# -*- coding: utf-8 -*-
"""
Created on Monday Mar 13, 2023

@author: martin singer
"""

import matplotlib.pyplot as plt
import numpy as np
from numpy import sqrt, exp

'stacking curve within the stacker'

'1. I_1 = I_2'
I_1_2           = np.array([1, 5, 10, 20, 60, 30])
signal_1        = np.array([4, 17, 29, 50, 86, 62])
signal_1_std    = np.array([1, 2, 2, 2, 2, 2])

plt.figure(num=0, clear=True)
plt.errorbar(I_1_2, signal_1, yerr=signal_1_std, fmt='.', markersize=10)
plt.xlabel('stacks [#]', size=10)
plt.ylabel('paddle signal [nVs]', size=10)
# plt.legend()
ax = plt.gca()
ax.tick_params(direction="in", top=True, right=True)
# plt.yscale('log')
plt.savefig('20230313-Stacking-Stack_Scan.jpg', dpi=300, bbox_inches='tight')
#%%
plt.figure(num=1, clear=True)
plt.errorbar(I_1_2, (signal_1/signal_1[0]), yerr=(signal_1_std/signal_1_std[0]), fmt='.', markersize=10)
plt.xlabel('stacks [#]', size=10)
plt.ylabel('signal / one stack', size=10)
plt.xlim([0,62])
plt.ylim([0,62])
ax = plt.gca()
ax.tick_params(direction="in", top=True, right=True)
# plt.yscale('log')
plt.savefig('20230313-Stacking-Stack_Scan-Normalized.jpg', dpi=300, bbox_inches='tight')