mater 0.14.0

Multi-regional Assessment of Technologies, Energy and Resources

MATER

Metabolic Analysis for Transdisciplinary Ecological Research

[TOC]

📋 Requirements

• Python 3.12 or higher

We recommend using one virtual environment per Python project to manage dependencies and maintain isolation. You can use a package manager like uv to help you with library dependencies and virtual environments.

Quick start

Use init-mater-project (recommended).
It provides a complete project structure, CLI tools, and data pipeline in minutes.

💡 Direct MATER Core usage is only recommended for:

• Custom integrations in existing codebases
• Advanced users building specialized tools
• Contributing to the MATER framework itself

Direct MATER Core usage

Install the mater package in your project via UV (recommended) or pip:

uv add mater
# Or
pip install mater

Example: Run a Simulation with an Excel file

Create a Python script in your working directory to run simulations. Ensure the required Excel input file (chose a compatible version) is located in the root of your working directory.

Create a file named simulate.py (or any name you prefer) with the following content:

"""
MATER simulation script.
"""

# === CONFIGURATION ===
INPUT_FILE = "your_file.xlsx"  # Name of your Excel input file
OUTPUT_FOLDER = "run0"         # Name of the folder where outputs are stored
START_TIME = 1901              # Initial time step
END_TIME = 2100                # Final time step
TIME_FREQUENCY = "YS"          # Time frequency
# === END CONFIGURATION ===

from mater import Mater

def main():
    """Run MATER simulation."""
    model = Mater()
    model.run_from_excel(
        OUTPUT_FOLDER,
        INPUT_FILE,
        simulation_start_time=START_TIME,
        simulation_end_time=END_TIME,
        time_frequency=TIME_FREQUENCY,
    )

if __name__ == "__main__":
    main()

To run your simulation:

1. Edit the configuration values at the top of the script

2. Save the file

3. Execute the script:

uv run simulate.py

Output variables description

Output variable	Unit	Definition	Example
control_flow	<object_unit>/time	Object footprint demand before trade between locations	Number of cars consumed in China (included the imported ones)
extraneous_flow	<object_unit>/time	Object consumption or coproduction	C02 coproduced (+ value) and coal consumed (- value) by the electricity production process of a coal power plant
in_use_stock	<object_unit>	Object in use stock	Number of cars in use
old_stock	<object_unit>	Object stock in landfill	Number of end of life cars unrecycled
process	<process_unit>/time	Number of process made by an object	Transportation process (km/year) made by cars
recycling_flow	<object_unit>/time	Quantity of recycled objects	Recycled end of life cars
reference_intensity_of_use	<process_unit>/<object_unit>/time	Intensity of use	Number of km per year made by a car
reference_stock	<object_unit>	How many objects should be in the in use stock	Installed power plant capacity to fulfill the electricity demand
secondary_production	<object_unit>/time	Coproduction due to recycling processes	Quantity of steel recycled (coproduce by recycling) in a year
self_disposal_flow	<object_unit>/time	End of life flow	Number of cars that cannot work anymore
traded_control_flow	<object_unit>/time	Object supply after trade between locations	Number of cars produced in China (included the exported ones)

Accessing the Results from a Python Script

Below is an example Python script using pandas and matplotlib to plot specific simulation results. Each folder in the output directory corresponds to a variable that can be loaded with pandas.

# Import the MATER package and matplotlib.pyplot
import matplotlib.pyplot as plt
from mater import Mater

# Create a Mater instance
model = Mater()

# Select the output directory where the run results are stored
model.set_output_dir()  # Defaults to the working directory

# Set the run directory name
model.set_run_name("run0")

# Get a variable
in_use_stock = model.get("in_use_stock")

# Transform the dataframe and plot the results
in_use_stock.groupby(level=["location", "object"]).sum().T.plot()
plt.show()

This example demonstrates how to access and plot variables from simulation outputs. Adjust the code to fit your analysis needs.

🤝 Contributing

We welcome contributions to the MATER project! To get started, please refer to the CONTRIBUTING file for detailed guidelines.

📚 Online Documentation

For more information, refer to the official MATER documentation.