact_industries_distrib (Optional[npt.ArrayLike], optional): A vector of equal size to the list of industries, stating the + share of the impact each industry should receive. Defaults to None. + gva_distrib (bool, optional): A boolean stating if the impact should be distributed proportionnaly to GVA. Defaults to False. + occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1. + duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1. + name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None. + + Raises: + ValueError: Raise if Impact is null, if len(industries) < 1 or if the sum of impact_industries_distrib differs from 1.0. + + Returns: + Event: An Event object or one of its subclass. + """ + if impact <= 0: + raise ValueError(f"Impact is null") + + if len(industries) < 1: + raise ValueError(f"Null sized affected industries ?") + + if isinstance(industries, list): + industries = pd.MultiIndex.from_tuples( + industries, names=["region", "sector"] + ) + + impact_vec = pd.Series(impact, dtype="float64", index=industries) + + if impact_industries_distrib: + if np.sum(impact_industries_distrib) != 1.0: + raise ValueError("Impact distribution doesn't sum up to 1.0") + else: + impact_vec *= impact_industries_distrib # type: ignore + + elif gva_distrib: + impact_vec = cls.distribute_impact_by_gva(impact_vec) + + else: + impact_vec = cls.distribute_impact_equally(impact_vec) + + return cls.from_series( + impact=impact_vec, + occurrence=occurrence, + duration=duration, + name=name, + **kwarg, ) - logger.debug( - f"Impact df after default distrib:\n {self.impact_df.loc[self.aff_industries]}" + + @classmethod + def from_scalar_regions_sectors( + cls, + impact: ScalarImpact, + *, + regions: RegionsList, + sectors: SectorsList, + impact_regional_distrib: Optional[npt.ArrayLike] = None, + impact_sectoral_distrib: Optional[Union[str, npt.ArrayLike]] = None, + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + **kwarg, + ) -> Event: + """Creates an Event from a scalar, a list of regions and a list of sectors affected. + + + + Args: + impact (ScalarImpact): The scalar impact. + regions (RegionsList): The list of regions affected. + sectors (SectorsList): The list of sectors affected in each region. + impact_regional_distrib (Optional[npt.ArrayLike], optional): A vector of equal size to the list of regions affected, stating the + share of the impact each industry should receive. Defaults to None. + impact_sectoral_distrib (Optional[Union[str, npt.ArrayLike]], optional): A vector of equal size to the list of sectors affected, stating the + share of the impact each industry should receive. Defaults to None. + occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1. + duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1. + name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None. + + Raises: + ValueError: Raise if Impact is null, if len(regions) or len(sectors) < 1, + + Returns: + Event: An Event object or one of its subclass + """ + if impact <= 0: + raise ValueError(f"Impact is null") + + if isinstance(regions, str): + regions = [regions] + + if isinstance(sectors, str): + sectors = [sectors] + + _regions = pd.Index(regions, name="region") + _sectors = pd.Index(sectors, name="sector") + + if len(_regions) < 1: + raise ValueError(f"Null sized affected regions ?") + + if len(_sectors) < 1: + raise ValueError(f"Null sized affected sectors ?") + + if _sectors.duplicated().any(): + warnings.warn( + UserWarning( + "Multiple presence of the same sector in affected sectors. (Will remove duplicate)" + ) + ) + _sectors = _sectors.drop_duplicates() + + if _regions.duplicated().any(): + warnings.warn( + UserWarning( + "Multiple presence of the same region in affected region. (Will remove duplicate)" + ) + ) + _regions = _regions.drop_duplicates() + + industries = pd.MultiIndex.from_product( + [_regions, _sectors], names=["region", "sector"] ) - def _finish_init(self): - logger.debug("Finishing Event init") - self.impact_vector = self.impact_df.to_numpy() - self.total_impact = self.impact_vector.sum() - self.impact_industries_distrib = ( - self.impact_vector[self.impact_vector > 0] / self.total_impact + impact_vec = pd.Series(impact, dtype="float64", index=industries) + + assert isinstance(_regions, pd.Index) + if impact_regional_distrib is None: + regional_distrib = pd.Series(1.0 / len(_regions), index=_regions) + elif not isinstance(impact_regional_distrib, pd.Series): + impact_regional_distrib = pd.Series(impact_regional_distrib, index=_regions) + regional_distrib = pd.Series(0.0, index=_regions) + regional_distrib.loc[ + impact_regional_distrib.index + ] = impact_regional_distrib + else: + regional_distrib = pd.Series(0.0, index=_regions) + try: + regional_distrib.loc[ + impact_regional_distrib.index + ] = impact_regional_distrib + except KeyError: + regional_distrib.loc[_regions] = impact_regional_distrib.values + + assert isinstance(_sectors, pd.Index) + if impact_sectoral_distrib is None: + sectoral_distrib = pd.Series(1.0 / len(_sectors), index=_sectors) + elif ( + isinstance(impact_sectoral_distrib, str) + and impact_sectoral_distrib == "gdp" + ): + gva = cls.gva_df.loc[(_regions, _sectors)] + sectoral_distrib = gva.groupby("region").transform(lambda x: x / sum(x)) + elif not isinstance(impact_sectoral_distrib, pd.Series): + impact_sectoral_distrib = pd.Series(impact_sectoral_distrib, index=_sectors) + sectoral_distrib = pd.Series(0.0, index=_sectors) + sectoral_distrib.loc[ + impact_sectoral_distrib.index + ] = impact_sectoral_distrib + else: + sectoral_distrib = pd.Series(0.0, index=_sectors) + try: + sectoral_distrib.loc[ + impact_sectoral_distrib.index + ] = impact_sectoral_distrib + except KeyError: + sectoral_distrib.loc[_sectors] = impact_sectoral_distrib.values + + logger.debug(f"{sectoral_distrib}") + logger.debug(f"{regional_distrib}") + if isinstance(sectoral_distrib.index, pd.MultiIndex): + industries_distrib = sectoral_distrib * regional_distrib + else: + industries_distrib = pd.Series( + np.outer(regional_distrib.values, sectoral_distrib.values).flatten(), # type: ignore + index=pd.MultiIndex.from_product( + [regional_distrib.index, sectoral_distrib.index] + ), + ) + + impact_vec *= industries_distrib + + return cls.from_series( + impact=impact_vec, + occurrence=occurrence, + duration=duration, + name=name, + **kwarg, ) - self.impact_regional_distrib = ( - self.impact_df.loc[self.aff_industries].groupby("region").sum().values - / self.total_impact + + @property + def impact_df(self) -> pd.Series: + r"""A pandas Series with all possible industries as index, holding the impact vector of the event. The impact is defined for each sectors in each region.""" + return self._impact_df + + @impact_df.setter + def impact_df(self, value: pd.Series): + self._impact_df = pd.Series( + 0, + dtype="float64", + index=pd.MultiIndex.from_product( + [self.possible_regions, self.possible_sectors], + names=["region", "sector"], + ), ) + self._impact_df[value.index] = value + logger.debug("Sorting impact Series") + self._impact_df.sort_index(inplace=True) + self.aff_industries = self.impact_df.loc[self.impact_df > 0].index # type: ignore + self.impact_industries_distrib = self.impact_df.transform(lambda x: x / sum(x)) + self.total_impact = self.impact_df.sum() + self.impact_vector = self.impact_df.values @property def aff_industries(self) -> pd.MultiIndex: @@ -565,12 +570,12 @@ def aff_industries(self, index: pd.MultiIndex): self.aff_regions = regions self.aff_sectors = sectors logger.debug( - f"Setting _aff_industries. There are {len(index)} affected industries" + f"Setting _aff_industries. There are {np.size(index)} affected industries" ) self._aff_industries = index self._aff_industries_idx = np.array( [ - len(self.possible_sectors) * ri + si + np.size(self.possible_sectors) * ri + si for ri in self._aff_regions_idx for si in self._aff_sectors_idx ] @@ -584,9 +589,9 @@ def occurrence(self) -> int: @occurrence.setter def occurrence(self, value: int): - if not 0 <= value <= self.temporal_unit_range: + if not 0 < value <= self.temporal_unit_range: raise ValueError( - "Occurrence of event is not in the range of simulation steps : {} not in [0-{}]".format( + "Occurrence of event is not in the range of simulation steps (cannot be 0) : {} not in ]0-{}]".format( value, self.temporal_unit_range ) ) @@ -619,16 +624,16 @@ def aff_regions(self) -> pd.Index: return self._aff_regions @property - def aff_regions_idx(self) -> np.ndarray: + def aff_regions_idx(self) -> npt.NDArray: r"""The array of lexicographically ordered affected region indexes""" return self._aff_regions_idx @aff_regions.setter - def aff_regions(self, value: ArrayLike | str): + def aff_regions(self, value: npt.ArrayLike | str): if isinstance(value, str): value = [value] - value = pd.Index(value) + value = pd.Index(value) # type: ignore impossible_regions = np.setdiff1d(value, self.possible_regions) if impossible_regions.size > 0: raise ValueError( @@ -646,16 +651,16 @@ def aff_sectors(self) -> pd.Index: return self._aff_sectors @property - def aff_sectors_idx(self) -> np.ndarray: + def aff_sectors_idx(self) -> npt.NDArray: r"""The array of lexicographically ordered affected sectors indexes""" return self._aff_sectors_idx @aff_sectors.setter - def aff_sectors(self, value: ArrayLike | str): + def aff_sectors(self, value: npt.ArrayLike | str): if isinstance(value, str): value = [value] - value = pd.Index(value, name="sector") + value = pd.Index(value, name="sector") # type: ignore impossible_sectors = np.setdiff1d(value, self.possible_sectors) if impossible_sectors.size > 0: raise ValueError( @@ -667,76 +672,33 @@ def aff_sectors(self, value: ArrayLike | str): self._aff_sectors_idx = np.searchsorted(self.possible_sectors, value) @property - def impact_regional_distrib(self) -> np.ndarray: - r"""The array specifying how damages are distributed among affected regions""" + def impact_regional_distrib(self) -> pd.Series: + r"""The series specifying how damages are distributed among affected regions""" return self._impact_regional_distrib - @impact_regional_distrib.setter - def impact_regional_distrib(self, value: ArrayLike): - if self.aff_regions is None: - raise AttributeError("Affected regions attribute isn't set yet") - value = np.array(value) - if value.size != self.aff_regions.size: - raise ValueError( - "There are {} affected regions by the event and length of given damage distribution is {}".format( - self.aff_regions.size, value.size - ) - ) - s = value.sum() - if not math.isclose(s, 1): - raise ValueError( - "Damage distribution doesn't sum up to 1 but to {}, which is not valid".format( - s - ) - ) - self._impact_regional_distrib = value - - # @property - # def impact_sectoral_distrib(self) -> np.ndarray: - # r"""The array specifying how damages are distributed among affected sectors""" - # return self._impact_sectoral_distrib - - # @impact_sectoral_distrib.setter - # def impact_sectoral_distrib(self, value: ArrayLike): - # if self.aff_sectors is None: - # raise AttributeError("Affected sectors attribute isn't set yet") - # value = np.array(value) - # if value.size != self.aff_sectors.size: - # raise ValueError( - # "There are {} affected sectors by the event and length of given damage distribution is {}".format( - # self.aff_sectors.size, value.size - # ) - # ) - # s = value.sum() - # if not math.isclose(s, 1): - # raise ValueError( - # "Damage distribution doesn't sum up to 1 but to {}, which is not valid".format( - # s - # ) - # ) - # self._impact_sectoral_distrib = value - @property - def impact_sectoral_distrib_type(self) -> str: - r"""The type of damages distribution among sectors (currently only 'gdp')""" + def impact_industries_distrib(self) -> pd.Series: + r"""The series specifying how damages are distributed among affected industries (regions,sectors)""" - return self._impact_sectoral_distrib_type + return self._impact_industries_distrib - @impact_sectoral_distrib_type.setter - def impact_sectoral_distrib_type(self, value: str): - logger.debug(f"Setting _impact_sectoral_distrib_type to {value}") - self._impact_sectoral_distrib_type = value + @impact_industries_distrib.setter + def impact_industries_distrib(self, value: pd.Series): + self._impact_industries_distrib = value + self._impact_regional_distrib = self._impact_industries_distrib.groupby( + "region" + ).sum() def __repr__(self): # TODO: find ways to represent long lists - return f""" [Representation WIP] + return f""" Event( name = {self.name}, occur = {self.occurrence}, duration = {self.duration} - aff_regions = {self.aff_regions}, - aff_sectors = {self.aff_sectors}, + aff_regions = {self.aff_regions.to_list()}, + aff_sectors = {self.aff_sectors.to_list()}, ) """ @@ -744,159 +706,336 @@ def __repr__(self): class EventArbitraryProd(Event): def __init__( self, - impact: Impact, - aff_regions: Optional[RegionsList] = None, - aff_sectors: Optional[SectorsList] = None, - aff_industries: Optional[IndustriesList] = None, - impact_industries_distrib=None, - impact_regional_distrib=None, - impact_sectoral_distrib_type="equally shared", - impact_sectoral_distrib=None, - name="Unnamed", - occurrence=1, - duration=1, + *, + impact: pd.Series, + recovery_time: int = 1, + recovery_function: str = "linear", + name: str | None = "Unnamed", + occurrence: int = 1, + duration: int = 1, ) -> None: + if (impact > 1.0).any(): + raise ValueError( + "Impact is greater than 100% (1.) for at least an industry." + ) + super().__init__( - impact, - aff_regions, - aff_sectors, - aff_industries, - impact_industries_distrib, - impact_regional_distrib, - impact_sectoral_distrib_type, - impact_sectoral_distrib, - name, - occurrence, - duration, + impact=impact, + name=name, + occurrence=occurrence, + duration=duration, + ) + + self._prod_cap_delta_arbitrary_0 = ( + self.impact_vector.copy() + ) # np.zeros(shape=len(self.possible_sectors)) + self.prod_cap_delta_arbitrary = ( + self.impact_vector.copy() + ) # type: ignore # np.zeros(shape=len(self.possible_sectors)) + self.recovery_time = recovery_time + r"""The characteristic recovery duration after the event is over""" + + self.recovery_function = recovery_function + + logger.info("Initialized") + + @classmethod + def _instantiate( + cls, + impact: pd.Series, + *, + recovery_time: int = 1, + recovery_function: str = "linear", + occurrence: int = 1, + duration: int = 1, + name: Optional[str] = None, + ): + return cls( + impact=impact, + occurrence=occurrence, + duration=duration, + name=name, + recovery_time=recovery_time, + recovery_function=recovery_function, ) - self._prod_cap_delta_arbitrary = np.zeros(shape=len(self.possible_sectors)) - self._aff_sectors_idx = None - self._aff_sectors = None - raise NotImplementedError() @property - def prod_cap_delta_arbitrary(self) -> np.ndarray: - r"""The optional array storing arbitrary (as in not related to kapital destroyed) production capacity loss""" + def prod_cap_delta_arbitrary(self) -> npt.NDArray: + r"""The optional array storing arbitrary (as in not related to productive_capital destroyed) production capacity loss""" return self._prod_cap_delta_arbitrary @prod_cap_delta_arbitrary.setter - def prod_cap_delta_arbitrary(self, value: dict[str, float]): - if self.aff_regions is None: - raise AttributeError("Affected regions attribute isn't set yet") - aff_sectors = np.array(list(value.keys())) - aff_shares = np.array(list(value.values())) - impossible_sectors = np.setdiff1d(aff_sectors, self.possible_sectors) - if impossible_sectors.size > 0: - raise ValueError( - "These sectors are not in the model : {}".format(impossible_sectors) + def prod_cap_delta_arbitrary(self, value: npt.NDArray): + self._prod_cap_delta_arbitrary = value + + @property + def recoverable(self) -> bool: + r"""A boolean stating if an event can start recover""" + return self._recoverable + + @recoverable.setter + def recoverable(self, current_temporal_unit: int): + reb = (self.occurrence + self.duration) <= current_temporal_unit + if reb and not self.recoverable: + logger.info( + "Temporal_Unit : {} ~ Event named {} that occured at {} in {} has started recovering (arbitrary production capacity loss)".format( + current_temporal_unit, + self.name, + self.occurrence, + self.aff_regions.to_list(), + ) ) - self._aff_sectors = aff_sectors - self._aff_sectors_idx = np.searchsorted(self.possible_sectors, aff_sectors) - aff_industries_idx = np.array( - [ - len(self.possible_sectors) * ri + si - for ri in self.regions_idx - for si in self._aff_sectors_idx - ] - ) - self._prod_cap_delta_arbitrary[aff_industries_idx] = np.tile( - aff_shares, self._aff_regions.size + self._recoverable = reb + + @property + def recovery_function(self) -> Callable: + r"""The recovery function used for recovery (`Callable`)""" + return self._recovery_fun + + @recovery_function.setter + def recovery_function(self, r_fun: str | Callable | None): + if r_fun is None: + r_fun = "instant" + if self.recovery_time is None: + raise AttributeError( + "Impossible to set recovery function if no recovery time is given." + ) + if isinstance(r_fun, str): + if r_fun == "linear": + fun = linear_recovery + elif r_fun == "convexe": + fun = convexe_recovery_scaled + elif r_fun == "convexe noscale": + fun = convexe_recovery + elif r_fun == "concave": + fun = concave_recovery + else: + raise NotImplementedError( + "No implemented recovery function corresponding to {}".format(r_fun) + ) + elif callable(r_fun): + r_fun_argsspec = inspect.getfullargspec(r_fun) + r_fun_args = r_fun_argsspec.args + r_fun_argsspec.kwonlyargs + if not all( + args in r_fun_args + for args in [ + "init_impact_stock", + "elapsed_temporal_unit", + "recovery_time", + ] + ): + raise ValueError( + "Recovery function has to have at least the following keyword arguments: {}".format( + [ + "init_impact_stock", + "elapsed_temporal_unit", + "recovery_time", + ] + ) + ) + fun = r_fun + + else: + raise ValueError("Given recovery function is not a str or callable") + + r_fun_partial = partial( + fun, + init_impact_stock=self._prod_cap_delta_arbitrary_0, + recovery_time=self.recovery_time, ) + self._recovery_fun = r_fun_partial + + def recovery(self, current_temporal_unit: int): + elapsed = current_temporal_unit - (self.occurrence + self.duration) + if elapsed < 0: + raise RuntimeError("Trying to recover before event is over") + if self.recovery_function is None: + raise RuntimeError( + "Trying to recover event while recovery function isn't set yet" + ) + res = self.recovery_function(elapsed_temporal_unit=elapsed) + precision = int(math.log10(self.model_monetary_factor)) + 1 + res = np.around(res, precision) + if not np.any(res): + self.over = True + self._prod_cap_delta_arbitrary = res -class EventKapitalDestroyed(Event, metaclass=abc.ABCMeta): +class EventKapitalDestroyed(Event, ABC): def __init__( self, - impact: Impact, - aff_regions: Optional[RegionsList] = None, - aff_sectors: Optional[SectorsList] = None, - aff_industries: Optional[IndustriesList] = None, - impact_industries_distrib=None, - impact_regional_distrib=None, - impact_sectoral_distrib_type="equally shared", - impact_sectoral_distrib=None, - name="Unnamed", - occurrence=1, - duration=1, + *, + impact: pd.Series, + households_impact: Optional[Impact] = None, + name: str | None = "Unnamed", + occurrence: int = 1, + duration: int = 1, + event_monetary_factor: Optional[int] = None, ) -> None: + if event_monetary_factor is None: + logger.info( + f"No event monetary factor given. Assuming it is the same as the model ({self.model_monetary_factor})" + ) + self.event_monetary_factor = self.model_monetary_factor + r"""The monetary factor for the impact of the event (e.g. 10**6, 10**3, ...)""" + + else: + self.event_monetary_factor = event_monetary_factor + if self.event_monetary_factor != self.model_monetary_factor: + logger.warning( + f"Event monetary factor ({self.event_monetary_factor}) differs from model monetary factor ({self.model_monetary_factor}). Be careful to define your impact with the correct unit (ie in event monetary factor)." + ) + + if (impact < LOW_DEMAND_THRESH / self.event_monetary_factor).all(): + raise ValueError( + "Impact is too small to be considered by the model. Check you units perhaps ?" + ) + super().__init__( - impact, - aff_regions, - aff_sectors, - aff_industries, - impact_industries_distrib, - impact_regional_distrib, - impact_sectoral_distrib_type, - impact_sectoral_distrib, - name, - occurrence, - duration, + impact=impact, + name=name, + occurrence=occurrence, + duration=duration, ) - # The only thing we have to do is affecting/computing the regional_sectoral_kapital_destroyed - self.total_kapital_destroyed = self.total_impact - self.total_kapital_destroyed /= self.monetary_factor - self.remaining_kapital_destroyed = self.total_kapital_destroyed - self._regional_sectoral_kapital_destroyed_0 = ( - self.impact_vector / self.monetary_factor + + # The only thing we have to do is affecting/computing the regional_sectoral_productive_capital_destroyed + self.impact_df = self.impact_df * ( + self.event_monetary_factor / self.model_monetary_factor + ) + self.total_productive_capital_destroyed = self.total_impact + logger.info( + f"Total impact on productive capital is {self.total_productive_capital_destroyed} (in model unit)" + ) + self.remaining_productive_capital_destroyed = ( + self.total_productive_capital_destroyed ) - self.regional_sectoral_kapital_destroyed = ( - self.impact_vector / self.monetary_factor + self._regional_sectoral_productive_capital_destroyed_0 = ( + self.impact_vector.copy() + ) + self.regional_sectoral_productive_capital_destroyed = self.impact_vector.copy() # type: ignore + self.households_impact_df: pd.Series = pd.Series( + 0, + dtype="float64", + index=pd.MultiIndex.from_product( + [self.possible_regions, self.possible_final_demand_cat], + names=["region", "final_demand_cat"], + ), + ) + r"""A pandas Series with all possible (regions, final_demand_cat) as index, holding the households impacts vector of the event. The impact is defined for each region and each final demand category.""" + + if households_impact: + try: + rebuilding_demand_idx = self.possible_final_demand_cat[ + self.possible_final_demand_cat.str.match( + REBUILDING_FINALDEMAND_CAT_REGEX + ) + ] # .values[0] + if len(rebuilding_demand_idx) > 1: + raise ValueError( + f"The rebuilding demand index ({rebuilding_demand_idx}) contains multiple values which is a problem. Contact the dev to update the matching regexp." + ) + + except IndexError: + logger.warning( + f"No final demand category matched common rebuilding final demand category, hence we will put it in the first available ({self.possible_final_demand_cat[0]})." + ) + rebuilding_demand_idx = self.possible_final_demand_cat[0] + if isinstance(households_impact, pd.Series): + logger.debug("Given household impact is a pandas Series") + self.households_impact_df.loc[ + households_impact.index, rebuilding_demand_idx + ] = households_impact # type: ignore + elif isinstance(households_impact, dict): + logger.debug("Given household impact is a dict") + households_impact = pd.Series(households_impact, dtype="float64") + self.households_impact_df.loc[ + households_impact.index, rebuilding_demand_idx + ] = households_impact # type: ignore + elif isinstance(households_impact, pd.DataFrame): + logger.debug("Given household impact is a dataframe") + households_impact = households_impact.squeeze() + if not isinstance(households_impact, pd.Series): + raise ValueError( + "The given households_impact DataFrame is not a Series after being squeezed" + ) + self.households_impact_df.loc[ + households_impact.index, rebuilding_demand_idx + ] = households_impact # type: ignore + elif isinstance(households_impact, (list, np.ndarray)): + if np.size(households_impact) != np.size(self.aff_regions): + raise ValueError( + f"Length mismatch between households_impact ({np.size(households_impact)} and affected regions ({np.size(self.aff_regions)}))" + ) + else: + self.households_impact_df.loc[ + self.aff_regions, rebuilding_demand_idx + ] = households_impact # type: ignore + elif isinstance(households_impact, (float, int)): + if self.impact_regional_distrib is not None: + logger.warning( + f"households impact ({households_impact}) given as scalar, distributing among region following `impact_regional_distrib` ({self.impact_regional_distrib}) to {self.aff_regions}" + ) + logger.debug(f"{rebuilding_demand_idx}") + self.households_impact_df.loc[ + self.aff_regions, rebuilding_demand_idx + ] = ( + households_impact * self.impact_regional_distrib + ) # type: ignore + self.households_impact_df *= ( + self.event_monetary_factor / self.model_monetary_factor + ) + logger.info( + f"Total impact on households is {self.households_impact_df.sum()} (in model unit)" ) @property - def regional_sectoral_kapital_destroyed(self) -> np.ndarray: - r"""The (optional) array of kapital destroyed per industry (ie region x sector)""" - return self._regional_sectoral_kapital_destroyed + def regional_sectoral_productive_capital_destroyed(self) -> npt.NDArray: + r"""The (optional) array of productive_capital destroyed per industry (ie region x sector)""" + return self._regional_sectoral_productive_capital_destroyed - @regional_sectoral_kapital_destroyed.setter - def regional_sectoral_kapital_destroyed(self, value: ArrayLike): + @regional_sectoral_productive_capital_destroyed.setter + def regional_sectoral_productive_capital_destroyed(self, value: npt.ArrayLike): value = np.array(value) + logger.debug( + f"Changing regional_sectoral_productive_capital_destroyed with {value}\n Sum is {value.sum()}" + ) if value.shape != self.x_shape: raise ValueError( - "Incorrect shape give for regional_sectoral_kapital_destroyed: {} given and {} expected".format( + "Incorrect shape give for regional_sectoral_productive_capital_destroyed: {} given and {} expected".format( value.shape, self.x_shape ) ) - self._regional_sectoral_kapital_destroyed = value + self._regional_sectoral_productive_capital_destroyed = value class EventKapitalRebuild(EventKapitalDestroyed): def __init__( self, - impact: Impact, - rebuilding_sectors: Union[dict[str, float], pd.Series], - rebuild_tau=60, - aff_regions: Optional[RegionsList] = None, - aff_sectors: Optional[SectorsList] = None, - aff_industries: Optional[IndustriesList] = None, - impact_industries_distrib=None, - impact_regional_distrib=None, - impact_sectoral_distrib_type="equally shared", - impact_sectoral_distrib=None, - name="Unnamed", - occurrence=1, - duration=1, - rebuilding_factor=None, + *, + impact: pd.Series, + households_impact: Impact | None = None, + name: str | None = "Unnamed", + occurrence: int = 1, + duration: int = 1, + event_monetary_factor: Optional[int] = None, + rebuild_tau: int, + rebuilding_sectors: dict[str, float] | pd.Series, + rebuilding_factor: float = 1.0, ) -> None: super().__init__( - impact, - aff_regions, - aff_sectors, - aff_industries, - impact_industries_distrib, - impact_regional_distrib, - impact_sectoral_distrib_type, - impact_sectoral_distrib, - name, - occurrence, - duration, + impact=impact, + households_impact=households_impact, + name=name, + occurrence=occurrence, + duration=duration, + event_monetary_factor=event_monetary_factor, ) - - self._rebuildable = None + self._rebuildable = False self.rebuild_tau = rebuild_tau self.rebuilding_sectors = rebuilding_sectors + self.rebuilding_factor = rebuilding_factor - rebuilding_demand = np.zeros(shape=self.z_shape) + industrial_rebuilding_demand = np.zeros(shape=self.z_shape) tmp = np.zeros(self.z_shape, dtype="float") mask = np.ix_( np.union1d( @@ -904,23 +1043,38 @@ def __init__( ), self._aff_industries_idx, ) - rebuilding_demand = np.outer( - self.rebuilding_sectors_shares, self.regional_sectoral_kapital_destroyed + industrial_rebuilding_demand = np.outer( + self.rebuilding_sectors_shares, + self.regional_sectoral_productive_capital_destroyed, + ) + industrial_rebuilding_demand = industrial_rebuilding_demand * rebuilding_factor + tmp[mask] = self.Z_distrib[mask] * industrial_rebuilding_demand[mask] + + precision = int(math.log10(self.model_monetary_factor)) + 1 + np.around(tmp, precision, tmp) + + self.rebuilding_demand_indus = tmp + + households_rebuilding_demand = np.zeros(shape=self.y_shape) + tmp = np.zeros(self.y_shape, dtype="float") + mask = np.ix_( # type: ignore + np.union1d( # type: ignore + self._rebuilding_industries_RoW_idx, self._rebuilding_industries_idx + ), + list(range(self.y_shape[1])), + ) + + households_rebuilding_demand = np.outer( + self.rebuilding_sectors_shares, self.households_impact_df.to_numpy() ) - rebuilding_demand = rebuilding_demand * rebuilding_factor + households_rebuilding_demand = households_rebuilding_demand * rebuilding_factor + tmp[mask] = self.Y_distrib[mask] * households_rebuilding_demand[mask] + np.around(tmp, precision, tmp) + self.rebuilding_demand_house = tmp logger.debug( - f"kapital destroyed vec is {self.regional_sectoral_kapital_destroyed}" + f"house rebuilding demand is: {pd.DataFrame(self.rebuilding_demand_house, index=pd.MultiIndex.from_product([self.possible_regions,self.possible_sectors]))}" ) - logger.debug(f"rebuilding sectors shares are {self.rebuilding_sectors_shares}") - logger.debug(f"Z shape is {self.z_shape}") - logger.debug(f"Z_distrib[mask] has shape {self.Z_distrib[mask].shape}") - logger.debug(f"reb_demand: {rebuilding_demand}") - logger.debug(f"reb_demand: {rebuilding_demand.shape}") - # reb_tiled = np.tile(rebuilding_demand, (len(self.possible_regions), 1)) - # reb_tiled = reb_tiled[mask] - tmp[mask] = self.Z_distrib[mask] * rebuilding_demand[mask] - self.rebuilding_demand_indus = tmp - self.rebuilding_demand_house = np.zeros(shape=self.y_shape) + self.event_dict["rebuilding_sectors"] = { sec: share for sec, share in zip( @@ -928,18 +1082,44 @@ def __init__( ) } + @classmethod + def _instantiate( + cls, + impact: pd.Series, + *, + households_impact: Optional[Impact] = None, + occurrence: int = 1, + duration: int = 1, + name: str | None = None, + event_monetary_factor: Optional[int] = None, + rebuild_tau: int, + rebuilding_sectors: dict[str, float] | pd.Series, + rebuilding_factor: float = 1.0, + ): + return cls( + impact=impact, + households_impact=households_impact, + occurrence=occurrence, + duration=duration, + name=name, + event_monetary_factor=event_monetary_factor, + rebuild_tau=rebuild_tau, + rebuilding_sectors=rebuilding_sectors, + rebuilding_factor=rebuilding_factor, + ) + @property def rebuilding_sectors(self) -> pd.Index: r"""The (optional) array of rebuilding sectors""" return self._rebuilding_sectors @property - def rebuilding_sectors_idx(self) -> np.ndarray: + def rebuilding_sectors_idx(self) -> npt.NDArray: r"""The (optional) array of indexes of the rebuilding sectors (in lexicographic order)""" return self._rebuilding_sectors_idx @property - def rebuilding_sectors_shares(self) -> np.ndarray: + def rebuilding_sectors_shares(self) -> npt.NDArray: r"""The array specifying how rebuilding demand is distributed along the rebuilding sectors""" return self._rebuilding_sectors_shares @@ -949,6 +1129,7 @@ def rebuilding_sectors(self, value: dict[str, float] | pd.Series): reb_sectors = pd.Series(value) else: reb_sectors = value + assert np.isclose(reb_sectors.sum(), 1.0) impossible_sectors = np.setdiff1d(reb_sectors.index, self.possible_sectors) if impossible_sectors.size > 0: raise ValueError( @@ -963,36 +1144,36 @@ def rebuilding_sectors(self, value: dict[str, float] | pd.Series): self._rebuilding_sectors_shares = np.zeros(self.x_shape) self._rebuilding_industries_idx = np.array( [ - len(self.possible_sectors) * ri + si + np.size(self.possible_sectors) * ri + si for ri in self._aff_regions_idx for si in self._rebuilding_sectors_idx ] ) self._rebuilding_industries_RoW_idx = np.array( [ - len(self.possible_sectors) * ri + si - for ri in range(len(self.possible_regions)) + np.size(self.possible_sectors) * ri + si + for ri in range(np.size(self.possible_regions)) if ri not in self._aff_regions_idx for si in self._rebuilding_sectors_idx ] ) self._rebuilding_sectors_shares[self._rebuilding_industries_idx] = np.tile( - np.array(reb_sectors.values), len(self.aff_regions) + np.array(reb_sectors.values), np.size(self.aff_regions) ) self._rebuilding_sectors_shares[ self._rebuilding_industries_RoW_idx ] = np.tile( np.array(reb_sectors.values), - (len(self.possible_regions) - len(self.aff_regions)), + (np.size(self.possible_regions) - np.size(self.aff_regions)), ) @property - def rebuilding_demand_house(self) -> np.ndarray: + def rebuilding_demand_house(self) -> npt.NDArray: r"""The optional array of rebuilding demand from households""" return self._rebuilding_demand_house @rebuilding_demand_house.setter - def rebuilding_demand_house(self, value: ArrayLike): + def rebuilding_demand_house(self, value: npt.ArrayLike): value = np.array(value) if value.shape != self.y_shape: raise ValueError( @@ -1000,15 +1181,16 @@ def rebuilding_demand_house(self, value: ArrayLike): value.shape, self.y_shape ) ) + value[value < LOW_DEMAND_THRESH / self.model_monetary_factor] = 0.0 self._rebuilding_demand_house = value @property - def rebuilding_demand_indus(self) -> np.ndarray: - r"""The optional array of rebuilding demand from industries (to rebuild their kapital)""" + def rebuilding_demand_indus(self) -> npt.NDArray: + r"""The optional array of rebuilding demand from industries (to rebuild their productive_capital)""" return self._rebuilding_demand_indus @rebuilding_demand_indus.setter - def rebuilding_demand_indus(self, value: ArrayLike): + def rebuilding_demand_indus(self, value: npt.ArrayLike): value = np.array(value) if value.shape != self.z_shape: raise ValueError( @@ -1016,9 +1198,12 @@ def rebuilding_demand_indus(self, value: ArrayLike): value.shape, self.z_shape ) ) + value[value < LOW_DEMAND_THRESH / self.model_monetary_factor] = 0.0 self._rebuilding_demand_indus = value - # Also update kapital destroyed - self._regional_sectoral_kapital_destroyed = value.sum(axis=0) + # Also update productive_capital destroyed + self.regional_sectoral_productive_capital_destroyed = value.sum(axis=0) * ( + 1 / self.rebuilding_factor + ) @property def rebuildable(self) -> bool: @@ -1034,8 +1219,8 @@ def rebuildable(self, current_temporal_unit: int): current_temporal_unit, self.name, self.occurrence, - self._aff_regions.values, - self.total_kapital_destroyed, + self.aff_regions.to_list(), + self.total_productive_capital_destroyed, ) ) self._rebuildable = reb @@ -1044,37 +1229,52 @@ def rebuildable(self, current_temporal_unit: int): class EventKapitalRecover(EventKapitalDestroyed): def __init__( self, - impact: Impact, + *, + impact: pd.Series, recovery_time: int, recovery_function: str = "linear", - aff_regions: Optional[RegionsList] = None, - aff_sectors: Optional[SectorsList] = None, - aff_industries: Optional[IndustriesList] = None, - impact_industries_distrib=None, - impact_regional_distrib=None, - impact_sectoral_distrib_type="equally shared", - impact_sectoral_distrib=None, - name="Unnamed", + households_impact: Optional[Impact] = None, + name: str | None = "Unnamed", occurrence=1, duration=1, + event_monetary_factor=None, ) -> None: super().__init__( - impact, - aff_regions, - aff_sectors, - aff_industries, - impact_industries_distrib, - impact_regional_distrib, - impact_sectoral_distrib_type, - impact_sectoral_distrib, - name, - occurrence, - duration, + impact=impact, + households_impact=households_impact, + name=name, + occurrence=occurrence, + duration=duration, + event_monetary_factor=event_monetary_factor, ) self.recovery_time = recovery_time self.recovery_function = recovery_function self._recoverable = False + @classmethod + def _instantiate( + cls, + impact: pd.Series, + *, + households_impact: Optional[Impact] = None, + occurrence: int = 1, + duration: int = 1, + name: str | None = None, + event_monetary_factor: Optional[int] = None, + recovery_time: int, + recovery_function: str = "linear", + ): + return cls( + impact=impact, + households_impact=households_impact, + occurrence=occurrence, + duration=duration, + name=name, + event_monetary_factor=event_monetary_factor, + recovery_time=recovery_time, + recovery_function=recovery_function, + ) + @property def recoverable(self) -> bool: r"""A boolean stating if an event can start recover""" @@ -1089,8 +1289,8 @@ def recoverable(self, current_temporal_unit: int): current_temporal_unit, self.name, self.occurrence, - self._aff_regions, - self.total_kapital_destroyed, + self.aff_regions.to_list(), + self.total_productive_capital_destroyed, ) ) self._recoverable = reb @@ -1127,7 +1327,7 @@ def recovery_function(self, r_fun: str | Callable | None): if not all( args in r_fun_args for args in [ - "init_kapital_destroyed", + "init_impact_stock", "elapsed_temporal_unit", "recovery_time", ] @@ -1135,7 +1335,7 @@ def recovery_function(self, r_fun: str | Callable | None): raise ValueError( "Recovery function has to have at least the following keyword arguments: {}".format( [ - "init_kapital_destroyed", + "init_impact_stock", "elapsed_temporal_unit", "recovery_time", ] @@ -1148,7 +1348,7 @@ def recovery_function(self, r_fun: str | Callable | None): r_fun_partial = partial( fun, - init_kapital_destroyed=self._regional_sectoral_kapital_destroyed_0, + init_impact_stock=self._regional_sectoral_productive_capital_destroyed_0, recovery_time=self.recovery_time, ) self._recovery_fun = r_fun_partial @@ -1162,8 +1362,9 @@ def recovery(self, current_temporal_unit: int): "Trying to recover event while recovery function isn't set yet" ) res = self.recovery_function(elapsed_temporal_unit=elapsed) - precision = int(math.log10(self.monetary_factor)) + 1 + precision = int(math.log10(self.model_monetary_factor)) + 1 res = np.around(res, precision) + res[res < 0] = 0.0 if not np.any(res): self.over = True - self.regional_sectoral_kapital_destroyed = res + self.regional_sectoral_productive_capital_destroyed = res diff --git a/boario/extended_models.py b/boario/extended_models.py index c12a30f..579367f 100644 --- a/boario/extended_models.py +++ b/boario/extended_models.py @@ -15,8 +15,9 @@ # along with this program. If not, see . from __future__ import annotations -from typing import Dict +from typing import Dict, Optional import numpy as np +import pandas as pd from boario import logger from boario.model_base import * from boario.event import * @@ -37,9 +38,16 @@ def __init__( rebuild_tau=60, main_inv_dur=90, monetary_factor=10**6, - psi_param=0.90, + temporal_units_by_step: int = 1, + iotable_year_to_temporal_unit_factor: int = 365, + infinite_inventories_sect: Optional[list] = None, + inventory_dict: Optional[dict] = None, + productive_capital_vector: Optional[ + pd.Series | np.ndarray | pd.DataFrame + ] = None, + productive_capital_to_VA_dict: Optional[dict] = None, + psi_param=0.80, inventory_restoration_tau: int | Dict[str, int] = 60, - **kwargs, ) -> None: """An ARIO3 model with some additional features @@ -55,7 +63,12 @@ def __init__( rebuild_tau=rebuild_tau, main_inv_dur=main_inv_dur, monetary_factor=monetary_factor, - **kwargs, + temporal_units_by_step=temporal_units_by_step, + iotable_year_to_temporal_unit_factor=iotable_year_to_temporal_unit_factor, + infinite_inventories_sect=infinite_inventories_sect, + inventory_dict=inventory_dict, + productive_capital_vector=productive_capital_vector, + productive_capital_to_VA_dict=productive_capital_to_VA_dict, ) logger.debug("Model is an ARIOPsiModel") @@ -66,10 +79,14 @@ def __init__( elif isinstance(psi_param, float): self.psi = psi_param + elif isinstance(psi_param, int): + self.psi = float(psi_param) else: raise ValueError( "'psi_param' parameter is neither a str rep of a float or a float" ) + if self.psi > 1.0: + raise ValueError("'psi_param' parameter must be less or equal than 1.") if isinstance(inventory_restoration_tau, int): restoration_tau = [ @@ -85,11 +102,19 @@ def __init__( ) for _, value in inventory_restoration_tau.items(): - if not isinstance(value, int): + if isinstance(value, float): + if not value.is_integer(): + raise ValueError( + f"Invalid value: {value} ({type(value)}) in inventory_restoration_tau, values should be integer: {inventory_restoration_tau}" + ) + elif not isinstance(value, int): raise ValueError( - "Invalid value in inventory_restoration_tau, values should be integer." + f"Invalid value: {value} ({type(value)}) in inventory_restoration_tau, values should be integer: {inventory_restoration_tau}" ) + inventory_restoration_tau = { + key: int(val) for key, val in inventory_restoration_tau.items() + } inventory_restoration_tau = dict(sorted(inventory_restoration_tau.items())) restoration_tau = [ (self.n_temporal_units_by_step / v) if v >= INV_THRESHOLD else v @@ -100,7 +125,7 @@ def __init__( f"Invalid inventory_restoration_tau: expected dict or int got {type(inventory_restoration_tau)}" ) self.restoration_tau = np.array(restoration_tau) - """numpy.ndarray of int: Array of size :math:`n` setting for each inputs its characteristic restoration time :math:`\tau_{\textrm{INV}}` in ``n_temporal_units_by_step``. (see :ref:`boario-math`).""" + r"""numpy.ndarray of int: Array of size :math:`n` setting for each inputs its characteristic restoration time :math:`\tau_{\textrm{INV}}` in ``n_temporal_units_by_step``. (see :ref:`boario-math`).""" ################################################################# @property @@ -117,50 +142,6 @@ def calc_production(self, current_temporal_unit: int) -> np.ndarray: r"""Computes and updates actual production. The difference with :class:`ARIOBaseModel` is in the way inventory constraints are computed. See :ref:`boario-math-prod`. - 1. Computes ``production_opt`` and ``inventory_constraints`` as : - - .. math:: - :nowrap: - - \begin{alignat*}{4} - \iox^{\textrm{Opt}}(t) &= (x^{\textrm{Opt}}_{f}(t))_{f \in \firmsset} &&= \left ( \min \left ( d^{\textrm{Tot}}_{f}(t), x^{\textrm{Cap}}_{f}(t) \right ) \right )_{f \in \firmsset} && \text{Optimal production}\\ - \ioinv^{\textrm{Cons}}(t) &= (\omega^{\textrm{Cons},f}_p(t))_{\substack{p \in \sectorsset\\f \in \firmsset}} &&= - \begin{bmatrix} - s^{1}_1 & \hdots & s^{p}_1 \\ - \vdots & \ddots & \vdots\\ - s^1_n & \hdots & s^{p}_n - \end{bmatrix} - \odot \begin{bmatrix} \iox^{\textrm{Opt}}(t)\\ - \vdots\\ - \iox^{\textrm{Opt}}(t) \end{bmatrix} \odot \ioa^{\sectorsset} && \text{Inventory constraints} \\ - &&&= \begin{bmatrix} - s^{1}_1 x^{\textrm{Opt}}_{1}(t) a_{11} & \hdots & s^{p}_1 x^{\textrm{Opt}}_{p}(t) a_{1p}\\ - \vdots & \ddots & \vdots\\ - s^1_n x^{\textrm{Opt}}_{1}(t) a_{n1} & \hdots & s^{p}_n x^{\textrm{Opt}}_{p}(t) a_{np} - \end{bmatrix} - \cdot \psi && \\ - &&&= \begin{bmatrix} - \tau^{1}_1 x^{\textrm{Opt}}_{1}(t) a_{11} & \hdots & \tau^{p}_1 x^{\textrm{Opt}}_{p}(t) a_{1p}\\ - \vdots & \ddots & \vdots\\ - \tau^1_n x^{\textrm{Opt}}_{1}(t) a_{n1} & \hdots & \tau^{p}_n x^{\textrm{Opt}}_{p}(t) a_{np} - \end{bmatrix} && \\ - \end{alignat*} - - 2. If stocks do not meet ``inventory_constraints`` for any inputs, then decrease production accordingly : - - .. math:: - :nowrap: - - \begin{alignat*}{4} - \iox^{a}(t) &= (x^{a}_{f}(t))_{f \in \firmsset} &&= \left \{ \begin{aligned} - & x^{\textrm{Opt}}_{f}(t) & \text{if $\omega_{p}^f(t) \geq \omega^{\textrm{Cons},f}_p(t)$}\\ - & x^{\textrm{Opt}}_{f}(t) \cdot \min_{p \in \sectorsset} \left ( \frac{\omega^s_{p}(t)}{\omega^{\textrm{Cons,f}}_p(t)} \right ) & \text{if $\omega_{p}^f(t) < \omega^{\textrm{Cons},f}_p(t)$} - \end{aligned} \right. \quad && - \end{alignat*} - - Also warns in logs if such shortages happen. - - Parameters ---------- current_temporal_unit : int diff --git a/boario/indicators.py b/boario/indicators.py index a4899e9..373724b 100644 --- a/boario/indicators.py +++ b/boario/indicators.py @@ -79,7 +79,7 @@ class Indicators(object): "overproduction", "rebuild_demand", "rebuild_prod", - "kapital_to_recover", + "productive_capital_to_recover", ] params_list = ["simulated_params", "simulated_events"] @@ -91,7 +91,7 @@ def __init__( params, regions, sectors, - kapital, + productive_capital, prod, prodmax, overprod, @@ -123,7 +123,9 @@ def __init__( self.n_rows = n_temporal_units_to_sim self.n_temporal_units_by_step = n_temporal_units_by_step - self.kapital_to_recover_df = create_df(kapital, regions, sectors) + self.productive_capital_to_recover_df = create_df( + productive_capital, regions, sectors + ) self.production_realised_df = create_df(prod, regions, sectors) self.production_capacity_df = create_df(prodmax, regions, sectors) self.overproduction_df = create_df(overprod, regions, sectors) @@ -187,15 +189,16 @@ def __init__( .reset_index() ) - self.limiting_inputs_df = pd.DataFrame( - limiting_stocks.reshape(n_temporal_units_to_sim * len(sectors), -1), - index=pd.MultiIndex.from_product( - [steps, sectors], names=["step", "stock of"] - ), - columns=pd.MultiIndex.from_product( - [regions, sectors], names=["region", "sector"] - ), - ) + self.limiting_inputs_df = limiting_stocks + # pd.DataFrame( + # limiting_stocks.reshape(n_temporal_units_to_sim * len(sectors), -1), + # index=pd.MultiIndex.from_product( + # [steps, sectors], names=["step", "stock of"] + # ), + # columns=pd.MultiIndex.from_product( + # [regions, sectors], names=["region", "sector"] + # ), + # ) self.aff_regions = [] self.aff_sectors = [] for e in events: @@ -259,9 +262,9 @@ def __init__( def from_simulation( cls, sim: Simulation, + results_storage: Optional[str | pathlib.Path] = None, stocks_treatment: bool = False, include_crash: bool = False, - results_storage: Optional[str | pathlib.Path] = None, ) -> Indicators: if isinstance(sim.model, ARIOPsiModel): psi = sim.model.psi @@ -270,27 +273,27 @@ def from_simulation( psi = None inventory_restoration_tau = None - prod = getattr(sim, "production_evolution") - kapital = getattr(sim, "regional_sectoral_kapital_destroyed_evolution") - prodmax = getattr(sim, "production_cap_evolution") - overprod = getattr(sim, "overproduction_evolution") - final_demand = getattr(sim, "final_demand_evolution") - io_demand = getattr(sim, "io_demand_evolution") - r_demand = getattr(sim, "rebuild_demand_evolution") - r_prod = getattr(sim, "rebuild_production_evolution") - fd_unmet = getattr(sim, "final_demand_unmet_evolution") + prod = getattr(sim, "production_realised") + productive_capital = getattr(sim, "productive_capital_to_recover") + prodmax = getattr(sim, "production_capacity") + overprod = getattr(sim, "overproduction") + final_demand = getattr(sim, "final_demand") + io_demand = getattr(sim, "intermediate_demand") + r_demand = getattr(sim, "rebuild_demand") + r_prod = getattr(sim, "rebuild_prod") + fd_unmet = getattr(sim, "final_demand_unmet") if stocks_treatment: - stocks = getattr(sim, "inputs_evolution") + stocks = getattr(sim, "inputs_stocks") else: stocks = None - limiting_stocks = getattr(sim, "limiting_inputs_evolution") + limiting_stocks = getattr(sim, "limiting_inputs") return cls( has_crashed=sim.has_crashed, params=sim.params_dict, regions=sim.model.regions, sectors=sim.model.sectors, - kapital=kapital, + productive_capital=productive_capital, prod=prod, prodmax=prodmax, overprod=overprod, @@ -373,8 +376,8 @@ def from_folder( dtype="float64", shape=(t, indexes["n_industries"]), ) - kapital = np.memmap( - records_path / "kapital_to_recover", + productive_capital = np.memmap( + records_path / "productive_capital_to_recover", mode="r+", dtype="float64", shape=(t, indexes["n_industries"]), @@ -450,7 +453,7 @@ def from_folder( params=params, regions=regions, sectors=sectors, - kapital=kapital, + productive_capital=productive_capital, prod=prod, prodmax=prodmax, overprod=overprod, @@ -706,8 +709,8 @@ def save_dfs(self): self.production_realised_df.to_parquet( self.parquets_path / "production_realised_df.parquet" ) - self.kapital_to_recover_df.to_parquet( - self.parquets_path / "kapital_to_recover_df.parquet" + self.productive_capital_to_recover_df.to_parquet( + self.parquets_path / "productive_capital_to_recover_df.parquet" ) self.production_capacity_df.to_parquet( self.parquets_path / "production_capacity_df.parquet" diff --git a/boario/model_base.py b/boario/model_base.py index 3d24c8f..1b3c63d 100644 --- a/boario/model_base.py +++ b/boario/model_base.py @@ -20,10 +20,17 @@ import typing from typing import Optional import numpy as np +import numpy.typing as npt import pandas as pd -from boario import logger, warn_once -from boario.event import * from pymrio.core.mriosystem import IOSystem + +from boario import logger, warn_once +from boario.event import ( + Event, + EventKapitalDestroyed, + EventKapitalRebuild, + EventArbitraryProd, +) from boario.utils.misc import lexico_reindex @@ -37,7 +44,9 @@ INV_THRESHOLD = 0 # 20 #temporal_units -TECHNOLOGY_THRESHOLD = 0.00001 +TECHNOLOGY_THRESHOLD = ( + 0.00001 # Do not care about input if producing requires less than this value +) VALUE_ADDED_NAMES = [ "VA", @@ -84,8 +93,10 @@ def __init__( iotable_year_to_temporal_unit_factor: int = 365, infinite_inventories_sect: Optional[list] = None, inventory_dict: Optional[dict] = None, - kapital_vector: Optional[pd.Series | np.ndarray | pd.DataFrame] = None, - kapital_to_VA_dict: Optional[dict] = None, + productive_capital_vector: Optional[ + pd.Series | npt.NDArray | pd.DataFrame + ] = None, + productive_capital_to_VA_dict: Optional[dict] = None, ) -> None: r"""The core of an ARIO model. Handles the different arrays containing the mrio tables. @@ -94,7 +105,7 @@ def __init__( An ARIOBaseModel instance based is build on the given IOSystem. Most default parameters are the same as in :cite:`2013:hallegatte` and - default order mechanism comes from :cite:`2020:guan`. By default each + default order mechanisms comes from :cite:`2020:guan`. By default each industry capital stock is 4 times its value added (:cite:`2008:hallegatte`). Parameters @@ -111,30 +122,30 @@ def __init__( alpha_tau : int, default 365 Characteristic time of overproduction :math:`\tau_{\alpha}` in ``n_temporal_units_by_step`` (default should be 365 days). rebuild_tau : int, default 60 - Rebuilding characteristic time :math:`\tau_{\textrm{REBUILD}}` (see :ref:`boario-math`). + Rebuilding characteristic time :math:`\tau_{\textrm{REBUILD}}` (see :ref:`boario-math`). Overwritten by per event value if it exists. main_inv_dur : int, default 90 The default numbers of days for inputs inventory to use if it is not defined for an input. monetary_factor : int, default 10**6 Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6). temporal_units_by_step: int, default 1 - The number of temporal_units between each step. (Current version of the model was not tested with values other than `1`). + The number of temporal_units between each step. (Current version of the model showed inconsistencies with values other than `1`). iotable_year_to_temporal_unit_factor : int, default 365 The (inverse of the) factor by which MRIO data should be multiplied in order to get "per temporal unit value", assuming IO data is yearly. infinite_inventories_sect: list, optional List of inputs (sector) considered never constraining for production. inventory_dict: dict, optional Dictionary in the form input:initial_inventory_size, (where input is a sector, and initial_inventory_size is in "temporal_unit" (defaults to a day)) - kapital_vector: ArrayLike, optional + productive_capital_vector: ArrayLike, optional Array of capital per industry if you need to give it exogenously. - kapital_to_VA_dict: dict, optional + productive_capital_to_VA_dict: dict, optional Dictionary in the form sector:ratio. Where ratio is used to estimate capital stock based on the value added of the sector. Notes ----- - It is recommended to use ``kapital_to_VA_dict`` if you have a more precise estimation of - the ratio of (Capital Stock / Value Added) per sectors. - You may also feed in directly a ``kapital_vector`` if you did your estimation before-hand. + It is recommended to use ``productive_capital_to_VA_dict`` if you have a more precise estimation of + the ratio of (Capital Stock / Value Added) per sectors than the default 4/1 ratio. + You may also feed in directly a ``productive_capital_vector`` if you did your estimation before-hand. (This is especially useful if you have events based of an exposure layer for instance) Regarding inventories, they default to 90 days for all inputs (ie sectors). @@ -142,18 +153,25 @@ def __init__( in ``infinite_inventories_sect`` or directly feed in a dictionary of the inventory duration for each input. - - Examples - -------- - FIXME: Add docs. - - """ logger.debug("Initiating new ARIOBaseModel instance") super().__init__() ################ Parameters variables ####################### - logger.info("IO system metadata :\n{}".format(str(pym_mrio.meta))) + try: + logger.info( + "IO system metadata :\n{}\n{}\n{}\n{}".format( + str(pym_mrio.meta.description), + str(pym_mrio.meta.name), + str(pym_mrio.meta.system), + str(pym_mrio.meta.version), + ) + ) + except AttributeError: + logger.warning( + "It seems the MRIOT you loaded doesn't have metadata to print." + ) + pym_mrio = lexico_reindex(pym_mrio) self.main_inv_dur: int = main_inv_dur r"""int, default 90: The default numbers of days for inputs inventory to use if it is not defined for an input.""" @@ -174,11 +192,16 @@ def __init__( self.n_sectors = len(sec) r"""int : The number :math:`n` of sectors.""" - self.industries = pd.MultiIndex.from_product([self.regions, self.sectors]) + self.industries = pd.MultiIndex.from_product( + [self.regions, self.sectors], names=["region", "sector"] + ) r"""pandas.MultiIndex : A pandas MultiIndex of the industries (region,sector) of the model.""" + self.n_industries = len(self.industries) + r"""int : The number :math:`m * n` of industries.""" + try: - self.fd_cat = np.array(sorted(list(pym_mrio.get_Y_categories()))) # type: ignore + self.final_demand_cat = np.array(sorted(list(pym_mrio.get_Y_categories()))) # type: ignore r"""numpy.ndarray of str: An array of the final demand categories of the model (``["Final demand"]`` if there is only one)""" self.n_fd_cat = len(pym_mrio.get_Y_categories()) # type: ignore @@ -186,12 +209,18 @@ def __init__( except KeyError: self.n_fd_cat = 1 - self.fd_cat = np.array(["Final demand"]) + self.final_demand_cat = np.array(["Final demand"]) except IndexError: self.n_fd_cat = 1 - self.fd_cat = np.array(["Final demand"]) + self.final_demand_cat = np.array(["Final demand"]) - self.monetary_factor = monetary_factor + if hasattr(pym_mrio, "monetary_factor"): + logger.warning( + f"Custom monetary factor found in the mrio pickle file, continuing with this one ({getattr(pym_mrio,'monetary_factor')})" + ) + self.monetary_factor = getattr(pym_mrio, "monetary_factor") + else: + self.monetary_factor = monetary_factor r"""int, default 10^6: Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6).""" self.n_temporal_units_by_step = temporal_units_by_step @@ -231,18 +260,24 @@ def __init__( r"""numpy.ndarray of int: Array :math:`\mathbf{s}` of size :math:`n` (sectors), setting for each input the initial number of ``n_temporal_units_by_step`` of stock for the input. (see :ref:`boario-math`).""" else: - inv = inventory_dict + infinite_inventories_sect = ( + [] if infinite_inventories_sect is None else infinite_inventories_sect + ) self.inventories = [ - np.inf if inv[k] == "inf" else inv[k] for k in sorted(inv.keys()) + np.inf + if inventory_dict[k] in ["inf", "Inf", "Infinity", "infinity"] + or k in infinite_inventories_sect + else inventory_dict[k] + for k in sorted(inventory_dict.keys()) ] + logger.debug(f"inventories: {self.inventories}") + logger.debug(f"n_temporal_units_by_step: {self.n_temporal_units_by_step}") self.inv_duration = np.array(self.inventories) / self.n_temporal_units_by_step if (self.inv_duration <= 1).any(): logger.warning( - "At least one product has inventory duration lower than the numbers of temporal units in one step ({}), model will set it to 2 by default, but you should probably check this !".format( - self.n_temporal_units_by_step - ) + f"At least one product has inventory duration lower than the numbers of temporal units in one step ({self.n_temporal_units_by_step}), model will set it to 2 by default, but you should probably check this !" ) self.inv_duration[self.inv_duration <= 1] = 2 ################################################################# @@ -263,7 +298,7 @@ def __init__( self._matrix_id = np.eye(self.n_sectors) self._matrix_I_sum = np.tile(self._matrix_id, self.n_regions) self.Z_0 = pym_mrio.Z.to_numpy() - r"""numpy.ndarray of float: 2-dim square matrix array :math:`\ioz` of size :math:`(n \times m, n \times m)` representing the intermediate (transaction) matrix (see :ref:`boario-math-init`).""" + r"""numpy.ndarray of float: 2-dim square matrix array :math:`\ioz` of size :math:`(n \times m, n \times m)` representing the daily intermediate (transaction) matrix (see :ref:`boario-math-init`).""" self.Z_C = self._matrix_I_sum @ self.Z_0 r"""numpy.ndarray of float: 2-dim matrix array :math:`\ioz^{\sectorsset}` of size :math:`(n, n \times m)` representing the intermediate (transaction) matrix aggregated by inputs (see :ref:`here `).""" @@ -277,17 +312,36 @@ def __init__( self.Z_distrib = np.nan_to_num(self.Z_distrib) self.Z_0 = pym_mrio.Z.to_numpy() * self.steply_factor + self.Y_0 = pym_mrio.Y.to_numpy() + self.Y_C = self._matrix_I_sum @ self.Y_0 + r"""numpy.ndarray of float: 2-dim matrix array :math:`\ioy^{\sectorsset}` of size :math:`(n, m \times \text{number of final demand categories})` representing the final demand matrix aggregated by inputs (see :ref:`here `).""" + + with np.errstate(divide="ignore", invalid="ignore"): + self.Y_distrib = np.divide( + self.Y_0, (np.tile(self.Y_C, (self.n_regions, 1))) + ) + r"""numpy.ndarray of float: math:`\ioy` normalised by :math:`\ioy^{\sectorsset}`, i.e. representing for each input the share of the total ordered transiting from an industry to final demand (see :ref:`here `).""" + + self.Y_distrib = np.nan_to_num(self.Y_distrib) + self.Y_0 = pym_mrio.Y.to_numpy() * self.steply_factor - r"""numpy.ndarray of float: 2-dim array :math:`\ioy` of size :math:`(n \times m, m \times \text{number of final demand categories})` representing the final demand matrix.""" + r"""numpy.ndarray of float: 2-dim array :math:`\ioy` of size :math:`(n \times m, m \times \text{number of final demand categories})` representing the daily final demand matrix.""" tmp = np.array(pym_mrio.x.T) self.X_0 = tmp.flatten() * self.steply_factor - r"""numpy.ndarray of float: Array :math:`\iox(0)` of size :math:`n \times m` representing the initial gross production.""" + r"""numpy.ndarray of float: Array :math:`\iox(0)` of size :math:`n \times m` representing the initial daily gross production.""" del tmp value_added = pym_mrio.x.T - pym_mrio.Z.sum(axis=0) value_added = value_added.reindex(sorted(value_added.index), axis=0) value_added = value_added.reindex(sorted(value_added.columns), axis=1) + if (value_added < 0).any().any(): + logger.warning( + f"""Found negative values in the value added, will set to 0. Note that sectors with null value added are not impacted by capital destruction. + industries with negative VA: {(value_added[value_added < 0].dropna(axis=1)).columns} + """ + ) + value_added[value_added < 0] = 0.0 self.gdp_df = value_added.groupby("region", axis=1).sum() r"""pandas.DataFrame: Dataframe of the total GDP of each region of the model""" @@ -298,18 +352,18 @@ def __init__( self.tech_mat = (self._matrix_I_sum @ pym_mrio.A).to_numpy() # type: ignore #to_numpy is not superfluous ! r"""numpy.ndarray of float: 2-dim array :math:`\ioa` of size :math:`(n \times m, n \times m)` representing the technical coefficients matrix.""" - if kapital_vector is not None: - self.k_stock = kapital_vector + if productive_capital_vector is not None: + self.k_stock = productive_capital_vector """numpy.ndarray of float: Array of size :math:`n \times m` representing the estimated stock of capital of each industry.""" if isinstance(self.k_stock, pd.DataFrame): self.k_stock = self.k_stock.squeeze().sort_index().to_numpy() - elif kapital_to_VA_dict is None: + elif productive_capital_to_VA_dict is None: logger.warning(f"No capital to VA dictionary given, considering 4/1 ratio") self.kstock_ratio_to_VA = 4 self.k_stock = self.VA_0 * self.kstock_ratio_to_VA else: - kratio = kapital_to_VA_dict + kratio = productive_capital_to_VA_dict kratio_ordered = [kratio[k] for k in sorted(kratio.keys())] self.kstock_ratio_to_VA = np.tile(np.array(kratio_ordered), self.n_regions) self.k_stock = self.VA_0 * self.kstock_ratio_to_VA @@ -354,7 +408,7 @@ def __init__( self.intmd_demand = self.Z_0.copy() self.final_demand = self.Y_0.copy() self.rebuilding_demand = None - self.kapital_lost = np.zeros(self.production.shape) + self.productive_capital_lost = np.zeros(self.production.shape) r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the estimated stock of capital currently destroyed for each industry.""" self.order_type = order_type @@ -362,10 +416,18 @@ def __init__( ################## SIMULATION TRACKING VARIABLES ################ self.in_shortage = False + r"""Boolean stating if at least one industry is in shortage (i.e.) if at least one of its inputs inventory is low enough to reduce production.""" + self.had_shortage = False + r"""Boolean stating if at least one industry was in shortage at some point.""" + self.rebuild_prod = np.zeros(shape=self.X_0.shape) + r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the remaining stock of rebuilding demand asked of each industry.""" + self.final_demand_not_met = np.zeros(self.Y_0.shape) - ############################################################################# + r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the final demand that could not be met at current step for each industry.""" + + ################################################################# ### Internals self._prod_delta_type = None @@ -375,59 +437,72 @@ def __init__( logger.debug( f"Setting possible regions (currently: {Event.possible_regions}) to: {self.regions}" ) - Event.possible_regions = self.regions.copy() + Event.possible_regions = pd.CategoricalIndex( + self.regions, name="region", copy=True + ) logger.debug(f"Possible regions is now {Event.possible_regions}") Event.regions_idx = np.arange(self.n_regions) - Event.possible_sectors = self.sectors.copy() + Event.possible_sectors = pd.CategoricalIndex( + self.sectors, name="sector", copy=True + ) + Event.possible_final_demand_cat = pd.CategoricalIndex( + pym_mrio.get_Y_categories(), name="final demand", copy=True + ) Event.sectors_idx = np.arange(self.n_sectors) Event.z_shape = self.Z_0.shape Event.y_shape = self.Y_0.shape Event.x_shape = self.X_0.shape - Event.monetary_factor = monetary_factor + Event.model_monetary_factor = monetary_factor Event.sectors_gva_shares = self.gdp_share_sector.copy() Event.Z_distrib = self.Z_distrib.copy() + Event.Y_distrib = self.Y_distrib.copy() + # logger.warning(f"{value_added}") + Event.gva_df = value_added.iloc[0].copy() meta = pym_mrio.meta.metadata + assert isinstance(meta, dict) + # meta = {"name":"unnamed", "description":"", "system":"unknown","version":"unknown"} try: - Event.mrio_name = ( - meta["name"] - + "_" - + meta["description"] - + "_" - + meta["system"] - + "_" - + meta["version"] + Event.mrio_name = "_".join( + [ + str(meta["name"]), + str(meta["description"]), + str(meta["system"]), + str(meta["version"]), + ] ) except TypeError: Event.mrio_name = "custom - method WIP" # initialize those (it's not very nice, but otherwise python complains) - self._indus_rebuild_demand_tot = None - self._house_rebuild_demand_tot = None - self._indus_rebuild_demand = None - self._house_rebuild_demand = None - self._tot_rebuild_demand = None - self._kapital_lost = np.zeros(self.VA_0.shape) - self._prod_cap_delta_kapital = None - self._prod_cap_delta_arbitrary = None - self._prod_cap_delta_tot = None + self._indus_rebuild_demand_tot: npt.NDArray = np.array([]) + self._house_rebuild_demand_tot: npt.NDArray = np.array([]) + self._indus_rebuild_demand: npt.NDArray = np.array([]) + self._house_rebuild_demand: npt.NDArray = np.array([]) + self._tot_rebuild_demand: npt.NDArray = np.array([]) + self._productive_capital_lost: npt.NDArray = np.zeros(self.VA_0.shape) + self._prod_cap_delta_productive_capital: npt.NDArray = np.zeros( + len(self.industries) + ) + self._prod_cap_delta_arbitrary: npt.NDArray = np.zeros(len(self.industries)) + self._prod_cap_delta_tot: npt.NDArray = np.zeros(len(self.industries)) ## Properties @property - def tot_rebuild_demand(self) -> Optional[np.ndarray]: + def tot_rebuild_demand(self) -> npt.NDArray: r"""Returns current total rebuilding demand (as the sum of rebuilding demand addressed to each industry)""" tmp = [] logger.debug("Trying to return tot_rebuilding demand") - if self._indus_rebuild_demand_tot is not None: + if np.any(self._indus_rebuild_demand_tot > 0): tmp.append(self._indus_rebuild_demand_tot) - if self._house_rebuild_demand_tot is not None: + if np.any(self._house_rebuild_demand_tot > 0): tmp.append(self._house_rebuild_demand_tot) if tmp: ret = np.concatenate(tmp, axis=1).sum(axis=1) self._tot_rebuild_demand = ret else: - self._tot_rebuild_demand = None + self._tot_rebuild_demand = np.array([]) return self._tot_rebuild_demand @tot_rebuild_demand.setter @@ -446,21 +521,18 @@ def tot_rebuild_demand(self, source: list[EventKapitalRebuild]): logger.debug(f"Trying to set tot_rebuilding demand from {source}") if not isinstance(source, list): ValueError( - "Setting tot_rebuild_demand can only be done with a list of events, not a {}".format( - type(source) - ) + f"Setting tot_rebuild_demand can only be done with a list of events, not a {type(source)}" ) self.house_rebuild_demand = source self.indus_rebuild_demand = source @property - def house_rebuild_demand(self) -> Optional[np.ndarray]: - r"""Returns household rebuilding demand matrix or `None` if - there is no such demand. + def house_rebuild_demand(self) -> npt.NDArray: + r"""Returns household rebuilding demand matrix. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\ioy`, containing the sum of all currently rebuildable final demand stock. """ @@ -468,13 +540,12 @@ def house_rebuild_demand(self) -> Optional[np.ndarray]: return self._house_rebuild_demand @property - def house_rebuild_demand_tot(self) -> Optional[np.ndarray]: - r"""Returns total household rebuilding demand vector or `None` if - there is no such demand. + def house_rebuild_demand_tot(self) -> npt.NDArray: + r"""Returns total household rebuilding demand vector. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\iox`, containing the sum of all currently rebuildable households demands. """ @@ -498,27 +569,23 @@ def house_rebuild_demand(self, source: list[EventKapitalRebuild]): events : 'list[Event]' A list of Event objects - Notes - ----- - - So far the model wasn't tested with such a rebuilding demand. Only intermediate demand was considered. """ tmp = [] - for ev in source: - if ev.rebuildable: - if ev.rebuild_tau is None: + for evnt in source: + if evnt.rebuildable: + if evnt.rebuild_tau is None: rebuild_tau = self.rebuild_tau else: - rebuild_tau = ev.rebuild_tau + rebuild_tau = evnt.rebuild_tau warn_once(logger, "Event has a custom rebuild_tau") - if ev.rebuilding_demand_house is not None: + if len(evnt.rebuilding_demand_house) > 0: tmp.append( - ev.rebuilding_demand_house + evnt.rebuilding_demand_house * (self.n_temporal_units_by_step / rebuild_tau) ) if not tmp: - self._house_rebuild_demand = None - self._house_rebuild_demand_tot = None + self._house_rebuild_demand = np.array([]) + self._house_rebuild_demand_tot = np.array([]) else: house_reb_dem = np.stack(tmp, axis=-1) tot = house_reb_dem.sum(axis=1) @@ -526,14 +593,12 @@ def house_rebuild_demand(self, source: list[EventKapitalRebuild]): self._house_rebuild_demand_tot = tot @property - def indus_rebuild_demand(self) -> Optional[np.ndarray]: - r"""Returns industrial rebuilding demand matrix or `None` if - there is no such demand. - + def indus_rebuild_demand(self) -> npt.NDArray: + r"""Returns industrial rebuilding demand matrix. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\ioz`, containing the sum of all currently rebuildable intermediate demand stock. """ @@ -541,13 +606,12 @@ def indus_rebuild_demand(self) -> Optional[np.ndarray]: return self._indus_rebuild_demand @property - def indus_rebuild_demand_tot(self) -> Optional[np.ndarray]: - r"""Returns total industrial rebuilding demand vector or `None` if - there is no such demand. + def indus_rebuild_demand_tot(self) -> npt.NDArray: + r"""Returns total industrial rebuilding demand vector. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\iox`, containing the sum of all currently rebuildable intermediate demands. """ @@ -570,42 +634,44 @@ def indus_rebuild_demand(self, source: list[EventKapitalRebuild]): """ tmp = [] - for ev in source: - if ev.rebuildable: - if ev.rebuild_tau is None: + for evnt in source: + if evnt.rebuildable: + if evnt.rebuild_tau is None: rebuild_tau = self.rebuild_tau else: - rebuild_tau = ev.rebuild_tau + rebuild_tau = evnt.rebuild_tau warn_once(logger, "Event has a custom rebuild_tau") tmp.append( - ev.rebuilding_demand_indus + evnt.rebuilding_demand_indus * (self.n_temporal_units_by_step / rebuild_tau) ) if not tmp: - self._indus_rebuild_demand = None - self._indus_rebuild_demand_tot = None + self._indus_rebuild_demand = np.array([]) + self._indus_rebuild_demand_tot = np.array([]) else: indus_reb_dem = np.stack(tmp, axis=-1) self._indus_rebuild_demand = indus_reb_dem self._indus_rebuild_demand_tot = indus_reb_dem.sum(axis=1) - logger.debug(f"Setting indus_rebuild_demand_tot to {indus_reb_dem}") + # logger.debug(f"Setting indus_rebuild_demand_tot to {indus_reb_dem}") @property - def kapital_lost(self) -> np.ndarray: + def productive_capital_lost(self) -> npt.NDArray: r"""Returns current stock of destroyed capital Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\iox`, containing the "stock" of capital currently destroyed for each industry. """ - return self._kapital_lost + return self._productive_capital_lost - @kapital_lost.setter - def kapital_lost(self, source: list[EventKapitalDestroyed] | np.ndarray) -> None: + @productive_capital_lost.setter + def productive_capital_lost( + self, source: list[EventKapitalDestroyed] | npt.NDArray + ) -> None: r"""Computes current capital lost and update production delta accordingly. Computes and sets the current stock of capital lost by each industry of @@ -615,58 +681,67 @@ def kapital_lost(self, source: list[EventKapitalDestroyed] | np.ndarray) -> None Parameters ---------- - source : list[EventKapitalDestroyed] | np.ndarray + source : list[EventKapitalDestroyed] | npt.NDArray Either a list of events to consider for the destruction of capital or directly a vector of destroyed capital for each industry. """ - logger.debug("Updating kapital lost from list of events") + logger.debug("Updating productive_capital lost from list of events") if isinstance(source, list): if source: - self._kapital_lost = np.add.reduce( - np.array([e.regional_sectoral_kapital_destroyed for e in source]) + self._productive_capital_lost = np.add.reduce( + np.array( + [ + e.regional_sectoral_productive_capital_destroyed + for e in source + ] + ) ) else: - self._kapital_lost = np.zeros(self.VA_0.shape) + self._productive_capital_lost = np.zeros(self.VA_0.shape) elif isinstance(source, np.ndarray): - self._kapital_lost = source + self._productive_capital_lost = source - productivity_loss_from_K = np.zeros(shape=self._kapital_lost.shape) + productivity_loss_from_capital_destroyed = np.zeros( + shape=self._productive_capital_lost.shape + ) np.divide( - self._kapital_lost, + self._productive_capital_lost, self.k_stock, - out=productivity_loss_from_K, + out=productivity_loss_from_capital_destroyed, where=self.k_stock != 0, ) - logger.debug("Updating production delta from kapital loss") - self._prod_cap_delta_kapital = productivity_loss_from_K - if (self._prod_cap_delta_kapital > 0.0).any(): + logger.debug("Updating production delta from productive_capital loss") + self._prod_cap_delta_productive_capital = ( + productivity_loss_from_capital_destroyed + ) + if (self._prod_cap_delta_productive_capital > 0.0).any(): if self._prod_delta_type is None: - self._prod_delta_type = "from_kapital" + self._prod_delta_type = "from_productive_capital" elif self._prod_delta_type == "from_arbitrary": - self._prod_delta_type = "mixed_from_kapital_from_arbitrary" + self._prod_delta_type = "mixed_from_productive_capital_from_arbitrary" @property - def prod_cap_delta_arbitrary(self) -> Optional[np.ndarray]: + def prod_cap_delta_arbitrary(self) -> npt.NDArray: r"""Return the possible "arbitrary" production capacity lost vector if it was set. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\iox`, stating the amount of production capacity lost arbitrarily (ie exogenous). """ return self._prod_cap_delta_arbitrary @prod_cap_delta_arbitrary.setter - def prod_cap_delta_arbitrary(self, source: list[Event] | np.ndarray): + def prod_cap_delta_arbitrary(self, source: list[EventArbitraryProd] | npt.NDArray): """Computes and sets the loss of production capacity from "arbitrary" sources. Parameters ---------- - source : list[Event] | np.ndarray + source : list[Event] | npt.NDArray Either a list of Event objects with arbitrary production losses set, or directly a vector of production capacity loss. @@ -674,42 +749,46 @@ def prod_cap_delta_arbitrary(self, source: list[Event] | np.ndarray): if isinstance(source, list): event_arb = np.array( - [ev for ev in source if ev.prod_cap_delta_arbitrary is not None] + [ + ev.prod_cap_delta_arbitrary + for ev in source + if len(ev.prod_cap_delta_arbitrary) > 0 + ] ) if event_arb.size == 0: self._prod_cap_delta_arbitrary = np.zeros(shape=self.X_0.shape) else: - self._prod_cap_delta_arbitrary = np.max.reduce(event_arb) + self._prod_cap_delta_arbitrary = np.maximum.reduce(event_arb) else: self._prod_capt_delta_arbitrary = source assert self._prod_cap_delta_arbitrary is not None if (self._prod_cap_delta_arbitrary > 0.0).any(): if self._prod_delta_type is None: self._prod_delta_type = "from_arbitrary" - elif self._prod_delta_type == "from_kapital": - self._prod_delta_type = "mixed_from_kapital_from_arbitrary" + elif self._prod_delta_type == "from_productive_capital": + self._prod_delta_type = "mixed_from_productive_capital_from_arbitrary" @property - def prod_cap_delta_kapital(self) -> Optional[np.ndarray]: + def prod_cap_delta_productive_capital(self) -> npt.NDArray: r"""Return the possible production capacity lost due to capital destroyed vector if it was set. Returns ------- - np.ndarray + npt.NDArray An array of same shape as math:`\iox`, stating the amount of production capacity lost due to capital destroyed. """ - return self._prod_cap_delta_kapital + return self._prod_cap_delta_productive_capital @property - def prod_cap_delta_tot(self) -> np.ndarray: + def prod_cap_delta_tot(self) -> npt.NDArray: r"""Computes and return total current production delta. Returns ------- - np.ndarray + npt.NDArray The total production delta (ie share of production capacity lost) for each industry. @@ -719,30 +798,30 @@ def prod_cap_delta_tot(self) -> np.ndarray: tmp = [] if self._prod_delta_type is None: raise AttributeError("Production delta doesn't appear to be set yet.") - elif self._prod_delta_type == "from_kapital": - logger.debug("Production delta is only set from kapital destruction") - tmp.append(self._prod_cap_delta_kapital) + elif self._prod_delta_type == "from_productive_capital": + logger.debug( + "Production delta is only set from productive_capital destruction" + ) + tmp.append(self._prod_cap_delta_productive_capital) elif self._prod_delta_type == "from_arbitrary": logger.debug("Production delta is only set from arbitrary delta") tmp.append(self._prod_cap_delta_arbitrary) - elif self._prod_delta_type == "mixed_from_kapital_from_arbitrary": + elif self._prod_delta_type == "mixed_from_productive_capital_from_arbitrary": logger.debug( - "Production delta is a mixed form of kapital destruction and arbitrary delta" + "Production delta is a mixed form of productive_capital destruction and arbitrary delta" ) - tmp.append(self._prod_cap_delta_kapital) + tmp.append(self._prod_cap_delta_productive_capital) tmp.append(self._prod_cap_delta_arbitrary) else: raise NotImplementedError( - "Production delta type {} not recognised".format(self._prod_delta_type) + f"Production delta type {self._prod_delta_type} not recognised" ) - tmp.append(np.ones(shape=self.X_0.shape)) + # tmp.append(np.ones(shape=self.X_0.shape)) # logger.debug("tmp: {}".format(tmp)) - self._prod_cap_delta_tot = np.amin(np.stack(tmp, axis=-1), axis=1) + self._prod_cap_delta_tot = np.amax(np.stack(tmp, axis=-1), axis=1) assert ( self._prod_cap_delta_tot.shape == self.X_0.shape - ), "expected shape {}, received {}".format( - self.X_0.shape, self._prod_cap_delta_tot.shape - ) + ), f"expected shape {self.X_0.shape}, received {self._prod_cap_delta_tot.shape}" return self._prod_cap_delta_tot @prod_cap_delta_tot.setter @@ -760,12 +839,10 @@ def prod_cap_delta_tot(self, source: list[Event]): logger.debug("Updating total production delta from list of events") if not isinstance(source, list): ValueError( - "Setting prod_cap_delta_tot can only be done with a list of events, not a {}".format( - type(source) - ) + f"Setting prod_cap_delta_tot can only be done with a list of events, not a {type(source)}" ) - self.kapital_lost = [ + self.productive_capital_lost = [ event for event in source if isinstance(event, EventKapitalDestroyed) ] self.prod_cap_delta_arbitrary = [ @@ -790,7 +867,7 @@ def update_system_from_events(self, events: list[Event]) -> None: ] @property - def production_cap(self) -> np.ndarray: + def production_cap(self) -> npt.NDArray: r"""Compute and update production capacity. Compute and update production capacity from current total production delta and overproduction. @@ -818,8 +895,8 @@ def production_cap(self) -> np.ndarray: return production_cap @property - def total_demand(self) -> np.ndarray: - r"""Computes and return total demand as the sum of intermediate demand (orders), final demand, and possible rebuilding demand.""" + def total_demand(self) -> npt.NDArray: + r"""Computes and returns total demand as the sum of intermediate demand (orders), final demand, and possible rebuilding demand.""" if (self.matrix_orders < 0).any(): raise RuntimeError("Some matrix orders are negative which shouldn't happen") @@ -827,12 +904,12 @@ def total_demand(self) -> np.ndarray: tot_dem = self.matrix_orders.sum(axis=1) + self.final_demand.sum(axis=1) if (tot_dem < 0).any(): raise RuntimeError("Some total demand are negative which shouldn't happen") - if self.tot_rebuild_demand is not None: + if len(self.tot_rebuild_demand) > 0: tot_dem += self.tot_rebuild_demand return tot_dem @property - def production_opt(self) -> np.ndarray: + def production_opt(self) -> npt.NDArray: r"""Computes and returns "optimal production" :math:`\iox^{textrm{Opt}}`, as the per industry minimum between total demand and production capacity. @@ -841,17 +918,17 @@ def production_opt(self) -> np.ndarray: return np.fmin(self.total_demand, self.production_cap) @property - def inventory_constraints_opt(self) -> np.ndarray: + def inventory_constraints_opt(self) -> npt.NDArray: r"""Computes and returns inventory constraints for "optimal production" (see :meth:`calc_inventory_constraints`)""" return self.calc_inventory_constraints(self.production_opt) @property - def inventory_constraints_act(self) -> np.ndarray: + def inventory_constraints_act(self) -> npt.NDArray: r"""Computes and returns inventory constraints for "actual production" (see :meth:`calc_inventory_constraints`)""" return self.calc_inventory_constraints(self.production) - def calc_production(self, current_temporal_unit: int) -> np.ndarray: + def calc_production(self, current_temporal_unit: int) -> npt.NDArray: r"""Computes and updates actual production. See :ref:`boario-math-prod`. 1. Computes ``production_opt`` and ``inventory_constraints`` as : @@ -906,9 +983,7 @@ def calc_production(self, current_temporal_unit: int) -> np.ndarray: ).any(): if not self.in_shortage: logger.info( - "At least one industry entered shortage regime. (step:{})".format( - current_temporal_unit - ) + f"At least one industry entered shortage regime. (step:{current_temporal_unit})" ) self.in_shortage = True self.had_shortage = True @@ -934,15 +1009,13 @@ def calc_production(self, current_temporal_unit: int) -> np.ndarray: if self.in_shortage: self.in_shortage = False logger.info( - "All industries exited shortage regime. (step:{})".format( - current_temporal_unit - ) + f"All industries exited shortage regime. (step:{current_temporal_unit})" ) assert not (production_opt < 0).any() self.production = production_opt return stock_constraint - def calc_inventory_constraints(self, production: np.ndarray) -> np.ndarray: + def calc_inventory_constraints(self, production: npt.NDArray) -> npt.NDArray: r"""Compute inventory constraints (no psi parameter, for the psi version, the recommended one, see :meth:`~boario.extended_models.ARIOPsiModel.calc_inventory_constraints`) @@ -950,12 +1023,12 @@ def calc_inventory_constraints(self, production: np.ndarray) -> np.ndarray: Parameters ---------- - production : np.ndarray + production : npt.NDArray The production vector to consider. Returns ------- - np.ndarray + npt.NDArray For each input, for each industry, the size of the inventory required to produce at `production` level for the duration goal (`inv_duration`). @@ -1054,16 +1127,23 @@ def distribute_production( """ if scheme != "proportional": - raise ValueError("Scheme %s not implemented" % scheme) + raise ValueError(f"Scheme {scheme} is currently not implemented") # list_of_demands = [self.matrix_orders, self.final_demand] ## 1. Calc demand from rebuilding requirements (with characteristic time rebuild_tau) + house_reb_dem_per_event = ( + house_reb_dem_tot_per_event + ) = indus_reb_dem_per_event = indus_reb_dem_tot_per_event = None + if rebuildable_events: logger.debug("There are rebuildable events") n_events = len(rebuildable_events) tot_rebuilding_demand_summed = self.tot_rebuild_demand.copy() # debugging assert - assert tot_rebuilding_demand_summed.shape == self.X_0.shape + if not tot_rebuilding_demand_summed.shape == self.X_0.shape: + raise RuntimeError( + f"received {tot_rebuilding_demand_summed.shape}, expected {self.X_0.shape}" + ) indus_reb_dem_tot_per_event = self.indus_rebuild_demand_tot.copy() indus_reb_dem_per_event = self.indus_rebuild_demand.copy() @@ -1155,14 +1235,8 @@ def distribute_production( if not np.allclose(stock_add, stock_use): self.matrix_stock = self.matrix_stock - stock_use + stock_add if (self.matrix_stock < 0).any(): - self.matrix_stock.dump(self.records_storage / "matrix_stock_dump.pkl") - logger.error( - "Negative values in the stocks, matrix has been dumped in the results dir : \n {}".format( - self.records_storage / "matrix_stock_dump.pkl" - ) - ) raise RuntimeError( - "stock_add (restocking) contains negative values, this should not happen" + "matrix_stock contains negative values, this should not happen" ) # 6. Compute final demand not met due to rationing @@ -1192,22 +1266,68 @@ def distribute_production( ) self.rebuild_prod = rebuild_prod.copy() + logger.debug( + f"tot_rebuilding_demand_summed: {pd.Series(tot_rebuilding_demand_summed, index=self.industries)}" + ) + if h_reb and ind_reb: - # logger.debug("Entering here") + logger.debug( + "There are both household rebuilding demand and industry rebuilding demand" + ) + logger.debug( + f"indus_reb_dem_tot_per_event: {pd.DataFrame(indus_reb_dem_tot_per_event, index=self.industries)}" + ) + logger.debug( + f"house_reb_dem_tot_per_event: {pd.DataFrame(house_reb_dem_tot_per_event, index=self.industries)}" + ) + logger.debug( + f"dem_tot_per_event: {pd.DataFrame(house_reb_dem_tot_per_event+indus_reb_dem_tot_per_event, index=self.industries)}" # type: ignore + ) + + tot_reb_dem_divider = np.tile( + tot_rebuilding_demand_summed[:, np.newaxis], + (1, indus_reb_dem_tot_per_event.shape[1]), + ) indus_shares = np.divide( indus_reb_dem_tot_per_event, - tot_rebuilding_demand_summed, - where=(tot_rebuilding_demand_summed != 0), + tot_reb_dem_divider, + out=np.zeros_like(indus_reb_dem_tot_per_event), + where=tot_reb_dem_divider != 0, ) house_shares = np.divide( house_reb_dem_tot_per_event, - tot_rebuilding_demand_summed, - where=(tot_rebuilding_demand_summed != 0), + tot_reb_dem_divider, + out=np.zeros_like(house_reb_dem_tot_per_event), + where=tot_reb_dem_divider != 0, + ) + # np.around(house_shares, 6, out=house_shares) + # np.around(indus_shares, 6, out=indus_shares) + logger.debug( + f"indus_shares: {pd.DataFrame(indus_shares, index=self.industries)}" + ) + logger.debug( + f"house_shares: {pd.DataFrame(house_shares, index=self.industries)}" ) - # logger.debug("indus_shares: {}".format(indus_shares)) + + # tot_reb = pd.Series(tot_rebuilding_demand_summed, index=self.industries) + # indus_reb_dem_tot = pd.DataFrame(indus_reb_dem_tot_per_event, index=self.industries)[0] + # house_reb_dem_tot = pd.DataFrame(house_reb_dem_tot_per_event, index=self.industries)[0] + # np.around(indus_shares,10,indus_shares) + # np.around(house_shares,10,house_shares) + # np.around(tmp,10,tmp) + # indus_df = pd.DataFrame(indus_shares,index=self.industries)[0] + # house_df = pd.DataFrame(house_shares,index=self.industries)[0] + # tmp_df=pd.DataFrame(tmp, index=self.industries)[0] + + # debug_df = pd.concat([tot_reb, indus_reb_dem_tot, house_reb_dem_tot, tot_reb-(house_reb_dem_tot+indus_reb_dem_tot), indus_df, house_df, tmp_df], axis=1) + # #logger.info(debug_df[debug_df.iloc[:,6]!=0].to_string()) + # debug_str = f""" + # {debug_df[debug_df.iloc[:,6]!=0].to_string()} + # """ assert np.allclose( - indus_shares + house_shares, np.ones(shape=indus_shares.shape) + (indus_shares + house_shares)[tot_rebuilding_demand_summed != 0], 1.0 ) + elif h_reb: house_shares = np.ones(house_reb_dem_tot_per_event.shape) indus_shares = np.zeros(indus_reb_dem_tot_per_event.shape) @@ -1218,9 +1338,23 @@ def distribute_production( else: return [] + logger.debug(f"rebuild_prod: {rebuild_prod}") + logger.debug(f"indus_shares: {indus_shares}") + indus_rebuild_prod = rebuild_prod[:, np.newaxis] * indus_shares # type:ignore house_rebuild_prod = rebuild_prod[:, np.newaxis] * house_shares # type:ignore + if ind_reb: + logger.debug( + f"indus_rebuild_prod: {pd.DataFrame(indus_rebuild_prod, index=self.industries)}" + ) + if h_reb: + logger.debug( + f"house_rebuild_prod: {pd.DataFrame(house_rebuild_prod, index=self.industries)}" + ) + # logger.debug(f"tot_rebuilding_demand_summed: {pd.DataFrame(tot_rebuilding_demand_summed, index=self.industries)}") + # logger.debug(f"house_rebuild_prod: {pd.Series(house_rebuild_prod.flatten(), index=self.industries)}") + indus_rebuild_prod_distributed = np.zeros(shape=indus_reb_dem_per_event.shape) house_rebuild_prod_distributed = np.zeros(shape=house_reb_dem_per_event.shape) if ind_reb: @@ -1252,7 +1386,7 @@ def distribute_production( if h_reb: house_rebuilding_demand_shares = np.zeros( - shape=indus_reb_dem_per_event.shape + shape=house_reb_dem_per_event.shape ) house_rebuilding_demand_broad = np.broadcast_to( house_reb_dem_tot_per_event[:, np.newaxis], @@ -1275,18 +1409,22 @@ def distribute_production( # update rebuilding demand events_to_remove = [] - for e_id, e in enumerate(rebuildable_events): - if e.rebuilding_demand_indus is not None: - e.rebuilding_demand_indus -= indus_rebuild_prod_distributed[:, :, e_id] - if e.rebuilding_demand_house is not None: - e.rebuilding_demand_house -= house_rebuild_prod_distributed[:, :, e_id] - if (e.rebuilding_demand_indus < (10 / self.monetary_factor)).all() and ( - e.rebuilding_demand_house < (10 / self.monetary_factor) + for e_id, evnt in enumerate(rebuildable_events): + if len(evnt.rebuilding_demand_indus) > 0: + evnt.rebuilding_demand_indus -= indus_rebuild_prod_distributed[ + :, :, e_id + ] + if len(evnt.rebuilding_demand_house) > 0: + evnt.rebuilding_demand_house -= house_rebuild_prod_distributed[ + :, :, e_id + ] + if (evnt.rebuilding_demand_indus < (10 / self.monetary_factor)).all() and ( + evnt.rebuilding_demand_house < (10 / self.monetary_factor) ).all(): - events_to_remove.append(e) + events_to_remove.append(evnt) return events_to_remove - def calc_matrix_stock_gap(self, matrix_stock_goal) -> np.ndarray: + def calc_matrix_stock_gap(self, matrix_stock_goal) -> npt.NDArray: """Computes and returns inputs stock gap matrix The gap is simply the difference between the goal (given as argument) @@ -1294,12 +1432,12 @@ def calc_matrix_stock_gap(self, matrix_stock_goal) -> np.ndarray: Parameters ---------- - matrix_stock_goal : np.ndarray of float + matrix_stock_goal : npt.NDArray of float The target inventories. Returns ------- - np.ndarray + npt.NDArray The (only positive) gap between goal and current inventories. Raises @@ -1340,7 +1478,7 @@ def calc_orders(self) -> None: with np.errstate(invalid="ignore"): matrix_stock_goal *= self.inv_duration[:, np.newaxis] if np.allclose(self.matrix_stock, matrix_stock_goal): - matrix_stock_gap = matrix_stock_goal * 0 + matrix_stock_gap = np.zeros(matrix_stock_goal.shape) else: matrix_stock_gap = self.calc_matrix_stock_gap(matrix_stock_goal) matrix_stock_gap += ( @@ -1363,7 +1501,7 @@ def calc_orders(self) -> None: self.matrix_orders = tmp def calc_overproduction(self) -> None: - r"""Computes and update the overproduction vector. + r"""Computes and updates the overproduction vector. See :ref:`Overproduction module ` @@ -1386,92 +1524,15 @@ def calc_overproduction(self) -> None: self.overprod += overprod_chg self.overprod[self.overprod < 1.0] = 1.0 - # def check_stock_increasing(self, current_temporal_unit: int): - # tmp = np.full(self.matrix_stock.shape, 0.0) - # mask = np.isfinite(self.matrix_stock_0) - # np.subtract(self.matrix_stock, self.matrix_stock_0, out=tmp, where=mask) - # check_1 = tmp > 0.0 - # tmp = np.full(self.matrix_stock.shape, 0.0) - # np.subtract( - # self.stocks_evolution[current_temporal_unit], - # self.stocks_evolution[current_temporal_unit - 1], - # out=tmp, - # where=mask, - # ) - # check_2 = tmp >= 0.0 - # return (check_1 & check_2).all() - - # def check_production_eq_strict(self): - # return ( - # (np.isclose(self.production, self.X_0)) - # | np.greater(self.production, self.X_0) - # ).all() - - # def check_production_eq_soft( - # self, current_temporal_unit: int, period: int = 10 - # ) -> bool: - # return self.check_monotony( - # self.production_evolution, current_temporal_unit, period - # ) - - # def check_stocks_monotony( - # self, current_temporal_unit: int, period: int = 10 - # ) -> bool: - # return self.check_monotony(self.stocks_evolution, current_temporal_unit, period) - - # def check_initial_equilibrium(self) -> bool: - # return np.allclose(self.production, self.X_0) and np.allclose( - # self.matrix_stock, self.matrix_stock_0 - # ) - - # def check_equilibrium_soft(self, current_temporal_unit: int): - # return ( - # self.check_stock_increasing(current_temporal_unit) - # and self.check_production_eq_strict - # ) - - # def check_equilibrium_monotony( - # self, current_temporal_unit: int, period: int = 10 - # ) -> bool: - # return self.check_production_eq_soft( - # current_temporal_unit, period - # ) and self.check_stocks_monotony(current_temporal_unit, period) - - # @staticmethod - # def check_monotony(x, current_temporal_unit: int, period: int = 10) -> bool: - # return np.allclose( - # x[current_temporal_unit], x[current_temporal_unit - period], atol=0.0001 - # ) - - # def check_crash(self, prod_threshold: float = 0.80) -> int: - # """Check for economic crash - - # This method look at the production vector and returns the number of - # industries which production is less than a certain share (default 20%) of the starting - # production. - - # Parameters - # ---------- - # prod_threshold : float, default: 0.8 - # An industry is counted as 'crashed' if its current production is less than its starting production times (1 - `prod_threshold`). - - # """ - # tmp = np.full(self.production.shape, 0.0) - # checker = np.full(self.production.shape, 0.0) - # mask = self.X_0 != 0 - # np.subtract(self.X_0, self.production, out=tmp, where=mask) - # np.divide(tmp, self.X_0, out=checker, where=mask) - # return np.where(checker >= prod_threshold)[0].size - def reset_module( self, ) -> None: """Resets the model to initial state [Deprecated] - This method is currently not functioning. + This method has not been checked extensively. """ - self.kapital_lost = np.zeros(self.production.shape) + self.productive_capital_lost = np.zeros(self.production.shape) self.overprod = np.full( (self.n_regions * self.n_sectors), self.overprod_base, dtype=np.float64 ) @@ -1481,51 +1542,18 @@ def reset_module( self.intmd_demand = self.Z_0.copy() self.final_demand = self.Y_0.copy() self.final_demand_not_met = np.zeros(self.Y_0.shape) - self.rebuilding_demand = None + self.rebuilding_demand = np.array([]) self.in_shortage = False self.had_shortage = False self._prod_delta_type = None - self._indus_rebuild_demand_tot = None - self._house_rebuild_demand_tot = None - self._indus_rebuild_demand = None - self._house_rebuild_demand = None - self._tot_rebuild_demand = None - self._prod_cap_delta_kapital = None - self._prod_cap_delta_arbitrary = None - self._prod_cap_delta_tot = None - - def update_params(self, new_params: dict) -> None: - """Update the parameters of the model. - - Replace each parameters with given new ones. - - .. warning:: - Be aware this method calls :meth:`~boario.model_base.reset_record_files`, which resets the memmap files located in the results directory ! - - Parameters - ---------- - new_params : dict - Dictionary of new parameters to use. - - """ - logger.warning("This method is quite probably deprecated") - self.n_temporal_units_by_step = new_params["n_temporal_units_by_step"] - self.iotable_year_to_temporal_unit_factor = new_params[ - "year_to_temporal_unit_factor" - ] - self.rebuild_tau = new_params["rebuild_tau"] - self.overprod_max = new_params["alpha_max"] - self.overprod_tau = new_params["alpha_tau"] - self.overprod_base = new_params["alpha_base"] - if self.records_storage != pathlib.Path( - new_params["output_dir"] + "/" + new_params["results_storage"] - ): - self.records_storage = pathlib.Path( - new_params["output_dir"] + "/" + new_params["results_storage"] - ) - self.reset_record_files( - new_params["n_temporal_units_to_sim"], new_params["register_stocks"] - ) + self._indus_rebuild_demand_tot = np.array([]) + self._house_rebuild_demand_tot = np.array([]) + self._indus_rebuild_demand = np.array([]) + self._house_rebuild_demand = np.array([]) + self._tot_rebuild_demand = np.array([]) + self._prod_cap_delta_productive_capital = np.array([]) + self._prod_cap_delta_arbitrary = np.array([]) + self._prod_cap_delta_tot = np.array([]) def write_index(self, index_file: str | pathlib.Path) -> None: """Write the indexes of the different dataframes of the model in a json file. @@ -1547,7 +1575,7 @@ def write_index(self, index_file: str | pathlib.Path) -> None: indexes = { "regions": list(self.regions), "sectors": list(self.sectors), - "fd_cat": list(self.fd_cat), + "fd_cat": list(self.final_demand_cat), "n_sectors": self.n_sectors, "n_regions": self.n_regions, "n_industries": self.n_sectors * self.n_regions, @@ -1555,10 +1583,10 @@ def write_index(self, index_file: str | pathlib.Path) -> None: if isinstance(index_file, str): index_file = pathlib.Path(index_file) index_file.parent.mkdir(parents=True, exist_ok=True) - with index_file.open("w") as f: - json.dump(indexes, f) + with index_file.open("w") as ffile: + json.dump(indexes, ffile) - def change_inv_duration(self, new_dur: int, old_dur: Optional[int] = None) -> None: + def change_inv_duration(self, new_dur, old_dur=None) -> None: # replace this method by a property ! if old_dur is None: old_dur = self.main_inv_dur diff --git a/boario/simulation.py b/boario/simulation.py index 0b4e4d7..43b045b 100644 --- a/boario/simulation.py +++ b/boario/simulation.py @@ -32,14 +32,21 @@ from typing import Optional, Union import numpy as np +import pandas as pd import progressbar from boario import DEBUGFORMATTER from boario import logger -from boario.event import * -from boario.extended_models import * +from boario.event import ( + Event, + EventArbitraryProd, + EventKapitalDestroyed, + EventKapitalRebuild, + EventKapitalRecover, +) +from boario.extended_models import ARIOPsiModel from boario.model_base import ARIOBaseModel -from boario.utils.misc import CustomNumpyEncoder, TempMemmap +from boario.utils.misc import CustomNumpyEncoder, TempMemmap, sizeof_fmt, print_summary __all__ = ["Simulation"] @@ -63,43 +70,58 @@ class Simulation: "rebuild_prod", "inputs_stocks", "limiting_inputs", - "kapital_to_recover", + "productive_capital_to_recover", ] __file_save_array_specs = { "production_realised": ( "float64", - "production_evolution", + "_production_evolution", "industries", np.nan, ), "production_capacity": ( "float64", - "production_cap_evolution", + "_production_cap_evolution", + "industries", + np.nan, + ), + "final_demand": ("float64", "_final_demand_evolution", "industries", np.nan), + "intermediate_demand": ( + "float64", + "_io_demand_evolution", + "industries", + np.nan, + ), + "rebuild_demand": ( + "float64", + "_rebuild_demand_evolution", + "industries", + np.nan, + ), + "overproduction": ( + "float64", + "_overproduction_evolution", "industries", np.nan, ), - "final_demand": ("float64", "final_demand_evolution", "industries", np.nan), - "intermediate_demand": ("float64", "io_demand_evolution", "industries", np.nan), - "rebuild_demand": ("float64", "rebuild_demand_evolution", "industries", np.nan), - "overproduction": ("float64", "overproduction_evolution", "industries", np.nan), "final_demand_unmet": ( "float64", - "final_demand_unmet_evolution", + "_final_demand_unmet_evolution", "industries", np.nan, ), "rebuild_prod": ( "float64", - "rebuild_production_evolution", + "_rebuild_production_evolution", "industries", np.nan, ), - "inputs_stocks": ("float64", "inputs_evolution", "stocks", np.nan), - "limiting_inputs": ("byte", "limiting_inputs_evolution", "stocks", -1), - "kapital_to_recover": ( + "inputs_stocks": ("float64", "_inputs_evolution", "stocks", np.nan), + "limiting_inputs": ("byte", "_limiting_inputs_evolution", "stocks", -1), + "productive_capital_to_recover": ( "float64", - "regional_sectoral_kapital_destroyed_evolution", + "_regional_sectoral_productive_capital_destroyed_evolution", "industries", np.nan, ), @@ -117,7 +139,7 @@ def __init__( save_index: bool = False, save_records: list | str = [], boario_output_dir: str | pathlib.Path = tempfile.mkdtemp(prefix="boario"), - results_dir_name: str = "results", + results_dir_name: Optional[str] = None, ) -> None: """A Simulation instance can be initialized with the following parameters: @@ -135,17 +157,36 @@ def __init__( An optional list of events to run the simulation with [WIP]. separate_sims : bool, default False Whether to run each event separately or during the same simulation [WIP]. + save_events: bool, default False + If True, saves a json file of the list of events to simulate when starting the model loop. + save_params: bool, default False + If True, saves a json file of the parameters used when starting the model loop. + save_index: bool, default False + If True, saves a json file of the list of industries in the model when starting the model loop (convenience). + save_records: list | str, default [] + The list of simulation variable records to save in corresponding files. boario_output_dir : str | pathlib.Path - An optional directory where to save files generated by the simulation. + An optional directory where to save files generated by the simulation. Defaults to a temporary directory prefixed by "boario". results_dir_name : str, default 'results' The name of the folder where simulation results will be stored. - Examples - -------- + """ + self.output_dir = pathlib.Path(boario_output_dir) + """pathlib.Path, optional: Optional path to the directory where output are stored.""" + self.results_storage = ( + self.output_dir.resolve() + if not results_dir_name + else self.output_dir.resolve() / results_dir_name + ) + """str: Name of the folder in `output_dir` where the results will be stored if saved.""" - See #add link to example page. + if not self.results_storage.exists(): + self.results_storage.mkdir(parents=True) - """ + tmp = logging.FileHandler(self.results_storage / "simulation.log") + tmp.setLevel(logging.INFO) + tmp.setFormatter(DEBUGFORMATTER) + logger.addHandler(tmp) if events_list is None: events_list = [] @@ -154,25 +195,27 @@ def __init__( self._save_params = save_params self._save_index = save_index self._register_stocks = register_stocks - self.output_dir = pathlib.Path(boario_output_dir) - """pathlib.Path, optional: Optional path to the directory where output are stored.""" + + # Pre-init record variables + self._production_evolution = np.array([]) + self._production_cap_evolution = np.array([]) + self._final_demand_evolution = np.array([]) + self._io_demand_evolution = np.array([]) + self._rebuild_demand_evolution = np.array([]) + self._overproduction_evolution = np.array([]) + self._final_demand_unmet_evolution = np.array([]) + self._rebuild_production_evolution = np.array([]) + self._inputs_evolution = np.array([]) + self._limiting_inputs_evolution = np.array([]) + self._regional_sectoral_productive_capital_destroyed_evolution = np.array([]) if save_records != [] or save_events or save_params or save_index: self.output_dir.resolve().mkdir(parents=True, exist_ok=True) if save_records != []: - self.results_storage = self.output_dir.resolve() / results_dir_name if not self.results_storage.exists(): self.results_storage.mkdir() - self.results_storage = ( - pathlib.Path(self.output_dir).resolve() / results_dir_name - ) - """str: Name of the folder in `output_dir` where the results will be stored if saved.""" - - if not self.results_storage.exists(): - self.results_storage.mkdir(parents=True) - self.model = model """Union[ARIOBaseModel, ARIOPsiModel] : The model to run the simulation with. See :class:`~boario.model_base.ARIOBaseModel`.""" @@ -184,6 +227,13 @@ def __init__( """list[Event]: A list containing all events that are happening at the current timestep of the simulation.""" self.events_timings = set() + if ( + not isinstance(n_temporal_units_to_sim, (int, np.integer)) + or n_temporal_units_to_sim <= 0 + ): + raise ValueError( + f"n_temporal_units_to_sim should be a positive integer (got {n_temporal_units_to_sim} of type {type(n_temporal_units_to_sim)})" + ) self.n_temporal_units_to_sim = n_temporal_units_to_sim """int: The total number of `temporal_units` to simulate.""" @@ -206,7 +256,7 @@ def __init__( # RECORDS FILES self.records_storage: pathlib.Path = self.results_storage / "records" - """Place where records are stored if stored""" + """pathlib.Path: Place where records are stored if stored""" self._files_to_record = [] @@ -227,15 +277,14 @@ def __init__( f"{impossible_records} are not possible records ({self.__possible_records})" ) logger.info(f"Will save {save_records} records") - logger.info("Records storage is: {}".format(self.records_storage)) + logger.info("Records storage is: {self.records_storage}") self.records_storage.mkdir(parents=True, exist_ok=True) self._save_index = True self._save_events = True self._save_params = True - self.init_records(save_records, register_stocks) + self._init_records(save_records) - Event.temporal_unit_range = self.n_temporal_units_to_sim self.params_dict = { "n_temporal_units_to_sim": self.n_temporal_units_to_sim, "output_dir": str(self.output_dir) @@ -262,8 +311,12 @@ def __init__( """dict: A dictionary saving the parameters the simulation was run with.""" logger.info("Initialized !") + formatted_params_dict = { + key: print_summary(value) if key == "inventory_restoration_tau" else value + for key, value in self.params_dict.items() + } logger.info( - "Simulation parameters:\n{}".format(pformat(self.params_dict, compact=True)) + f"Simulation parameters:\n{pformat(formatted_params_dict, compact=True)}" ) def loop(self, progress: bool = True): @@ -282,21 +335,12 @@ def loop(self, progress: bool = True): If True, shows a progress bar of the loop in the console. """ logger.info( - "Starting model loop for at most {} steps".format( - self.n_temporal_units_to_sim // self.model.n_temporal_units_by_step + 1 - ) + f"Starting model loop for at most {self.n_temporal_units_to_sim // self.model.n_temporal_units_by_step + 1} steps" ) logger.info( - "One step is {}/{} of a year".format( - self.model.n_temporal_units_by_step, - self.model.iotable_year_to_temporal_unit_factor, - ) + f"One step is {self.model.n_temporal_units_by_step}/{self.model.iotable_year_to_temporal_unit_factor} of a year" ) - tmp = logging.FileHandler(self.results_storage / "simulation.log") - tmp.setLevel(logging.INFO) - tmp.setFormatter(DEBUGFORMATTER) - logger.addHandler(tmp) - logger.info("Events : {}".format(self.all_events)) + logger.info("Events : {self.all_events}") run_range = range( 0, @@ -310,9 +354,9 @@ def loop(self, progress: bool = True): ) with ( pathlib.Path(self.results_storage) / "jsons" / "simulated_events.json" - ).open("w") as f: + ).open("w") as ffile: event_dicts = [ev.event_dict for ev in self.all_events] - json.dump(event_dicts, f, indent=4, cls=CustomNumpyEncoder) + json.dump(event_dicts, ffile, indent=4, cls=CustomNumpyEncoder) if progress: widgets = [ "Processed: ", @@ -348,7 +392,7 @@ def loop(self, progress: bool = True): if step_res == 1: self.has_crashed = True logger.warning( - f"""Economy seems to have crashed. + f"""Economy or model seems to have crashed. - At step : {self.current_temporal_unit} """ ) @@ -361,8 +405,8 @@ def loop(self, progress: bool = True): ) break - if self._files_to_record != []: - self.flush_memmaps() + if self._files_to_record: + self._flush_memmaps() if self._save_index: self.model.write_index(self.results_storage / "jsons" / "indexes.json") @@ -373,14 +417,14 @@ def loop(self, progress: bool = True): if self._save_params: with ( pathlib.Path(self.results_storage) / "jsons" / "simulated_params.json" - ).open("w") as f: - json.dump(self.params_dict, f, indent=4, cls=CustomNumpyEncoder) + ).open("w") as ffile: + json.dump(self.params_dict, ffile, indent=4, cls=CustomNumpyEncoder) with ( pathlib.Path(self.results_storage) / "jsons" / "equilibrium_checks.json" - ).open("w") as f: + ).open("w") as ffile: json.dump( {str(k): v for k, v in self.equi.items()}, - f, + ffile, indent=4, cls=CustomNumpyEncoder, ) @@ -399,7 +443,7 @@ def next_step( First it checks if an event is planned to occur at the current step and if so, shocks the model with the - corresponding event. Then it : + corresponding event. Then: 0) If at least one step elapsed, it computes the new overproduction vector for the next step (using :meth:`~boario.model_base.ARIOBaseModel.calc_overproduction`) @@ -407,7 +451,7 @@ def next_step( 2) Distribute the `realised` production towards the different demands (intermediate, final, rebuilding) and compute the changes in the inputs stock matrix (see :meth:`~boario.model_base.ARIOBaseModel.distribute_production`) - Note that it is during this step that the model checks if an event is completely rebuild/recovered. + Note that it is during this step that the model checks if an event is completely rebuild/recovered and removes it from the list the case being. 3) Computes the orders matrix (i.e. the intermediate demand) for the next step (see :meth:`~boario.model_base.ARIOBaseModel.calc_orders`) @@ -423,86 +467,98 @@ def next_step( The minimum number of failing regions required to consider economy has crashed. """ - if min_steps_check is None: - min_steps_check = self.n_temporal_units_to_sim // 5 - if min_failing_regions is None: - min_failing_regions = self.model.n_regions * self.model.n_sectors // 3 - - # Check if there are new events to add, - # if some happening events can start rebuilding (if rebuildable), - # and updates the internal model production_cap decrease and rebuild_demand - self.check_happening_events() - - if "inputs_evolution" in self._files_to_record: - self.write_stocks() - - if self.current_temporal_unit > 1: - self.model.calc_overproduction() - - if "overproduction_evolution" in self._files_to_record: - self.write_overproduction() - if "rebuild_demand_evolution" in self._files_to_record: - self.write_rebuild_demand() - if "final_evolution" in self._files_to_record: - self.write_final_demand() - if "io_demand_evolution" in self._files_to_record: - self.write_io_demand() - - constraints = self.model.calc_production(self.current_temporal_unit) - - if "limiting_inputs_evolution" in self._files_to_record: - self.write_limiting_stocks(constraints) - if "production_evolution" in self._files_to_record: - self.write_production() - if "production_cap_evolution" in self._files_to_record: - self.write_production_max() - if "regional_sectoral_kapital_destroyed_evolution" in self._files_to_record: - self.write_kapital_lost() - try: - rebuildable_events = [ - ev - for ev in self.currently_happening_events - if isinstance(ev, EventKapitalRebuild) and ev.rebuildable - ] - events_to_remove = self.model.distribute_production( - rebuildable_events, self.scheme - ) - if "final_demand_unmet_evolution" in self._files_to_record: - self.write_final_demand_unmet() - if "rebuild_production_evolution" in self._files_to_record: - self.write_rebuild_prod() - except RuntimeError as e: - logger.exception("This exception happened:", e) - return 1 - events_to_remove = events_to_remove + [ - ev for ev in self.currently_happening_events if ev.over - ] - if events_to_remove: - self.currently_happening_events = [ - e for e in self.currently_happening_events if e not in events_to_remove + if min_steps_check is None: + min_steps_check = self.n_temporal_units_to_sim // 5 + if min_failing_regions is None: + min_failing_regions = self.model.n_regions * self.model.n_sectors // 3 + + # Check if there are new events to add, + # if some happening events can start rebuilding (if rebuildable), + # and updates the internal model production_cap decrease and rebuild_demand + self._check_happening_events() + + if "_inputs_evolution" in self._files_to_record: + self._write_stocks() + + # 0) + if self.current_temporal_unit > 1: + self.model.calc_overproduction() + + if "_overproduction_evolution" in self._files_to_record: + self._write_overproduction() + if "_rebuild_demand_evolution" in self._files_to_record: + self._write_rebuild_demand() + if "_final_evolution" in self._files_to_record: + self._write_final_demand() + if "_io_demand_evolution" in self._files_to_record: + self._write_io_demand() + + # 1) + constraints = self.model.calc_production(self.current_temporal_unit) + + if "_limiting_inputs_evolution" in self._files_to_record: + self._write_limiting_stocks(constraints) + if "_production_evolution" in self._files_to_record: + self._write_production() + if "_production_cap_evolution" in self._files_to_record: + self._write_production_max() + if ( + "_regional_sectoral_productive_capital_destroyed_evolution" + in self._files_to_record + ): + self._write_productive_capital_lost() + + # 2) + try: + rebuildable_events = [ + ev + for ev in self.currently_happening_events + if isinstance(ev, EventKapitalRebuild) and ev.rebuildable + ] + events_to_remove = self.model.distribute_production( + rebuildable_events, self.scheme + ) + if "_final_demand_unmet_evolution" in self._files_to_record: + self._write_final_demand_unmet() + if "_rebuild_production_evolution" in self._files_to_record: + self._write_rebuild_prod() + except RuntimeError: + logger.exception("An exception happened:") + self.model.matrix_stock.dump( + self.results_storage / "matrix_stock_dump.pkl" + ) + logger.error( + f"Negative values in the stocks, matrix has been dumped in the results dir : \n {self.results_storage / 'matrix_stock_dump.pkl'}" + ) + return 1 + events_to_remove = events_to_remove + [ + ev for ev in self.currently_happening_events if ev.over ] - for e in events_to_remove: - if isinstance(e, EventKapitalDestroyed): - logger.info( - "Temporal_Unit : {} ~ Event named {} that occured at {} in {} for {} damages is completely rebuilt/recovered".format( - self.current_temporal_unit, - e.name, - e.occurrence, - e.aff_regions, - e.total_kapital_destroyed, + if events_to_remove: + self.currently_happening_events = [ + e + for e in self.currently_happening_events + if e not in events_to_remove + ] + for evnt in events_to_remove: + if isinstance(evnt, EventKapitalDestroyed): + logger.info( + f"""Temporal_Unit : {self.current_temporal_unit} ~ Event named {evnt.name} that occured at {evnt.occurrence} in {evnt.aff_regions.to_list()} for {evnt.total_productive_capital_destroyed} damages is completely rebuilt/recovered""" ) - ) - self.model.calc_orders() + self.model.calc_orders() - n_checks = self.current_temporal_unit // check_period - if n_checks > self._n_checks: - self.check_equilibrium(n_checks) - self._n_checks += 1 + n_checks = self.current_temporal_unit // check_period + if n_checks > self._n_checks: + self.check_equilibrium(n_checks) + self._n_checks += 1 - self.current_temporal_unit += self.model.n_temporal_units_by_step - return 0 + self.current_temporal_unit += self.model.n_temporal_units_by_step + return 0 + except Exception: + logger.exception(f"The following exception happened:") + return 1 def check_equilibrium(self, n_checks: int): """Checks the status of production, stocks and rebuilding demand. @@ -545,43 +601,18 @@ def check_equilibrium(self, n_checks: int): (n_checks, self.current_temporal_unit, "rebuilding") ] = "not finished" - def read_events_from_list(self, events_list: list[dict]): - raise NotImplementedError("I have to redo this one") - - # """Import a list of events (as a list of dictionaries) into the model. - - # Also performs various checks on the events to avoid badly written events. - # See :ref:`How to define Events ` to understand how to write events dictionaries or JSON files. - - # Parameters - # ---------- - # events_list : - # List of events as dictionaries. - - # """ - # logger.info("Reading events from given list and adding them to the model") - # if not isinstance(events_list, list): - # if isinstance(events_list, dict): - # raise TypeError( - # "read_events_from_list() takes a list of event dicts as an argument, not a single event dict, you might want to use read_event(your_event) instead." - # ) - # else: - # raise TypeError( - # "read_events_from_list() takes a list of event dicts as an argument, not a {}".format( - # type(events_list) - # ) - # ) - # for ev_dic in events_list: - # self.read_event(ev_dic) - - def read_event(self, ev_dic: dict): - raise NotImplementedError("I have to redo this one") - - # if ev_dic["aff_sectors"] == "all": - # ev_dic["aff_sectors"] = list(self.model.sectors) - # ev = Event(ev_dic) - # self.all_events.append(ev) - # self.events_timings.add(ev.occurrence) + def add_events(self, events: list[Event]): + """Add a list of events to the simulation. + + Parameters + ---------- + events : list[Event] + The events to add. + """ + if not isinstance(events, list): + raise TypeError(f"list[Event] expected, {type(events)} received.") + for ev in events: + self.add_event(ev) def add_event(self, ev: Event): """Add an event to the simulation. @@ -591,7 +622,8 @@ def add_event(self, ev: Event): ev : Event The event to add. """ - + if not isinstance(ev, Event): + raise ValueError(f"Event expected, {type(ev)} received.") self.all_events.append(ev) self.events_timings.add(ev.occurrence) @@ -603,7 +635,12 @@ def reset_sim_with_same_events(self): self._n_checks = 0 self.n_temporal_units_simulated = 0 self.has_crashed = False - self.reset_records() + self.equi = { + (int(0), int(0), "production"): "equi", + (int(0), int(0), "stocks"): "equi", + (int(0), int(0), "rebuilding"): "equi", + } + self._reset_records() self.model.reset_module() def reset_sim_full(self): @@ -615,29 +652,6 @@ def reset_sim_full(self): self.currently_happening_events = [] self.events_timings = set() - def update_params(self, new_params: dict): - """Update the parameters of the model. - - Replace the ``params`` attribute with ``new_params`` and logs the update. - This method also checks if the directory specified to save the results exists and create it otherwise. - - .. warning:: - Be aware this method calls :meth:`~boario.model_base.ARIOBaseModel.update_params`, which resets the memmap files located in the results directory ! - - Parameters - ---------- - new_params : dict - New dictionnary of parameters to use. - - """ - raise NotImplementedError("To fix") - logger.info("Updating model parameters") - self.params_dict = new_params - results_storage = pathlib.Path(self.results_storage) - if not results_storage.exists(): - results_storage.mkdir(parents=True) - self.model.update_params(self.params_dict) - def write_index(self, index_file: Union[str, pathlib.Path]): """Write the index of the dataframes used in the model in a json file. @@ -651,39 +665,7 @@ def write_index(self, index_file: Union[str, pathlib.Path]): """ self.model.write_index(index_file) - def read_events(self, events_file: Union[str, pathlib.Path]): - """Read events from a json file. - - Parameters - ---------- - events_file : - path to a json file - - Raises - ------ - FileNotFoundError - If file does not exist - - """ - raise NotImplementedError("I have to redo this one") - # logger.info( - # "Reading events from {} and adding them to the model".format(events_file) - # ) - # if isinstance(events_file, str): - # events_file = pathlib.Path(events_file) - # elif not isinstance(events_file, pathlib.Path): - # raise TypeError("Given index file is not an str or a Path") - # if not events_file.exists(): - # raise FileNotFoundError("This file does not exist: ", events_file) - # else: - # with events_file.open("r") as f: - # events = json.load(f) - # if isinstance(events, list): - # self.read_events_from_list(events) - # else: - # self.read_events_from_list([events]) - - def check_happening_events(self) -> None: + def _check_happening_events(self) -> None: """Updates the status of all events. Check the `all_events` attribute and `current_temporal_unit` and @@ -691,112 +673,110 @@ def check_happening_events(self) -> None: rebuild/recover) """ - for ev in self.all_events: - if not ev.happened: + for evnt in self.all_events: + if not evnt.happened: if ( (self.current_temporal_unit - self.model.n_temporal_units_by_step) - <= ev.occurrence + <= evnt.occurrence <= self.current_temporal_unit ): logger.info( - "Temporal_Unit : {} ~ Shocking model with new event".format( - self.current_temporal_unit - ) + f"Temporal_Unit : {self.current_temporal_unit} ~ Shocking model with new event" ) - logger.info("Affected regions are : {}".format(ev.aff_regions)) - ev.happened = True - self.currently_happening_events.append(ev) - for ev in self.currently_happening_events: - if isinstance(ev, EventKapitalRebuild): - ev.rebuildable = self.current_temporal_unit - if isinstance(ev, EventKapitalRecover): - ev.recoverable = self.current_temporal_unit - if ev.recoverable: - ev.recovery(self.current_temporal_unit) + logger.info(f"Affected regions are : {evnt.aff_regions.to_list()}") + evnt.happened = True + self.currently_happening_events.append(evnt) + for evnt in self.currently_happening_events: + if isinstance(evnt, EventKapitalRebuild): + evnt.rebuildable = self.current_temporal_unit + if isinstance(evnt, (EventKapitalRecover, EventArbitraryProd)): + evnt.recoverable = self.current_temporal_unit + if evnt.recoverable: + evnt.recovery(self.current_temporal_unit) self.model.update_system_from_events(self.currently_happening_events) - def write_production(self) -> None: + def _write_production(self) -> None: """Saves the current production vector to the memmap.""" - self.production_evolution[self.current_temporal_unit] = self.model.production + self._production_evolution[self.current_temporal_unit] = self.model.production - def write_rebuild_prod(self) -> None: + def _write_rebuild_prod(self) -> None: """Saves the current rebuilding production vector to the memmap.""" logger.debug( - f"self.rebuild_production_evolution shape : {self.rebuild_production_evolution.shape}, self.model.rebuild_prod shape : {self.model.rebuild_prod.shape}" + f"self._rebuild_production_evolution shape : {self._rebuild_production_evolution.shape}, self.model.rebuild_prod shape : {self.model.rebuild_prod.shape}" ) - self.rebuild_production_evolution[ + self._rebuild_production_evolution[ self.current_temporal_unit ] = self.model.rebuild_prod - def write_kapital_lost(self) -> None: - """Saves the current remaining kapital to rebuild vector to the memmap.""" - self.regional_sectoral_kapital_destroyed_evolution[ + def _write_productive_capital_lost(self) -> None: + """Saves the current remaining productive_capital to rebuild vector to the memmap.""" + self._regional_sectoral_productive_capital_destroyed_evolution[ self.current_temporal_unit - ] = self.model.kapital_lost + ] = self.model.productive_capital_lost - def write_production_max(self) -> None: + def _write_production_max(self) -> None: """Saves the current production capacity vector to the memmap.""" - self.production_cap_evolution[ + self._production_cap_evolution[ self.current_temporal_unit ] = self.model.production_cap - def write_io_demand(self) -> None: + def _write_io_demand(self) -> None: """Saves the current (total per industry) intermediate demand vector to the memmap.""" - self.io_demand_evolution[ + self._io_demand_evolution[ self.current_temporal_unit ] = self.model.matrix_orders.sum(axis=1) - def write_final_demand(self) -> None: + def _write_final_demand(self) -> None: """Saves the current (total per industry) final demand vector to the memmap.""" - self.final_demand_evolution[ + self._final_demand_evolution[ self.current_temporal_unit ] = self.model.final_demand.sum(axis=1) - def write_rebuild_demand(self) -> None: + def _write_rebuild_demand(self) -> None: """Saves the current (total per industry) rebuilding demand vector to the memmap.""" to_write = np.full(self.model.n_regions * self.model.n_sectors, 0.0) - if (r_dem := self.model.tot_rebuild_demand) is not None: - self.rebuild_demand_evolution[self.current_temporal_unit] = r_dem # type: ignore + if len(r_dem := self.model.tot_rebuild_demand) > 0: + self._rebuild_demand_evolution[self.current_temporal_unit] = r_dem # type: ignore else: - self.rebuild_demand_evolution[self.current_temporal_unit] = to_write # type: ignore - - def write_limiting_stocks(self, limiting_stock: np.ndarray) -> None: - """Saves the current limiting inputs matrix to the memmap.""" - self.limiting_inputs_evolution[self.current_temporal_unit] = limiting_stock # type: ignore + self._rebuild_demand_evolution[self.current_temporal_unit] = to_write # type: ignore - def write_overproduction(self) -> None: + def _write_overproduction(self) -> None: """Saves the current overproduction vector to the memmap.""" - self.overproduction_evolution[self.current_temporal_unit] = self.model.overprod + self._overproduction_evolution[self.current_temporal_unit] = self.model.overprod - def write_final_demand_unmet(self) -> None: + def _write_final_demand_unmet(self) -> None: """Saves the unmet final demand (for this step) vector to the memmap.""" - self.final_demand_unmet_evolution[ + self._final_demand_unmet_evolution[ self.current_temporal_unit ] = self.model.final_demand_not_met - def write_stocks(self) -> None: + def _write_stocks(self) -> None: """Saves the current inputs stock matrix to the memmap.""" - self.inputs_evolution[self.current_temporal_unit] = self.model.matrix_stock + self._inputs_evolution[self.current_temporal_unit] = self.model.matrix_stock - def write_limiting_stocks(self, limiting_stock: np.ndarray) -> None: + def _write_limiting_stocks(self, limiting_stock: np.ndarray) -> None: """Saves the current limiting inputs matrix to the memmap.""" - self.limiting_inputs_evolution[self.current_temporal_unit] = limiting_stock # type: ignore + self._limiting_inputs_evolution[self.current_temporal_unit] = limiting_stock # type: ignore - def flush_memmaps(self) -> None: + def _flush_memmaps(self) -> None: """Saves files to record""" - for at in self._files_to_record: - if not hasattr(self, at): + for attr in self._files_to_record: + if not hasattr(self, attr): raise RuntimeError( - f"{at} should be a member yet it isn't. This shouldn't happen." + f"{attr} should be a member yet it isn't. This shouldn't happen." ) else: - getattr(self, at).flush() + getattr(self, attr).flush() - def init_records(self, save_records, register_stocks): + def _init_records(self, save_records): for rec in self.__possible_records: - if rec == "input_stocks" and not register_stocks: - pass + if rec == "inputs_stocks" and not self._register_stocks: + logger.debug("Will not save inputs stocks") else: + if rec == "inputs_stocks": + logger.info( + f"Simulation will save inputs stocks. Estimated size is {sizeof_fmt(self.n_temporal_units_to_sim * self.model.n_sectors * self.model.n_sectors * self.model.n_regions * 64)}" + ) save = rec in save_records filename = rec dtype, attr_name, shapev, fillv = self.__file_save_array_specs[rec] @@ -824,14 +804,181 @@ def init_records(self, save_records, register_stocks): self._files_to_record.append(attr_name) setattr(self, attr_name, memmap_array) - def reset_records( + def _reset_records( self, ): for rec in self.__possible_records: - dtype, attr_name, shapev, fillv = self.__file_save_array_specs[rec] + _, attr_name, _, fillv = self.__file_save_array_specs[rec] if rec == "input_stocks" and not self._register_stocks: pass else: memmap_array = getattr(self, attr_name) memmap_array.fill(fillv) setattr(self, attr_name, memmap_array) + + @property + def production_realised(self) -> pd.DataFrame: + """Return the evolution of the production realised by each industry (region,sector) + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the production realised, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._production_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def production_capacity(self) -> pd.DataFrame: + """Return the evolution of the production capacity of each industry (region,sector) + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the production capacity, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._production_cap_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def final_demand(self) -> pd.DataFrame: + """Return the evolution of final demand asked of each industry. + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the final demand asked, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._final_demand_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def intermediate_demand(self) -> pd.DataFrame: + """Return the evolution of intermediate demand asked of each industry (Total orders). + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the intermediate demand asked, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._io_demand_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def rebuild_demand(self) -> pd.DataFrame: + """Return the evolution of rebuild demand asked of each industry (Total orders). + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the rebuild demand asked, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._rebuild_demand_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def overproduction(self) -> pd.DataFrame: + """Return the evolution of the overproduction factor of each industry (region,sector) + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the overproduction factor, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._overproduction_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def final_demand_unmet(self) -> pd.DataFrame: + """Return the evolution of the final demand that could not be answered by industries. + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the final demand not met, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._final_demand_unmet_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def rebuild_prod(self) -> pd.DataFrame: + """Return the production allocated for the rebuilding demand by each industry (region,sector). + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the production allocated, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._rebuild_production_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") + + @property + def inputs_stocks(self) -> pd.DataFrame: + """Return the evolution of the inventory amount of each input for each industry (region,sector). Not this is not available if "record_stocks" is not set to True, + as the DataFrame can be quite large for "classic" MRIOTs. + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the amount in inventory, the columns are the industries + and the index are the step and input considered (MultiIndex). + """ + return pd.DataFrame( + self._inputs_evolution.reshape( + self.n_temporal_units_to_sim * self.model.n_sectors, -1 + ), + columns=self.model.industries, + copy=True, + index=pd.MultiIndex.from_product( + [list(range(self.n_temporal_units_to_sim)), self.model.sectors], + names=["step", "input"], + ), + ) + + @property + def limiting_inputs(self) -> pd.DataFrame: + """Return the evolution of the inputs lacking for each industry (region,sector) + + Returns: + pd.DataFrame: A pandas DataFrame where the value is a boolean set to 1 if considered input constrains production, the columns are the industries + and the index are the step and input considered (MultiIndex). + """ + return pd.DataFrame( + self._limiting_inputs_evolution.reshape( + self.n_temporal_units_to_sim * self.model.n_sectors, -1 + ), + columns=self.model.industries, + copy=True, + index=pd.MultiIndex.from_product( + [list(range(self.n_temporal_units_to_sim)), self.model.sectors], + names=["step", "input"], + ), + ) + + @property + def productive_capital_to_recover(self) -> pd.DataFrame: + """Return the evolution of remaining capital destroyed/to recover for each industry (region,sector) if it exists. + + Returns: + pd.DataFrame: A pandas DataFrame where the value is the amount of capital (still) destroyed, the columns are the industries + and the index is the step considered. + """ + return pd.DataFrame( + self._regional_sectoral_productive_capital_destroyed_evolution, + columns=self.model.industries, + copy=True, + ).rename_axis("step") diff --git a/boario/utils/misc.py b/boario/utils/misc.py index 90f7f74..c3c77d5 100644 --- a/boario/utils/misc.py +++ b/boario/utils/misc.py @@ -15,6 +15,7 @@ # along with this program. If not, see . from collections.abc import Iterable +import textwrap import pymrio import numpy import json @@ -118,3 +119,42 @@ def lexico_reindex(mrio: pymrio.IOSystem) -> pymrio.IOSystem: mrio.A = mrio.A.reindex(sorted(mrio.A.columns), axis=1) return mrio + + +def sizeof_fmt(num, suffix="B"): + for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]: + if abs(num) < 1024.0: + return f"{num:3.1f}{unit}{suffix}" + num /= 1024.0 + return f"{num:.1f}Yi{suffix}" + + +def print_summary(my_list): + if my_list: + current_element = None + current_count = 0 + summary = [] + for element in my_list: + if element != current_element: + if current_element is not None: + if current_count == 1: + summary.append(str(current_element)) + else: + summary.append(f"{current_element} (x {current_count})") + current_element = element + current_count = 1 + else: + current_count += 1 + if current_element is not None: + if current_count == 1: + summary.append(str(current_element)) + else: + summary.append(f"{current_element} (x {current_count})") + total_length = len(my_list) + total_sum = sum(my_list) + summary_string = ( + "[" + ", ".join(summary) + f"] (len: {total_length}, sum: {total_sum})" + ) + return textwrap.wrap(summary_string, width=80) + else: + return "" diff --git a/boario/utils/recovery_functions.py b/boario/utils/recovery_functions.py new file mode 100644 index 0000000..d8775f6 --- /dev/null +++ b/boario/utils/recovery_functions.py @@ -0,0 +1,132 @@ +from typing import Optional +import numpy as np + + +def linear_recovery( + elapsed_temporal_unit: int, + init_impact_stock: np.ndarray, + recovery_time: int, +): + r"""Linear Initial impact recovery function + + Initial impact is entirely recovered when `recovery_time` has passed since event + started recovering + + Parameters + ---------- + + init_impact_stock : float + Initial Initial impact destroyed + + elapsed_temporal_unit : int + Elapsed time since event started recovering + + recovery_time : int + Total time it takes the event to fully recover + + """ + + return init_impact_stock * (1 - (elapsed_temporal_unit / recovery_time)) + + +def convexe_recovery( + elapsed_temporal_unit: int, + init_impact_stock: np.ndarray, + recovery_time: int, +): + r"""Convexe Initial impact recovery function + + Initial impact is recovered with characteristic time `recovery_time`. (This doesn't mean Initial impact is fully recovered after this time !) + This function models a recovery similar as the one happening in the rebuilding case, for the same characteristic time. + + Parameters + ---------- + + init_impact_stock : float + Initial Initial impact destroyed + + elapsed_temporal_unit : int + Elapsed time since event started recovering + + recovery_time : int + Total time it takes the event to fully recover + + """ + + return init_impact_stock * (1 - (1 / recovery_time)) ** elapsed_temporal_unit + + +def convexe_recovery_scaled( + elapsed_temporal_unit: int, + init_impact_stock: np.ndarray, + recovery_time: int, + scaling_factor: float = 4, +): + r"""Convexe Initial impact recovery function (scaled to match other recovery duration) + + Initial impact is mostly recovered (>95% by default for most cases) when `recovery_time` has passed since event + started recovering. + + Parameters + ---------- + + init_impact_stock : float + Initial Initial impact destroyed + + elapsed_temporal_unit : int + Elapsed time since event started recovering + + recovery_time : int + Total time it takes the event to fully recover + + scaling_factor: float + Used to scale the exponent in the function so that Initial impact is mostly rebuilt after `recovery_time`. A value of 4 insure >95% of Initial impact is recovered for a reasonable range of `recovery_time` values. + + """ + + return init_impact_stock * (1 - (1 / recovery_time)) ** ( + scaling_factor * elapsed_temporal_unit + ) + + +def concave_recovery( + elapsed_temporal_unit: int, + init_impact_stock: np.ndarray, + recovery_time: int, + steep_factor: float = 0.000001, + half_recovery_time: Optional[int] = None, +): + r"""Concave (s-shaped) Initial impact recovery function + + Initial impact is mostly (>95% in most cases) recovered when `recovery_time` has passed since event started recovering. + + Parameters + ---------- + + init_impact_stock : float + Initial Initial impact destroyed + + elapsed_temporal_unit : int + Elapsed time since event started recovering + + recovery_time : int + Total time it takes the event to fully recover + + steep_factor: float + This coefficient governs the slope of the central part of the s-shape, smaller values lead to a steeper slope. As such it also affect the percentage of Initial impact rebuilt after `recovery_time` has elapsed. A value of 0.000001 should insure 95% of the initial impact is rebuild for a reasonable range of recovery duration. + + half_recovery_time : int + This can by use to change the time the inflexion point of the s-shape curve is attained. By default it is set to half the recovery duration. + + """ + + if half_recovery_time is None: + tau_h = 2 + else: + tau_h = recovery_time / half_recovery_time + exponent = (np.log(recovery_time) - np.log(steep_factor)) / ( + np.log(recovery_time) - np.log(tau_h) + ) + return (init_impact_stock * recovery_time) / ( + recovery_time + steep_factor * (elapsed_temporal_unit**exponent) + ) diff --git a/docs/build/doctrees/autoapi/boario.doctree b/docs/build/doctrees/autoapi/boario.doctree index a2c26a2..ce16be8 100644 Binary files a/docs/build/doctrees/autoapi/boario.doctree and b/docs/build/doctrees/autoapi/boario.doctree differ diff --git a/docs/build/doctrees/autoapi/boario.event.doctree b/docs/build/doctrees/autoapi/boario.event.doctree index 43a95ea..008760b 100644 Binary files a/docs/build/doctrees/autoapi/boario.event.doctree and b/docs/build/doctrees/autoapi/boario.event.doctree differ diff --git a/docs/build/doctrees/autoapi/boario.extended_models.doctree 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When it is not found, a full rebuild will be done. -config: 451af383ec1f62424fe2b190b4f160ff +config: 9b60168294691cd51a01b397a85f5935 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_modules/boario/event.html b/docs/build/html/_modules/boario/event.html index 6136ee2..0ce6f92 100644 --- a/docs/build/html/_modules/boario/event.html +++ b/docs/build/html/_modules/boario/event.html @@ -1,3 +1,1704 @@ +<<<<<<< HEAD + + + + + + + + + + + boario.event — BoARIO documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Last updated on Apr 24, 2023. +
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Source code for boario.event

