add all .py files

.gitignore

@@ -1,16 +1,7 @@
-# 1. Broad Ignores
+# Ignore all contents of these directories
-/Data/*
-/attach/*
-/results/*
-/enarcelona/*
-.env
-__pycache__/
-*.pyc
-
-# 2. Ignore virtual environments COMPLETELY
-# This must come BEFORE the unignore rule
-env*/
-
-# 3. The "Unignore" rule (Whitelisting)
-# We only unignore .py files that aren't already blocked by the rules above
 !**/*.py
+/Data/
+/attach/
+/results/
+/enarcelona/
+.env

Data/show_plots.py

@@ -1,570 +0,0 @@
-# %% Scatter
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-
-# Load your data from TSV file
-file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
-df = pd.read_csv(file_path, sep='\t')
-
-# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
-df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
-df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
-
-# Convert to float (handle invalid entries gracefully)
-df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
-df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
-
-# Drop rows where either column is NaN
-df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
-
-# Create scatter plot
-plt.figure(figsize=(8, 6))
-plt.scatter(df_clean['GT_EDSS'], df_clean['LLM_Results'], alpha=0.7, color='blue')
-
-# Add labels and title
-plt.xlabel('GT_EDSS')
-plt.ylabel('LLM_Results')
-plt.title('Comparison of GT_EDSS vs LLM_Results')
-
-# Optional: Add a diagonal line for reference (perfect prediction)
-plt.plot([0, max(df_clean['GT_EDSS'])], [0, max(df_clean['GT_EDSS'])], color='red', linestyle='--', label='Perfect Prediction')
-plt.legend()
-
-# Show plot
-plt.grid(True)
-plt.tight_layout()
-plt.show()
-
-##
-
-
-# %% Bland0-altman
-
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-import statsmodels.api as sm
-
-# Load your data from TSV file
-file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_results+MS_Briefe_400_with_unique_id_SHA3_explore_cleaned.tsv'
-df = pd.read_csv(file_path, sep='\t')
-
-# Replace comma with dot for numeric conversion in GT_EDSS and LLM_Results
-df['GT_EDSS'] = df['GT_EDSS'].astype(str).str.replace(',', '.')
-df['LLM_Results'] = df['LLM_Results'].astype(str).str.replace(',', '.')
-
-# Convert to float (handle invalid entries gracefully)
-df['GT_EDSS'] = pd.to_numeric(df['GT_EDSS'], errors='coerce')
-df['LLM_Results'] = pd.to_numeric(df['LLM_Results'], errors='coerce')
-
-# Drop rows where either column is NaN
-df_clean = df.dropna(subset=['GT_EDSS', 'LLM_Results'])
-
-# Create Bland-Altman plot
-f, ax = plt.subplots(1, figsize=(8, 5))
-sm.graphics.mean_diff_plot(df_clean['GT_EDSS'], df_clean['LLM_Results'], ax=ax)
-
-# Add labels and title
-ax.set_title('Bland-Altman Plot: GT_EDSS vs LLM_Results')
-ax.set_xlabel('Mean of GT_EDSS and LLM_Results')
-ax.set_ylabel('Difference between GT_EDSS and LLM_Results')
-
-# Display Bland-Altman plot
-plt.tight_layout()
-plt.show()
-
-# Print some statistics
-mean_diff = np.mean(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
-std_diff = np.std(df_clean['GT_EDSS'] - df_clean['LLM_Results'])
-print(f"Mean difference: {mean_diff:.3f}")
-print(f"Standard deviation of differences: {std_diff:.3f}")
-print(f"95% Limits of Agreement: [{mean_diff - 1.96*std_diff:.3f}, {mean_diff + 1.96*std_diff:.3f}]")
-
-##
-
-
-
-# %%  Confusion matrix
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.metrics import confusion_matrix, classification_report
-import seaborn as sns
-
-# Load your data from TSV file
-file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
-df = pd.read_csv(file_path, sep='\t')
-
-# Replace comma with dot for numeric conversion in GT.EDSS and result.EDSS
-df['GT.EDSS'] = df['GT.EDSS'].astype(str).str.replace(',', '.')
-df['result.EDSS'] = df['result.EDSS'].astype(str).str.replace(',', '.')
