app.py

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)
    
    center_x, center_y = sim_data.shape[1] // 2, sim_data.shape[0] // 2
    sim_data = sim_data.iloc[center_y - 1500:center_y + 1500, center_x - 1500:center_x + 1500]

    # 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)