+# BoARIO : The Adaptative Regional Input Output model in python.
+# Copyright (C) 2022  Samuel Juhel
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+from __future__ import annotations
+import abc
+from typing import Callable, Optional, Union, List, Tuple
+import numpy.typing as npt
+import numpy as np
+import pandas as pd
+from boario import logger
+import math
+import inspect
+from functools import partial
+
+from boario.utils.recovery_functions import (
+    concave_recovery,
+    convexe_recovery,
+    linear_recovery,
+    convexe_recovery_scaled,
+)
+
+__all__ = [
+    "Event",
+    "EventKapitalDestroyed",
+    "EventArbitraryProd",
+    "EventKapitalRecover",
+    "EventKapitalRebuild",
+    "Impact",
+    "IndustriesList",
+    "SectorsList",
+    "RegionsList",
+]
+
+Impact = Union[int, float, list, dict, npt.NDArray, pd.DataFrame, pd.Series]
+IndustriesList = Union[List[Tuple[str, str]], pd.MultiIndex, npt.NDArray]
+SectorsList = Union[List[str], pd.Index, npt.NDArray]
+RegionsList = Union[List[str], pd.Index, npt.NDArray]
+FinalCatList = Union[List[str], pd.Index, npt.NDArray]
+
+rebuilding_finaldemand_cat_regex = r".*[hH]ousehold.*|HFCE"
+
+
+
[docs]class Event(metaclass=abc.ABCMeta):
+    # Class Attributes
+    __required_class_attributes = [
+        "possible_sectors",
+        "possible_regions",
+        "temporal_unit_range",
+        "z_shape",
+        "y_shape",
+        "x_shape",
+        "regions_idx",
+        "sectors_idx",
+        "model_monetary_factor",
+        "sectors_gva_shares",
+        "Z_distrib",
+        "mrio_name",
+    ]
+    possible_sectors: pd.Index = pd.Index([])
+    r"""List of sectors present in the MRIO used by the model"""
+
+    possible_regions: pd.Index = pd.Index([])
+    r"""List of regions present in the MRIO used by the model"""
+
+    possible_final_demand_cat: pd.Index = pd.Index([])
+    r"""List of final demand categories present in the MRIO used by the model"""
+
+    temporal_unit_range: int = 0
+    r"""Maximum temporal unit simulated"""
+
+    z_shape: tuple[int, int] = (0, 0)
+    r"""Shape of the Z (intermediate consumption) matrix in the model"""
+
+    y_shape: tuple[int, int] = (0, 0)
+    r"""Shape of the Y (final demand) matrix in the model"""
+
+    x_shape: tuple[int, int] = (0, 0)
+    r"""Shape of the x (production) vector in the model"""
+
+    regions_idx: npt.NDArray = np.array([])
+    r"""lexicographic region indexes"""
+
+    sectors_idx: npt.NDArray = np.array([])
+    r"""lexicographic sector indexes"""
+
+    model_monetary_factor: int = 0
+    r"""Amount of unitary currency used in the MRIO (e.g. 1000000 if in € millions)"""
+
+    sectors_gva_shares: npt.NDArray = np.array([])
+    r"""Fraction of total (regional) GVA for each sectors"""
+
+    Z_distrib: npt.NDArray = np.array([])
+    r"""Normalized intermediate consumption matrix"""
+
+    Y_distrib: npt.NDArray = np.array([])
+    r"""Normalized final consumption matrix"""
+
+    mrio_name: str = ""
+    r"""MRIO identification"""
+
+    def __init__(
+        self,
+        *,
+        impact: Impact,
+        aff_regions: RegionsList = [],
+        aff_sectors: SectorsList = [],
+        aff_industries: IndustriesList = [],
+        impact_industries_distrib: Optional[npt.ArrayLike] = None,
+        impact_regional_distrib: Optional[npt.ArrayLike] = None,
+        impact_sectoral_distrib_type: str = "custom",
+        impact_sectoral_distrib: Optional[npt.ArrayLike] = None,
+        name: str = "Unnamed",
+        occurrence: int = 1,
+        duration: int = 1,
+        event_monetary_factor: Optional[int] = None,
+    ) -> None:
+        r"""Create an event shocking the model from a dictionary.
+
+        An Event object stores all information about a unique shock during simulation such as
+        time of occurrence, duration, type of shock, amount of damages. Computation
+        of recovery or initially requested rebuilding demand is also done in this
+        class.
+
+        Parameters
+        ----------
+
+        event : dict
+            A dictionary holding the necessary information to define an event.
+
+        Examples
+        --------
+            FIXME: Add docs.
+
+        """
+
+        self._aff_sectors_idx = np.array([])
+        self._aff_sectors = pd.Index([])
+        self._aff_regions_idx = np.array([])
+        self._aff_regions = pd.Index([])
+        impact_regional_distrib = (
+            np.array(impact_regional_distrib)
+            if impact_regional_distrib
+            else np.array([])
+        )
+        impact_sectoral_distrib = (
+            np.array(impact_sectoral_distrib)
+            if impact_sectoral_distrib
+            else np.array([])
+        )
+        impact_industries_distrib = (
+            np.array(impact_industries_distrib)
+            if impact_sectoral_distrib
+            else np.array([])
+        )
+        logger.info("Initializing new Event")
+        logger.debug("Checking required Class attributes are defined")
+
+        if event_monetary_factor is None:
+            logger.info(
+                f"No event monetary factor given. Assuming it is the same as the model ({self.model_monetary_factor})"
+            )
+            self.event_monetary_factor = self.model_monetary_factor
+        else:
+            self.event_monetary_factor = event_monetary_factor
+            if self.event_monetary_factor != self.model_monetary_factor:
+                logger.warning(
+                    f"Event monetary factor ({self.event_monetary_factor}) differs from model monetary factor ({self.model_monetary_factor}). Be careful to define your impact with the correct unit (ie in event monetary factor)."
+                )
+
+        for v in Event.__required_class_attributes:
+            if Event.__dict__[v] is None:
+                raise AttributeError(
+                    "Required Event Class attribute {} is not set yet so instantiating an Event isn't possible".format(
+                        v
+                    )
+                )
+
+        self.name: str = name
+        r"""An identifying name for the event (for convenience mostly)"""
+
+        self.occurrence = occurrence
+        self.duration = duration
+        self.impact_df: pd.Series = pd.Series(
+            0,
+            dtype="float64",
+            index=pd.MultiIndex.from_product(
+                [self.possible_regions, self.possible_sectors],
+                names=["region", "sector"],
+            ),
+        )
+        r"""A pandas Series with all possible industries as index, holding the impact vector of the event. The impact is defined for each sectors in each region."""
+
+        ################## DATAFRAME INIT #################
+        # CASE VECTOR 1 (everything is there and regrouped) (only df creation)
+        if isinstance(impact, pd.Series):
+            logger.debug("Given impact is a pandas Series")
+            self.impact_df.loc[impact.index] = impact
+            if self.name == "Unnamed" and not impact.name is None:
+                self.name = str(impact.name)
+        elif isinstance(impact, dict):
+            logger.debug("Given impact is a dict, converting it to pandas Series")
+            impact = pd.Series(impact)
+            self.impact_df.loc[impact.index] = impact
+        elif isinstance(impact, pd.DataFrame):
+            logger.debug("Given impact is a pandas DataFrame, squeezing it to a Series")
+            impact = impact.squeeze()
+            if not isinstance(impact, pd.Series):
+                raise ValueError(
+                    "The given impact DataFrame is not a Series after being squeezed"
+                )
+            self.impact_df.loc[impact.index] = impact
+        # CASE VECTOR 2 (everything is there but not regrouped) AND CASE SCALAR (Only df creation)
+        elif (
+            isinstance(impact, (int, float, list, np.ndarray))
+            and len(aff_industries) > 0
+        ):
+            logger.debug(
+                f"Given impact is a {type(impact)} and list of impacted industries given. Proceeding."
+            )
+            self.impact_df.loc[aff_industries] = impact
+        elif (
+            isinstance(impact, (int, float, list, np.ndarray))
+            and len(aff_regions) > 0
+            and len(aff_sectors) > 0
+        ):
+            logger.debug(
+                f"Given impact is a {type(impact)} and lists of impacted regions and sectors given. Proceeding."
+            )
+            if isinstance(aff_regions, str):
+                aff_regions = [aff_regions]
+            if isinstance(aff_sectors, str):
+                aff_sectors = [aff_sectors]
+
+            self.impact_df.loc[
+                pd.MultiIndex.from_product([aff_regions, aff_sectors])
+            ] = impact
+        else:
+            raise ValueError("Invalid input format. Could not initiate pandas Series.")
+
+        # Check for <0 values and remove 0.
+        if (self.impact_df < 0).any():
+            logger.warning(
+                "Found negative values in impact vector. This should raise concern"
+            )
+
+        # SORT DF
+        # at this point impact_df is built, and can be sorted. Note that if impact was a scalar, impact_df contains copies of this scalar.
+        logger.debug("Sorting impact Series")
+        self.impact_df = self.impact_df.sort_index()
+
+        # Init self.impact_sectoral_distrib_type,
+        self.impact_sectoral_distrib_type = impact_sectoral_distrib_type
+        #################################################
+
+        # SET INDEXES ATTR
+        # note that the following also sets aff_regions and aff_sectors
+        assert isinstance(self.impact_df.index, pd.MultiIndex)
+        # Only look for industries where impact is greater than 0
+        self.aff_industries = self.impact_df.loc[self.impact_df > 0].index
+
+        logger.debug(
+            f"impact df at the moment:\n {self.impact_df.loc[self.aff_industries]}"
+        )
+
+        ###### SCALAR DISTRIBUTION ######################
+        # if impact_industries_distrib is given, set it. We assume impact is scalar !
+        # CASE SCALAR + INDUS DISTRIB
+        if len(impact_industries_distrib) > 0 and not isinstance(
+            impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray)
+        ):
+            logger.debug("impact is Scalar and impact_industries_distrib was given")
+            self.impact_industries_distrib = np.array(impact_industries_distrib)
+            self.impact_df.loc[self.aff_industries] = (
+                self.impact_df.loc[self.aff_industries] * self.impact_industries_distrib
+            )
+        # if impact_reg_dis and sec_dis are give, deduce the rest. We also assume impact is scalar !
+        # CASE SCALAR + REGION and SECTOR DISTRIB
+        elif (
+            len(impact_regional_distrib) > 0
+            and len(impact_sectoral_distrib) > 0
+            and not isinstance(
+                impact,
+                (pd.Series, dict, pd.DataFrame, list, np.ndarray),
+            )
+        ):
+            logger.debug(
+                "impact is Scalar and impact_regional_distrib and impact_sectoral_distrib were given"
+            )
+            if len(impact_regional_distrib) != len(self.aff_regions) or len(
+                impact_sectoral_distrib
+            ) != len(self.aff_sectors):
+                raise ValueError(
+                    "Lengths of `impact_regional_distrib` and/or `impact_sectoral_distrib` are incompatible with `aff_regions` and/or `aff_sectors`."
+                )
+            else:
+                self.impact_regional_distrib = impact_regional_distrib
+                self.impact_sectoral_distrib = impact_sectoral_distrib
+                self.impact_industries_distrib = (
+                    self.impact_regional_distrib[:, np.newaxis]
+                    * self.impact_sectoral_distrib
+                ).flatten()
+                self.impact_df.loc[self.aff_industries] = (
+                    self.impact_df.loc[self.aff_industries]
+                    * self.impact_industries_distrib
+                )
+        # CASE SCALAR + 'gdp' distrib
+        elif (
+            len(impact_regional_distrib) > 0
+            and len(impact_sectoral_distrib_type) > 0
+            and impact_sectoral_distrib_type == "gdp"
+            and not isinstance(
+                impact,
+                (pd.Series, dict, pd.DataFrame, list, np.ndarray),
+            )
+        ):
+            logger.debug("impact is Scalar and impact_sectoral_distrib_type is 'gdp'")
+
+            self.impact_regional_distrib = impact_regional_distrib
+
+            shares = self.sectors_gva_shares.reshape(
+                (len(self.possible_regions), len(self.possible_sectors))
+            )
+            self.impact_sectoral_distrib = (
+                shares[self._aff_regions_idx][:, self._aff_sectors_idx]
+                / shares[self._aff_regions_idx][:, self._aff_sectors_idx].sum(axis=1)[
+                    :, np.newaxis
+                ]
+            )
+            self.impact_industries_distrib = (
+                self.impact_regional_distrib[:, np.newaxis]
+                * self.impact_sectoral_distrib
+            ).flatten()
+            self.impact_df.loc[self.aff_industries] = (
+                self.impact_df.loc[self.aff_industries] * self.impact_industries_distrib
+            )
+            self.impact_sectoral_distrib_type = "gdp"
+        # CASE SCALAR + NO DISTRIB + list of industries
+        # if neither was given, we use default values. Again impact should be scalar here !
+        elif isinstance(aff_industries, (list, np.ndarray)) and not isinstance(
+            impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray)
+        ):
+            logger.debug(
+                "impact is Scalar and no distribution was given but a list of affected industries was given"
+            )
+            self._default_distribute_impact_from_industries_list()
+            self.impact_sectoral_distrib_type = "default (shared equally between affected regions and then affected sectors)"
+        # CASE SCALAR + NO DISTRIB + list of region + list of sectors
+        elif (
+            len(aff_regions) > 0
+            and len(aff_sectors) > 0
+            and not isinstance(
+                impact,
+                (pd.Series, dict, pd.DataFrame, list, np.ndarray),
+            )
+        ):
+            logger.debug(
+                "impact is Scalar and no distribution was given but lists of regions and sectors affected were given"
+            )
+            self._default_distribute_impact_from_industries_list()
+            self.impact_sectoral_distrib_type = "default (shared equally between affected regions and then affected sectors)"
+        elif not isinstance(impact, (pd.Series, dict, pd.DataFrame, list, np.ndarray)):
+            raise ValueError(f"Invalid input format: Could not compute impact")
+
+        self._finish_init()
+        ##################################################
+
+        self.happened: bool = False
+        r"""States if the event happened"""
+
+        self.over: bool = False
+        r"""States if the event is over"""
+
+        self.event_dict: dict = {
+            "name": str(self.name),
+            "occurrence": self.occurrence,
+            "duration": self.duration,
+            "aff_regions": list(self.aff_regions),
+            "aff_sectors": list(self.aff_sectors),
+            "impact": self.total_impact,
+            "impact_industries_distrib": list(self.impact_industries_distrib),
+            "impact_regional_distrib": list(self.impact_regional_distrib),
+            "impact_sectoral_distrib_type": self.impact_sectoral_distrib_type,
+            "globals_vars": {
+                "possible_sectors": list(self.possible_sectors),
+                "possible_regions": list(self.possible_regions),
+                "temporal_unit_range": self.temporal_unit_range,
+                "z_shape": self.z_shape,
+                "y_shape": self.y_shape,
+                "x_shape": self.x_shape,
+                "model_monetary_factor": self.model_monetary_factor,
+                "event_monetary_factor": self.event_monetary_factor,
+                "mrio_used": self.mrio_name,
+            },
+        }
+        r"""Store relevant information about the event"""
+
+    def _default_distribute_impact_from_industries_list(self):
+        # at this point, impact should still be scalar.
+        logger.debug("Using default impact distribution to industries")
+        logger.debug(
+            f"impact df at the moment:\n {self.impact_df.loc[self.aff_industries]}"
+        )
+        self.impact_regional_distrib = np.full(
+            len(self.aff_regions), 1 / len(self.aff_regions)
+        )
+
+        logger.debug(
+            f"self.impact_regional_distrib: {list(self.impact_regional_distrib)}"
+        )
+        logger.debug(f"len aff_regions: {len(self.aff_regions)}")
+        self.impact_df.loc[self.aff_industries] = (
+            self.impact_df.loc[self.aff_industries] * 1 / len(self.aff_regions)
+        )
+        impact_sec_vec = np.array(
+            [
+                1 / len(self.aff_industries.to_series().loc[reg])
+                for reg in self.aff_regions
+            ]
+        )
+        self.impact_df.loc[self.aff_industries] = (
+            self.impact_df.loc[self.aff_industries] * impact_sec_vec
+        )
+        logger.debug(
+            f"impact df after default distrib:\n {self.impact_df.loc[self.aff_industries]}"
+        )
+
+    def _finish_init(self):
+        logger.debug("Finishing Event init")
+        self.impact_vector = self.impact_df.to_numpy()
+        self.total_impact = self.impact_vector.sum()
+        self.impact_industries_distrib = (
+            self.impact_vector[self.impact_vector > 0] / self.total_impact
+        )
+        self.impact_regional_distrib = (
+            self.impact_df.loc[self.aff_industries].groupby("region").sum().values
+            / self.total_impact
+        )
+
+    @property
+    def aff_industries(self) -> pd.MultiIndex:
+        r"""The industries affected by the event.
+
+        Parameters
+        ----------
+
+        index : pd.MultiIndex
+             The affected industries as a pandas MultiIndex
+
+        Returns
+        -------
+             A pandas MultiIndex with the regions affected as first level, and sectors affected as second level
+
+        """
+
+        return self._aff_industries
+
+    @aff_industries.setter
+    def aff_industries(self, index: pd.MultiIndex):
+        if not isinstance(index, pd.MultiIndex):
+            raise ValueError(
+                "Given impact vector does not have a (region,sector) MultiIndex"
+            )
+        if index.names != ["region", "sector"]:
+            raise ValueError("MultiIndex must have levels named 'region' and 'sector'")
+
+        regions = index.get_level_values("region").unique()
+        sectors = index.get_level_values("sector").unique()
+        if not set(regions).issubset(self.possible_regions):
+            raise ValueError(
+                f"Regions {set(regions) - set(self.possible_regions)} are not in the possible regions list"
+            )
+        if not set(sectors).issubset(self.possible_sectors):
+            raise ValueError(
+                f"Sectors {set(sectors) - set(self.possible_sectors)} are not in the possible sectors list"
+            )
+
+        self.aff_regions = regions
+        self.aff_sectors = sectors
+        logger.debug(
+            f"Setting _aff_industries. There are {len(index)} affected industries"
+        )
+        self._aff_industries = index
+        self._aff_industries_idx = np.array(
+            [
+                len(self.possible_sectors) * ri + si
+                for ri in self._aff_regions_idx
+                for si in self._aff_sectors_idx
+            ]
+        )
+
+    @property
+    def occurrence(self) -> int:
+        r"""The temporal unit of occurrence of the event."""
+
+        return self._occur
+
+    @occurrence.setter
+    def occurrence(self, value: int):
+        if not 0 <= value <= self.temporal_unit_range:
+            raise ValueError(
+                "Occurrence of event is not in the range of simulation steps : {} not in [0-{}]".format(
+                    value, self.temporal_unit_range
+                )
+            )
+        else:
+            logger.debug(f"Setting occurence to {value}")
+            self._occur = value
+
+    @property
+    def duration(self) -> int:
+        r"""The duration of the event."""
+
+        return self._duration
+
+    @duration.setter
+    def duration(self, value: int):
+        if not 0 <= self.occurrence + value <= self.temporal_unit_range:
+            raise ValueError(
+                "Occurrence + duration of event is not in the range of simulation steps : {} + {} not in [0-{}]".format(
+                    self.occurrence, value, self.temporal_unit_range
+                )
+            )
+        else:
+            logger.debug(f"Setting duration to {value}")
+            self._duration = value
+
+    @property
+    def aff_regions(self) -> pd.Index:
+        r"""The array of regions affected by the event"""
+
+        return self._aff_regions
+
+    @property
+    def aff_regions_idx(self) -> npt.NDArray:
+        r"""The array of lexicographically ordered affected region indexes"""
+
+        return self._aff_regions_idx
+
+    @aff_regions.setter
+    def aff_regions(self, value: npt.ArrayLike | str):
+        if isinstance(value, str):
+            value = [value]
+        value = pd.Index(value)
+        impossible_regions = np.setdiff1d(value, self.possible_regions)
+        if impossible_regions.size > 0:
+            raise ValueError(
+                "These regions are not in the model : {}".format(impossible_regions)
+            )
+        else:
+            self._aff_regions = pd.Index(value, name="region")
+            logger.debug(f"Setting _aff_regions to {self._aff_regions}")
+            self._aff_regions_idx = np.searchsorted(self.possible_regions, value)
+
+    @property
+    def aff_sectors(self) -> pd.Index:
+        r"""The array of affected sectors by the event"""
+
+        return self._aff_sectors
+
+    @property
+    def aff_sectors_idx(self) -> npt.NDArray:
+        r"""The array of lexicographically ordered affected sectors indexes"""
+
+        return self._aff_sectors_idx
+
+    @aff_sectors.setter
+    def aff_sectors(self, value: npt.ArrayLike | str):
+        if isinstance(value, str):
+            value = [value]
+        value = pd.Index(value, name="sector")
+        impossible_sectors = np.setdiff1d(value, self.possible_sectors)
+        if impossible_sectors.size > 0:
+            raise ValueError(
+                "These sectors are not in the model : {}".format(impossible_sectors)
+            )
+        else:
+            self._aff_sectors = pd.Index(value, name="sector")
+            logger.debug(f"Setting _aff_sectors to {self._aff_sectors}")
+            self._aff_sectors_idx = np.searchsorted(self.possible_sectors, value)
+
+    @property
+    def impact_regional_distrib(self) -> npt.NDArray:
+        r"""The array specifying how damages are distributed among affected regions"""
+
+        return self._impact_regional_distrib
+
+    @impact_regional_distrib.setter
+    def impact_regional_distrib(self, value: npt.ArrayLike):
+        if self.aff_regions is None:
+            raise AttributeError("Affected regions attribute isn't set yet")
+        value = np.array(value)
+        if value.size != self.aff_regions.size:
+            raise ValueError(
+                "There are {} affected regions by the event and length of given damage distribution is {}".format(
+                    self.aff_regions.size, value.size
+                )
+            )
+        s = value.sum()
+        if not math.isclose(s, 1):
+            raise ValueError(
+                "Damage distribution doesn't sum up to 1 but to {}, which is not valid".format(
+                    s
+                )
+            )
+        self._impact_regional_distrib = value
+
+    @property
+    def impact_sectoral_distrib_type(self) -> str:
+        r"""The type of damages distribution among sectors (currently only 'gdp')"""
+
+        return self._impact_sectoral_distrib_type
+
+    @impact_sectoral_distrib_type.setter
+    def impact_sectoral_distrib_type(self, value: str):
+        logger.debug(f"Setting _impact_sectoral_distrib_type to {value}")
+        self._impact_sectoral_distrib_type = value
+
+    def __repr__(self):
+        # TODO: find ways to represent long lists
+        return f"""
+        Event(
+              name = {self.name},
+              occur = {self.occurrence},
+              duration = {self.duration}
+              aff_regions = {self.aff_regions.to_list()},
+              aff_sectors = {self.aff_sectors.to_list()},
+             )
+        """

