Adding Date to Dashboard

.gitignore

@@ -7,10 +7,6 @@
 __pycache__/
 *.pyc
 
-=======
-/reference/
-*.svg
->>>>>>> Stashed changes
 # 2. Ignore virtual environments COMPLETELY
 # This must come BEFORE the unignore rule
 env*/

Data/style2.py

@@ -0,0 +1,135 @@
+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

@@ -0,0 +1,74 @@
+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")

figure1.py

@@ -263,60 +263,3 @@ plt.legend(frameon=False, loc='upper center', bbox_to_anchor=(0.5, -0.05))
 plt.tight_layout()
 plt.show()
 ##
-
-
-
-
-# %% name
-import matplotlib.pyplot as plt
-
-# Data
-data = {
-    'Visit': [9, 8, 7, 6, 5, 4, 3, 2, 1],
-    'patient_count': [2, 3, 3, 6, 13, 17, 28, 24, 32]
-}
-
-# Create figure and axis
-fig, ax = plt.subplots(figsize=(10, 6))
-
-# Plot the bar chart
-bars = ax.bar(data['Visit'], data['patient_count'], color='darkblue', label='Patients by Visit Count')
-
-# Add labels and title
-ax.set_xlabel('Visit Number (from last to first)', fontsize=12)
-ax.set_ylabel('Number of Patients', fontsize=12)
-ax.set_title('Patient Visits by Visit Number', fontsize=14)
-
-# Invert x-axis to show Visit 9 on the left (descending order) if desired, but keep natural order (1–9 left to right)
-# For descending order (9→1 from left to right), we'd need to reverse:
-# Visit = data['Visit'][::-1], patient_count = data['patient_count'][::-1]
-# But standard practice is ascending (1 to 9), so we'll sort accordingly:
-# Let's sort by Visit to ensure left-to-right: 1,2,...,9
-
-# Actually, your current Visit list is [9,8,...,1], which is descending.
-# Let's sort by Visit for intuitive left-to-right increasing order:
-sorted_indices = sorted(range(len(data['Visit'])), key=lambda i: data['Visit'][i])
-visit_sorted = [data['Visit'][i] for i in sorted_indices]
-count_sorted = [data['patient_count'][i] for i in sorted_indices]
-
-# Re-plot with sorted x-axis:
-ax.clear()
-bars = ax.bar(visit_sorted, count_sorted, color='darkblue', label='Patients by Visit Count')
-
-# Re-apply labels, etc.
-ax.set_xlabel('Number of Visits', fontsize=12)
-ax.set_ylabel('Number of Unique Patients', fontsize=12)
-#ax.set_title('Number of Patients by Visit Number', fontsize=14)
-
-# Add legend
-ax.legend()
-
-# Improve layout and grid
-ax.grid(axis='y', linestyle='--', alpha=0.7)
-plt.xticks(visit_sorted)  # Ensure all integer visit numbers are shown
-
-# Show the plot
-plt.tight_layout()
-plt.show()
-
-##