Parameter reference

time

start

Year in which the model starts

• Type: int

• Default: 2020

• Can be false: False

• Min: 1900

• Max: 2100

Example usage:

params = load_params()
params["time"]["start"] = 2020
model = MIMOSA(params)

end

Last year of the model run

• Type: int

• Default: 2150

• Can be false: False

• Min: 1901

• Max: 2300

Example usage:

params = load_params()
params["time"]["end"] = 2150
model = MIMOSA(params)

dt

Timestep in years

• Type: float

• Default: 5

• Can be false: False

• Min: 0.5

• Max: 20

Example usage:

params = load_params()
params["time"]["dt"] = 5
model = MIMOSA(params)

SSP

SSP, used for population, baseline GDP and baseline emissions

• Type: enum

• Default: SSP2

• Can be false: False

• Allowed values:

  • SSP1
  • SSP2
  • SSP3
  • SSP4
  • SSP5

Example usage:

params = load_params()
params["SSP"] = "SSP2"
model = MIMOSA(params)

economics

PRTP

Pure rate of time preference

• Type: float

• Default: 0.015

• Can be false: False

• Min: 0

• Max: 0.2

Example usage:

params = load_params()
params["economics"]["PRTP"] = 0.015
model = MIMOSA(params)

elasmu

Elasticity of marginal utility

• Type: float

• Default: 1.001

• Can be false: False

• Min: 0.1

• Max: 10

Example usage:

params = load_params()
params["economics"]["elasmu"] = 1.001
model = MIMOSA(params)

inequal_aversion

Parameter of inequality aversion. Should be between 0 and elasmu. Only used when welfare_module='inequal_aversion_general'

• Type: float

• Default: 0.5

• Can be false: False

• Min: 0.0

• Max: 3

Example usage:

params = load_params()
params["economics"]["inequal_aversion"] = 0.5
model = MIMOSA(params)

economics > GDP

alpha

Output elasticity of capital

• Type: float

• Default: 0.3

• Can be false: False

• Min: 0

• Max: 1

Example usage:

params = load_params()
params["economics"]["GDP"]["alpha"] = 0.3
model = MIMOSA(params)

depreciation of capital

Yearly depreciation rate of capital stock

• Type: float

• Default: 0.05

• Can be false: False

• Min: 0

• Max: inf

Example usage:

params = load_params()
params["economics"]["GDP"]["depreciation of capital"] = 0.05
model = MIMOSA(params)

savings rate

Fraction of GDP used for investments

• Type: float

• Default: 0.21

• Can be false: False

• Min: 0

• Max: 1

Example usage:

params = load_params()
params["economics"]["GDP"]["savings rate"] = 0.21
model = MIMOSA(params)

economics > MAC

beta

Power factor of the MAC curve

• Type: float

• Default: 3

• Can be false: False

• Min: 0.01

• Max: 10

Example usage:

params = load_params()
params["economics"]["MAC"]["beta"] = 3
model = MIMOSA(params)

gamma

Calibration level of the MAC (carbon price for 100% reduction)

• Type: quantity

• Default: 2601 USD2005/tCO2

• Can be false: False

• Unit: currency_unit/emissionsrate_unit

Example usage:

params = load_params()
params["economics"]["MAC"]["gamma"] = "2601 USD2005/tCO2"
model = MIMOSA(params)

LBD_rate

Learning by doing rate: reduction in marginal mitigation costs for doubling cumulative mitigation capacity. Empirical studies show values between 0.65 (high learning) and 0.95 (low learning)

• Type: float

• Default: 0.82

• Can be false: False

• Min: 0.1

• Max: 1

Example usage:

params = load_params()
params["economics"]["MAC"]["LBD_rate"] = 0.82
model = MIMOSA(params)

LBD_scaling

Scaling factor for learning by doing to transform the units of cumulative mitigation in relative terms (compared to baseline emissions in t=0). Only used for calibration, and should not be used to change the amount of LBD (for this, use the economics > MAC > rho parameter)

• Type: quantity

• Default: 40 GtCO2

• Can be false: False

• Unit: emissions_unit

Example usage:

params = load_params()
params["economics"]["MAC"]["LBD_scaling"] = "40 GtCO2"
model = MIMOSA(params)

LOT_rate

Learning rate of exogenous learning (learning over time). By default, there is no exogenous learning assumed, as all the technological learning happens endogenously (learning by doing).