+
+
+
[docs]class EventArbitraryProd(Event):
+    def __init__(
+        self,
+        *,
+        impact: Impact,
+        recovery_time: int = 1,
+        recovery_function: str = "linear",
+        aff_regions: RegionsList = [],
+        aff_sectors: SectorsList = [],
+        aff_industries: IndustriesList = [],
+        impact_industries_distrib: Optional[npt.ArrayLike] = None,
+        impact_regional_distrib: Optional[npt.ArrayLike] = None,
+        impact_sectoral_distrib_type="equally shared",
+        impact_sectoral_distrib: Optional[npt.ArrayLike] = None,
+        name: str = "Unnamed",
+        occurrence: int = 1,
+        duration: int = 1,
+    ) -> None:
+        if not isinstance(impact, pd.Series):
+            raise NotImplementedError(
+                "Arbitrary production capacity shock currently require to be setup from a vector indexed by the industries (regions,sectors) affected, and where values are the share of production capacity lost."
+            )
+
+        if (impact > 1.0).any():
+            raise ValueError(
+                "Impact is greater than 100% (1.) for at least an industry."
+            )
+
+        super().__init__(
+            impact=impact,
+            aff_regions=aff_regions,
+            aff_sectors=aff_sectors,
+            aff_industries=aff_industries,
+            impact_industries_distrib=impact_industries_distrib,
+            impact_regional_distrib=impact_regional_distrib,
+            impact_sectoral_distrib_type=impact_sectoral_distrib_type,
+            impact_sectoral_distrib=impact_sectoral_distrib,
+            name=name,
+            occurrence=occurrence,
+            duration=duration,
+            event_monetary_factor=None,
+        )
+
+        self._prod_cap_delta_arbitrary_0 = (
+            self.impact_vector.copy()
+        )  # np.zeros(shape=len(self.possible_sectors))
+        self.prod_cap_delta_arbitrary = (
+            self.impact_vector.copy()
+        )  # np.zeros(shape=len(self.possible_sectors))
+        self.recovery_time = recovery_time
+        r"""The characteristic recovery duration after the event is over"""
+
+        self.recovery_function = recovery_function
+
+        logger.info("Initialized")
+
+    @property
+    def prod_cap_delta_arbitrary(self) -> npt.NDArray:
+        r"""The optional array storing arbitrary (as in not related to productive_capital destroyed) production capacity loss"""
+        return self._prod_cap_delta_arbitrary
+
+    @prod_cap_delta_arbitrary.setter
+    def prod_cap_delta_arbitrary(self, value: npt.NDArray):
+        self._prod_cap_delta_arbitrary = value
+
+    @property
+    def recoverable(self) -> bool:
+        r"""A boolean stating if an event can start recover"""
+        return self._recoverable
+
+    @recoverable.setter
+    def recoverable(self, current_temporal_unit: int):
+        reb = (self.occurrence + self.duration) <= current_temporal_unit
+        if reb and not self.recoverable:
+            logger.info(
+                "Temporal_Unit : {} ~ Event named {} that occured at {} in {} has started recovering (arbitrary production capacity loss)".format(
+                    current_temporal_unit,
+                    self.name,
+                    self.occurrence,
+                    self.aff_regions.to_list(),
+                )
+            )
+        self._recoverable = reb
+
+    @property
+    def recovery_function(self) -> Callable:
+        r"""The recovery function used for recovery (`Callable`)"""
+        return self._recovery_fun
+
+    @recovery_function.setter
+    def recovery_function(self, r_fun: str | Callable | None):
+        if r_fun is None:
+            r_fun = "instant"
+        if self.recovery_time is None:
+            raise AttributeError(
+                "Impossible to set recovery function if no recovery time is given."
+            )
+        if isinstance(r_fun, str):
+            if r_fun == "linear":
+                fun = linear_recovery
+            elif r_fun == "convexe":
+                fun = convexe_recovery_scaled
+            elif r_fun == "convexe noscale":
+                fun = convexe_recovery
+            elif r_fun == "concave":
+                fun = concave_recovery
+            else:
+                raise NotImplementedError(
+                    "No implemented recovery function corresponding to {}".format(r_fun)
+                )
+        elif callable(r_fun):
+            r_fun_argsspec = inspect.getfullargspec(r_fun)
+            r_fun_args = r_fun_argsspec.args + r_fun_argsspec.kwonlyargs
+            if not all(
+                args in r_fun_args
+                for args in [
+                    "init_impact_stock",
+                    "elapsed_temporal_unit",
+                    "recovery_time",
+                ]
+            ):
+                raise ValueError(
+                    "Recovery function has to have at least the following keyword arguments: {}".format(
+                        [
+                            "init_impact_stock",
+                            "elapsed_temporal_unit",
+                            "recovery_time",
+                        ]
+                    )
+                )
+            fun = r_fun
+
+        else:
+            raise ValueError("Given recovery function is not a str or callable")
+
+        r_fun_partial = partial(
+            fun,
+            init_impact_stock=self._prod_cap_delta_arbitrary_0,
+            recovery_time=self.recovery_time,
+        )
+        self._recovery_fun = r_fun_partial
+
+    def recovery(self, current_temporal_unit: int):
+        elapsed = current_temporal_unit - (self.occurrence + self.duration)
+        if elapsed < 0:
+            raise RuntimeError("Trying to recover before event is over")
+        if self.recovery_function is None:
+            raise RuntimeError(
+                "Trying to recover event while recovery function isn't set yet"
+            )
+        res = self.recovery_function(elapsed_temporal_unit=elapsed)
+        precision = int(math.log10(self.model_monetary_factor)) + 1
+        res = np.around(res, precision)
+        if not np.any(res):
+            self.over = True
+        self._prod_cap_delta_arbitrary = res

+
+
+
[docs]class EventKapitalDestroyed(Event, metaclass=abc.ABCMeta):
+    def __init__(
+        self,
+        *,
+        impact: Impact,
+        households_impact: Optional[Impact] = None,
+        aff_regions: RegionsList = [],
+        aff_sectors: SectorsList = [],
+        aff_industries: IndustriesList = [],
+        impact_industries_distrib=None,
+        impact_regional_distrib=None,
+        impact_sectoral_distrib_type="equally shared",
+        impact_sectoral_distrib=None,
+        name="Unnamed",
+        occurrence=1,
+        duration=1,
+        event_monetary_factor=None,
+    ) -> None:
+        super().__init__(
+            impact=impact,
+            aff_regions=aff_regions,
+            aff_sectors=aff_sectors,
+            aff_industries=aff_industries,
+            impact_industries_distrib=impact_industries_distrib,
+            impact_regional_distrib=impact_regional_distrib,
+            impact_sectoral_distrib_type=impact_sectoral_distrib_type,
+            impact_sectoral_distrib=impact_sectoral_distrib,
+            name=name,
+            occurrence=occurrence,
+            duration=duration,
+            event_monetary_factor=event_monetary_factor,
+        )
+        # The only thing we have to do is affecting/computing the regional_sectoral_productive_capital_destroyed
+        self.total_productive_capital_destroyed = self.total_impact
+        self.total_productive_capital_destroyed *= (
+            self.event_monetary_factor / self.model_monetary_factor
+        )
+        logger.info(
+            f"Total impact on productive capital is {self.total_productive_capital_destroyed} (in model unit)"
+        )
+        self.remaining_productive_capital_destroyed = (
+            self.total_productive_capital_destroyed
+        )
+        self._regional_sectoral_productive_capital_destroyed_0 = self.impact_vector * (
+            self.event_monetary_factor / self.model_monetary_factor
+        )
+        self.regional_sectoral_productive_capital_destroyed = self.impact_vector * (
+            self.event_monetary_factor / self.model_monetary_factor
+        )
+        self.households_impact_df: pd.Series = pd.Series(
+            0,
+            dtype="float64",
+            index=pd.MultiIndex.from_product(
+                [self.possible_regions, self.possible_final_demand_cat],
+                names=["region", "final_demand_cat"],
+            ),
+        )
+        r"""A pandas Series with all possible (regions, final_demand_cat) as index, holding the households impacts vector of the event. The impact is defined for each region and each final demand category."""
+
+        if households_impact:
+            try:
+                rebuilding_demand_idx = self.possible_final_demand_cat[
+                    self.possible_final_demand_cat.str.match(
+                        rebuilding_finaldemand_cat_regex
+                    )
+                ]  # .values[0]
+
+            except IndexError:
+                rebuilding_demand_idx = self.possible_final_demand_cat[0]
+            if isinstance(households_impact, pd.Series):
+                logger.debug("Given household impact is a pandas Series")
+                self.households_impact_df.loc[
+                    households_impact.index, rebuilding_demand_idx
+                ] = households_impact
+            elif isinstance(households_impact, dict):
+                logger.debug("Given household impact is a dict")
+                households_impact = pd.Series(households_impact)
+                self.households_impact_df.loc[
+                    households_impact.index, rebuilding_demand_idx
+                ] = households_impact
+            elif isinstance(households_impact, pd.DataFrame):
+                logger.debug("Given household impact is a dataframe")
+                households_impact = households_impact.squeeze()
+                if not isinstance(households_impact, pd.Series):
+                    raise ValueError(
+                        "The given households_impact DataFrame is not a Series after being squeezed"
+                    )
+                self.households_impact_df.loc[
+                    households_impact.index, rebuilding_demand_idx
+                ] = households_impact
+            elif isinstance(households_impact, (list, np.ndarray)):
+                if len(households_impact) != len(self.aff_regions):
+                    raise ValueError(
+                        f"Length mismatch between households_impact ({len(households_impact)} and affected regions ({len(self.aff_regions)}))"
+                    )
+                else:
+                    self.households_impact_df.loc[
+                        self.aff_regions, rebuilding_demand_idx
+                    ] = households_impact
+            elif isinstance(households_impact, (float, int)):
+                if self.impact_regional_distrib is not None:
+                    logger.warning(
+                        f"households impact given as scalar, distributing among region following `impact_regional_distrib` ({self.impact_regional_distrib}) "
+                    )
+                    self.households_impact_df.loc[
+                        self.aff_regions, rebuilding_demand_idx
+                    ] = (households_impact * self.impact_regional_distrib)
+            self.households_impact_df *= (
+                self.event_monetary_factor / self.model_monetary_factor
+            )
+        logger.info(
+            f"Total impact on households is {self.households_impact_df.sum()} (in model unit)"
+        )
+
+    @property
+    def regional_sectoral_productive_capital_destroyed(self) -> npt.NDArray:
+        r"""The (optional) array of productive_capital destroyed per industry (ie region x sector)"""
+        return self._regional_sectoral_productive_capital_destroyed
+
+    @regional_sectoral_productive_capital_destroyed.setter
+    def regional_sectoral_productive_capital_destroyed(self, value: npt.ArrayLike):
+        value = np.array(value)
+        logger.debug(
+            f"Changing regional_sectoral_productive_capital_destroyed with {value}\n Sum is {value.sum()}"
+        )
+        if value.shape != self.x_shape:
+            raise ValueError(
+                "Incorrect shape give for regional_sectoral_productive_capital_destroyed: {} given and {} expected".format(
+                    value.shape, self.x_shape
+                )
+            )
+        self._regional_sectoral_productive_capital_destroyed = value

+
+
+
[docs]class EventKapitalRebuild(EventKapitalDestroyed):
+    def __init__(
+        self,
+        *,
+        impact: Impact,
+        rebuilding_sectors: Union[dict[str, float], pd.Series],
+        rebuild_tau=60,
+        households_impact: Optional[Impact] = None,
+        aff_regions: RegionsList = [],
+        aff_sectors: SectorsList = [],
+        aff_industries: IndustriesList = [],
+        impact_industries_distrib=None,
+        impact_regional_distrib=None,
+        impact_sectoral_distrib_type="equally shared",
+        impact_sectoral_distrib=None,
+        name="Unnamed",
+        occurrence=1,
+        duration=1,
+        rebuilding_factor: float = 1.0,
+        event_monetary_factor=None,
+    ) -> None:
+        super().__init__(
+            impact=impact,
+            households_impact=households_impact,
+            aff_regions=aff_regions,
+            aff_sectors=aff_sectors,
+            aff_industries=aff_industries,
+            impact_industries_distrib=impact_industries_distrib,
+            impact_regional_distrib=impact_regional_distrib,
+            impact_sectoral_distrib_type=impact_sectoral_distrib_type,
+            impact_sectoral_distrib=impact_sectoral_distrib,
+            name=name,
+            occurrence=occurrence,
+            duration=duration,
+            event_monetary_factor=event_monetary_factor,
+        )
+
+        self._rebuildable = False
+        self.rebuild_tau = rebuild_tau
+        self.rebuilding_sectors = rebuilding_sectors
+        self.rebuilding_factor = rebuilding_factor
+
+        industrial_rebuilding_demand = np.zeros(shape=self.z_shape)
+        tmp = np.zeros(self.z_shape, dtype="float")
+        mask = np.ix_(
+            np.union1d(
+                self._rebuilding_industries_RoW_idx, self._rebuilding_industries_idx
+            ),
+            self._aff_industries_idx,
+        )
+        industrial_rebuilding_demand = np.outer(
+            self.rebuilding_sectors_shares,
+            self.regional_sectoral_productive_capital_destroyed,
+        )
+        industrial_rebuilding_demand = industrial_rebuilding_demand * rebuilding_factor
+        tmp[mask] = self.Z_distrib[mask] * industrial_rebuilding_demand[mask]
+
+        precision = int(math.log10(self.model_monetary_factor)) + 1
+        np.around(tmp, precision, tmp)
+
+        self.rebuilding_demand_indus = tmp
+
+        households_rebuilding_demand = np.zeros(shape=self.y_shape)
+        tmp = np.zeros(self.y_shape, dtype="float")
+        mask = np.ix_(  # type: ignore
+            np.union1d(  # type: ignore
+                self._rebuilding_industries_RoW_idx, self._rebuilding_industries_idx
+            ),
+            list(range(self.y_shape[1])),
+        )
+
+        households_rebuilding_demand = np.outer(
+            self.rebuilding_sectors_shares, self.households_impact_df.to_numpy()
+        )
+        households_rebuilding_demand = households_rebuilding_demand * rebuilding_factor
+        tmp[mask] = self.Y_distrib[mask] * households_rebuilding_demand[mask]
+        np.around(tmp, precision, tmp)
+        self.rebuilding_demand_house = tmp
+        logger.debug(
+            f"house rebuilding demand is: {pd.DataFrame(self.rebuilding_demand_house, index=pd.MultiIndex.from_product([self.possible_regions,self.possible_sectors]))}"
+        )
+
+        self.event_dict["rebuilding_sectors"] = {
+            sec: share
+            for sec, share in zip(
+                self.rebuilding_sectors, self.rebuilding_sectors_shares
+            )
+        }
+
+    @property
+    def rebuilding_sectors(self) -> pd.Index:
+        r"""The (optional) array of rebuilding sectors"""
+        return self._rebuilding_sectors
+
+    @property
+    def rebuilding_sectors_idx(self) -> npt.NDArray:
+        r"""The (optional) array of indexes of the rebuilding sectors (in lexicographic order)"""
+        return self._rebuilding_sectors_idx
+
+    @property
+    def rebuilding_sectors_shares(self) -> npt.NDArray:
+        r"""The array specifying how rebuilding demand is distributed along the rebuilding sectors"""
+        return self._rebuilding_sectors_shares
+
+    @rebuilding_sectors.setter
+    def rebuilding_sectors(self, value: dict[str, float] | pd.Series):
+        if isinstance(value, dict):
+            reb_sectors = pd.Series(value)
+        else:
+            reb_sectors = value
+        assert np.isclose(reb_sectors.sum(), 1.0)
+        impossible_sectors = np.setdiff1d(reb_sectors.index, self.possible_sectors)
+        if impossible_sectors.size > 0:
+            raise ValueError(
+                "These sectors are not in the model : {}".format(impossible_sectors)
+            )
+        else:
+            self._rebuilding_sectors = reb_sectors.index
+            self._rebuilding_sectors_idx = np.searchsorted(
+                self.possible_sectors, reb_sectors.index
+            )
+
+            self._rebuilding_sectors_shares = np.zeros(self.x_shape)
+            self._rebuilding_industries_idx = np.array(
+                [
+                    len(self.possible_sectors) * ri + si
+                    for ri in self._aff_regions_idx
+                    for si in self._rebuilding_sectors_idx
+                ]
+            )
+            self._rebuilding_industries_RoW_idx = np.array(
+                [
+                    len(self.possible_sectors) * ri + si
+                    for ri in range(len(self.possible_regions))
+                    if ri not in self._aff_regions_idx
+                    for si in self._rebuilding_sectors_idx
+                ]
+            )
+            self._rebuilding_sectors_shares[self._rebuilding_industries_idx] = np.tile(
+                np.array(reb_sectors.values), len(self.aff_regions)
+            )
+            self._rebuilding_sectors_shares[
+                self._rebuilding_industries_RoW_idx
+            ] = np.tile(
+                np.array(reb_sectors.values),
+                (len(self.possible_regions) - len(self.aff_regions)),
+            )
+
+    @property
+    def rebuilding_demand_house(self) -> npt.NDArray:
+        r"""The optional array of rebuilding demand from households"""
+        return self._rebuilding_demand_house
+
+    @rebuilding_demand_house.setter
+    def rebuilding_demand_house(self, value: npt.ArrayLike):
+        value = np.array(value)
+        if value.shape != self.y_shape:
+            raise ValueError(
+                "Incorrect shape give for rebuilding_demand_house: {} given and {} expected".format(
+                    value.shape, self.y_shape
+                )
+            )
+        value[value < 10 / self.model_monetary_factor] = 0.0
+        self._rebuilding_demand_house = value
+
+    @property
+    def rebuilding_demand_indus(self) -> npt.NDArray:
+        r"""The optional array of rebuilding demand from industries (to rebuild their productive_capital)"""
+        return self._rebuilding_demand_indus
+
+    @rebuilding_demand_indus.setter
+    def rebuilding_demand_indus(self, value: npt.ArrayLike):
+        value = np.array(value)
+        if value.shape != self.z_shape:
+            raise ValueError(
+                "Incorrect shape give for rebuilding_demand_indus: {} given and {} expected".format(
+                    value.shape, self.z_shape
+                )
+            )
+        value[value < 10 / self.model_monetary_factor] = 0.0
+        self._rebuilding_demand_indus = value
+        # Also update productive_capital destroyed
+        self.regional_sectoral_productive_capital_destroyed = value.sum(axis=0) * (
+            1 / self.rebuilding_factor
+        )
+
+    @property
+    def rebuildable(self) -> bool:
+        r"""A boolean stating if an event can start rebuild"""
+        return self._rebuildable
+
+    @rebuildable.setter
+    def rebuildable(self, current_temporal_unit: int):
+        reb = (self.occurrence + self.duration) <= current_temporal_unit
+        if reb and not self.rebuildable:
+            logger.info(
+                "Temporal_Unit : {} ~ Event named {} that occured at {} in {} for {} damages has started rebuilding".format(
+                    current_temporal_unit,
+                    self.name,
+                    self.occurrence,
+                    self.aff_regions.to_list(),
+                    self.total_productive_capital_destroyed,
+                )
+            )
+        self._rebuildable = reb

