python for reading data from recording

read_recording.py

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+# 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()
+
+