• Type: float

• Default: 0

• Can be false: False

• Min: 0

• Max: inf

Example usage:

params = load_params()
params["economics"]["MAC"]["LOT_rate"] = 0
model = MIMOSA(params)

regional calibration factor

Column from mac.csv to be used for the regional MACs. The MACs are calibrated from TIMER SSP2-RCP2.6 MACs at a given year and a given carbon price / abatement level.

• Type: enum

• Default: kappa_rel_abatement_0.75_2050

• Can be false: False

• Allowed values:

  • kappa_carbonprice_200_2030
  • kappa_carbonprice_200_2050
  • kappa_carbonprice_200_2070
  • kappa_carbonprice_200_2100
  • kappa_carbonprice_300_2030
  • kappa_carbonprice_300_2050
  • kappa_carbonprice_300_2070
  • kappa_carbonprice_300_2100
  • kappa_carbonprice_500_2030
  • kappa_carbonprice_500_2050
  • kappa_carbonprice_500_2070
  • kappa_carbonprice_500_2100
  • kappa_carbonprice_1000_2030
  • kappa_carbonprice_1000_2050
  • kappa_carbonprice_1000_2070
  • kappa_carbonprice_1000_2100
  • kappa_rel_abatement_0.25_2030
  • kappa_rel_abatement_0.25_2050
  • kappa_rel_abatement_0.25_2070
  • kappa_rel_abatement_0.25_2100
  • kappa_rel_abatement_0.4_2030
  • kappa_rel_abatement_0.4_2050
  • kappa_rel_abatement_0.4_2070
  • kappa_rel_abatement_0.4_2100
  • kappa_rel_abatement_0.5_2050
  • kappa_rel_abatement_0.5_2070
  • kappa_rel_abatement_0.5_2100
  • kappa_rel_abatement_0.75_2050
  • kappa_rel_abatement_0.75_2070
  • kappa_rel_abatement_0.75_2100

Example usage:

params = load_params()
params["economics"]["MAC"]["regional calibration factor"] = "kappa_rel_abatement_0.75_2050"
model = MIMOSA(params)

economics > MAC > SSP_calibration_factor

SSP2

Dictionary of year-value pairs giving time dependent multiplication factor of the SSP2 MAC. Linear interpolation is taken in between keyframes. Used for time-dependent changes between SSPs.

• Type: dict

• Default: {2020: 1}

• Can be false: False

Example usage:

params = load_params()
params["economics"]["MAC"]["SSP_calibration_factor"]["SSP2"] = {2020: 1}
model = MIMOSA(params)

SSP1

Dictionary of year-value pairs giving time dependent multiplication factor of the SSP2 MAC. Linear interpolation is taken in between keyframes. Used for time-dependent changes between SSPs.

• Type: dict

• Default: {2020: 1, 2100: 0.618}

• Can be false: False

Example usage:

params = load_params()
params["economics"]["MAC"]["SSP_calibration_factor"]["SSP1"] = {2020: 1, 2100: 0.618}
model = MIMOSA(params)

SSP3

Dictionary of year-value pairs giving time dependent multiplication factor of the SSP2 MAC. Linear interpolation is taken in between keyframes. Used for time-dependent changes between SSPs.

• Type: dict

• Default: {2020: 1, 2050: 1.265, 2100: 1.3184}

• Can be false: False

Example usage:

params = load_params()
params["economics"]["MAC"]["SSP_calibration_factor"]["SSP3"] = {2020: 1, 2050: 1.265, 2100: 1.3184}
model = MIMOSA(params)

SSP4

Dictionary of year-value pairs giving time dependent multiplication factor of the SSP2 MAC. Linear interpolation is taken in between keyframes. Used for time-dependent changes between SSPs.

• Type: dict

• Default: {2020: 1}

• Can be false: False

Example usage:

params = load_params()
params["economics"]["MAC"]["SSP_calibration_factor"]["SSP4"] = {2020: 1}
model = MIMOSA(params)

SSP5

Dictionary of year-value pairs giving time dependent multiplication factor of the SSP2 MAC. Linear interpolation is taken in between keyframes. Used for time-dependent changes between SSPs.