-
-# Convert to float (handle invalid entries gracefully)
-df['GT.EDSS'] = pd.to_numeric(df['GT.EDSS'], errors='coerce')
-df['result.EDSS'] = pd.to_numeric(df['result.EDSS'], errors='coerce')
-
-# Drop rows where either column is NaN
-df_clean = df.dropna(subset=['GT.EDSS', 'result.EDSS'])
-
-# For confusion matrix, we need to categorize the values
-# Let's create categories up to 10 (0-1, 1-2, 2-3, ..., 9-10)
-def categorize_edss(value):
-    if pd.isna(value):
-        return np.nan
-    elif value <= 1.0:
-        return '0-1'
-    elif value <= 2.0:
-        return '1-2'
-    elif value <= 3.0:
-        return '2-3'
-    elif value <= 4.0:
-        return '3-4'
-    elif value <= 5.0:
-        return '4-5'
-    elif value <= 6.0:
-        return '5-6'
-    elif value <= 7.0:
-        return '6-7'
-    elif value <= 8.0:
-        return '7-8'
-    elif value <= 9.0:
-        return '8-9'
-    elif value <= 10.0:
-        return '9-10'
-    else:
-        return '10+'
-
-# Create categorical versions
-df_clean['GT.EDSS_cat'] = df_clean['GT.EDSS'].apply(categorize_edss)
-df_clean['result.EDSS_cat'] = df_clean['result.EDSS'].apply(categorize_edss)
-
-# Remove any NaN categories
-df_clean = df_clean.dropna(subset=['GT.EDSS_cat', 'result.EDSS_cat'])
-
-# Create confusion matrix
-cm = confusion_matrix(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat'],
-                     labels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
-
-# Plot confusion matrix
-plt.figure(figsize=(10, 8))
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
-            xticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'],
-            yticklabels=['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10'])
-plt.title('Confusion Matrix: Ground truth EDSS vs interferred EDSS (Categorized 0-10)')
-plt.xlabel('LLM Generated EDSS')
-plt.ylabel('Ground Truth EDSS')
-plt.tight_layout()
-plt.show()
-
-# Print classification report
-print("Classification Report:")
-print(classification_report(df_clean['GT.EDSS_cat'], df_clean['result.EDSS_cat']))
-
-# Print raw counts
-print("\nConfusion Matrix (Raw Counts):")
-print(cm)
-
-##
-
-
-
-# %% Classification 
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.metrics import confusion_matrix
-import numpy as np
-
-# Load your data from TSV file
-file_path ='/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
-
-df = pd.read_csv(file_path, sep='\t')
-
-# Check data structure
-print("Data shape:", df.shape)
-print("First few rows:")
-print(df.head())
-print("\nColumn names:")
-for col in df.columns:
-    print(f"  {col}")
-
-# Function to safely convert to boolean
-def safe_bool_convert(series):
-    '''Safely convert series to boolean, handling various input formats'''
-    # Convert to string first, then to boolean
-    series_str = series.astype(str).str.strip().str.lower()
-
-    # Handle different true/false representations
-    bool_map = {
-        'true': True, '1': True, 'yes': True, 'y': True,
-        'false': False, '0': False, 'no': False, 'n': False
-    }
-
-    converted = series_str.map(bool_map)
-
-    # Handle remaining NaN values
-    converted = converted.fillna(False)  # or True, depending on your preference
-
-    return converted
-
-# Convert columns safely
-if 'result.klassifizierbar' in df.columns:
-    print("\nresult.klassifizierbar column info:")
-    print(df['result.klassifizierbar'].head(10))
-    print("Unique values:", df['result.klassifizierbar'].unique())
-
-    df['result.klassifizierbar'] = safe_bool_convert(df['result.klassifizierbar'])
-    print("After conversion:")
-    print(df['result.klassifizierbar'].value_counts())
-
-if 'GT.klassifizierbar' in df.columns:
-    print("\nGT.klassifizierbar column info:")
-    print(df['GT.klassifizierbar'].head(10))
-    print("Unique values:", df['GT.klassifizierbar'].unique())
-
-    df['GT.klassifizierbar'] = safe_bool_convert(df['GT.klassifizierbar'])
-    print("After conversion:")
-    print(df['GT.klassifizierbar'].value_counts())
