Update of tutorial

Reviewed-on: #1

.gitea/workflows/test.yaml

@@ -20,6 +20,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
+        python -m pip install bec-lib
         python -m pip install .
         pip install flake8 pytest
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
@@ -32,11 +33,3 @@ jobs:
     - name: Test with pytest
       run: |
         python -m pytest -vv tests
-
-  release-test:
-    needs: [build_test]
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checking that the above worked
-      run: | 
-       echo "🎉 The job was automatically triggered by the success of build_test."

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools >= 77.0.3"]
+requires = ["setuptools"]# >= 77.0.3"]
 build-backend = "setuptools.build_meta"
 
 [tool.setuptools]
@@ -8,9 +8,9 @@ package-dir = {"" = "src"}
 
 [project]
 name = "AMBER-ds4ms"
-version = "0.0.4"
+version = "1.0.1"
 dependencies = [
-  "numpy>=2",
+  "numpy>=1.14",
   "scipy>=1.7",
   "torch>=2",
   "matplotlib>=3.4",
@@ -27,7 +27,7 @@ maintainers = [
 ]
 description = "AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence"
 readme = "README.md"
-license-files = ["LICENSE"]
+#license-files = ["LICENSE"]
 keywords = ["Machine Learning", "Signal Segmentation", "Background Determination"]
 classifiers = [
   "Development Status :: 4 - Beta",

source/conf.py

@@ -9,14 +9,15 @@
 project = 'AMBER'
 copyright = '2025, J. Lass, V. Cohen, B. B. Haro, & D. G. Mazzone'
 author = 'J. Lass, V. Cohen, B. B. Haro, & D. G. Mazzone'
-release = '0.0.4'
+release = '1.0.1'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
     'nbsphinx', # allows notebooks
-    'myst_parser' # allows markdown
+    'myst_parser', # allows markdown
+    'IPython.sphinxext.ipython_console_highlighting',
 ]
 source_suffix = {
     '.rst': 'restructuredtext',
@@ -24,7 +25,7 @@ source_suffix = {
     '.md': 'markdown',
 }
 templates_path = ['_templates']
-exclude_patterns = []
+exclude_patterns = ['**.ipynb_checkpoints']
 
 
 

src/AMBER/background.py

@@ -612,11 +612,11 @@ class background():
         Me = self.set_e_design_matrix(mu_)
 
         # Loss function
-        loss = np.zeros(n_epochs, dtype=self.dtype)
-        loss[1] = 1.0
-        k = 1
-
-        while (np.abs(loss[k] - loss[k-1]) > 1e-3) and (k < n_epochs-1):
+        loss = []#np.zeros(n_epochs, dtype=self.dtype)
+        old_loss = 0
+        new_loss = 0
+        k = 0
+        while ((old_loss - new_loss > 1e-3) and (k < n_epochs)) or k < 2:
             # Compute A = Y - B by filling the nans with 0s
             A = np.where(np.isnan(Y_r - b_tmp) == True, 0.0, Y_r - b_tmp)
 
@@ -640,17 +640,18 @@ class background():
             b_tmp = self.R_operator(self.b)
 
             # ######################### Compute loss function ##################
-            loss[k] = 0.5 * np.nansum((Y_r - self.X - b_tmp) ** 2) + lambda_ * np.nansum(np.abs(self.X))
+            loss.append(0.5 * np.nansum((Y_r - self.X - b_tmp) ** 2) + lambda_ * np.nansum(np.abs(self.X)))
 
             for e in range(self.E_size):
-                loss[k] += (beta_/2) * np.matmul(self.b[e, :], np.matmul(Lb_lst[e], self.b[e, :].T))
+                loss[-1] += (beta_/2) * np.matmul(self.b[e, :], np.matmul(Lb_lst[e], self.b[e, :].T))
 
-            loss[k] += (mu_ / 2) * np.trace(np.matmul(self.X.T, np.matmul(Le, self.X)))
+            loss[-1] += (mu_ / 2) * np.trace(np.matmul(self.X.T, np.matmul(Le, self.X)))
 
             if verbose:
-                print(" Iteration ", str(k))
-                print(" Loss function: ", loss[k].item())
-
+                print(" Iteration ", str(k+1))
+                print(" Loss function: ", loss[-1].item())
+            old_loss = new_loss
+            new_loss = loss[-1]
             k += 1
 
         # Compute the propagated background