Python & R API

The application programming interface (API) for MESSAGEix model developers is implemented in Python:

Support for R usage of the core classes is provided through the reticulate package. For instance:

> library(reticulate)
> ixmp <- import('ixmp')
> message_ix <- import('message_ix')
> mp <- ixmp$Platform(...)
> scen <- message_ix$Scenario(mp, ...)

ixmp package

ixmp provides three classes. These are fully described by the ixmp documentation, which is cross-linked from many places in the MESSAGEix documentation.

Platform(*args[, name, backend]) Instance of the modeling platform.
TimeSeries(mp, model, scenario[, version, …]) Collection of data in time series format.
Scenario(mp, model, scenario[, version, …]) Collection of model-related data.

ixmp also provides some utility classes and methods:

ixmp.config Configuration for ixmp.
ixmp.model.MODELS Mapping from names to available models.
ixmp.model.get_model(name, **model_options) Return a model for name (or the default) with model_options.
ixmp.testing.make_dantzig

message_ix package

MESSAGEix models are created using the message_ix.Scenario class. Several utility methods are also provided in the module message_ix.utils.

class message_ix.Scenario(mp, model, scenario=None, version=None, annotation=None, cache=False)

Bases: ixmp.core.Scenario

MESSAGEix Scenario.

See ixmp.TimeSeries for the meaning of arguments mp, model, scenario, version, and annotation; ixmp.Scenario for the meaning of cache. The scheme of a newly-created Scenario is always ‘MESSAGE’.

This class extends ixmp.Scenario and ixmp.TimeSeries and inherits all their methods. Documentation of these inherited methods is included here for convenience. message_ix.Scenario defines additional methods specific to MESSAGEix:

add_cat(name, cat, keys[, is_unique]) Map elements from keys to category cat within set name.
add_horizon(data) Add sets related to temporal dimensions of the model.
add_spatial_sets(data) Add sets related to spatial dimensions of the model.
cat(name, cat) Return a list of all set elements mapped to a category.
cat_list(name) Return a list of all categories for a mapping set.
firstmodelyear The first model year of the scenario.
read_excel(fname[, add_units, commit_steps]) Read Excel file data and load into the scenario.
rename(name, mapping[, keep]) Rename an element in a set
to_excel(fname) Save a scenario as an Excel file.
vintage_and_active_years([ya_args, in_horizon]) Return sets of vintage and active years for use in data input.
years_active(node, tec, yr_vtg) Return years in which tec of yr_vtg can be active in node.
add_cat(name, cat, keys, is_unique=False)

Map elements from keys to category cat within set name.

Parameters:
  • name (str) – Name of the set.
  • cat (str) – Name of the category.
  • keys (str or list of str) – Element keys to be added to the category mapping.
  • is_unique (bool, optional) – If True, then cat must have only one element. An exception is raised if cat already has an element, or if len(keys) > 1.
add_geodata(df)

Add geodata (layers) to the TimeSeries.

Parameters:df (pandas.DataFrame) –

Data to add. df must have the following columns:

  • region
  • variable
  • time
  • unit
  • year
  • value
  • meta
add_horizon(data)

Add sets related to temporal dimensions of the model.

Parameters:data (dict-like) – Year sets. “year” is a required key. “firstmodelyear” is optional; if not provided, the first element of “year” is used.

Examples

>>> s = message_ix.Scenario()
>>> s.add_horizon({'year': [2010, 2020]})
>>> s.add_horizon({'year': [2010, 2020], 'firstmodelyear': 2020})
add_par(name, key_or_data=None, value=None, unit=None, comment=None, key=None, val=None)

Set the values of a parameter.

Parameters:
  • name (str) – Name of the parameter.
  • key_or_data (str or iterable of str or range or dict or pandas.DataFrame) – Element(s) to be added.
  • value (numeric or iterable of numeric, optional) – Values.
  • unit (str or iterable of str, optional) – Unit symbols.
  • comment (str or iterable of str, optional) – Comment(s) for the added values.
add_set(name, key, comment=None)

Add elements to an existing set.

Parameters:
  • name (str) – Name of the set.
  • key (str or iterable of str or dict or pandas.DataFrame) – Element(s) to be added. If name exists, the elements are appended to existing elements.
  • comment (str or iterable of str, optional) – Comment describing the element(s). If given, there must be the same number of comments as elements.
Raises:
add_spatial_sets(data)

Add sets related to spatial dimensions of the model.

Parameters:data (dict) –

Mapping of levelmember. Each member may be:

  • A single label for elements.
  • An iterable of labels for elements.
  • A recursive dict following the same convention, defining sub-levels and their members.

Examples

>>> s = message_ix.Scenario()
>>> s.add_spatial_sets({'country': 'Austria'})
>>> s.add_spatial_sets({'country': ['Austria', 'Germany']})
>>> s.add_spatial_sets({'country': {
...     'Austria': {'state': ['Vienna', 'Lower Austria']}}})
add_timeseries(df, meta=False)

Add data to the TimeSeries.

Parameters:
  • df (pandas.DataFrame) –

    Data to add. df must have the following columns:

    • region or node
    • variable
    • unit

    Additional column names may be either of:

    • year and value—long, or ‘tabular’, format.
    • one or more specific years—wide, or ‘IAMC’ format.
  • meta (bool, optional) – If True, store df as metadata. Metadata is treated specially when Scenario.clone() is called for Scenarios created with scheme='MESSAGE'.
cat(name, cat)

Return a list of all set elements mapped to a category.

Parameters:
  • name (str) – Name of the set.
  • cat (str) – Name of the category.
Returns:

Return type:

list of str

cat_list(name)

Return a list of all categories for a mapping set.

Parameters:name (str) – Name of the set.
change_scalar(name, val, unit, comment=None)

Set the value and unit of a scalar.

Parameters:
  • name (str) – Name of the scalar.
  • val (number) – New value of the scalar.
  • unit (str) – New unit of the scalar.
  • comment (str, optional) – Description of the change.
check_out(timeseries_only=False)

Check out the TimeSeries for modification.

clear_cache(name=None, ix_type=None)

clear the Python cache of item elements

Parameters:
  • name (str, optional) – item name (None clears entire Python cache)
  • ix_type (str, optional) – type of item (if provided, cache clearing is faster)
clone(*args, **kwargs)

Clone the current scenario and return the clone.

See ixmp.Scenario.clone() for other parameters.

Parameters:
  • keep_solution (bool, optional) – If True, include all timeseries data and the solution (vars and equs) from the source scenario in the clone. Otherwise, only timeseries data marked as meta=True (see TimeSeries.add_timeseries()) or prior to first_model_year (see TimeSeries.add_timeseries()) are cloned.
  • shift_first_model_year (int, optional) – If given, the values of the solution are transfered to parameters historical_*, parameter resource_volume is updated, and the first_model_year is shifted. The solution is then discarded, see TimeSeries.remove_solution().
commit(comment)

Commit all changed data to the database.

If the TimeSeries was newly created (with version='new'), version is updated with a new version number assigned by the backend. Otherwise, commit() does not change the version.

Parameters:comment (str) – Description of the changes being committed.
discard_changes()

Discard all changes and reload from the database.

equ(name, filters=None, **kwargs)

return a dataframe of (filtered) elements for a specific equation

Parameters:
  • name (str) – name of the equation
  • filters (dict) – index names mapped list of index set elements
equ_list()

List all defined equations.

firstmodelyear

The first model year of the scenario.

Returns:
Return type:int
classmethod from_url(url, errors='warn')

Instantiate a Scenario given an ixmp-scheme URL.

The following are equivalent:

from ixmp import Platform, Scenario
mp = Platform(name='example')
scen = Scenario(mp 'model', 'scenario', version=42)

and:

from ixmp import Scenario
scen, mp = Scenario.from_url('ixmp://example/model/scenario#42')
Parameters:
  • url (str) – See parse_url.
  • errors ('warn' or 'raise') – If ‘warn’, a failure to load the Scenario is logged as a warning, and the platform is still returned. If ‘raise’, the exception is raised.
Returns:

scenario, platform – The Scenario and Platform referred to by the URL.

Return type:

2-tuple of (Scenario, Platform)

get_geodata()

Fetch geodata and return it as dataframe.

Returns:Specified data.
Return type:pandas.DataFrame
get_meta(name=None)

get scenario metadata

Parameters:name (str, optional) – metadata attribute name
has_equ(name)

check whether the scenario has an equation with that name

has_par(name)

check whether the scenario has a parameter with that name

has_set(name)

Check whether the scenario has a set name.

has_solution()

Return True if the Scenario has been solved.

If has_solution() == True, model solution data exists in the db.

has_var(name)

check whether the scenario has a variable with that name

idx_names(name)

return the list of index names for an item (set, par, var, equ)

Parameters:name (str) – name of the item
idx_sets(name)

Return the list of index sets for an item (set, par, var, equ)

Parameters:name (str) – name of the item
init_equ(name, idx_sets=None, idx_names=None)

Initialize a new equation.

Parameters:
  • name (str) – name of the item
  • idx_sets (list of str) – index set list
  • idx_names (list of str, optional) – index name list
init_par(name, idx_sets, idx_names=None)

Initialize a new parameter.

Parameters:
  • name (str) – Name of the parameter.
  • idx_sets (list of str) – Names of sets that index this parameter.
  • idx_names (list of str, optional) – Names of the dimensions indexed by idx_sets.
init_scalar(name, val, unit, comment=None)

Initialize a new scalar.

Parameters:
  • name (str) – Name of the scalar
  • val (number) – Initial value of the scalar.
  • unit (str) – Unit of the scalar.
  • comment (str, optional) – Description of the scalar.
init_set(name, idx_sets=None, idx_names=None)

Initialize a new set.

Parameters:
  • name (str) – Name of the set.
  • idx_sets (list of str, optional) – Names of other sets that index this set.
  • idx_names (list of str, optional) – Names of the dimensions indexed by idx_sets.
Raises:
  • ValueError – If the set (or another object with the same name) already exists.
  • RuntimeError – If the Scenario is not checked out (see check_out()).
init_var(name, idx_sets=None, idx_names=None)

initialize a new variable in the scenario

Parameters:
  • name (str) – name of the item
  • idx_sets (list of str) – index set list
  • idx_names (list of str, optional) – index name list
is_default()

Return True if the version is the default version.

last_update()

get the timestamp of the last update/edit of this TimeSeries

load_scenario_data()

Load all Scenario data into memory.

Raises:ValueError – If the Scenario was instantiated with cache=False.
par(name, filters=None, **kwargs)

return a dataframe of (filtered) elements for a specific parameter

Parameters:
  • name (str) – name of the parameter
  • filters (dict) – index names mapped list of index set elements
par_list()

List all defined parameters.

preload_timeseries()

Preload timeseries data to in-memory cache. Useful for bulk updates.

read_excel(fname, add_units=False, commit_steps=False)

Read Excel file data and load into the scenario.

Parameters:
  • fname (string) – path to file
  • add_units (bool) – add missing units, if any, to the platform instance. default: False
  • commit_steps (bool) – commit changes after every data addition. default: False
remove_geodata(df)

Remove geodata from the TimeSeries instance.

Parameters:df (pandas.DataFrame) –

Data to remove. df must have the following columns:

  • region
  • variable
  • unit
  • time
  • year
remove_par(name, key=None)

Remove parameter values or an entire parameter.

Parameters:
  • name (str) – Name of the parameter.
  • key (dataframe or key list or concatenated string, optional) – elements to be removed
remove_set(name, key=None)

delete a set from the scenario or remove an element from a set (if key is specified)

Parameters:
  • name (str) – name of the set
  • key (dataframe or key list or concatenated string) – elements to be removed
remove_solution(first_model_year=None)

Remove the solution from the scenario

This function removes the solution (variables and equations) and timeseries data marked as meta=False from the scenario (see TimeSeries.add_timeseries()).