-
-# Create bar chart showing only True values for klassifizierbar
-if 'result.klassifizierbar' in df.columns and 'GT.klassifizierbar' in df.columns:
-    # Get counts for True values only
-    llm_true_count = df['result.klassifizierbar'].sum()
-    gt_true_count = df['GT.klassifizierbar'].sum()
-
-    # Plot using matplotlib directly
-    fig, ax = plt.subplots(figsize=(8, 6))
-
-    x = np.arange(2)
-    width = 0.35
-
-    bars1 = ax.bar(x[0] - width/2, llm_true_count, width, label='LLM', color='skyblue', alpha=0.8)
-    bars2 = ax.bar(x[1] + width/2, gt_true_count, width, label='GT', color='lightcoral', alpha=0.8)
-
-    # Add value labels on bars
-    ax.annotate(f'{llm_true_count}',
-                xy=(x[0], llm_true_count),
-                xytext=(0, 3),
-                textcoords="offset points",
-                ha='center', va='bottom')
-
-    ax.annotate(f'{gt_true_count}',
-                xy=(x[1], gt_true_count),
-                xytext=(0, 3),
-                textcoords="offset points",
-                ha='center', va='bottom')
-
-    ax.set_xlabel('Classification Status (klassifizierbar)')
-    ax.set_ylabel('Count')
-    ax.set_title('True Values Comparison: LLM vs GT for "klassifizierbar"')
-    ax.set_xticks(x)
-    ax.set_xticklabels(['LLM', 'GT'])
-    ax.legend()
-
-    plt.tight_layout()
-    plt.show()
-
-# Create confusion matrix if both columns exist
-if 'result.klassifizierbar' in df.columns and 'GT.klassifizierbar' in df.columns:
-    try:
-        # Ensure both columns are boolean
-        llm_bool = df['result.klassifizierbar'].fillna(False).astype(bool)
-        gt_bool = df['GT.klassifizierbar'].fillna(False).astype(bool)
-
-        cm = confusion_matrix(gt_bool, llm_bool)
-
-        # Plot confusion matrix
-        fig, ax = plt.subplots(figsize=(8, 6))
-        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
-                   xticklabels=['False ', 'True '],
-                   yticklabels=['False', 'True '],
-                   ax=ax)
-        ax.set_xlabel('LLM Predictions ')
-        ax.set_ylabel('GT Labels ')
-        ax.set_title('Confusion Matrix: LLM vs GT for "klassifizierbar"')
-
-        plt.tight_layout()
-        plt.show()
-
-        print("Confusion Matrix:")
-        print(cm)
-
-    except Exception as e:
-        print(f"Error creating confusion matrix: {e}")
-
-# Show final data info
-print("\nFinal DataFrame info:")
-print(df[['result.klassifizierbar', 'GT.klassifizierbar']].info())
-
-##
-
-
-
-
-# %% Boxplot
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-
-# Load your data from TSV file
-file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/join_results_unique.tsv'
-df = pd.read_csv(file_path, sep='\t')
-
-# Replace comma with dot for numeric conversion in GT.EDSS and result.EDSS
-df['GT.EDSS'] = df['GT.EDSS'].astype(str).str.replace(',', '.')
-df['result.EDSS'] = df['result.EDSS'].astype(str).str.replace(',', '.')
-
-# Convert to float (handle invalid entries gracefully)
-df['GT.EDSS'] = pd.to_numeric(df['GT.EDSS'], errors='coerce')
-df['result.EDSS'] = pd.to_numeric(df['result.EDSS'], errors='coerce')
-
-# Drop rows where either column is NaN
-df_clean = df.dropna(subset=['GT.EDSS', 'result.EDSS'])
-
-# 1. DEFINE CATEGORY ORDER
-# This ensures the X-axis is numerically logical (0-1 comes before 1-2)
-category_order = ['0-1', '1-2', '2-3', '3-4', '4-5', '5-6', '6-7', '7-8', '8-9', '9-10', '10+']
-
-# Convert the column to a Categorical type with the specific order
-df_clean['GT.EDSS_cat'] = pd.Categorical(df_clean['GT.EDSS'].apply(categorize_edss), 
-                                         categories=category_order, 
-                                         ordered=True)
-
-plt.figure(figsize=(14, 8))
-
-# 2. ADD HUE FOR LEGEND
-# Assigning x to 'hue' allows Seaborn to generate a legend automatically
-box_plot = sns.boxplot(
-    data=df_clean, 
-    x='GT.EDSS_cat', 
-    y='result.EDSS',
-    hue='GT.EDSS_cat',  # Added hue
-    palette='viridis', 
