Create Wearable_Data.py

pages/Wearable_Data.py

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+import streamlit as st
+import csv
+import io
+import random
+from datetime import datetime, timedelta
+
+st.title("Wearable Data Generator")
+
+# User inputs
+num_patients = st.number_input("Number of Patients", min_value=1, value=40)
+num_measurements = st.number_input("Measurements per Patient", min_value=1, value=10)
+start_date = st.date_input("Start Date", value=datetime(2024, 12, 1))
+start_time = st.time_input("Start Time", value=datetime(2024, 12, 1, 8, 0).time())
+
+st.write("This tool generates mock wearable data for patients, some of whom have cancer and may exhibit signs of chemo brain.")
+
+# Additional parameters
+cancer_rate = st.slider("Percentage of Patients with Cancer", 0, 100, 30)  # 30% by default
+chemo_brain_effect = st.slider("Chemo Brain Impact on Activity Level (in % reduction)", 0, 50, 20)
+
+# Generate data when user clicks
+if st.button("Generate Data"):
+    # Convert start_date and start_time into a datetime
+    start_datetime = datetime.combine(start_date, start_time)
+
+    # Time interval: 1 hour increments
+    time_interval = timedelta(hours=1)
+    
+    # Generate unique PRIMARY_PERSON_KEYs
+    # We'll create a pattern of keys - hex-like strings
+    base_key = "7D2E50BB328E34917766B7A"
+    patient_keys = [base_key + format(i, 'X') for i in range(1, num_patients + 1)]
+
+    # Determine which patients have cancer
+    # Randomly assign cancer to the given percentage of patients
+    num_cancer_patients = int((cancer_rate / 100) * num_patients)
+    cancer_patients = set(random.sample(patient_keys, num_cancer_patients))
+
+    # Generate data
+    # Baseline measurements
+    baseline_activity = 2000
+    baseline_heart_rate = 80
+    baseline_o2 = 98.2
+
+    # For chemo brain patients, reduce activity by a percentage
+    # e.g., chemo brain patient might have a 20% reduction in activity
+    # also maybe increase heart rate slightly
+    activity_reduction_factor = (100 - chemo_brain_effect) / 100.0
+    chemo_heart_rate_increase = 5  # Increase HR by 5 bpm to simulate stress
+
+    data_rows = []
+    
+    # Prepare timestamps
+    timestamps = [start_datetime + i * time_interval for i in range(num_measurements)]
+
+    # Generate random variability
+    for pkey in patient_keys:
+        is_cancer = pkey in cancer_patients
+        for ts in timestamps:
+            # Introduce some random variability
+            activity_var = random.randint(-300, 300)
+            hr_var = random.randint(-3, 3)
+            o2_var = random.uniform(-0.3, 0.3)
+
+            if is_cancer:
+                # Apply chemo brain effects
+                activity = int((baseline_activity + activity_var) * activity_reduction_factor)
+                heart_rate = baseline_heart_rate + hr_var + chemo_heart_rate_increase
+            else:
+                activity = baseline_activity + activity_var
+                heart_rate = baseline_heart_rate + hr_var
+
+            o2_sat = baseline_o2 + o2_var
+
+            # Ensure no negative values
+            if activity < 0:
+                activity = 0
+            if heart_rate < 50:
+                heart_rate = 50
+            if o2_sat < 90:
+                o2_sat = 90.0
+
+            data_rows.append([
+                pkey,
+                ts.strftime("%Y-%m-%d %H:%M:%S"),
+                activity,
+                heart_rate,
+                round(o2_sat, 1)
+            ])
+
+    # Create CSV in memory
+    output = io.StringIO()
+    writer = csv.writer(output)
+    writer.writerow(["PRIMARY_PERSON_KEY", "Measurement_Timestamp", "Activity_Level", "Heart_Rate", "O2_Saturation"])
+    writer.writerows(data_rows)
+
+    csv_data = output.getvalue().encode('utf-8')
+
+    st.success("Data generated successfully!")
+    st.download_button(
+        label="Download CSV",
+        data=csv_data,
+        file_name="Wearable_Data.csv",
+        mime="text/csv"
+    )