def plot_DC(setting, ds, ax):
data = sort(se.search(setting, ds))
ts = np.array([int(d[0].split('e')[-1].split('.')[0])
for d in data]) / 1000
t_fit = np.linspace(ts[0], ts[-1], 100)
guess = [0, 600]
popt, pcov = scp.curve_fit(linear_fit,
ts,
se.noises(data),
guess,
bounds=((0, -np.inf), (np.inf, np.inf)),
sigma=se.error(data),
absolute_sigma=True)
ax.plot(t_fit, linear_fit(t_fit, *popt), 'k--')
ax.errorbar(ts, se.noises(data), color='r', fmt='o-',
markersize=20) #, capsize = 4)
ax.set_xlabel('Tid', fontsize=16)
ax.set_ylabel('Noise', fontsize=16)
ax.set_title('Dark Charge som funktion af tid', fontsize=16)
ax.legend()
print(int(setting[2].split('b')[-1])**2)
return popt[0] / ((int(setting[2].split('b')[-1]))**2)
def find_effektiv_a(setting, ds):
data = sort(se.search(setting, ds))
ts = np.array([int(d[0].split('e')[-1].split('.')[0])
for d in data]) / 1000
guess = [0, 600]
popt, pcov = scp.curve_fit(linear_fit,
ts,
se.noises(data),
guess,
bounds=((0, -np.inf), (np.inf, np.inf)),
sigma=se.error(data),
absolute_sigma=True)
b = 1
for sett in setting:
if 'b' in sett:
b = sett
return popt[0], np.sqrt(np.diag(pcov))[0]