• Type: dict

• Default: {2020: 1, 2030: 1.0724, 2040: 1.16, 2050: 1.17, 2100: 1.198}

• Can be false: False

Example usage:

params = load_params()
params["economics"]["MAC"]["SSP_calibration_factor"]["SSP5"] = {2020: 1, 2030: 1.0724, 2040: 1.16, 2050: 1.17, 2100: 1.198}
model = MIMOSA(params)

rel_mitigation_costs_min_level

Minimum level of mitigation costs (rel to GDP). By default, this is 0: no negative abatement costs are allowed. For certain burden sharing regimes, this value can become negative to allow certain (small) financial transfers.

• Type: float

• Default: 0

• Can be false: False

• Min: -2

• Max: 0

Example usage:

params = load_params()
params["economics"]["MAC"]["rel_mitigation_costs_min_level"] = 0
model = MIMOSA(params)

economics > emission trade

min rel payment level

Which percentage of the area under the MAC of a region should it pay itself (minimum)? If false: no limt

• Type: float

• Default: False

• Can be false: True

• Min: 0

• Max: 1

Example usage:

params = load_params()
params["economics"]["emission trade"]["min rel payment level"] = False
model = MIMOSA(params)

max rel payment level

Which percentage of the area under the MAC of a region should it pay itself (maximum)? If false: no limit

• Type: float

• Default: False

• Can be false: True

• Min: 1

• Max: inf

Example usage:

params = load_params()
params["economics"]["emission trade"]["max rel payment level"] = False
model = MIMOSA(params)

economics > damages

percentage reversible

Factor of damages that are reversible

• Type: float

• Default: 1

• Can be false: False

• Min: 0

• Max: 1

Example usage:

params = load_params()
params["economics"]["damages"]["percentage reversible"] = 1
model = MIMOSA(params)

scale factor

Manual scaling factor to increase or decrease damages

• Type: float

• Default: 1

• Can be false: False

• Min: -inf

• Max: inf

Example usage:

params = load_params()
params["economics"]["damages"]["scale factor"] = 1
model = MIMOSA(params)

ignore damages

Flag to not take into account the damages in the GDP (but damages are calculated)

• Type: bool

• Default: False

• Can be false: False

Example usage:

params = load_params()
params["economics"]["damages"]["ignore damages"] = False
model = MIMOSA(params)

quantile

Damage quantile (Only used for COACCH)

• Type: enum

• Default: 0.5

• Can be false: False

• Allowed values:

  • 0.025
  • 0.05
  • 0.16
  • 0.25
  • 0.33
  • 0.5
  • 0.67
  • 0.75
  • 0.84
  • 0.95
  • 0.975

Example usage:

params = load_params()
params["economics"]["damages"]["quantile"] = 0.5
model = MIMOSA(params)

coacch_slr_withadapt

Flag to use the SLR-with-Adapation damage functions (Only used for COACCH)

• Type: bool

• Default: True

• Can be false: False

Example usage:

params = load_params()
params["economics"]["damages"]["coacch_slr_withadapt"] = True
model = MIMOSA(params)

coacch_combined_slr_nonslr_damages

If true, do not model SLR damages separately from non-SLR, but use the combined damage functions (Only used for COACCH)

• Type: bool

• Default: False

• Can be false: False

Example usage:

params = load_params()
params["economics"]["damages"]["coacch_combined_slr_nonslr_damages"] = False
model = MIMOSA(params)

emissions

carbonbudget

Value of the carbon budget. Example: "800 GtCO2" (the unit is important). If set to False, no carbon budget is imposed: this is cost-benefit mode. Default: False.

