Modernize PythonVRFT packaging and update core dependencies to recent versions

README.md

@@ -16,21 +16,22 @@ Our code is released under the MIT license (refer to the [LICENSE](https://githu
 
 ## Requirements
 
-To run the library you need atleast Python 3.5.
+To run the library you need at least Python 3.5.
 
 Other dependencies:
-- NumPy (1.19.5)
-- SciPy (1.6.0)
+- NumPy
+- SciPy
 
 ## Installation
 
 - Install from source: git clone this repo and from the root folder execute the command ```pip install .```
+- Install with modern Python tooling: ```uv sync``` (requires [uv](https://github.com/astral-sh/uv))
 
 ## Usage/Examples
 
 You can import the library by typing ```python import vrft``` in your code.
 
-To learn how to use the library, check the examples located in the examples/ folder. At the moment there are examples available. 
+To learn how to use the library, check the examples located in the examples/ folder. At the moment there are examples available.
 Check example3 to see usage of instrumental variables.
 
 In general the code has the following structure
@@ -73,7 +74,7 @@ theta, _, _, C = compute_vrft(data, ref_model, control, pre_filter)
 To execute tests run the following command from the root folder of the repo
 ```sh
 python -m unittest
-``` 
+```
 
 ## Changelog
 
@@ -97,4 +98,3 @@ If you find this code useful in your research, please, consider citing it:
 >}
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-

pyproject.toml

@@ -0,0 +1,43 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pythonvrft"
+version = "0.0.7"
+description = "VRFT Python Library"
+readme = "README.md"
+keywords = ["VRFT", "Virtual Reference Feedback Tuning", "Data Driven Control", "Adaptive Control"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Development Status :: 3 - Alpha",
+    "Topic :: Scientific/Engineering",
+]
+requires-python = ">=3.5"
+dependencies = [
+    "scipy",
+    "numpy",
+]
+
+[[project.authors]]
+name = "Alessio Russo"
+email = "alessior@kth.se"
+
+[project.urls]
+Homepage = "https://github.com/rssalessio/PythonVRFT/"
+
+[project.license]
+text = "GPL3"
+
+[project.optional-dependencies]
+test = ["pytest"]
+
+[dependency-groups]
+dev = [
+  "pytest"
+]
+
+[tool.setuptools.packages.find]
+include = ["vrft", "test"]

test/test_utils.py

@@ -33,7 +33,7 @@ def test_deconvolve(self):
         data = iddata(y, u, t_step, [0])
         r1, _ = virtual_reference(data, sys.num, sys.den)
         r2 = deconvolve_signal(sys, data.y)
-        self.assertTrue(np.linalg.norm(r2-r1[:r2.size], np.infty) <  1e-3)
+        self.assertTrue(np.linalg.norm(r2-r1[:r2.size], np.inf) <  1e-3)
 
 
     def test_check_system(self):

test/test_vrft.py

@@ -27,7 +27,7 @@ def test_vrft(self):
 
         expected_theta = np.array([1.93220784, -1.05808206, 1.26623764, 0.0088772])
         expected_loss = 0.00064687904235295
-        
+
         for t_end in t_ends:
             t = np.arange(t_start, t_end, t_step)
             u = np.ones(len(t)).tolist()
@@ -50,20 +50,20 @@ def test_vrft(self):
             theta1, _, loss1, _ = compute_vrft(data, refModel, control, prefilter)
             theta2, _, loss2, _ = compute_vrft([data], refModel, control, prefilter)
             theta3, _, loss3, _ = compute_vrft([data, data], refModel, control, prefilter)
-            
+
             self.assertTrue(np.isclose(loss1, loss2))
             self.assertTrue(np.isclose(loss1, loss3))
             self.assertTrue(np.linalg.norm(theta1-theta2)<1e-15)
             self.assertTrue(np.linalg.norm(theta1-theta3)<1e-15)
-            self.assertTrue(np.linalg.norm(theta1-expected_theta, np.infty) < 1e-5)
+            self.assertTrue(np.linalg.norm(theta1-expected_theta, np.inf) < 1e-5)
             self.assertTrue(abs(expected_loss - loss1) < 1e-5)
 
     def test_iv(self):
         t_start = 0
         t_step = 1e-2
         t_ends = [10, 10 + t_step]
 
-        
+
         for t_end in t_ends:
             t = np.arange(t_start, t_end, t_step)
             u = np.ones(len(t)).tolist()
@@ -78,7 +78,7 @@ def test_iv(self):
             _, y = scipysig.dlsim(sys, u, t)
             y = y.flatten() + 1e-2 * np.random.normal(size=t.size)
             data2 = iddata(y,u,t_step,[0])
-            
+
 
             refModel = ExtendedTF([0.2], [1, -0.8], dt=t_step)
             prefilter = refModel * (1-refModel)