visuals.py

import matplotlib.pyplot as plt
import numpy as np
from analytics import moving_average, fft_analysis
import matplotlib.dates as mdates


def plot_health_trends(df):
    """Plot glucose and heart rate trends with smoothing + anomaly markers and formatted date axis."""
    fig, ax = plt.subplots(figsize=(8, 4))

    # Smoothed series
    smooth_glucose = moving_average(df["Glucose"])
    smooth_hr = moving_average(df["Heart Rate"])

    # --- Glucose ---
    ax.plot(df.index, df["Glucose"], label="Glucose (raw)", alpha=0.3)
    ax.plot(df.index, smooth_glucose, label="Glucose (smoothed)", linewidth=2, color="tab:blue")

    # Highlight anomalies
    g_high = df["Glucose"] > 180
    g_low = df["Glucose"] < 70
    ax.scatter(df.index[g_high], df["Glucose"][g_high], color="red", label="Glucose High", zorder=5)
    ax.scatter(df.index[g_low], df["Glucose"][g_low], color="orange", label="Glucose Low", zorder=5)

    # --- Heart Rate ---
    ax.plot(df.index, df["Heart Rate"], label="Heart Rate (raw)", alpha=0.3)
    ax.plot(df.index, smooth_hr, label="Heart Rate (smoothed)", linewidth=2, color="tab:red")

    # Highlight anomalies
    h_high = df["Heart Rate"] > 100
    h_low = df["Heart Rate"] < 60
    ax.scatter(df.index[h_high], df["Heart Rate"][h_high], color="purple", label="HR High", zorder=5)
    ax.scatter(df.index[h_low], df["Heart Rate"][h_low], color="pink", label="HR Low", zorder=5)

    # Titles and labels
    ax.set_title("Health Trends with Anomalies Highlighted")
    ax.set_xlabel("Date")
    ax.set_ylabel("Levels")
    ax.legend()
    ax.grid(True)

    # --- Fix date label formatting ---
    ax.xaxis.set_major_locator(mdates.AutoDateLocator())
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
    fig.autofmt_xdate(rotation=45, ha="right")

    return fig


def plot_fft(df):
    """Plot frequency spectrum of glucose and heart rate."""
    fig, ax = plt.subplots(figsize=(8, 4))
    freqs_g, mag_g = fft_analysis(df["Glucose"])
    freqs_h, mag_h = fft_analysis(df["Heart Rate"])

    ax.plot(freqs_g, mag_g, label="Glucose Frequency Spectrum", color="tab:blue")
    ax.plot(freqs_h, mag_h, label="Heart Rate Frequency Spectrum", color="tab:red")

    ax.set_title("Frequency Analysis (FFT)")
    ax.set_xlabel("Frequency (1/day)")
    ax.set_ylabel("Signal Strength")
    ax.legend()
    ax.grid(True)
    return fig


def generate_anomaly_summary(df):
    """
    Generates a short textual summary of glucose and heart rate anomalies.
    Returns a string.
    """
    high_glucose = (df["Glucose"] > 180).sum()
    low_glucose = (df["Glucose"] < 70).sum()
    high_hr = (df["Heart Rate"] > 100).sum()
    low_hr = (df["Heart Rate"] < 60).sum()

    summary = []
    if high_glucose > 0:
        summary.append(f"⚠ {high_glucose} high glucose readings detected.")
    if low_glucose > 0:
        summary.append(f"⚠ {low_glucose} low glucose readings detected.")
    if high_hr > 0:
        summary.append(f"💓 {high_hr} elevated heart rate readings detected.")
    if low_hr > 0:
        summary.append(f"💤 {low_hr} low heart rate readings detected.")

    if not summary:
        summary.append("✅ No major anomalies detected — great job maintaining stability!")

    return " ".join(summary)