new description

.ipynb_checkpoints/app-checkpoint.py

@@ -463,7 +463,8 @@ def pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v, animate=True):
     end = (N)*60000
     if animate:
         line = ax.plot(volumes[start:(start+1)], pressures[start:(start+1)], lw=1, color='b')
-        point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5)
+        point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5)#, label='End Diastole')
+        #point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5, label='End Systole')
     else:
         line = ax.plot(volumes[start:end], pressures[start:end], lw=1, color='b')
     
@@ -523,7 +524,11 @@ description = """
 <p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa <br></p>
 <p> We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. Check out our <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>code.</a> \n \n 
 We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here. To run example predictions on samples from the <a href='https://echonet.github.io/dynamic/'>EchoNet</a> dataset, click the first button. \n \n 
-Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted by the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> hydraulic analogy model by pressing 'Run simulation.'</p>
+</p>
+"""
+
+description2 = """\n \n
+Our model uses a hydraulic analogy model of cardiac function from <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a>. Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted from the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> model, which is an ordinary differential equation. Tune parameters and press 'Run simulation.'
 """
 
 gr.Markdown(title)
@@ -551,9 +556,11 @@ with gr.Blocks() as demo:
                 Vd = gr.Number(label="Theoretical zero pressure volume (Vd) ml:")
                 Tc = gr.Number(label="Cycle duration (Tc) s:")
                 start_v = gr.Number(label="Initial volume (start_v) ml:")
-                
-            simulation_button = gr.Button("Run simulation")
+
+            
+            gr.Markdown(description2)
             
+            simulation_button = gr.Button("Run simulation")
             
             
             with gr.Row():

app.py

@@ -463,7 +463,8 @@ def pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v, animate=True):
     end = (N)*60000
     if animate:
         line = ax.plot(volumes[start:(start+1)], pressures[start:(start+1)], lw=1, color='b')
-        point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5)
+        point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5)#, label='End Diastole')
+        #point = ax.scatter(volumes[start:(start+1)], pressures[start:(start+1)], c="b", s=5, label='End Systole')
     else:
         line = ax.plot(volumes[start:end], pressures[start:end], lw=1, color='b')
     
@@ -523,7 +524,11 @@ description = """
 <p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa <br></p>
 <p> We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. Check out our <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>code.</a> \n \n 
 We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here. To run example predictions on samples from the <a href='https://echonet.github.io/dynamic/'>EchoNet</a> dataset, click the first button. \n \n 
-Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted by the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> hydraulic analogy model by pressing 'Run simulation.'</p>
+</p>
+"""
+
+description2 = """\n \n
+Our model uses a hydraulic analogy model of cardiac function from <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a>. Below you can input values of predicted parameters and output a simulated pressure-volume loop predicted from the <a href='https://ieeexplore.ieee.org/document/4729737/keywords#keywords'>Simaan et al 2008</a> model, which is an ordinary differential equation. Tune parameters and press 'Run simulation.'
 """
 
 gr.Markdown(title)
@@ -551,9 +556,11 @@ with gr.Blocks() as demo:
                 Vd = gr.Number(label="Theoretical zero pressure volume (Vd) ml:")
                 Tc = gr.Number(label="Cycle duration (Tc) s:")
                 start_v = gr.Number(label="Initial volume (start_v) ml:")
-                
-            simulation_button = gr.Button("Run simulation")
+
+            
+            gr.Markdown(description2)
             
+            simulation_button = gr.Button("Run simulation")
             
             
             with gr.Row():