-    linewidth=1.5,
-    legend=True         # Ensure legend is enabled
-)
-
-# 3. CUSTOMIZE PLOT
-plt.title('Distribution of result.EDSS by GT.EDSS Category', fontsize=18, pad=20)
-plt.xlabel('Ground Truth EDSS Category', fontsize=14)
-plt.ylabel('LLM Predicted EDSS', fontsize=14)
-
-# Move legend to the side or top
-plt.legend(title="EDSS Categories", bbox_to_anchor=(1.05, 1), loc='upper left')
-
-plt.xticks(rotation=45, ha='right', fontsize=10)
-plt.grid(True, axis='y', alpha=0.3)
-plt.tight_layout()
-
-plt.show()
-##
-
-
-# %% Postproccessing Column names
-
-import pandas as pd
-
-# Read the TSV file
-file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
-df = pd.read_csv(file_path, sep='\t')
-
-# Create a mapping dictionary for German to English column names
-column_mapping = {
-    'EDSS':'GT.EDSS',
-    'klassifizierbar': 'GT.klassifizierbar',
-    'Sehvermögen': 'GT.VISUAL_OPTIC_FUNCTIONS',
-    'Cerebellum': 'GT.CEREBELLAR_FUNCTIONS',
-    'Hirnstamm': 'GT.BRAINSTEM_FUNCTIONS',
-    'Sensibiliät': 'GT.SENSORY_FUNCTIONS',
-    'Pyramidalmotorik': 'GT.PYRAMIDAL_FUNCTIONS',
-    'Ambulation': 'GT.AMBULATION',
-    'Cerebrale_Funktion': 'GT.CEREBRAL_FUNCTIONS',
-    'Blasen-_und_Mastdarmfunktion': 'GT.BOWEL_AND_BLADDER_FUNCTIONS'
-}
-
-# Rename columns
-df = df.rename(columns=column_mapping)
-
-# Save the modified dataframe back to TSV file
-df.to_csv(file_path, sep='\t', index=False)
-
-print("Columns have been successfully renamed!")
-print("Renamed columns:")
-for old_name, new_name in column_mapping.items():
-    if old_name in df.columns:
-        print(f"  {old_name} -> {new_name}")
-
-
-##
-
-
-
-
-# %% Styled table
-import pandas as pd
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-import dataframe_image as dfi
-# Load data
-df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
-
-# 1. Identify all GT and result columns
-gt_columns = [col for col in df.columns if col.startswith('GT.')]
-result_columns = [col for col in df.columns if col.startswith('result.')]
-
-print("GT Columns found:", gt_columns)
-print("Result Columns found:", result_columns)
-
-# 2. Create proper mapping between GT and result columns
-# Handle various naming conventions (spaces, underscores, etc.)
-column_mapping = {}
-
-for gt_col in gt_columns:
-    base_name = gt_col.replace('GT.', '')
-
-    # Clean the base name for matching - remove spaces, underscores, etc.
-    # Try different matching approaches
-    candidates = [
-        f'result.{base_name}',  # Exact match
-        f'result.{base_name.replace(" ", "_")}',  # With underscores
-        f'result.{base_name.replace("_", " ")}',  # With spaces
-        f'result.{base_name.replace(" ", "")}',   # No spaces
-        f'result.{base_name.replace("_", "")}'    # No underscores
-    ]
-
-    # Also try case-insensitive matching
-    candidates.append(f'result.{base_name.lower()}')
-    candidates.append(f'result.{base_name.upper()}')
-
-    # Try to find matching result column
-    matched = False
-    for candidate in candidates:
-        if candidate in result_columns:
-            column_mapping[gt_col] = candidate
-            matched = True
-            break
-
-    # If no exact match found, try partial matching
-    if not matched:
-        # Try to match by removing special characters and comparing
-        base_clean = ''.join(e for e in base_name if e.isalnum() or e in ['_', ' '])
-        for result_col in result_columns:
-            result_base = result_col.replace('result.', '')
-            result_clean = ''.join(e for e in result_base if e.isalnum() or e in ['_', ' '])
-            if base_clean.lower() == result_clean.lower():
-                column_mapping[gt_col] = result_col
-                matched = True
-                break
-
-print("Column mapping:", column_mapping)
-
-# 3. Faster, vectorized computation using the corrected mapping
-data_list = []
-
-for gt_col, result_col in column_mapping.items():
-    print(f"Processing {gt_col} vs {result_col}")
-
