upload initial

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+
+st.title("Phase Error Analysis")
+
+# File Uploaders
+de_sim_file = st.file_uploader("Upload simulation CSV", type="csv")
+de_exp_file = st.file_uploader("Upload experiment CSV", type="csv")
+
+if de_sim_file and de_exp_file:
+    # Read CSV files
+    sim_data = pd.read_csv(de_sim_file, header=None)
+    exp_data = pd.read_csv(de_exp_file, header=None)
+
+    # User input for line number
+    line_num = st.slider("Select line number", 0, min(sim_data.shape[0], exp_data.shape[0])-1, 1499)
+
+    x_values = np.arange(3000)
+    sim_selected_row = sim_data.iloc[line_num, :3000]
+    exp_selected_row = exp_data.iloc[line_num, :3000]
+
+    # Linear Regression for simulation and experiment
+    sim_linear_reg = LinearRegression()
+    exp_linear_reg = LinearRegression()
+
+    sim_linear_reg.fit(x_values.reshape(-1, 1), sim_selected_row)
+    exp_linear_reg.fit(x_values.reshape(-1, 1), exp_selected_row)
+
+    sim_pred_values = sim_linear_reg.predict(np.arange(3000).reshape(-1, 1))
+    exp_pred_values = exp_linear_reg.predict(np.arange(3000).reshape(-1, 1))
+
+    # Cacluate as percentage
+    chkbox = st.checkbox("Calculate as percentage")
+
+    sim_diff = sim_selected_row - sim_pred_values
+    exp_diff = exp_selected_row - exp_pred_values
+
+    if chkbox:
+        sim_diff = sim_diff / (2 * np.pi) * 100
+        exp_diff = exp_diff / (2 * np.pi) * 100
+
+    # Plotting
+    plt.figure(figsize=(20, 6))
+    plt.plot(np.arange(500, 2500), exp_diff[500:2500], color='red', linestyle='-', label='experiment')
+    plt.plot(np.arange(500, 2500), sim_diff[500:2500], color='blue', linestyle='--', label='simulation')
+    plt.xlabel('x')
+    if chkbox:
+        plt.ylabel('phase error(%)')
+    else:
+        plt.ylabel('phase error(rad)')
+    plt.legend()
+    plt.grid(True)
+
+    st.pyplot(plt)

requirements.txt

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+matplotlib
+scikit-learn