init

app.py

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+from fmpy import *
+from fmpy import read_model_description, extract
+from fmpy.fmi2 import FMU2Slave
+import numpy as np
+import shutil
+import pandas as pd
+import random
+import plotly.graph_objects as go
+def add_noise(min, max, std, mean):
+        return np.clip(np.random.normal(mean, std), min, max)
+
+def gen_pulsive_noise(count, min_val, max_val):
+    pulse = 0
+    n = count // 100  # Use integer division to get the floor of count / 100
+    start = 100 * n
+    end = start + 50
+    if (count > start and count < end and n % 15 == 0):
+        pulse = random.randint(min_val, max_val)
+    
+    return pulse
+def simulation(input_noise, output_noise, min_val, max_val, kp, ki):
+    vrs = {}
+    fmu = 'Pharmacokinetics_4_comportmental_model_PI_ref_FMU_base4_OAAS_lnx.fmu'
+    model_description = read_model_description(fmu)
+    for variable in model_description.modelVariables:
+        vrs[variable.name] = variable.valueReference
+    start_time = 0.0
+    stop_time = 7000
+    step_size = 1
+    unzipdir = extract(fmu)
+
+    fmu = FMU2Slave(guid=model_description.guid,
+                    unzipDirectory=unzipdir,
+                    modelIdentifier=model_description.coSimulation.modelIdentifier,
+                    instanceName='instance1')
+
+    # initialize
+    fmu.instantiate()
+    fmu.setupExperiment(startTime=start_time)
+    fmu.enterInitializationMode()
+    fmu.exitInitializationMode()
+
+    fmu.setReal([vrs["amesim_interface.Age_year"]], [60])
+    fmu.setReal([vrs["amesim_interface.BIS0"]], [95.6])
+    fmu.setReal([vrs["amesim_interface.BISmin"]], [8.9])
+    fmu.setReal([vrs["amesim_interface.Drug_concentration_mgmL"]], [20])
+    fmu.setReal([vrs["amesim_interface.EC50"]], [2.23])
+    fmu.setReal([vrs["amesim_interface.Gamma"]], [1.58])
+    fmu.setReal([vrs["amesim_interface.Gender_0male_1female"]], [1])
+    fmu.setReal([vrs["amesim_interface.Height_cm"]], [168])
+    fmu.setReal([vrs["amesim_interface.Infusion_rate_mLh"]], [200])
+    fmu.setReal([vrs["amesim_interface.Weight_kg"]], [75])
+    vr_input = vrs["amesim_interface.Infusion_rate_mLh"]
+    vr_output = vrs["amesim_interface.BIS_Index"]
+
+    
+    rows = []  # list to record the results
+    time = start_time
+    infusion_rate = 200
+    i = 0
+    target = 30
+    # simulation loop
+    while time < stop_time:
+
+        if time >= 2.4e3 and time < 4.5e3:
+            target = 70
+            p = 0
+            i = 0
+        if time >= 4.5e3:
+            target = 30
+            p = 0
+            i = 0
+
+        bis = fmu.getReal([int(vr_output)])[0] if time > step_size else 95.6
+        if input_noise:
+            bis += add_noise(5, 10, 2, 7)
+        p = bis - target
+        i = i + p
+        infusion_rate = np.clip(kp*p + ki*i, 0, 200)
+        # infusion_rate += gen_pulsive_noise(time, 50, 150)
+        if output_noise:
+            infusion_rate += gen_pulsive_noise(time, min_val, max_val)
+        
+        fmu.setReal([vr_input], [int(infusion_rate)])
+            
+        # perform one step
+        fmu.doStep(currentCommunicationPoint=time, communicationStepSize=step_size)
+
+        # advance the time
+        time += step_size
+        # get the values for 'inputs' and 'outputs[4]'
+        inputs, outputs = fmu.getReal([int(vr_input), int(vr_output)])
+
+        # append the results
+        rows.append((time, bis, inputs))
+
+    fmu.terminate()
+    fmu.freeInstance()
+    shutil.rmtree(unzipdir, ignore_errors=True)
+    result = np.array(rows, dtype=np.dtype([('time', np.float64), ('BIS', np.float64), ('Infusion', np.float64)]))
+    df = pd.DataFrame(result)  
+    trace1 = go.Scatter(x=df.index, y=df['BIS'], mode='lines', name='BIS')
+    fig1 = go.Figure(data=trace1)
+    fig1.update_layout(height=300, width=900, title_text="BIS evolution")
+
+    # Add a line trace for column_2 in the second subplot
+    trace2 = go.Scatter(x=df.index, y=df['Infusion'], mode='lines', name='Infusion')
+    fig2 = go.Figure(data=trace2)
+    fig2.update_layout(height=300, width=900, title_text="Infusion evolution")
+
+    return fig1, fig2  
+import gradio as gr 
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("**BIS noise**")
+            input_noise = gr.inputs.Checkbox(label="Add noise")
+            gr.Markdown("**Infusion noise**")
+            with gr.Blocks():
+                output_noise = gr.inputs.Checkbox(label="Add noise")
+                with gr.Accordion("noise range"):
+                    min_slider = gr.inputs.Slider(minimum=0, maximum=50, step=1, default=50, label="noise min")
+                    max_slider = gr.inputs.Slider(minimum=0, maximum=150, step=1, default=150, label="noise max")
+            gr.Markdown("**PI controller paramters**")
+            with gr.Blocks():
+                kp_slider = gr.inputs.Slider(minimum=0, maximum=19, default=4, label="kp")
+                ki_slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.01, label="ki")
+            button = gr.Button("Simulate")
+        with gr.Column(scale=5):
+            plot1 = gr.Plot(label="BIS evolution")
+            plot2 = gr.Plot(label="Infusion evolution")
+    button.click(simulation, inputs=[input_noise, output_noise, min_slider, max_slider, kp_slider, ki_slider], outputs=[plot1, plot2])
+demo.launch()