-    # Convert to numeric, forcing errors to NaN
-    s1 = pd.to_numeric(df[gt_col], errors='coerce').astype(float)
-    s2 = pd.to_numeric(df[result_col], errors='coerce').astype(float)
-
-    # Calculate matches (abs difference <= 0.5)
-    diff = np.abs(s1 - s2)
-    matches = (diff <= 0.5).sum()
-
-    # Determine the denominator (total valid comparisons)
-    valid_count = diff.notna().sum()
-
-    if valid_count > 0:
-        percentage = (matches / valid_count) * 100
-    else:
-        percentage = 0
-
-    # Extract clean base name for display
-    base_name = gt_col.replace('GT.', '')
-
-    data_list.append({
-        'GT': base_name,
-        'Match %': round(percentage, 1)
-    })
-
-
-
-
-# 4. Prepare Data
-match_df = pd.DataFrame(data_list)
-# Clean up labels: Replace underscores with spaces and capitalize
-match_df['GT'] = match_df['GT'].str.replace('_', ' ').str.title()
-match_df = match_df.sort_values('Match %', ascending=False)
-
-# 5. Create a "Beautiful" Table using Seaborn Heatmap
-def create_luxury_table(df, output_file="edss_agreement.png"):
-    # Set the aesthetic style
-    sns.set_theme(style="white", font="sans-serif")
-
-    # Prepare data for heatmap
-    plot_data = df.set_index('GT')[['Match %']]
-
-    # Initialize the figure
-    # Height is dynamic based on number of rows
-    fig, ax = plt.subplots(figsize=(8, len(df) * 0.6))
-
-    # Create a custom diverging color map (Deep Red -> Mustard -> Emerald)
-    # This looks more professional than standard 'RdYlGn'
-    cmap = sns.diverging_palette(15, 135, s=80, l=55, as_cmap=True)
-
-    # Draw the heatmap
-    sns.heatmap(
-        plot_data,
-        annot=True,
-        fmt=".1f",
-        cmap=cmap,
-        center=85,      # Centers the color transition
-        vmin=50, vmax=100, # Range of the gradient
-        linewidths=2,
-        linecolor='white',
-        cbar=False,     # Remove color bar for a "table" look
-        annot_kws={"size": 14, "weight": "bold", "family": "sans-serif"}
-    )
-
-    # Styling the Axes (Turning the heatmap into a table)
-    ax.set_xlabel("")
-    ax.set_ylabel("")
-    ax.xaxis.tick_top() # Move "Match %" label to top
-    ax.set_xticklabels(['Agreement (%)'], fontsize=14, fontweight='bold', color='#2c3e50')
-    ax.tick_params(axis='y', labelsize=12, labelcolor='#2c3e50', length=0)
-
-    # Add a thin border around the plot
-    for _, spine in ax.spines.items():
-        spine.set_visible(True)
-        spine.set_color('#ecf0f1')
-
-    plt.title('EDSS Subcategory Consistency Analysis', fontsize=16, pad=40, fontweight='bold', color='#2c3e50')
-
-    # Add a subtle footer
-    plt.figtext(0.5, 0.0, "Tolerance: ±0.5 points",
-                wrap=True, horizontalalignment='center', fontsize=10, color='gray', style='italic')
-
-    # Save with high resolution
-    plt.tight_layout()
-    plt.savefig(output_file, dpi=300, bbox_inches='tight')
-    print(f"Beautiful table saved as {output_file}")
-
-# Execute
-create_luxury_table(match_df)
-
-
-# Run the function
-save_styled_table(match_df)
-# 6. Save as SVG
-
-plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
-print("Successfully saved agreement_table.svg")
-
-# Show plot if running in a GUI environment
-plt.show()
-
-##

Data/style2.py

@@ -1,135 +0,0 @@
-import pandas as pd
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-import dataframe_image as dfi
-# Load data
-df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
-
-# 1. Identify all GT and result columns
-gt_columns = [col for col in df.columns if col.startswith('GT.')]
-result_columns = [col for col in df.columns if col.startswith('result.')]
-
-print("GT Columns found:", gt_columns)
-print("Result Columns found:", result_columns)
-
-# 2. Create proper mapping between GT and result columns
-# Handle various naming conventions (spaces, underscores, etc.)
-column_mapping = {}
-
-for gt_col in gt_columns:
-    base_name = gt_col.replace('GT.', '')
-
-    # Clean the base name for matching - remove spaces, underscores, etc.
