Metadata-Version: 2.4
Name: mater
Version: 0.15.0
Summary: Multi-regional Assessment of Technologies, Energy and Resources
Project-URL: Documentation, https://isterre-dynamic-modeling.gricad-pages.univ-grenoble-alpes.fr/mater-project/mater/
Project-URL: repository, https://gricad-gitlab.univ-grenoble-alpes.fr/isterre-dynamic-modeling/mater-project/mater
Author-email: francois-verzier <francois.verzier@univ-grenoble-alpes.fr>, Lauranne Sarribouette <lauranne.sarribouette@univ-grenoble-alpes.fr>
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License-File: AUTHORS
License-File: LICENSE
Keywords: ecological,energy,materials,research,scientific
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Requires-Python: <4.0,>=3.10
Requires-Dist: numpy>=2.1.0; python_version == '3.10'
Requires-Dist: numpy>=2.3.0; python_version >= '3.11'
Requires-Dist: pandas>=2.3.3
Requires-Dist: pandera>=0.31.1
Requires-Dist: pyarrow>=21.0.0
Requires-Dist: pydantic>=2.12.4
Requires-Dist: tqdm>=4.67.3
Description-Content-Type: text/markdown

# MATER

**MATER** (*Multi-regional Assessments of Technologies, Energy and Resources*) is an open-source Python library for building dynamic stock-flow models of socioeconomic systems.

MATER is designed to support:

- dynamic Mass Flow Analysis (dMFA);
- dynamic Input-Output Analysis (dIOA);
- biophysical assessments of technologies, infrastructures, resources, materials and pollutants;
- modelling of constrained transition scenarios.

The package is organized around:

- multidimensional pandas variables;
- documented systems of equations called presets;
- validated variable specifications;
- input and output adapters;
- a ports-and-adapters software architecture.

> MATER is currently under active development. Equations, data examples and taxonomies may evolve in future versions.

---

## Requirements

- Python 3.10 or higher

We recommend using one virtual environment per Python project to manage dependencies and maintain isolation.

You can use [uv](https://docs.astral.sh/uv/) or `pip`.

---

## Installation

Install MATER with `uv`:

```bash
uv add mater
```

Or with `pip`:

```bash
pip install mater
```

---

## Version status

MATER `0.15.0` is a major restructuring release.

It introduces a new package architecture, a new public input API based on `run_from_dict`, validated variable specifications, documented default equations, and a clearer separation between presets, adapters, application services and core services.

MATER `0.15.x` should be considered the new main development line.

Projects based on `0.14.x` should not assume direct compatibility with `0.15.x`. The `0.14.x` series is treated as a legacy maintenance line for existing projects.

If you are starting a new project, use `0.15.x`.

If your project already depends on `0.14.x`, either stay on the `0.14.x` maintenance line or plan an explicit migration.

---

<!-- ## Recommended usage

For most users, we recommend starting from the companion project template:

**[init-mater-project](https://gricad-gitlab.univ-grenoble-alpes.fr/isterre-dynamic-modeling/mater-project/init-mater-project)**

It provides a complete project structure, CLI tools and a data pipeline to help you run MATER simulations more easily.

Direct usage of the `mater` package is recommended for:

* custom integrations in existing Python codebases;
* advanced users building specialized tools;
* users who already have MATER-ready pandas variables;
* contributors to the MATER framework itself.

--- -->

## Public input API

The stable public input contract of MATER is a dictionary mapping variable names to pandas objects:

```python
dict[str, pd.Series | pd.DataFrame | None]
```

Each key is the name of a variable expected by the selected preset.

Each value can be:

* `pd.Series` for one simulation time step;
* `pd.DataFrame` for a full time series;
* `None` for variables that are not initialized yet and will be computed later.

When a variable is represented as a `pd.DataFrame`, its columns must be a `pd.DatetimeIndex`.

The selected preset validates the variables before the simulation starts.

