def error(x,
y,
color="C0",
zorder=20,
fmt=" ",
bottom=None,
top=None,
left=None,
right=None,
**kwargs):
assert len(x) == len(y)
mask = np.repeat(True, len(x))
if left is not None:
mask &= x >= left
if right is not None:
mask &= x <= right
if bottom is not None:
mask &= y >= bottom
if top is not None:
mask &= y <= top
x = x[mask]
y = y[mask]
plt.errorbar(unv(x),
unv(y),
xerr=usd(x) if any(usd(x)) else None,
yerr=usd(y) if any(usd(y)) else None,
fmt=fmt,
color=color,
zorder=zorder,
**kwargs)
def fitspaceY(datax,
datay,
function,
p0=None,
xfit=None,
range=None,
**kwargs):
if range is None:
range = min(unv(datax)), max(unv(datax))
if xfit is None:
xfit = np.linspace(*range, **kwargs)
p = fitY(datax, datay, function, p0=p0, **kwargs)
return xfit, function(xfit, *p), p
def fit(x,
y,
sigma=1,
alpha=0.4,
alphaData=0.7,
label=None,
color="C1",
zorder=10,
filltype="y",
**kwargs):
plt.plot(unv(x),
unv(y),
alpha=alphaData,
label=label + r"$\pm %s\sigma$" % sigma if label else label,
color=color,
zorder=zorder,
**kwargs)
if filltype == "y":
plt.fill_between(unv(x),
unv(y) - sigma * usd(y),
unv(y) + sigma * usd(y),
alpha=alpha,
color=color,
zorder=zorder,
**kwargs)
if filltype == "x":
plt.fill_betweenx(unv(y),
unv(x) - sigma * usd(x),
unv(x) + sigma * usd(x),
alpha=alpha,
color=color,
zorder=zorder,
**kwargs)
def fitspaceXY(datax,
datay,
function,
p0,
xfit=None,
range=None,
num=50,
functionUnc=None,
**kwargs):
if range is None:
range = min(unv(datax)), max(unv(datax))
if xfit is None:
xfit = np.linspace(*range, num)
if functionUnc is None:
functionUnc = function
p = fitXY(datax, datay, function, p0=p0, **kwargs)
return xfit, functionUnc(xfit, *p), p
def fitY(datax,
datay,
function,
p0=None,
epsfcn=0.0001,
maxfev=10000,
**kwargs):
yerr = usd(datay) if uncertain(datay) else None
pfit, pcov = curve_fit(function,
unv(datax),
unv(datay),
p0=p0,
sigma=usd(yerr),
epsfcn=epsfcn,
maxfev=maxfev,
**kwargs)
perr = np.sqrt(np.diag(pcov))
return unp.uarray(pfit, perr)
time = np.array(messung3["Messzeit"])
counts = stat(messung3["Counts"]) / time
messung3 = winkel, counts
# %% 1. Verzögerungsdauer
def gauss(x, A, x0, sigma, y0):
return A * np.exp(-((x-x0)/sigma)**2/2) + y0
def gaussUnc(x,A,x0,sigma,y0):
return A * unp.exp(-((x-x0)/sigma)**2/2) + y0
fig = plt.Figure(figsize=fullscreen)
dt, counts = messung1
plt.errorbar(unv(dt), unv(counts), xerr=usd(dt), yerr=usd(counts), fmt=" ", color="C0", zorder=10, label="Messung")
p0 = [1200, 0.333, 0.1, 0]
wechselT = 0.199
xdata, ydata = zip(*[(t,c) for t,c in zip(dt,counts) if t >= wechselT])
p = (A,x0,d,y0) = fit.fitXY(xdata, ydata, gauss, p0)
eff = gaussUnc(0.333, *p) / A
print("eff = %s" % eff)
latex.SI(eff, "", data_out, "messung1_eff")
latex.SI(A.format("3f"), "", data_out, "messung1_A")
latex.SI(x0*C.kilo, "\\nano\\second", data_out, "messung1_x0")
latex.SI(d*C.kilo, "\\nano\\second", data_out, "messung1_d")
latex.SI(y0, "", data_out, "messung1_y0")
xfit1 = np.linspace(wechselT, max(unv(dt)), num=250)
def interpolatespace(datax, datay, range=None, **kwargs):
if range is None:
range = min(unv(datax)), max(unv(datax))
xfit = np.linspace(*range, **kwargs)
return xfit, interpolate(datax, datay)(xfit)
def fitXY(datax, datay, function, p0, **kwargs):
model = Model(lambda p, x: function(x, *p))
realdata = RealData(unv(datax), unv(datay), sx=usd(datax), sy=usd(datay))
odr = ODR(realdata, model, beta0=p0, **kwargs)
out = odr.run()
return unp.uarray(out.beta, out.sd_beta)