Upload app.py

app.py

@@ -138,6 +138,34 @@ def create_sample_data() -> pd.DataFrame:
     
     return df
 
+def detect_column_names(df: pd.DataFrame) -> Dict[str, str]:
+    """Auto-detect column names for EIS data"""
+    columns = df.columns.tolist()
+    mapping = {}
+    
+    # Frequency column detection
+    freq_candidates = ['frequency', 'freq', 'f', 'Hz', 'Frequency']
+    for col in columns:
+        if any(candidate.lower() in col.lower() for candidate in freq_candidates):
+            mapping['frequency'] = col
+            break
+    
+    # Real impedance column detection
+    real_candidates = ['z_real', 'real', 'zreal', 'z\'', 'realImpedance', 'real_impedance', 'Re(Z)', 'Re_Z']
+    for col in columns:
+        if any(candidate.lower() in col.lower().replace('(', '').replace(')', '') for candidate in real_candidates):
+            mapping['z_real'] = col
+            break
+    
+    # Imaginary impedance column detection
+    imag_candidates = ['z_imag', 'imag', 'zimag', 'z\'\'', 'imagImpedance', 'imag_impedance', 'Im(Z)', 'Im_Z', '-z\'\'']
+    for col in columns:
+        if any(candidate.lower() in col.lower().replace('(', '').replace(')', '') for candidate in imag_candidates):
+            mapping['z_imag'] = col
+            break
+    
+    return mapping
+
 def analyze_eis_optimized(
     df: pd.DataFrame,
     circuit_model: str = "auto",
@@ -159,9 +187,30 @@ def analyze_eis_optimized(
             return {"error": "Insufficient memory available"}, None, None
     
     try:
-        # Prepare impedance data
-        Z = df['z_real'].values + 1j * df['z_imag'].values
-        freq = df['frequency'].values
+        # Auto-detect column names
+        column_mapping = detect_column_names(df)
+        
+        if 'frequency' not in column_mapping:
+            return {"error": "Could not find frequency column. Expected names: frequency, freq, f, Hz"}, None, None
+        if 'z_real' not in column_mapping:
+            return {"error": "Could not find real impedance column. Expected names: z_real, real, realImpedance, Re(Z)"}, None, None
+        if 'z_imag' not in column_mapping:
+            return {"error": "Could not find imaginary impedance column. Expected names: z_imag, imag, imagImpedance, Im(Z)"}, None, None
+        
+        # Prepare impedance data using detected column names
+        freq = df[column_mapping['frequency']].values
+        z_real = df[column_mapping['z_real']].values
+        z_imag = df[column_mapping['z_imag']].values
+        
+        # Handle imaginary part sign convention
+        # EIS convention: Z = Z' - jZ'' (imaginary part should be negative for typical circuits)
+        # If most imaginary values are positive, we might need to flip the sign
+        if np.mean(z_imag) > 0:
+            z_imag = -z_imag  # Flip sign to follow EIS convention
+            if progress_callback:
+                progress_callback(0.15, "Adjusted imaginary impedance sign...")
+        
+        Z = z_real + 1j * z_imag
         
         # Use optimized parameters for HF
         params = HF_OPTIMIZED_PARAMS.copy() if use_hf_params else {}
@@ -267,7 +316,9 @@ def analyze_eis_optimized(
             "fit_parameters": fitted_params if fitted_params else {},
             "fit_error": float(fit_error) if fit_error else None,
             "chi_squared": float(chi_squared) if chi_squared else None,
-            "memory_usage_mb": get_memory_usage()
+            "memory_usage_mb": get_memory_usage(),
+            "column_mapping": column_mapping,
+            "data_points": len(freq)
         }
         
         if progress_callback:
@@ -363,10 +414,13 @@ def create_interface():
                     
                 with gr.Row():
                     gr.Markdown("""
-                    **Expected CSV Format:**
-                    - Column 1: `frequency` (Hz)
-                    - Column 2: `z_real` (Ω)
-                    - Column 3: `z_imag` (Ω)
+                    **Supported CSV Formats (auto-detected):**
+                    
+                    **Frequency column**: `frequency`, `freq`, `f`, `Hz`
+                    **Real impedance**: `z_real`, `real`, `realImpedance`, `Re(Z)`  
+                    **Imaginary impedance**: `z_imag`, `imag`, `imagImpedance`, `Im(Z)`
+                    
+                    ✅ **Your file format is automatically detected!**
                     
                     Leave empty to use sample data.
                     """)
@@ -460,9 +514,24 @@ def create_interface():
                 return df.head(10), f"Memory: {get_memory_usage():.1f} MB"
             try:
                 df = pd.read_csv(file.name)
-                return df.head(10), f"Memory: {get_memory_usage():.1f} MB"
-            except:
-                return None, f"Memory: {get_memory_usage():.1f} MB"
+                # Detect column mapping
+                mapping = detect_column_names(df)
+                missing = []
+                if 'frequency' not in mapping:
+                    missing.append("frequency")
+                if 'z_real' not in mapping:
+                    missing.append("real impedance")
+                if 'z_imag' not in mapping:
+                    missing.append("imaginary impedance")
+                
+                if missing:
+                    status = f"Memory: {get_memory_usage():.1f} MB | ⚠️ Missing: {', '.join(missing)}"
+                else:
+                    status = f"Memory: {get_memory_usage():.1f} MB | ✅ All columns detected"
+                
+                return df.head(10), status
+            except Exception as e:
+                return None, f"Memory: {get_memory_usage():.1f} MB | ❌ Error: {str(e)}"
         
         def clear_all():
             gc.collect()