-    # Try different matching approaches
-    candidates = [
-        f'result.{base_name}',  # Exact match
-        f'result.{base_name.replace(" ", "_")}',  # With underscores
-        f'result.{base_name.replace("_", " ")}',  # With spaces
-        f'result.{base_name.replace(" ", "")}',   # No spaces
-        f'result.{base_name.replace("_", "")}'    # No underscores
-    ]
-
-    # Also try case-insensitive matching
-    candidates.append(f'result.{base_name.lower()}')
-    candidates.append(f'result.{base_name.upper()}')
-
-    # Try to find matching result column
-    matched = False
-    for candidate in candidates:
-        if candidate in result_columns:
-            column_mapping[gt_col] = candidate
-            matched = True
-            break
-
-    # If no exact match found, try partial matching
-    if not matched:
-        # Try to match by removing special characters and comparing
-        base_clean = ''.join(e for e in base_name if e.isalnum() or e in ['_', ' '])
-        for result_col in result_columns:
-            result_base = result_col.replace('result.', '')
-            result_clean = ''.join(e for e in result_base if e.isalnum() or e in ['_', ' '])
-            if base_clean.lower() == result_clean.lower():
-                column_mapping[gt_col] = result_col
-                matched = True
-                break
-
-print("Column mapping:", column_mapping)
-
-# 3. Faster, vectorized computation using the corrected mapping
-data_list = []
-
-for gt_col, result_col in column_mapping.items():
-    print(f"Processing {gt_col} vs {result_col}")
-
-    # Convert to numeric, forcing errors to NaN
-    s1 = pd.to_numeric(df[gt_col], errors='coerce').astype(float)
-    s2 = pd.to_numeric(df[result_col], errors='coerce').astype(float)
-
-    # Calculate matches (abs difference <= 0.5)
-    diff = np.abs(s1 - s2)
-    matches = (diff <= 0.5).sum()
-
-    # Determine the denominator (total valid comparisons)
-    valid_count = diff.notna().sum()
-
-    if valid_count > 0:
-        percentage = (matches / valid_count) * 100
-    else:
-        percentage = 0
-
-    # Extract clean base name for display
-    base_name = gt_col.replace('GT.', '')
-
-    data_list.append({
-        'GT': base_name,
-        'Match %': round(percentage, 1)
-    })
-
-
-
-# 4. Prepare Data for Plotting
-match_df = pd.DataFrame(data_list)
-match_df = match_df.sort_values('Match %', ascending=False) # Sort for better visual flow
-
-# 5. Create the Styled Gradient Table
-def style_agreement_table(df):
-    return (df.style
-        .format({'Match %': '{:.1f}%'}) # Add % sign
-        .background_gradient(cmap='RdYlGn', subset=['Match %'], vmin=50, vmax=100) # Red to Green gradient
-        .set_properties(**{
-            'text-align': 'center',
-            'font-size': '12pt',
-            'border-collapse': 'collapse',
-            'border': '1px solid #D3D3D3'
-        })
-        .set_table_styles([
-            # Style the header
-            {'selector': 'th', 'props': [
-                ('background-color', '#404040'), 
-                ('color', 'white'),
-                ('font-weight', 'bold'),
-                ('text-transform', 'uppercase'),
-                ('padding', '10px')
-            ]},
-            # Add hover effect
-            {'selector': 'tr:hover', 'props': [('background-color', '#f5f5f5')]}
-        ])
-        .set_caption("EDSS Agreement Analysis: Ground Truth vs. Results (Tolerance ±0.5)")
-    )
-
-# To display in a Jupyter Notebook:
-styled_table = style_agreement_table(match_df)
-styled_table
-
-dfi.export(styled_table, "styled_table.png")
-#styled_table.to_html("agreement_report.html")
-# 6. Save as SVG
-
-#plt.savefig("agreement_table.svg", format='svg', dpi=300, bbox_inches='tight')
-#print("Successfully saved agreement_table.svg")
-
-# Show plot if running in a GUI environment
-plt.show()
-
-

Data/styled_tables.py

@@ -1,74 +0,0 @@
-import pandas as pd
-import numpy as np
-import seaborn as sns
-
-# Sample data (replace with your actual df)
-df = pd.read_csv("/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv", sep='\t')
-
-# Identify GT and Result columns
-gt_columns = [col for col in df.columns if col.startswith('GT.')]
-result_columns = [col for col in df.columns if col.startswith('result.')]
