Update app.py

app.py

@@ -18,7 +18,7 @@ def gen_pulsive_noise(count, min_val, max_val):
         pulse = random.randint(min_val, max_val)
     
     return pulse
-def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul, kp, ki):
+def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul, kp, ki, kd):
     vrs = {}
     fmu = 'Pharmacokinetics_4_comportmental_model_PI_ref_FMU_base4_OAAS_lnx.fmu'
     model_description = read_model_description(fmu)
@@ -59,6 +59,7 @@ def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul,
     infusion_rate = 200
     i = 0
     target = 30
+    last_error = 0
     # simulation loop
     while time < stop_time:
 
@@ -76,8 +77,9 @@ def simulation(input_noise, output_noise, min, max, std, mean, min_pul, max_pul,
             bis += add_noise(min, max, std, mean)
         p = bis - target
         i = i + p
-        infusion_rate = np.clip(kp*p + ki*i, 0, 200)
-        # infusion_rate += gen_pulsive_noise(time, 50, 150)
+        d = p - last_error
+        last_error = p
+        infusion_rate = np.clip(kp*p + ki*i + kd*d, 0, 200)
         if output_noise:
             infusion_rate += gen_pulsive_noise(time, min_pul, max_pul)
         
@@ -130,9 +132,10 @@ with gr.Blocks() as demo:
             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")
+                kd_slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.01, label="kd")
             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_gaussian, max_gaussian, std_gaussian, mean_gaussian, min_pul, max_pul, kp_slider, ki_slider], outputs=[plot1, plot2])
+    button.click(simulation, inputs=[input_noise, output_noise, min_gaussian, max_gaussian, std_gaussian, mean_gaussian, min_pul, max_pul, kp_slider, ki_slider, kd_slider], outputs=[plot1, plot2])
 demo.launch()