def __init__(self):
super(NEW_meta, self).__init__()
# Load the database objects
self._det_geo = load_db.DetectorGeo()
self._pmt_data = load_db.DataPMT()
self._sipm_data = load_db.DataSiPM()
self.skim_det_info(self._det_geo)
def test_compute_drift_v_when_moving_edge():
edge = np.random.uniform(530, 570)
Nevents = 100 * 1000
data = np.random.uniform(450, edge, Nevents)
data = np.random.normal(data, 1)
dv, dvu = compute_drift_v(data, 60, [500, 600], [1500, 550, 1, 0], 'new')
dv_th = DB.DetectorGeo('new').ZMAX[0] / edge
assert dv_th == approx(dv, abs=5 * dvu)
def __init__(self):
super(io_manager, self).__init__()
# Load the database objects
self._det_geo = load_db.DetectorGeo()
self._pmt_data = load_db.DataPMT()
self._sipm_data = load_db.DataSiPM()
self._events = []
self._entry = 0
self._max_entry = 0
self._run = 5
def compute_drift_v(zdata: np.array,
nbins: int = 35,
zrange: Tuple[float, float] = (500, 640),
seed: Tuple[float, float, float, float] = None,
detector: str = 'new') -> Tuple[float, float]:
"""
Computes the drift velocity for a given distribution
using the sigmoid function to get the cathode edge.
Parameters
----------
zdata: array_like
Values of Z coordinate.
nbins: int (optional)
The number of bins in the z coordinate for the binned fit.
zrange: length-2 tuple (optional)
Fix the range in z.
seed: length-4 tuple (optional)
Seed for the fit.
detector: string (optional)
Used to get the cathode position from DB.
plot_fit: boolean (optional)
Flag for plotting the results.
Returns
-------
dv: float
Drift velocity.
dvu: float
Drift velocity uncertainty.
"""
y, x = np.histogram(zdata, nbins, zrange)
x = shift_to_bin_centers(x)
if seed is None: seed = np.max(y), np.mean(zrange), 0.5, np.min(y)
z_cathode = DB.DetectorGeo(detector).ZMAX[0]
try:
f = fitf.fit(sigmoid,
x,
y,
seed,
sigma=poisson_sigma(y),
fit_range=zrange)
dv = z_cathode / f.values[1]
dvu = dv / f.values[1] * f.errors[1]
except RuntimeError:
print(
"WARNING: Sigmoid fit for dv computation fails. NaN value will be set in its place."
)
dv, dvu = np.nan, np.nan
return dv, dvu
def compute_drift_v(zdata: np.array,
nbins: int = 35,
zrange: Tuple[float, float] = (500, 640),
seed: Tuple[float, float, float, float] = None,
detector: str = 'new',
plot_fit: bool = False) -> Tuple[float, float]:
"""
Computes the drift velocity for a given distribution
using the sigmoid function to get the cathode edge.
Parameters
----------
zdata: array_like
Values of Z coordinate.
nbins: int (optional)
The number of bins in the z coordinate for the binned fit.
zrange: length-2 tuple (optional)
Fix the range in z.
seed: length-4 tuple (optional)
Seed for the fit.
detector: string (optional)
Used to get the cathode position from DB.
plot_fit: boolean (optional)
Flag for plotting the results.
Returns
-------
dv: float
Drift velocity.
dvu: float
Drift velocity uncertainty.
"""
y, x = np.histogram(zdata, nbins, zrange)
x = shift_to_bin_centers(x)
if seed is None: seed = np.max(y), np.mean(zrange), 0.5, np.min(y)
f = fitf.fit(sigmoid, x, y, seed, sigma=poisson_sigma(y), fit_range=zrange)
z_cathode = DB.DetectorGeo(detector).ZMAX[0]
dv = z_cathode / f.values[1]
dvu = dv / f.values[1] * f.errors[1]
if plot_fit:
plt.figure()
plt.hist(zdata, nbins, zrange)
xx = np.linspace(zrange[0], zrange[1], nbins)
plt.plot(xx, sigmoid(xx, *f[1]), color='red')
return dv, dvu