+
+
+
[docs]class EventKapitalRecover(EventKapitalDestroyed):
+    def __init__(
+        self,
+        *,
+        impact: Impact,
+        recovery_time: int,
+        recovery_function: str = "linear",
+        households_impact: Impact | None = None,
+        aff_regions: RegionsList = [],
+        aff_sectors: SectorsList = [],
+        aff_industries: IndustriesList = [],
+        impact_industries_distrib=None,
+        impact_regional_distrib=None,
+        impact_sectoral_distrib_type="equally shared",
+        impact_sectoral_distrib=None,
+        name="Unnamed",
+        occurrence=1,
+        duration=1,
+        event_monetary_factor=None,
+    ) -> None:
+        super().__init__(
+            impact=impact,
+            households_impact=households_impact,
+            aff_regions=aff_regions,
+            aff_sectors=aff_sectors,
+            aff_industries=aff_industries,
+            impact_industries_distrib=impact_industries_distrib,
+            impact_regional_distrib=impact_regional_distrib,
+            impact_sectoral_distrib_type=impact_sectoral_distrib_type,
+            impact_sectoral_distrib=impact_sectoral_distrib,
+            name=name,
+            occurrence=occurrence,
+            duration=duration,
+            event_monetary_factor=event_monetary_factor,
+        )
+        self.recovery_time = recovery_time
+        self.recovery_function = recovery_function
+        self._recoverable = False
+
+    @property
+    def recoverable(self) -> bool:
+        r"""A boolean stating if an event can start recover"""
+        return self._recoverable
+
+    @recoverable.setter
+    def recoverable(self, current_temporal_unit: int):
+        reb = (self.occurrence + self.duration) <= current_temporal_unit
+        if reb and not self.recoverable:
+            logger.info(
+                "Temporal_Unit : {} ~ Event named {} that occured at {} in {} for {} damages has started recovering (no rebuilding demand)".format(
+                    current_temporal_unit,
+                    self.name,
+                    self.occurrence,
+                    self.aff_regions.to_list(),
+                    self.total_productive_capital_destroyed,
+                )
+            )
+        self._recoverable = reb
+
+    @property
+    def recovery_function(self) -> Callable:
+        r"""The recovery function used for recovery (`Callable`)"""
+        return self._recovery_fun
+
+    @recovery_function.setter
+    def recovery_function(self, r_fun: str | Callable | None):
+        if r_fun is None:
+            r_fun = "linear"
+        if self.recovery_time is None:
+            raise AttributeError(
+                "Impossible to set recovery function if no recovery time is given."
+            )
+        if isinstance(r_fun, str):
+            if r_fun == "linear":
+                fun = linear_recovery
+            elif r_fun == "convexe":
+                fun = convexe_recovery_scaled
+            elif r_fun == "convexe noscale":
+                fun = convexe_recovery
+            elif r_fun == "concave":
+                fun = concave_recovery
+            else:
+                raise NotImplementedError(
+                    "No implemented recovery function corresponding to {}".format(r_fun)
+                )
+        elif callable(r_fun):
+            r_fun_argsspec = inspect.getfullargspec(r_fun)
+            r_fun_args = r_fun_argsspec.args + r_fun_argsspec.kwonlyargs
+            if not all(
+                args in r_fun_args
+                for args in [
+                    "init_impact_stock",
+                    "elapsed_temporal_unit",
+                    "recovery_time",
+                ]
+            ):
+                raise ValueError(
+                    "Recovery function has to have at least the following keyword arguments: {}".format(
+                        [
+                            "init_impact_stock",
+                            "elapsed_temporal_unit",
+                            "recovery_time",
+                        ]
+                    )
+                )
+            fun = r_fun
+
+        else:
+            raise ValueError("Given recovery function is not a str or callable")
+
+        r_fun_partial = partial(
+            fun,
+            init_impact_stock=self._regional_sectoral_productive_capital_destroyed_0,
+            recovery_time=self.recovery_time,
+        )
+        self._recovery_fun = r_fun_partial
+
+    def recovery(self, current_temporal_unit: int):
+        elapsed = current_temporal_unit - (self.occurrence + self.duration)
+        if elapsed < 0:
+            raise RuntimeError("Trying to recover before event is over")
+        if self.recovery_function is None:
+            raise RuntimeError(
+                "Trying to recover event while recovery function isn't set yet"
+            )
+        res = self.recovery_function(elapsed_temporal_unit=elapsed)
+        precision = int(math.log10(self.model_monetary_factor)) + 1
+        res = np.around(res, precision)
+        res[res < 0] = 0.0
+        if not np.any(res):
+            self.over = True
+        self.regional_sectoral_productive_capital_destroyed = res

+
+ +
+ + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+
+
+ + + + + +
+
+ +
+ +
+

+ + © Copyright 2022, Samuel Juhel. +
+ +

+
+ +
+

+ Created using Sphinx 6.1.3. +
+

+
+ +
+ + +
+ +

+ Built with the PyData Sphinx Theme 0.13.0. +

+ +
+ +
+ +
+ + +||||||| 66c6efed +======= @@ -1590,4 +3291,5 @@

Source code for boario.event

 
   
   
-
\ No newline at end of file
+
+>>>>>>> master
diff --git a/docs/build/html/_modules/boario/extended_models.html b/docs/build/html/_modules/boario/extended_models.html
index a1829e3..aa6dd53 100644
--- a/docs/build/html/_modules/boario/extended_models.html
+++ b/docs/build/html/_modules/boario/extended_models.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -18,12 +17,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -34,14 +33,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -157,7 +157,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -182,7 +182,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -269,7 +269,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -283,7 +283,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -372,8 +372,9 @@ 
    Source code for boario.extended_models
     # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 from __future__ import annotations
-from typing import Dict
+from typing import Dict, Optional
 import numpy as np
+import pandas as pd
 from boario import logger
 from boario.model_base import *
 from boario.event import *
@@ -394,11 +395,18 @@ 
    Source code for boario.extended_models
             rebuild_tau=60,
         main_inv_dur=90,
         monetary_factor=10**6,
-        psi_param=0.90,
+        temporal_units_by_step: int = 1,
+        iotable_year_to_temporal_unit_factor: int = 365,
+        infinite_inventories_sect: Optional[list] = None,
+        inventory_dict: Optional[dict] = None,
+        productive_capital_vector: Optional[
+            pd.Series | np.ndarray | pd.DataFrame
+        ] = None,
+        productive_capital_to_VA_dict: Optional[dict] = None,
+        psi_param=0.80,
         inventory_restoration_tau: int | Dict[str, int] = 60,
-        **kwargs,
     ) -> None:
-        """An ARIO3 model with some additional features
+        """An ARIO3 model with some additional features
 
         Added feature are parameter psi of production adjustment inventories constraint threshold, as well as a characteristic time of inventories resupplying.
         """
@@ -412,21 +420,30 @@ 
    Source code for boario.extended_models
                 rebuild_tau=rebuild_tau,
             main_inv_dur=main_inv_dur,
             monetary_factor=monetary_factor,
-            **kwargs,
+            temporal_units_by_step=temporal_units_by_step,
+            iotable_year_to_temporal_unit_factor=iotable_year_to_temporal_unit_factor,
+            infinite_inventories_sect=infinite_inventories_sect,
+            inventory_dict=inventory_dict,
+            productive_capital_vector=productive_capital_vector,
+            productive_capital_to_VA_dict=productive_capital_to_VA_dict,
         )
 
         logger.debug("Model is an ARIOPsiModel")
 
         if isinstance(psi_param, str):
             self.psi = float(psi_param.replace("_", "."))
-            """float: Value of the psi parameter. (see :ref:`boario-math`)."""
+            """float: Value of the psi parameter. (see :ref:`boario-math`)."""
 
         elif isinstance(psi_param, float):
             self.psi = psi_param
+        elif isinstance(psi_param, int):
+            self.psi = float(psi_param)
         else:
             raise ValueError(
                 "'psi_param' parameter is neither a str rep of a float or a float"
             )
+        if self.psi > 1.0:
+            raise ValueError("'psi_param' parameter must be less or equal than 1.")
 
         if isinstance(inventory_restoration_tau, int):
             restoration_tau = [
@@ -442,11 +459,19 @@ 
    Source code for boario.extended_models
                     )
 
             for _, value in inventory_restoration_tau.items():
-                if not isinstance(value, int):
+                if isinstance(value, float):
+                    if not value.is_integer():
+                        raise ValueError(
+                            f"Invalid value: {value} ({type(value)}) in inventory_restoration_tau, values should be integer: {inventory_restoration_tau}"
+                        )
+                elif not isinstance(value, int):
                     raise ValueError(
-                        "Invalid value in inventory_restoration_tau, values should be integer."
+                        f"Invalid value: {value} ({type(value)}) in inventory_restoration_tau, values should be integer: {inventory_restoration_tau}"
                     )
 
+            inventory_restoration_tau = {
+                key: int(val) for key, val in inventory_restoration_tau.items()
+            }
             inventory_restoration_tau = dict(sorted(inventory_restoration_tau.items()))
             restoration_tau = [
                 (self.n_temporal_units_by_step / v) if v >= INV_THRESHOLD else v
@@ -457,7 +482,7 @@ 
    Source code for boario.extended_models
                     f"Invalid inventory_restoration_tau: expected dict or int got {type(inventory_restoration_tau)}"
             )
         self.restoration_tau = np.array(restoration_tau)
-        """numpy.ndarray of int: Array of size :math:`n` setting for each inputs its characteristic restoration time :math:`\tau_{\textrm{INV}}` in ``n_temporal_units_by_step``. (see :ref:`boario-math`)."""
+        r"""numpy.ndarray of int: Array of size :math:`n` setting for each inputs its characteristic restoration time :math:`\tau_{\textrm{INV}}` in ``n_temporal_units_by_step``. (see :ref:`boario-math`)."""
         #################################################################
 
     @property
@@ -471,7 +496,7 @@ 
    Source code for boario.extended_models
         # This is just so that theres a docstring for this one, it actually does the same as its parent method,
     # the difference is in the inventory constraints computation.
 
    [docs]    def calc_production(self, current_temporal_unit: int) -> np.ndarray:
-        r"""Computes and updates actual production. The difference with :class:`ARIOBaseModel` is in the way
+        r"""Computes and updates actual production. The difference with :class:`ARIOBaseModel` is in the way
         inventory constraints are computed. See :ref:`boario-math-prod`.
 
         1. Computes ``production_opt`` and ``inventory_constraints`` as :
@@ -527,7 +552,7 @@ 
    Source code for boario.extended_models
             return super().calc_production(current_temporal_unit)
    
 
 
    [docs]    def calc_inventory_constraints(self, production: np.ndarray) -> np.ndarray:
-        r"""Compute inventory constraints (with psi parameter, for the non psi version,
+        r"""Compute inventory constraints (with psi parameter, for the non psi version,
         see :meth:`~boario.model_base.ARIOBaseModel.calc_inventory_constraints`)
 
         Parameters
@@ -566,9 +591,15 @@ 
    Source code for boario.extended_models
                   
               
                 
    
-                  
+                  
+
    
+  
+    
    
 
    
+
    
+  
 
    
+
                 
    
               
             
    
@@ -578,7 +609,7 @@ 
    Source code for boario.extended_models
                 
           
    
           
    
-            
    
+            
           
    
         
       
@@ -586,8 +617,8 @@ 
    Source code for boario.extended_models
       
    
   
   
-  
-
+  
+
 
   
    
 
    
@@ -605,7 +636,7 @@ 
    Source code for boario.extended_models
           
         
    
   
    
-    Created using Sphinx 6.1.3.
+    Created using Sphinx 7.0.1.
     
    
   
    
 
    
@@ -616,7 +647,7 @@ 
    Source code for boario.extended_models
         
    
       
         
    
-  Built with the PyData Sphinx Theme 0.13.0.
+  Built with the PyData Sphinx Theme 0.13.3.
 
    
       
     
    
diff --git a/docs/build/html/_modules/boario/indicators.html b/docs/build/html/_modules/boario/indicators.html
index 61c143f..d74ba3e 100644
--- a/docs/build/html/_modules/boario/indicators.html
+++ b/docs/build/html/_modules/boario/indicators.html
@@ -1,3 +1,1180 @@
+<<<<<<< HEAD
+
+
+
+
+
+
+
+  
+    
+    
+    boario.indicators — BoARIO  documentation
+  
+  
+  
+  
+  
+  
+  
+
+
+
+  
+  
+  
+
+
+
+    
+    
+    
+    
+  
+  
+  
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+    
+    
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+    
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+  
+  
+  
+  
+  
+  
    
+    
    
+    
    
+
    
+  
+  
+  Ctrl+K
+
    
+  
    
+  
+    
+  
+  
    
+    
    
+      
+      
    
+        
+
+  
+  
    
+    
+    
+      
    
+        
+          
    
+        
+      
    
+    
+    
+    
+      
    
+        
+          
    
+  Last updated on Apr 24, 2023.
+  
    
+
    
+        
+          
    
+
    
+        
+      
    
+    
+  
    
+  
+  
+  
    
+  
    
+  
+  
+
+
+      
    
+      
+      
    
+        
+        
+          
    
+            
    
+              
+              
    
+
    
+  
+    
    
+      
+        
    
+
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+      
+    
    
+  
+  
+
    
+
    
+              
+              
+              
+                
+
+                
    
+                  
+  
    Source code for boario.indicators
    +# BoARIO : The Adaptative Regional Input Output model in python.
+# Copyright (C) 2022  Samuel Juhel
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+from __future__ import annotations
+
+from typing import Optional, Union
+
+from boario.extended_models import ARIOPsiModel
+from boario.simulation import Simulation
+import numpyencoder
+import json
+import pathlib
+import numpy as np
+import pandas as pd
+import itertools
+from boario.utils import misc
+import dask.dataframe as da
+from boario import logger
+from boario.event import *
+
+__all__ = ["Indicators"]
+
+
+def create_df(data, regions, sectors):
+    df = pd.DataFrame(
+        data,
+        columns=pd.MultiIndex.from_product(
+            [regions, sectors], names=["region", "sector"]
+        ),
+    )
+    df = df.interpolate()
+    df = df.rename_axis("step")
+    return df
+
+
+def df_from_memmap(memmap: np.memmap, indexes: dict) -> pd.DataFrame:
+    a = pd.DataFrame(
+        memmap,
+        columns=pd.MultiIndex.from_product([indexes["regions"], indexes["sectors"]]),
+    )
+    a["step"] = a.index
+    return a
+
+
+def stock_df_from_memmap(
+    memmap: np.memmap, indexes: dict, timesteps: int, timesteps_simulated: int
+) -> pd.DataFrame:
+    a = pd.DataFrame(
+        memmap.reshape(timesteps * indexes["n_sectors"], -1),
+        index=pd.MultiIndex.from_product(
+            [timesteps, indexes["sectors"]], names=["step", "stock of"]
+        ),
+        columns=pd.MultiIndex.from_product([indexes["regions"], indexes["sectors"]]),
+    )
+    a = a.loc[pd.IndexSlice[:timesteps_simulated, :]]
+    return a
+
+
+
    [docs]class Indicators(object):
+    record_files_list = [
+        "final_demand",
+        "intermediate_demand",
+        "final_demand_unmet",
+        "production_capacity",
+        "production_realised",
+        "limiting_inputs",
+        "overproduction",
+        "rebuild_demand",
+        "rebuild_prod",
+        "productive_capital_to_recover",
+    ]
+
+    params_list = ["simulated_params", "simulated_events"]
+
+    def __init__(
+        self,
+        *,
+        has_crashed,
+        params,
+        regions,
+        sectors,
+        productive_capital,
+        prod,
+        prodmax,
+        overprod,
+        final_demand,
+        io_demand,
+        r_demand,
+        r_prod,
+        fd_unmet,
+        limiting_stocks,
+        n_temporal_units_to_sim: int,
+        n_temporal_units_by_step: int,
+        events: list[Event],
+        results_storage: Optional[str | pathlib.Path] = None,
+        psi_param: Optional[float] = None,
+        inventory_restoration_tau: Optional[int | list[int]] = None,
+        stocks=None,
+        stocks_treatment=False,
+        include_crash: bool = False,
+    ) -> None:
+        logger.info("Instantiating indicators")
+        if not include_crash:
+            if has_crashed:
+                raise RuntimeError(
+                    "Simulation crashed and include_crash is False, I won't compute indicators"
+                )
+        steps = [i for i in range(n_temporal_units_to_sim)]
+
+        self.params = params
+        self.n_rows = n_temporal_units_to_sim
+        self.n_temporal_units_by_step = n_temporal_units_by_step
+
+        self.productive_capital_to_recover_df = create_df(
+            productive_capital, regions, sectors
+        )
+        self.production_realised_df = create_df(prod, regions, sectors)
+        self.production_capacity_df = create_df(prodmax, regions, sectors)
+        self.overproduction_df = create_df(overprod, regions, sectors)
+        self.final_demand_df = create_df(final_demand, regions, sectors)
+        self.intermediate_demand_df = create_df(io_demand, regions, sectors)
+        self.rebuild_demand_df = create_df(r_demand, regions, sectors)
+        self.rebuild_prod_df = create_df(r_prod, regions, sectors)
+        self.final_demand_unmet_df = create_df(fd_unmet, regions, sectors)
+
+        if stocks_treatment:
+            stocks_df = pd.DataFrame(
+                stocks.reshape(n_temporal_units_to_sim * len(sectors), -1),
+                index=pd.MultiIndex.from_product(
+                    [steps, sectors], names=["step", "stock of"]
+                ),
+                columns=pd.MultiIndex.from_product(
+                    [regions, sectors],
+                    names=["region", "sector"],
+                ),
+            )
+            stocks_df = stocks_df.replace([np.inf, -np.inf], np.nan).dropna(how="all")
+            stocks_df = stocks_df.astype(np.float32)
+            stocks_df = (
+                stocks_df.groupby("stock of")
+                .pct_change()
+                .fillna(0)
+                .add(1)
+                .groupby("stock of")
+                .cumprod()
+                .sub(1)
+            )  # type: ignore
+            stocks_df = stocks_df.melt(ignore_index=False).rename(
+                columns={
+                    "variable_0": "region",
+                    "variable_1": "sector",
+                    "variable_2": "stock of",
+                }
+            )
+            stocks_df = stocks_df.reset_index()
+            stocks_df["step"] = stocks_df["step"].astype("uint16")
+            stocks_df["stock of"] = stocks_df["stock of"].astype("category")
+            stocks_df["region"] = stocks_df["region"].astype("category")
+            stocks_df["sector"] = stocks_df["sector"].astype("category")
+
+        else:
+            stocks_df = None
+
+        self.inputs_stocks_df = stocks_df
+        del stocks_df
+        # self.df_stocks = self.df_stocks.interpolate()
+
+        self.treated_loss_df = (
+            self.final_demand_unmet_df.melt(ignore_index=False)
+            .rename(
+                columns={
+                    "variable_0": "region",
+                    "variable_1": "fd_cat",
+                    "value": "fdloss",
+                }
+            )
+            .reset_index()
+        )
+
+        self.limiting_inputs_df = limiting_stocks
+        # pd.DataFrame(
+        #     limiting_stocks.reshape(n_temporal_units_to_sim * len(sectors), -1),
+        #     index=pd.MultiIndex.from_product(
+        #         [steps, sectors], names=["step", "stock of"]
+        #     ),
+        #     columns=pd.MultiIndex.from_product(
+        #         [regions, sectors], names=["region", "sector"]
+        #     ),
+        # )
+        self.aff_regions = []
+        self.aff_sectors = []
+        for e in events:
+            if isinstance(e, dict):
+                self.aff_sectors.append(e["aff_sectors"])
+                self.aff_regions.append(e["aff_regions"])
+            elif isinstance(e, Event):
+                self.aff_sectors.append(e.aff_sectors)
+                self.aff_regions.append(e.aff_regions)
+            else:
+                raise ValueError(f"Unrecognised event of type {type(e)}")
+
+        self.aff_regions = list(misc.flatten(self.aff_regions))
+        self.aff_sectors = list(misc.flatten(self.aff_sectors))
+
+        self.rebuilding_sectors = []
+
+        # This is working but wrong ! I need to fix it
+        for e in events:
+            if isinstance(e, dict):
+                self.rebuilding_sectors.append(e.get("rebuilding_sectors"))
+            if isinstance(e, EventKapitalRebuild):
+                self.rebuilding_sectors.append(list(e.rebuilding_sectors))
+            else:
+                self.rebuilding_sectors = []
+
+        self.rebuilding_sectors = list(misc.flatten(self.rebuilding_sectors))
+        # This is also wrong but will settle for now
+        gdp_dmg_share = -1.0
+        self.indicators = {
+            "region": self.aff_regions,
+            "gdp_dmg_share": gdp_dmg_share,
+            "tot_fd_unmet": "unset",
+            "aff_fd_unmet": "unset",
+            "rebuild_durations": "unset",
+            "shortage_b": False,
+            "shortage_date_start": "unset",
+            "shortage_date_end": "unset",
+            "shortage_date_max": "unset",
+            "shortage_ind_max": "unset",
+            "shortage_ind_mean": "unset",
+            "10_first_shortages_(step,region,sector,stock_of)": "unset",
+            "prod_gain_tot": "unset",
+            "prod_lost_tot": "unset",
+            "prod_gain_unaff": "unset",
+            "prod_lost_unaff": "unset",
+            "psi": psi_param,
+            "inv_tau": inventory_restoration_tau,
+            "n_temporal_units_to_sim": n_temporal_units_to_sim,
+            "has_crashed": has_crashed,
+        }
+        if results_storage is not None:
+            self.storage_path = (pathlib.Path(results_storage)).resolve() / "indicators"
+            self.parquets_path = (pathlib.Path(results_storage)).resolve() / "parquets"
+            self.storage = self.storage_path / "indicators.json"
+            self.parquets_path.mkdir(parents=True, exist_ok=True)
+            self.storage_path.mkdir(parents=True, exist_ok=True)
+            self.save_dfs()
+
+    @classmethod
+    def from_simulation(
+        cls,
+        sim: Simulation,
+        results_storage: Optional[str | pathlib.Path] = None,
+        stocks_treatment: bool = False,
+        include_crash: bool = False,
+    ) -> Indicators:
+        if isinstance(sim.model, ARIOPsiModel):
+            psi = sim.model.psi
+            inventory_restoration_tau = list(sim.model.restoration_tau)
+        else:
+            psi = None
+            inventory_restoration_tau = None
+
+        prod = getattr(sim, "production_realised")
+        productive_capital = getattr(sim, "productive_capital_to_recover")
+        prodmax = getattr(sim, "production_capacity")
+        overprod = getattr(sim, "overproduction")
+        final_demand = getattr(sim, "final_demand")
+        io_demand = getattr(sim, "intermediate_demand")
+        r_demand = getattr(sim, "rebuild_demand")
+        r_prod = getattr(sim, "rebuild_prod")
+        fd_unmet = getattr(sim, "final_demand_unmet")
+        if stocks_treatment:
+            stocks = getattr(sim, "inputs_stocks")
+        else:
+            stocks = None
+
+        limiting_stocks = getattr(sim, "limiting_inputs")
+        return cls(
+            has_crashed=sim.has_crashed,
+            params=sim.params_dict,
+            regions=sim.model.regions,
+            sectors=sim.model.sectors,
+            productive_capital=productive_capital,
+            prod=prod,
+            prodmax=prodmax,
+            overprod=overprod,
+            final_demand=final_demand,
+            io_demand=io_demand,
+            r_demand=r_demand,
+            r_prod=r_prod,
+            fd_unmet=fd_unmet,
+            limiting_stocks=limiting_stocks,
+            n_temporal_units_to_sim=sim.n_temporal_units_to_sim,
+            n_temporal_units_by_step=sim.model.n_temporal_units_by_step,
+            events=sim.all_events,
+            results_storage=results_storage,
+            psi_param=psi,
+            inventory_restoration_tau=inventory_restoration_tau,
+            stocks=stocks,
+            stocks_treatment=stocks_treatment,
+            include_crash=include_crash,
+        )
+
+    @classmethod
+    def from_folder(
+        cls,
+        folder: Union[str, pathlib.Path],
+        include_crash: bool = False,
+    ) -> Indicators:
+        if not isinstance(folder, pathlib.Path):
+            folder = pathlib.Path(folder)
+            if not folder.exists():
+                raise FileNotFoundError(str("Directory does not exist:" + str(folder)))
+
+        with (folder / "jsons" / "indexes.json").open() as f:
+            indexes = json.load(f)
+
+        params_folder = folder / "jsons"
+        records_folder = folder / "records"
+        params_file = {f.stem: f for f in params_folder.glob("*.json")}
+        absentee = [f for f in cls.params_list if f not in params_file.keys()]
+        if absentee:
+            raise FileNotFoundError(
+                "Some of the required parameters files not found (looked for {} in {}".format(
+                    cls.params_list, folder
+                )
+            )
+
+        record_files = [f for f in records_folder.glob("*") if f.is_file()]
+        absentee = [
+            f
+            for f in cls.record_files_list
+            if f not in [fn.name for fn in record_files]
+        ]
+        if absentee:
+            raise FileNotFoundError(
+                "Some of the required records are not there : {}".format(absentee)
+            )
+
+        with params_file["simulated_params"].open("r") as f:
+            params = json.load(f)
+
+        with params_file["simulated_events"].open("r") as f:
+            events = json.load(f)
+
+        if "has_crashed" in params:
+            has_crashed = params["has_crashed"]
+        else:
+            has_crashed = False
+
+        results_storage = folder.absolute()
+        records_path = records_folder.absolute()
+        n_temporal_units_to_sim = params["n_temporal_units_to_sim"]
+        t = n_temporal_units_to_sim
+        n_temporal_units_by_step = params["n_temporal_units_by_step"]
+        regions = indexes["regions"]
+        sectors = indexes["sectors"]
+        psi = params.get("psi_param")
+        inventory_restoration_tau = params.get("inventory_restoration_tau")
+        prod = np.memmap(
+            records_path / "production_realised",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        productive_capital = np.memmap(
+            records_path / "productive_capital_to_recover",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        prodmax = np.memmap(
+            records_path / "production_capacity",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        overprod = np.memmap(
+            records_path / "overproduction",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        final_demand = np.memmap(
+            records_path / "final_demand",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        io_demand = np.memmap(
+            records_path / "intermediate_demand",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        r_demand = np.memmap(
+            records_path / "rebuild_demand",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        r_prod = np.memmap(
+            records_path / "rebuild_prod",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_industries"]),
+        )
+        fd_unmet = np.memmap(
+            records_path / "final_demand_unmet",
+            mode="r+",
+            dtype="float64",
+            shape=(t, indexes["n_regions"] * indexes["n_sectors"]),
+        )
+        limiting_stocks = np.memmap(
+            records_path / "limiting_inputs",
+            mode="r+",
+            dtype="byte",
+            shape=(t * indexes["n_sectors"], indexes["n_industries"]),
+        )
+        if params.get("register_stocks", False):
+            if not (records_path / "inputs_stocks").exists():
+                raise FileNotFoundError(
+                    "Stocks record file was not found {}".format(
+                        records_path / "stocks_record"
+                    )
+                )
+            stocks = np.memmap(
+                records_path / "stocks_record",
+                mode="r+",
+                dtype="float64",
+                shape=(t * indexes["n_sectors"], indexes["n_industries"]),
+            )
+            stocks_treatment = True
+        else:
+            stocks = None
+            stocks_treatment = False
+
+        return cls(
+            has_crashed=has_crashed,
+            params=params,
+            regions=regions,
+            sectors=sectors,
+            productive_capital=productive_capital,
+            prod=prod,
+            prodmax=prodmax,
+            overprod=overprod,
+            final_demand=final_demand,
+            io_demand=io_demand,
+            r_demand=r_demand,
+            r_prod=r_prod,
+            fd_unmet=fd_unmet,
+            limiting_stocks=limiting_stocks,
+            n_temporal_units_to_sim=n_temporal_units_to_sim,
+            n_temporal_units_by_step=n_temporal_units_by_step,
+            events=events,
+            results_storage=results_storage,
+            psi_param=psi,
+            inventory_restoration_tau=inventory_restoration_tau,
+            stocks=stocks,
+            stocks_treatment=stocks_treatment,
+            include_crash=include_crash,
+        )
+
+    def calc_top_failing_sect(self):
+        pass
+
+    def calc_tot_fd_unmet(self):
+        self.indicators["tot_fd_unmet"] = self.treated_loss_df["fdloss"].sum()
+
+    def calc_aff_fd_unmet(self):
+        # TODO: check this
+        self.indicators["aff_fd_unmet"] = self.treated_loss_df[
+            self.treated_loss_df.region.isin(self.aff_regions)
+        ]["fdloss"].sum()
+
+    def calc_rebuild_durations(self):
+        rebuilding = (
+            self.rebuild_demand_df.reset_index()
+            .melt(
+                id_vars="step",
+                var_name=["region", "sector"],
+                value_name="rebuild_demand",
+            )
+            .groupby("step")
+            .sum(numeric_only=True)
+            .ne(0)
+            .rebuild_demand.to_numpy()
+        )
+        self.indicators["rebuild_durations"] = [
+            sum(1 for _ in group) for key, group in itertools.groupby(rebuilding) if key
+        ]
+
+    def calc_recovery_duration(self):
+        pass
+
+    def calc_general_shortage(self):
+        # TODO: replace hard values by soft.
+        n_regions = self.limiting_inputs_df.columns.levels[0].size
+        n_sectors = self.limiting_inputs_df.columns.levels[1].size
+        # have only steps in index (next step not possible with multiindex)
+        a = self.limiting_inputs_df.unstack()
+        # select only simulated steps (we can't store nan in bool or byte dtype array)
+        a = a.iloc[lambda x: x.index % self.n_temporal_units_by_step == 0]
+        # We put -1 initially, so this should check we have correctly selected the simulated rows
+        assert (a >= 0).all().all()
+        # put input stocks as columns and the rest as index
+        a = a.stack().T.stack(level=0)
+        # sum for all input and divide by n_sector to get "input shortage fraction"
+        # by industry
+        b = a.sum(axis=1).groupby(["step", "region", "sector"]).sum() / n_sectors
+        # by sector
+        c = b.groupby(["step", "region"]).sum() / n_sectors
+        # by region
+        c = c.groupby("step").sum() / n_regions
+        if not c.ne(0).any():
+            self.indicators["shortage_b"] = False
+        else:
+            self.indicators["shortage_b"] = True
+            shortage_date_start = c.ne(0.0).idxmax()
+            self.indicators["shortage_date_start"] = shortage_date_start
+            shortage_date_end = c.loc[shortage_date_start:].eq(0).idxmax()
+            self.indicators["shortage_date_end"] = shortage_date_end
+            self.indicators["shortage_date_max"] = c.idxmax()
+            self.indicators["shortage_ind_max"] = c.max()
+            self.indicators["shortage_ind_mean"] = c.loc[
+                shortage_date_start:shortage_date_end
+            ].mean()
+
+    def calc_first_shortages(self):
+        a = self.limiting_inputs_df.stack([0, 1])  # type: ignore
+        a = a.swaplevel(1, 2).swaplevel(2, 3)
+        b = a[a > 0]
+        b = b[:10]
+        res = list(b.index)  # type:ignore
+        self.indicators["10_first_shortages_(step,region,sector,stock_of)"] = res
+
+    def calc_tot_prod_change(self):
+        df2 = self.production_realised_df.copy()
+        # df2.columns=df2.columns.droplevel(0)
+        prod_chg = df2 - df2.iloc[0, :]
+        # Round to € to avoid floating error differences
+        prod_chg = prod_chg.round(6)
+        # Aggregate rebuilding and non-rebuilding sectors
+        prod_chg_agg_1 = pd.concat(
+            [
+                prod_chg.loc[:, pd.IndexSlice[:, self.rebuilding_sectors]]
+                .groupby("region", axis=1)
+                .sum(),
+                prod_chg.loc[
+                    prod_chg.index.difference(
+                        prod_chg.loc[
+                            :, pd.IndexSlice[:, self.rebuilding_sectors]
+                        ].columns
+                    )
+                ]
+                .groupby("region", axis=1)
+                .sum(),
+            ],
+            keys=["rebuilding", "non-rebuilding"],
+            names=["sectors affected"],
+            axis=1,
+        )
+
+        n_semesters = self.n_rows // (self.params["year_to_temporal_unit_factor"] // 2)
+        row_to_semester = self.params["year_to_temporal_unit_factor"] // 2
+        modulo = self.params["year_to_temporal_unit_factor"] % 2
+        logger.info(
+            "There are {} semesters [{} rows, each representing a {}th of a year]".format(
+                n_semesters, self.n_rows, self.params["year_to_temporal_unit_factor"]
+            )
+        )
+        prod_chg_sect_sem_l = []
+        for sem in range(0, n_semesters):
+            prod_chg_sect_sem_l.append(
+                prod_chg_agg_1.iloc[
+                    sem * row_to_semester : ((sem + 1) * row_to_semester)
+                    + (sem % 2) * modulo
+                ]
+                .sum()
+                .T
+            )
+
+        prod_chg_region = pd.concat(
+            prod_chg_sect_sem_l,
+            keys=["semester {}".format(v + 1) for v in range(n_semesters)],
+            names=["semester"],
+        )
+        aff_regions = "~".join(self.aff_regions)
+        self.prod_chg_region = pd.DataFrame({aff_regions: prod_chg_region}).T
+        prod_chg_sect = prod_chg.sum()
+        tmp = prod_chg_sect.sort_values(ascending=False, key=abs).head(5).to_dict()
+        self.indicators["top_5_sector_chg"] = {str(k): v for k, v in tmp.items()}
+
+        self.indicators["prod_gain_tot"] = prod_chg.mul(prod_chg.gt(0)).sum().sum()
+        self.indicators["prod_lost_tot"] = prod_chg.mul(~prod_chg.gt(0)).sum().sum() * (
+            -1
+        )
+        prod_chg = prod_chg.drop(self.aff_regions, axis=1)
+        self.indicators["prod_gain_unaff"] = prod_chg.mul(prod_chg.gt(0)).sum().sum()
+        self.indicators["prod_lost_unaff"] = prod_chg.mul(
+            ~prod_chg.gt(0)
+        ).sum().sum() * (-1)
+
+    def update_indicators(self):
+        logger.info("(Re)computing all indicators")
+        logger.info("Tot fd unmet")
+        self.calc_tot_fd_unmet()
+        logger.info("aff fd unmet")
+        self.calc_aff_fd_unmet()
+        logger.info("rebuild durations")
+        self.calc_rebuild_durations()
+        logger.info("recovery duration")
+        self.calc_recovery_duration()
+        logger.info("general shortage")
+        self.calc_general_shortage()
+        logger.info("tot prod change")
+        self.calc_tot_prod_change()
+        logger.info("fd loss region")
+        self.calc_fd_loss_region()
+        logger.info("first shortages")
+        self.calc_first_shortages()
+
+    def write_indicators(self, storage_path=None):
+        if storage_path is None and not hasattr(self, "storage_path"):
+            raise ValueError(
+                f"You are attempting to save indicators but no storage path was specified either in the Indicators class or to this method."
+            )
+        storage_path = self.storage_path if storage_path is None else storage_path
+        storage_path = pathlib.Path(storage_path).resolve()
+        logger.info("Writing indicators to json")
+        if hasattr(self, "prod_chg_region"):
+            self.prod_chg_region.to_json(
+                storage_path / "prod_chg.json", indent=4, orient="split"
+            )
+        if hasattr(self, "fd_loss_region"):
+            self.fd_loss_region.to_json(
+                storage_path / "fd_loss.json", indent=4, orient="split"
+            )
+        with (storage_path / "indicators.json").open("w") as f:
+            json.dump(self.indicators, f, cls=numpyencoder.NumpyEncoder, indent=4)
+
+    def calc_fd_loss_region(self):
+        fd_loss = self.final_demand_unmet_df.copy().round(6)
+        fd_loss_agg_1 = pd.concat(
+            [
+                fd_loss.loc[:, pd.IndexSlice[:, self.rebuilding_sectors]]
+                .groupby("region", axis=1)
+                .sum(),
+                fd_loss.loc[
+                    fd_loss.index.difference(
+                        fd_loss.loc[
+                            :, pd.IndexSlice[:, self.rebuilding_sectors]
+                        ].columns
+                    )
+                ]
+                .groupby("region", axis=1)
+                .sum(),
+            ],
+            keys=["rebuilding", "non-rebuilding"],
+            names=["sectors affected"],
+            axis=1,
+        )
+
+        n_semesters = self.n_rows // (self.params["year_to_temporal_unit_factor"] // 2)
+        row_to_semester = self.params["year_to_temporal_unit_factor"] // 2
+        modulo = self.params["year_to_temporal_unit_factor"] % 2
+        logger.info(
+            "There are {} semesters [{} rows, each representing a {}th of a year]".format(
+                n_semesters, self.n_rows, self.params["year_to_temporal_unit_factor"]
+            )
+        )
+        fd_loss_sect_sem_l = []
+        for sem in range(0, n_semesters):
+            fd_loss_sect_sem_l.append(
+                fd_loss_agg_1.iloc[
+                    sem * row_to_semester : ((sem + 1) * row_to_semester)
+                    + (sem % 2) * modulo
+                ]
+                .sum()
+                .T
+            )
+
+        fd_loss_region = pd.concat(
+            fd_loss_sect_sem_l,
+            keys=["semester {}".format(v + 1) for v in range(n_semesters)],
+            names=["semester"],
+        )
+        aff_regions = "~".join(self.aff_regions)
+        self.fd_loss_region = pd.DataFrame({aff_regions: fd_loss_region}).T
+        fd_loss_sect = fd_loss.sum()
+        tmp = fd_loss_sect.sort_values(ascending=False, key=abs).head(5).to_dict()
+        self.indicators["top_5_sector_fdloss"] = {str(k): v for k, v in tmp.items()}
+
+    def save_dfs(self):
+        logger.info("Saving computed dataframe to results folder")
+        self.production_realised_df.to_parquet(
+            self.parquets_path / "production_realised_df.parquet"
+        )
+        self.productive_capital_to_recover_df.to_parquet(
+            self.parquets_path / "productive_capital_to_recover_df.parquet"
+        )
+        self.production_capacity_df.to_parquet(
+            self.parquets_path / "production_capacity_df.parquet"
+        )
+        self.overproduction_df.to_parquet(
+            self.parquets_path / "overproduction_df.parquet"
+        )
+        self.final_demand_unmet_df.to_parquet(
+            self.parquets_path / "final_demand_unmet_df.parquet"
+        )
+        self.final_demand_df.to_parquet(self.parquets_path / "final_demand_df.parquet")
+        self.intermediate_demand_df.to_parquet(
+            self.parquets_path / "intermediate_demand_df.parquet"
+        )
+        self.rebuild_demand_df.to_parquet(
+            self.parquets_path / "rebuild_demand_df.parquet"
+        )
+        self.treated_loss_df.to_parquet(self.parquets_path / "treated_df_loss.parquet")
+        if self.inputs_stocks_df is not None:
+            ddf = da.from_pandas(self.inputs_stocks_df, chunksize=10000000)
+            ddf.to_parquet(
+                self.parquets_path / "treated_df_stocks.parquet", engine="pyarrow"
+            )
+        if self.limiting_inputs_df is not None:
+            # ddf_l = da.from_pandas(self.df_limiting, chunksize=10000000)
+            # ddf_l = ddf_l.melt(ignore_index=False).rename(columns={'variable_0':'region','variable_1':'sector', 'variable_2':'stock of'})
+            # ddf_l = ddf_l.reset_index()
+            # ddf_l['step'] = ddf_l['step'].astype("uint16")
+            # ddf_l['stock of'] = ddf_l['stock of'].astype("category")
+            # ddf_l['region'] = ddf_l['region'].astype("category")
+            # ddf_l['sector'] = ddf_l['sector'].astype("category")
+            self.limiting_inputs_df.to_parquet(
+                self.parquets_path / "treated_df_limiting_inputs.parquet",
+                engine="pyarrow",
+            )
    
+        # self.df_limiting.to_feather(self.parquets_path/"treated_df_limiting.feather")
+
    
+
+                
    
+              
+              
+              
+                
    
+                  
+
    
+
    
+                
    
+              
+            
    
+            
+            
+              
+            
+          
    
+          
    
+            
    
+          
    
+        
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    Source code for boario.indicators
     
   
    
   
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+  Last updated on Apr 24, 2023.
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+  
    Source code for boario.model_base
    +# BoARIO : The Adaptative Regional Input Output model in python.
+# Copyright (C) 2022  Samuel Juhel
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from __future__ import annotations
+import json
+import pathlib
+import typing
+from typing import Optional
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from boario import logger, warn_once
+from boario.event import *
+from pymrio.core.mriosystem import IOSystem
+from boario.utils.misc import lexico_reindex
+
+
+__all__ = [
+    "ARIOBaseModel",
+    "INV_THRESHOLD",
+    "VALUE_ADDED_NAMES",
+    "VA_idx",
+    "lexico_reindex",
+]
+
+INV_THRESHOLD = 0  # 20 #temporal_units
+
+TECHNOLOGY_THRESHOLD = 0.00001
+
+VALUE_ADDED_NAMES = [
+    "VA",
+    "Value Added",
+    "value added",
+    "factor inputs",
+    "factor_inputs",
+    "Factors Inputs",
+    "Satellite Accounts",
+    "satellite accounts",
+    "satellite_accounts",
+    "satellite",
+]
+
+VA_idx = np.array(
+    [
+        "Taxes less subsidies on products purchased: Total",
+        "Other net taxes on production",
+        "Compensation of employees; wages, salaries, & employers' social contributions: Low-skilled",
+        "Compensation of employees; wages, salaries, & employers' social contributions: Medium-skilled",
+        "Compensation of employees; wages, salaries, & employers' social contributions: High-skilled",
+        "Operating surplus: Consumption of fixed capital",
+        "Operating surplus: Rents on land",
+        "Operating surplus: Royalties on resources",
+        "Operating surplus: Remaining net operating surplus",
+    ],
+    dtype=object,
+)
+
+
+
    [docs]class ARIOBaseModel:
+    def __init__(
+        self,
+        pym_mrio: IOSystem,
+        *,
+        order_type: str = "alt",
+        alpha_base: float = 1.0,
+        alpha_max: float = 1.25,
+        alpha_tau: int = 365,
+        rebuild_tau: int = 60,
+        main_inv_dur: int = 90,
+        monetary_factor: int = 10**6,
+        temporal_units_by_step: int = 1,
+        iotable_year_to_temporal_unit_factor: int = 365,
+        infinite_inventories_sect: Optional[list] = None,
+        inventory_dict: Optional[dict] = None,
+        productive_capital_vector: Optional[
+            pd.Series | npt.NDArray | pd.DataFrame
+        ] = None,
+        productive_capital_to_VA_dict: Optional[dict] = None,
+    ) -> None:
+        r"""The core of an ARIO model.  Handles the different arrays containing the mrio tables.
+
+        `ARIOBaseModel` wrap all the data and functions used in the core of the most basic version of the ARIO
+        model (based on :cite:`2013:hallegatte` and :cite:`2020:guan`).
+
+        An ARIOBaseModel instance based is build on the given IOSystem.
+        Most default parameters are the same as in :cite:`2013:hallegatte` and
+        default order mechanism comes from :cite:`2020:guan`. By default each
+        industry capital stock is 4 times its value added (:cite:`2008:hallegatte`).
+
+        Parameters
+        ----------
+        pym_mrio : IOSystem
+            The IOSystem to base the model on.
+        order_type : str, default "alt"
+            The type of orders mechanism to use. Currently, can be "alt" or
+            "noalt". See :ref:`boario-math`
+        alpha_base : float, default 1.0
+            Base value of overproduction factor :math:`\alpha^{b}` (Default to 1.0).
+        alpha_max : float, default 1.25
+            Maximum factor of overproduction :math:`\alpha^{\textrm{max}}` (default should be 1.25).
+        alpha_tau : int, default 365
+            Characteristic time of overproduction :math:`\tau_{\alpha}` in ``n_temporal_units_by_step`` (default should be 365 days).
+        rebuild_tau : int, default 60
+            Rebuilding characteristic time :math:`\tau_{\textrm{REBUILD}}` (see :ref:`boario-math`).
+        main_inv_dur : int, default 90
+            The default numbers of days for inputs inventory to use if it is not defined for an input.
+        monetary_factor : int, default 10**6
+            Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6).
+        temporal_units_by_step: int, default 1
+            The number of temporal_units between each step. (Current version of the model was not tested with values other than `1`).
+        iotable_year_to_temporal_unit_factor : int, default 365
+            The (inverse of the) factor by which MRIO data should be multiplied in order to get "per temporal unit value", assuming IO data is yearly.
+        infinite_inventories_sect: list, optional
+            List of inputs (sector) considered never constraining for production.
+        inventory_dict: dict, optional
+            Dictionary in the form input:initial_inventory_size, (where input is a sector, and initial_inventory_size is in "temporal_unit" (defaults to a day))
+        productive_capital_vector: ArrayLike, optional
+            Array of capital per industry if you need to give it exogenously.
+        productive_capital_to_VA_dict: dict, optional
+            Dictionary in the form sector:ratio. Where ratio is used to estimate capital stock based on the value added of the sector.
+
+        Notes
+        -----
+
+        It is recommended to use ``productive_capital_to_VA_dict`` if you have a more precise estimation of
+        the ratio of (Capital Stock / Value Added) per sectors.
+        You may also feed in directly a ``productive_capital_vector`` if you did your estimation before-hand.
+        (This is especially useful if you have events based of an exposure layer for instance)
+
+        Regarding inventories, they default to 90 days for all inputs (ie sectors).
+        You can set some inputs to be never constraining for production by listing them
+        in ``infinite_inventories_sect`` or directly feed in a dictionary of the inventory
+        duration for each input.
+
+
+        Examples
+        --------
+        FIXME: Add docs.
+
+
+        """
+
+        logger.debug("Initiating new ARIOBaseModel instance")
+        super().__init__()
+        ################ Parameters variables #######################
+        logger.info(
+            "IO system metadata :\n{}\n{}\n{}\n{}".format(
+                str(pym_mrio.meta.description),
+                str(pym_mrio.meta.name),
+                str(pym_mrio.meta.system),
+                str(pym_mrio.meta.version),
+            )
+        )
+        pym_mrio = lexico_reindex(pym_mrio)
+        self.main_inv_dur: int = main_inv_dur
+        r"""int, default 90: The default numbers of days for inputs inventory to use if it is not defined for an input."""
+
+        reg = pym_mrio.get_regions()
+        reg = typing.cast("list[str]", reg)
+        self.regions = np.array(sorted(reg))
+        r"""numpy.ndarray of str : An array of the regions of the model."""
+
+        self.n_regions = len(reg)
+        r"""int : The number :math:`m` of regions."""
+
+        sec = pym_mrio.get_sectors()
+        sec = typing.cast("list[str]", sec)
+        self.sectors = np.array(sorted(sec))
+        r"""numpy.ndarray of str: An array of the sectors of the model."""
+
+        self.n_sectors = len(sec)
+        r"""int : The number :math:`n` of sectors."""
+
+        self.industries = pd.MultiIndex.from_product(
+            [self.regions, self.sectors], names=["region", "sector"]
+        )
+        r"""pandas.MultiIndex : A pandas MultiIndex of the industries (region,sector) of the model."""
+
+        self.n_industries = len(self.industries)
+        r"""int : The number :math:`m * n` of industries."""
+
+        try:
+            self.final_demand_cat = np.array(sorted(list(pym_mrio.get_Y_categories())))  # type: ignore
+            r"""numpy.ndarray of str: An array of the final demand categories of the model (``["Final demand"]`` if there is only one)"""
+
+            self.n_fd_cat = len(pym_mrio.get_Y_categories())  # type: ignore
+            r"""int: The numbers of final demand categories."""
+
+        except KeyError:
+            self.n_fd_cat = 1
+            self.final_demand_cat = np.array(["Final demand"])
+        except IndexError:
+            self.n_fd_cat = 1
+            self.final_demand_cat = np.array(["Final demand"])
+
+        if hasattr(pym_mrio, "monetary_factor"):
+            logger.warning(
+                f"Custom monetary factor found in the mrio pickle file, continuing with this one ({getattr(pym_mrio,'monetary_factor')})"
+            )
+            self.monetary_factor = getattr(pym_mrio, "monetary_factor")
+        else:
+            self.monetary_factor = monetary_factor
+        r"""int, default 10^6: Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6)."""
+
+        self.n_temporal_units_by_step = temporal_units_by_step
+        r"""int, default 1: The number of temporal_units between each step. (Current version of the model was not tested with values other than `1`)."""
+
+        self.iotable_year_to_temporal_unit_factor = iotable_year_to_temporal_unit_factor
+        r"""int, default 365: The (inverse of the) factor by which MRIO data should be multiplied in order to get "per temporal unit value", assuming IO data is yearly."""
+
+        if self.iotable_year_to_temporal_unit_factor != 365:
+            logger.warning(
+                "iotable_to_daily_step_factor is not set to 365 (days). This should probably not be the case if the IO tables you use are on a yearly basis."
+            )
+
+        self.steply_factor = (
+            self.n_temporal_units_by_step / self.iotable_year_to_temporal_unit_factor
+        )
+        self.rebuild_tau = rebuild_tau
+        r"""int: Rebuilding characteristic time :math:`\tau_{\textrm{REBUILD}}` (see :ref:`boario-math`)."""
+
+        self.overprod_max = alpha_max
+        r"""float: Maximum factor of overproduction :math:`\alpha^{\textrm{max}}` (default should be 1.25)."""
+
+        self.overprod_tau = self.n_temporal_units_by_step / alpha_tau
+        r"""float: Characteristic time of overproduction :math:`\tau_{\alpha}` in ``n_temporal_units_by_step`` (default should be 365 days)."""
+
+        self.overprod_base = alpha_base
+        r"""float: Base value of overproduction factor :math:`\alpha^{b}` (Default to 1.0)."""
+
+        if inventory_dict is None:
+            infinite_inventories_sect = (
+                [] if infinite_inventories_sect is None else infinite_inventories_sect
+            )
+            self.inventories = [
+                np.inf if sector in infinite_inventories_sect else main_inv_dur
+                for sector in self.sectors
+            ]
+            r"""numpy.ndarray of int: Array :math:`\mathbf{s}` of size :math:`n` (sectors), setting for each input the initial number of ``n_temporal_units_by_step`` of stock for the input. (see :ref:`boario-math`)."""
+
+        else:
+            inv = inventory_dict
+            self.inventories = [
+                np.inf if inv[k] in ["inf", "Inf", "Infinity", "infinity"] else inv[k]
+                for k in sorted(inv.keys())
+            ]
+
+        logger.debug(f"inventories: {self.inventories}")
+        logger.debug(f"n_temporal_units_by_step: {self.n_temporal_units_by_step}")
+        self.inv_duration = np.array(self.inventories) / self.n_temporal_units_by_step
+
+        if (self.inv_duration <= 1).any():
+            logger.warning(
+                "At least one product has inventory duration lower than the numbers of temporal units in one step ({}), model will set it to 2 by default, but you should probably check this !".format(
+                    self.n_temporal_units_by_step
+                )
+            )
+            self.inv_duration[self.inv_duration <= 1] = 2
+        #################################################################
+
+        ######## INITIAL MRIO STATE (in step temporality) ###############
+        if pym_mrio.Z is None:
+            raise ValueError(
+                "Z attribute of given MRIO doesn't exist, this is a problem"
+            )
+        if pym_mrio.Y is None:
+            raise ValueError(
+                "Y attribute of given MRIO doesn't exist, this is a problem"
+            )
+        if pym_mrio.x is None:
+            raise ValueError(
+                "x attribute of given MRIO doesn't exist, this is a problem"
+            )
+        self._matrix_id = np.eye(self.n_sectors)
+        self._matrix_I_sum = np.tile(self._matrix_id, self.n_regions)
+        self.Z_0 = pym_mrio.Z.to_numpy()
+        r"""numpy.ndarray of float: 2-dim square matrix array :math:`\ioz` of size :math:`(n \times m, n \times m)` representing the intermediate (transaction) matrix (see :ref:`boario-math-init`)."""
+
+        self.Z_C = self._matrix_I_sum @ self.Z_0
+        r"""numpy.ndarray of float: 2-dim matrix array :math:`\ioz^{\sectorsset}` of size :math:`(n, n \times m)` representing the intermediate (transaction) matrix aggregated by inputs (see :ref:`here <boario-math-z-agg>`)."""
+
+        with np.errstate(divide="ignore", invalid="ignore"):
+            self.Z_distrib = np.divide(
+                self.Z_0, (np.tile(self.Z_C, (self.n_regions, 1)))
+            )
+            r"""numpy.ndarray of float: math:`\ioz` normalised by :math:`\ioz^{\sectorsset}`, i.e. representing for each input the share of the total ordered transiting from an industry to another (see :ref:`here <boario-math-z-agg>`)."""
+
+        self.Z_distrib = np.nan_to_num(self.Z_distrib)
+        self.Z_0 = pym_mrio.Z.to_numpy() * self.steply_factor
+
+        self.Y_0 = pym_mrio.Y.to_numpy()
+        self.Y_C = self._matrix_I_sum @ self.Y_0
+        r"""numpy.ndarray of float: 2-dim matrix array :math:`\ioy^{\sectorsset}` of size :math:`(n, m \times \text{number of final demand categories})` representing the final demand matrix aggregated by inputs (see :ref:`here <boario-math-z-agg>`)."""
+
+        with np.errstate(divide="ignore", invalid="ignore"):
+            self.Y_distrib = np.divide(
+                self.Y_0, (np.tile(self.Y_C, (self.n_regions, 1)))
+            )
+            r"""numpy.ndarray of float: math:`\ioy` normalised by :math:`\ioy^{\sectorsset}`, i.e. representing for each input the share of the total ordered transiting from an industry to final demand (see :ref:`here <boario-math-z-agg>`)."""
+
+        self.Y_distrib = np.nan_to_num(self.Y_distrib)
+
+        self.Y_0 = pym_mrio.Y.to_numpy() * self.steply_factor
+        r"""numpy.ndarray of float: 2-dim array :math:`\ioy` of size :math:`(n \times m, m \times \text{number of final demand categories})` representing the final demand matrix."""
+
+        tmp = np.array(pym_mrio.x.T)
+        self.X_0 = tmp.flatten() * self.steply_factor
+        r"""numpy.ndarray of float: Array :math:`\iox(0)` of size :math:`n \times m` representing the initial gross production."""
+
+        del tmp
+        value_added = pym_mrio.x.T - pym_mrio.Z.sum(axis=0)
+        value_added = value_added.reindex(sorted(value_added.index), axis=0)
+        value_added = value_added.reindex(sorted(value_added.columns), axis=1)
+        value_added[value_added < 0] = 0.0
+        self.gdp_df = value_added.groupby("region", axis=1).sum()
+        r"""pandas.DataFrame: Dataframe of the total GDP of each region of the model"""
+
+        self.VA_0 = value_added.to_numpy().flatten()
+        r"""numpy.ndarray of float: Array :math:`\iov` of size :math:`n \times m` representing the total value added for each sectors."""
+
+        self.tech_mat = (self._matrix_I_sum @ pym_mrio.A).to_numpy()  # type: ignore #to_numpy is not superfluous !
+        r"""numpy.ndarray of float: 2-dim array :math:`\ioa` of size :math:`(n \times m, n \times m)` representing the technical coefficients matrix."""
+
+        if productive_capital_vector is not None:
+            self.k_stock = productive_capital_vector
+            """numpy.ndarray of float: Array of size :math:`n \times m` representing the estimated stock of capital of each industry."""
+
+            if isinstance(self.k_stock, pd.DataFrame):
+                self.k_stock = self.k_stock.squeeze().sort_index().to_numpy()
+        elif productive_capital_to_VA_dict is None:
+            logger.warning(f"No capital to VA dictionary given, considering 4/1 ratio")
+            self.kstock_ratio_to_VA = 4
+            self.k_stock = self.VA_0 * self.kstock_ratio_to_VA
+        else:
+            kratio = productive_capital_to_VA_dict
+            kratio_ordered = [kratio[k] for k in sorted(kratio.keys())]
+            self.kstock_ratio_to_VA = np.tile(np.array(kratio_ordered), self.n_regions)
+            self.k_stock = self.VA_0 * self.kstock_ratio_to_VA
+        if value_added.ndim > 1:
+            self.gdp_share_sector = (
+                self.VA_0
+                / value_added.sum(axis=0).groupby("region").transform("sum").to_numpy()
+            )
+        else:
+            self.gdp_share_sector = (
+                self.VA_0 / value_added.groupby("region").transform("sum").to_numpy()
+            )
+        self.gdp_share_sector = self.gdp_share_sector.flatten()
+        r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the estimated share of a sector in its regional economy."""
+
+        self.matrix_share_thresh = (
+            self.Z_C > np.tile(self.X_0, (self.n_sectors, 1)) * TECHNOLOGY_THRESHOLD
+        )  # [n_sectors, n_regions*n_sectors]
+        r"""numpy.ndarray of float: 2-dim square matrix array of size :math:`(n , n \times m)` representing the threshold under which an input is not considered being an input (0.00001)."""
+        #################################################################
+
+        ####### SIMULATION VARIABLES ####################################
+        self.overprod = np.full(
+            (self.n_regions * self.n_sectors), self.overprod_base, dtype=np.float64
+        )
+        r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the overproduction coefficients vector :math:`\mathbf{\alpha}(t)`."""
+
+        with np.errstate(divide="ignore", invalid="ignore"):
+            self.matrix_stock = (
+                np.tile(self.X_0, (self.n_sectors, 1)) * self.tech_mat
+            ) * self.inv_duration[:, np.newaxis]
+        self.matrix_stock = np.nan_to_num(self.matrix_stock, nan=np.inf, posinf=np.inf)
+        r"""numpy.ndarray of float: 2-dim square matrix array :math:`\ioinv` of size :math:`(n \times m, n \times m)` representing the stock of inputs (see :ref:`boario-math-init`)."""
+
+        self.matrix_stock_0 = self.matrix_stock.copy()
+        self.matrix_orders = self.Z_0.copy()
+        r"""numpy.ndarray of float: 2-dim square matrix array :math:`\ioorders` of size :math:`(n \times m, n \times m)` representing the matrix of orders."""
+
+        self.production = self.X_0.copy()
+        r"""numpy.ndarray of float: Array :math:`\iox(t)` of size :math:`n \times m` representing the current gross production."""
+
+        self.intmd_demand = self.Z_0.copy()
+        self.final_demand = self.Y_0.copy()
+        self.rebuilding_demand = None
+        self.productive_capital_lost = np.zeros(self.production.shape)
+        r"""numpy.ndarray of float: Array of size :math:`n \times m` representing the estimated stock of capital currently destroyed for each industry."""
+
+        self.order_type = order_type
+        #################################################################
+
+        ################## SIMULATION TRACKING VARIABLES ################
+        self.in_shortage = False
+        self.had_shortage = False
+        self.rebuild_prod = np.zeros(shape=self.X_0.shape)
+        self.final_demand_not_met = np.zeros(self.Y_0.shape)
+        #############################################################################
+
+        ### Internals
+        self._prod_delta_type = None
+
+        #### POST INIT ####
+        ### Event Class Attribute setting
+        logger.debug(
+            f"Setting possible regions (currently: {Event.possible_regions}) to: {self.regions}"
+        )
+        Event.possible_regions = pd.CategoricalIndex(
+            self.regions, name="region", copy=True
+        )
+        logger.debug(f"Possible regions is now {Event.possible_regions}")
+        Event.regions_idx = np.arange(self.n_regions)
+        Event.possible_sectors = pd.CategoricalIndex(
+            self.sectors, name="sector", copy=True
+        )
+        Event.possible_final_demand_cat = pd.CategoricalIndex(
+            pym_mrio.get_Y_categories(), name="final demand", copy=True
+        )
+        Event.sectors_idx = np.arange(self.n_sectors)
+        Event.z_shape = self.Z_0.shape
+        Event.y_shape = self.Y_0.shape
+        Event.x_shape = self.X_0.shape
+        Event.model_monetary_factor = monetary_factor
+        Event.sectors_gva_shares = self.gdp_share_sector.copy()
+        Event.Z_distrib = self.Z_distrib.copy()
+        Event.Y_distrib = self.Y_distrib.copy()
+
+        meta = pym_mrio.meta.metadata
+        assert isinstance(meta, dict)
+        # meta = {"name":"unnamed", "description":"", "system":"unknown","version":"unknown"}
+        try:
+            Event.mrio_name = "_".join(
+                [
+                    str(meta["name"]),
+                    str(meta["description"]),
+                    str(meta["system"]),
+                    str(meta["version"]),
+                ]
+            )
+        except TypeError:
+            Event.mrio_name = "custom - method WIP"
+
+        # initialize those (it's not very nice, but otherwise python complains)
+        self._indus_rebuild_demand_tot: npt.NDArray = np.array([])
+        self._house_rebuild_demand_tot: npt.NDArray = np.array([])
+        self._indus_rebuild_demand: npt.NDArray = np.array([])
+        self._house_rebuild_demand: npt.NDArray = np.array([])
+        self._tot_rebuild_demand: npt.NDArray = np.array([])
+        self._productive_capital_lost: npt.NDArray = np.zeros(self.VA_0.shape)
+        self._prod_cap_delta_productive_capital: npt.NDArray = np.zeros(
+            len(self.industries)
+        )
+        self._prod_cap_delta_arbitrary: npt.NDArray = np.zeros(len(self.industries))
+        self._prod_cap_delta_tot: npt.NDArray = np.zeros(len(self.industries))
+
+    ## Properties
+
+    @property
+    def tot_rebuild_demand(self) -> npt.NDArray:
+        r"""Returns current total rebuilding demand (as the sum of rebuilding demand addressed to each industry)"""
+        tmp = []
+        logger.debug("Trying to return tot_rebuilding demand")
+        if np.any(self._indus_rebuild_demand_tot > 0):
+            tmp.append(self._indus_rebuild_demand_tot)
+        if np.any(self._house_rebuild_demand_tot > 0):
+            tmp.append(self._house_rebuild_demand_tot)
+        if tmp:
+            ret = np.concatenate(tmp, axis=1).sum(axis=1)
+            self._tot_rebuild_demand = ret
+        else:
+            self._tot_rebuild_demand = np.array([])
+        return self._tot_rebuild_demand
+
+    @tot_rebuild_demand.setter
+    def tot_rebuild_demand(self, source: list[EventKapitalRebuild]):
+        r"""Computes and updates total rebuilding demand based on a list of events.
+
+        Compute and update rebuilding demand for the given list of events. Only events
+        tagged as rebuildable are accounted for. Both `house_rebuild_demand` and
+        `indus_rebuild_demand` are updated.
+
+        Parameters
+        ----------
+        events : 'list[EventKapitalRebuild]'
+            A list of EventKapitalRebuild objects
+        """
+        logger.debug(f"Trying to set tot_rebuilding demand from {source}")
+        if not isinstance(source, list):
+            ValueError(
+                "Setting tot_rebuild_demand can only be done with a list of events, not a {}".format(
+                    type(source)
+                )
+            )
+        self.house_rebuild_demand = source
+        self.indus_rebuild_demand = source
+
+    @property
+    def house_rebuild_demand(self) -> npt.NDArray:
+        r"""Returns household rebuilding demand matrix.
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\ioy`, containing the sum of all currently
+            rebuildable final demand stock.
+        """
+
+        return self._house_rebuild_demand
+
+    @property
+    def house_rebuild_demand_tot(self) -> npt.NDArray:
+        r"""Returns total household rebuilding demand vector.
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\iox`, containing the sum of all currently
+            rebuildable households demands.
+        """
+
+        return self._house_rebuild_demand_tot
+
+    @house_rebuild_demand.setter
+    def house_rebuild_demand(self, source: list[EventKapitalRebuild]):
+        r"""Computes rebuild demand from household from a list of events
+
+        Computes and sets rebuilding final households demand for the given list of events
+        by summing the `rebuilding_demand_house member` of each event and multiplying it
+        by the characteristic rebuilding time (inverse of its value). Only events
+        tagged as rebuildable are accounted for.
+
+        If the event has its own rebuilding characteristic time :math:`\tau_{\textrm{REBUILD}}` it is applied,
+        else the model rebuilding characteristic time is used.
+
+        Parameters
+        ----------
+        events : 'list[Event]'
+            A list of Event objects
+
+        """
+        tmp = []
+        for ev in source:
+            if ev.rebuildable:
+                if ev.rebuild_tau is None:
+                    rebuild_tau = self.rebuild_tau
+                else:
+                    rebuild_tau = ev.rebuild_tau
+                    warn_once(logger, "Event has a custom rebuild_tau")
+                if len(ev.rebuilding_demand_house) > 0:
+                    tmp.append(
+                        ev.rebuilding_demand_house
+                        * (self.n_temporal_units_by_step / rebuild_tau)
+                    )
+        if not tmp:
+            self._house_rebuild_demand = np.array([])
+            self._house_rebuild_demand_tot = np.array([])
+        else:
+            house_reb_dem = np.stack(tmp, axis=-1)
+            tot = house_reb_dem.sum(axis=1)
+            self._house_rebuild_demand = house_reb_dem
+            self._house_rebuild_demand_tot = tot
+
+    @property
+    def indus_rebuild_demand(self) -> npt.NDArray:
+        r"""Returns industrial rebuilding demand matrix.
+
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\ioz`, containing the sum of all currently
+            rebuildable intermediate demand stock.
+        """
+
+        return self._indus_rebuild_demand
+
+    @property
+    def indus_rebuild_demand_tot(self) -> npt.NDArray:
+        r"""Returns total industrial rebuilding demand vector.
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\iox`, containing the sum of all currently
+            rebuildable intermediate demands.
+        """
+
+        return self._indus_rebuild_demand_tot
+
+    @indus_rebuild_demand.setter
+    def indus_rebuild_demand(self, source: list[EventKapitalRebuild]):
+        r"""Computes rebuild demand from economic sectors (i.e. not households)
+
+        Computes and sets rebuilding 'intermediate demand' for the given list of events
+        by summing the `rebuildind_demand_indus` of each event. Only events
+        tagged as rebuildable are accounted for.
+
+        Parameters
+        ----------
+        events : 'list[Event]'
+            A list of Event objects
+
+        """
+
+        tmp = []
+        for ev in source:
+            if ev.rebuildable:
+                if ev.rebuild_tau is None:
+                    rebuild_tau = self.rebuild_tau
+                else:
+                    rebuild_tau = ev.rebuild_tau
+                    warn_once(logger, "Event has a custom rebuild_tau")
+                tmp.append(
+                    ev.rebuilding_demand_indus
+                    * (self.n_temporal_units_by_step / rebuild_tau)
+                )
+
+        if not tmp:
+            self._indus_rebuild_demand = np.array([])
+            self._indus_rebuild_demand_tot = np.array([])
+        else:
+            indus_reb_dem = np.stack(tmp, axis=-1)
+            self._indus_rebuild_demand = indus_reb_dem
+            self._indus_rebuild_demand_tot = indus_reb_dem.sum(axis=1)
+            # logger.debug(f"Setting indus_rebuild_demand_tot to {indus_reb_dem}")
+
+    @property
+    def productive_capital_lost(self) -> npt.NDArray:
+        r"""Returns current stock of destroyed capital
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\iox`, containing the "stock"
+        of capital currently destroyed for each industry.
+        """
+
+        return self._productive_capital_lost
+
+    @productive_capital_lost.setter
+    def productive_capital_lost(
+        self, source: list[EventKapitalDestroyed] | npt.NDArray
+    ) -> None:
+        r"""Computes current capital lost and update production delta accordingly.
+
+        Computes and sets the current stock of capital lost by each industry of
+        the model due to the given list of events. Also update the production
+        capacity lost accordingly by computing the ratio of capital lost of
+        capital stock.
+
+        Parameters
+        ----------
+        source : list[EventKapitalDestroyed] | npt.NDArray
+            Either a list of events to consider for the destruction
+        of capital or directly a vector of destroyed capital for each industry.
+
+        """
+
+        logger.debug("Updating productive_capital lost from list of events")
+        if isinstance(source, list):
+            if source:
+                self._productive_capital_lost = np.add.reduce(
+                    np.array(
+                        [
+                            e.regional_sectoral_productive_capital_destroyed
+                            for e in source
+                        ]
+                    )
+                )
+            else:
+                self._productive_capital_lost = np.zeros(self.VA_0.shape)
+        elif isinstance(source, np.ndarray):
+            self._productive_capital_lost = source
+
+        productivity_loss_from_K = np.zeros(shape=self._productive_capital_lost.shape)
+        np.divide(
+            self._productive_capital_lost,
+            self.k_stock,
+            out=productivity_loss_from_K,
+            where=self.k_stock != 0,
+        )
+        logger.debug("Updating production delta from productive_capital loss")
+        self._prod_cap_delta_productive_capital = productivity_loss_from_K
+        if (self._prod_cap_delta_productive_capital > 0.0).any():
+            if self._prod_delta_type is None:
+                self._prod_delta_type = "from_productive_capital"
+            elif self._prod_delta_type == "from_arbitrary":
+                self._prod_delta_type = "mixed_from_productive_capital_from_arbitrary"
+
+    @property
+    def prod_cap_delta_arbitrary(self) -> npt.NDArray:
+        r"""Return the possible "arbitrary" production capacity lost vector if
+        it was set.
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\iox`, stating the amount of production
+        capacity lost arbitrarily (ie exogenous).
+        """
+        return self._prod_cap_delta_arbitrary
+
+    @prod_cap_delta_arbitrary.setter
+    def prod_cap_delta_arbitrary(self, source: list[EventArbitraryProd] | npt.NDArray):
+        """Computes and sets the loss of production capacity from "arbitrary" sources.
+
+        Parameters
+        ----------
+        source : list[Event] | npt.NDArray
+            Either a list of Event objects with arbitrary production losses
+            set, or directly a vector of production capacity loss.
+
+        """
+
+        if isinstance(source, list):
+            event_arb = np.array(
+                [
+                    ev.prod_cap_delta_arbitrary
+                    for ev in source
+                    if len(ev.prod_cap_delta_arbitrary) > 0
+                ]
+            )
+            if event_arb.size == 0:
+                self._prod_cap_delta_arbitrary = np.zeros(shape=self.X_0.shape)
+            else:
+                self._prod_cap_delta_arbitrary = np.maximum.reduce(event_arb)
+        else:
+            self._prod_capt_delta_arbitrary = source
+        assert self._prod_cap_delta_arbitrary is not None
+        if (self._prod_cap_delta_arbitrary > 0.0).any():
+            if self._prod_delta_type is None:
+                self._prod_delta_type = "from_arbitrary"
+            elif self._prod_delta_type == "from_productive_capital":
+                self._prod_delta_type = "mixed_from_productive_capital_from_arbitrary"
+
+    @property
+    def prod_cap_delta_productive_capital(self) -> npt.NDArray:
+        r"""Return the possible production capacity lost due to capital destroyed vector if
+        it was set.
+
+        Returns
+        -------
+        npt.NDArray
+            An array of same shape as math:`\iox`, stating the amount of production
+        capacity lost due to capital destroyed.
+        """
+
+        return self._prod_cap_delta_productive_capital
+
+    @property
+    def prod_cap_delta_tot(self) -> npt.NDArray:
+        r"""Computes and return total current production delta.
+
+        Returns
+        -------
+        npt.NDArray
+            The total production delta (ie share of production capacity lost)
+            for each industry.
+
+        """
+
+        logger.debug("Trying to retrieve current production delta")
+        tmp = []
+        if self._prod_delta_type is None:
+            raise AttributeError("Production delta doesn't appear to be set yet.")
+        elif self._prod_delta_type == "from_productive_capital":
+            logger.debug(
+                "Production delta is only set from productive_capital destruction"
+            )
+            tmp.append(self._prod_cap_delta_productive_capital)
+        elif self._prod_delta_type == "from_arbitrary":
+            logger.debug("Production delta is only set from arbitrary delta")
+            tmp.append(self._prod_cap_delta_arbitrary)
+        elif self._prod_delta_type == "mixed_from_productive_capital_from_arbitrary":
+            logger.debug(
+                "Production delta is a mixed form of productive_capital destruction and arbitrary delta"
+            )
+            tmp.append(self._prod_cap_delta_productive_capital)
+            tmp.append(self._prod_cap_delta_arbitrary)
+        else:
+            raise NotImplementedError(
+                "Production delta type {} not recognised".format(self._prod_delta_type)
+            )
+        # tmp.append(np.ones(shape=self.X_0.shape))
+        # logger.debug("tmp: {}".format(tmp))
+        self._prod_cap_delta_tot = np.amax(np.stack(tmp, axis=-1), axis=1)
+        assert (
+            self._prod_cap_delta_tot.shape == self.X_0.shape
+        ), "expected shape {}, received {}".format(
+            self.X_0.shape, self._prod_cap_delta_tot.shape
+        )
+        return self._prod_cap_delta_tot
+
+    @prod_cap_delta_tot.setter
+    def prod_cap_delta_tot(self, source: list[Event]):
+        r"""Computes and sets the loss of production capacity from both "arbitrary" sources and
+        capital destroyed.
+
+        Parameters
+        ----------
+        source : list[Event]
+            A list of Event objects.
+
+        """
+
+        logger.debug("Updating total production delta from list of events")
+        if not isinstance(source, list):
+            ValueError(
+                "Setting prod_cap_delta_tot can only be done with a list of events, not a {}".format(
+                    type(source)
+                )
+            )
+
+        self.productive_capital_lost = [
+            event for event in source if isinstance(event, EventKapitalDestroyed)
+        ]
+        self.prod_cap_delta_arbitrary = [
+            event for event in source if isinstance(event, EventArbitraryProd)
+        ]
+
+
    [docs]    def update_system_from_events(self, events: list[Event]) -> None:
+        """Update model variables according to given list of events
+
+        Computes and sets both the total production delta from all events, and the total rebuilding demand.
+
+        Parameters
+        ----------
+        events : 'list[Event]'
+            List of events (as Event objects) to consider.
+
+        """
+        logger.debug("Updating system from list of events")
+        self.prod_cap_delta_tot = events
+        self.tot_rebuild_demand = [
+            event for event in events if isinstance(event, EventKapitalRebuild)
+        ]
    
