initial dataset commit

Signed-off-by: vincent-maillou <vmaillou@iis.ee.ethz.ch>

.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+# Matrix market files
+*.mtx filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,2 @@
----
-license: bsd-3-clause
----
+# NEST: NEw Sparse maTrix dataset
+The matrices are stored in the Matrix Market format as well as `scipy.sparse.npz` format.

device_modeling/README.md

@@ -0,0 +1,37 @@
+# Device Modeling
+This section of the dataset contains sparse matrices related to device modeling. More specifically, this contains matrices related to the modeling of semiconductor devices, including MOSFETs, ReRAM, and other devices.
+
+## kmc_
+Domain: 
+
+### Caracteristics
+- Type:
+- Size: ,  nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+|          |         |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References
+- [Accelerated Atomistic Kinetic Monte Carlo Simulations of Resistive Memory Arrays](https://ieeexplore.ieee.org/abstract/document/10793135)
+
+
+
+## qbit_
+Domain: 
+
+### Caracteristics
+- Type:
+- Size: ,  nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+|          |         |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References

legacy_references/README.md

@@ -0,0 +1,37 @@
+# Legacy References
+This section of the dataset contains references sparse matrices that have been used as standard benchmarks for permutation and fill-in reduction algorithms as well as for evaluating the performance of sparse matrix factorization algorithms.
+
+## west0479
+Domain: Chemical engineering plant models
+
+### Caracteristics
+- Type: real unsymmetric
+- Size: 479x479, 1910 nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+| west0479 | 1910    |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References
+- [State-of-The-Art Sparse Direct Solvers](https://arxiv.org/abs/1907.05309)
+
+
+## Freescale/circuit5M_dc
+Domain: Circuit Simulation Problem
+
+### Caracteristics
+- Type: real unsymmetric
+- Size: 3523317x3523317, 19194193 nnz
+
+|              | Initial  | LU | Fill-in | Depth |
+|--------------|----------|----|---------|-------|
+| circuit5M_dc | 19194193 |    |         |       |
+| RCM          |          |    |         |       |
+| AMD          |          |    |         |       |
+| ND           |          |    |         |       |
+
+### References
+- []()

legacy_references/circuit5m_dc/circuit5m_dc.mtx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:435d665495cdc1e7e6afb8e4d61cab905c479d6b0820d2a7f52d5bfb7b65d42e
+size 621990406

legacy_references/circuit5m_dc/circuit5m_dc.npz

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+oid sha256:b5f14a80342ca185a89448cf01f1ce1d52a6806bb11312887a9dc7584fd86714
+size 31888094

legacy_references/west0479/west0479.mtx

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legacy_references/west0479/west0479.npz

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+oid sha256:7cfdb7f59e58475050d93516111620596ec0bee230a15df5e5c10eb458900ee2
+size 13461

quantum_transport/README.md

@@ -0,0 +1,55 @@
+# Quantum Transport
+This section of the dataset contains sparse matrices related to quantum transport using the non-equilibrium Green's function method (NEGF).
+
+## cnt_cp2k
+Domain: Quantum transport
+Description: Hamiltonian (CP2K) of a carbon nano-tube made of 704 carbon atoms. Every carbon atom has 13 basis (9152 basis in total).
+
+### Caracteristics
+- Type: complex symmetric
+- Size: 9152x9152, 6154350 nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+| cnt_cp2k | 6154350 |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References
+
+
+## cnt_w90
+Domain: Quantum transport
+Description: Hamiltonian (Wannier 90) of a carbon nano-tube.
+
+### Caracteristics
+- Type: complex symmetric
+- Size: 768x768, 71680 nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+| cnt_cp2k | 71680   |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References
+
+
+## sinw_w90
+Domain: Quantum transport
+Description: Hamiltonian (Wannier 90) of a silicon nano-wire.
+
+### Caracteristics
+- Type: complex symmetric
+- Size: 7488x7488, 5310160 nnz
+
+|          | Initial | LU | Fill-in | Depth |
+|----------|---------|----|---------|-------|
+| sinw_w90 | 5310160 |    |         |       |
+| RCM      |         |    |         |       |
+| AMD      |         |    |         |       |
+| ND       |         |    |         |       |
+
+### References

