from matplotlib import pyplot as plt
import numpy as np
import pandas as pd






def plotData(data):
    """Plot the data contained in the given DataFrame."""

    fig, ax = plt.subplots(2, 1)
    ax[0].plot(data['v'], data['i_dmm'], label='Keithley')
    ax[0].plot(data['v'], data['i_hv'], label='HV port')
    ax[0].plot(data['v'], data['i_calc'], label='Calculated')
    ax[0].set_xlabel('Voltage (V)')
    ax[0].set_ylabel('Current (1 uA)')
    ax[0].legend()
    ax[1].plot(data['v'], data['i_dmm'] - data['i_calc'], label='Difference KEI')
    ax[1].plot(data['v'], data['i_hv'] - data['i_calc'], label='Difference HV')
    ax[1].set_xlabel('Voltage (V)')
    ax[1].grid()
    ax[1].set_ylabel('Current difference (uA)')
    ax[1].legend()
    plt.show()


#import excel file 
data = pd.read_excel('data_biasTEST.xlsx')
# data['ppm'] = (data['i_kei'] - data['i_hv']) / data['i_kei'] * 1e6
# sample_cal = round(len(data) * 0.8)
# print(f"Sample for calibration: {data['ppm'].iloc[sample_cal:sample_cal+20].mean()} ppm")

# linear regression for i_kei
from sklearn.linear_model import LinearRegression
X = data['v'].values.reshape(-1, 1)
y = data['i_hv'].values
reg = LinearRegression().fit(X, y)
data['i_calc'] = reg.predict(X)
print(f"Linear regression: {reg.coef_[0]} A/V")
print(f"Linear regression: {reg.intercept_} A")


print(data)
plotData(data)