---

## Quick start: run from pandas variables

Create a Python script named `simulate.py`:

```python
"""
MATER simulation script.
"""

from mater.main import run_from_dict

# These variables must be pandas Series or DataFrames following the
# specifications of the selected preset.
#
# For the default preset, see:
# mater.presets.mater_default.variable_specs

variables = {
    "stock": stock,
    "flow_balance": flow_balance,
    "process_capacity_assignment": process_capacity_assignment,
    "process_ref_capacity": process_ref_capacity,
    "allocation_input": allocation_input,
    "life_time_mean_value": life_time_mean_value,
    "reference_stock_input": reference_stock_input,
    "process_input": process_input,
}

run_from_dict(
    variables=variables,
    simulation_name="run0",
    preset_name="mater_default",
    simulation_start_time=1901,
    simulation_end_time=2100,
)
```

Run the script:

```bash
uv run python simulate.py
```

By default, outputs are written to:

```text
outputs/run0/
```

---

## Convenience usage: run from Excel

MATER also provides an Excel input adapter as a convenience loader.

This is useful for examples, tests and manual input files. However, the recommended stable API is `run_from_dict`.

```python
"""
MATER simulation script using an Excel input file.
"""

from mater.main import run_from_excel

run_from_excel(
    file_path="input.xlsx",
    simulation_name="run0",
    preset_name="mater_default",
    simulation_start_time=1901,
    simulation_end_time=2100,
)
```

Run the script:

```bash
uv run python simulate.py
```

The Excel adapter reads an Excel file and converts it into the same dictionary format expected by `run_from_dict`.

---

## Default preset

The default preset is:

```python
"mater_default"
```

It defines the default MATER variables, equations and validation rules.

The default preset is implemented in:

```python
mater.presets.mater_default
```

Important modules include:

```python
mater.presets.mater_default.variable_specs
mater.presets.mater_default.variables
mater.presets.mater_default.equations
```

The default equations include:

* `reference_stock_computation`
* `theoretical_flow_computation`
* `process_computation`
* `flow_computation`
* `transport_computation`
* `end_of_life_computation`
* `stock_computation`

---

## Outputs

Simulation outputs are written by the configured repository adapter.

By default, MATER uses a Parquet repository and writes results to:

```text
outputs/<simulation_name>/
```

A typical output structure is:

```text
outputs/
└── run0/
    ├── stock/
    │   ├── metadata.json
    │   └── data/
    ├── flow/
    │   ├── metadata.json
    │   └── data/
    └── process/
        ├── metadata.json
        └── data/
```

Each variable folder contains:

* `metadata.json`, with variable-level metadata such as name and unit;
* a `data/` folder containing repository-specific data files.

With the default Parquet repository, data are stored as `.mater` files.

---

## Reading outputs

Outputs can be loaded with pandas or with MATER repository utilities.

A simple pandas-based example:

```python
from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

stock = pd.read_parquet(Path("outputs") / "first_run" / "stock" / "data")

stock.unstack("time")["value"].loc[:, :, :, "in_use", :].T.plot()
plt.show()
```

The exact loading strategy depends on the repository adapter used for the simulation.

---

## Documentation

The online documentation is available here:

[https://isterre-dynamic-modeling.gricad-pages.univ-grenoble-alpes.fr/mater-project/mater/](https://isterre-dynamic-modeling.gricad-pages.univ-grenoble-alpes.fr/mater-project/mater/)

The documentation includes:

* getting started guide;
* user guide;
* modelling concepts;
* default preset variables and equations;
* API reference;
* developer guide.

---

## Citation

If you use MATER in academic work, please cite the associated thesis and the software version used.

The scientific framework behind MATER is associated with:

François Verzier. *Modélisation du métabolisme de la société et de ses trajectoires de transition biophysique*. 2025. DOI: 10.70675/89172c3czbb2bz45abz8b5fza3bb49f30f92

Machine-readable software metadata are provided in `codemeta.json`.

---

## License

MATER is distributed under the terms of the **GNU Lesser General Public License v3.0 or later**.

SPDX identifier:

```text
LGPL-3.0-or-later
```

---

## Contributing

Contributions are welcome.

Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for development guidelines, testing instructions and contribution rules.

---

## Development

Clone the repository:

```bash
git clone https://gricad-gitlab.univ-grenoble-alpes.fr/isterre-dynamic-modeling/mater-project/mater.git
cd mater
```

Install dependencies:

```bash
uv sync
```

Run the test suite:

```bash
uv run pytest
```

Build the documentation locally:

```bash
uv run sphinx-build -b html docs/source docs/build/html
```