-
-# Create mapping
-column_mapping = {}
-for gt_col in gt_columns:
-    base_name = gt_col.replace('GT.', '')
-    result_col = f'result.{base_name}'
-    if result_col in result_columns:
-        column_mapping[gt_col] = result_col
-
-# Function to compute match percentage for each GT-Result pair
-def compute_match_percentages(df, column_mapping):
-    percentages = []
-    for gt_col, result_col in column_mapping.items():
-        count = 0
-        total = len(df)
-
-        for _, row in df.iterrows():
-            gt_val = row[gt_col]
-            result_val = row[result_col]
-
-            # Handle NaN values
-            if pd.isna(gt_val) or pd.isna(result_val):
-                continue
-
-            # Handle non-numeric values
-            try:
-                gt_float = float(gt_val)
-                result_float = float(result_val)
-            except (ValueError, TypeError):
-                # Skip rows with non-numeric values
-                continue
-
-            # Check if values are within 0.5 tolerance
-            if abs(gt_float - result_float) <= 0.5:
-                count += 1
-
-        percentage = (count / total) * 100
-        percentages.append({
-            'GT_Column': gt_col,
-            'Result_Column': result_col,
-            'Match_Percentage': round(percentage, 1)
-        })
-
-    return pd.DataFrame(percentages)
-
-# Compute match percentages
-match_df = compute_match_percentages(df, column_mapping)
-
-# Create a pivot table for gradient display (optional but helpful)
-pivot_table = match_df.set_index(['GT_Column', 'Result_Column'])['Match_Percentage'].unstack(fill_value=0)
-
-# Apply gradient background
-cm = sns.light_palette("green", as_cmap=True)
-styled_table = pivot_table.style.background_gradient(cmap=cm, axis=None)
-
-# Display result
-print("Agreement Percentage Table (with gradient):")
-styled_table
-
-
-
-# Save the styled table to a file
-styled_table.to_html("agreement_report.html")
-print("Report saved to agreement_report.html")

app.py

@@ -214,8 +214,3 @@ if __name__ == "__main__":
     print(f"Results saved to {output_json}")
 ##
 
-
-
-# %% name
-eXXXXXXXX
-##

total_app.py

@@ -1,149 +0,0 @@
-import time
-import json
-import os
-from datetime import datetime
-import pandas as pd
-from openai import OpenAI
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# === CONFIGURATION ===
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL")
-MODEL_NAME = "GPT-OSS-120B"
-HEALTH_URL = f"{OPENAI_BASE_URL}/health"  # Placeholder - actual health check would need to be implemented
-CHAT_URL = f"{OPENAI_BASE_URL}/chat/completions"
-# File paths
-INPUT_CSV = "/home/shahin/Lab/Doktorarbeit/Barcelona/Data/MS_Briefe_400_with_unique_id_SHA3_explore_cleaned_unique.csv"
-EDSS_INSTRUCTIONS_PATH = "/home/shahin/Lab/Doktorarbeit/Barcelona/attach/Komplett.txt"
-#GRAMMAR_FILE = "/home/shahin/Lab/Doktorarbeit/Barcelona/attach/just_edss_schema.gbnf"
-# Initialize OpenAI client
-client = OpenAI(
-    api_key=OPENAI_API_KEY,
-    base_url=OPENAI_BASE_URL
-)
-# Read EDSS instructions from file
-with open(EDSS_INSTRUCTIONS_PATH, 'r') as f:
-    EDSS_INSTRUCTIONS = f.read().strip()
-
-# === RUN INFERENCE 2 ===
-def run_inference(patient_text, max_retries=3):
-    prompt = f'''Du bist ein medizinischer Assistent, der spezialisiert darauf ist, EDSS-Scores (Expanded Disability Status Scale) sowie alle Unterkategorien aus klinischen Berichten zu extrahieren.
-### Regeln für die Ausgabe:
-1. **Reason**: Erstelle eine prägnante Zusammenfassung (max. 400 Zeichen) der Befunde auf **DEUTSCH**, die zur Einstufung führen.
-2. **klassifizierbar**:
-   - Setze dies auf **true**, wenn ein EDSS-Wert identifiziert, berechnet oder basierend auf den klinischen Hinweisen plausibel geschätzt werden kann.
-   - Setze dies auf **false**, NUR wenn die Daten absolut unzureichend oder so widersprüchlich sind, dass keinerlei Einstufung möglich ist.
-3. **EDSS**:
-   - Dieses Feld ist **VERPFLICHTEND**, wenn "klassifizierbar" auf true steht.
-   - Es muss eine Zahl zwischen 0.0 und 10.0 sein.
-   - Versuche stets, den EDSS-Wert so präzise wie möglich zu bestimmen, auch wenn die Datenlage dünn ist (nutze verfügbare Informationen zu Gehstrecke und Funktionssystemen).
-   - Dieses Feld **DARF NICHT ERSCHEINEN**, wenn "klassifizierbar" auf false steht.
