for freq in range(RF_Freq_Start / RF_Freq_Interval,
RF_Freq_End / RF_Freq_Interval + 1):
### read this file
name_of_file = RF_Power + '_' + str(freq * 10) + 'MHz.csv'
result = []
with open(folder + name_of_file, 'r') as f:
for line in f:
result.append(map(str, line.split(',')))
I = [float(result[i][1]) for i in range(0, len(result))]
Q = [
float(result[i][2].replace("\n", ""))
for i in range(0, len(result))
]
### fit I, Q with lmfit and get five values and their 1-sigma standard error
x_c, x_c_err, y_c, y_c_err, x_dim, x_dim_err, y_dim, y_dim_err, angle, angle_err = Fitter.Fit_Ellipse(
I, Q)
ellipse_array.append([
x_c, x_c_err, y_c, y_c_err, x_dim, x_dim_err, y_dim, y_dim_err,
angle, angle_err
])
###
A_I = np.sqrt(x_dim**2 * np.cos(angle)**2 +
y_dim**2 * np.sin(angle)**2)
A_Q = np.sqrt(x_dim**2 * np.sin(angle)**2 +
y_dim**2 * np.cos(angle)**2)
alpha_1 = np.arctan2(y_dim * np.sin(angle), x_dim * np.cos(angle))
alpha_2 = np.pi - np.arctan2(y_dim * np.cos(angle),
x_dim * np.sin(angle))
gamma = alpha_1 - alpha_2
mixer_para_array.append([A_I, A_Q, alpha_1, alpha_2, gamma])
