def test_tuple_input():
xs = np.array([0.5, 1.5])
ys = np.array([1.5, 0.5])
bins = np.array([0, 1, 2])
h1 = Hist2D((xs, ys), bins=bins)
counts, edgesx, edgesy = np.histogram2d(xs, ys, bins)
h2 = Hist2D.from_bincounts(counts, (edgesx, edgesy))
assert h1 == h2
def to_yahist(h, overflow=False):
if "TH1" in str(type(h)):
c, e = h.to_numpy(flow=overflow)
if overflow:
c[1] += c[0]
c[-2] += c[-1]
c = c[1:-1]
e = e[1:-1]
h = Hist1D.from_bincounts(c, e)
else:
c, ex, ey = h.to_numpy(flow=False)
h = Hist2D.from_bincounts(c.T, (ex, ey))
return h
def test_basic():
xs = np.array([0.5, 1.5])
ys = np.array([1.5, 0.5])
bins = np.array([0, 1, 2])
h1 = Hist2D(np.c_[xs, ys], bins=bins)
counts, edgesx, edgesy = np.histogram2d(xs, ys, bins)
h2 = Hist2D.from_bincounts(counts, (edgesx, edgesy))
assert h1.dim == 2
assert h1 == h2
assert h1.nbins == (2, 2)
assert h1.integral == 2.0
assert h1.integral_error == 2.0**0.5
allclose(h1.edges[0], edgesx)
allclose(h1.edges[1], edgesy)
assert h1 == h1.transpose()
def __init__(self, year=2018):
self.year = year
if self.year == 2016:
muonScaleFactor_bins = [np.array([10., 20., 25., 30., 40., 50., 60., 100.]),
np.array([0., 0.9, 1.2, 2.1, 2.4]),]
muonScaleFactor_counts = np.array([[0.8964, 0.9560, 0.9732, 0.9833, 0.9837, 0.9672, 0.9812],
[0.8871, 0.9518, 0.9622, 0.9720, 0.9769, 0.9727, 0.9797],
[0.9727, 0.9736, 0.9794, 0.9839, 0.9855, 0.9801, 0.9920],
[0.9059, 0.9197, 0.9318, 0.9388, 0.9448, 0.9355, 0.9481],])
self.h_muonScaleFactor = Hist2D.from_bincounts(muonScaleFactor_counts, muonScaleFactor_bins)
trackingSF_bins = np.array([-2.4, -2.1, -1.6, -1.2, -0.9, -0.6, -0.3, -0.2, 0.2, 0.3, 0.6, 0.9, 1.2, 1.6, 2.1, 2.4])
trackingSF_counts = np.array([0.991237, 0.994853, 0.996413, 0.997157, 0.997512, 0.99756, 0.996745, 0.996996, 0.99772, 0.998604, 0.998321, 0.997682, 0.995252, 0.994919, 0.987334])
self.h_trackingSF = Hist1D.from_bincounts(trackingSF_counts, trackingSF_bins)
electronScaleFactor_legacy_bins = [np.array([10., 20., 35., 50., 100., 200., 300.]),
np.array([-2.500, -2.000, -1.566, -1.444, -0.800, 0.000, 0.800, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactor_legacy_counts = np.array([[1.0634, 0.9979, 0.9861, 0.9949, 1.0214, 0.7836],
[0.9313, 0.9300, 0.9431, 0.9619, 0.9882, 0.9927],
[1.0123, 0.9336, 0.9259, 0.9366, 0.9216, 1.0170],
[0.9516, 0.9176, 0.9339, 0.9334, 0.9215, 0.9272],
[0.9588, 0.9277, 0.9336, 0.9351, 0.9382, 0.9366],
[0.9735, 0.9426, 0.9471, 0.9480, 0.9537, 0.9238],
[0.9210, 0.8963, 0.9155, 0.9185, 0.9074, 0.9028],
[1.0384, 0.8672, 0.9029, 0.9167, 0.8905, 0.8326],
[0.9239, 0.9107, 0.9310, 0.9569, 0.9314, 0.9973],
[0.9779, 0.9431, 0.9554, 0.9662, 1.0301, 1.0125],])
self.h_electronScaleFactor_legacy = Hist2D.from_bincounts(electronScaleFactor_legacy_counts, electronScaleFactor_legacy_bins)
