Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+# Title of the app
+st.title("Waveform Simulator for Diode Clipping Circuit")
+
+# Sidebar for user inputs
+st.sidebar.header("Circuit Parameters")
+battery_voltage = st.sidebar.number_input("DC Battery Voltage (V)", min_value=-20.0, max_value=20.0, value=5.0, step=0.1)
+diode_type = st.sidebar.selectbox("Diode Type", ["Germanium (Ge)", "Silicon (Si)"])
+signal_type = st.sidebar.selectbox("Input Signal Type", ["AC", "DC"])
+
+# Diode voltage drops
+V_diode = 0.3 if diode_type == "Germanium (Ge)" else 0.7
+
+# Define input voltage points for analysis
+vin_points = [10, 8, 6, 5, 4, 3, 2, 1, 0.7, 0.3, 0]
+if signal_type == "AC":
+    vin_full = vin_points + [0] + [-x for x in vin_points[::-1]]  # Positive and negative half-cycles
+else:
+    vin_full = vin_points  # DC only (positive values)
+
+# Circuit analysis function
+def analyze_circuit(vin, v_bat, v_diode_drop):
+    """Analyze a simple diode clipping circuit with battery bias."""
+    v_out = []
+    v_diode = []
+    
+    for v in vin:
+        # Positive half-cycle or DC: Diode conducts if Vin > Vbat + Vdiode
+        if v > (v_bat + v_diode_drop):
+            v_o = v_bat  # Clipped at battery voltage
+            v_d = v_diode_drop  # Diode forward voltage
+        elif v < -v_diode_drop and signal_type == "AC":
+            # Negative half-cycle: Diode conducts in reverse (idealized model)
+            v_o = v  # Passes through (no clipping in this simple model)
+            v_d = 0  # Diode off
+        else:
+            # Diode off: Vout = Vin (within battery bounds)
+            v_o = v
+            v_d = 0
+        
+        v_out.append(v_o)
+        v_diode.append(v_d)
+    
+    return v_diode, v_out
+
+# Perform analysis
+v_diode_values, v_out_values = analyze_circuit(vin_full, battery_voltage, V_diode)
+
+# Create table
+data = {
+    "V[in] (V)": vin_full,
+    f"V[{diode_type[:2]}] (V)": v_diode_values,
+    "V[out] (V)": v_out_values
+}
+df = pd.DataFrame(data)
+st.subheader("Point-to-Point Analysis Table")
+st.dataframe(df.style.format("{:.2f}"))
+
+# Waveform plotting
+fig = go.Figure()
+fig.add_trace(go.Scatter(x=np.arange(len(vin_full)), y=vin_full, mode='lines+markers', name='V[in]', line=dict(color='blue')))
+fig.add_trace(go.Scatter(x=np.arange(len(vin_full)), y=v_out_values, mode='lines+markers', name='V[out]', line=dict(color='red')))
+fig.update_layout(
+    title="Input and Output Waveforms",
+    xaxis_title="Sample Points",
+    yaxis_title="Voltage (V)",
+    legend=dict(x=0, y=1),
+    template="plotly_white"
+)
+st.plotly_chart(fig)
+
+# Explanation
+st.subheader("How It Works")
+st.write(f"""
+- **Input Signal**: {signal_type}. For AC, both positive and negative cycles are analyzed; for DC, only positive values are used.
+- **Diode**: {diode_type} with a forward voltage drop of {V_diode}V.
+- **Battery**: Clips the output at {battery_voltage}V when the diode conducts.
+- **Analysis**: For each V[in], the output V[out] is calculated based on diode conduction and battery bias.
+- **Waveform**: Blue line is V[in], red line is V[out].
+""")