quantum_transport/cnt_cp2k/cnt_cp2k.mtx

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+oid sha256:5e23a6569ea80456970ea0bdb6ec01741262f6ce990189a5d811d0176c60c643
+size 193277075

quantum_transport/cnt_cp2k/cnt_cp2k.npz

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+oid sha256:3c12515b163f89c64ec940dff7d994c9ec6c777563d2c586223da60f6755989c
+size 48785865

quantum_transport/cnt_w90/cnt_w90.mtx

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+size 1668885

quantum_transport/cnt_w90/cnt_w90.npz

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+oid sha256:a49650281d17c28276cb1220903edf6cf6c879d8b4ed5fe0cce0d615f5f01477
+size 337563

quantum_transport/sinw_w90/sinw_w90.mtx

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quantum_transport/sinw_w90/sinw_w90.npz

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+oid sha256:7e9608d54b3ae97b6629a7bb8e1728db0bf455b8002a854efff34b9a2507cdc5
+size 33100999

statistical_modeling/README.md

@@ -0,0 +1,75 @@
+# Statistical Modeling
+This section of the dataset contains sparse matrices related to statistical modeling. More specifically, this contain precision matrices as used within the integrated nested Laplace approximation (INLA) framework.
+
+## temp_prior_st1
+Domain: Prior precision matrix from spatio-temporal statistical modeling applied to temperature prediction
+
+### Caracteristics
+- Type: positive-definite symmetric
+- Size: 1000506x1000506, 50870502 nnz
+
+|                | Initial  | Cholesky | Fill-in | Depth |
+|----------------|----------|----------|---------|-------|
+| temp_prior_st1 | 50870502 |          |         |       |
+| RCM            |          |          |         |       |
+| AMD            |          |          |         |       |
+| ND             |          |          |         |       |
+
+### References
+- [Integrated Nested Laplace Approximations for Large-Scale Spatiotemporal Bayesian Modeling](https://epubs.siam.org/doi/full/10.1137/23M1561531)
+
+## temp_conditional_st1
+Domain: Conditional precision matrix from spatio-temporal statistical modeling applied to temperature prediction
+
+### Caracteristics
+- Type: positive-definite symmetric
+- Size: 1000506x1000506, 62837532 nnz
+
+|                      | Initial  | Cholesky | Fill-in | Depth |
+|----------------------|----------|----------|---------|-------|
+| temp_conditional_st1 | 62837532 |          |         |       |
+| RCM                  |          |          |         |       |
+| AMD                  |          |          |         |       |
+| ND                   |          |          |         |       |
+
+### References
+- [Integrated Nested Laplace Approximations for Large-Scale Spatiotemporal Bayesian Modeling](https://epubs.siam.org/doi/full/10.1137/23M1561531)
+
+
+
+## airpoll_prior_st3
+Domain: Prior precision matrix from a coregional (trivariate) spatio-temporal statistical modeling applied to air pollution
+
+### Caracteristics
+- Type: positive-definite symmetric
+- Size: 8499x8499, 1205931 nnz
+
+|                   | Initial | Cholesky | Fill-in | Depth |
+|-------------------|---------|----------|---------|-------|
+| airpoll_prior_st3 | 1205931 |          |         |       |
+| RCM               |         |          |         |       |
+| AMD               |         |          |         |       |
+| ND                |         |          |         |       |
+
+### References
+- []()
+- [Multivariate disaggregation modeling of air pollutants: a case-study of PM2.5, PM10 and ozone prediction in Portugal and Italy](https://arxiv.org/abs/2503.12394)
+
+
+## airpoll_conditional_st3
+Domain: Conditional precision matrix from a coregional (trivariate) spatio-temporal statistical modeling applied to air pollution
+
+### Caracteristics
+- Type: positive-definite symmetric
+- Size: 8499x8499, 1218651 nnz
+
+|                         | Initial | Cholesky | Fill-in | Depth |
+|-------------------------|---------|----------|---------|-------|
+| airpoll_conditional_st3 | 1218651 |          |         |       |
+| RCM                     |         |          |         |       |
+| AMD                     |         |          |         |       |
+| ND                      |         |          |         |       |
+
+### References
+- []()
+- [Multivariate disaggregation modeling of air pollutants: a case-study of PM2.5, PM10 and ozone prediction in Portugal and Italy](https://arxiv.org/abs/2503.12394)

statistical_modeling/airpoll_conditional_st3/airpoll_conditional_st3.mtx

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statistical_modeling/airpoll_conditional_st3/airpoll_conditional_st3.npz