-4. **Unterkategorien**:
-   - Extrahiere alle folgenden Unterkategorien aus dem Bericht:
-   - VISUAL OPTIC FUNCTIONS (max. 6.0)
-   - BRAINSTEM FUNCTIONS (max. 6.0)
-   - PYRAMIDAL FUNCTIONS (max. 6.0)
-   - CEREBELLAR FUNCTIONS (max. 6.0)
-   - SENSORY FUNCTIONS (max. 6.0)
-   - BOWEL AND BLADDER FUNCTIONS (max. 6.0)
-   - CEREBRAL FUNCTIONS (max. 6.0)
-   - AMBULATION (max. 10.0)
-   - Jede Unterkategorie sollte eine Zahl zwischen 0.0 und der jeweiligen Obergrenze enthalten, wenn sie klassifizierbar ist
-   - Wenn eine Unterkategorie nicht klassifizierbar ist, setze den Wert auf null
-### Einschränkungen:
-- Erfinde keine Fakten, aber nutze klinische Herleitungen aus dem Bericht, um den EDSS und die Unterkategorien zu bestimmen.
-- Priorisiere die Vergabe eines EDSS-Wertes gegenüber der Markierung als nicht klassifizierbar.
-- Halte dich strikt an die JSON-Struktur.
-- Die Unterkategorien müssen immer enthalten sein, auch wenn sie null sind.
-EDSS-Bewertungsrichtlinien:
-{EDSS_INSTRUCTIONS}
-Patientenbericht:
-{patient_text}
-'''
-
-    start_time = time.time()
-    for attempt in range(max_retries + 1):
-        try:
-            response = client.chat.completions.create(
-                messages=[
-                    {
-                        "role": "system",
-                        "content": "You extract EDSS scores and all subcategories. You prioritize providing values even if data is partial, by using clinical inference."
-                    },
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
-                ],
-                model=MODEL_NAME,
-                max_tokens=2048,
-                temperature=0.0,
-                response_format={"type": "json_object"}
-            )
-            content = response.choices[0].message.content
-
-            if content is None or content.strip() == "":
-                raise ValueError("API returned empty or None response content")
-
-            parsed = json.loads(content)
-            inference_time = time.time() - start_time
-            return {
-                "success": True,
-                "result": parsed,
-                "inference_time_sec": inference_time
-            }
-
-        except Exception as e:
-            print(f"Attempt {attempt + 1} failed: {e}")
-            if attempt < max_retries:
-                time.sleep(2 ** attempt)  # Exponential backoff
-                continue
-            else:
-                print("All retries exhausted.")
-                return {
-                    "success": False,
-                    "error": str(e),
-                    "inference_time_sec": -1
-                }
-
-# === BUILD PATIENT TEXT ===
-def build_patient_text(row):
-    # Handle potential NaN or None values in the row
-    summary = str(row.get("T_Zusammenfassung", "")) if pd.notna(row.get("T_Zusammenfassung")) else ""
-    diagnoses = str(row.get("Diagnosen", "")) if pd.notna(row.get("Diagnosen")) else ""
-    clinical = str(row.get("T_KlinBef", "")) if pd.notna(row.get("T_KlinBef")) else ""
-    findings = str(row.get("T_Befunde", "")) if pd.notna(row.get("T_Befunde")) else ""
-    return "\n".join([summary, diagnoses, clinical, findings]).strip()
-if __name__ == "__main__":
-    # Read CSV file ONLY inside main block
-    df = pd.read_csv(INPUT_CSV, sep=';')
-    results = []
-    # Process each row
-    for idx, row in df.iterrows():
-        print(f"Processing row {idx + 1}/{len(df)}")
-        try:
-            patient_text = build_patient_text(row)
-            result = run_inference(patient_text)
-            # Add unique_id and MedDatum to result for tracking
-            result["unique_id"] = row.get("unique_id", f"row_{idx}")
-            result["MedDatum"] = row.get("MedDatum", None)
-            results.append(result)
-            print(json.dumps(result, indent=2, ensure_ascii=False))
-        except Exception as e:
-            print(f"Error processing row {idx}: {e}")
-            results.append({
-                "success": False,
-                "error": str(e),
-                "unique_id": row.get("unique_id", f"row_{idx}"),
-                "MedDatum": row.get("MedDatum", None)
-            })
-    # Save results to a JSON file
-    output_json = INPUT_CSV.replace(".csv", "_results_total.json")
-    with open(output_json, 'w', encoding='utf-8') as f:
-        json.dump(results, f, indent=2, ensure_ascii=False)
-    print(f"Results saved to {output_json}")
-
-