+
+    @property
+    def production_cap(self) -> npt.NDArray:
+        r"""Compute and update production capacity.
+
+        Compute and update production capacity from current total production delta and overproduction.
+
+        .. math::
+
+            x^{Cap}_{f}(t) = \alpha_{f}(t) (1 - \Delta_{f}(t)) x_{f}(t)
+
+        Raises
+        ------
+        ValueError
+            Raised if any industry has negative production.
+        """
+        production_cap = self.X_0.copy()
+        if self._prod_delta_type is not None:
+            prod_delta_tot = self.prod_cap_delta_tot.copy()
+            if (prod_delta_tot > 0.0).any():
+                production_cap = production_cap * (1 - prod_delta_tot)
+        if (self.overprod > 1.0).any():
+            production_cap = production_cap * self.overprod
+        if (production_cap < 0).any():
+            raise ValueError(
+                "Production capacity was found negative for at least on industry"
+            )
+        return production_cap
+
+    @property
+    def total_demand(self) -> npt.NDArray:
+        r"""Computes and return total demand as the sum of intermediate demand (orders), final demand, and possible rebuilding demand."""
+
+        if (self.matrix_orders < 0).any():
+            raise RuntimeError("Some matrix orders are negative which shouldn't happen")
+
+        tot_dem = self.matrix_orders.sum(axis=1) + self.final_demand.sum(axis=1)
+        if (tot_dem < 0).any():
+            raise RuntimeError("Some total demand are negative which shouldn't happen")
+        if len(self.tot_rebuild_demand) > 0:
+            tot_dem += self.tot_rebuild_demand
+        return tot_dem
+
+    @property
+    def production_opt(self) -> npt.NDArray:
+        r"""Computes and returns "optimal production" :math:`\iox^{textrm{Opt}}`, as the per industry minimum between
+        total demand and production capacity.
+
+        """
+
+        return np.fmin(self.total_demand, self.production_cap)
+
+    @property
+    def inventory_constraints_opt(self) -> npt.NDArray:
+        r"""Computes and returns inventory constraints for "optimal production" (see :meth:`calc_inventory_constraints`)"""
+
+        return self.calc_inventory_constraints(self.production_opt)
+
+    @property
+    def inventory_constraints_act(self) -> npt.NDArray:
+        r"""Computes and returns inventory constraints for "actual production" (see :meth:`calc_inventory_constraints`)"""
+        return self.calc_inventory_constraints(self.production)
+
+
    [docs]    def calc_production(self, current_temporal_unit: int) -> npt.NDArray:
+        r"""Computes and updates actual production. See :ref:`boario-math-prod`.
+
+        1. Computes ``production_opt`` and ``inventory_constraints`` as :
+
+        .. math::
+           :nowrap:
+
+                \begin{alignat*}{4}
+                      \iox^{\textrm{Opt}}(t) &= (x^{\textrm{Opt}}_{f}(t))_{f \in \firmsset} &&= \left ( \min \left ( d^{\textrm{Tot}}_{f}(t), x^{\textrm{Cap}}_{f}(t) \right ) \right )_{f \in \firmsset} && \text{Optimal production}\\
+                      \mathbf{\ioinv}^{\textrm{Cons}}(t) &= (\omega^{\textrm{Cons},f}_p(t))_{\substack{p \in \sectorsset\\f \in \firmsset}} &&=
+                    \begin{bmatrix}
+                        \tau^{1}_1 & \hdots & \tau^{p}_1 \\
+                        \vdots & \ddots & \vdots\\
+                        \tau^1_n & \hdots & \tau^{p}_n
+                    \end{bmatrix}
+                    \odot \begin{bmatrix} \iox^{\textrm{Opt}}(t)\\ \vdots\\ \iox^{\textrm{Opt}}(t) \end{bmatrix} \odot \ioa^{\sectorsset} && \text{Inventory constraints} \\
+                    &&&= \begin{bmatrix}
+                        \tau^{1}_1 x^{\textrm{Opt}}_{1}(t) a_{11} & \hdots & \tau^{p}_1 x^{\textrm{Opt}}_{p}(t) a_{1p}\\
+                        \vdots & \ddots & \vdots\\
+                        \tau^1_n x^{\textrm{Opt}}_{1}(t) a_{n1} & \hdots & \tau^{p}_n x^{\textrm{Opt}}_{p}(t) a_{np}
+                    \end{bmatrix} && \\
+                \end{alignat*}
+
+        2. If stocks do not meet ``inventory_constraints`` for any inputs, then decrease production accordingly :
+
+        .. math::
+           :nowrap:
+
+                \begin{alignat*}{4}
+                    \iox^{a}(t) &= (x^{a}_{f}(t))_{f \in \firmsset} &&= \left \{ \begin{aligned}
+                                                           & x^{\textrm{Opt}}_{f}(t) & \text{if $\omega_{p}^f(t) \geq \omega^{\textrm{Cons},f}_p(t)$}\\
+                                                           & x^{\textrm{Opt}}_{f}(t) \cdot \min_{p \in \sectorsset} \left ( \frac{\omega^s_{p}(t)}{\omega^{\textrm{Cons,f}}_p(t)} \right ) & \text{if $\omega_{p}^f(t) < \omega^{\textrm{Cons},f}_p(t)$}
+                                                           \end{aligned} \right. \quad &&
+                \end{alignat*}
+
+        Also warns in logs if such shortages happen.
+
+
+        Parameters
+        ----------
+        current_temporal_unit : int
+            current step number
+
+        """
+        # 1.
+        production_opt = self.production_opt.copy()
+        inventory_constraints = self.inventory_constraints_opt.copy()
+        # 2.
+        if (
+            stock_constraint := (self.matrix_stock < inventory_constraints)
+            * self.matrix_share_thresh
+        ).any():
+            if not self.in_shortage:
+                logger.info(
+                    "At least one industry entered shortage regime. (step:{})".format(
+                        current_temporal_unit
+                    )
+                )
+            self.in_shortage = True
+            self.had_shortage = True
+            production_ratio_stock = np.ones(shape=self.matrix_stock.shape)
+            np.divide(
+                self.matrix_stock,
+                inventory_constraints,
+                out=production_ratio_stock,
+                where=(self.matrix_share_thresh * (inventory_constraints != 0)),
+            )
+            production_ratio_stock[production_ratio_stock > 1] = 1
+            if (production_ratio_stock < 1).any():
+                production_max = (
+                    np.tile(production_opt, (self.n_sectors, 1))
+                    * production_ratio_stock
+                )
+                assert not (np.min(production_max, axis=0) < 0).any()
+                self.production = np.min(production_max, axis=0)
+            else:
+                assert not (production_opt < 0).any()
+                self.production = production_opt
+        else:
+            if self.in_shortage:
+                self.in_shortage = False
+                logger.info(
+                    "All industries exited shortage regime. (step:{})".format(
+                        current_temporal_unit
+                    )
+                )
+            assert not (production_opt < 0).any()
+            self.production = production_opt
+        return stock_constraint
    
+
+
    [docs]    def calc_inventory_constraints(self, production: npt.NDArray) -> npt.NDArray:
+        r"""Compute inventory constraints (no psi parameter, for the psi version,
+        the recommended one, see :meth:`~boario.extended_models.ARIOPsiModel.calc_inventory_constraints`)
+
+        See :meth:`calc_production` for how inventory constraints are computed.
+
+        Parameters
+        ----------
+        production : npt.NDArray
+            The production vector to consider.
+
+        Returns
+        -------
+        npt.NDArray
+            For each input, for each industry, the size of the inventory required to produce at `production` level
+        for the duration goal (`inv_duration`).
+
+        """
+        inventory_constraints = np.tile(production, (self.n_sectors, 1)) * self.tech_mat
+        tmp = np.tile(
+            np.nan_to_num(self.inv_duration, posinf=0.0)[:, np.newaxis],
+            (1, self.n_regions * self.n_sectors),
+        )
+        return inventory_constraints * tmp
    
+
+
    [docs]    def distribute_production(
+        self,
+        rebuildable_events: list[EventKapitalRebuild],
+        scheme: str = "proportional",
+    ) -> list[EventKapitalRebuild]:
+        r"""Production distribution module
+
+    #. Computes rebuilding demand for each rebuildable events (applying the `rebuild_tau` characteristic time)
+
+    #. Creates/Computes total demand matrix (Intermediate + Final + Rebuild)
+
+    #. Assesses if total demand is greater than realized production, hence requiring rationing
+
+    #. Distributes production proportionally to demand such that :
+
+        .. math::
+           :nowrap:
+
+               \begin{alignat*}{4}
+                   &\ioorders^{\textrm{Received}}(t) &&= \left (\frac{o_{ff'}(t)}{d^{\textrm{Tot}}_f(t)} \cdot x^a_f(t) \right )_{f,f'\in \firmsset}\\
+                   &\ioy^{\textrm{Received}}(t) &&= \left ( \frac{y_{f,c}}{d^{\textrm{Tot}}_f(t)}\cdot x^a_f(t) \right )_{f\in \firmsset, c \in \catfdset}\\
+                   &\Damage^{\textrm{Repaired}}(t) &&= \left ( \frac{\gamma_{f,c}}{d^{\textrm{Tot}}_f(t)} \cdot x^a_f(t) \right )_{f\in \firmsset, c \in \catfdset}\\
+               \end{alignat*}
+
+        Where :
+
+        - :math:`\ioorders^{\textrm{Received}}(t)` is the received orders matrix,
+
+        - :math:`\ioy^{\textrm{Received}}(t)` is the final demand received matrix,
+
+        - :math:`\Damage^{\textrm{Repared}}(t)` is the rebuilding/repair achieved matrix,
+
+        - :math:`d^{\textrm{Tot}}_f(t)` is the total demand to industry :math:`f`,
+
+        - :math:`x^a_f(t)` is :math:`f`'s realized production,
+
+        - :math:`o_{ff'}(t)` is the quantity of product ordered by industry :math:`f'` to industry :math:`f`,
+
+        - :math:`y_{fc}(t)` is the quantity of product ordered by household :math:`c` to industry :math:`f`,
+
+        - :math:`\gamma_{fc}(t)` is the repaired/rebuilding demand ordered to :math:`f`.
+
+    #. Updates stocks matrix. (Only if `np.allclose(stock_add, stock_use).all()` is false)
+
+        .. math::
+           :nowrap:
+
+               \begin{alignat*}{4}
+                   &\ioinv(t+1) &&= \ioinv(t) + \left ( \mathbf{I}_{\textrm{sum}} \cdot \ioorders^{\textrm{Received}}(t) \right ) - \left ( \colvec{\iox^{\textrm{a}}(t)}{\iox^{\textrm{a}}(t)} \odot \ioa^{\sectorsset} \right )\\
+               \end{alignat*}
+
+        Where :
+
+        - :math:`\ioinv` is the inventory matrix,
+        - :math:`\mathbf{I}_{\textrm{sum}}` is a row summation matrix,
+        - :math:`\ioa^{\sectorsset}` is the (input not specific to region) technical coefficients matrix.
+
+    #. Computes final demand not met due to rationing and write it.
+
+    #. Updates rebuilding demand for each event (by substracting distributed production)
+
+    Parameters
+    ----------
+    current_temporal_unit : int
+        Current temporal unit (day|week|... depending on parameters) (required to write the final demand not met)
+    events : 'list[Event]'
+        List of rebuildable events
+    scheme : str
+        Placeholder for future distribution scheme
+    separate_rebuilding : bool
+        Currently unused.
+
+    Returns
+    -------
+    list[Event]
+        The list of events to remove from current events (as they are totally rebuilt)
+
+    Raises
+    ------
+    RuntimeError
+        If negative values are found in places there's should not be any
+    ValueError
+        If an attempt to run an unimplemented distribution scheme is tried
+
+"""
+
+        if scheme != "proportional":
+            raise ValueError("Scheme %s not implemented" % scheme)
+
+        # list_of_demands = [self.matrix_orders, self.final_demand]
+        ## 1. Calc demand from rebuilding requirements (with characteristic time rebuild_tau)
+        house_reb_dem_per_event = (
+            house_reb_dem_tot_per_event
+        ) = indus_reb_dem_per_event = indus_reb_dem_tot_per_event = None
+
+        if rebuildable_events:
+            logger.debug("There are rebuildable events")
+            n_events = len(rebuildable_events)
+            tot_rebuilding_demand_summed = self.tot_rebuild_demand.copy()
+            # debugging assert
+            assert tot_rebuilding_demand_summed.shape == self.X_0.shape
+            indus_reb_dem_tot_per_event = self.indus_rebuild_demand_tot.copy()
+            indus_reb_dem_per_event = self.indus_rebuild_demand.copy()
+
+            # expected shape assert (debug also)
+            exp_shape_indus_per_event = (
+                self.n_sectors * self.n_regions,
+                self.n_sectors * self.n_regions,
+                n_events,
+            )
+            exp_shape_indus_tot_per_event = (self.n_sectors * self.n_regions, n_events)
+            assert (
+                indus_reb_dem_per_event.shape == exp_shape_indus_per_event
+            ), "expected shape is {}, given shape is {}".format(
+                exp_shape_indus_per_event, indus_reb_dem_per_event.shape
+            )
+            assert (
+                indus_reb_dem_tot_per_event.shape == exp_shape_indus_tot_per_event
+            ), "expected shape is {}, given shape is {}".format(
+                exp_shape_indus_tot_per_event, indus_reb_dem_tot_per_event.shape
+            )
+
+            house_reb_dem_tot_per_event = self.house_rebuild_demand_tot.copy()
+            house_reb_dem_per_event = self.house_rebuild_demand.copy()
+
+            # expected shape assert (debug also)
+            exp_shape_house = (
+                self.n_sectors * self.n_regions,
+                self.n_fd_cat * self.n_regions,
+                n_events,
+            )
+            exp_shape_house_tot = (self.n_sectors * self.n_regions, n_events)
+            assert house_reb_dem_per_event.shape == exp_shape_house
+            assert house_reb_dem_tot_per_event.shape == exp_shape_house_tot
+            h_reb = (house_reb_dem_tot_per_event > 0).any()
+            ind_reb = (indus_reb_dem_tot_per_event > 0).any()
+        else:
+            tot_rebuilding_demand_summed = np.zeros(shape=self.X_0.shape)
+            h_reb = False
+            ind_reb = False
+
+        ## 2. Concat to have total demand matrix (Intermediate + Final + Rebuild)
+        tot_demand = np.concatenate(
+            [
+                self.matrix_orders,
+                self.final_demand,
+                np.expand_dims(tot_rebuilding_demand_summed, 1),
+            ],
+            axis=1,
+        )
+        ## 3. Does production meet total demand
+        logger.debug(f"tot demand shape : {tot_demand.shape}")
+        rationing_required = (self.production - tot_demand.sum(axis=1)) < (
+            -1 / self.monetary_factor
+        )
+        rationning_mask = np.tile(
+            rationing_required[:, np.newaxis], (1, tot_demand.shape[1])
+        )
+        demand_share = np.full(tot_demand.shape, 0.0)
+        tot_dem_summed = np.expand_dims(
+            np.sum(tot_demand, axis=1, where=rationning_mask), 1
+        )
+        # Get demand share
+        np.divide(
+            tot_demand, tot_dem_summed, where=(tot_dem_summed != 0), out=demand_share
+        )
+        distributed_production = tot_demand.copy()
+        # 4. distribute production proportionally to demand
+        np.multiply(
+            demand_share,
+            np.expand_dims(self.production, 1),
+            out=distributed_production,
+            where=rationning_mask,
+        )
+        intmd_distribution = distributed_production[
+            :, : self.n_sectors * self.n_regions
+        ]
+        # Stock use is simply production times technical coefs
+        stock_use = np.tile(self.production, (self.n_sectors, 1)) * self.tech_mat
+        if (stock_use < 0).any():
+            raise RuntimeError(
+                "Stock use contains negative values, this should not happen"
+            )
+        # 5. Restock is the production from each supplier, summed.
+        stock_add = self._matrix_I_sum @ intmd_distribution
+        if (stock_add < 0).any():
+            raise RuntimeError(
+                "stock_add (restocking) contains negative values, this should not happen"
+            )
+        if not np.allclose(stock_add, stock_use):
+            self.matrix_stock = self.matrix_stock - stock_use + stock_add
+            if (self.matrix_stock < 0).any():
+                raise RuntimeError(
+                    "matrix_stock contains negative values, this should not happen"
+                )
+
+        # 6. Compute final demand not met due to rationing
+        final_demand_not_met = (
+            self.final_demand
+            - distributed_production[
+                :,
+                self.n_sectors
+                * self.n_regions : (
+                    self.n_sectors * self.n_regions + self.n_fd_cat * self.n_regions
+                ),
+            ]
+        )
+        final_demand_not_met = final_demand_not_met.sum(axis=1)
+        # avoid -0.0 (just in case)
+        final_demand_not_met[final_demand_not_met == 0.0] = 0.0
+        self.final_demand_not_met = final_demand_not_met.copy()
+
+        # 7. Compute production delivered to rebuilding
+        logger.debug(f"distributed prod shape : {distributed_production.shape}")
+        rebuild_prod = (
+            distributed_production[
+                :, (self.n_sectors * self.n_regions + self.n_fd_cat * self.n_regions) :
+            ]
+            .copy()
+            .flatten()
+        )
+        self.rebuild_prod = rebuild_prod.copy()
+
+        logger.debug(
+            f"tot_rebuilding_demand_summed: {pd.Series(tot_rebuilding_demand_summed, index=self.industries)}"
+        )
+
+        if h_reb and ind_reb:
+            logger.debug(
+                "There are both household rebuilding demand and industry rebuilding demand"
+            )
+            logger.debug(
+                f"indus_reb_dem_tot_per_event: {pd.DataFrame(indus_reb_dem_tot_per_event, index=self.industries)}"
+            )
+            logger.debug(
+                f"house_reb_dem_tot_per_event: {pd.DataFrame(house_reb_dem_tot_per_event, index=self.industries)}"
+            )
+            logger.debug(
+                f"dem_tot_per_event: {pd.DataFrame(house_reb_dem_tot_per_event+indus_reb_dem_tot_per_event, index=self.industries)}"  # type: ignore
+            )
+
+            tot_reb_dem_divider = np.tile(
+                tot_rebuilding_demand_summed[:, np.newaxis],
+                (1, indus_reb_dem_tot_per_event.shape[1]),
+            )
+            indus_shares = np.divide(
+                indus_reb_dem_tot_per_event,
+                tot_reb_dem_divider,
+                out=np.zeros_like(indus_reb_dem_tot_per_event),
+                where=tot_reb_dem_divider != 0,
+            )
+            house_shares = np.divide(
+                house_reb_dem_tot_per_event,
+                tot_reb_dem_divider,
+                out=np.zeros_like(house_reb_dem_tot_per_event),
+                where=tot_reb_dem_divider != 0,
+            )
+            # np.around(house_shares, 6, out=house_shares)
+            # np.around(indus_shares, 6, out=indus_shares)
+            logger.debug(
+                f"indus_shares: {pd.DataFrame(indus_shares, index=self.industries)}"
+            )
+            logger.debug(
+                f"house_shares: {pd.DataFrame(house_shares, index=self.industries)}"
+            )
+
+            #             tot_reb = pd.Series(tot_rebuilding_demand_summed, index=self.industries)
+            #             indus_reb_dem_tot = pd.DataFrame(indus_reb_dem_tot_per_event, index=self.industries)[0]
+            #             house_reb_dem_tot = pd.DataFrame(house_reb_dem_tot_per_event, index=self.industries)[0]
+            #             np.around(indus_shares,10,indus_shares)
+            #             np.around(house_shares,10,house_shares)
+            #             np.around(tmp,10,tmp)
+            #             indus_df = pd.DataFrame(indus_shares,index=self.industries)[0]
+            #             house_df = pd.DataFrame(house_shares,index=self.industries)[0]
+            #             tmp_df=pd.DataFrame(tmp, index=self.industries)[0]
+
+            #             debug_df = pd.concat([tot_reb, indus_reb_dem_tot, house_reb_dem_tot, tot_reb-(house_reb_dem_tot+indus_reb_dem_tot), indus_df, house_df, tmp_df], axis=1)
+            #             #logger.info(debug_df[debug_df.iloc[:,6]!=0].to_string())
+            #             debug_str = f"""
+            # {debug_df[debug_df.iloc[:,6]!=0].to_string()}
+            # """
+            assert np.allclose(
+                (indus_shares + house_shares)[tot_rebuilding_demand_summed != 0], 1.0
+            )
+
+        elif h_reb:
+            house_shares = np.ones(house_reb_dem_tot_per_event.shape)
+            indus_shares = np.zeros(indus_reb_dem_tot_per_event.shape)
+        elif ind_reb:
+            house_shares = np.zeros(house_reb_dem_tot_per_event.shape)
+            indus_shares = np.ones(indus_reb_dem_tot_per_event.shape)
+            # logger.debug("indus_shares: {}".format(indus_shares.shape))
+        else:
+            return []
+
+        logger.debug(f"rebuild_prod: {rebuild_prod}")
+        logger.debug(f"indus_shares: {indus_shares}")
+
+        indus_rebuild_prod = rebuild_prod[:, np.newaxis] * indus_shares  # type:ignore
+        house_rebuild_prod = rebuild_prod[:, np.newaxis] * house_shares  # type:ignore
+
+        if ind_reb:
+            logger.debug(
+                f"indus_rebuild_prod: {pd.DataFrame(indus_rebuild_prod, index=self.industries)}"
+            )
+        if h_reb:
+            logger.debug(
+                f"house_rebuild_prod: {pd.DataFrame(house_rebuild_prod, index=self.industries)}"
+            )
+            # logger.debug(f"tot_rebuilding_demand_summed: {pd.DataFrame(tot_rebuilding_demand_summed, index=self.industries)}")
+        # logger.debug(f"house_rebuild_prod: {pd.Series(house_rebuild_prod.flatten(), index=self.industries)}")
+
+        indus_rebuild_prod_distributed = np.zeros(shape=indus_reb_dem_per_event.shape)
+        house_rebuild_prod_distributed = np.zeros(shape=house_reb_dem_per_event.shape)
+        if ind_reb:
+            # 1. We normalize rebuilding demand by total rebuilding demand (i.e. we get for each client asking, the share of the total demand)
+            # This is done by broadcasting total demand and then dividing. (Perhaps this is not efficient ?)
+            indus_rebuilding_demand_shares = np.zeros(
+                shape=indus_reb_dem_per_event.shape
+            )
+            indus_rebuilding_demand_broad = np.broadcast_to(
+                indus_reb_dem_tot_per_event[:, np.newaxis],
+                indus_reb_dem_per_event.shape,
+            )
+            np.divide(
+                indus_reb_dem_per_event,
+                indus_rebuilding_demand_broad,
+                where=(indus_rebuilding_demand_broad != 0),
+                out=indus_rebuilding_demand_shares,
+            )
+
+            # 2. Then we multiply those shares by the total production (each client get production proportional to its demand relative to total demand)
+            indus_rebuild_prod_broad = np.broadcast_to(
+                indus_rebuild_prod[:, np.newaxis], indus_reb_dem_per_event.shape
+            )
+            np.multiply(
+                indus_rebuilding_demand_shares,
+                indus_rebuild_prod_broad,
+                out=indus_rebuild_prod_distributed,
+            )
+
+        if h_reb:
+            house_rebuilding_demand_shares = np.zeros(
+                shape=house_reb_dem_per_event.shape
+            )
+            house_rebuilding_demand_broad = np.broadcast_to(
+                house_reb_dem_tot_per_event[:, np.newaxis],
+                house_reb_dem_per_event.shape,
+            )
+            np.divide(
+                house_reb_dem_per_event,
+                house_rebuilding_demand_broad,
+                where=(house_rebuilding_demand_broad != 0),
+                out=house_rebuilding_demand_shares,
+            )
+            house_rebuild_prod_broad = np.broadcast_to(
+                house_rebuild_prod[:, np.newaxis], house_reb_dem_per_event.shape
+            )
+            np.multiply(
+                house_rebuilding_demand_shares,
+                house_rebuild_prod_broad,
+                out=house_rebuild_prod_distributed,
+            )
+
+        # update rebuilding demand
+        events_to_remove = []
+        for e_id, e in enumerate(rebuildable_events):
+            if len(e.rebuilding_demand_indus) > 0:
+                e.rebuilding_demand_indus -= indus_rebuild_prod_distributed[:, :, e_id]
+            if len(e.rebuilding_demand_house) > 0:
+                e.rebuilding_demand_house -= house_rebuild_prod_distributed[:, :, e_id]
+            if (e.rebuilding_demand_indus < (10 / self.monetary_factor)).all() and (
+                e.rebuilding_demand_house < (10 / self.monetary_factor)
+            ).all():
+                events_to_remove.append(e)
+        return events_to_remove
    
+
+
    [docs]    def calc_matrix_stock_gap(self, matrix_stock_goal) -> npt.NDArray:
+        """Computes and returns inputs stock gap matrix
+
+        The gap is simply the difference between the goal (given as argument)
+        and the current stock.
+
+        Parameters
+        ----------
+        matrix_stock_goal : npt.NDArray of float
+            The target inventories.
+
+        Returns
+        -------
+        npt.NDArray
+            The (only positive) gap between goal and current inventories.
+
+        Raises
+        ------
+        RuntimeError
+            If NaN are found in the result.
+
+        """
+        matrix_stock_gap = np.zeros(matrix_stock_goal.shape)
+        # logger.debug("matrix_stock_goal: {}".format(matrix_stock_goal.shape))
+        # logger.debug("matrix_stock: {}".format(self.matrix_stock.shape))
+        # logger.debug("matrix_stock_goal_finite: {}".format(matrix_stock_goal[np.isfinite(matrix_stock_goal)].shape))
+        # logger.debug("matrix_stock_finite: {}".format(self.matrix_stock[np.isfinite(self.matrix_stock)].shape))
+        matrix_stock_gap[np.isfinite(matrix_stock_goal)] = (
+            matrix_stock_goal[np.isfinite(matrix_stock_goal)]
+            - self.matrix_stock[np.isfinite(self.matrix_stock)]
+        )
+        if np.isnan(matrix_stock_gap).any():
+            raise RuntimeError("NaN in matrix stock gap")
+        matrix_stock_gap[matrix_stock_gap < 0] = 0
+        return matrix_stock_gap
    
+
+
    [docs]    def calc_orders(self) -> None:
+        r"""Computes and sets the orders (intermediate demand) for the next step.
+
+        See :ref:`Order module <boario-math-orders>`
+
+        Raises
+        ------
+        RuntimeError
+            If negative orders are found, which shouldn't happen.
+        """
+
+        # total_demand = self.total_demand
+        production_opt = self.production_opt.copy()
+        matrix_stock_goal = np.tile(production_opt, (self.n_sectors, 1)) * self.tech_mat
+        # Check this !
+        with np.errstate(invalid="ignore"):
+            matrix_stock_goal *= self.inv_duration[:, np.newaxis]
+        if np.allclose(self.matrix_stock, matrix_stock_goal):
+            matrix_stock_gap = matrix_stock_goal * 0
+        else:
+            matrix_stock_gap = self.calc_matrix_stock_gap(matrix_stock_goal)
+        matrix_stock_gap += (
+            np.tile(self.production, (self.n_sectors, 1)) * self.tech_mat
+        )
+        if self.order_type == "alt":
+            prod_ratio = np.ones(shape=self.X_0.shape)
+            np.divide(
+                self.production_cap, self.X_0, out=prod_ratio, where=self.X_0 != 0
+            )
+            Z_prod = self.Z_0 * prod_ratio[:, np.newaxis]
+            Z_Cprod = np.tile(self._matrix_I_sum @ Z_prod, (self.n_regions, 1))
+            out = np.zeros(shape=Z_prod.shape)
+            np.divide(Z_prod, Z_Cprod, out=out, where=Z_Cprod != 0)
+            tmp = np.tile(matrix_stock_gap, (self.n_regions, 1)) * out
+        else:
+            tmp = np.tile(matrix_stock_gap, (self.n_regions, 1)) * self.Z_distrib
+        if (tmp < 0).any():
+            raise RuntimeError("Negative orders computed")
+        self.matrix_orders = tmp
    
+
+
    [docs]    def calc_overproduction(self) -> None:
+        r"""Computes and update the overproduction vector.
+
+        See :ref:`Overproduction module <boario-math-overprod>`
+
+        """
+
+        scarcity = np.full(self.production.shape, 0.0)
+        total_demand = self.total_demand.copy()
+        scarcity[total_demand != 0] = (
+            total_demand[total_demand != 0] - self.production[total_demand != 0]
+        ) / total_demand[total_demand != 0]
+        scarcity[np.isnan(scarcity)] = 0
+        overprod_chg = (
+            ((self.overprod_max - self.overprod) * scarcity * self.overprod_tau)
+            + (
+                (self.overprod_base - self.overprod)
+                * (scarcity == 0)
+                * self.overprod_tau
+            )
+        ).flatten()
+        self.overprod += overprod_chg
+        self.overprod[self.overprod < 1.0] = 1.0
    
+
+    # def check_stock_increasing(self, current_temporal_unit: int):
+    #     tmp = np.full(self.matrix_stock.shape, 0.0)
+    #     mask = np.isfinite(self.matrix_stock_0)
+    #     np.subtract(self.matrix_stock, self.matrix_stock_0, out=tmp, where=mask)
+    #     check_1 = tmp > 0.0
+    #     tmp = np.full(self.matrix_stock.shape, 0.0)
+    #     np.subtract(
+    #         self.stocks_evolution[current_temporal_unit],
+    #         self.stocks_evolution[current_temporal_unit - 1],
+    #         out=tmp,
+    #         where=mask,
+    #     )
+    #     check_2 = tmp >= 0.0
+    #     return (check_1 & check_2).all()
+
+    # def check_production_eq_strict(self):
+    #     return (
+    #         (np.isclose(self.production, self.X_0))
+    #         | np.greater(self.production, self.X_0)
+    #     ).all()
+
+    # def check_production_eq_soft(
+    #     self, current_temporal_unit: int, period: int = 10
+    # ) -> bool:
+    #     return self.check_monotony(
+    #         self.production_evolution, current_temporal_unit, period
+    #     )
+
+    # def check_stocks_monotony(
+    #     self, current_temporal_unit: int, period: int = 10
+    # ) -> bool:
+    #     return self.check_monotony(self.stocks_evolution, current_temporal_unit, period)
+
+    # def check_initial_equilibrium(self) -> bool:
+    #     return np.allclose(self.production, self.X_0) and np.allclose(
+    #         self.matrix_stock, self.matrix_stock_0
+    #     )
+
+    # def check_equilibrium_soft(self, current_temporal_unit: int):
+    #     return (
+    #         self.check_stock_increasing(current_temporal_unit)
+    #         and self.check_production_eq_strict
+    #     )
+
+    # def check_equilibrium_monotony(
+    #     self, current_temporal_unit: int, period: int = 10
+    # ) -> bool:
+    #     return self.check_production_eq_soft(
+    #         current_temporal_unit, period
+    #     ) and self.check_stocks_monotony(current_temporal_unit, period)
+
+    # @staticmethod
+    # def check_monotony(x, current_temporal_unit: int, period: int = 10) -> bool:
+    #     return np.allclose(
+    #         x[current_temporal_unit], x[current_temporal_unit - period], atol=0.0001
+    #     )
+
+    # def check_crash(self, prod_threshold: float = 0.80) -> int:
+    #     """Check for economic crash
+
+    #     This method look at the production vector and returns the number of
+    #     industries which production is less than a certain share (default 20%) of the starting
+    #     production.
+
+    #     Parameters
+    #     ----------
+    #     prod_threshold : float, default: 0.8
+    #         An industry is counted as 'crashed' if its current production is less than its starting production times (1 - `prod_threshold`).
+
+    #     """
+    #     tmp = np.full(self.production.shape, 0.0)
+    #     checker = np.full(self.production.shape, 0.0)
+    #     mask = self.X_0 != 0
+    #     np.subtract(self.X_0, self.production, out=tmp, where=mask)
+    #     np.divide(tmp, self.X_0, out=checker, where=mask)
+    #     return np.where(checker >= prod_threshold)[0].size
+
+
    [docs]    def reset_module(
+        self,
+    ) -> None:
+        """Resets the model to initial state [Deprecated]
+
+        This method is currently not functioning.
+        """
+
+        self.productive_capital_lost = np.zeros(self.production.shape)
+        self.overprod = np.full(
+            (self.n_regions * self.n_sectors), self.overprod_base, dtype=np.float64
+        )
+        self.matrix_stock = self.matrix_stock_0.copy()
+        self.matrix_orders = self.Z_0.copy()
+        self.production = self.X_0.copy()
+        self.intmd_demand = self.Z_0.copy()
+        self.final_demand = self.Y_0.copy()
+        self.final_demand_not_met = np.zeros(self.Y_0.shape)
+        self.rebuilding_demand = np.array([])
+        self.in_shortage = False
+        self.had_shortage = False
+        self._prod_delta_type = None
+        self._indus_rebuild_demand_tot = np.array([])
+        self._house_rebuild_demand_tot = np.array([])
+        self._indus_rebuild_demand = np.array([])
+        self._house_rebuild_demand = np.array([])
+        self._tot_rebuild_demand = np.array([])
+        self._prod_cap_delta_productive_capital = np.array([])
+        self._prod_cap_delta_arbitrary = np.array([])
+        self._prod_cap_delta_tot = np.array([])
    
+
+
    [docs]    def update_params(self, new_params: dict) -> None:
+        """Update the parameters of the model.
+
+        Replace each parameters with given new ones.
+
+        .. warning::
+            Be aware this method calls :meth:`~boario.model_base.reset_record_files`, which resets the memmap files located in the results directory !
+
+        Parameters
+        ----------
+        new_params : dict
+            Dictionary of new parameters to use.
+
+        """
+        logger.warning("This method is quite probably deprecated")
+        self.n_temporal_units_by_step = new_params["n_temporal_units_by_step"]
+        self.iotable_year_to_temporal_unit_factor = new_params[
+            "year_to_temporal_unit_factor"
+        ]
+        self.rebuild_tau = new_params["rebuild_tau"]
+        self.overprod_max = new_params["alpha_max"]
+        self.overprod_tau = new_params["alpha_tau"]
+        self.overprod_base = new_params["alpha_base"]
+        if self.records_storage != pathlib.Path(
+            new_params["output_dir"] + "/" + new_params["results_storage"]
+        ):
+            self.records_storage = pathlib.Path(
+                new_params["output_dir"] + "/" + new_params["results_storage"]
+            )
    
+
+
    [docs]    def write_index(self, index_file: str | pathlib.Path) -> None:
+        """Write the indexes of the different dataframes of the model in a json file.
+
+        In order to easily rebuild the dataframes from the 'raw' data, this
+        method create a JSON file with all columns and indexes names, namely :
+
+        * regions names
+        * sectors names
+        * final demand categories
+        * number of regions, sectors and industries (regions * sectors)
+
+        Parameters
+        ----------
+        index_file : pathlib.Path
+            Path to the file to save the indexes.
+        """
+
+        indexes = {
+            "regions": list(self.regions),
+            "sectors": list(self.sectors),
+            "fd_cat": list(self.final_demand_cat),
+            "n_sectors": self.n_sectors,
+            "n_regions": self.n_regions,
+            "n_industries": self.n_sectors * self.n_regions,
+        }
+        if isinstance(index_file, str):
+            index_file = pathlib.Path(index_file)
+        index_file.parent.mkdir(parents=True, exist_ok=True)
+        with index_file.open("w") as f:
+            json.dump(indexes, f)
    
+
+    def change_inv_duration(self, new_dur, old_dur=None) -> None:
+        # replace this method by a property !
+        if old_dur is None:
+            old_dur = self.main_inv_dur
+        old_dur = float(old_dur) / self.n_temporal_units_by_step
+        new_dur = float(new_dur) / self.n_temporal_units_by_step
+        logger.info(
+            "Changing (main) inventories duration from {} steps to {} steps (there are {} temporal units by step so duration is {})".format(
+                old_dur,
+                new_dur,
+                self.n_temporal_units_by_step,
+                new_dur * self.n_temporal_units_by_step,
+            )
+        )
+        self.inv_duration = np.where(
+            self.inv_duration == old_dur, new_dur, self.inv_duration
+        )
    
+
    
+
+                
    
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+      © Copyright 2022, Samuel Juhel.
+      
    
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+    Created using Sphinx 6.1.3.
+    
    
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+        
    
+  Built with the PyData Sphinx Theme 0.13.0.
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+
+||||||| 66c6efed
+=======
 
 
 
@@ -1998,4 +4110,5 @@ 
    Source code for boario.model_base
     
   
   
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+
+>>>>>>> master
diff --git a/docs/build/html/_modules/boario/simulation.html b/docs/build/html/_modules/boario/simulation.html
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+<<<<<<< HEAD
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+    
+    boario.simulation — BoARIO  documentation
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+        
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+  Last updated on Apr 24, 2023.
+  
    
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+  
    Source code for boario.simulation
    +# BoARIO : The Adaptative Regional Input Output model in python.
+# Copyright (C) 2022  Samuel Juhel
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+"""
+Simulation module
+
+This module defines the Simulation object, which represent a BoARIO simulation environment.
+
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import math
+import pathlib
+import tempfile
+from pprint import pformat
+from typing import Optional, Union
+
+import numpy as np
+import pandas as pd
+import progressbar
+
+from boario import DEBUGFORMATTER
+from boario import logger
+from boario.event import *
+from boario.extended_models import *
+from boario.model_base import ARIOBaseModel
+from boario.utils.misc import CustomNumpyEncoder, TempMemmap, sizeof_fmt, print_summary
+
+__all__ = ["Simulation"]
+
+
+
    [docs]class Simulation:
+    """Defines a simulation object with a set of parameters and an IOSystem.
+
+    This class wraps a :class:`~boario.model_base.ARIOBaseModel` or descendant, and create the context for
+    simulations using this model. It stores execution parameters as well as events perturbing
+    the model.
+    """
+
+    __possible_records = [
+        "production_realised",
+        "production_capacity",
+        "final_demand",
+        "intermediate_demand",
+        "rebuild_demand",
+        "overproduction",
+        "final_demand_unmet",
+        "rebuild_prod",
+        "inputs_stocks",
+        "limiting_inputs",
+        "productive_capital_to_recover",
+    ]
+
+    __file_save_array_specs = {
+        "production_realised": (
+            "float64",
+            "_production_evolution",
+            "industries",
+            np.nan,
+        ),
+        "production_capacity": (
+            "float64",
+            "_production_cap_evolution",
+            "industries",
+            np.nan,
+        ),
+        "final_demand": ("float64", "_final_demand_evolution", "industries", np.nan),
+        "intermediate_demand": (
+            "float64",
+            "_io_demand_evolution",
+            "industries",
+            np.nan,
+        ),
+        "rebuild_demand": (
+            "float64",
+            "_rebuild_demand_evolution",
+            "industries",
+            np.nan,
+        ),
+        "overproduction": (
+            "float64",
+            "_overproduction_evolution",
+            "industries",
+            np.nan,
+        ),
+        "final_demand_unmet": (
+            "float64",
+            "_final_demand_unmet_evolution",
+            "industries",
+            np.nan,
+        ),
+        "rebuild_prod": (
+            "float64",
+            "_rebuild_production_evolution",
+            "industries",
+            np.nan,
+        ),
+        "inputs_stocks": ("float64", "_inputs_evolution", "stocks", np.nan),
+        "limiting_inputs": ("byte", "_limiting_inputs_evolution", "stocks", -1),
+        "productive_capital_to_recover": (
+            "float64",
+            "_regional_sectoral_productive_capital_destroyed_evolution",
+            "industries",
+            np.nan,
+        ),
+    }
+
+    def __init__(
+        self,
+        model: Union[ARIOBaseModel, ARIOPsiModel],
+        register_stocks: bool = False,
+        n_temporal_units_to_sim: int = 365,
+        events_list: Optional[list[Event]] = None,
+        separate_sims: bool = False,
+        save_events: bool = False,
+        save_params: bool = False,
+        save_index: bool = False,
+        save_records: list | str = [],
+        boario_output_dir: str | pathlib.Path = tempfile.mkdtemp(prefix="boario"),
+        results_dir_name: Optional[str] = None,
+    ) -> None:
+        """A Simulation instance can be initialized with the following parameters:
+
+        Parameters
+        ----------
+        model : Union[ARIOBaseModel, ARIOPsiModel, ARIOClimadaModel]
+            The model to run the simulation with.
+        register_stocks : bool, default False
+            A boolean stating if stocks evolution should be registered in a file.
+            Be aware that such arrays have timesteps*sectors*sectors*regions size
+            which can rapidly lead to very large files.
+        n_temporal_units_to_sim : int, default 365
+            The number of temporal units to simulates.
+        events_list : list[Event], optional
+            An optional list of events to run the simulation with [WIP].
+        separate_sims : bool, default False
+            Whether to run each event separately or during the same simulation [WIP].
+        boario_output_dir : str | pathlib.Path
+            An optional directory where to save files generated by the simulation.
+        results_dir_name : str, default 'results'
+            The name of the folder where simulation results will be stored.
+
+        Examples
+        --------
+
+        See #add link to example page.
+
+        """
+
+        if events_list is None:
+            events_list = []
+        logger.info("Initializing new simulation instance")
+        self._save_events = save_events
+        self._save_params = save_params
+        self._save_index = save_index
+        self._register_stocks = register_stocks
+        self.output_dir = pathlib.Path(boario_output_dir)
+        """pathlib.Path, optional: Optional path to the directory where output are stored."""
+
+        # Pre-init record variables
+        self._production_evolution = np.array([])
+        self._production_cap_evolution = np.array([])
+        self._final_demand_evolution = np.array([])
+        self._io_demand_evolution = np.array([])
+        self._rebuild_demand_evolution = np.array([])
+        self._overproduction_evolution = np.array([])
+        self._final_demand_unmet_evolution = np.array([])
+        self._rebuild_production_evolution = np.array([])
+        self._inputs_evolution = np.array([])
+        self._limiting_inputs_evolution = np.array([])
+        self._regional_sectoral_productive_capital_destroyed_evolution = np.array([])
+
+        if save_records != [] or save_events or save_params or save_index:
+            self.output_dir.resolve().mkdir(parents=True, exist_ok=True)
+
+        self.results_storage = (
+            self.output_dir.resolve()
+            if not results_dir_name
+            else self.output_dir.resolve() / results_dir_name
+        )
+        """str: Name of the folder in `output_dir` where the results will be stored if saved."""
+
+        if save_records != []:
+            if not self.results_storage.exists():
+                self.results_storage.mkdir()
+
+        if not self.results_storage.exists():
+            self.results_storage.mkdir(parents=True)
+
+        self.model = model
+        """Union[ARIOBaseModel, ARIOPsiModel] : The model to run the simulation with.
+        See :class:`~boario.model_base.ARIOBaseModel`."""
+
+        self.all_events: list[Event] = events_list
+        """list[Event]: A list containing all events associated with the simulation."""
+
+        self.currently_happening_events: list[Event] = []
+        """list[Event]: A list containing all events that are happening at the current timestep of the simulation."""
+
+        self.events_timings = set()
+        if not isinstance(n_temporal_units_to_sim,int) or n_temporal_units_to_sim <= 0:
+            raise ValueError(f"n_temporal_units_to_sim should be a positive integer (got {n_temporal_units_to_sim} of type {type(n_temporal_units_to_sim)})")
+        self.n_temporal_units_to_sim = n_temporal_units_to_sim
+        """int: The total number of `temporal_units` to simulate."""
+
+        Event.temporal_unit_range = self.n_temporal_units_to_sim
+        self.current_temporal_unit = 0
+        """int: Tracks the number of `temporal_units` elapsed since simulation start.
+        This may differs from the number of `steps` if the parameter `n_temporal_units_by_step`
+        differs from 1 temporal_unit as `current_temporal_unit` is actually `step` * `n_temporal_units_by_step`."""
+
+        self.equi = {
+            (int(0), int(0), "production"): "equi",
+            (int(0), int(0), "stocks"): "equi",
+            (int(0), int(0), "rebuilding"): "equi",
+        }
+        self.n_temporal_units_simulated = 0
+        self._n_checks = 0
+        self._monotony_checker = 0
+        self.scheme = "proportional"
+        self.has_crashed = False
+        # RECORDS FILES
+
+        self.records_storage: pathlib.Path = self.results_storage / "records"
+        """Place where records are stored if stored"""
+
+        self._files_to_record = []
+
+        if save_records != []:
+            if isinstance(save_records, str):
+                if save_records == "all":
+                    save_records = self.__possible_records
+                else:
+                    raise ValueError(
+                        f'save_records argument has to be either "all" or a sublist of {self.__possible_records}'
+                    )
+
+            impossible_records = set(save_records).difference(
+                set(self.__possible_records)
+            )
+            if not len(impossible_records) == 0:
+                raise ValueError(
+                    f"{impossible_records} are not possible records ({self.__possible_records})"
+                )
+            logger.info(f"Will save {save_records} records")
+            logger.info("Records storage is: {}".format(self.records_storage))
+            self.records_storage.mkdir(parents=True, exist_ok=True)
+            self._save_index = True
+            self._save_events = True
+            self._save_params = True
+
+        self._init_records(save_records)
+
+        self.params_dict = {
+            "n_temporal_units_to_sim": self.n_temporal_units_to_sim,
+            "output_dir": str(self.output_dir)
+            if hasattr(self, "output_dir")
+            else "none",
+            "results_storage": self.results_storage.stem
+            if hasattr(self, "results_storage")
+            else "none",
+            "model_type": self.model.__class__.__name__,
+            "psi_param": self.model.psi
+            if isinstance(self.model, ARIOPsiModel)
+            else None,
+            "order_type": self.model.order_type,
+            "n_temporal_units_by_step": self.model.n_temporal_units_by_step,
+            "year_to_temporal_unit_factor": self.model.iotable_year_to_temporal_unit_factor,
+            "inventory_restoration_tau": list(self.model.restoration_tau)
+            if isinstance(self.model, ARIOPsiModel)
+            else None,
+            "alpha_base": self.model.overprod_base,
+            "alpha_max": self.model.overprod_max,
+            "alpha_tau": self.model.overprod_tau,
+            "rebuild_tau": self.model.rebuild_tau,
+        }
+        """dict: A dictionary saving the parameters the simulation was run with."""
+
+        logger.info("Initialized !")
+        formatted_params_dict = {
+            key: print_summary(value) if key == "inventory_restoration_tau" else value
+            for key, value in self.params_dict.items()
+        }
+        logger.info(
+            "Simulation parameters:\n{}".format(
+                pformat(formatted_params_dict, compact=True)
+            )
+        )
+
+
    [docs]    def loop(self, progress: bool = True):
+        r"""Launch the simulation loop.
+
+        This method launch the simulation for the number of steps to simulate
+        described by the attribute ``self.n_temporal_units_to_sim``, calling the
+        :meth:`next_step` method. For convenience, it dumps the
+        parameters used in the logs just before running the loop. Once the loop
+        is completed, it flushes the different memmaps generated.
+
+        Parameters
+        ----------
+
+        progress: bool, default: True
+            If True, shows a progress bar of the loop in the console.
+        """
+        logger.info(
+            "Starting model loop for at most {} steps".format(
+                self.n_temporal_units_to_sim // self.model.n_temporal_units_by_step + 1
+            )
+        )
+        logger.info(
+            "One step is {}/{} of a year".format(
+                self.model.n_temporal_units_by_step,
+                self.model.iotable_year_to_temporal_unit_factor,
+            )
+        )
+        tmp = logging.FileHandler(self.results_storage / "simulation.log")
+        tmp.setLevel(logging.INFO)
+        tmp.setFormatter(DEBUGFORMATTER)
+        logger.addHandler(tmp)
+        logger.info("Events : {}".format(self.all_events))
+
+        run_range = range(
+            0,
+            self.n_temporal_units_to_sim,
+            math.floor(self.model.n_temporal_units_by_step),
+        )
+
+        if self._save_events:
+            (pathlib.Path(self.results_storage) / "jsons").mkdir(
+                parents=True, exist_ok=True
+            )
+            with (
+                pathlib.Path(self.results_storage) / "jsons" / "simulated_events.json"
+            ).open("w") as f:
+                event_dicts = [ev.event_dict for ev in self.all_events]
+                json.dump(event_dicts, f, indent=4, cls=CustomNumpyEncoder)
+        if progress:
+            widgets = [
+                "Processed: ",
+                progressbar.Counter("Step: %(value)d"),
+                " ~ ",
+                progressbar.Percentage(),
+                " ",
+                progressbar.ETA(),
+            ]
+            bar = progressbar.ProgressBar(widgets=widgets, redirect_stdout=True)
+            for _ in bar(run_range):
+                step_res = self.next_step()
+                self.n_temporal_units_simulated = self.current_temporal_unit
+                if step_res == 1:
+                    self.has_crashed = True
+                    logger.warning(
+                        f"""Economy seems to have crashed.
+                    - At step : {self.current_temporal_unit}
+                    """
+                    )
+                    break
+                elif self._monotony_checker > 3:
+                    logger.warning(
+                        f"""Economy seems to have found an equilibrium
+                    - At step : {self.current_temporal_unit}
+                    """
+                    )
+            bar.finish()
+        else:
+            for _ in run_range:
+                step_res = self.next_step()
+                self.n_temporal_units_simulated = self.current_temporal_unit
+                if step_res == 1:
+                    self.has_crashed = True
+                    logger.warning(
+                        f"""Economy or model seems to have crashed.
+                    - At step : {self.current_temporal_unit}
+                    """
+                    )
+                    break
+                elif self._monotony_checker > 3:
+                    logger.warning(
+                        f"""Economy seems to have found an equilibrium
+                    - At step : {self.current_temporal_unit}
+                    """
+                    )
+                    break
+
+        if self._files_to_record != []:
+            self._flush_memmaps()
+
+        if self._save_index:
+            self.model.write_index(self.results_storage / "jsons" / "indexes.json")
+
+        self.params_dict["n_temporal_units_simulated"] = self.n_temporal_units_simulated
+        self.has_crashed = self.has_crashed
+
+        if self._save_params:
+            with (
+                pathlib.Path(self.results_storage) / "jsons" / "simulated_params.json"
+            ).open("w") as f:
+                json.dump(self.params_dict, f, indent=4, cls=CustomNumpyEncoder)
+            with (
+                pathlib.Path(self.results_storage) / "jsons" / "equilibrium_checks.json"
+            ).open("w") as f:
+                json.dump(
+                    {str(k): v for k, v in self.equi.items()},
+                    f,
+                    indent=4,
+                    cls=CustomNumpyEncoder,
+                )
+        logger.info("Loop complete")
    
+
+
    [docs]    def next_step(
+        self,
+        check_period: int = 182,
+        min_steps_check: Optional[int] = None,
+        min_failing_regions: Optional[int] = None,
+    ):
+        """Advance the model to the next temporal step.
+
+        This method wraps all computation required to advance to the next step
+        of the simulation.
+
+        First it checks if an event is planned to
+        occur at the current step and if so, shocks the model with the
+        corresponding event. Then it :
+
+        0) If at least one step elapsed, it computes the new overproduction vector for the next step (using :meth:`~boario.model_base.ARIOBaseModel.calc_overproduction`)
+
+        1) Computes production for the current step. (See :meth:`~boario.model_base.ARIOBaseModel.calc_production`)
+
+        2) Distribute the `realised` production towards the different demands (intermediate, final, rebuilding) and compute the changes in the inputs stock matrix (see :meth:`~boario.model_base.ARIOBaseModel.distribute_production`)
+
+        Note that it is during this step that the model checks if an event is completely rebuild/recovered.
+
+        3) Computes the orders matrix (i.e. the intermediate demand) for the next step (see :meth:`~boario.model_base.ARIOBaseModel.calc_orders`)
+
+        Additionally, once every `check_period` steps elapsed, it checks for crash or equilibrium of the economy (see :meth:`~boario.simulation.check_equilibrium`).
+
+        Parameters
+        ----------
+        check_period : int
+            The time period in number of temporal units to wait between each "crash/equilibrium" check.
+        min_steps_check : Optional[int]
+            The minimum wait before the first check.
+        min_failing_regions : Optional[int]
+            The minimum number of failing regions required to consider economy has crashed.
+
+        """
+        try:
+            if min_steps_check is None:
+                min_steps_check = self.n_temporal_units_to_sim // 5
+            if min_failing_regions is None:
+                min_failing_regions = self.model.n_regions * self.model.n_sectors // 3
+
+            # Check if there are new events to add,
+            # if some happening events can start rebuilding (if rebuildable),
+            # and updates the internal model production_cap decrease and rebuild_demand
+            self._check_happening_events()
+
+            if "_inputs_evolution" in self._files_to_record:
+                self._write_stocks()
+
+            if self.current_temporal_unit > 1:
+                self.model.calc_overproduction()
+
+            if "_overproduction_evolution" in self._files_to_record:
+                self._write_overproduction()
+            if "_rebuild_demand_evolution" in self._files_to_record:
+                self._write_rebuild_demand()
+            if "_final_evolution" in self._files_to_record:
+                self._write_final_demand()
+            if "_io_demand_evolution" in self._files_to_record:
+                self._write_io_demand()
+
+            constraints = self.model.calc_production(self.current_temporal_unit)
+
+            if "_limiting_inputs_evolution" in self._files_to_record:
+                self._write_limiting_stocks(constraints)
+            if "_production_evolution" in self._files_to_record:
+                self._write_production()
+            if "_production_cap_evolution" in self._files_to_record:
+                self._write_production_max()
+            if (
+                "_regional_sectoral_productive_capital_destroyed_evolution"
+                in self._files_to_record
+            ):
+                self._write_productive_capital_lost()
+
+            try:
+                rebuildable_events = [
+                    ev
+                    for ev in self.currently_happening_events
+                    if isinstance(ev, EventKapitalRebuild) and ev.rebuildable
+                ]
+                events_to_remove = self.model.distribute_production(
+                    rebuildable_events, self.scheme
+                )
+                if "_final_demand_unmet_evolution" in self._files_to_record:
+                    self._write_final_demand_unmet()
+                if "_rebuild_production_evolution" in self._files_to_record:
+                    self._write_rebuild_prod()
+            except RuntimeError as e:
+                logger.exception("This exception happened:", e)
+                self.model.matrix_stock.dump(
+                    self.results_storage / "matrix_stock_dump.pkl"
+                )
+                logger.error(
+                    "Negative values in the stocks, matrix has been dumped in the results dir : \n {}".format(
+                        self.results_storage / "matrix_stock_dump.pkl"
+                    )
+                )
+                return 1
+            events_to_remove = events_to_remove + [
+                ev for ev in self.currently_happening_events if ev.over
+            ]
+            if events_to_remove:
+                self.currently_happening_events = [
+                    e
+                    for e in self.currently_happening_events
+                    if e not in events_to_remove
+                ]
+                for e in events_to_remove:
+                    if isinstance(e, EventKapitalDestroyed):
+                        logger.info(
+                            "Temporal_Unit : {} ~ Event named {} that occured at {} in {} for {} damages is completely rebuilt/recovered".format(
+                                self.current_temporal_unit,
+                                e.name,
+                                e.occurrence,
+                                e.aff_regions.to_list(),
+                                e.total_productive_capital_destroyed,
+                            )
+                        )
+
+            self.model.calc_orders()
+
+            n_checks = self.current_temporal_unit // check_period
+            if n_checks > self._n_checks:
+                self.check_equilibrium(n_checks)
+                self._n_checks += 1
+
+            self.current_temporal_unit += self.model.n_temporal_units_by_step
+            return 0
+        except Exception as e:
+            logger.exception(f"The following exception happened: {e}")
+            return 1
    
+
+
    [docs]    def check_equilibrium(self, n_checks: int):
+        """Checks the status of production, stocks and rebuilding demand.
+
+        This methods checks and store the status of production, inputs stocks
+        and rebuilding demand and store the information in ``self.equi``.
+
+        At the moment, the following status are implemented:
+
+        - `production` and `stocks` can be `greater` (ie all industries are producing more), `equi` (ie all industries produce almost the same as at initial equilibrium (0.01 atol)), `not equi` (ie neither of the previous case)
+        - `rebuilding demand` can be `finished` or `not finished` depending if some events still have some rebuilding demand unanswered or if all are completely rebuilt.
+
+        Parameters
+        ----------
+        n_checks : int
+            The number of checks counter.
+
+        """
+        if np.greater_equal(self.model.production, self.model.X_0).all():
+            self.equi[(n_checks, self.current_temporal_unit, "production")] = "greater"
+        elif np.allclose(self.model.production, self.model.X_0, atol=0.01):
+            self.equi[(n_checks, self.current_temporal_unit, "production")] = "equi"
+        else:
+            self.equi[(n_checks, self.current_temporal_unit, "production")] = "not equi"
+
+        if np.greater_equal(self.model.matrix_stock, self.model.matrix_stock_0).all():
+            self.equi[(n_checks, self.current_temporal_unit, "stocks")] = "greater"
+        elif np.allclose(self.model.production, self.model.X_0, atol=0.01):
+            self.equi[(n_checks, self.current_temporal_unit, "stocks")] = "equi"
+        else:
+            self.equi[(n_checks, self.current_temporal_unit, "stocks")] = "not equi"
+
+        if (
+            self.model.tot_rebuild_demand is None
+            or not self.model.tot_rebuild_demand.any()
+        ):
+            self.equi[(n_checks, self.current_temporal_unit, "rebuilding")] = "finished"
+        else:
+            self.equi[
+                (n_checks, self.current_temporal_unit, "rebuilding")
+            ] = "not finished"
    
+
+
    [docs]    def add_events(self, events: list[Event]):
+        """Add a list of events to the simulation.
+
+        Parameters
+        ----------
+        events : list[Event]
+            The events to add.
+        """
+        if not isinstance(events,list):
+            raise TypeError(f"list[Event] expected, {type(events)} received.")
+        for ev in events:
+            self.add_event(ev)
    
+
+
    [docs]    def add_event(self, ev: Event):
+        """Add an event to the simulation.
+
+        Parameters
+        ----------
+        ev : Event
+            The event to add.
+        """
+        if not isinstance(ev, Event):
+            raise ValueError(f"Event expected, {type(ev)} received.")
+        self.all_events.append(ev)
+        self.events_timings.add(ev.occurrence)
    
+
+
    [docs]    def reset_sim_with_same_events(self):
+        """Resets the model to its initial status (without removing the events). [WIP]"""
+        logger.info("Resetting model to initial status (with same events)")
+        self.current_temporal_unit = 0
+        self._monotony_checker = 0
+        self._n_checks = 0
+        self.n_temporal_units_simulated = 0
+        self.has_crashed = False
+        self._reset_records()
+        self.model.reset_module()
    
+
+
    [docs]    def reset_sim_full(self):
+        """Resets the model to its initial status and remove all events."""
+
+        self.reset_sim_with_same_events()
+        logger.info("Resetting events")
+        self.all_events = []
+        self.currently_happening_events = []
+        self.events_timings = set()
    
+
+
    [docs]    def write_index(self, index_file: Union[str, pathlib.Path]):
+        """Write the index of the dataframes used in the model in a json file.
+
+        See :meth:`~boario.model_base.ARIOBaseModel.write_index` for a more detailed documentation.
+
+        Parameters
+        ----------
+        index_file : Union[str, pathlib.Path]
+            name of the file to save the indexes to.
+
+        """
+        self.model.write_index(index_file)
    
+
+    def _check_happening_events(self) -> None:
+        """Updates the status of all events.
+
+        Check the `all_events` attribute and `current_temporal_unit` and
+        updates the events accordingly (ie if they happened, if they can start
+        rebuild/recover)
+
+        """
+        for ev in self.all_events:
+            if not ev.happened:
+                if (
+                    (self.current_temporal_unit - self.model.n_temporal_units_by_step)
+                    <= ev.occurrence
+                    <= self.current_temporal_unit
+                ):
+                    logger.info(
+                        "Temporal_Unit : {} ~ Shocking model with new event".format(
+                            self.current_temporal_unit
+                        )
+                    )
+                    logger.info(
+                        "Affected regions are : {}".format(ev.aff_regions.to_list())
+                    )
+                    ev.happened = True
+                    self.currently_happening_events.append(ev)
+        for ev in self.currently_happening_events:
+            if isinstance(ev, EventKapitalRebuild):
+                ev.rebuildable = self.current_temporal_unit
+            if isinstance(ev, (EventKapitalRecover, EventArbitraryProd)):
+                ev.recoverable = self.current_temporal_unit
+                if ev.recoverable:
+                    ev.recovery(self.current_temporal_unit)
+        self.model.update_system_from_events(self.currently_happening_events)
+
+    def _write_production(self) -> None:
+        """Saves the current production vector to the memmap."""
+        self._production_evolution[self.current_temporal_unit] = self.model.production
+
+    def _write_rebuild_prod(self) -> None:
+        """Saves the current rebuilding production vector to the memmap."""
+        logger.debug(
+            f"self._rebuild_production_evolution shape : {self._rebuild_production_evolution.shape}, self.model.rebuild_prod shape : {self.model.rebuild_prod.shape}"
+        )
+        self._rebuild_production_evolution[
+            self.current_temporal_unit
+        ] = self.model.rebuild_prod
+
+    def _write_productive_capital_lost(self) -> None:
+        """Saves the current remaining productive_capital to rebuild vector to the memmap."""
+        self._regional_sectoral_productive_capital_destroyed_evolution[
+            self.current_temporal_unit
+        ] = self.model.productive_capital_lost
+
+    def _write_production_max(self) -> None:
+        """Saves the current production capacity vector to the memmap."""
+        self._production_cap_evolution[
+            self.current_temporal_unit
+        ] = self.model.production_cap
+
+    def _write_io_demand(self) -> None:
+        """Saves the current (total per industry) intermediate demand vector to the memmap."""
+        self._io_demand_evolution[
+            self.current_temporal_unit
+        ] = self.model.matrix_orders.sum(axis=1)
+
+    def _write_final_demand(self) -> None:
+        """Saves the current (total per industry) final demand vector to the memmap."""
+        self._final_demand_evolution[
+            self.current_temporal_unit
+        ] = self.model.final_demand.sum(axis=1)
+
+    def _write_rebuild_demand(self) -> None:
+        """Saves the current (total per industry) rebuilding demand vector to the memmap."""
+        to_write = np.full(self.model.n_regions * self.model.n_sectors, 0.0)
+        if len(r_dem := self.model.tot_rebuild_demand) > 0:
+            self._rebuild_demand_evolution[self.current_temporal_unit] = r_dem  # type: ignore
+        else:
+            self._rebuild_demand_evolution[self.current_temporal_unit] = to_write  # type: ignore
+
+    def _write_overproduction(self) -> None:
+        """Saves the current overproduction vector to the memmap."""
+        self._overproduction_evolution[self.current_temporal_unit] = self.model.overprod
+
+    def _write_final_demand_unmet(self) -> None:
+        """Saves the unmet final demand (for this step) vector to the memmap."""
+        self._final_demand_unmet_evolution[
+            self.current_temporal_unit
+        ] = self.model.final_demand_not_met
+
+    def _write_stocks(self) -> None:
+        """Saves the current inputs stock matrix to the memmap."""
+        self._inputs_evolution[self.current_temporal_unit] = self.model.matrix_stock
+
+    def _write_limiting_stocks(self, limiting_stock: np.ndarray) -> None:
+        """Saves the current limiting inputs matrix to the memmap."""
+        self._limiting_inputs_evolution[self.current_temporal_unit] = limiting_stock  # type: ignore
+
+    def _flush_memmaps(self) -> None:
+        """Saves files to record"""
+        for at in self._files_to_record:
+            if not hasattr(self, at):
+                raise RuntimeError(
+                    f"{at} should be a member yet it isn't. This shouldn't happen."
+                )
+            else:
+                getattr(self, at).flush()
+
+    def _init_records(self, save_records):
+        for rec in self.__possible_records:
+            if rec == "inputs_stocks" and not self._register_stocks:
+                logger.debug("Will not save inputs stocks")
+                pass
+            else:
+                if rec == "inputs_stocks":
+                    logger.info(
+                        f"Simulation will save inputs stocks. Estimated size is {sizeof_fmt(self.n_temporal_units_to_sim * self.model.n_sectors * self.model.n_sectors * self.model.n_regions * 64)}"
+                    )
+                save = rec in save_records
+                filename = rec
+                dtype, attr_name, shapev, fillv = self.__file_save_array_specs[rec]
+                if shapev == "industries":
+                    shape = (
+                        self.n_temporal_units_to_sim,
+                        self.model.n_sectors * self.model.n_regions,
+                    )
+                elif shapev == "stocks":
+                    shape = (
+                        self.n_temporal_units_to_sim,
+                        self.model.n_sectors,
+                        self.model.n_sectors * self.model.n_regions,
+                    )
+                else:
+                    raise RuntimeError(f"shapev {shapev} unrecognised")
+                memmap_array = TempMemmap(
+                    filename=(self.records_storage / filename),
+                    dtype=dtype,
+                    mode="w+",
+                    shape=shape,
+                    save=save,
+                )
+                memmap_array.fill(fillv)
+                self._files_to_record.append(attr_name)
+                setattr(self, attr_name, memmap_array)
+
+    def _reset_records(
+        self,
+    ):
+        for rec in self.__possible_records:
+            _, attr_name, _, fillv = self.__file_save_array_specs[rec]
+            if rec == "input_stocks" and not self._register_stocks:
+                pass
+            else:
+                memmap_array = getattr(self, attr_name)
+                memmap_array.fill(fillv)
+                setattr(self, attr_name, memmap_array)
+
+    @property
+    def production_realised(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._production_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def production_capacity(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._production_cap_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def final_demand(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._final_demand_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def intermediate_demand(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._io_demand_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def rebuild_demand(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._rebuild_demand_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def overproduction(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._overproduction_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def final_demand_unmet(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._final_demand_unmet_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def rebuild_prod(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._rebuild_production_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
+
+    @property
+    def inputs_stocks(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._inputs_evolution.reshape(
+                self.n_temporal_units_to_sim * self.model.n_sectors, -1
+            ),
+            columns=self.model.industries,
+            copy=True,
+            index=pd.MultiIndex.from_product(
+                [list(range(self.n_temporal_units_to_sim)), self.model.sectors],
+                names=["step", "input"],
+            ),
+        )
+
+    @property
+    def limiting_inputs(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._limiting_inputs_evolution.reshape(
+                self.n_temporal_units_to_sim * self.model.n_sectors, -1
+            ),
+            columns=self.model.industries,
+            copy=True,
+            index=pd.MultiIndex.from_product(
+                [list(range(self.n_temporal_units_to_sim)), self.model.sectors],
+                names=["step", "input"],
+            ),
+        )
+
+    @property
+    def productive_capital_to_recover(self) -> pd.DataFrame:
+        return pd.DataFrame(
+            self._regional_sectoral_productive_capital_destroyed_evolution,
+            columns=self.model.industries,
+            copy=True,
+        ).rename_axis("step")
    
+
    
+
+                
    
+              
+              
+              
+                
    
+                  
+
    
+
    
+                
    
+              
+            
    
+            
+            
+              
+            
+          
    
+          
    
+            
    
+          
    
+        
+      
    
+    
    
+  
    
+  
+  
+  
+
+
+  
    
+
    
+  
+    
    
+      
+        
    
+  
    
+    
+      © Copyright 2022, Samuel Juhel.
+      
    
+    
+  
    
+
    
+      
+        
    
+  
    
+    Created using Sphinx 6.1.3.
+    
    
+  
    
+
    
+      
+    
    
+  
+  
+    
    
+      
+        
    
+  Built with the PyData Sphinx Theme 0.13.0.
+
    
+      
+    
    
+  
+
    
+
+  
    
+  
+
+||||||| 66c6efed
+=======
 
 
 
@@ -1258,4 +2601,5 @@ 
    Source code for boario.simulation
     
   
   
-
\ No newline at end of file
+
+>>>>>>> master
diff --git a/docs/build/html/_modules/boario/utils/misc.html b/docs/build/html/_modules/boario/utils/misc.html
index 44a4c21..c1b220f 100644
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+++ b/docs/build/html/_modules/boario/utils/misc.html
@@ -1,3 +1,588 @@
+<<<<<<< HEAD
+
+
+
+
+
+
+
+  
+    
+    
+    boario.utils.misc — BoARIO  documentation
+  
+  
+  
+  
+  
+  
+  
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+  
+  
+  
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+    
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+    
    
+    
    
+
    
+  
+  
+  Ctrl+K
+
    
+  
    
+  
+    
+  
+  
    
+    
    
+      
+      
    
+        
+
+  
+  
    
+    
+    
+      
    
+        
+          
    
+        
+      
    
+    
+    
+    
+      
    
+        
+          
    
+  Last updated on Apr 24, 2023.
+  
    
+
    
+        
+          
    
+
    
+        
+      
    
+    
+  
    
+  
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+  
    
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+      
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+
    
+
    
+              
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+                
+
+                
    
+                  
+  
    Source code for boario.utils.misc
    +# BoARIO : The Adaptative Regional Input Output model in python.
+# Copyright (C) 2022  Samuel Juhel
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from collections.abc import Iterable
+import textwrap
+import pymrio
+import numpy
+import json
+import tempfile
+
+
+class TempMemmap(numpy.memmap):
+    def __new__(
+        subtype,
+        filename,
+        dtype=numpy.float64,
+        mode="r+",
+        offset=0,
+        shape=None,
+        order="C",
+        save=False,
+    ):
+        if save:
+            self = numpy.memmap.__new__(
+                subtype,
+                filename=filename,
+                dtype=dtype,
+                mode=mode,
+                offset=offset,
+                shape=shape,
+                order=order,
+            )
+            return self
+        else:
+            ntf = tempfile.NamedTemporaryFile()
+            self = numpy.memmap.__new__(
+                subtype,
+                ntf,
+                dtype=dtype,
+                mode=mode,
+                offset=offset,
+                shape=shape,
+                order=order,
+            )
+            self._tmpfile = ntf
+            return self
+
+    def __del__(self):
+        if hasattr(self, "_tmpfile") and self._tmpfile is not None:
+            self._tmpfile.close()
+            del self._tmpfile
+
+
+class CustomNumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, numpy.integer):
+            return int(obj)
+        elif isinstance(obj, numpy.floating):
+            return float(obj)
+        elif isinstance(obj, numpy.ndarray):
+            return obj.tolist()
+        else:
+            return super(CustomNumpyEncoder, self).default(obj)
+
+
+def flatten(l):
+    for el in l:
+        if isinstance(el, Iterable) and not isinstance(el, (str, bytes)):
+            yield from flatten(el)
+        else:
+            yield el
+
+
+
    [docs]def lexico_reindex(mrio: pymrio.IOSystem) -> pymrio.IOSystem:
+    """Reindex IOSystem lexicographicaly
+
+    Sort indexes and columns of the dataframe of a ``pymrio`` `IOSystem <https://pymrio.readthedocs.io/en/latest/intro.html>` by
+    lexical order.
+
+    Parameters
+    ----------
+    mrio : pymrio.IOSystem
+        The IOSystem to sort
+
+    Returns
+    -------
+    pymrio.IOSystem
+        The sorted IOSystem
+
+
+    """
+    if mrio.Z is None:
+        raise ValueError("Given mrio has no Z attribute set")
+    mrio.Z = mrio.Z.reindex(sorted(mrio.Z.index), axis=0)
+    mrio.Z = mrio.Z.reindex(sorted(mrio.Z.columns), axis=1)
+    if mrio.Y is None:
+        raise ValueError("Given mrio has no Y attribute set")
+    mrio.Y = mrio.Y.reindex(sorted(mrio.Y.index), axis=0)
+    mrio.Y = mrio.Y.reindex(sorted(mrio.Y.columns), axis=1)
+    if mrio.x is None:
+        raise ValueError("Given mrio has no x attribute set")
+    mrio.x = mrio.x.reindex(sorted(mrio.x.index), axis=0)
+    if mrio.A is None:
+        raise ValueError("Given mrio has no A attribute set")
+    mrio.A = mrio.A.reindex(sorted(mrio.A.index), axis=0)
+    mrio.A = mrio.A.reindex(sorted(mrio.A.columns), axis=1)
+
+    return mrio
    
+
+
+def sizeof_fmt(num, suffix="B"):
+    for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
+        if abs(num) < 1024.0:
+            return f"{num:3.1f}{unit}{suffix}"
+        num /= 1024.0
+    return f"{num:.1f}Yi{suffix}"
+
+
+def print_summary(my_list):
+    current_element = None
+    current_count = 0
+    summary = []
+    for element in my_list:
+        if element != current_element:
+            if current_element is not None:
+                if current_count == 1:
+                    summary.append(str(current_element))
+                else:
+                    summary.append(f"{current_element} (x {current_count})")
+            current_element = element
+            current_count = 1
+        else:
+            current_count += 1
+    if current_element is not None:
+        if current_count == 1:
+            summary.append(str(current_element))
+        else:
+            summary.append(f"{current_element} (x {current_count})")
+    total_length = len(my_list)
+    total_sum = sum(my_list)
+    summary_string = (
+        "[" + ", ".join(summary) + f"] (len: {total_length}, sum: {total_sum})"
+    )
+    return textwrap.wrap(summary_string, width=80)
+
    
+
+                
    
+              
+              
+              
+                
    
+                  
+
    
+
    
+                
    
+              
+            
    
+            
+            
+              
+            
+          
    
+          
    
+            
    
+          
    
+        
+      
    
+    
    
+  
    
+  
+  
+  
+
+
+  
    
+
    
+  
+    
    
+      
+        
    
+  
    
+    
+      © Copyright 2022, Samuel Juhel.
+      
    
+    
+  
    
+
    
+      
+        
    
+  
    
+    Created using Sphinx 6.1.3.
+    
    
+  
    
+
    
+      
+    
    
+  
+  
+    
    
+      
+        
    
+  Built with the PyData Sphinx Theme 0.13.0.
+
    
+      
+    
    
+  
+
    
+
+  
    
+  
+
+||||||| 66c6efed
+=======
 
 
 
@@ -541,4 +1126,5 @@ 
    Source code for boario.utils.misc
     
   
   
-
\ No newline at end of file
+
+>>>>>>> master
diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index b0079b2..7753d5d 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -18,12 +17,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -34,14 +33,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -157,7 +157,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -182,7 +182,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -269,7 +269,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -283,7 +283,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -365,9 +365,15 @@ 
    All modules for which code is available
    
               
               
                 
    
-                  
+                  
+
    
+  
+    
    
 
    
+
    
+  
 
    
+
                 
    
               
             
    
@@ -377,7 +383,7 @@ 
    All modules for which code is available
    
             
           
    
           
    
-            
    
+            
           
    
         
       
@@ -385,8 +391,8 @@ 
    All modules for which code is available
    
   
    
   
   
-  
-
+  
+
 
   
    
 
    
@@ -404,7 +410,7 @@ 
    All modules for which code is available
    
       
         
    
   
    
-    Created using Sphinx 6.1.3.
+    Created using Sphinx 7.0.1.
     
    
   
    
 
    
@@ -415,7 +421,7 @@ 
    All modules for which code is available
    
     
    
       
         
    
-  Built with the PyData Sphinx Theme 0.13.0.
+  Built with the PyData Sphinx Theme 0.13.3.
 
    
       
     
    
diff --git a/docs/build/html/_sources/autoapi/boario.event.rst.txt b/docs/build/html/_sources/autoapi/boario.event.rst.txt
index 896bf49..9487c1a 100644
--- a/docs/build/html/_sources/autoapi/boario.event.rst.txt
+++ b/docs/build/html/_sources/autoapi/boario.event.rst.txt
@@ -88,25 +88,25 @@ Variables
 
    .. code-block:: text
 
-      typing.Union[int, float, list, dict, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series]
+      typing.Union[int, float, list, dict, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]], pandas.core.frame.DataFrame, pandas.core.series.Series]
 
 .. autodata:: IndustriesList
    :annotation:
 
    .. code-block:: text
 
-      typing.Union[typing.List[typing.Tuple[str, str]], pandas.core.indexes.multi.MultiIndex, numpy.ndarray]
+      typing.Union[typing.List[typing.Tuple[str, str]], pandas.core.indexes.multi.MultiIndex, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
 
 .. autodata:: SectorsList
    :annotation:
 
    .. code-block:: text
 
-      typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray]
+      typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
 
 .. autodata:: RegionsList
    :annotation:
 
    .. code-block:: text
 
-      typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray]
+      typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
diff --git a/docs/build/html/_sources/boario-events.rst.txt b/docs/build/html/_sources/boario-events.rst.txt
index 6e61ef7..bce0379 100644
--- a/docs/build/html/_sources/boario-events.rst.txt
+++ b/docs/build/html/_sources/boario-events.rst.txt
@@ -9,33 +9,39 @@ Introduction
 ===============
 
 The BoARIO model is used to study the indirect economic impacts consequent to `local` event [#local]_.
-At the moment, a shock is represented by a quantity of damages expressed in monetary value and leading to:
+At the moment, a shock is represented either by:
 
-1. A destruction of capital leading to a reduction in production capacity production (See :ref:`boario-math-prod`).
-2. A possible rebuilding demand, corresponding to destroyed capital or a part of it and addressed to a set of rebuilding sectors (See :ref:`boario-math-rebuilding-demand`).
+* A quantity of damages expressed in monetary value and leading to:
+
+  1. A destruction of capital leading to a reduction in production capacity production (See :ref:`boario-math-prod`).
+  2. A possible rebuilding demand, corresponding to destroyed capital or a part of it and addressed to a set of rebuilding sectors.
+
+* Directly by a fraction of production capacity lost for a certain period, which is then recovered exogenously.
 
 To define such an event, we hence need the following information:
 
-1. A quantity of damages
+1. A quantity of damages (or fraction of production capacity)
 2. The region(s) affected
 3. The sector(s) affected
 4. In the case a rebuilding demand is considered: the sector(s) considered to be rebuilding sectors
 
 In addition, it is also possible to define a duration (corresponding to the time before rebuilding can start)
-as well as time of occurrence (when studying interaction between multiple events).
+as well as time of occurrence (when studying interaction between multiple events), and a recovery duration (:ref:`boario-math-recovery`).
 
 During a simulation BoARIO uses :class:`~boario.event.Event` objects.
 This page details the different aspect of instantiating such objects.
 
+.. [#local] At the moment events are local to the regional unit of the MRIOT used (e.g. a country/world region in the case of EXIOBASE3 MRIO).
+
 Simulation context
 ------------------
 
 Creating events requires a simulation context to exist,
 notably to assess the validity of the event (the affected regions, sectors, the occurrence, ...). Hence,
-trying to instantiate an ``Event`` object without already having a ``Simulation`` (and therefore a ``ARIOPsiModel``)
+trying to instantiate an ``Event`` object without already having a ``Simulation`` (and therefore an ``ARIOPsiModel``)
 will raise an error.
 
-For the purpose of this page we will suppose the following code was run initially:
+In the following, we will assume the following code was run initially:
 
 .. code:: python
 
@@ -63,23 +69,23 @@ Here are the regions and sectors defined in this context:
                  'other', 'trade', 'transport'], dtype='>> import pandas as pd
+   >>> impact = pd.Series(
+   ...     data=[0.3, 0.1],
+   ...     index=pd.MultiIndex.from_product(
+   ...         [["reg2"], ["manufactoring", "mining"]], names=["region", "sector"]
+   ...     ),
+   ... )
+   >>> impact
+
+   region  sector
+   reg2    manufactoring    0.3
+           mining           0.1
+   dtype: float64
+
+.. code:: python
+
+          ev = EventArbitraryProd.from_series(
+              impact=impact,
+              occurrence=5,
+              duration=7,
+              recovery_function="linear",
+              recovery_time=5,
+          )
+
 ================================
 Defining events from a scalar
 ================================
 
-You can also define an event from a scalar impact.
+You can also define an event from a scalar impact 
+(except for :class:`~boario.event.EventArbitraryProd` at the moment).
 This requires to define which industries are affected and
 how the impact is distributed among the industries.
 
+You can take a look at the quickstart example 
+`here `_.
+
 In this section, we go over the different cases first, and then show
 code examples for each case.
 
 In order to define which industries are affected you can:
 
-1. Directly give them as a pandas MultiIndex with affected regions as the first level and affected sectors at the second.
+1. Directly give them as a pandas MultiIndex with affected regions as the first level 
+   and affected sectors at the second.
 
-2. Give them as a list of regions affected, as well as a list of sectors affected. The resulting affected industries being the cartesian product of those two lists.
+2. Give them as a list of regions affected, as well as a list of sectors affected. 
+   The resulting affected industries being the cartesian product of those two lists.
 
 .. warning::
 
   Note that the second option does not allow to have different sectors affected in each region.
 
-By default, the impact will be uniformally distributed among the affected regions and the impact per region
-is then also uniformally distributed among the affected sector in the region.
+By default, the impact will be uniformally distributed among the affected regions and 
+the impact per region is then also uniformally distributed among the affected sector in the region.
 
-Otherwise, there are multiple ways to parameters a custom distribution:
+Otherwise, there are multiple ways to setup a custom distribution:
 
-1. Directly give a vector (any variable that can be transformed as a numpy array) of per affected industry share of the impact (although in this case you should probably directly give the impact vector).
+1. Directly give a vector (any variable that can be transformed as a numpy array) 
+   of per affected industry share of the impact (although in this case you should 
+   probably directly give the impact vector).
 2. Give a vector of the share per region, and the share per sector.
-3. Give a vector of the share per region, and specify ``"gdp"`` for the per sector distribution. This will distribute each regional impact toward each affected sector proportionally to their share of the regional GDP among the affected sectors. For example: Suppose we look at the impact in region ``"reg2"``, and suppose ``"manufactoring"`` and ``"mining"`` are both affected. Now suppose ``"manufactoring"`` account for 40% of ``"reg2"``'s GDP and ``"mining"`` for 10%. The ``"manufactoring"`` sector will receive :math:`(40 * 100) / (40 + 10) = 80\%` of the impact and ``"mining"`` the remaining :math:`(10 * 100) / (40 + 10) = 20\%`.
+3. Give a vector of the share per region, and specify ``"gdp"`` for the per sector 
+   distribution. This will distribute each regional impact toward each affected sector 
+   proportionally to their share of the regional GDP among the affected sectors. 
+   For example: Suppose we look at the impact in region ``"reg2"``, and suppose 
+   ``"manufactoring"`` and ``"mining"`` are both affected. Now suppose 
+   ``"manufactoring"`` account for 40% of ``"reg2"``'s GDP and ``"mining"`` 
+   for 10%. The ``"manufactoring"`` sector will receive :math:`(40 * 100) / (40 + 10) = 80\%` 
+   of the impact and ``"mining"`` the remaining :math:`(10 * 100) / (40 + 10) = 20\%`.
 
 .. note::
 
@@ -214,9 +275,10 @@ Otherwise, there are multiple ways to parameters a custom distribution:
 
 .. warning::
 
-  You should not assume the default impact distribution setting is a good representation of the general case,
-  as different regions and sectors are most probably differently impacted by an event.
-  It is strongly advised to setup your own distribution in accordance with your study.
+  You should not assume the default impact distribution setting is a good representation
+  of the general case, as different regions and sectors are most probably differently
+  impacted by an event. It is strongly advised to setup your own distribution in accordance
+  with your study.
 
 .. code:: pycon
 
diff --git a/docs/build/html/_sources/boario-installation.rst.txt b/docs/build/html/_sources/boario-installation.rst.txt
index 7fa63ec..c199f93 100644
--- a/docs/build/html/_sources/boario-installation.rst.txt
+++ b/docs/build/html/_sources/boario-installation.rst.txt
@@ -23,14 +23,27 @@ on the repository, or `contact the developer`_.
 
 .. _contact the developer: pro@sjuhel.org
 
+Install from a specific branch
+=================================
+
+Note that you can directly install BoARIO from a branch of its github repository using:
+
+.. code-block:: console
+
+   pip install git+https://github.com/spjuhel/boario.git@branch-name
 
 Requirements
 ===============
 
-BoARIO is currently developed using python ``3.8``. It might work with older python versions, but no check were made regarding this.
+BoARIO is currently developed using python ``3.9``. It might work with older python versions, but no check were made regarding this.
 
 Installing via pip should install all the required dependencies.
 
 You can also check the ``pyproject.toml`` file in the sources to see all current dependencies.
 
 If you are experiencing difficulties in setting up a working environment feel free to open an issue on the Github repository or to contact the developer.
+
+Platform
+==========
+
+The code is known to run under both Linux and Windows operating systems.
diff --git a/docs/build/html/_sources/boario-math.rst.txt b/docs/build/html/_sources/boario-math.rst.txt
index f0f4fb8..762ad11 100644
--- a/docs/build/html/_sources/boario-math.rst.txt
+++ b/docs/build/html/_sources/boario-math.rst.txt
@@ -1,10 +1,55 @@
 .. _boario-math:
 
 ###################################
-Detailed model description
+Model description
 ###################################
 
-This page details the mathematical background of the ARIO model.
+Background and overview
+=======================
+	 	 	 	
+Adaptive Regional Input-Output (ARIO) is an I-O model, designed to compute indirect
+costs from exogenous shocks. Its first version dates back to 2008 and has originally
+been developed to assess the indirect costs of natural disasters (Hallegatte 2008).
+In this paper we present BoARIO, a generic python implementation, similar to the one
+described in (Hallegatte 2014) with some additions (e.g. inspired by Guan et al. 2020).
+
+The economy is modelled as a set of economic sectors and a set of regions.
+In the following, we call an industry a specific (sector, region) couple.
+Each economic sector produces its generic product and draws inputs from an inventory.
+Each industry answers to a total demand consisting of a final demand
+(household consumption, public spending and private investments) coming from all
+regions (i.e. both local demand and exports), intermediate demand (inventory resupply).
+An initial equilibrium state of the economy is built based on multi-regional
+input-output tables (MRIO tables). Apart from parameters specific to the MRIOT
+region-sector typology, BoARIO supposedly handles any MRIOT in the same manner.
+
+Multiple kinds of shocks can be implemented: 
+
+* On the production capacity directly (one or multiple industries are arbitrarily
+  forced to produce less)
+* On the productive capital (one or multiple industries arbitrarily 
+  lose some part of their factors of production and are thus forced to both
+  produce less and to build back their capital stock).
+* On the households (households of affected regions lose some part of their goods and
+  seek to get them back).
+
+The model then describes how exogenous shocks propagates across the economy at each
+time step (a day in this study): the imbalance between production and demand is
+resolved by a proportional rationing scheme, which then leads some industries and
+households to not receive the totality of their orders. Industries can buffer
+these unfilled orders with their “inventories” of input and increase or shift
+their resupply demand. Currently, households simply register the loss as
+final consumption not met and do not postpone their demand. During the simulation,
+industries adjust their production and orders based on both demand and their inventories.
+
+Direct economic impact consists in the valuation of the initial exogenous shock,
+while total economic impact includes also indirect costs consequent to the propagation.
+
+Total economic impact can be measured in two ways: 
+
+* Final demand not met, i.e. goods that households couldn’t buy due to rationing.
+* Relative production change, i.e. diminished or increased production of industries relative to their initial production.
+
 
 .. _boario-math-notations:
 
@@ -23,8 +68,8 @@ Notations
 
   Additionally, we use :math:`A = B \odot C`, the Hadamard product (element wise product) (i.e. :math:`a_{ij} = b_{ij} \cdot c_{ij}`)
 
-Model(s) details
-===================
+Mathematical formalism
+=======================
 
 .. _boario-math-init:
 
@@ -33,11 +78,8 @@ Initial state
 
 The initial state refers to an economic equilibrium before any exogenous shocks. Initial values for intermediate orders :math:`\ioorders`, final consumption :math:`\ioy`, and production :math:`\iox` are derived from MRIO tables. Inventories :math:`\ioinv` are stocks of inputs an industry can draw into and :math:`\ioinv(t=0)` is initialized using the initial intermediate orders :math:`\ioorders(0) = \ioz` : by default, it is assumed that every industry has :math:`\mathbf{s}` days of inputs ahead in their stocks.
 
-In the rest of this section, we describe this initialisation in more details.
-
-We build :math:`\ioz`, :math:`\ioy` and :math:`\iov` using input output tables.
-We compute initial production :math:`\iox_0`, technical matrix :math:`\ioa` and
-value added technical matrix :math:`\ioava` as follows (consistent with Leontief I-O theory):
+Initial production :math:`\iox_0`, technical matrix :math:`\ioa` and
+value added technical matrix :math:`\ioava` are consistent with Leontief I-O theory:
 
 .. math::
    :nowrap:
@@ -56,9 +98,10 @@ Where:
 
 .. note::
 
-   Note that we divide these yearly values by the ``iotable_year_to_temporal_unit_factor`` :ref:`parameter ` in order to obtain an approximation of the productions and demands per :ref:`temporal unit` (most often days).
+   We divide these yearly values by the ``iotable_year_to_temporal_unit_factor`` :ref:`parameter ` in order to obtain an approximation of the productions and demands per :ref:`temporal unit` (most often days).
 
-We also compute the following :
+We also require technology and transactions information at the sector level,
+regardless of the region of provenance, so we compute the following :
 
 .. _boario-math-z-agg:
 
@@ -94,7 +137,7 @@ Where :
    :math:`\oslash` is the matrix element-wise division, defined such that dividing by zero gives zero. (If initial order to an industry was null, the share ordered is also null)
 
 
-Hence, :math:`\ioa^{\sectorsset}` and :math:`\ioz^{\sectorsset}` are respectfully the technical matrix and the intermediate demand matrix, that are aggregated by sector. They indicate technology and transactions on the sector level, regardless of the region of provenance.
+Hence, :math:`\ioa^{\sectorsset}` and :math:`\ioz^{\sectorsset}` are respectfully the technical matrix and the intermediate demand matrix, that are aggregated by sector.
 
 :math:`\ioz^{\textrm{Share}}` represents for each firm and for each of their input, the share of the total ordered to a supplier, initially (i.e. :math:`\ioz` normalized for each input). Or alternatively, the initial "market shares".
 
@@ -122,11 +165,11 @@ Such that :math:`\omega_{if}(0) = s_i \cdot x_{f}(0) \cdot a_{if}` is the
 exact amount of product :math:`i` required by industry :math:`f` to produce
 :math:`x_{f}(0)` (i.e. the initial equilibrium production of :math:`f`) during :math:`s_i` temporal units. Or, alternatively, that :math:`f` could (technically) produce during :math:`s_i` temporal units without receiving input from any industry producing :math:`i`.
 
-Hence, all industries start with a stock of each of their intermediate inputs equal to the amount required for :math:`s_i` temporal units of production at initial production capacity.
+As such, all industries start with a stock of each of their intermediate inputs equal to the amount required for :math:`s_i` temporal units of production at initial production capacity.
 
 .. note::
 
-  :math:`s_i` does not differ on a per-industry basis, only on a per-product basis, (although this could be implemented easily).
+  At the moment :math:`s_i` does not differ on a per-industry basis, only on a per-product basis.
 
 The order matrix :math:`\ioorders` is initialized to be equal to :math:`\ioz` :
 
@@ -183,7 +226,7 @@ Once we have production capacity, we can compute actual production:
              \end{bmatrix}
     \odot \begin{bmatrix} \iox^{\textrm{Opt}}(t)\\
     \vdots\\
-    \iox^{\textrm{Opt}}(t) \end{bmatrix} \odot \ioa^{\sectorsset} && \\
+    \iox^{\textrm{Opt}}(t) \end{bmatrix} \odot \ioa^{\sectorsset} \cdot \psi && \\
     &&&= \begin{bmatrix}
     s^{1}_1 x^{\textrm{Opt}}_{1}(t) a_{11} & \cdots & s^{p}_1 x^{\textrm{Opt}}_{p}(t) a_{1p}\\
     \vdots & \ddots & \vdots\\
@@ -198,19 +241,28 @@ Once we have production capacity, we can compute actual production:
     \end{alignat*}
 
 
-First we compute the total demand directed towards each industry with eq. :math:`\text{Total demand matrix}`. Then we compute optimal production without inventory constraints for each industry as the minimum between production capacity (possibly reduced by damages) and total demand, assuming an industry will not produce more than its clients demand (eq. :math:`\text{Optimal production}`).
+First we compute the total demand directed towards each industry with **Eq.** :math:`\text{Total demand matrix}`.
+Then we compute optimal production without inventory constraints for each industry as the minimum between
+production capacity (possibly reduced by damages) and total demand, assuming an industry will not produce more
+than its clients demand (**Eq.** :math:`\text{Optimal production}`).
 
-We define inventory constraints :math:`\ioinv^{\textrm{Cons}}` for each input, as a share :math:`\psi` of the amount of stocks required to produce :math:`s_p^f` temporal units of production at the level of production of the previous step (eq. :math:`\text{Inventory constraints}`).
+We define inventory constraints :math:`\ioinv^{\textrm{Cons}}` for each input, as a share :math:`\psi` of the amount
+of stocks required to produce :math:`s_p^f` temporal units of production at the level of production of the previous
+step (**Eq.** :math:`\text{Inventory constraints}`).
 
 .. seealso::
 
-   You can take a look at the documentation of the method here: :py:meth:`~boario.extended_models.ARIOPsiModel.calc_production` (as well as the source code, via the ``source`` button)
+   You can take a look at the documentation of the method
+   here: :py:meth:`~boario.extended_models.ARIOPsiModel.calc_production`
+   (as well as the source code, via the ``source`` button)
 
 .. hint::
 
   :class:`ARIOBaseModel` offers a simplified version of the model where :math:`\psi = 1` (among other simplifications).
 
-If the inventory of product :math:`p \in \sectorsset` of an industry :math:`f` is lower than its required level, then :math:`f`'s production is reduced. An inventory shortage of :math:`x` % (w.r.t. its constraint) leads to a :math:`x` % reduction of production.
+If the inventory of product :math:`p \in \sectorsset` of an industry :math:`f` is lower than its required level,
+then :math:`f`'s production is reduced. An inventory shortage of :math:`x` % (w.r.t. its constraint) leads to
+a :math:`x` % reduction of production.
 
 
 .. _boario-math-distrib:
@@ -220,7 +272,8 @@ Distribution and inventory module
 
 The distribution module defines how actual production is allocated towards the various demands:
 
-If :math:`d_f^{\textrm{Tot}}(t) = x_f(t)`, each client receive their order. If :math:`d_f^{\textrm{Tot}}(t) > x_f(t)`, each client receive a share of their order as per a proportional rationing scheme:
+If :math:`d_f^{\textrm{Tot}}(t) = x_f(t)`, each client receive their order. If :math:`d_f^{\textrm{Tot}}(t) > x_f(t)`,
+each client receive a share of their order as per a proportional rationing scheme:
 
 .. math::
    :nowrap:
@@ -231,7 +284,8 @@ If :math:`d_f^{\textrm{Tot}}(t) = x_f(t)`, each client receive their order. If :
       &\Damage^{\textrm{Repaired}}(t) &&= \left ( \frac{\gamma_{f} \cdot \tau_{\textrm{REBUILD}}}{d^{\textrm{Tot}}_f(t)} \cdot x^a_f(t) \right )_{f\in \firmsset}\\
     \end{alignat*}
 
-Where :math:`\damage_f` is the total rebuilding demand towards industry :math:`f` and :math:`\tau_{\textrm{REBUILD}}` is the rebuilding characteristic time.
+Where :math:`\damage_f` is the total rebuilding demand towards industry :math:`f` and
+:math:`\tau_{\textrm{REBUILD}}` is the rebuilding characteristic time.
 
 Inventory resupply
 __________________
@@ -269,9 +323,11 @@ __________
 Order module
 ^^^^^^^^^^^^^^
 
-After distribution, the model can compute the orders made by each industries towards their suppliers in order to resupply their inventories of inputs.
+After distribution, the model can compute the orders made by each industries towards their suppliers in order
+to resupply their inventories of inputs.
 
-Industries seek to restore the inventory of each of their input to their goal level, that is the initial level w.r.t. its current optimal production level (see :ref:`boario-math-prod`).
+Industries seek to restore the inventory of each of their input to their goal level, that is the initial
+level w.r.t. its current optimal production level (see :ref:`boario-math-prod`).
 
 .. attention::
 
@@ -279,7 +335,10 @@ Industries seek to restore the inventory of each of their input to their goal le
 
 .. note::
 
-  In :class:`ARIOBaseModel`, the total order matrix is simply the difference between :math:`\ioinv^{*}(t)` and :math:`\ioinv(t)`, whereas in :class:`ARIOPsiModel`, only a fraction of missing inventories are ordered, but in addition, the totality of inputs used for production during this step is also ordered. This difference is highlighted in red in the next equations (the red part is added in the ``ARIOPsiModel`` case)
+  In :class:`ARIOBaseModel`, the total order matrix is simply the difference between :math:`\ioinv^{*}(t)` and
+  :math:`\ioinv(t)`, whereas in :class:`ARIOPsiModel`, only a fraction of missing inventories are ordered,
+  but in addition, the totality of inputs used for production during this step is also ordered.
+  This difference is highlighted in red in the next equations (the red part is added in the ``ARIOPsiModel`` case)
 
 .. math::
    :nowrap:
@@ -291,10 +350,10 @@ Industries seek to restore the inventory of each of their input to their goal le
        &\ioorders(t) &&= \left ( \ioorders^{\sectorsset}(t) \otimes \underbrace{\begin{bmatrix} 1 \\ \vdots\\ 1 \end{bmatrix}}_{\text{m-sized}} \right ) \odot  {\color{green}{\ioz^{\textrm{*}}}} &&\quad && \text{Intermediate demand orders}
     \end{alignat*}
 
-#. In eq. :math:`\text{Inventory goals}` we compute inventory goals based on optimal production. (Note that, in the version with ``psi``, :math:`\Omega^* = \frac{\Omega^{\textrm{Cons}}}{\psi}`)
-#. In eq. :math:`\text{Inventory gaps}` we compute the inventory gaps. :math:`(\mathbf{A} - \mathbf{B})_{\geq 0}` denotes the resulting matrix of :math:`\mathbf{A} - \mathbf{B}` where negative values are replaced by 0.
-#. In eq. :math:`\text{Intermediate total demand orders}` we compute aggregate orders for intermediate demand.
-#. In eq. :math:`\text{Intermediate demand orders}` we compute the actual order matrix, by distributing total orders along the different suppliers. The model has two variants based on the value of :math:`\color{green}{\ioz^{\textrm{*}}}` which are described bellow.
+#. In **Eq.** :math:`\text{Inventory goals}` we compute inventory goals based on optimal production. (Note that, in the version with ``psi``, :math:`\Omega^* = \frac{\Omega^{\textrm{Cons}}}{\psi}`)
+#. In **Eq.** :math:`\text{Inventory gaps}` we compute the inventory gaps. :math:`(\mathbf{A} - \mathbf{B})_{\geq 0}` denotes the resulting matrix of :math:`\mathbf{A} - \mathbf{B}` where negative values are replaced by 0.
+#. In **Eq.** :math:`\text{Intermediate total demand orders}` we compute aggregate orders for intermediate demand.
+#. In **Eq.** :math:`\text{Intermediate demand orders}` we compute the actual order matrix, by distributing total orders along the different suppliers. The model has two variants based on the value of :math:`\color{green}{\ioz^{\textrm{*}}}`, which are described bellow.
 
 .. _alt_orders:
 
@@ -307,7 +366,7 @@ A. The model uses the initial transaction shares: :math:`\ioz^{\textrm{*}}(t) =
 
        \ioz^{\textrm{Share}} =  \ioz \oslash \left ( \left ( \mathbf{I_{\textrm{sum}}} \right ) \cdot  \ioz \otimes \underbrace{\begin{bmatrix} 1 \\ \vdots\\ 1 \end{bmatrix}}_{\text{m-sized}} \right )
 
-B. The model uses the initial transaction share, but weighted by suppliers current production level, relative to their initial production, ie we replace both occurrence of :math:`\ioz` in the previous equation by the following:
+B. The model uses the initial transaction share, but now weighted by suppliers current production level relative to their initial production, this is done by replacing both occurrences of :math:`\ioz` in the previous equation by the following:
 
 .. math::
 
@@ -352,10 +411,11 @@ In ARIO, industries can temporarily increase their production capacity via an ov
 Event impact
 --------------
 
-Currently, the model allows representing the impact of the event via two effects :
+Currently, the model allows representing the impact of an event via three effects :
 
-1. A decrease of the production capacity of the impacted industries.
+1. A decrease of the production capacity of the impacted industries consequent to the destruction of productive capital.
 2. An additional final demand corresponding to the capital destroyed and addressed towards the rebuilding sectors.
+3. An arbitrary reduction of the production capacity for a given period.
 
 See :ref:`boario-events`.
 
diff --git a/docs/build/html/_sources/boario-quickstart.rst.txt b/docs/build/html/_sources/boario-quickstart.rst.txt
index 2747e3b..07d16f7 100644
--- a/docs/build/html/_sources/boario-quickstart.rst.txt
+++ b/docs/build/html/_sources/boario-quickstart.rst.txt
@@ -16,66 +16,13 @@ on the repository, or `contact the developer`_.
 .. _contact the developer: pro@sjuhel.org
 
 
-Minimal working full example
-__________________________________
-
-In the following example, we use the "test" MRIO of the pymrio module,
-which doesn't require to load additional data and has a low number of sectors and
-regions, allowing fast computation.
-
-.. code:: python
-
-          # import pymrio for the test MRIO
-          import pymrio
-
-          # import pandas for the plot
-          import pandas as pd
+Quickstart example
+___________________
 
-          # import the different classes
-          from boario.simulation import Simulation  # Simulation wraps the model
-          from boario.extended_models import ARIOPsiModel  # The core of the model
-
-          # A class computing and storing indicators based on a simulation
-          from boario.indicators import Indicators
-          from boario.event import EventKapitalRecover  # A class defining a shock on capital
-
-          # Load the IOSystem from pymrio
-          mrio = pymrio.load_test().calc_all()
-
-          # Instantiate the model and the simulation
-          model = ARIOPsiModel(mrio)
-          sim = Simulation(model)
-
-          # Instantiate an event.
-          ev = EventKapitalRebuild(
-              impact=10000000,
-              aff_regions=["reg1"],
-              aff_sectors=["manufactoring", "mining"],
-              rebuilding_sectors={"construction": 1.0},
-              rebuilding_factor=1.0,
-          )
-
-          # Add the event to the simulation
-          sim.add_event(ev)
-
-          # Launch the simulation
-          sim.loop()
-
-          # You should be able to generate a dataframe of
-          # the production with the following line
-          df = pd.DataFrame(sim.production_evolution, columns=model.industries)
-
-          # This allows to normalize production at its initial level
-          df = df / df.loc[0]
-
-          df.loc[:, ("reg1", slice(None))].plot()
-
-You should get a plot looking like this one:
-
-.. image:: ./imgs/boario-minimal-example-1-plot.png
-  :width: 600
-  :alt: Example of results plot
+.. toctree::
+   :maxdepth: 2
 
+   notebooks/boario-quickstart
 
 ARIO vs :class:`~boario.model_base.ARIOBaseModel` vs :class:`~boario.extended_models.ARIOPsiModel`
 _____________________________________________________________________________________________________________
@@ -160,8 +107,10 @@ Parameters are set when instantiating the model. The following block shows all c
               iotable_year_to_temporal_unit_factor=365,
               infinite_inventories_sect=None,
               inventory_dict=None,
-              kapital_vector=self.stock_exp,
-              kapital_to_VA_dict=None,
+              productive_capital_vector=None,
+              productive_capital_to_VA_dict=None,
+              psi_param = 0.80,
+              inventory_restoration_tau = 60,
           )
 
 
@@ -187,9 +136,11 @@ Here a quick description of each parameters. Please refer to both :ref:`the math
 
 * ``iotable_year_to_temporal_unit_factor`` defines the `temporal unit` assuming the MRIO contains yearly values. Note that this has not been extensively tested and should be used with care.
 
-* ``kapital_to_VA_dict`` should be a dictionary of ``sector:ratio`` format, where ratio is an estimate of Capital Stock over Value Added ratio. This is used to estimate the capital stock of each sector. By default the ratio is 4/1 for all sectors.
+* ``productive_capital_to_VA_dict`` should be a dictionary of ``sector:ratio`` format, where ratio is an estimate of Capital Stock over Value Added ratio. This is used to estimate the capital stock of each sector. By default the ratio is 4/1 for all sectors.
+
+* ``productive_capital_vector`` can directly set the capital stock for all industries (ie regions*sectors sized). This overrides ``kapital_to_VA_dict``.
 
-* ``kapital_vector`` can directly set the capital stock for all industries (ie regions*sectors sized). This overrides ``kapital_to_VA_dict``.
+* ``psi_param`` and ``inventory_restoration_tau`` : see :ref:`boario-math-dyn`
 
 .. note::
 
@@ -207,8 +158,8 @@ Monitoring the model variables
 
 By default, simulations record the evolution of variables in temporary files, and the arrays
 are accessible directly as attributes as long as the ``Simulation`` object exists.
-For convenience all such attributes ends with ``_evolution``.
-The arrays contains the variables values for each regions for each sector for each temporal unit.
+
+Their available as DataFrame and contain the variables values for each regions for each sector for each temporal unit.
 Each row represent a temporal unit. The columns are all the possible (region,sector) tuples, ie industries,
 ordered in lexicographic order.
 
@@ -217,41 +168,41 @@ Here is a commented list of these attributes:
 .. code:: python
 
         # The realised production
-        sim.production_evolution
+        sim.production_realised
 
         # The production capacity
-        sim.production_cap_evolution
+        sim.production_capacity
 
         # The share of realised production distributed to rebuilding
-        sim.rebuild_production_evolution
+        sim.rebuild_prod
 
         # The overproduction factor
-        sim.overproduction_evolution
+        sim.overproduction
 
         # The (total) intermediate demand (ie how much intermediate demand was addressed to sector i in region j)
-        sim.io_demand_evolution
+        sim.intermediate_demand
 
-        # The (total) final demand (note the final demand is currently fix in the model)
-        sim.final_demand_evolution
+        # The (total) final demand (note that the final demand is currently fix in the model)
+        sim.final_demand
 
         # The (total) rebuild demand
-        sim.rebuild_demand_evolution
+        sim.rebuild_demand
 
         # The amount of final demand that couldn't be satisfied
-        sim.final_demand_unmet_evolution
+        sim.final_demand_unmet
 
         # The remaining amount of destroyed (ie not recovered/rebuilt) capital
-        sim.regional_sectoral_kapital_destroyed_evolution
+        sim.productive_capital_to_recover
 
         # Note that the following array have one more dimension,
         # their shape is (temporal units, sectors, regions*sectors)
         # This one states for each temporal unit, for each input, for each (region,sector)
         # if the input was limiting production. For efficiency, information is stored as a
         # byte, -1 for False, 1 for True
-        sim.limiting_inputs_evolution
+        sim.limiting_inputs
 
 It is also possible to record the inputs stocks, but this is disabled by defaults as its shape is the same as
-``limiting_inputs_evolution``, but its ``dtype`` is ``float64``, which can very rapidly lead to huge files.
+``limiting_inputs``, but its ``dtype`` is ``float64``, which can very rapidly lead to huge files.
 
