# Cell 1: Import des bibliothèques
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Configuration pour des graphiques lisibles
sns.set(style="whitegrid")
plt.rcParams['figure.figsize'] = (14, 6)

# Chargement des données
df = pd.read_csv("../recordings/recording_20250718_112807.csv", parse_dates=["timestamp"])

df_PatientSkid = df[df['pu'] == 'PatientSkid'].copy()

# Cellule finale : Affichage multi-PU par grandeur
import matplotlib.dates as mdates

reference_lines = {
    'Qperm': 1200,
    'Pdilute': 2.5
}

quantities = ['Qperm', 'Qdilute', 'Qdrain', 'Pro', 'Pdilute','MV07_sp']
n_quantities = len(quantities)
pus_all =pus =  ['PU_1','PU_2','PU_3']

fig, axes = plt.subplots(n_quantities, 1, figsize=(14, 3 * n_quantities), sharex=True)
fig.suptitle("Évolution des grandeurs par PU", fontsize=16)

for i, quantity in enumerate(quantities):
    ax = axes[i]
    for pu in pus_all:
        df_pu = df[df['pu'] == pu]
        if quantity in df_pu.columns:
            ax.plot(df_pu['timestamp'], df_pu[quantity], label=pu)
        if quantity in reference_lines:
            ax.axhline(reference_lines[quantity], linestyle='--', color='red')
    if quantity == 'Qdilute':
        ax.plot(df_PatientSkid['timestamp'], df_PatientSkid['QSkid'], label='QSkid')
    ax.set_ylabel(quantity)
    ax.grid(True)
    ax.legend(loc='upper right')
    if i == n_quantities - 1:
        ax.set_xlabel("Timestamp")
    else:
        ax.set_xlabel("")
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M:%S"))

plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.show()

# Analyse initiale pour PU_1
df_pu_1 = df[df['pu'] == 'PU_1'].copy()
df_pu_1.sort_values('timestamp', inplace=True)
df_pu_1['delta_t'] = df_pu_1['timestamp'].diff().dt.total_seconds()
df_pu_1 = df_pu_1.iloc[1:]  # Supprimer la première valeur NaN

plt.figure('Time between messages',figsize=(10, 4))
sns.histplot(df_pu_1['delta_t'], bins=10,stat='probability')
plt.title("Time between messages for PU_1")
plt.xlabel("Timestamp")
plt.ylabel("Δt (seconds)")
plt.grid(True)
plt.tight_layout()
# plt.show()

print("Average time is ", df_pu_1['delta_t'].mean())


def plot_pu_data(pu_name):
    # Filtrage
    df_pu = df[df['pu'] == pu_name].copy()
    df_pu['timestamp'] = pd.to_datetime(df_pu['timestamp'], errors='coerce')
    df_pu = df_pu.dropna(subset=['timestamp'])

    # --------- Plot 1: Débits ---------
    flow_cols = ['Qperm', 'Qdilute', 'Qdrain', 'Qrecirc']
    available_flows = [col for col in flow_cols if col in df_pu.columns]
    if available_flows:
        fig, ax = plt.subplots(figsize=(10, 4))
        for col in available_flows:
            ax.plot(df_pu['timestamp'], df_pu[col], label=col)
        ax.plot(df_PatientSkid['timestamp'], df_PatientSkid['QSkid'],label='QSkid')
        ax.set_title(f'{pu_name} - Flow Rates')
        ax.set_xlabel("Timestamp")
        ax.set_ylabel("Flow (L/min)")
        ax.legend(loc='upper right')
        ax.grid(True)
        fig.tight_layout()
        # plt.show()

    # --------- Plot 2: Pressions ---------
    pressure_cols = ['Pro', 'Pdilute', 'Pretentate']
    available_pressures = [col for col in pressure_cols if col in df_pu.columns]
    if available_pressures:
        fig, ax = plt.subplots(figsize=(10, 4))
        for col in available_pressures:
            ax.plot(df_pu['timestamp'], df_pu[col], label=col)
        ax.set_title(f'{pu_name} - Pressures')
        ax.set_xlabel("Timestamp")
        ax.set_ylabel("Pressure (bar)")
        ax.legend(loc='upper right')
        ax.grid(True)
        fig.tight_layout()
        # plt.show()

    # --------- Plot 3: Motor Valve Positions ---------
    mv_indices = range(2, 9)  # MV02 à MV08
    fig, axes = plt.subplots(3, 3, figsize=(15, 10), sharex=True)
    fig.suptitle(f'{pu_name} - Motor Valve Positions vs Setpoints', fontsize=16)

    plot_index = 0
    for mv in mv_indices:
        mv_real = f"MV0{mv}"
        mv_sp = f"MV0{mv}_sp"
        row, col = divmod(plot_index, 3)
        ax = axes[row, col]

        if mv_real in df_pu.columns and mv_sp in df_pu.columns:
            ax.plot(df_pu['timestamp'], df_pu[mv_real], label='Actual', color='blue')
            ax.plot(df_pu['timestamp'], df_pu[mv_sp], label='Setpoint', linestyle='--', color='orange')
            ax.set_title(f"{mv_real}")
            ax.set_ylabel("Position (%)")
            ax.grid(True)
            if row == 2:
                ax.set_xlabel("Timestamp")
        else:
            ax.set_visible(False)

        plot_index += 1

    # Cacher les sous-graphiques inutilisés
    while plot_index < 9:
        row, col = divmod(plot_index, 3)
        axes[row, col].set_visible(False)
        plot_index += 1

    handles, labels = axes[0][0].get_legend_handles_labels()
    fig.legend(handles, labels, loc='upper right')
    fig.tight_layout(rect=[0, 0, 1, 0.96])
    # plt.show()


# Cell final : Affichage pour tous les PU
pus = df['pu'].dropna().unique()
print("PU disponibles :", pus)
pus =  ['PU_1']
for pu in pus:
    print(f"\n--- Data for {pu} ---\n")
    plot_pu_data(pu)
plt.show()