Added pyproject with dependencies

.gitignore

@@ -49,9 +49,20 @@ obj/table.c.o
 /regr_smlp/code/test*.*
 /regr_smlp/code/__pycache__/
 /regr_smlp/code/all_log.txt
+/regr_smlp/master/all_log.txt
 /regr_smlp/code/logs.log
 
 # ignore files for tests 130,131 as the data set is too big for the repository
 /regr_smlp/data/smlp_s2_tx.csv
 /regr_smlp/master/Test130_*.*
 /regr_smlp/master/Test131_*.*
+
+*.log
+# ignore experiments
+experiment_outputs/*
+# ignore toy_out_dir
+toy_out_dir/*
+
+justfile
+.idea
+tags

.pre-commit-config.yaml

@@ -0,0 +1,18 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.14.1
+    hooks:
+      # Run the linter.
+      - id: ruff-check
+      # Run the formatter.
+      - id: ruff-format

.python-version

@@ -0,0 +1 @@
+3.11

README.md

@@ -3,18 +3,18 @@
 SMLP is a tool for optimization, synthesis and design space exploration. It is
 based on statistical and machine learning techniques combined with formal verification
 approaches that allows selection of optimal configurations with respect to given
-constraints on the inputs and outputs of the system under consideration.  
+constraints on the inputs and outputs of the system under consideration.
 
 SMLP has been applied at Intel for hardware design optimization. It is a gneral
 purpose optimization and verification tool applicable to any domain where ML models
-can be trained on data. The supported modes in SMLP include processing data and 
+can be trained on data. The supported modes in SMLP include processing data and
 training models (NNs, tree-based, and polynomial models), querying data and models,
 certifying and verifying assertions, synthesis and pareto-optimization of configurations,
 design of experiments (to simulate systems and produce data), feature selection,
 rule learning/subgroup discovery, root-cause analysis, and more.
 
 If you want to try out SMLP on your optimization problems
-and require support, please contact the developers through 
+and require support, please contact the developers through
 the [discussion page](https://github.com/SMLP-Systems/smlp/discussions).
 
 When you use this tool, please cite our corresponding CAV 2024 tool paper,
@@ -153,7 +153,7 @@ following of SMLP's modes:
 - doe
 
 Detailed outputs of each tests will be generated in the regr_smlp/code directory.
-We refer to the manual for further information. 
+We refer to the manual for further information.
 
 ## A note on external solvers
 
@@ -192,7 +192,7 @@ directory, run the following commands:
     -mode optimize -pareto f -sat_thresh f -resp o0 \
     -feat Byte,CH,RANK,Timing,i0,i1,i2,i3 \
     -model nn_keras -nn_keras_epochs 20 -data_scaler min_max \
-    -epsilon 0.05 -log_time f -plots f  -spec ../specs/smlp_s2_tx 
+    -epsilon 0.05 -log_time f -plots f  -spec ../specs/smlp_s2_tx
 
 These runs will take longer than the regression tests provided earlier,
 usually between one and three hours, depending on the machine.