 .. code:: python
 
@@ -259,14 +210,7 @@ It is also possible to record the inputs stocks, but this is disabled by default
           sim = simulation(model, register_stocks=True)
 
           # Access the array
-          sim.inputs_evolution
-
-These files are raw numpy arrays (for efficiency purpose) but you can easily transform them to pandas DataFrame as ``model.industries`` stores
-the relevant MultiIndex.
-
-.. code:: python
-
-          df = pd.DataFrame(sim.production_evolution, columns=model.industries)
+          sim.inputs_stocks
 
 .. _index_records:
 
@@ -277,18 +221,18 @@ In order to keep experiments organized and reproducible,
 the following arguments can be used when instantiating a
 ``Simulation`` object:
 
-* ``"save_index"`` : ``True|False``, if ``True``, save a file :file:`boario_output_dir/results/jsons/indexes.json`, where the indexes (regions, sectors, final demand categories, etc.) are stored.
+* ``"save_index"`` : ``True|False``, if ``True``, saves a file :file:`boario_output_dir/results/jsons/indexes.json`, where the indexes (regions, sectors, final demand categories, etc.) are stored.
 
-* ``"save_params"`` : ``True|False``, if ``True``, save a file :file:`boario_output_dir/results/jsons/simulated_params.json`, where the simulation parameters are stored.
+* ``"save_params"`` : ``True|False``, if ``True``, saves a file :file:`boario_output_dir/results/jsons/simulated_params.json`, where the simulation parameters are stored.
 
-* ``"save_events"`` : ``True|False``, if ``True``, save a file :file:`boario_output_dir/results/jsons/simulated_events.json`, where the indexes (regions, sectors, final demand categories, etc.) are stored.
+* ``"save_events"`` : ``True|False``, if ``True``, saves a file :file:`boario_output_dir/results/jsons/simulated_events.json`, where the indexes (regions, sectors, final demand categories, etc.) are stored.
 
 .. _recording:
 
 Record files
 -------------
 
-By defaults the arrays recording the evolution of variables are temporary files (:ref:`variables_evolution`),
+By defaults the arrays recording the evolution of variables are temporary files,
 which are deleted when the ``Simulation`` object is destroyed.
 
 It is however possible to ask the ``Simulation`` object to save any selection of these raw arrays,
@@ -314,7 +258,7 @@ in the specified folder (or to a temporary directory prefixed by ``"boario"`` by
           )
 
 
-Files saved like this can then be read with:
+Files saved like this are raw numpy arrays and can then be read with:
 
 .. code:: python
 
@@ -344,41 +288,5 @@ Files saved like this can then be read with:
               shape=(n_sectors * n_temporal_units, n_sectors * n_regions),
           )
 
-Indicators and parquet files
--------------------------------
-
-You may also pass the ``Simulation`` object to ``indic = Indicators.from_sim()`` to create an :class:`~boario.indicators.Indicators` object,
-which will among other things, generate all the pandas dataframe. These dataframes can then be accessed by ``ind.production_realised_df`` for example.
-
-If you also specify a ``results_storage`` argument, instantiating will also produce a ``parquets`` folder at the path given
-, inside which all dataframes will be stored as `parquets`_ files (which can then be read easily using :py:func:`~pandas.read_parquet`).
-
-.. code:: python
-
-   from boario.indicators import Indicators
-
-   indic = Indicators.from_sim(sim, results_storage="path/to/save/results")
-
-Calling ``indic.update_indicators()`` fills the ``indic.indicators`` dictionary with the following indicators:
-
-- The total (whole world, all sectors) final consumption not met during the simulation ``indicator['tot_fd_unmet']``.
-
-- The final consumption not met in the region(s) affected by the shock ``indicator['aff_fd_unmet']``.
-
-- The rebuild duration (ie the number of step during which rebuild demand is not zero) ``indicator['rebuild_durations']``.
-
-- If there was a shortage (``indicator['shortage_b']``), its start and end dates ``indicator['shortage_date_start']`` and ``indicator['shortage_date_end']``.
-
-- The top five `(region,sectors)` tuples where there was the biggest absolute change of production compared to a no shock scenario.
-
-It also produce dataframes indicators :
-
-- The production change by region over the simulation (``indic.prod_chg_region``) giving for each region the total gain or loss in production during the simulation.
-
-- The final consumption not met by region over the simulation (``indic.df_loss_region``) giving for each region the total loss in consumption during the simulation.
-
-Finally calling ``indic.write_indicators(storage_path="/path/of/your/choosing")`` will write the ``indic.indicators`` dictionary to :file:`indicators/indicators.json`, as well as :file:`indicators/prod_chg.json` and :file:`indicators/fd_loss.json`. The parent folder defaults to the ``results_storage`` argument given when instantiating the ``Indicators`` object and is overridden if ``storage_path`` is specified. Note that if neither was given, an error is raised.
-
-.. _`parquets`: https://parquet.apache.org/
 .. _github repository: https://github.com/spjuhel/BoARIO
 .. _here: https://zenodo.org/record/5589597
diff --git a/docs/build/html/_sources/boario-references.rst.txt b/docs/build/html/_sources/boario-references.rst.txt
index 9497bae..552f68f 100644
--- a/docs/build/html/_sources/boario-references.rst.txt
+++ b/docs/build/html/_sources/boario-references.rst.txt
@@ -1,4 +1,37 @@
+.. _references:
+
+ARIO Academic work
+==========================
+
+.. image:: https://raw.githubusercontent.com/spjuhel/BoARIO/master/imgs/chronology.svg?sanitize=true
+           :width: 900
+           :alt: ADEME Logo
+
+
 References
 =============
 
+
+:cite:empty:`2008:hallegatte`
+:cite:empty:`2010:ranger`
+:cite:empty:`2011:hallegatte`
+:cite:empty:`2012:henriet`
+:cite:empty:`2013:hallegatte`
+:cite:empty:`2014:wenz`
+:cite:empty:`2015:koks`
+:cite:empty:`2016:koks`
+:cite:empty:`2018:wang`
+:cite:empty:`2018:zhang`
+:cite:empty:`2019:avelino19`
+:cite:empty:`2019:colon`
+:cite:empty:`2019:koks`
+:cite:empty:`2020:guan`
+:cite:empty:`2020:guan`
+:cite:empty:`2020:huang`
+:cite:empty:`2020:mendoza`
+:cite:empty:`2020:shughrue`
+:cite:empty:`2020:wang`
+:cite:empty:`2021:zhang`
+:cite:empty:`2022:huang`
+
 .. bibliography::
diff --git a/docs/build/html/_sources/boario-terminology.rst.txt b/docs/build/html/_sources/boario-terminology.rst.txt
index ab0b305..dc95a39 100644
--- a/docs/build/html/_sources/boario-terminology.rst.txt
+++ b/docs/build/html/_sources/boario-terminology.rst.txt
@@ -2,11 +2,8 @@
 Terminology
 ###########
 
-.. currentmodule:: boario
-
 So far, there is no consistent terminology for MRIO systems and parameters in the scientific community.
-For BoARIO, the following variable names (i.e. attributes of the :py:class:`~boario.mrio.system.MrioSystem`)
-are used (the alias columns are other names and abbreviations often found in the literature):
+For BoARIO, the following variable names are used (the alias columns are other names and abbreviations often found in the literature):
 
 .. list-table::
    :widths: 30 70 30
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index 6ec8165..7786b24 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -1,9 +1,11 @@
 Welcome to BoARIO's documentation!
-==================================
+##################################
+
+Navigation
+**********
 
 .. toctree::
    :maxdepth: 1
-   :caption: Contents:
 
    user-guide
    boario-api-reference
@@ -11,8 +13,8 @@ Welcome to BoARIO's documentation!
    release-note
    boario-references
 
-Indices and tables
-==================
+Indexes
+*******
 
 * :ref:`genindex`
 * :ref:`modindex`
diff --git a/docs/build/html/_sources/release-note.rst.txt b/docs/build/html/_sources/release-note.rst.txt
index 15863ed..2b8d773 100644
--- a/docs/build/html/_sources/release-note.rst.txt
+++ b/docs/build/html/_sources/release-note.rst.txt
@@ -1,4 +1,10 @@
 Release notes
 ================
 
-Placeholder
+0.5.0 (06/2023)
+---------------
+
+Features
+*********
+
+* WIP
\ No newline at end of file
diff --git a/docs/build/html/_sources/user-guide.rst.txt b/docs/build/html/_sources/user-guide.rst.txt
index 239e21a..0a3c175 100644
--- a/docs/build/html/_sources/user-guide.rst.txt
+++ b/docs/build/html/_sources/user-guide.rst.txt
@@ -1,9 +1,8 @@
 User guide
-==============
+##########
 
 .. toctree::
    :maxdepth: 1
-   :caption: Contents:
 
    boario-what-is
    boario-installation
diff --git a/docs/build/html/_static/copybutton.css b/docs/build/html/_static/copybutton.css
index 40eafe5..f1916ec 100644
--- a/docs/build/html/_static/copybutton.css
+++ b/docs/build/html/_static/copybutton.css
@@ -35,7 +35,8 @@ div.highlight  {
     position: relative;
 }
 
-.highlight:hover button.copybtn {
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
 	opacity: 1;
 }
 
diff --git a/docs/build/html/_static/copybutton.js b/docs/build/html/_static/copybutton.js
index 9f9dc28..bb5393d 100644
--- a/docs/build/html/_static/copybutton.js
+++ b/docs/build/html/_static/copybutton.js
@@ -20,7 +20,7 @@ const messages = {
   },
   'fr' : {
     'copy': 'Copier',
-    'copy_to_clipboard': 'Copié dans le presse-papier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
     'copy_success': 'Copié !',
     'copy_failure': 'Échec de la copie',
   },
@@ -102,18 +102,25 @@ const clearSelection = () => {
   }
 }
 
-// Changes tooltip text for two seconds, then changes it back
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
 const temporarilyChangeTooltip = (el, oldText, newText) => {
   el.setAttribute('data-tooltip', newText)
   el.classList.add('success')
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
-  setTimeout(() => el.classList.remove('success'), 2000)
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
 }
 
 // Changes the copy button icon for two seconds, then changes it back
 const temporarilyChangeIcon = (el) => {
   el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, 2000)
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
 }
 
 const addCopyButtonToCodeCells = () => {
@@ -125,7 +132,8 @@ const addCopyButtonToCodeCells = () => {
   }
 
   // Add copybuttons to all of our code cells
-  const codeCells = document.querySelectorAll('div.highlight pre')
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
   codeCells.forEach((codeCell, index) => {
     const id = codeCellId(index)
     codeCell.setAttribute('id', id)
@@ -141,10 +149,25 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
     var regexp;
     var match;
 
@@ -199,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl
 
 var copyTargetText = (trigger) => {
   var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-  return formatCopyText(target.innerText, '>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: ', false, true, true, true, '', '')
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: ', false, true, true, true, '', '')
 }
 
   // Initialize with a callback so we can modify the text before copy
diff --git a/docs/build/html/_static/copybutton_funcs.js b/docs/build/html/_static/copybutton_funcs.js
index b9168c5..dbe1aaa 100644
--- a/docs/build/html/_static/copybutton_funcs.js
+++ b/docs/build/html/_static/copybutton_funcs.js
@@ -2,10 +2,25 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
     var regexp;
     var match;
 
diff --git a/docs/build/html/_static/scripts/bootstrap.js b/docs/build/html/_static/scripts/bootstrap.js
index 05b3474..766173a 100644
--- a/docs/build/html/_static/scripts/bootstrap.js
+++ b/docs/build/html/_static/scripts/bootstrap.js
@@ -1,7 +1,3 @@
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NAME(){return"carousel"}next(){this._slide("next")}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide("prev")}pause(){this._isSliding&&Ht(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval(()=>this.nextWhenVisible(),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?fe.one(this._element,"slid.bs.carousel",()=>this.cycle()):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void fe.one(this._element,"slid.bs.carousel",()=>this.to(t));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?"next":"prev";this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return 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e=Le[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=Te.findOne(".active",this._indicatorsElement);e.classList.remove("active"),e.removeAttribute("aria-current");const i=Te.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add("active"),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n="next"===t,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>fe.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r("slide.bs.carousel").defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),Vt(s),i.classList.add(l),s.classList.add(l);this._queueCallback(()=>{s.classList.remove(l,c),s.classList.add("active"),i.classList.remove("active",c,l),this._isSliding=!1,r("slid.bs.carousel")},i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return Te.findOne(".active.carousel-item",this._element)}_getItems(){return Te.find(".carousel-item",this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Qt()?"left"===t?"prev":"next":"left"===t?"next":"prev"}_orderToDirection(t){return Qt()?"prev"===t?"left":"right":"prev"===t?"right":"left"}static jQueryInterface(t){return this.each((function(){const e=Ie.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Mt(this);if(!e||!e.classList.contains("carousel"))return;t.preventDefault();const i=Ie.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ve.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,"load.bs.carousel.data-api",()=>{const t=Te.find('[data-bs-ride="carousel"]');for(const e of t)Ie.getOrCreateInstance(e)}),Xt(Ie);const Ne={parent:null,toggle:!0},Pe={parent:"(null|element)",toggle:"boolean"};class je extends we{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=Te.find('[data-bs-toggle="collapse"]');for(const t of i){const e=jt(t),i=Te.find(e).filter(t=>t===this._element);null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return Ne}static get DefaultType(){return Pe}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter(t=>t!==this._element).map(t=>je.getOrCreateInstance(t,{toggle:!1}))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,"show.bs.collapse").defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove("collapse"),this._element.classList.add("collapsing"),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i="scroll"+(e[0].toUpperCase()+e.slice(1));this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse","show"),this._element.style[e]="",fe.trigger(this._element,"shown.bs.collapse")},this._element,!0),this._element.style[e]=this._element[i]+"px"}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=this._element.getBoundingClientRect()[t]+"px",Vt(this._element),this._element.classList.add("collapsing"),this._element.classList.remove("collapse","show");for(const t of this._triggerArray){const e=Mt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0;this._element.style[t]="",this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse"),fe.trigger(this._element,"hidden.bs.collapse")},this._element,!0)}_isShown(t=this._element){return t.classList.contains("show")}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren('[data-bs-toggle="collapse"]');for(const e of t){const t=Mt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=Te.find(":scope .collapse .collapse",this._config.parent);return Te.find(t,this._config.parent).filter(t=>!e.includes(t))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=je.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,"click.bs.collapse.data-api",'[data-bs-toggle="collapse"]',(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=jt(this),i=Te.find(e);for(const t of i)je.getOrCreateInstance(t,{toggle:!1}).toggle()})),Xt(je);const Me="ArrowUp",He="ArrowDown",We='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Fe=Qt()?"top-end":"top-start",Be=Qt()?"top-start":"top-end",ze=Qt()?"bottom-end":"bottom-start",qe=Qt()?"bottom-start":"bottom-end",Re=Qt()?"left-start":"right-start",Ve=Qt()?"right-start":"left-start",$e={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ke={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Qe extends we{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=Te.next(this._element,".dropdown-menu")[0]||Te.prev(this._element,".dropdown-menu")[0]||Te.findOne(".dropdown-menu",this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return $e}static get DefaultType(){return Ke}static get NAME(){return"dropdown"}toggle(){return this._isShown()?this.hide():this.show()}show(){if(zt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,"show.bs.dropdown",t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add("show"),this._element.classList.add("show"),fe.trigger(this._element,"shown.bs.dropdown",t)}}hide(){if(zt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),ve.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,"hidden.bs.dropdown",t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Wt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_createPopper(){if(void 0===n)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Wt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=It(t,this._menu,e)}_isShown(){return this._menu.classList.contains("show")}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Re;if(t.classList.contains("dropstart"))return Ve;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Be:Fe:e?qe:ze}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ve.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=Te.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(t=>Bt(t));i.length&&Gt(i,e,t===He,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Qe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=Te.find('[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled).show');for(const i of e){const e=Qe.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Me,He].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(We)?this:Te.prev(this,We)[0]||Te.next(this,We)[0]||Te.findOne(We,t.delegateTarget.parentNode),o=Qe.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,"keydown.bs.dropdown.data-api",We,Qe.dataApiKeydownHandler),fe.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Qe.dataApiKeydownHandler),fe.on(document,"click.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"keyup.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"click.bs.dropdown.data-api",We,(function(t){t.preventDefault(),Qe.getOrCreateInstance(this).toggle()})),Xt(Qe);class Xe{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"padding-right",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right",e=>e+t),this._setElementAttributes(".sticky-top","margin-right",e=>e-t)}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"padding-right"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right"),this._resetElementAttributes(".sticky-top","margin-right")}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,i(Number.parseFloat(s))+"px")})}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ve.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=ve.getDataAttribute(t,e);null!==i?(ve.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)})}_applyManipulationCallback(t,e){if(Wt(t))e(t);else for(const i of Te.find(t,this._element))e(i)}}const Ye={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ue={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ge extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Ye}static get DefaultType(){return Ue}static get NAME(){return"backdrop"}show(t){if(!this._config.isVisible)return void Yt(t);this._append();const e=this._getElement();this._config.isAnimated&&Vt(e),e.classList.add("show"),this._emulateAnimation(()=>{Yt(t)})}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),Yt(t)})):Yt(t)}dispose(){this._isAppended&&(fe.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,"mousedown.bs.backdrop",()=>{Yt(this._config.clickCallback)}),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Je={autofocus:!0,trapElement:null},Ze={autofocus:"boolean",trapElement:"element"};class ti extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return Je}static get DefaultType(){return Ze}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,".bs.focustrap"),fe.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),fe.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,".bs.focustrap"))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=Te.focusableChildren(e);0===i.length?e.focus():"backward"===this._lastTabNavDirection?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}}const ei={backdrop:!0,focus:!0,keyboard:!0},ii={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class ni extends we{constructor(t,e){super(t,e),this._dialog=Te.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Xe,this._addEventListeners()}static get Default(){return ei}static get DefaultType(){return ii}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown||this._isTransitioning)return;fe.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._backdrop.show(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;fe.trigger(this._element,"hide.bs.modal").defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove("show"),this._queueCallback(()=>this._hideModal(),this._element,this._isAnimated()))}dispose(){for(const t of[window,this._dialog])fe.off(t,".bs.modal");this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ge({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=Te.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Vt(this._element),this._element.classList.add("show");this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.modal",t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()}),fe.on(window,"resize.bs.modal",()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()}),fe.on(this._element,"mousedown.dismiss.bs.modal",t=>{fe.one(this._element,"click.dismiss.bs.modal",e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())})})}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,"hidden.bs.modal")})}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains("modal-static")||(t||(this._element.style.overflowY="hidden"),this._element.classList.add("modal-static"),this._queueCallback(()=>{this._element.classList.remove("modal-static"),this._queueCallback(()=>{this._element.style.overflowY=e},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Qt()?"paddingLeft":"paddingRight";this._element.style[t]=e+"px"}if(!i&&t){const t=Qt()?"paddingRight":"paddingLeft";this._element.style[t]=e+"px"}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=Mt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,"show.bs.modal",t=>{t.defaultPrevented||fe.one(e,"hidden.bs.modal",()=>{Bt(this)&&this.focus()})});const i=Te.findOne(".modal.show");i&&ni.getInstance(i).hide();ni.getOrCreateInstance(e).toggle(this)})),Ae(ni),Xt(ni);const si=".bs.offcanvas",oi=`load${si}.data-api`,ri=`click${si}.data-api`,ai={backdrop:!0,keyboard:!0,scroll:!1},li={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class ci extends we{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return ai}static get DefaultType(){return li}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown)return;if(fe.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented)return;this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Xe).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("showing");this._queueCallback(()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add("show"),this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0)}hide(){if(!this._isShown)return;if(fe.trigger(this._element,"hide.bs.offcanvas").defaultPrevented)return;this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add("hiding"),this._backdrop.hide();this._queueCallback(()=>{this._element.classList.remove("show","hiding"),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Xe).reset(),fe.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ge({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas")}:null})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas"))})}static jQueryInterface(t){return this.each((function(){const e=ci.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,ri,'[data-bs-toggle="offcanvas"]',(function(t){const e=Mt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this))return;fe.one(e,"hidden.bs.offcanvas",()=>{Bt(this)&&this.focus()});const i=Te.findOne(".offcanvas.show");i&&i!==e&&ci.getInstance(i).hide();ci.getOrCreateInstance(e).toggle(this)})),fe.on(window,oi,()=>{for(const t of Te.find(".offcanvas.show"))ci.getOrCreateInstance(t).show()}),fe.on(window,"resize.bs.offcanvas",()=>{for(const t of Te.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&ci.getOrCreateInstance(t).hide()}),Ae(ci),Xt(ci);const ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),hi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,di=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,fi=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!ui.has(i)||Boolean(hi.test(t.nodeValue)||di.test(t.nodeValue)):e.filter(t=>t instanceof RegExp).some(t=>t.test(i))},pi={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]};const gi={allowList:pi,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
    "},mi={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},_i={entry:"(string|element|function|null)",selector:"(string|element)"};class bi extends ye{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return gi}static get DefaultType(){return mi}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map(t=>this._resolvePossibleFunction(t)).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},_i)}_setContent(t,e,i){const n=Te.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Wt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)fi(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const vi=new Set(["sanitize","allowList","sanitizeFn"]),yi={AUTO:"auto",TOP:"top",RIGHT:Qt()?"left":"right",BOTTOM:"bottom",LEFT:Qt()?"right":"left"},wi={allowList:pi,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},Ai={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class Ei extends we{constructor(t,e){if(void 0===n)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return wi}static get DefaultType(){return Ai}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this.tip&&this.tip.remove(),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(qt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this.tip&&(this.tip.remove(),this.tip=null);const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper?this._popper.update():this._popper=this._createPopper(i),i.classList.add("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback(()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1},this.tip,this._isAnimated())}hide(){if(!this._isShown())return;if(fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented)return;const t=this._getTipElement();if(t.classList.remove("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,this._isHovered=null;this._queueCallback(()=>{this._isWithActiveTrigger()||(this._isHovered||t.remove(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")),this._disposePopper())},this.tip,this._isAnimated())}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove("fade","show"),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add("fade"),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new bi({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains("fade")}_isShown(){return this.tip&&this.tip.classList.contains("show")}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=yi[e.toUpperCase()];return It(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,t=>{this._initializeOnDelegatedTarget(t).toggle()});else if("manual"!==e){const t="hover"===e?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i="hover"===e?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0,e._enter()}),fe.on(this._element,i,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget),e._leave()})}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout(()=>{this._isHovered&&this.show()},this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout(()=>{this._isHovered||this.hide()},this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ve.getDataAttributes(this._element);for(const t of Object.keys(e))vi.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=Ei.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Ei);const Ci={...Ei.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},Ti={...Ei.DefaultType,content:"(null|string|element|function)"};class Oi extends Ei{static get Default(){return Ci}static get DefaultType(){return Ti}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=Oi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Oi);const xi={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ki={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Li extends we{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xi}static get DefaultType(){return ki}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?t.offset+"px 0px -30%":t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map(t=>Number.parseFloat(t))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,"click.bs.scrollspy"),fe.on(this._config.target,"click.bs.scrollspy","[href]",t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}}))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver(t=>this._observerCallback(t),t)}_observerCallback(t){const e=t=>this._targetLinks.get("#"+t.target.id),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=Te.find("[href]",this._config.target);for(const e of t){if(!e.hash||zt(e))continue;const t=Te.findOne(e.hash,this._element);Bt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add("active"),this._activateParents(t),fe.trigger(this._element,"activate.bs.scrollspy",{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))Te.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add("active");else for(const e of Te.parents(t,".nav, .list-group"))for(const t of Te.prev(e,".nav-link, .nav-item > .nav-link, .list-group-item"))t.classList.add("active")}_clearActiveClass(t){t.classList.remove("active");const e=Te.find("[href].active",t);for(const t of e)t.classList.remove("active")}static jQueryInterface(t){return this.each((function(){const e=Li.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,"load.bs.scrollspy.data-api",()=>{for(const t of Te.find('[data-bs-spy="scroll"]'))Li.getOrCreateInstance(t)}),Xt(Li);const Di="ArrowLeft",Si="ArrowRight",Ii="ArrowUp",Ni="ArrowDown",Pi='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',ji='.nav-link:not(.dropdown-toggle), .list-group-item:not(.dropdown-toggle), [role="tab"]:not(.dropdown-toggle), '+Pi;class Mi extends we{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,"keydown.bs.tab",t=>this._keydown(t)))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,"hide.bs.tab",{relatedTarget:t}):null;fe.trigger(t,"show.bs.tab",{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){if(!t)return;t.classList.add("active"),this._activate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,"shown.bs.tab",{relatedTarget:e})):t.classList.add("show")},t,t.classList.contains("fade"))}_deactivate(t,e){if(!t)return;t.classList.remove("active"),t.blur(),this._deactivate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,"hidden.bs.tab",{relatedTarget:e})):t.classList.remove("show")},t,t.classList.contains("fade"))}_keydown(t){if(![Di,Si,Ii,Ni].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Si,Ni].includes(t.key),i=Gt(this._getChildren().filter(t=>!zt(t)),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Mi.getOrCreateInstance(i).show())}_getChildren(){return Te.find(ji,this._parent)}_getActiveElem(){return this._getChildren().find(t=>this._elemIsActive(t))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Mt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby","#"+t.id))}_toggleDropDown(t,e){const 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isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",We=".data-api",Fe="next",Be="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ke=`keydown${He}`,Qe=`mouseenter${He}`,Xe=`mouseleave${He}`,Ye=`dragstart${He}`,Ue=`load${He}${We}`,Ge=`click${He}${We}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&de.on(this._element,Ke,(t=>this._keydown(t))),"hover"===this._config.pause&&(de.on(this._element,Qe,(()=>this.pause())),de.on(this._element,Xe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))de.on(t,Ye,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
    "},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return Cs}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(de.off(this._config.target,_s),de.on(this._config.target,_s,ys,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const 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Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){de.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),de.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(de.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),de.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){de.on(this._element,Qs,(t=>this._onInteraction(t,!0))),de.on(this._element,Xs,(t=>this._onInteraction(t,!1))),de.on(this._element,Ys,(t=>this._onInteraction(t,!0))),de.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}var ao;we(ro),Qt(ro),ao=function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new us(t,{delay:{show:500,hide:100}})}))},"loading"!=document.readyState?ao():document.addEventListener("DOMContentLoaded",ao)})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/docs/build/html/_static/scripts/pydata-sphinx-theme.js b/docs/build/html/_static/scripts/pydata-sphinx-theme.js
index 79e86a9..3cbeb9b 100644
--- a/docs/build/html/_static/scripts/pydata-sphinx-theme.js
+++ b/docs/build/html/_static/scripts/pydata-sphinx-theme.js
@@ -1 +1,2 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/docs/build/html/_static/styles/bootstrap.css b/docs/build/html/_static/styles/bootstrap.css
index b258fd9..0aa5f54 100644
--- a/docs/build/html/_static/styles/bootstrap.css
+++ b/docs/build/html/_static/styles/bootstrap.css
@@ -1,6 +1,6 @@
 /*!
-   * Bootstrap  v5.2.2 (https://getbootstrap.com/)
-   * Copyright 2011-2022 The Bootstrap Authors
-   * Copyright 2011-2022 Twitter, Inc.
-   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-   */:root{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-body-color-rgb:33,37,41;--bs-body-bg-rgb:255,255,255;--bs-font-sans-serif:system-ui,-apple-system,"Segoe 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+ * Bootstrap  v5.2.3 (https://getbootstrap.com/)
+ * Copyright 2011-2022 The Bootstrap Authors
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var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:#fff;--bs-pagination-border-width:1px;--bs-pagination-border-color:#dee2e6;--bs-pagination-border-radius:0.375rem;--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:#e9ecef;--bs-pagination-hover-border-color:#dee2e6;--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:#e9ecef;--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:#6c757d;--bs-pagination-disabled-bg:#fff;--bs-pagination-disabled-border-color:#dee2e6;display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{background-color:#cfe2ff;color:#084298}.list-group-item-primary.list-group-item-action:focus,.list-group-item-primary.list-group-item-action:hover{background-color:#bacbe6;color:#084298}.list-group-item-primary.list-group-item-action.active{background-color:#084298;border-color:#084298;color:#fff}.list-group-item-secondary{background-color:#e2e3e5;color:#41464b}.list-group-item-secondary.list-group-item-action:focus,.list-group-item-secondary.list-group-item-action:hover{background-color:#cbccce;color:#41464b}.list-group-item-secondary.list-group-item-action.active{background-color:#41464b;border-color:#41464b;color:#fff}.list-group-item-success{background-color:#d1e7dd;color:#0f5132}.list-group-item-success.list-group-item-action:focus,.list-group-item-success.list-group-item-action:hover{background-color:#bcd0c7;color:#0f5132}.list-group-item-success.list-group-item-action.active{background-color:#0f5132;border-color:#0f5132;color:#fff}.list-group-item-info{background-color:#cff4fc;color:#055160}.list-group-item-info.list-group-item-action:focus,.list-group-item-info.list-group-item-action:hover{background-color:#badce3;color:#055160}.list-group-item-info.list-group-item-action.active{background-color:#055160;border-color:#055160;color:#fff}.list-group-item-warning{background-color:#fff3cd;color:#664d03}.list-group-item-warning.list-group-item-action:focus,.list-group-item-warning.list-group-item-action:hover{background-color:#e6dbb9;color:#664d03}.list-group-item-warning.list-group-item-action.active{background-color:#664d03;border-color:#664d03;color:#fff}.list-group-item-danger{background-color:#f8d7da;color:#842029}.list-group-item-danger.list-group-item-action:focus,.list-group-item-danger.list-group-item-action:hover{background-color:#dfc2c4;color:#842029}.list-group-item-danger.list-group-item-action.active{background-color:#842029;border-color:#842029;color:#fff}.list-group-item-light{background-color:#fefefe;color:#636464}.list-group-item-light.list-group-item-action:focus,.list-group-item-light.list-group-item-action:hover{background-color:#e5e5e5;color:#636464}.list-group-item-light.list-group-item-action.active{background-color:#636464;border-color:#636464;color:#fff}.list-group-item-dark{background-color:#d3d3d4;color:#141619}.list-group-item-dark.list-group-item-action:focus,.list-group-item-dark.list-group-item-action:hover{background-color:#bebebf;color:#141619}.list-group-item-dark.list-group-item-action.active{background-color:#141619;border-color:#141619;color:#fff}.btn-close{background:transparent url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3E%3C/svg%3E") 50%/1em auto no-repeat;border:0;border-radius:.375rem;box-sizing:content-box;color:#000;height:1em;opacity:.5;padding:.25em;width:1em}.btn-close:hover{color:#000;opacity:.75;text-decoration:none}.btn-close:focus{box-shadow:0 0 0 .25rem rgba(13,110,253,.25);opacity:1;outline:0}.btn-close.disabled,.btn-close:disabled{opacity:.25;pointer-events:none;user-select:none}.btn-close-white{filter:invert(1) grayscale(100%) brightness(200%)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: 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\ No newline at end of file
diff --git a/docs/build/html/_static/styles/pydata-sphinx-theme.css b/docs/build/html/_static/styles/pydata-sphinx-theme.css
index 65988ff..ca295d8 100644
--- a/docs/build/html/_static/styles/pydata-sphinx-theme.css
+++ b/docs/build/html/_static/styles/pydata-sphinx-theme.css
@@ -1 +1 @@
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caption{caption-side:top;color:var(--pst-color-text-muted);text-align:center}td.text-align\:left,th.text-align\:left{text-align:left}td.text-align\:right,th.text-align\:right{text-align:right}td.text-align\:center,th.text-align\:center{text-align:center}.toctree-wrapper p.caption{font-size:1.5em;margin-bottom:0}.toctree-wrapper>ul{padding-left:0}.toctree-wrapper li[class^=toctree-l]{list-style:none;margin-bottom:.2em}.toctree-wrapper li[class^=toctree-l]>a{font-size:1.1em;list-style:none}.toctree-wrapper li[class^=toctree-l]>ul{list-style:none;padding-inline-start:1.5em}.toctree-wrapper .toctree-l1>a{font-size:1.3em}div.topic.contents ul.simple,nav.contents ul.simple{list-style:none;padding-left:0}div.math,span.math{align-items:center;display:flex;max-width:100%;overflow:hidden}span.math{display:inline-flex}div.math{flex-direction:row-reverse;gap:.5em}div.math span.eqno a.headerlink{font-size:1em;position:relative}div.math 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ul.ablog-cloud{flex-direction:row;flex-flow:wrap;gap:.5rem}.ablog-sidebar-item ul.ablog-cloud li{align-items:center;display:flex}.ablog__prev-next{display:flex;font-size:1.2em;padding:1rem 0}.ablog__prev-next>span{display:flex;max-width:45%}.ablog__prev-next>span a{align-items:center;display:flex;gap:1rem;line-height:1.5rem;margin-left:auto}.ablog__prev-next>span a i:before{color:var(--pst-color-text-base)}.ablog__prev-next span.ablog__prev i.fa-arrow-circle-left:before{content:var(--pst-icon-angle-left)}.ablog__prev-next span.ablog__spacer{display:none}.ablog__prev-next span.ablog__next{margin-left:auto;text-align:right}.ablog__prev-next span.ablog__next i.fa-arrow-circle-right:before{content:var(--pst-icon-angle-right)}.ablog__collection,.postlist{padding-left:0}.ablog__collection .ablog-post,.postlist .ablog-post{list-style:none}.ablog__collection .ablog-post .ablog-archive,.postlist .ablog-post .ablog-archive{display:flex;flex-direction:row;flex-wrap:wrap;font-size:.75rem;gap:1rem;list-style:none;padding-left:0}.ablog__collection .ablog-post .ablog-post-title,.postlist .ablog-post .ablog-post-title{font-size:1.25rem;margin-top:0}.ablog__collection .ablog-post .ablog-post-title a,.postlist .ablog-post .ablog-post-title a{font-weight:700}.ablog__collection .ablog-post .ablog-post-expand,.postlist .ablog-post .ablog-post-expand{margin-bottom:.5rem}.docutils.container{margin-left:unset;margin-right:unset;max-width:unset;padding-left:unset;padding-right:unset;width:unset}div.highlight button.copybtn{align-items:center;background-color:unset;background-color:var(--pst-color-surface);border:none;display:flex;justify-content:center}div.highlight button.copybtn:not(.success){color:var(--pst-color-muted)}div.highlight button.copybtn:hover:not(.success){color:var(--pst-color-text)}div.highlight button.copybtn.o-tooltip--left:after{background-color:var(--pst-color-surface);color:var(--pst-color-text)}#ethical-ad-placement .ethical-footer a,#ethical-ad-placement .ethical-footer a:active,#ethical-ad-placement .ethical-footer a:hover,#ethical-ad-placement .ethical-footer a:visited,#ethical-ad-placement .ethical-sidebar a,#ethical-ad-placement .ethical-sidebar a:active,#ethical-ad-placement .ethical-sidebar a:hover,#ethical-ad-placement .ethical-sidebar a:visited{color:var(--pst-color-text-base)}#ethical-ad-placement .ethical-footer,#ethical-ad-placement .ethical-sidebar{background-color:var(--pst-color-background);border:1px solid var(--pst-color-border);border-radius:5px;color:var(--pst-color-text-base);font-size:14px;line-height:20px}.bd-content div.jupyter_container{background-color:unset;border:none;box-shadow:none}.bd-content div.jupyter_container div.highlight,.bd-content div.jupyter_container div.output{border-radius:.25rem}.bd-content div.jupyter_container div.highlight{background-color:var(--pst-color-surface)}.bd-content div.jupyter_container .cell_input,.bd-content div.jupyter_container .cell_output{border-radius:.25rem}.bd-content div.jupyter_container .cell_input pre,.bd-content div.jupyter_container .cell_output pre{padding:1rem}.xr-wrap[hidden]{display:block!important}@use "../variables/color" as 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.sd-shadow-lg,html[data-theme=light] .sd-shadow-md,html[data-theme=light] .sd-shadow-sm,html[data-theme=light] .sd-shadow-xs{box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem var(--pst-color-shadow)!important}.bd-content .sd-card{border:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-header{background-color:var(--pst-color-panel-background);border-bottom:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-footer{border-top:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-body,.bd-content .sd-card .sd-card-footer{background-color:var(--pst-color-panel-background)}.bd-content .sd-tab-set>input:checked+label,.bd-content .sd-tab-set>input:not(:checked)+label:hover{border-color:var(--pst-color-primary);color:var(--pst-color-primary)}.bd-content .sd-tab-set>input:not(:checked)+label:hover{opacity:.5}.bd-content .sd-tab-set>label{color:var(--pst-color-text-muted)}html .bd-content .sd-tab-set>label:hover{border-color:var(--pst-color-primary);color:var(--pst-color-primary);opacity:.5}details.sd-dropdown{border:0!important;box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem var(--pst-color-shadow)!important}details.sd-dropdown summary.sd-card-header{border:0!important}details.sd-dropdown summary.sd-card-header+div.sd-summary-content{border:0}details.sd-dropdown summary.sd-card-header{align-items:center;background-color:unset!important;border-left:.2rem solid var(--pst-sd-dropdown-color)!important;color:var(--pst-color-text)!important;display:flex;font-weight:600;padding-bottom:.5rem;padding-top:.5rem;position:relative}details.sd-dropdown summary.sd-card-header,details.sd-dropdown summary.sd-card-header+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-card-border)}details.sd-dropdown summary.sd-card-header.sd-bg-primary,details.sd-dropdown summary.sd-card-header.sd-bg-primary+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-primary)}details.sd-dropdown summary.sd-card-header.sd-bg-secondary,details.sd-dropdown summary.sd-card-header.sd-bg-secondary+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-secondary)}details.sd-dropdown summary.sd-card-header.sd-bg-success,details.sd-dropdown summary.sd-card-header.sd-bg-success+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-success)}details.sd-dropdown summary.sd-card-header.sd-bg-info,details.sd-dropdown summary.sd-card-header.sd-bg-info+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-info)}details.sd-dropdown summary.sd-card-header.sd-bg-warning,details.sd-dropdown summary.sd-card-header.sd-bg-warning+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-warning)}details.sd-dropdown summary.sd-card-header.sd-bg-danger,details.sd-dropdown summary.sd-card-header.sd-bg-danger+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-danger)}details.sd-dropdown summary.sd-card-header.sd-bg-light,details.sd-dropdown summary.sd-card-header.sd-bg-light+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-light)}details.sd-dropdown summary.sd-card-header.sd-bg-muted,details.sd-dropdown summary.sd-card-header.sd-bg-muted+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-muted)}details.sd-dropdown summary.sd-card-header.sd-bg-dark,details.sd-dropdown summary.sd-card-header.sd-bg-dark+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-dark)}details.sd-dropdown summary.sd-card-header.sd-bg-black,details.sd-dropdown summary.sd-card-header.sd-bg-black+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-black)}details.sd-dropdown summary.sd-card-header.sd-bg-white,details.sd-dropdown summary.sd-card-header.sd-bg-white+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-white)}details.sd-dropdown summary.sd-card-header:before{background-color:var(--pst-sd-dropdown-color);content:"";height:100%;left:0;opacity:.1;pointer-events:none;position:absolute;top:0;width:100%}details.sd-dropdown summary.sd-card-header+div.sd-summary-content{border-bottom-left-radius:calc(.25rem - 1px);border-left:.2rem solid var(--pst-sd-dropdown-color)!important}details.sd-dropdown summary.sd-card-header span.sd-summary-icon{align-items:center;color:var(--pst-sd-dropdown-color)!important;display:inline-flex}details.sd-dropdown summary.sd-card-header .sd-summary-down,details.sd-dropdown summary.sd-card-header .sd-summary-up{top:.7rem}.bd-content .admonition button.toggle-button{color:inherit}.bd-content details.toggle-details summary{border-left:3px solid var(--pst-color-primary)}html div.rendered_html html .jp-RenderedHTMLCommon table{table-layout:auto}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html{background-color:var(--pst-color-text-base);border-radius:.25rem;color:var(--pst-color-on-background);padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}div.cell_output .output::-webkit-scrollbar{height:.5rem;width:.5rem}div.cell_output .output::-webkit-scrollbar-thumb{background:var(--pst-color-text-muted);border-radius:.25rem}div.cell_output .output::-webkit-scrollbar-track{background:transparent}div.cell_output .output::-webkit-scrollbar-thumb{background:var(--pst-color-on-surface)}div.cell_output .output::-webkit-scrollbar-thumb:hover,div.cell_output .output:hover::-webkit-scrollbar-thumb{background:var(--pst-color-text-muted)}html[data-theme=dark] .bd-content div.cell_output .text_html,html[data-theme=dark] .bd-content div.cell_output img{background-color:var(--pst-color-text-base);border-radius:.25rem;color:var(--pst-color-on-background);padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:0}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{list-style:none;margin:0}.bd-search-container ul.search li{background-image:none;border-top:1px solid var(--pst-color-text-muted);margin:1rem 0;padding:1rem 0}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0}.bd-search-container ul.search li div.context a:before,.bd-search-container ul.search li p.context a:before{color:var(--pst-color-text-muted);content:"#";padding-right:.2em}
\ No newline at end of file
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diff --git a/docs/build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css b/docs/build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css
index b9a01fb..aad4b1d 100644
--- a/docs/build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css
+++ b/docs/build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css
@@ -2,5 +2,4 @@
  * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
  * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
  * Copyright 2022 Fonticons, Inc.
- */
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\ No newline at end of file
diff --git a/docs/build/html/_static/webpack-macros.html b/docs/build/html/_static/webpack-macros.html
index 65389d9..58db785 100644
--- a/docs/build/html/_static/webpack-macros.html
+++ b/docs/build/html/_static/webpack-macros.html
@@ -4,7 +4,7 @@
 -->
 {# Load FontAwesome icons #}
 {% macro head_pre_icons() %}
-  
+  
   
 
 
@@ -12,19 +12,19 @@
 
 {% macro head_pre_assets() %}
   
-  
-
-
+  
+
+
 {% endmacro %}
 
 {% macro head_js_preload() %}
   
-  
-
+  
+
 {% endmacro %}
 
 {% macro body_post() %}
   
-  
-
+  
+
 {% endmacro %}
\ No newline at end of file
diff --git a/docs/build/html/autoapi/boario.event.html b/docs/build/html/autoapi/boario.event.html
index 6ba0de4..8585957 100644
--- a/docs/build/html/autoapi/boario.event.html
+++ b/docs/build/html/autoapi/boario.event.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario.event — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,14 +34,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -160,7 +160,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -185,7 +185,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -276,7 +276,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -290,7 +290,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -406,7 +406,7 @@ 
    Classes
 
    
-class Event(impact, aff_regions=None, aff_sectors=None, aff_industries=None, impact_industries_distrib=None, impact_regional_distrib=None, impact_sectoral_distrib_type='custom', impact_sectoral_distrib=None, name='Unnamed', occurrence=1, duration=1)[source]#
    
+class Event(*, impact, name='Unnamed', occurrence=1, duration=1)[source]#
 
    Create an event shocking the model from a dictionary.
    
 
    An Event object stores all information about a unique shock during simulation such as
 time of occurrence, duration, type of shock, amount of damages. Computation
@@ -418,32 +418,38 @@ 
    ClassesParameters:
 
    
 
    
 
 
    Examples
    
 
    FIXME: Add docs.
    
 
    Inheritance
    
-
    Inheritance diagram of Event
    
+
    Inheritance diagram of Event
    
 
-
+
 
    Attributes and Methods
    
 
    
 
    
-possible_sectors: List[str] | Index | ndarray = Index([], dtype='object')#
    
+possible_sectors: Index = Index([], dtype='object')#
 
    List of sectors present in the MRIO used by the model
    
 
    
 
 
    
 
    
-possible_regions: List[str] | Index | ndarray = Index([], dtype='object')#
    
+possible_regions: Index = Index([], dtype='object')#
 
    List of regions present in the MRIO used by the model
    
 
    
 
+
    
+
    
+possible_final_demand_cat: Index = Index([], dtype='object')#
    
+
    List of final demand categories present in the MRIO used by the model
    
+
    
+
 
    
 
    
 temporal_unit_range: int = 0#
    
@@ -470,34 +476,46 @@ 
    Classes
 
    
-regions_idx: ndarray = array([], dtype=float64)#
    
+regions_idx: ndarray[Any, dtype[ScalarType]] = array([], dtype=float64)#
 
    lexicographic region indexes
    
 
    
 
 
    
 
    
-sectors_idx: ndarray = array([], dtype=float64)#
    
+sectors_idx: ndarray[Any, dtype[ScalarType]] = array([], dtype=float64)#
 
    lexicographic sector indexes
    
 
    
 
 
    
-
    
-monetary_factor: int = 0#
    
+
    
+model_monetary_factor: int = 1#
    
 
    Amount of unitary currency used in the MRIO (e.g. 1000000 if in € millions)
    
 
    
 
+
    
+
    
+gva_df: Series = Series([], dtype: float64)#
    
+
    GVA per (region,sector)
    
+
    
+
 
    
 
    
-sectors_gva_shares: ndarray = array([], dtype=float64)#
    
+sectors_gva_shares: ndarray[Any, dtype[ScalarType]] = array([], dtype=float64)#
 
    Fraction of total (regional) GVA for each sectors
    
 
    
 
 
    
 
    
-Z_distrib: ndarray = array([], dtype=float64)#
    
+Z_distrib: ndarray[Any, dtype[ScalarType]] = array([], dtype=float64)#
 
    Normalized intermediate consumption matrix
    
 
    
 
+
    
+
    
+Y_distrib: ndarray[Any, dtype[ScalarType]] = array([], dtype=float64)#
    
+
    Normalized final consumption matrix
    
+
    
+
 
    
 
    
 mrio_name: str = ''#
    
@@ -510,12 +528,6 @@ 
    Classes
-
    
-impact_df: Series#
    
-
    A pandas Series with all possible industries as index, holding the impact vector of the event. The impact is defined for each sectors in each region.
    
-
    
-
 
    
 
    
 happened: bool#
    
@@ -534,9 +546,195 @@ 
    Classes
+
    
+classmethod from_series(impact, *, occurrence=1, duration=1, name=None, **kwarg)[source]#
    
+
    Create an event from an impact pd.Series.
    
+
    
+
    Args:
    impact (pd.Series): A vector definition of the impact per (region,sector)
+occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
+duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1.
+name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None.
    
+
    
+
    Raises:
    ValueError: Raised if impact is empty or contains negative values.
    
+
    
+
    Returns:
    Event: an Event object (or one of its subclass).
    
+
    
+
    
+
    
+
    Parameters:
    
+
      
+
    • impact (Series) – 
      • 
+
    • occurrence (int) – 
      • 
+
    • duration (int) – 
      • 
+
    • name (str | None) – 
      • 
+
    
+
    
+
    Return type:
    
+
    Event
    
+
    
+
    
+
    
+
+
    
+
    
+classmethod from_dataframe(impact, *, occurrence=1, duration=1, name=None, **kwarg)[source]#
    
+
    Convenience function for DataFrames. See from_series()
    
+
    
+
    Raises:
    ValueError: If impact cannot be squeezed to a Series
    
+
    
+
    Returns:
    Event: an Event object (or one of its subclass).
    
+
    
+
    
+
    
+
    Parameters:
    
+
      
+
    • impact (DataFrame) – 
      • 
+
    • occurrence (int) – 
      • 
+
    • duration (int) – 
      • 
+
    • name (str | None) – 
      • 
+
    
+
    
+
    Return type:
    
+
    Event
    
+
    
+
    
+
    
+
+
    
+
    
+classmethod distribute_impact_by_gva(impact_vec)[source]#
    
+
    Distribute a vector of impact by the GVA of affected industries.
    
+
    Each values of the given impact are mutliplied by the share of the GVA
+the industry has over the GVA of all affected industries.
    
+
    
+
    Args:
    impact_vec (pd.Series): The impact values to be reweigthed.
+Current use-case assumes all values are the total impact.
    
+
    
+
    Returns:
    pd.Series: The impact where each value was multiplied by the share
+of GVA of each affected industry (over total GVA affected).
    
+
    
+
    
+
    
+
    Parameters:
    
+
    impact_vec (Series) – 
    
+
    
+
    Return type:
    
+
    Series
    
+
    
+
    
+
    
+
+
    
+
    
+classmethod distribute_impact_equally(impact_vec)[source]#
    
+
    Distribute an impact equally between all affected regions.
    
+
    Assume impact is given as a vector with all value being the
+total impact to distribute.
    
+
    
+
    Args:
    impact_vec (pd.Series): The impact to distribute.
    
+
    
+
    Returns:
    pd.Series: The impact vector equally distributed among affected industries.
    
+
    
+
    
+
    
+
    Parameters:
    
+
    impact_vec (Series) – 
    
+
    
+
    Return type:
    
+
    Series
    
+
    
+
    
+
    
+
+
    
+
    
+classmethod from_scalar_industries(impact, *, industries, impact_industries_distrib=None, gva_distrib=False, occurrence=1, duration=1, name=None, **kwarg)[source]#
    
+
    Creates an Event from a scalar and a list of industries affected.
    
+
    The scalar impact is distributed evenly by default. Otherwise it can be distributed proportionnaly to the GVA of
+affected industries, or to a custom distribution.
    
+
    
+
    Args:
    impact (ScalarImpact): The scalar impact.
+industries (IndustriesList): The list of industries affected by the impact
+impact_industries_distrib (Optional[npt.ArrayLike], optional): A vector of equal size to the list of industries, stating the
+share of the impact each industry should receive. Defaults to None.
+gva_distrib (bool, optional): A boolean stating if the impact should be distributed proportionnaly to GVA. Defaults to False.
+occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
+duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1.
+name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None.
    
+
    
+
    Raises:
    ValueError: Raise if Impact is null, if len(industries) < 1 or if the sum of impact_industries_distrib differs from 1.0.
    
+
    
+
    Returns:
    Event: An Event object or one of its subclass.
    
+
    
+
    
+
    
+
    Parameters:
    
+
    
+
    
+
    Return type:
    
+
    Event
    
+
    
+
    
+
    
+
+
    
+
    
+classmethod from_scalar_regions_sectors(impact, *, regions, sectors, impact_regional_distrib=None, impact_sectoral_distrib=None, occurrence=1, duration=1, name=None, **kwarg)[source]#
    
+
    Creates an Event from a scalar, a list of regions and a list of sectors affected.
    
+
    
+
    Args:
    impact (ScalarImpact): The scalar impact.
+regions (RegionsList): The list of regions affected.
+sectors (SectorsList): The list of sectors affected in each region.
+impact_regional_distrib (Optional[npt.ArrayLike], optional): A vector of equal size to the list of regions affected, stating the
+share of the impact each industry should receive. Defaults to None.
+impact_sectoral_distrib (Optional[Union[str, npt.ArrayLike]], optional): A vector of equal size to the list of sectors affected, stating the
+share of the impact each industry should receive. Defaults to None.
+occurrence (int, optional): The ordinal of occurrence of the event (requires to be > 0). Defaults to 1.
+duration (int, optional): The duration of the event (entire impact applied during this number of steps). Defaults to 1.
+name (Optional[str], optional): A possible name for the event, for convenience. Defaults to None.
    
+
    
+
    Raises:
    ValueError: Raise if Impact is null, if len(regions) or len(sectors) < 1,
    
+
    
+
    Returns:
    Event: An Event object or one of its subclass
    
+
    
+
    
+
    
+
    Parameters:
    
+
    
+
    
+
    Return type:
    
+
    Event
    
+
    
+
    
+
    
+
+
    
+
    
+property impact_df: Series#
    
+
    A pandas Series with all possible industries as index, holding the impact vector of the event. The impact is defined for each sectors in each region.
    
+
    
+
 
    
 
    
-property aff_industries: MultiIndex#
    
+property aff_industries: MultiIndex#
 
    The industries affected by the event.
    
 
    
 
    Parameters:
    
@@ -562,45 +760,45 @@ 
    Classes
 
    
-property aff_regions_idx: ndarray#
    
+property aff_regions_idx: ndarray[Any, dtype[ScalarType]]#
 
    The array of lexicographically ordered affected region indexes
    
 
    
 
 
    
 
    
-property aff_regions: Index#
    
+property aff_regions: Index#
 
    The array of regions affected by the event
    
 
    
 
 
    
 
    
-property aff_sectors_idx: ndarray#
    
+property aff_sectors_idx: ndarray[Any, dtype[ScalarType]]#
 
    The array of lexicographically ordered affected sectors indexes
    
 
    
 
 
    
 
    
-property aff_sectors: Index#
    
+property aff_sectors: Index#
 
    The array of affected sectors by the event
    
 
    
 
 
    
 
    
-property impact_regional_distrib: ndarray#
    
-
    The array specifying how damages are distributed among affected regions
    
+property impact_regional_distrib: Series#
+
    The series specifying how damages are distributed among affected regions
    
 
    
 
 
    
-
    
-property impact_sectoral_distrib_type: str#
    
-
    The type of damages distribution among sectors (currently only ‘gdp’)
    
+
    
+property impact_industries_distrib: Series#
    
+
    The series specifying how damages are distributed among affected industries (regions,sectors)
    
 
    
 
 
    
 
 
    
 
    
-class EventKapitalDestroyed(impact, aff_regions=None, aff_sectors=None, aff_industries=None, impact_industries_distrib=None, impact_regional_distrib=None, impact_sectoral_distrib_type='equally shared', impact_sectoral_distrib=None, name='Unnamed', occurrence=1, duration=1)[source]#
    
+class EventKapitalDestroyed(*, impact, households_impact=None, name='Unnamed', occurrence=1, duration=1, event_monetary_factor=None)[source]#
 
    Create an event shocking the model from a dictionary.
    
 
    An Event object stores all information about a unique shock during simulation such as
 time of occurrence, duration, type of shock, amount of damages. Computation
@@ -612,33 +810,46 @@ 
    ClassesParameters:
 
    
 
    
 
    
 
    Examples
    
 
    FIXME: Add docs.
    
 
    Inheritance
    
-
    Inheritance diagram of EventKapitalDestroyed
    
+
    Inheritance diagram of EventKapitalDestroyed
    
 
-
-
+
+
 
    Attributes and Methods
    
+
    
+
    
+event_monetary_factor#
    
+
    The monetary factor for the impact of the event (e.g. 10**6, 10**3, …)
    
+
    
+
+
    
+
    
+households_impact_df: Series#
    
+
    A pandas Series with all possible (regions, final_demand_cat) as index, holding the households impacts vector of the event. The impact is defined for each region and each final demand category.
    
+
    
+
 
    
-
    
-property regional_sectoral_kapital_destroyed: ndarray#
    
-
    The (optional) array of kapital destroyed per industry (ie region x sector)
    
+
    
+property regional_sectoral_productive_capital_destroyed: ndarray[Any, dtype[ScalarType]]#
    
+
    The (optional) array of productive_capital destroyed per industry (ie region x sector)
    
 
    
 
 
    
 
 
    
 
    
-class EventArbitraryProd(impact, aff_regions=None, aff_sectors=None, aff_industries=None, impact_industries_distrib=None, impact_regional_distrib=None, impact_sectoral_distrib_type='equally shared', impact_sectoral_distrib=None, name='Unnamed', occurrence=1, duration=1)[source]#
    
+class EventArbitraryProd(*, impact, recovery_time=1, recovery_function='linear', name='Unnamed', occurrence=1, duration=1)[source]#
 
    Create an event shocking the model from a dictionary.
    
 
    An Event object stores all information about a unique shock during simulation such as
 time of occurrence, duration, type of shock, amount of damages. Computation
@@ -650,33 +861,52 @@ 
    ClassesParameters:
 
    
 
    
 
    
 
    Examples
    
 
    FIXME: Add docs.
    
 
    Inheritance
    
-
    Inheritance diagram of EventArbitraryProd
    
+
    Inheritance diagram of EventArbitraryProd
    
 
-
-
+
+
 
    Attributes and Methods
    
+
    
+
    
+recovery_time#
    
+
    The characteristic recovery duration after the event is over
    
+
    
+
 
    
 
    
-property prod_cap_delta_arbitrary: ndarray#
    
-
    The optional array storing arbitrary (as in not related to kapital destroyed) production capacity loss
    
+property prod_cap_delta_arbitrary: ndarray[Any, dtype[ScalarType]]#
+
    The optional array storing arbitrary (as in not related to productive_capital destroyed) production capacity loss
    
+
    
+
+
    
+
    
+property recoverable: bool#
    
+
    A boolean stating if an event can start recover
    
+
    
+
+
    
+
    
+property recovery_function: Callable#
    
+
    The recovery function used for recovery (Callable)
    
 
    
 
 
 
 
    
 
    
-class EventKapitalRecover(impact, recovery_time, recovery_function='linear', aff_regions=None, aff_sectors=None, aff_industries=None, impact_industries_distrib=None, impact_regional_distrib=None, impact_sectoral_distrib_type='equally shared', impact_sectoral_distrib=None, name='Unnamed', occurrence=1, duration=1)[source]#
    
+class EventKapitalRecover(*, impact, recovery_time, recovery_function='linear', households_impact=None, name='Unnamed', occurrence=1, duration=1, event_monetary_factor=None)[source]#
 
    Create an event shocking the model from a dictionary.
    
 
    An Event object stores all information about a unique shock during simulation such as
 time of occurrence, duration, type of shock, amount of damages. Computation
@@ -688,12 +918,10 @@ 
    ClassesParameters:
 
    
 
    
@@ -701,11 +929,11 @@ 
    ClassesExamples
    
 
    FIXME: Add docs.
    
 
    Inheritance
    
-
    Inheritance diagram of EventKapitalRecover
    
+
    Inheritance diagram of EventKapitalRecover
    
 
-
-
-
+
+
+
 
    Attributes and Methods
    
 
    
 
    
@@ -723,7 +951,7 @@ 
    Classes
 
    
-class EventKapitalRebuild(impact, rebuilding_sectors, rebuild_tau=60, aff_regions=None, aff_sectors=None, aff_industries=None, impact_industries_distrib=None, impact_regional_distrib=None, impact_sectoral_distrib_type='equally shared', impact_sectoral_distrib=None, name='Unnamed', occurrence=1, duration=1, rebuilding_factor=None)[source]#
    
+class EventKapitalRebuild(*, impact, households_impact=None, name='Unnamed', occurrence=1, duration=1, event_monetary_factor=None, rebuild_tau, rebuilding_sectors, rebuilding_factor=1.0)[source]#
    
 
    Create an event shocking the model from a dictionary.
    
 
    An Event object stores all information about a unique shock during simulation such as
 time of occurrence, duration, type of shock, amount of damages. Computation
@@ -735,52 +963,55 @@ 
    ClassesParameters:
 
      
-
    • impact (Impact) – 
      • 
-
    • rebuilding_sectors (Union[dict[str, float], pd.Series]) – 
      • 
-
    • aff_regions (Optional[RegionsList]) – 
      • 
-
    • aff_sectors (Optional[SectorsList]) – 
      • 
-
    • aff_industries (Optional[IndustriesList]) – 
      • 
+
    • impact (pd.Series) – 
      • 
+
    • households_impact (Impact | None) – 
      • 
 
    • name (str) – 
      • 
+
    • occurrence (int) – 
      • 
+
    • duration (int) – 
      • 
+
    • event_monetary_factor (Optional[int]) – 
      • 
+
    • rebuild_tau (int) – 
      • 
+
    • rebuilding_sectors (dict[str, float] | pd.Series) – 
      • 
+
    • rebuilding_factor (float) – 
      • 
 
    
 
    
 
    
 
    Examples
    
 
    FIXME: Add docs.
    
