Return ValueError for mismatched samples, UserWarning for missing dat…

…a columns

.github/workflows/main.yml

@@ -2,10 +2,14 @@ name: Test and publish to PyPI
 
 on:
   push:
-    branches: [ main ]
+    branches:
+      - main
+      - development
     tags: [ 'v*.*.*' ]
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - development
   release:
     types: [ created ]
 
@@ -66,7 +70,8 @@ jobs:
         python -m pip install --upgrade pip;
         python -m pip install build
         pip install setuptools wheel twine
-        python setup.py sdist bdist_wheel
+        python -m build
+        # python setup.py sdist bdist_wheel
     - uses: actions/upload-artifact@v2
       with:
         name: dist

docs/Changelog.rst

@@ -2,6 +2,12 @@
 Change Log
 ==========
 
+
+Version 0.6.2
+=============
+Return ValueError for mismatched samples in peak height and composition DataFrames, UserWarning for missing data columns in composition DataFrame.
+
+
 Version 0.6.1
 =============
 Update composition-dependent epsilon inversions to add Shi et al., 2024 values for H2Ot, 3550 and carbonate, slight correction. Correct data export paths in calculate_baselines and calculate_concentrations. Update PC vector creation.

src/PyIRoGlass/_version.py

@@ -5,4 +5,4 @@
 # 1) we don't load dependencies by storing it in __init__.py
 # 2) we can import it in setup.py for the same reason
 # 3) we can import it into your module
-__version__ = "0.6.1"
+__version__ = "0.6.2"

src/PyIRoGlass/core.py

@@ -115,23 +115,41 @@ def load_chemistry_thickness(self):
         chem_thickness = pd.read_csv(self.chemistry_thickness_path)
         chem_thickness.set_index("Sample", inplace=True)
 
+        oxides = [
+            "SiO2",
+            "TiO2",
+            "Al2O3",
+            "Fe2O3",
+            "FeO",
+            "MnO",
+            "MgO",
+            "CaO",
+            "Na2O",
+            "K2O",
+            "P2O5",
+        ]
+
+        thicknesses = [
+            "Thickness",
+            "Sigma_Thickness"
+        ]
+
+        missing_columns = [col for col in oxides+thicknesses if col 
+                           not in chem_thickness.columns]
+        if missing_columns:
+            warnings.warn(f"Missing required columns: {missing_columns}. "
+                          f"Column created and filled with NaN. "
+                          f"Please correct the values.",
+                          UserWarning,
+                          stacklevel=2)
+            for col in missing_columns:
+                chem_thickness[col] = np.nan
+
         chemistry = chem_thickness.loc[
             :,
-            [
-                "SiO2",
-                "TiO2",
-                "Al2O3",
-                "Fe2O3",
-                "FeO",
-                "MnO",
-                "MgO",
-                "CaO",
-                "Na2O",
-                "K2O",
-                "P2O5",
-            ],
+            oxides,
         ].fillna(0)
-        thickness = chem_thickness.loc[:, ["Thickness", "Sigma_Thickness"]]
+        thickness = chem_thickness.loc[:, thicknesses]
 
         self.chemistry = chemistry
         self.thickness = thickness
@@ -1281,8 +1299,8 @@ def calculate_epsilon(composition, T, P):
     or glass composition dataset.
 
     Parameters:
-        composition (dictionary): Dictionary containing the weight percentages
-            of each oxide in the glass composition
+        composition (pd.DataFrame): Dataframe containing oxide weight
+            percentages for the glass composition.
         T (int): temperature at which the density is calculated (in Celsius)
         P (int): pressure at which the density is calculated (in bars)
 
@@ -1416,8 +1434,8 @@ def calculate_concentrations(Volatile_PH, composition, thickness,
             of total H2O (3550 cm^-1), molecular H2O (1635 cm^-1), and
             carbonate peaks (1515 and 1430 cm^-1). Each row represents a
             different sample.
-        composition (dictionary): Dictionary with keys as oxide names and
-            values as their weight percentages in the glass composition.
+        composition (pd.DataFrame): Dataframe containing oxide weight
+            percentages for the glass composition.
         thickness (pd.DataFrame): DataFrame with "Thickness" column
             indicating wafer thickness in micrometers (µm) for each sample.
         N (int): Number of Monte Carlo simulations to perform for uncertainty
@@ -1442,6 +1460,17 @@ def calculate_concentrations(Volatile_PH, composition, thickness,
         the accuracy of the results.
     """
 
+    # Check if sample names match between Volatile_PH and composition
+    Volatile_PH_samples = set(Volatile_PH.index)
+    composition_samples = set(composition.index)
+    mismatched_samples = Volatile_PH_samples.symmetric_difference(composition_samples)
+
+    if mismatched_samples:
+        raise ValueError(
+            f"Sample names in Volatile_PH and composition do not match. "
+            f"Mismatched samples: {mismatched_samples}"
+        )
+
     if export_path is not None:
 
         warnings.filterwarnings("ignore", category=DeprecationWarning)