mmodel 0.5.0

Modular modeling framework for scientific modeling and prototyping.

MModel is a lightweight and modular model-building framework for small-scale and nonlinear models. The package aims to solve scientific program prototyping and distribution difficulties, making it easier to create modular, fast, and user-friendly packages. The package is fully tested.

Quickstart

To create a nonlinear model that has the result of (x + y)log(x + y, base):

from mmodel import ModelGraph, Model, MemHandler
import math
import numpy as np

def func(sum_xy, log_xy):
    """Function that adds a value to the multiplied inputs."""
    return sum_xy * log_xy + 6

The graph is defined using grouped edges (the networkx syntax of edge the definition also works.)

# create graph edges
grouped_edges = [
    ("add", ["log", "function node"]),
    ("log", "function node"),
]

The functions are then added to node attributes. The order of definition is node_name, node_func, output, input (if different from original function), and modifiers.

# define note objects
node_objects = [
    ("add", np.add, "sum_xy", ["x", "y"]),
    ("log", math.log, "log_xy", ["sum_xy", "log_base"]),
    ("function node", func, "result"),
]

G = ModelGraph(name="example_graph")
G.add_grouped_edges_from(grouped_edges)
G.set_node_objects_from(node_objects)

To define the model, the name, graph, and handler need to be specified. Additional parameters include modifiers, descriptions, and returns lists. The input parameters of the model are determined based on the node information.

example_model = Model("example_model", G, handler=(MemHandler, {}), description="Test model.")

The model behaves like a Python function, with additional metadata. The graph can be plotted using the draw method.

>>> print(example_model)
example_model(log_base, x, y)
returns: z
handler: MemHandler()

Test model.

>>> example_model(2, 5, 3) # (5 + 3)log(5 + 3, 2) + 6
30.0

>>> example_model.draw()

The resulting graph contains the model metadata and detailed node information.

One key feature of mmodel that differs from other workflow is modifiers, which modify callables post definition. Modifiers work on both the node level and model level.

Example: Using modifier and graph to loop the nodes that require the “log_base” parameter.

from mmodel import loop_modifier

H = G.subgraph(inputs=["log_base"])
loop_node = Model(
    "loop_submodel",
    H,
    handler=(MemHandler, {}),
    modifiers=[(loop_modifier, {"parameter": "log_base"})],
)
looped_G = G.replace_subgraph(H, "loop_node", loop_node, output="looped_z")
looped_model = Model("looped_model", looped_G, loop_node.handler)

We can inspect the loop node as well as the new model.

>>> print(loop_node)
loop_submodel(log_base, sum_xy)
returns: z
handler: MemHandler()
modifiers:
  - loop_modifier('log_base')

>>> print(looped_model)
looped_model(log_base, x, y)
returns: looped_z
handler: MemHandler()

>>> looped_model([2, 4], 5, 3) # (5 + 3)log(5 + 3, 2) + 6
[30.0, 18.0]

Use the draw method to draw the graph. There are three styles “plain”, “short”, and “full”, which differ by the level of detail of the node information. A graph output is displayed in Jupyter Notebook or can be saved using the export option.

G.draw(style="short")
example_model.draw(style="plain", export="example.pdf") # default to draw_graph

Installation

Graphviz installation

To view the graph, Graphviz needs to be installed: Graphviz Installation For windows installation, please choose “add Graphviz to the system PATH for all users/current users” during the setup.

MModel installation

pip install mmodel

Development installation

MModel uses poetry as the build system. The package works with both pip and poetry installation.

To install test and docs, despondencies run:

pip install .[test] .[docs]

To run the tests in different python environments and cases (py38, py39, py310, py311, coverage and docs):

tox

To create the documentation, run under the “/docs” directory:

make html