optimize dashboard

Data/show_plots.py

@@ -629,3 +629,111 @@ plt.show()
 
 
 
+
+
+# %% Dashboard 
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+import numpy as np
+
+# Load the data
+file_path = '/home/shahin/Lab/Doktorarbeit/Barcelona/Data/Join_edssandsub.tsv'
+df = pd.read_csv(file_path, sep='\t')
+
+# Rename columns to remove 'result.' prefix and handle spaces
+column_mapping = {}
+for col in df.columns:
+    if col.startswith('result.'):
+        new_name = col.replace('result.', '')
+        # Handle spaces in column names (replace with underscores if needed)
+        new_name = new_name.replace(' ', '_')
+        column_mapping[col] = new_name
+df = df.rename(columns=column_mapping)
+
+# Convert MedDatum to datetime
+df['MedDatum'] = pd.to_datetime(df['MedDatum'])
+
+# Check what columns actually exist in the dataset
+print("Available columns:")
+print(df.columns.tolist())
+print("\nFirst few rows:")
+print(df.head())
+
+# Hardcode specific patient names
+patient_names = ['6ccda8c6']
+
+# Define the functional systems (columns to plot) - adjust based on actual column names
+functional_systems = ['EDSS', 'Visual', 'Sensory', 'Motor', 'Brainstem', 'Cerebellar', 'Autonomic', 'Bladder', 'Intellectual']
+
+# Create subplots horizontally (2 columns, adjust rows as needed)
+num_plots = len(functional_systems)
+num_cols = 2
+num_rows = (num_plots + num_cols - 1) // num_cols  # Ceiling division
+
+fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 4*num_rows), sharex=True)
+if num_plots == 1:
+    axes = [axes]
+elif num_rows == 1:
+    axes = axes
+else:
+    axes = axes.flatten()
+
+# Plot for the hardcoded patient
+for i, system in enumerate(functional_systems):
+    # Filter data for this specific patient
+    patient_data = df[df['unique_id'] == patient_names[0]].sort_values('MedDatum')
+
+    # Check if patient data exists
+    if patient_data.empty:
+        print(f"No data found for patient: {patient_names[0]}")
+        continue
+
+    # Check if the system column exists in the data
+    if system in patient_data.columns:
+        # Plot the specific functional system
+        if not patient_data[system].isna().all():
+            axes[i].plot(patient_data['MedDatum'], patient_data[system], marker='o', linewidth=2, label=system)
+            axes[i].set_ylabel('Score')
+            axes[i].set_title(f'Functional System: {system}')
+            axes[i].grid(True, alpha=0.3)
+            axes[i].legend()
+        else:
+            axes[i].set_title(f'Functional System: {system} (No data)')
+            axes[i].set_ylabel('Score')
+            axes[i].grid(True, alpha=0.3)
+    else:
+        # Try to find column with similar name (case insensitive)
+        found_column = None
+        for col in df.columns:
+            if system.lower() in col.lower():
+                found_column = col
+                break
+
+        if found_column:
+            print(f"Found similar column: {found_column}")
+            if not patient_data[found_column].isna().all():
+                axes[i].plot(patient_data['MedDatum'], patient_data[found_column], marker='o', linewidth=2, label=found_column)
+                axes[i].set_ylabel('Score')
+                axes[i].set_title(f'Functional System: {system} (found as: {found_column})')
+                axes[i].grid(True, alpha=0.3)
+                axes[i].legend()
+        else:
+            axes[i].set_title(f'Functional System: {system} (Column not found)')
+            axes[i].set_ylabel('Score')
+            axes[i].grid(True, alpha=0.3)
+
+# Hide empty subplots
+for i in range(len(functional_systems), len(axes)):
+    axes[i].set_visible(False)
+
+# Set x-axis label for the last row only
+for i in range(len(functional_systems)):
+    if i >= len(axes) - num_cols:  # Last row
+        axes[i].set_xlabel('Date')
+
+plt.tight_layout()
+plt.show()
+
+##