Model

class Model(_impl)

Top-level container representing a complete modelx model.

Model is the root object in the modelx object hierarchy and serves as the primary container for organizing spaces, cells, and references. Each Model represents an independent, self-contained computational model with its own namespace and execution environment.

A Model contains:

• UserSpace objects (top-level spaces)

• Global references accessible throughout the model

• Serialization and I/O specifications

Key Characteristics:

• Top-level container: Root of the object hierarchy

• Independent namespace: Each model has isolated global references

• Serializable: Can be saved to/loaded from files or zip archives

• Exportable: Can be exported as a Python package

Creation:

Models are created using the new_model() function:

>>> import modelx as mx
>>> model = mx.new_model()
>>> model
<Model Model1>

>>> # Create with specific name
>>> model = mx.new_model('MyModel')
>>> model
<Model MyModel>

Adding Spaces:

Create child spaces to organize cells and nested structures:

>>> space = model.new_space('Calculations')
>>> space
<UserSpace Calculations in MyModel>

Global References:

Set global references accessible from all spaces in the model:

>>> import numpy as np
>>> model.np = np
>>> model.discount_rate = 0.05

Persistence:

Models can be saved and loaded in multiple formats:

>>> # Save as directory structure with text files
>>> model.write('path/to/model')

>>> # Save as zip archive
>>> model.zip('path/to/model.zip')

>>> # Load a saved model
>>> loaded = mx.read_model('path/to/model')

Memory Management:

Clear calculated values to free memory:

>>> model.clear_all()  # Clear all cells and dynamic spaces in the model

Multiple Models:

Multiple models can coexist in the same session:

>>> model1 = mx.new_model('Model1')
>>> model2 = mx.new_model('Model2')
>>> mx.get_models()
{'Model1': <Model Model1>, 'Model2': <Model Model2>}

Accessing Current Model:

Get the currently active model:

>>> mx.cur_model()
<Model Model2>

See also

• new_model(): Create a new model

• read_model(): Load a model from files

• get_models(): Get all models in the session

• cur_model(): Get the current model

• UserSpace: Container for cells and nested spaces

• write(): Save model to files

• zip(): Save model to zip archive

Changed in version 0.18.0: Added pandas and module update operations

Model properties

name	Name of the object.
fullname	Dotted name of the object.
doc	Documentation string
path	A Path object representing the model's path.
model	The model this object belongs to.
parent	The parent of this object.
allow_none	Whether a cells can have None as its value.
properties
spaces	A mapping of the names of child spaces to the Space objects
refs	Return a mapping of global references.
macros	Return a mapping of macros.
iospecs	List of BaseIOSpec objects
tracegraph	A directed graph of cells.
info	An object whose repr summarizes this Interface.

Model operations

close()	Close the model.
rename(name[, rename_old])	Rename the model itself
set_property(name, value)	Set property name

Macro operations

new_macro([name, formula])

Create a new Macro in this model.

Saving operations

write(model_path[, backup, log_input])	Write model to files.
zip(model_path[, backup, log_input, ...])	Archive model to a zip file.
export(path)	Export the model as a Python package.

Child Space operations

cur_space([name])	Set the current space to Space name and return it.
new_space([name, bases, formula, refs])	Create a child space.
clear_all()	Clears Cells and ItemSpace.
import_module([module, recursive])	Create a child space from an module.
new_space_from_csv(filepath[, space, cells, ...])	Create spaces from from a comma-separated values (csv) file.
new_space_from_excel(book, range_[, sheet, ...])	Create a child space from an Excel range.
new_space_from_module(module[, recursive])	Create a child space from an module.
new_space_from_pandas(obj[, space, cells, ...])	Create child spaces from Pandas DataFrame or Series.
new_space_from_model(source_model[, name, ...])	Create a top-level UserSpace from another Model.
compare_cells(func)	Tentative: Compare cells with the same name across different spaces in the model.

Reference operations

new_pandas(name, path, data[, file_type, ...])	Create a Reference bound to a pandas DataFrame or Series associating a new PandasData object.
new_module(name, path, module)	Assigns a user module to a Reference associating a new ModuleData object
new_excel_range(name, path, range_[, sheet, ...])	Creates a Reference to an Excel range
update_pandas(old_data[, new_data])	Update a pandas object assigned to References
update_module(old_module[, new_module])	Update an user-defined module assigned to References
get_spec(data)	Get IOSpec associated with data
del_spec(data)	Delete IOSpec associate with data

Run operations

generate_actions(targets[, step_size])	Generates actions for memory-optimized run
execute_actions(actions)	Performs memory-optimized run