• Type: quantity

• Default: False

• Can be false: True

• Unit: emissions_unit

Example usage:

params = load_params()
params["emissions"]["carbonbudget"] = False
model = MIMOSA(params)

global min level

Limit on the emission level (globally), mostly used for negative emissions. Can also be false, then no limit is imposed

• Type: quantity

• Default: -20 GtCO2/yr

• Can be false: True

• Unit: emissionsrate_unit

Example usage:

params = load_params()
params["emissions"]["global min level"] = "-20 GtCO2/yr"
model = MIMOSA(params)

regional min level

Limit on the emission level (per region), mostly used for negative emissions. Can also be false, then no limit is imposed

• Type: quantity

• Default: -10 GtCO2/yr

• Can be false: True

• Unit: emissionsrate_unit

Example usage:

params = load_params()
params["emissions"]["regional min level"] = "-10 GtCO2/yr"
model = MIMOSA(params)

not positive after budget year

If true, impose net-zero emissions after budget year (2100)

• Type: bool

• Default: True

• Can be false: False

Example usage:

params = load_params()
params["emissions"]["not positive after budget year"] = True
model = MIMOSA(params)

non increasing emissions after 2100

If true, the regional emissions after 2100 are not allowed to climb.

• Type: bool

• Default: True

• Can be false: False

Example usage:

params = load_params()
params["emissions"]["non increasing emissions after 2100"] = True
model = MIMOSA(params)

baseline carbon intensity

If true, use baseline carbon intensity to calculate baseline emissions. If false, the SSP baseline emissions are used, regardless of lower GDP.

• Type: bool

• Default: True

• Can be false: False

Example usage:

params = load_params()
params["emissions"]["baseline carbon intensity"] = True
model = MIMOSA(params)

emissions > inertia

global

Maximum reduction speed, in % of initial emissions (should be negative) Can also be false, then no inertia limit is imposed

• Type: float

• Default: False

• Can be false: True

• Min: -inf

• Max: 0

Example usage:

params = load_params()
params["emissions"]["inertia"]["global"] = False
model = MIMOSA(params)

regional

Maximum reduction speed, in % of initial emissions (should be negative) Can also be false, then no inertia limit is imposed

• Type: float

• Default: -0.05

• Can be false: True

• Min: -inf

• Max: 0

Example usage:

params = load_params()
params["emissions"]["inertia"]["regional"] = -0.05
model = MIMOSA(params)

cumulative_emissions_trapz

If true, calculate cumulative emissions using trapezoidal interpolation. If false, cum. emissions are simply cum_emissions[t-1] + dt * cum_emissions[t]. This is less accurate, but better for numerical stability. For small dt the approximation is usually still acceptable.

• Type: bool

• Default: True

• Can be false: False

Example usage:

params = load_params()
params["emissions"]["cumulative_emissions_trapz"] = True
model = MIMOSA(params)

effort sharing

regime

Type of effort sharing to be used

• Type: enum

• Default: noregime

• Can be false: False

• Allowed values:

  • noregime
  • equal_mitigation_costs
  • equal_total_costs
  • per_cap_convergence
  • ability_to_pay
  • equal_cumulative_per_cap

Example usage:

params = load_params()
params["effort sharing"]["regime"] = "noregime"
model = MIMOSA(params)

percapconv_year

Year of convergence to per capita emission allowance (only used when effort sharing - regime is per_cap_convergence) Can also be false, then always use grandfathering

• Type: float

• Default: 2050

• Can be false: True

• Min: 2020

• Max: 2200

Example usage:

params = load_params()
params["effort sharing"]["percapconv_year"] = 2050
model = MIMOSA(params)

ecpc_discount_rate

Discount rate for historical debt in the ECPC effort sharing regime (Equal Cumulative Per Capita regime). Only used when effort sharing - regime is equal_cumulative_per_cap

• Type: float

• Default: 0.03

• Can be false: False

• Min: 0

• Max: 1

Example usage:

params = load_params()
params["effort sharing"]["ecpc_discount_rate"] = 0.03
model = MIMOSA(params)

ecpc_start_year

Start year for historical debt in the ECPC effort sharing regime (Equal Cumulative Per Capita regime). Only used when effort sharing - regime is equal_cumulative_per_cap

• Type: float

• Default: 1850

• Can be false: False

• Min: 1800

• Max: 2019

Example usage:

params = load_params()
params["effort sharing"]["ecpc_start_year"] = 1850
model = MIMOSA(params)

ecpc_repayment_endyear

Year at which the repayment of the historical emission debt should be finished in the ECPC effort sharing regime. Before this year, the debt repayment is lowered linearly.