Parameters:first_model_year (int, optional) – If given, timeseries data marked as meta=False is removed only for years from first_model_year onwards.
Raises:ValueError – If Scenario has no solution or if first_model_year is not int.
remove_timeseries(df)

Remove timeseries data from the TimeSeries instance.

Parameters:df (pandas.DataFrame) –

Data to remove. df must have the following columns:

  • region or node
  • variable
  • unit
  • year
rename(name, mapping, keep=False)

Rename an element in a set

Parameters:
  • name (str) – name of the set to change (e.g., ‘technology’)
  • mapping (str) – mapping of old (current) to new set element names
  • keep (bool, optional, default: False) – keep the old values in the model
run_id()

get the run id of this TimeSeries

scalar(name)

Return the value and unit of a scalar.

Parameters:name (str) – Name of the scalar.
Returns:{‘value’
Return type:value, ‘unit’: unit}
set(name, filters=None, **kwargs)

Return the (filtered) elements of a set.

Parameters:
  • name (str) – Name of the set.
  • filters (dict) – Mapping of dimension_nameelements, where dimension_name is one of the idx_names given when the set was initialized (see init_set()), and elements is an iterable of labels to include in the return value.
Returns:

Return type:

pandas.DataFrame

set_as_default()

Set the current version as the default.

set_list()

List all defined sets.

set_meta(name, value)

set scenario metadata

Parameters:
  • name (str) – metadata attribute name
  • value (str or number or bool) – metadata attribute value
solve(model='MESSAGE', solve_options={}, **kwargs)

Solve MESSAGE or MESSAGE-MACRO for the Scenario.

By default, ixmp.Scenario.solve() is called with ‘MESSAGE’ as the model argument. model may also be overwritten, e.g.:

>>> s.solve(model='MESSAGE-MACRO')
Parameters:
  • model (str, optional) – Type of model to solve, e.g. ‘MESSAGE’ or ‘MESSAGE-MACRO’.
  • solve_options (dict (str -> str), optional) – Name to value mapping to use for GAMS CPLEX solver options file. See MESSAGE and DEFAULT_CPLEX_OPTIONS.
  • kwargs – Many other options control the execution of the underlying GAMS code; see GAMSModel.
timeseries(region=None, variable=None, unit=None, year=None, iamc=False)

Retrieve TimeSeries data.

Parameters:
  • iamc (bool, default: False) – Return data in wide/’IAMC’ format. If False, return data in long/’tabular’ format; see add_timeseries().
  • region (str or list of strings) – Regions to include in returned data.
  • variable (str or list of strings) – Variables to include in returned data.
  • unit (str or list of strings) – Units to include in returned data.
  • year (str, int or list of strings or integers) – Years to include in returned data.
Returns:

Specified data.

Return type:

pandas.DataFrame

to_excel(fname)

Save a scenario as an Excel file. NOTE: Cannot export solution currently (only model data) due to limitations in excel sheet names (cannot have multiple sheet names which are identical except for upper/lower case).

Parameters:fname (string) – path to file
var(name, filters=None, **kwargs)

return a dataframe of (filtered) elements for a specific variable

Parameters:
  • name (str) – name of the variable
  • filters (dict) – index names mapped list of index set elements
var_list()

List all defined variables.

vintage_and_active_years(ya_args=None, in_horizon=True)

Return sets of vintage and active years for use in data input.

For a valid pair (year_vtg, year_act), the following conditions are satisfied:

  1. Both the vintage year (year_vtg) and active year (year_act) are in the model’s year set.
  2. year_vtg <= year_act.
  3. year_act <= the model’s first year or year_act is in the smaller subset ixmp.Scenario.years_active() for the given ya_args.
Parameters:
  • ya_args (tuple of (node, technology, year_vtg), optional) – Arguments to ixmp.Scenario.years_active().
  • in_horizon (bool, optional) – Restrict years returned to be within the current model horizon.
Returns:

with columns, “year_vtg” and “year_act”, in which each row is a valid pair.

Return type:

pandas.DataFrame

years_active(node, tec, yr_vtg)

Return years in which tec of yr_vtg can be active in node.

Parameters:
  • node (str) – Node name.
  • tec (str) – Technology name.
  • yr_vtg (str) – Vintage year.
Returns:

Return type:

list of int

Model classes

message_ix.models.DEFAULT_CPLEX_OPTIONS = {'advind': 0, 'epopt': 1e-06, 'lpmethod': 2, 'threads': 4}

Solver options used by message_ix.Scenario.solve(). These configure the GAMS CPLEX solver (or another solver, if selected); see the solver documentation for possible values.

class message_ix.models.MESSAGE(name=None, **model_options)

Bases: ixmp.model.gams.GAMSModel

The MESSAGE Python class encapsulates the GAMS code for the core MESSAGE mathematical formulation. The model_options arguments are received from Scenario.solve(), and—except for solve_options—are passed on to the parent class GAMSModel; see there for a full list of options.

name = 'MESSAGE'
defaults = dict(...)

Default model options. The paths to MESSAGE GAMS source files use the MODEL_PATH configuration setting. MODEL_PATH, in turn, defaults to “message_ix/model” inside the directory where message_ix is installed.

Key Value
MESSAGE defaults
model_file '{MODEL_PATH}/{model_name}_run.gms'
in_file '{MODEL_PATH}/data/MsgData_{case}.gdx'
out_file '{MODEL_PATH}/output/MsgOutput_{case}.gdx'
solve_args ['--in="{in_file}"', '--out="{out_file}"', '--iter="{MODEL_PATH}/output/MsgIterationReport_{case}.gdx"']
Inherited from GAMSModel
case '{scenario.model}_{scenario.scenario}'
gams_args ['LogOption=4']
check_solution True
comment None
equ_list None
var_list None
classmethod read_version()

Retrieve MESSAGE version string from version.gms.

run(scenario)

Execute the model.

MESSAGE creates a file named cplex.opt in the model directory, containing the options in DEFAULT_CPLEX_OPTIONS, or any overrides passed to solve().

class message_ix.models.MESSAGE_MACRO(*args, **kwargs)

Bases: message_ix.models.MESSAGE

name = 'MESSAGE-MACRO'
GAMS_min_version = '24.8.1'

MESSAGE-MACRO uses the GAMS break; statement, and thus requires GAMS 24.8.1 or later.

Utility methods

message_ix.utils.make_df(base, **kwargs)

Extend or overwrite base with new values from kwargs.

Parameters:
Returns:

base modified with kwargs.

Return type:

pandas.DataFrame

Examples

Scalar values in base or kwargs are broadcast. The number of rows in the returned pandas.DataFrame equals the length of the longest item in either argument.

>>> base = {'foo': 'bar'}
>>> make_df(base, baz=[42, 43, 44])
    foo     baz
0   bar     42
1   bar     43
2   bar     44

Testing utilities

message_ix.testing.make_dantzig(mp, solve=False, multi_year=False)

Return an message_ix.Scenario for Dantzig’s canning problem.

Parameters:
  • mp (ixmp.Platform) – Platform on which to create the scenario.
  • solve (bool, optional) – If True, the scenario is solved.
  • multi_year (bool, optional) – If True, the scenario has years 1963–1965 inclusive. Otherwise, the scenario has the single year 1963.
message_ix.testing.make_westeros(mp, emissions=False, solve=False)

Return an message_ix.Scenario for the Westeros model.

This is the same model used in the westeros_baseline.ipynb tutorial.

Parameters:
  • mp (ixmp.Platform) – Platform on which to create the scenario.
  • emissions (bool, optional) – If True, the emissions_factor parameter is also populated for CO2.
  • solve (bool, optional) – If True, the scenario is solved.