shb84 · shb84 · Dec 4, 2025 · Nov 30, 2025 · Dec 1, 2025 · Dec 1, 2025
diff --git a/.github/workflows/jenn-ci.yml b/.github/workflows/jenn-ci.yml
@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.x"]
     steps:
       - uses: actions/checkout@v4
 
@@ -29,10 +29,29 @@ jobs:
           environments: dev # Specify the environment to install and activate
 
       - name: Check linting 
-        run: pixi run -e dev --frozen lint 
+        run: pixi run -e dev lint 
 
-      - name: Run tests 
-        run: pixi run -e dev --frozen test 
+
+      - name: Run tests (Python 3.9)
+        run: pixi run -e py39 test 
+
+      - name: Run tests (Python 3.10)
+        run: pixi run -e py310 test 
+
+      - name: Run tests (Python 3.11)
+        run: pixi run -e py311 test 
+
+      - name: Run tests (Python 3.12)
+        run: pixi run -e py312 test 
+
+      - name: Run tests (Python 3.13)
+        run: pixi run -e py313 test 
+
+      - name: Run tests (Python 3.14)
+        run: pixi run -e py314 test 
+
+      - name: Run tests (latest Python version)
+        run: pixi run -e dev test 
 
       - name: ⬆️ Upload build artifacts
         uses: actions/upload-artifact@v4
@@ -53,12 +72,12 @@ jobs:
           environments: dev # Specify the environment to install and activate
 
       - name: Build docs using sphinx
-        run: pixi run -e dev --frozen docs 
+        run: pixi run -e dev docs 
 
       - name: 💾 Build distribution files
         shell: bash -el {0}
         run: | 
-          pixi run -e dev --frozen build
+          pixi run -e dev build
 
       - name: ⬆️ Upload build artifacts
         uses: actions/upload-artifact@v4

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -18,7 +18,11 @@ build: Changes to the build process or tools.
 
 # Changelog
 
-## v1.0.9 (YYYY-MM-DD)
+## v2.0.0 (YYYY-MM-DD)
+
+### Feat 
+
+- Added prediction error histogram
 
 ### Fix 
 
@@ -28,22 +32,27 @@ build: Changes to the build process or tools.
 
 ### Test 
 
-- Updated optimization tests to reflect line search changes
-- Updated unit tests to reflect changes to synthetic data functions  
-- Updated unit tests to reflect changes to `Parameters.load()` method  
+- Updated unit tests to reflect refactoring changes 
+- Added testing for all supported Python versions (leveraging pixi)
 
 ### Build
 
 - Added `pixi.toml` (rather than putting everything in `pyproject.toml`)
-- Updated CI worflow 
+- Cleanup CI and updated worflow to test all supported python versions using pixi 
 
 ### Docs
 
 - Simplified CONTRIBUTING 
-- Updated docs to include API changes
+- Updated README to reflect refactoring changes 
+- Updated docs to reflect refactoring changes
+
+### Style 
+
+- Updated linting and annotations 
 
 ### Refactor
 
+- Dropped support for Python 3.8 (because SMT no longer supports it and it doesn't handle annotations as well)
 - Changed API by moving module `model.py` into `core` 
 - Changed API by moving module `synthetic.py` into `synthetic_data` (synthetic functions are now modules not classes)
 - Changed API by moving module `plot` into `post_processing` 
@@ -55,6 +64,7 @@ build: Changes to the build process or tools.
   - _Previous pattern: `reloaded = NeuralNet(layer_sizes=[1, 2, 3]).load("save_params.json")`_
 - Converted `Parameters.load(...)` to a classmethod such that `reloaded = Parameters.load("save_params.json")`
   - _Previous pattern: `reloaded = Parameters(layer_sizes=[1, 2, 3]).load("save_params.json")`_
+- Replaced `NeuralNet.evaluate(x)` by `NeuralNet.__call__(x)`
 