Can also be false: then the historical debt repayment is spread out equally over every time period.

- Type: float

- Default: 2050

- Can be false: True



- Min: 2030

- Max: 2150



Example usage:


```python hl_lines="2"
params = load_params()
params["effort sharing"]["ecpc_repayment_endyear"] = 2050
model = MIMOSA(params)
```

temperature

initial

Temperature in initial year of model run (2020 by default).

• Type: quantity

• Default: 1.16 delta_degC

• Can be false: False

• Unit: temperature_unit

Example usage:

params = load_params()
params["temperature"]["initial"] = "1.16 delta_degC"
model = MIMOSA(params)

TCRE

Transient Climate Response to CO2 Emissions

• Type: quantity

• Default: 0.62 delta_degC/(TtCO2)

• Can be false: False

• Unit: (temperature_unit)/(emissions_unit)

Example usage:

params = load_params()
params["temperature"]["TCRE"] = "0.62 delta_degC/(TtCO2)"
model = MIMOSA(params)

target

Temperature target in 2100 (and beyond). Can also be false, then no temperature target is imposed

• Type: quantity

• Default: False

• Can be false: True

• Unit: temperature_unit

Example usage:

params = load_params()
params["temperature"]["target"] = False
model = MIMOSA(params)

model

damage module

Damage module to be used

• Type: enum

• Default: COACCH

• Can be false: False

• Allowed values:

  • COACCH
  • nodamage

Example usage:

params = load_params()
params["model"]["damage module"] = "COACCH"
model = MIMOSA(params)

emissiontrade module

Emission trade module to be used

• Type: enum

• Default: notrade

• Can be false: False

• Allowed values:

  • notrade
  • emissiontrade
  • globalcostpool

Example usage:

params = load_params()
params["model"]["emissiontrade module"] = "notrade"
model = MIMOSA(params)

financialtransfer module

Financial transfer module to be used

• Type: enum

• Default: notransfer

• Can be false: False

• Allowed values:

  • notransfer
  • globaldamagepool

Example usage:

params = load_params()
params["model"]["financialtransfer module"] = "notransfer"
model = MIMOSA(params)

welfare module

Welfare and utility module to be used

• Type: enum

• Default: welfare_loss_minimising

• Can be false: False

• Allowed values:

  • welfare_loss_minimising
  • cost_minimising
  • inequal_aversion_general

Example usage:

params = load_params()
params["model"]["welfare module"] = "welfare_loss_minimising"
model = MIMOSA(params)

objective module

Objective module to be used

• Type: enum

• Default: utility

• Can be false: False

• Allowed values:

  • utility
  • globalcosts

Example usage:

params = load_params()
params["model"]["objective module"] = "utility"
model = MIMOSA(params)

regionstype

Name of the region definition. Used in the mapping of the regional parameters.

• Type: enum

• Default: IMAGE26

• Can be false: False

• Allowed values:

  • IMAGE26
  • SSP5
  • World

Example usage:

params = load_params()
params["regionstype"] = "IMAGE26"
model = MIMOSA(params)

regionsmappings

List of region types and their conversion tables. Only used for regional parameters, not for aggregating or disaggregating variables or other output.

• Type: list

• Default: [{'regionstype1': 'IMAGE26', 'regionstype2': 'COACCH', 'conversiontable': 'inputdata/regions/IMAGE26_COACCH.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2010', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2010.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2012', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2012.csv'}]

• Can be false: False

Example usage:

params = load_params()
params["regionsmappings"] = [{'regionstype1': 'IMAGE26', 'regionstype2': 'COACCH', 'conversiontable': 'inputdata/regions/IMAGE26_COACCH.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2010', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2010.csv'}, {'regionstype1': 'IMAGE26', 'regionstype2': 'ADRICE2012', 'conversiontable': 'inputdata/regions/IMAGE26_ADRICE2012.csv'}]
model = MIMOSA(params)

regional_parameter_files

Dictionary of regional parameter files. If the regionstype of the file is different from the regionstype of the model, the file is converted using the regionsmappings parameter.

