from scipy.signal import savgol_filter
from scipy.interpolate import interp1d
if len(disp_curves) == 0:
print("no new correlations found to be processed!")
else:
f = np.array([])
avg = np.array([])
sterr = np.array([])
for frequency in dictionary:
f = np.append(f, float(frequency))
avg = np.append(avg, np.mean(dictionary[frequency]))
sterr = np.append(sterr, np.std(dictionary[frequency]))
avg = avg[f.argsort()]
sterr = sterr[f.argsort()]
f = f[f.argsort()]
minf = f[0]
maxf = f[-1]
func = interp1d(f, avg)
f2 = np.arange(int(
(maxf - minf) / 0.002)) * 0.002 + (int(minf * 1000) + 1) / 1000.
avg_ip = func(f2)
if len(avg_ip) > 91:
smooth_curve1 = savgol_filter(avg_ip, 91, 3)
else:
smooth_curve1 = avg_ip
a, b, c, d, e = np.polyfit(f, avg, 4)