 ## v1.0.8 (2024-06-26)
 

diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 Jacobian-Enhanced Neural Networks (JENN) are fully connected multi-layer
 perceptrons, whose training process is modified to predict partial 
-derivatives accurately. This is accomplished by minimizing a modified version 
+derivatives more accurately. This is accomplished by minimizing a modified version 
 of the Least Squares Estimator (LSE) that accounts for Jacobian prediction error (see [paper](https://doi.org/10.48550/arXiv.2406.09132)). 
 The main benefit of jacobian-enhancement is better accuracy with
 fewer training points compared to standard fully connected neural nets, as illustrated below. 
@@ -35,21 +35,16 @@ If you use JENN in a scientific publication, please consider citing it:
 }
 ```
 
-----
 # Main Features
 
 * Multi-Task Learning : predict more than one output with same model Y = f(X) where Y = [y1, y2, ...]
 * Jacobian prediction : analytically compute the Jacobian (_i.e._ forward propagation of dY/dX)
 * Gradient-Enhancement: minimize prediction error of partials (_i.e._ back-prop accounts for dY/dX)
 
-----
-
 # Installation
 
     pip install jenn 
 
-----
-
 # Example Usage
 
 _See [demo](./docs/examples/) notebooks for more details_
@@ -60,23 +55,26 @@ Import library:
 
 Generate example training and test data:  
 
-    x_train, y_train, dydx_train = jenn.synthetic.Sinusoid.sample(
-        m_lhs=0, 
+    x_train, y_train, dydx_train = jenn.utilities.sample(
+        f=jenn.synthetic_data.sinusoid.compute, 
+        f_prime=jenn.synthetic_data.sinusoid.compute_partials, 
+        m_random=0, 
         m_levels=4, 
         lb=-3.14, 
         ub=3.14,
     )
-    x_test, y_test, dydx_test = jenn.synthetic.Sinusoid.sample(
-        m_lhs=30, 
+    x_test, y_test, dydx_test = jenn.utilities.sample(
+        f=jenn.synthetic_data.sinusoid.compute, 
+        f_prime=jenn.synthetic_data.sinusoid.compute_partials, 
+        m_random=30, 
         m_levels=0, 
         lb=-3.14, 
         ub=3.14,
     )
 
-
 Train a model: 
 
-    nn = jenn.model.NeuralNet(
+    nn = jenn.NeuralNet(
         layer_sizes=[1, 12, 1],
     ).fit(
         x=x_train,  
@@ -88,7 +86,7 @@ Train a model:
 
  Make predictions: 
 
-    y, dydx = nn.evaluate(x)
+    y, dydx = nn(x) 
 
     # OR 
 
@@ -102,36 +100,38 @@ Save model (parameters) for later use:
 
 Reload saved parameters into new model: 
 
-    reloaded = jenn.model.NeuralNet(layer_sizes=[1, 12, 1]).load('parameters.json')
+    reloaded = jenn.NeuralNet.load('parameters.json')
 
-Optionally, if `matplotlib` is installed, import plotting utilities:  
+Check goodness of fit: 
 
-    from jenn.utils import plot
+    jenn.plot_goodness_of_fit(
+        y_true=y_test, 
+        y_pred=nn.predict(x_test), 
+        title="y (JENN)"
+    )
 
-Optionally, if `matplotlib` is installed, check goodness of fit: 
+Check goodness of fit of partials: 
 
-    plot.goodness_of_fit(
-        y_true=dydx_test[0], 
-        y_pred=nn.predict_partials(x_test)[0], 
-        title="Partial Derivative: dy/dx (JENN)"
+    jenn.plot_goodness_of_fit(
+        y_true=dydx_test, 
+        y_pred=nn.predict_partials(x_test), 
+        title="dy/dx (JENN)"
     )
 
-Optionally, if `matplotlib` is installed, show sensitivity profiles:
+Show sensitivity profiles:
 
-    plot.sensitivity_profiles(
-        f=[jenn.synthetic.Sinusoid.evaluate, nn.predict], 
+    jenn.plot_sensitivity_profiles(
+        func=[jenn.synthetic_data.sinusoid.compute, nn.predict], 
         x_min=x_train.min(), 
         x_max=x_train.max(), 
         x_true=x_train, 
         y_true=y_train, 
         resolution=100, 
-        legend=['true', 'pred'], 
+        legend_label=['true', 'pred'], 
         xlabels=['x'], 
         ylabels=['y'],
     )
 
-----
-
 # Use Case
 
 JENN is intended for the field of computer aided design, where there is often 
@@ -147,8 +147,6 @@ However, in the special case of gradient-enhanced methods, there is the addition
 are accurate which is a critical property for one important use-case: **surrogate-based optimization**. The field of 
 aerospace engineering is rich in [applications](https://doi.org/10.1002/9780470686652.eae496) of such a use-case. 
 
-----
-
 # Limitations
 
 Gradient-enhanced methods require responses to be continuous and smooth, 
@@ -157,12 +155,9 @@ is not excessive in the first place (e.g. adjoint methods), or if the need for a
 computing the partials. Users should therefore carefully weigh the benefit of 
 gradient-enhanced methods relative to the needs of their application. 
 
---- 
 # License
 Distributed under the terms of the MIT License.
 
-----
-
 # Acknowledgement
 
 This code used the code by Prof. Andrew Ng in the

diff --git a/docs/examples/.ipynb_checkpoints/demo_1_sinusoid-checkpoint.ipynb b/docs/examples/.ipynb_checkpoints/demo_1_sinusoid-checkpoint.ipynb
diff --git a/docs/examples/.ipynb_checkpoints/demo_2_rastrigin-checkpoint.ipynb b/docs/examples/.ipynb_checkpoints/demo_2_rastrigin-checkpoint.ipynb
diff --git a/docs/examples/demo_1_sinusoid.ipynb b/docs/examples/demo_1_sinusoid.ipynb
diff --git a/docs/examples/demo_2_rastrigin.ipynb b/docs/examples/demo_2_rastrigin.ipynb
diff --git a/docs/examples/demo_3_airfoil.ipynb b/docs/examples/demo_3_airfoil.ipynb
@@ -316,7 +316,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.9"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,

diff --git a/docs/examples/demo_4_rosenbrock.ipynb b/docs/examples/demo_4_rosenbrock.ipynb
@@ -636,7 +636,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.9"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,

diff --git a/docs/pics/example_goodness_of_fit.png b/docs/pics/example_goodness_of_fit.png
diff --git a/docs/pics/example_sensitivity_profile.png b/docs/pics/example_sensitivity_profile.png
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -38,6 +38,7 @@
     "sphinx.ext.mathjax",
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
+    "sphinx_copybutton",
 ]
 templates_path = ["_templates"]
 html_static_path = ["_static"]

diff --git a/docs/source/sections/api.rst b/docs/source/sections/api.rst
@@ -20,6 +20,9 @@ Core API
 
 The core API implements all theory described in the `paper`_. This section is intended for developers.  
 
+.. automodule:: jenn.core.model
+   :members:
+
 .. automodule:: jenn.core.activation
    :members: