import numpy as np

##Sampling Rate and Duration

sampling_rate = 250 #Hz

duration = 10 #seconds

leads = ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']

## Gamma, a (12,5) matrix to modify the five waves' amplitudes of 12 leads (P, Q, R, S, T)

gamma = np.array([[1, 0.1, 1, 1.2, 1],

                   [2, 0.2, 0.2, 0.2, 3],

                   [1, -0.1, -0.8, -1.1, 2.5],

                   [-1, -0.05, -0.8, -0.5, -1.2],

                   [0.05, 0.05, 1, 1, 1],

                   [1, -0.05, -0.1, -0.1, 3],

                   [-0.5, 0.05, 0.2, 0.5, 1],

                   [0.05, 0.05, 1.3, 2.5, 2],

                   [1, 0.05, 1, 2, 1],

                   [1.2, 0.05, 1, 2, 2],

                   [1.5, 0.1, 0.8, 1, 2],

                   [1.8, 0.05, 0.5, 0.1, 2]])

## Normal ECG Parameters

Anoise = 0.01

#heart rate

mu_hr_1 = 60          # mean of the heart rate

sigma_hr_1 = 7        # variance of the heart rate

#noise

min_noise_1 = 0.01      

max_noise_1 = 0.1

#For PQRST five spikes

# t, the starting position along the circle of each interval in radius

mu_t_1 = np.array((-70, -15, 0, 15, 100))  

sigma_t_1 = np.ones(5)*3

# a, the amplitude of each spike; b, the width of each spike

mu_a_1 = np.array((1.2, -5, 30, -7.5, 0.75))

mu_b_1 = np.array((0.25, 0.1, 0.1, 0.1, 0.4))

sigma_a_1 = np.abs(mu_a_1/5)

sigma_b_1 = np.abs(mu_b_1/5)

## Abnormal ECG Parameters

#heart rate

mu_hr_2 = 60          # mean of the heart rate

sigma_hr_2 = 7        # variance of the heart rate

#noise

min_noise_2 = 0.01      

max_noise_2 = 0.1

#t, a, b

mu_t_2 = np.array((-70, -15, 0, 15, 100))

mu_a_2 = np.array((1.2, -4, 25, -6.5, 0.75))

mu_b_2 = np.array((0.25, 0.1, 0.1, 0.1, 0.4))

sigma_t_2 = np.ones(5)*3

sigma_a_2 = np.abs(mu_a_1/5)

sigma_b_2 = np.abs(mu_b_1/5)