 
    Inheritance
    
-
    Inheritance diagram of EventKapitalRebuild
    
+
    Inheritance diagram of EventKapitalRebuild
    
 
-
-
-
+
+
+
 
    Attributes and Methods
    
 
    
 
    
-property rebuilding_sectors_idx: ndarray#
    
+property rebuilding_sectors_idx: ndarray[Any, dtype[ScalarType]]#
 
    The (optional) array of indexes of the rebuilding sectors (in lexicographic order)
    
 
    
 
 
    
 
    
-property rebuilding_sectors_shares: ndarray#
    
+property rebuilding_sectors_shares: ndarray[Any, dtype[ScalarType]]#
 
    The array specifying how rebuilding demand is distributed along the rebuilding sectors
    
 
    
 
 
    
 
    
-property rebuilding_sectors: Index#
    
+property rebuilding_sectors: Index#
 
    The (optional) array of rebuilding sectors
    
 
    
 
 
    
 
    
-property rebuilding_demand_house: ndarray#
    
+property rebuilding_demand_house: ndarray[Any, dtype[ScalarType]]#
 
    The optional array of rebuilding demand from households
    
 
    
 
 
    
 
    
-property rebuilding_demand_indus: ndarray#
    
-
    The optional array of rebuilding demand from industries (to rebuild their kapital)
    
+property rebuilding_demand_indus: ndarray[Any, dtype[ScalarType]]#
+
    The optional array of rebuilding demand from industries (to rebuild their productive_capital)
    
 
    
 
 
    
@@ -803,40 +1034,40 @@ 
    Variables
 
    
 Impact#
    
-
    typing.Union[int, float, list, dict, numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series]
+
    typing.Union[int, float, list, dict, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]], pandas.core.frame.DataFrame, pandas.core.series.Series]
 
    
 
    
-
    alias of Union[int, float, list, dict, ndarray, DataFrame, Series]
    
+
    alias of Union[list, dict, ndarray, DataFrame, Series, int, float, integer]
    
 
    
 
 
    
 
    
 IndustriesList#
    
-
    typing.Union[typing.List[typing.Tuple[str, str]], pandas.core.indexes.multi.MultiIndex, numpy.ndarray]
+
    typing.Union[typing.List[typing.Tuple[str, str]], pandas.core.indexes.multi.MultiIndex, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
 
    
 
    
-
    alias of Union[List[Tuple[str, str]], MultiIndex, ndarray]
    
+
    alias of Union[List[Tuple[str, str]], MultiIndex]
    
 
    
 
 
    
 
    
 SectorsList#
    
-
    typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray]
+
    typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
 
    
 
    
-
    alias of Union[List[str], Index, ndarray]
    
+
    alias of Union[List[str], Index, str]
    
 
    
 
 
    
 
    
 RegionsList#
    
-
    typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray]
+
    typing.Union[typing.List[str], pandas.core.indexes.base.Index, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]]
 
    
 
    
-
    alias of Union[List[str], Index, ndarray]
    
+
    alias of Union[List[str], Index, str]
    
 
    
 
-
    alias of Union[List[str], Index, ndarray]
    
+
    alias of Union[List[str], Index, str]
    
 
 
 
@@ -846,7 +1077,10 @@ 
    Variables
-                  
+                  
+
    
+  
+    
    
 
    
     Variables
       
     
+
    
+  
 
    
+
                 
    
               
             
    
@@ -885,21 +1122,30 @@ 
    VariablesEvent
 
 
  • EventKapitalDestroyed
 
    • 
 
  • EventArbitraryProd
 
    • 
 
  • EventKapitalRecover
    • 
diff --git a/docs/build/html/autoapi/boario.extended_models.html b/docs/build/html/autoapi/boario.extended_models.html
index 7704a76..80e7320 100644
--- a/docs/build/html/autoapi/boario.extended_models.html
+++ b/docs/build/html/autoapi/boario.extended_models.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario.extended_models — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,15 +34,16 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
-    
+    
+    
     
     
     
@@ -162,7 +162,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -187,7 +187,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -278,7 +278,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -292,7 +292,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -399,7 +399,7 @@ 
    Classes
 
    
-class ARIOPsiModel(pym_mrio, *, order_type='alt', alpha_base=1.0, alpha_max=1.25, alpha_tau=365, rebuild_tau=60, main_inv_dur=90, monetary_factor=1000000, psi_param=0.9, inventory_restoration_tau=60, **kwargs)[source]#
    
+class ARIOPsiModel(pym_mrio, *, order_type='alt', alpha_base=1.0, alpha_max=1.25, alpha_tau=365, rebuild_tau=60, main_inv_dur=90, monetary_factor=1000000, temporal_units_by_step=1, iotable_year_to_temporal_unit_factor=365, infinite_inventories_sect=None, inventory_dict=None, productive_capital_vector=None, productive_capital_to_VA_dict=None, psi_param=0.8, inventory_restoration_tau=60)[source]#
 
    An ARIO3 model with some additional features
    
 
    Added feature are parameter psi of production adjustment inventories constraint threshold, as well as a characteristic time of inventories resupplying.
    
 
    Inheritance
    
@@ -412,6 +412,12 @@ 
    ClassesParameters:
 
    
 
    
@@ -419,7 +425,7 @@ 
    Classes
 
    
 psi#
    
-
    Value of the psi parameter. (see Detailed model description).
    
+
    Value of the psi parameter. (see Model description).
    
 
    
 
    Type:
    
 
    float
    
@@ -430,7 +436,7 @@ 
    Classes
 
    
 restoration_tau#
    
-
    Array of size \(n\) setting for each inputs its characteristic restoration time :math:`       au_{    extrm{INV}}` in n_temporal_units_by_step. (see Detailed model description).
    
+
    Array of size \(n\) setting for each inputs its characteristic restoration time \(\tau_{\textrm{INV}}\) in n_temporal_units_by_step. (see Model description).
    
 
    
 
    Type:
    
 
    numpy.ndarray of int
    
@@ -537,13 +543,13 @@ 
    Classes
 
    Parameters:
    
-
    matrix_stock_goal (np.ndarray of float) – The target inventories.
    
+
    matrix_stock_goal (npt.NDArray of float) – The target inventories.
    
 
    
 
    Returns:
    
 
    The (only positive) gap between goal and current inventories.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    Raises:
    
 
    RuntimeError – If NaN are found in the result.
    
@@ -562,7 +568,10 @@ 
    Classes
-                  
+                  
+
    
+  
+    
    
 
    
     Classes
     
+
    
+  
 
    
+
                 
               
             
    
@@ -641,7 +653,7 @@ 
    Classes
-            
    
+            
           
         
       
@@ -649,8 +661,8 @@ 
    Classes
-
+  
+
 
   
    
diff --git a/docs/build/html/autoapi/boario.html b/docs/build/html/autoapi/boario.html
index b21480a..81b72b7 100644
--- a/docs/build/html/autoapi/boario.html
+++ b/docs/build/html/autoapi/boario.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,14 +34,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -160,7 +160,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -185,7 +185,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -276,7 +276,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -290,7 +290,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -395,21 +395,30 @@ 
    SubmodulesEvent
 
 
  • EventKapitalDestroyed
 
    • 
 
  • EventArbitraryProd
 
    • 
 
  • EventKapitalRecover
 
    • 
@@ -566,25 +585,22 @@ 
    SubmodulesSimulation.loop()
 
  • Simulation.next_step()
    • 
 
  • Simulation.check_equilibrium()
    • 
+
  • Simulation.add_events()
    • 
 
  • Simulation.add_event()
    • 
 
  • Simulation.reset_sim_with_same_events()
    • 
 
  • Simulation.reset_sim_full()
    • 
-
  • Simulation.update_params()
    • 
 
  • Simulation.write_index()
    • 
-
  • Simulation.read_events()
    • 
-
  • Simulation.check_happening_events()
    • 
-
  • Simulation.write_production()
    • 
-
  • Simulation.write_rebuild_prod()
    • 
-
  • Simulation.write_kapital_lost()
    • 
-
  • Simulation.write_production_max()
    • 
-
  • Simulation.write_io_demand()
    • 
-
  • Simulation.write_final_demand()
    • 
-
  • Simulation.write_rebuild_demand()
    • 
-
  • Simulation.write_overproduction()
    • 
-
  • Simulation.write_final_demand_unmet()
    • 
-
  • Simulation.write_stocks()
    • 
-
  • Simulation.write_limiting_stocks()
    • 
-
  • Simulation.flush_memmaps()
    • 
+
  • Simulation.production_realised
    • 
+
  • Simulation.production_capacity
    • 
+
  • Simulation.final_demand
    • 
+
  • Simulation.intermediate_demand
    • 
+
  • Simulation.rebuild_demand
    • 
+
  • Simulation.overproduction
    • 
+
  • Simulation.final_demand_unmet
    • 
+
  • Simulation.rebuild_prod
    • 
+
  • Simulation.inputs_stocks
    • 
+
  • Simulation.limiting_inputs
    • 
+
  • Simulation.productive_capital_to_recover
    • 
 
 
 
@@ -602,7 +618,10 @@ 
    Submodules
-                  
+                  
+
    
+  
+    
    
 
    
     Submodules
       
     
+
    
+  
 
    
+
                 
               
             
    
@@ -669,7 +691,7 @@ 
    Submodules
           
    
-            
    
+            
           
    
         
       
@@ -677,8 +699,8 @@ 
    Submodules
   
   
-  
-
+  
+
 
   
    
diff --git a/docs/build/html/autoapi/boario.indicators.html b/docs/build/html/autoapi/boario.indicators.html
index d7b2884..cc17c04 100644
--- a/docs/build/html/autoapi/boario.indicators.html
+++ b/docs/build/html/autoapi/boario.indicators.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario.indicators — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,14 +34,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -160,7 +160,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -185,7 +185,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -276,7 +276,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -290,7 +290,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -397,7 +397,7 @@ 
    Classes
 
    
-class Indicators(*, has_crashed, params, regions, sectors, kapital, prod, prodmax, overprod, final_demand, io_demand, r_demand, r_prod, fd_unmet, limiting_stocks, n_temporal_units_to_sim, n_temporal_units_by_step, events, results_storage=None, psi_param=None, inventory_restoration_tau=None, stocks=None, stocks_treatment=False, include_crash=False)[source]#
    
+class Indicators(*, has_crashed, params, regions, sectors, productive_capital, prod, prodmax, overprod, final_demand, io_demand, r_demand, r_prod, fd_unmet, limiting_stocks, n_temporal_units_to_sim, n_temporal_units_by_step, events, results_storage=None, psi_param=None, inventory_restoration_tau=None, stocks=None, stocks_treatment=False, include_crash=False)[source]#
 
    Inheritance
    
 
    Inheritance diagram of Indicators
    
 
@@ -427,7 +427,10 @@ 
    Classes
-                  
+                  
+
    
+  
+    
    
 
    
     Classes
     
+
    
+  
 
    
+
                 
               
             
    
@@ -497,7 +503,7 @@ 
    Classes
-            
    
+            
           
         
       
@@ -505,8 +511,8 @@ 
    Classes
-
+  
+
 
   
    
diff --git a/docs/build/html/autoapi/boario.model_base.html b/docs/build/html/autoapi/boario.model_base.html
index ce42af2..c4d1549 100644
--- a/docs/build/html/autoapi/boario.model_base.html
+++ b/docs/build/html/autoapi/boario.model_base.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario.model_base — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,15 +34,16 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
-    
+    
+    
     
     
     
@@ -162,7 +162,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -187,7 +187,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -278,7 +278,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -292,7 +292,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -430,32 +430,32 @@ 
    Classes
 
    
-class ARIOBaseModel(pym_mrio, *, order_type='alt', alpha_base=1.0, alpha_max=1.25, alpha_tau=365, rebuild_tau=60, main_inv_dur=90, monetary_factor=1000000, temporal_units_by_step=1, iotable_year_to_temporal_unit_factor=365, infinite_inventories_sect=None, inventory_dict=None, kapital_vector=None, kapital_to_VA_dict=None)[source]#
    
+class ARIOBaseModel(pym_mrio, *, order_type='alt', alpha_base=1.0, alpha_max=1.25, alpha_tau=365, rebuild_tau=60, main_inv_dur=90, monetary_factor=1000000, temporal_units_by_step=1, iotable_year_to_temporal_unit_factor=365, infinite_inventories_sect=None, inventory_dict=None, productive_capital_vector=None, productive_capital_to_VA_dict=None)[source]#
 
    The core of an ARIO model.  Handles the different arrays containing the mrio tables.
    
 
    ARIOBaseModel wrap all the data and functions used in the core of the most basic version of the ARIO
-model (based on [Hal13] and [GWH+20]).
    
+model (based on [Hal13] and [GWH+20]).
    
 
    An ARIOBaseModel instance based is build on the given IOSystem.
-Most default parameters are the same as in [Hal13] and
-default order mechanism comes from [GWH+20]. By default each
-industry capital stock is 4 times its value added ([Hal08]).
    
+Most default parameters are the same as in [Hal13] and
+default order mechanisms comes from [GWH+20]. By default each
+industry capital stock is 4 times its value added ([Hal08]).
    
 
    
 
    Parameters:
    
 
      
 
    • pym_mrio (IOSystem) – The IOSystem to base the model on.
      • 
 
    • order_type (str, default “alt”) – The type of orders mechanism to use. Currently, can be “alt” or
-“noalt”. See Detailed model description
      • 
+“noalt”. See Model description
      
 
    • alpha_base (float, default 1.0) – Base value of overproduction factor \(\alpha^{b}\) (Default to 1.0).
      • 
 
    • alpha_max (float, default 1.25) – Maximum factor of overproduction \(\alpha^{\textrm{max}}\) (default should be 1.25).
      • 
 
    • alpha_tau (int, default 365) – Characteristic time of overproduction \(\tau_{\alpha}\) in n_temporal_units_by_step (default should be 365 days).
      • 
-
    • rebuild_tau (int, default 60) – Rebuilding characteristic time \(\tau_{\textrm{REBUILD}}\) (see Detailed model description).
      • 
+
    • rebuild_tau (int, default 60) – Rebuilding characteristic time \(\tau_{\textrm{REBUILD}}\) (see Model description). Overwritten by per event value if it exists.
      • 
 
    • main_inv_dur (int, default 90) – The default numbers of days for inputs inventory to use if it is not defined for an input.
      • 
 
    • monetary_factor (int, default 10**6) – Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6).
      • 
-
    • temporal_units_by_step (int, default 1) – The number of temporal_units between each step. (Current version of the model was not tested with values other than 1).
      • 
+
    • temporal_units_by_step (int, default 1) – The number of temporal_units between each step. (Current version of the model showed inconsistencies with values other than 1).
      • 
 
    • iotable_year_to_temporal_unit_factor (int, default 365) – The (inverse of the) factor by which MRIO data should be multiplied in order to get “per temporal unit value”, assuming IO data is yearly.
      • 
 
    • infinite_inventories_sect (list, optional) – List of inputs (sector) considered never constraining for production.
      • 
 
    • inventory_dict (dict, optional) – Dictionary in the form input:initial_inventory_size, (where input is a sector, and initial_inventory_size is in “temporal_unit” (defaults to a day))
      • 
-
    • kapital_vector (ArrayLike, optional) – Array of capital per industry if you need to give it exogenously.
      • 
-
    • kapital_to_VA_dict (dict, optional) – Dictionary in the form sector:ratio. Where ratio is used to estimate capital stock based on the value added of the sector.
      • 
+
    • productive_capital_vector (ArrayLike, optional) – Array of capital per industry if you need to give it exogenously.
      • 
+
    • productive_capital_to_VA_dict (dict, optional) – Dictionary in the form sector:ratio. Where ratio is used to estimate capital stock based on the value added of the sector.
      • 
 
    
 
    
 
    Parameters:
    
@@ -472,22 +472,20 @@ 
    Classesint) – 
    
 
  • infinite_inventories_sect (Optional[list]) – 
    • 
 
  • inventory_dict (Optional[dict]) – 
    • 
-
  • kapital_vector (Optional[pd.Series | np.ndarray | pd.DataFrame]) – 
    • 
-
  • kapital_to_VA_dict (Optional[dict]) – 
    • 
+
  • productive_capital_vector (Optional[pd.Series | npt.NDArray | pd.DataFrame]) – 
    • 
+
  • productive_capital_to_VA_dict (Optional[dict]) – 
    • 
 
 
    
 
 
    Notes
    
-
    It is recommended to use kapital_to_VA_dict if you have a more precise estimation of
-the ratio of (Capital Stock / Value Added) per sectors.
-You may also feed in directly a kapital_vector if you did your estimation before-hand.
+
    It is recommended to use productive_capital_to_VA_dict if you have a more precise estimation of
+the ratio of (Capital Stock / Value Added) per sectors than the default 4/1 ratio.
+You may also feed in directly a productive_capital_vector if you did your estimation before-hand.
 (This is especially useful if you have events based of an exposure layer for instance)
    
 
    Regarding inventories, they default to 90 days for all inputs (ie sectors).
 You can set some inputs to be never constraining for production by listing them
 in infinite_inventories_sect or directly feed in a dictionary of the inventory
 duration for each input.
    
-
    Examples
    
-
    FIXME: Add docs.
    
 
    Inheritance
    
 
    Inheritance diagram of ARIOBaseModel
    
 
@@ -554,40 +552,40 @@ 
    Classes
 
    Type:
    
-
    pandas.MultiIndex
    
+
    pandas.MultiIndex
    
 
    
 
 
 
 
    
-
    
-fd_cat#
    
-
    An array of the final demand categories of the model (["Final demand"] if there is only one)
    
+
    
+n_industries#
    
+
    The number \(m * n\) of industries.
    
 
    
 
    Type:
    
-
    numpy.ndarray of str
    
+
    int
    
 
    
 
    
 
    
 
 
    
-
    
-n_fd_cat#
    
-
    The numbers of final demand categories.
    
+
    
+final_demand_cat#
    
+
    An array of the final demand categories of the model (["Final demand"] if there is only one)
    
 
    
 
    Type:
    
-
    int
    
+
    numpy.ndarray of str
    
 
    
 
    
 
    
 
 
    
-
    
-monetary_factor#
    
-
    Monetary unit factor (i.e. if the tables unit is 10^6 € instead of 1 €, it should be set to 10^6).
    
+
    
+n_fd_cat#
    
+
    The numbers of final demand categories.
    
 
    
 
    Type:
    
-
    int, default 10^6
    
+
    int
    
 
    
 
    
 
    
@@ -617,7 +615,7 @@ 
    Classes
 
    
 rebuild_tau#
    
-
    Rebuilding characteristic time \(\tau_{\textrm{REBUILD}}\) (see Detailed model description).
    
+
    Rebuilding characteristic time \(\tau_{\textrm{REBUILD}}\) (see Model description).
    
 
    
 
    Type:
    
 
    int
    
@@ -661,7 +659,7 @@ 
    Classes
 
    
 inventories#
    
-
    Array \(\mathbf{s}\) of size \(n\) (sectors), setting for each input the initial number of n_temporal_units_by_step of stock for the input. (see Detailed model description).
    
+
    Array \(\mathbf{s}\) of size \(n\) (sectors), setting for each input the initial number of n_temporal_units_by_step of stock for the input. (see Model description).
    
 
    
 
    Type:
    
 
    numpy.ndarray of int
    
@@ -694,7 +692,29 @@ 
    Classes
 
    
 Z_0#
    
-
    2-dim square matrix array \(\ioz\) of size \((n \times m, n \times m)\) representing the intermediate (transaction) matrix (see Initial state).
    
+
    2-dim square matrix array \(\ioz\) of size \((n \times m, n \times m)\) representing the daily intermediate (transaction) matrix (see Initial state).
    
+
    
+
    Type:
    
+
    numpy.ndarray of float
    
+
    
+
    
+
    
+
+
    
+
    
+Y_C#
    
+
    2-dim matrix array \(\ioy^{\sectorsset}\) of size \((n, m \times \text{number of final demand categories})\) representing the final demand matrix aggregated by inputs (see here).
    
+
    
+
    Type:
    
+
    numpy.ndarray of float
    
+
    
+
    
+
    
+
+
    
+
    
+Y_distrib#
    
+
    math:ioy normalised by \(\ioy^{\sectorsset}\), i.e. representing for each input the share of the total ordered transiting from an industry to final demand (see here).
    
 
    
 
    Type:
    
 
    numpy.ndarray of float
    
@@ -705,7 +725,7 @@ 
    Classes
 
    
 Y_0#
    
-
    2-dim array \(\ioy\) of size \((n \times m, m \times \text{number of final demand categories})\) representing the final demand matrix.
    
+
    2-dim array \(\ioy\) of size \((n \times m, m \times \text{number of final demand categories})\) representing the daily final demand matrix.
    
 
    
 
    Type:
    
 
    numpy.ndarray of float
    
@@ -716,7 +736,7 @@ 
    Classes
 
    
 X_0#
    
-
    Array \(\iox(0)\) of size \(n \times m\) representing the initial gross production.
    
+
    Array \(\iox(0)\) of size \(n \times m\) representing the initial daily gross production.
    
 
    
 
    Type:
    
 
    numpy.ndarray of float
    
@@ -730,7 +750,7 @@ 
    Classes
 
    Type:
    
-
    pandas.DataFrame
    
+
    pandas.DataFrame
    
 
    
 
    
 
    
@@ -834,79 +854,109 @@ 
    Classes
+
    
+in_shortage#
    
+
    Boolean stating if at least one industry is in shortage (i.e.) if at least one of its inputs inventory is low enough to reduce production.
    
+
    
+
+
    
+
    
+had_shortage#
    
+
    Boolean stating if at least one industry was in shortage at some point.
    
+
    
+
+
    
+
    
+rebuild_prod#
    
+
    Array of size \(n \times m\) representing the remaining stock of rebuilding demand asked of each industry.
    
+
    
+
    Type:
    
+
    numpy.ndarray of float
    
+
    
+
    
+
    
+
+
    
+
    
+final_demand_not_met#
    
+
    Array of size \(n \times m\) representing the final demand that could not be met at current step for each industry.
    
+
    
+
    Type:
    
+
    numpy.ndarray of float
    
+
    
+
    
+
    
+
 
    
 
    
-property tot_rebuild_demand: ndarray | None#
    
+property tot_rebuild_demand: ndarray[Any, dtype[ScalarType]]#
 
    Returns current total rebuilding demand (as the sum of rebuilding demand addressed to each industry)
    
 
    
 
 
    
 
    
-property house_rebuild_demand_tot: ndarray | None#
    
-
    Returns total household rebuilding demand vector or None if
-there is no such demand.
    
+property house_rebuild_demand_tot: ndarray[Any, dtype[ScalarType]]#
+
    Returns total household rebuilding demand vector.
    
 
    
 
    Returns:
    
 
    An array of same shape as math:iox, containing the sum of all currently
 rebuildable households demands.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    
 
    
 
 
    
 
    
-property house_rebuild_demand: ndarray | None#
    
-
    Returns household rebuilding demand matrix or None if
-there is no such demand.
    
+property house_rebuild_demand: ndarray[Any, dtype[ScalarType]]#
+
    Returns household rebuilding demand matrix.
    
 
    
 
    Returns:
    
 
    An array of same shape as math:ioy, containing the sum of all currently
 rebuildable final demand stock.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    
 
    
 
 
    
 
    
-property indus_rebuild_demand_tot: ndarray | None#
    
-
    Returns total industrial rebuilding demand vector or None if
-there is no such demand.
    
+property indus_rebuild_demand_tot: ndarray[Any, dtype[ScalarType]]#
+
    Returns total industrial rebuilding demand vector.
    
 
    
 
    Returns:
    
 
    An array of same shape as math:iox, containing the sum of all currently
 rebuildable intermediate demands.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    
 
    
 
 
    
 
    
-property indus_rebuild_demand: ndarray | None#
    
-
    Returns industrial rebuilding demand matrix or None if
-there is no such demand.
    
+property indus_rebuild_demand: ndarray[Any, dtype[ScalarType]]#
+
    Returns industrial rebuilding demand matrix.
    
 
    
 
    Returns:
    
 
    An array of same shape as math:ioz, containing the sum of all currently
 rebuildable intermediate demand stock.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    
 
    
 
 
    
-
    
-property kapital_lost: ndarray#
    
+
    
+property productive_capital_lost: ndarray[Any, dtype[ScalarType]]#
    
 
    Array of size \(n \times m\) representing the estimated stock of capital currently destroyed for each industry.
    
 
    
 
    Type:
    
@@ -917,13 +967,13 @@ 
    Classes
 
    
-property prod_cap_delta_arbitrary: ndarray | None#
    
+property prod_cap_delta_arbitrary: ndarray[Any, dtype[ScalarType]]#
 
    Return the possible “arbitrary” production capacity lost vector if
 it was set.
    
 
    
 
    Returns:
    
 
      
-
    • np.ndarray – An array of same shape as math:iox, stating the amount of production
      • 
+
    • npt.NDArray – An array of same shape as math:iox, stating the amount of production
      • 
 
    • capacity lost arbitrarily (ie exogenous).
      • 
 
    
 
    
@@ -932,14 +982,14 @@ 
    Classes
-
    
-property prod_cap_delta_kapital: ndarray | None#
    
+
    
+property prod_cap_delta_productive_capital: ndarray[Any, dtype[ScalarType]]#
    
 
    Return the possible production capacity lost due to capital destroyed vector if
 it was set.
    
 
    
 
    Returns:
    
 
      
-
    • np.ndarray – An array of same shape as math:iox, stating the amount of production
      • 
+
    • npt.NDArray – An array of same shape as math:iox, stating the amount of production
      • 
 
    • capacity lost due to capital destroyed.
      • 
 
    
 
    
@@ -949,7 +999,7 @@ 
    Classes
 
    
-property prod_cap_delta_tot: ndarray#
    
+property prod_cap_delta_tot: ndarray[Any, dtype[ScalarType]]#
 
    Computes and return total current production delta.
    
 
    
 
    Returns:
    
@@ -957,7 +1007,7 @@ 
    ClassesReturn type:
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    
 
    
@@ -982,7 +1032,7 @@ 
    Classes
 
    
-property production_cap: ndarray#
    
+property production_cap: ndarray[Any, dtype[ScalarType]]#
 
    Compute and update production capacity.
    
 
    Compute and update production capacity from current total production delta and overproduction.
    
 
    
@@ -996,26 +1046,26 @@ 
    Classes
 
    
-property total_demand: ndarray#
    
-
    Computes and return total demand as the sum of intermediate demand (orders), final demand, and possible rebuilding demand.
    
+property total_demand: ndarray[Any, dtype[ScalarType]]#
+
    Computes and returns total demand as the sum of intermediate demand (orders), final demand, and possible rebuilding demand.
    
 
    
 
 
    
 
    
-property production_opt: ndarray#
    
+property production_opt: ndarray[Any, dtype[ScalarType]]#
 
    Computes and returns “optimal production” \(\iox^{textrm{Opt}}\), as the per industry minimum between
 total demand and production capacity.
    
 
    
 
 
    
 
    
-property inventory_constraints_opt: ndarray#
    
+property inventory_constraints_opt: ndarray[Any, dtype[ScalarType]]#
 
    Computes and returns inventory constraints for “optimal production” (see calc_inventory_constraints())
    
 
    
 
 
    
 
    
-property inventory_constraints_act: ndarray#
    
+property inventory_constraints_act: ndarray[Any, dtype[ScalarType]]#
 
    Computes and returns inventory constraints for “actual production” (see calc_inventory_constraints())
    
 
    
 
@@ -1059,7 +1109,7 @@ 
    Classes
    current_temporal_unit (int) – 
    
 
    
 
    Return type:
    
-
    ndarray
    
+
    ndarray[Any, dtype[ScalarType]]
    
 
    
 
    
 
    
@@ -1072,20 +1122,20 @@ 
    Classescalc_production() for how inventory constraints are computed.
    
 
    
 
    Parameters:
    
-
    production (np.ndarray) – The production vector to consider.
    
+
    production (npt.NDArray) – The production vector to consider.
    
 
    
 
    Returns:
    
 
      
-
    • np.ndarray – For each input, for each industry, the size of the inventory required to produce at production level
      • 
+
    • npt.NDArray – For each input, for each industry, the size of the inventory required to produce at production level
      • 
 
    • for the duration goal (inv_duration).
      • 
 
    
 
    
 
    
 
    Parameters:
    
-
    production (ndarray) – 
    
+
    production (ndarray[Any, dtype[ScalarType]]) – 
    
 
    
 
    Return type:
    
-
    ndarray
    
+
    ndarray[Any, dtype[ScalarType]]
    
 
    
 
    
 
    
@@ -1172,13 +1222,13 @@ 
    Classes
 
    Parameters:
    
-
    matrix_stock_goal (np.ndarray of float) – The target inventories.
    
+
    matrix_stock_goal (npt.NDArray of float) – The target inventories.
    
 
    
 
    Returns:
    
 
    The (only positive) gap between goal and current inventories.
    
 
    
 
    Return type:
    
-
    np.ndarray
    
+
    npt.NDArray
    
 
    
 
    Raises:
    
 
    RuntimeError – If NaN are found in the result.
    
@@ -1204,7 +1254,7 @@ 
    Classes
 
    
 calc_overproduction()[source]#
    
-
    Computes and update the overproduction vector.
    
+
    Computes and updates the overproduction vector.
    
 
    See Overproduction module
    
 
    
 
    Return type:
    
@@ -1217,30 +1267,8 @@ 
    Classes
 reset_module()[source]#
 
    Resets the model to initial state [Deprecated]
    
-
    This method is currently not functioning.
    
-
    
-
    Return type:
    
-
    None
    
-
    
-
    
-
    
-
-
    
-
    
-update_params(new_params)[source]#
    
-
    Update the parameters of the model.
    
-
    Replace each parameters with given new ones.
    
-
    
-
    Warning
    
-
    Be aware this method calls reset_record_files(), which resets the memmap files located in the results directory !
    
-
    
+
    This method has not been checked extensively.
    
 
    
-
    Parameters:
    
-
    new_params (dict) – Dictionary of new parameters to use.
    
-
    
-
    Parameters:
    
-
    new_params (dict) – 
    
-
    
 
    Return type:
    
 
    None
    
 
    
@@ -1447,7 +1475,10 @@ 
    Variables
-                  
+                  
+
    
+  
+    
    
 
    
     Variables
       
     
+
    
+  
 
    
+
                 
               
             
    
@@ -1494,9 +1528,9 @@ 
    VariablesARIOBaseModel.sectors
 
  • ARIOBaseModel.n_sectors
    • 
 
  • ARIOBaseModel.industries
    • 
-
  • ARIOBaseModel.fd_cat
    • 
+
  • ARIOBaseModel.n_industries
    • 
+
  • ARIOBaseModel.final_demand_cat
    • 
 
  • ARIOBaseModel.n_fd_cat
    • 
-
  • ARIOBaseModel.monetary_factor
    • 
 
  • ARIOBaseModel.n_temporal_units_by_step
    • 
 
  • ARIOBaseModel.iotable_year_to_temporal_unit_factor
    • 
 
  • ARIOBaseModel.rebuild_tau
    • 
@@ -1507,6 +1541,8 @@ 
    VariablesARIOBaseModel.Z_C
 
  • ARIOBaseModel.Z_distrib
    • 
 
  • ARIOBaseModel.Z_0
    • 
+
  • ARIOBaseModel.Y_C
    • 
+
  • ARIOBaseModel.Y_distrib
    • 
 
  • ARIOBaseModel.Y_0
    • 
 
  • ARIOBaseModel.X_0
    • 
 
  • ARIOBaseModel.gdp_df
    • 
@@ -1519,14 +1555,18 @@ 
    VariablesARIOBaseModel.matrix_stock
 
  • ARIOBaseModel.matrix_orders
    • 
 
  • ARIOBaseModel.production
    • 
+
  • ARIOBaseModel.in_shortage
    • 
+
  • ARIOBaseModel.had_shortage
    • 
+
  • ARIOBaseModel.rebuild_prod
    • 
+
  • ARIOBaseModel.final_demand_not_met
    • 
 
  • ARIOBaseModel.tot_rebuild_demand
    • 
 
  • ARIOBaseModel.house_rebuild_demand_tot
    • 
 
  • ARIOBaseModel.house_rebuild_demand
    • 
 
  • ARIOBaseModel.indus_rebuild_demand_tot
    • 
 
  • ARIOBaseModel.indus_rebuild_demand
    • 
-
  • ARIOBaseModel.kapital_lost
    • 
+
  • ARIOBaseModel.productive_capital_lost
    • 
 
  • ARIOBaseModel.prod_cap_delta_arbitrary
    • 
-
  • ARIOBaseModel.prod_cap_delta_kapital
    • 
+
  • ARIOBaseModel.prod_cap_delta_productive_capital
    • 
 
  • ARIOBaseModel.prod_cap_delta_tot
    • 
 
  • ARIOBaseModel.update_system_from_events()
    • 
 
  • ARIOBaseModel.production_cap
    • 
@@ -1541,7 +1581,6 @@ 
    VariablesARIOBaseModel.calc_orders()
 
  • ARIOBaseModel.calc_overproduction()
    • 
 
  • ARIOBaseModel.reset_module()
    • 
-
  • ARIOBaseModel.update_params()
    • 
 
  • ARIOBaseModel.write_index()
    • 
 
 
@@ -1584,7 +1623,7 @@ 
    Variables
           
    
-            
    
+            
           
    
         
       
@@ -1592,8 +1631,8 @@ 
    Variables
   
   
-  
-
+  
+
 
   
    
diff --git a/docs/build/html/autoapi/boario.simulation.html b/docs/build/html/autoapi/boario.simulation.html
index b415797..2a34649 100644
--- a/docs/build/html/autoapi/boario.simulation.html
+++ b/docs/build/html/autoapi/boario.simulation.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     boario.simulation — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,14 +34,15 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
@@ -160,7 +160,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -185,7 +185,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -276,7 +276,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -290,7 +290,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -399,7 +399,7 @@ 
    Classes
 
    
-class Simulation(model, register_stocks=False, n_temporal_units_to_sim=365, events_list=None, separate_sims=False, save_events=False, save_params=False, save_index=False, save_records=[], boario_output_dir='/tmp/boario2t1bw66t', results_dir_name='results')[source]#
    
+class Simulation(model, register_stocks=False, n_temporal_units_to_sim=365, events_list=None, separate_sims=False, save_events=False, save_params=False, save_index=False, save_records=[], boario_output_dir='/tmp/boarioqjw4zuwv', results_dir_name=None)[source]#
 
    Defines a simulation object with a set of parameters and an IOSystem.
    
 
    This class wraps a ARIOBaseModel or descendant, and create the context for
 simulations using this model. It stores execution parameters as well as events perturbing
@@ -415,7 +415,11 @@ 
    ClassesClassesbool) – 
    
 
  • save_records (list | str) – 
    • 
 
  • boario_output_dir (str | pathlib.Path) – 
    • 
-
  • results_dir_name (str) – 
    • 
+
  • results_dir_name (Optional[str]) – 
    • 
 
 
    
 
@@ -526,6 +530,11 @@ 
    Classes
 records_storage: Path#
 
    Place where records are stored if stored
    
+
    
+
    Type:
    
+
    pathlib.Path
    
+
    
+
    
 
    
 
 
    
@@ -566,13 +575,13 @@ 
    Classes
 
  • If at least one step elapsed, it computes the new overproduction vector for the next step (using calc_overproduction())
    • 
 
  • Computes production for the current step. (See calc_production())
    • 
 
  • Distribute the realised production towards the different demands (intermediate, final, rebuilding) and compute the changes in the inputs stock matrix (see distribute_production())
    • 
 
-
    Note that it is during this step that the model checks if an event is completely rebuild/recovered.
    
+
    Note that it is during this step that the model checks if an event is completely rebuild/recovered and removes it from the list the case being.
    
 
      
 
    1. Computes the orders matrix (i.e. the intermediate demand) for the next step (see calc_orders())
      2. 
 
    
@@ -616,6 +625,20 @@ 
    Classes
+
    
+add_events(events)[source]#
    
+
    Add a list of events to the simulation.
    
+
    
+
    Parameters:
    
+
    events (list[Event]) – The events to add.
    
+
    
+
    Parameters:
    
+
    events (list[boario.event.Event]) – 
    
+
    
+
    
+
    
+
 
    
 
    
 add_event(ev)[source]#
    
@@ -642,26 +665,6 @@ 
    Classes
-
    
-update_params(new_params)[source]#
    
-
    Update the parameters of the model.
    
-
    Replace the params attribute with new_params and logs the update.
-This method also checks if the directory specified to save the results exists and create it otherwise.
    
-
    
-
    Warning
    
-
    Be aware this method calls update_params(), which resets the memmap files located in the results directory !
    
-
    
-
    
-
    Parameters:
    
-
    new_params (dict) – New dictionnary of parameters to use.
    
-
    
-
    Parameters:
    
-
    new_params (dict) – 
    
-
    
-
    
-
    
-
 
    
 
    
 write_index(index_file)[source]#
    
@@ -677,168 +680,124 @@ 
    Classes
-
    
-read_events(events_file)[source]#
    
-
    Read events from a json file.
    
-
    
-
    Parameters:
    
-
    events_file – path to a json file
    
-
    
-
    Raises:
    
-
    FileNotFoundError – If file does not exist
    
-
    
-
    Parameters:
    
-
    events_file (str | Path) – 
    
-
    
-
    
-
    
-
-
    
-
    
-check_happening_events()[source]#
    
-
    Updates the status of all events.
    
-
    Check the all_events attribute and current_temporal_unit and
-updates the events accordingly (ie if they happened, if they can start
-rebuild/recover)
    
-
    
-
    Return type:
    
-
    None
    
-
    
-
    
-
    
-
-
    
-
    
-write_production()[source]#
    
-
    Saves the current production vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property production_realised: DataFrame#
    
+
    Return the evolution of the production realised by each industry (region,sector)
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the production realised, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_rebuild_prod()[source]#
    
-
    Saves the current rebuilding production vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property production_capacity: DataFrame#
    
+
    Return the evolution of the production capacity of each industry (region,sector)
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the production capacity, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_kapital_lost()[source]#
    
-
    Saves the current remaining kapital to rebuild vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property final_demand: DataFrame#
    
+
    Return the evolution of final demand asked of each industry.
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the final demand asked, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_production_max()[source]#
    
-
    Saves the current production capacity vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property intermediate_demand: DataFrame#
    
+
    Return the evolution of intermediate demand asked of each industry (Total orders).
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the intermediate demand asked, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_io_demand()[source]#
    
-
    Saves the current (total per industry) intermediate demand vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property rebuild_demand: DataFrame#
    
+
    Return the evolution of rebuild demand asked of each industry (Total orders).
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the rebuild demand asked, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_final_demand()[source]#
    
-
    Saves the current (total per industry) final demand vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
-
    
-
    
-
    
-
-
    
-
    
-write_rebuild_demand()[source]#
    
-
    Saves the current (total per industry) rebuilding demand vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property overproduction: DataFrame#
    
+
    Return the evolution of the overproduction factor of each industry (region,sector)
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the overproduction factor, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_overproduction()[source]#
    
-
    Saves the current overproduction vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property final_demand_unmet: DataFrame#
    
+
    Return the evolution of the final demand that could not be answered by industries.
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the final demand not met, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_final_demand_unmet()[source]#
    
-
    Saves the unmet final demand (for this step) vector to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property rebuild_prod: DataFrame#
    
+
    Return the production allocated for the rebuilding demand by each industry (region,sector).
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the production allocated, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
 
-
    
-
    
-write_stocks()[source]#
    
-
    Saves the current inputs stock matrix to the memmap.
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property inputs_stocks: DataFrame#
    
+
    Return the evolution of the inventory amount of each input for each industry (region,sector). Not this is not available if “record_stocks” is not set to True,
+as the DataFrame can be quite large for “classic” MRIOTs.
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the amount in inventory, the columns are the industries
+and the index are the step and input considered (MultiIndex).
    
 
    
 
    
 
    
 
-
    
-
    
-write_limiting_stocks(limiting_stock)[source]#
    
-
    Saves the current limiting inputs matrix to the memmap.
    
-
    
-
    Parameters:
    
-
    limiting_stock (ndarray) – 
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property limiting_inputs: DataFrame#
    
+
    Return the evolution of the inputs lacking for each industry (region,sector)
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is a boolean set to 1 if considered input constrains production, the columns are the industries
+and the index are the step and input considered (MultiIndex).
    
 
    
 
    
 
    
 
-
    
-
    
-flush_memmaps()[source]#
    
-
    Saves files to record
    
-
    
-
    Return type:
    
-
    None
    
+
    
+
    
+property productive_capital_to_recover: DataFrame#
    
+
    Return the evolution of remaining capital destroyed/to recover for each industry (region,sector) if it exists.
    
+
    
+
    Returns:
    pd.DataFrame: A pandas DataFrame where the value is the amount of capital (still) destroyed, the columns are the industries
+and the index is the step considered.
    
 
    
 
    
 
    
@@ -854,7 +813,10 @@ 
    Classes
-                  
+                  
+
    
+  
+    
    
 
    
     Classes
     
+
    
+  
 
    
+
                 
               
             
    
@@ -903,25 +868,22 @@ 
    ClassesSimulation.loop()
 
  • Simulation.next_step()
    • 
 
  • Simulation.check_equilibrium()
    • 
+
  • Simulation.add_events()
    • 
 
  • Simulation.add_event()
    • 
 
  • Simulation.reset_sim_with_same_events()
    • 
 
  • Simulation.reset_sim_full()
    • 
-
  • Simulation.update_params()
    • 
 
  • Simulation.write_index()
    • 
-
  • Simulation.read_events()
    • 
-
  • Simulation.check_happening_events()
    • 
-
  • Simulation.write_production()
    • 
-
  • Simulation.write_rebuild_prod()
    • 
-
  • Simulation.write_kapital_lost()
    • 
-
  • Simulation.write_production_max()
    • 
-
  • Simulation.write_io_demand()
    • 
-
  • Simulation.write_final_demand()
    • 
-
  • Simulation.write_rebuild_demand()
    • 
-
  • Simulation.write_overproduction()
    • 
-
  • Simulation.write_final_demand_unmet()
    • 
-
  • Simulation.write_stocks()
    • 
-
  • Simulation.write_limiting_stocks()
    • 
-
  • Simulation.flush_memmaps()
    • 
+
  • Simulation.production_realised
    • 
+
  • Simulation.production_capacity
    • 
+
  • Simulation.final_demand
    • 
+
  • Simulation.intermediate_demand
    • 
+
  • Simulation.rebuild_demand
    • 
+
  • Simulation.overproduction
    • 
+
  • Simulation.final_demand_unmet
    • 
+
  • Simulation.rebuild_prod
    • 
+
  • Simulation.inputs_stocks
    • 
+
  • Simulation.limiting_inputs
    • 
+
  • Simulation.productive_capital_to_recover
    • 
 
 
 
@@ -957,7 +919,7 @@ 
    Classes
-            
    
+            
           
         
       
@@ -965,8 +927,8 @@ 
    Classes
-
+  
+
 
   
    
diff --git a/docs/build/html/boario-api-reference.html b/docs/build/html/boario-api-reference.html
index b527f5c..99e029a 100644
--- a/docs/build/html/boario-api-reference.html
+++ b/docs/build/html/boario-api-reference.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     API Reference — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,20 +34,21 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
+    
     
     
     
     
     
-    
+    
   
   
   
@@ -160,7 +160,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -185,7 +185,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -276,7 +276,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -290,7 +290,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -420,7 +420,10 @@
               
               
                 
               
             
    
@@ -477,7 +483,7 @@
             
           
    
           
    
-            
    
+            
           
    
         
       
@@ -485,8 +491,8 @@
   
    
   
   
-  
-
+  
+
 
   
    
 
    
@@ -504,7 +510,7 @@
       
         
    
   
    
-    Created using Sphinx 6.1.3.
+    Created using Sphinx 7.0.1.
     
    
   
    
 
    
@@ -515,7 +521,7 @@
     
    
       
         
    
-  Built with the PyData Sphinx Theme 0.13.0.
+  Built with the PyData Sphinx Theme 0.13.3.
 
    
       
     
    
diff --git a/docs/build/html/boario-events.html b/docs/build/html/boario-events.html
index 1e8aa79..18177f6 100644
--- a/docs/build/html/boario-events.html
+++ b/docs/build/html/boario-events.html
@@ -1,5 +1,4 @@
 
-
 
 
 
@@ -7,7 +6,7 @@
 
   
     
-    
+    
 
     How to define Events — BoARIO  documentation
   
@@ -19,12 +18,12 @@
   
   
   
-  
-
-
+  
+
+
 
   
-  
+  
   
 
 
@@ -35,22 +34,23 @@
     
   
   
-  
-
+  
+
 
     
     
     
     
     
-    
+    
+    
     
     
     
     
     
-    
-    
+    
+    
   
   
   
@@ -162,7 +162,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -187,7 +187,7 @@
       
       
         
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
       
@@ -278,7 +278,7 @@
 
                     
  • 
                       
-                        References
+                        ARIO Academic work
                       
                     
    • 
                 
@@ -292,7 +292,7 @@
       
    
         
           
    
-  Last updated on Mar 13, 2023.
+  Last updated on Jun 07, 2023.
   
    
 
    
         
@@ -315,14 +315,16 @@
         
    
@@ -385,29 +387,40 @@
 
    
 
    Introduction#
    
 
    The BoARIO model is used to study the indirect economic impacts consequent to local event [1].
-At the moment, a shock is represented by a quantity of damages expressed in monetary value and leading to:
    
+At the moment, a shock is represented either by:
    
+
      
+
    • A quantity of damages expressed in monetary value and leading to:
      
 
        
 
      1. A destruction of capital leading to a reduction in production capacity production (See Production module).
        2. 
-
      2. A possible rebuilding demand, corresponding to destroyed capital or a part of it and addressed to a set of rebuilding sectors (See boario-math-rebuilding-demand).
        3. 
+
      3. A possible rebuilding demand, corresponding to destroyed capital or a part of it and addressed to a set of rebuilding sectors.
        4. 
 
      
+
      • 
+
    • Directly by a fraction of production capacity lost for a certain period, which is then recovered exogenously.
      • 
+
    
 
    To define such an event, we hence need the following information:
    
 
      
-
    1. A quantity of damages
      2. 
+
    2. A quantity of damages (or fraction of production capacity)
      3. 
 
    3. The region(s) affected
      4. 
 
    4. The sector(s) affected
      5. 
 
    5. In the case a rebuilding demand is considered: the sector(s) considered to be rebuilding sectors
      6. 
 
    
 
    In addition, it is also possible to define a duration (corresponding to the time before rebuilding can start)
-as well as time of occurrence (when studying interaction between multiple events).
    
+as well as time of occurrence (when studying interaction between multiple events), and a recovery duration (Recovery).
    
 
    During a simulation BoARIO uses Event objects.
 This page details the different aspect of instantiating such objects.
    
+
 
    
 
    Simulation context#
    
 
    Creating events requires a simulation context to exist,
 notably to assess the validity of the event (the affected regions, sectors, the occurrence, …). Hence,
-trying to instantiate an Event object without already having a Simulation (and therefore a ARIOPsiModel)
+trying to instantiate an Event object without already having a Simulation (and therefore an ARIOPsiModel)
 will raise an error.
    
-
    For the purpose of this page we will suppose the following code was run initially:
    
+
    In the following, we will assume the following code was run initially:
    
 
    import pymrio
 import pandas as pd
 
@@ -430,33 +443,26 @@ 
    Simulation context       'other', 'trade', 'transport'], dtype='<U13')
 
    
 
    
-
 
    
 
    
 
    
 
    Different types of Event objects#
    
-
    Currently two types of events are implemented and both consider
+
    Currently three types of events are implemented and both consider
 a destruction of capital as the impact:
    
-
    
-
      
-
    • EventKapitalRecover defines events
-for which the destroyed capital is recovered (i.e. regained
-along a specified recovery curve without an associated
-rebuilding demand and rebuilding sectors)
      • 
-
    • EventKapitalRebuild define events for
-which the destroyed capital is rebuild (i.e. creates a corresponding
-rebuilding demand addressed toward a set of rebuilding sectors)
      • 
+
        
+
      • EventKapitalRecover defines events for which the destroyed
+capital is recovered (i.e. regained along a specified recovery curve without an
+associated rebuilding demand and rebuilding sectors)
        • 
+
      • EventKapitalRebuild define events for which the destroyed
+capital is rebuild (i.e. creates a corresponding rebuilding demand addressed toward
+a set of rebuilding sectors)
        • 
+
      • EventArbitraryProd define events for which production capacity
+is arbitrarily decrease for a set of industries.
        • 
 
      
-
    
 
    
 
    
 
    Defining Event from an impact vector#
    
-
    The more direct way to instantiate an event is to use a pandas.Series object
+
    The more direct way to instantiate an event is to use a pandas.Series object
 where the index is the set of affected industries and the values are the impact for each.
    
 
    Suppose you want to represent an event impacting the “manufactoring” an “mining” sectors of region “reg2” for respectively
 5’000’000€ and 3’000’000€ (assuming the MRIO is in €). You can define the following Series:
    
@@ -478,19 +484,22 @@ 
    Defining Event from an impact vector
 
    Create a EventKapitalRecover#
    
 
    Here is how you can create an EventKapitalRecover from this impact.
    
-
    For this type of event you need to specify the characteristic time for recovery "recovery_time": let us use 30 days here.
    
+
    For this type of event you need to specify the characteristic time for recovery "recovery_time":
+let us use 30 days here.
    
 
    You can also choose a recovery function/curve between "linear" (by default), "concave"
-and "convexe" following what is done in [KTA+19, KT16].
    
-
    You may also choose a specific occurrence (default is 1) which is especially useful if you simulate multiple events.
    
+and "convexe" following what is done in [KTA+19, KT16].
    
+
    You may also choose a specific occurrence (default is 1) which is especially useful if you
+simulate multiple events.
    
 
    
 
    Warning
    
-
    Note that it is not advised to set the occurrence at 0 as some indicators require the first step to be at equilibrium.
    
+
    Note that it is not advised to set the occurrence at 0 as some indicators require the first
+step to be at equilibrium.
    
 
    
-
    You may as well choose a duration for the event. The duration is the amount of temporal units before which recovery
-starts. It allow the possibility to represent delayed recovery due to the event (e.g. for a flood the region inaccessible
-because of the water)
    
+
    You may as well choose a duration for the event. The duration is the amount of temporal units
+before which recovery starts. It allow the possibility to represent delayed recovery due to the event
+(e.g. for a flood the region inaccessible because of the water)
    
 
    Finally for convenience you can give a name for the event.
    
-
    ev = EventKapitalRecover(
+
    ev = EventKapitalRecover.from_series(
     impact=impact,
     recovery_time=30,
     recovery_function="concave",
@@ -501,22 +510,24 @@ 
    Create a 
-
    Create a EventKapitalRebuilding#
    
+
    
+
    Create a EventKapitalRebuild#
    
 
    When creating this type of event, you need to specify the rebuilding characteristic time "rebuild_tau"
 as well which are the rebuilding sectors and how the rebuilding demand is distributed among them.
    
-
    The rebuilding sectors can be given as a Series where the index are the sectors, and the values are the share of
-the rebuilding demand they will answer (hence, it should sum to 1).
    
+
    The rebuilding sectors can be given as a Series where the index are the sectors, and the values
+are the share of the rebuilding demand they will answer (hence, it should sum to 1).
    
 
    
 
    Hint
    
 
    The rebuilding sectors can also be given as a dict, where keys are sectors and values are shares.
    
 
    
-
    By default, the rebuilding demand is equal to the totality of the impact (assuming all the value that was destroyed is rebuilt),
-but you may set a "rebuilding_factor" (default 1) to define a lower (or greater) rebuilding demand.
    
-
    Otherwise, you can also set occurrence, duration and name  similarly to EventKapitalRecover.
    
-
    The following code defines an event for which the rebuilding demand is 90% of the capital destroyed, and where 80% of the demand is
-answered by the construction sector, and 20% by the manufactoring sector.
    
-
    ev = EventKapitalRebuild(
+
    By default, the rebuilding demand is equal to the totality of the impact (assuming all the value that
+was destroyed is rebuilt), but you may set a "rebuilding_factor" (default 1) to define a lower
+(or greater) rebuilding demand.
    
+
    Otherwise, you can also set occurrence, duration and name  similarly to
+EventKapitalRecover.
    
+
    The following code defines an event for which the rebuilding demand is 90% of the capital destroyed,
+and where 80% of the demand is answered by the construction sector, and 20% by the manufactoring sector.
    
+
    ev = EventKapitalRebuild.from_series(
     impact=impact,
     rebuild_tau=60,
     rebuilding_sectors={"construction": 0.8, "manufactoring": 0.2},
@@ -528,30 +539,75 @@ 
    Create a 
    
 
    
 
    
+
    
+
    Create a EventArbitraryProd#
    
+
    When creating this type of event, the impact values should be value between 0 and 1 stating
+the fraction of production capacity unavailable due to the event.
    
+
    As for EventKapitalRecover, a recovery function and a recovery time may be given.
+Otherwise, production capacity is restored instantaneously after the duration of the event has elapsed.
    
+
    >>> import pandas as pd
+>>> impact = pd.Series(
+...     data=[0.3, 0.1],
+...     index=pd.MultiIndex.from_product(
+...         [["reg2"], ["manufactoring", "mining"]], names=["region", "sector"]
+...     ),
+... )
+>>> impact
+
+region  sector
+reg2    manufactoring    0.3
+        mining           0.1
+dtype: float64
+
    
+
    
+
    ev = EventArbitraryProd.from_series(
+    impact=impact,
+    occurrence=5,
+    duration=7,
+    recovery_function="linear",
+    recovery_time=5,
+)
+
    
+
    
+
    
 
    
 
    
 
    Defining events from a scalar#
    
-
    You can also define an event from a scalar impact.
+
    You can also define an event from a scalar impact
+(except for EventArbitraryProd at the moment).
 This requires to define which industries are affected and
 how the impact is distributed among the industries.
    
+
    You can take a look at the quickstart example
+here.
    
 
    In this section, we go over the different cases first, and then show
 code examples for each case.
    
 
    In order to define which industries are affected you can:
    
 
      
-
    1. Directly give them as a pandas MultiIndex with affected regions as the first level and affected sectors at the second.
      2. 
-
    2. Give them as a list of regions affected, as well as a list of sectors affected. The resulting affected industries being the cartesian product of those two lists.
      3. 
+
    3. Directly give them as a pandas MultiIndex with affected regions as the first level
+and affected sectors at the second.
      4. 
+
    4. Give them as a list of regions affected, as well as a list of sectors affected.
+The resulting affected industries being the cartesian product of those two lists.
      5. 
 
    
 
    
 
    Warning
    
 
    Note that the second option does not allow to have different sectors affected in each region.
    
 
    
-
    By default, the impact will be uniformally distributed among the affected regions and the impact per region
-is then also uniformally distributed among the affected sector in the region.
    
-
    Otherwise, there are multiple ways to parameters a custom distribution:
    
+
    By default, the impact will be uniformally distributed among the affected regions and
+the impact per region is then also uniformally distributed among the affected sector in the region.
    
+
    Otherwise, there are multiple ways to setup a custom distribution:
    
 
      
-
    1. Directly give a vector (any variable that can be transformed as a numpy array) of per affected industry share of the impact (although in this case you should probably directly give the impact vector).
      2. 
+
    2. Directly give a vector (any variable that can be transformed as a numpy array)
+of per affected industry share of the impact (although in this case you should
+probably directly give the impact vector).
      3. 
 
    3. Give a vector of the share per region, and the share per sector.
      4. 
-
    4. Give a vector of the share per region, and specify "gdp" for the per sector distribution. This will distribute each regional impact toward each affected sector proportionally to their share of the regional GDP among the affected sectors. For example: Suppose we look at the impact in region "reg2", and suppose "manufactoring" and "mining" are both affected. Now suppose "manufactoring" account for 40% of "reg2"’s GDP and "mining" for 10%. The "manufactoring" sector will receive \((40 * 100) / (40 + 10) = 80\%\) of the impact and "mining" the remaining \((10 * 100) / (40 + 10) = 20\%\).
      5. 
+
    5. Give a vector of the share per region, and specify "gdp" for the per sector
+distribution. This will distribute each regional impact toward each affected sector
+proportionally to their share of the regional GDP among the affected sectors.
+For example: Suppose we look at the impact in region "reg2", and suppose
+"manufactoring" and "mining" are both affected. Now suppose
+"manufactoring" account for 40% of "reg2"’s GDP and "mining"
+for 10%. The "manufactoring" sector will receive \((40 * 100) / (40 + 10) = 80\%\)
+of the impact and "mining" the remaining \((10 * 100) / (40 + 10) = 20\%\).
      6. 
 
    
 
    
 
    Note
    
@@ -561,9 +617,10 @@ 
    Defining events from a scalar
 
    Warning
    
-
    You should not assume the default impact distribution setting is a good representation of the general case,
-as different regions and sectors are most probably differently impacted by an event.
-It is strongly advised to setup your own distribution in accordance with your study.
    
+
    You should not assume the default impact distribution setting is a good representation
+of the general case, as different regions and sectors are most probably differently
+impacted by an event. It is strongly advised to setup your own distribution in accordance
+with your study.
    
 
    
 
    >>> impact = 1000000
 >>> aff_industries = pd.MultiIndex(
@@ -580,15 +637,18 @@ 
    Defining events from a scalar
-                  
+                  
+
    
+  
+    
    
 
    
     
       
       
    
         
    previous
    
-        
    Quickstart and examples
    
+        
    Quickstart example
    
       
    
         
     Defining events from a scalar
       
    
         
    next
    
-        
    Detailed model description
    
+        
    Model description
    
       
    
       
         
+
    
+  
 
    
+
                 
    
               
             
    
@@ -622,7 +685,8 @@ 
    Defining events from a scalarDifferent types of Event objects
 
  • Defining Event from an impact vector
 
    • 
 
  • Defining events from a scalar
    • 
@@ -657,7 +721,7 @@ 
    Defining events from a scalar
-            
    
+            
           
         
       
@@ -665,8 +729,8 @@ 
    Defining events from a scalar
-
+  
+
    diff --git a/docs/build/html/boario-installation.html b/docs/build/html/boario-installation.html index 9c820f3..de68c46 100644 --- a/docs/build/html/boario-installation.html +++ b/docs/build/html/boario-installation.html @@ -1,5 +1,4 @@ - @@ -7,7 +6,7 @@ - + Installation — BoARIO documentation @@ -19,12 +18,12 @@ - - - + + + - + @@ -35,14 +34,15 @@ - - + + + @@ -160,7 +160,7 @@
  • - References + ARIO Academic work
    • @@ -185,7 +185,7 @@

    - Last updated on Mar 13, 2023. + Last updated on Jun 07, 2023.

    @@ -276,7 +276,7 @@
  • - References + ARIO Academic work
    • @@ -290,7 +290,7 @@

    - Last updated on Mar 13, 2023. + Last updated on Jun 07, 2023.

    @@ -313,14 +313,13 @@
    @@ -390,13 +389,24 @@

    Installationraise an issue on the repository, or contact the developer.

    +
    +

    Install from a specific branch#

    +

    Note that you can directly install BoARIO from a branch of its github repository using:

    +
    pip install git+https://github.com/spjuhel/boario.git@branch-name
+
    +
    +

    Requirements#

    -

    BoARIO is currently developed using python 3.8. It might work with older python versions, but no check were made regarding this.

    +

    BoARIO is currently developed using python 3.9. It might work with older python versions, but no check were made regarding this.

    Installing via pip should install all the required dependencies.

    You can also check the pyproject.toml file in the sources to see all current dependencies.

    If you are experiencing difficulties in setting up a working environment feel free to open an issue on the Github repository or to contact the developer.

    +
    +

    Platform#

    +

    The code is known to run under both Linux and Windows operating systems.

    +
    @@ -405,7 +415,10 @@

    Requirements - + +
    + +
    Requirements +
    +
    +

    @@ -441,7 +457,9 @@

    Requirements @@ -473,7 +491,7 @@

    Requirements -
    + @@ -481,8 +499,8 @@

    Requirements - + +