import numpy as np
import matplotlib.pyplot as plt
from models.LeakyIntegrateFireModel import LIFModel


# def calculate_step(current_v, tau, i_b, step_size=0.01):
    # return current_v + (step_size * (-current_v + mem_res * i_b)) / tau

def function_e(x):
    return (0-15) * np.e**(-x/1) + 15

# x_values = np.arange(0, 5, 0.01)
# plt.plot(x_values, [function_e(x) for x in x_values])
# plt.show()


# def function_f(i_base, tau=1, threshold=10, reset=0):
#     return -1/(tau*np.log((threshold-i_base)/(reset - i_base)))
#
# x_values = np.arange(0, 20, 0.001)
# plt.plot(x_values, [function_f(x) for x in x_values])
# plt.show()


# LIF test:
# Rm = 100 MOhm, Cm = 200pF
step_size = 0.01  # ms
mem_res = 100*1000000
tau = 1
base_freq = 30
v_threshold = 10
base_input = -(- v_threshold / (np.e**(-1/(base_freq*tau))) + 1) / mem_res

stim1 = int(1000/step_size) * [base_input]

stimulus = []
stimulus.extend(stim1)

lif = LIFModel(mem_res, tau, 0, 0, stimulus, 10)

voltage, spikes_b = lif.calculate_response()
y_spikes = []
x_spikes = []
for i in range(len(spikes_b)):
    if spikes_b[i]:
        y_spikes.append(10.5)
        x_spikes.append(i*step_size)

time = np.arange(0, 1000, step_size)
plt.plot(time, voltage)
plt.plot(x_spikes, y_spikes, 'o')
plt.show()
plt.close()