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Metabolic Analysis for Transdisciplinary Ecological Research
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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.
Install the mater
package via pip:
pip install mater
The mater
command line interface (CLI) makes it easy to run simulations. Ensure the required Excel input file (chose a compatible version) is located in the root of your working directory.
Run the following command to start a simulation from your excel file:
mater run -i <YOUR_INPUT_FILE_NAME>
Simulation results are stored locally in Parquet (.mater) files.
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) |
Results can be visualize in a built-in user interface. Go to the directory containing the result folder and use the following command :
mater plot -o <YOUR_RESULT_FOLDER_NAME>
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.
We welcome contributions to the MATER project! To get started, please refer to the CONTRIBUTING file for detailed guidelines.
For more information, refer to the official MATER documentation.
FAQs
Multi-regional Assessment of Technologies, Energy and Resources
We found that mater demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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