• Type: dict

• Default: {'economics': {'filename': 'inputdata/regionalparams/economics.csv', 'regionstype': 'IMAGE26'}, 'MAC': {'filename': 'inputdata/regionalparams/mac.csv', 'regionstype': 'IMAGE26'}, 'COACCH': {'filename': 'inputdata/regionalparams/COACCH.csv', 'regionstype': 'COACCH'}}

• Can be false: False

Example usage:

params = load_params()
params["regional_parameter_files"] = {'economics': {'filename': 'inputdata/regionalparams/economics.csv', 'regionstype': 'IMAGE26'}, 'MAC': {'filename': 'inputdata/regionalparams/mac.csv', 'regionstype': 'IMAGE26'}, 'COACCH': {'filename': 'inputdata/regionalparams/COACCH.csv', 'regionstype': 'COACCH'}}
model = MIMOSA(params)

regions

Dictionary of all regions with optional dictionaries defining, optionally, manual values for certain parameters for that specific region.

• Type: dict

• Default: {'CAN': None, 'USA': None, 'MEX': None, 'RCAM': None, 'BRA': None, 'RSAM': None, 'NAF': None, 'WAF': None, 'EAF': None, 'SAF': None, 'WEU': None, 'CEU': None, 'TUR': None, 'UKR': None, 'STAN': None, 'RUS': None, 'ME': None, 'INDIA': None, 'KOR': None, 'CHN': None, 'SEAS': None, 'INDO': None, 'JAP': None, 'OCE': None, 'RSAS': None, 'RSAF': None}

• Can be false: False

Example usage:

params = load_params()
params["regions"] = {'CAN': None, 'USA': None, 'MEX': None, 'RCAM': None, 'BRA': None, 'RSAM': None, 'NAF': None, 'WAF': None, 'EAF': None, 'SAF': None, 'WEU': None, 'CEU': None, 'TUR': None, 'UKR': None, 'STAN': None, 'RUS': None, 'ME': None, 'INDIA': None, 'KOR': None, 'CHN': None, 'SEAS': None, 'INDO': None, 'JAP': None, 'OCE': None, 'RSAS': None, 'RSAF': None}
model = MIMOSA(params)

input

input > variables

GDP

Data source of GDP

• Type: datasource

• Default: {'variable': 'GDP|PPP', 'unit': 'currency_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

• Can be false: False

Example usage:

params = load_params()
params["input"]["variables"]["GDP"] = {'variable': 'GDP|PPP', 'unit': 'currency_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)

emissions

Data source of baseline emissions

• Type: datasource

• Default: {'variable': 'Emissions|CO2', 'unit': 'emissionsrate_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

• Can be false: False

Example usage:

params = load_params()
params["input"]["variables"]["emissions"] = {'variable': 'Emissions|CO2', 'unit': 'emissionsrate_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)

population

Data source of population

• Type: datasource

• Default: {'variable': 'Population', 'unit': 'population_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}

• Can be false: False

Example usage:

params = load_params()
params["input"]["variables"]["population"] = {'variable': 'Population', 'unit': 'population_unit', 'scenario': '{SSP}-Ref-SPA0-V17', 'model': 'IMAGE', 'file': 'inputdata/data/data_IMAGE_SSP.csv'}
model = MIMOSA(params)

simulation

simulationmode

If true, the model is run in simulation mode: then some variables will be imposed exogenously and fixed. If false, constraint_variables and deactivated_constraints are ignored.

• Type: bool

• Default: False

• Can be false: False

Example usage:

params = load_params()
params["simulation"]["simulationmode"] = False
model = MIMOSA(params)

constraint_variables

Dictionary of variable names with associated path to file containing values for that variable

• Type: dict

• Default: None

• Can be false: False

Example usage:

params = load_params()
params["simulation"]["constraint_variables"] = None
model = MIMOSA(params)

deactivated_constraints

List of constraint names to be disabled

• Type: list

• Default: None

• Can be false: False

Example usage:

params = load_params()
params["simulation"]["deactivated_constraints"] = None
model = MIMOSA(params)

custom_mapping

Custom mapping of parameter values or variables

• Type: dict

• Default: None

• Can be false: False

Example usage:

params = load_params()
params["simulation"]["custom_mapping"] = None
model = MIMOSA(params)