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statistical_modeling/airpoll_prior_st3/airpoll_prior_st3.mtx

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statistical_modeling/airpoll_prior_st3/airpoll_prior_st3.npz

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statistical_modeling/temp_conditional_st1/temp_conditional_st1.mtx

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statistical_modeling/temp_conditional_st1/temp_conditional_st1.npz

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statistical_modeling/temp_prior_st1/temp_prior_st1.mtx

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statistical_modeling/temp_prior_st1/temp_prior_st1.npz

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utils/README.md

@@ -0,0 +1,15 @@
+# Utils
+
+## matutils.py
+This module contains utility functions for matrix handling and conversion from/to matrix market coo format and scipy npz format.
+
+Example usage:
+```Python
+# Load the west0479.mtx file, convert it to npz format and save it to the specified directory
+python matutils.py mm2npz /home/vmaillou/Documents/Repositories/bigbird_dataset/legacy_references/west0479/west0479.mtx /home/vmaillou/Documents/Repositories/bigbird_dataset/  legacy_references/west0479/clear
+```
+
+```Python
+# Load the west0479.npz file, convert it to Matrix Market format and save it to the specified directory
+python matutils.py npz2mm /home/vmaillou/Documents/Repositories/bigbird_dataset/legacy_references/west0479/west0479.npz /home/vmaillou/Documents/Repositories/bigbird_dataset/legacy_references/west0479/
+```

utils/matutils.py

@@ -0,0 +1,62 @@
+import os
+import argparse
+from pathlib import Path
+import scipy as sp
+
+def print_matinfos(matrix: sp.sparse.coo_matrix) -> None:
+    print(f"    - Size of the matrix: {matrix.shape}")
+    print(f"    - Number of non-zero elements: {matrix.nnz}")
+    print(f"    - Data type of the matrix: {matrix.dtype}")
+
+
+def load_matrixmarket(path_input: Path) -> sp.sparse.coo_matrix:
+    """
+    Load a Matrix Market file and return it as scipy.sparse.coo_matrix.
+    """
+    print(f"    - Loaded Matrix Market file from: {path_input}")
+    matrix = sp.io.mmread(path_input)
+    print_matinfos(matrix)
+    return matrix
+
+def save_matrixmarket(matrix: sp.sparse.coo_matrix, path_output: Path) -> None:
+    """
+    Save a scipy.sparse.coo_matrix to a Matrix Market file.
+    """
+    print(f"    - Saving Matrix Market file to: {path_output}")
+    sp.io.mmwrite(
+        path_output, 
+        matrix, 
+    )
+    print("    - Successfully saved Matrix Market file.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Matrix Market utilities")
+    parser.add_argument("command", choices=["mm2npz", "npz2mm"], help="Command to execute")
+    parser.add_argument("input_file", help="Input file path")
+    parser.add_argument("output_file", help="Output file path")
+
+    args = parser.parse_args()
+
+    print("args:", args)
+
+    if args.command == "mm2npz":
+        print("Converting Matrix Market to NPZ format:")
+        matrix_coo = load_matrixmarket(args.input_file)
+        input_matrix_name = os.path.splitext(os.path.basename(args.input_file))[0]
+        output_matrix_name = Path.joinpath(
+            Path(args.output_file), f"{input_matrix_name}.npz"
+        )
+        print(f"    - Saving to NPZ format: {output_matrix_name}")
+        sp.sparse.save_npz(output_matrix_name, matrix_coo)
+    elif args.command == "npz2mm":
+        print("Converting NPZ to Matrix Market format:")
+        matrix_coo = sp.sparse.load_npz(args.input_file).tocoo()
+        print(f"    - Loaded NPZ file from: {args.input_file}")
+        print_matinfos(matrix_coo)
+        input_matrix_name = os.path.splitext(os.path.basename(args.input_file))[0]
+        output_matrix_name = Path.joinpath(
+            Path(args.output_file), f"{input_matrix_name}.mtx"
+        )
+        print(f"    - Saving to Matrix Market format: {output_matrix_name}")
+        save_matrixmarket(matrix_coo, output_matrix_name)