electronScaleFactorReco_legacy_bins = [np.array([10., 20., 45., 75., 100., 200.]),
np.array([-2.500, -2.000, -1.566, -1.444, -1.000, -0.500, 0.000, 0.500, 1.000, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactorReco_legacy_counts = np.array([[1.0423, 1.0164, 1.0021, 1.0180, 0.9843],
[0.9744, 0.9979, 0.9969, 1.0154, 1.0000],
[1.4277, 0.9908, 0.9622, 1.0329, 1.0022],
[0.9894, 0.9918, 0.9918, 1.0081, 0.9869],
[0.9820, 0.9867, 0.9878, 1.0051, 0.9939],
[0.9820, 0.9836, 0.9868, 0.9969, 0.9858],
[0.9820, 0.9836, 0.9868, 0.9969, 0.9858],
[0.9820, 0.9867, 0.9878, 1.0051, 0.9939],
[0.9894, 0.9918, 0.9918, 1.0081, 0.9869],
[1.4277, 0.9908, 0.9622, 1.0329, 1.0022],
[0.9744, 0.9979, 0.9969, 1.0154, 1.0000],
[1.0423, 1.0164, 1.0021, 1.0180, 0.9843],])
#can later rebin these by abs eta
self.h_electronScaleFactorReco_legacy = Hist2D.from_bincounts(electronScaleFactorReco_legacy_counts, electronScaleFactorReco_legacy_bins)
if self.year == 2017:
muonScaleFactor_Medium_bins = [np.array([20., 25., 30., 40., 50., 60., 100.]),
np.array([0, 0.9, 1.2, 2.1, 2.4]),]
muonScaleFactor_Medium_counts = np.array([[0.9946, 0.9943, 0.9985, 0.9967, 0.9938, 0.9973],
[1.0046, 0.9963, 0.9995, 0.9975, 0.9967, 0.9957],
[0.9955, 0.9935, 0.9982, 0.9959, 0.9946, 0.9964],
[0.9760, 0.9746, 0.9791, 0.9764, 0.9711, 0.9810]])
self.h_muonScaleFactor_Medium = Hist2D.from_bincounts(muonScaleFactor_Medium_counts, muonScaleFactor_Medium_bins)
muonScaleFactor_RunBCDEF_bins = [np.array([20.,25.,30.,40.,50.,60.]),
np.array([0,0.9,1.2,2.1,2.4]),]
muonScaleFactor_RunBCDEF_counts = np.array([[0.9920, 0.9957, 0.9969, 0.9980, 0.9990],
[0.9932, 0.9913, 0.9948, 0.9970, 0.9990],
[0.9945, 0.9957, 0.9979, 0.9990, 0.9990],
[0.9904, 0.9958, 0.9980, 0.9990, 1.0030]])
self.h_muonScaleFactor_RunBCDEF = Hist2D.from_bincounts(muonScaleFactor_RunBCDEF_counts, muonScaleFactor_RunBCDEF_bins)
electronScaleFactor_RunBCDEF_bins = [np.array([10., 20., 35., 50., 100., 200., 300.]),
np.array([-2.500, -2.000, -1.566, -1.444, -0.800, 0.000, 0.800, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactor_RunBCDEF_counts = np.array([[0.8110, 0.7964, 0.8392, 0.8922, 0.9225, 0.8807],
[0.8088, 0.8230, 0.8713, 0.9195, 0.9652, 1.0230],
[1.0723, 0.8844, 0.9108, 0.9390, 0.9500, 0.9424],
[0.9052, 0.8773, 0.9025, 0.9183, 0.9436, 0.9426],
[0.9279, 0.9041, 0.9245, 0.9358, 0.9560, 0.9518],
[0.9836, 0.9071, 0.9288, 0.9388, 0.9581, 0.9547],
[0.9607, 0.8810, 0.9032, 0.9211, 0.9373, 0.9231],
[0.9872, 0.8527, 0.9018, 0.9243, 1.0209, 0.7952],
[0.8348, 0.8121, 0.8671, 0.9230, 0.9889, 0.8837],
[0.8348, 0.7849, 0.8309, 0.8838, 0.9965, 0.9757],])
self.h_electronScaleFactor_RunBCDEF = Hist2D.from_bincounts(electronScaleFactor_RunBCDEF_counts, electronScaleFactor_RunBCDEF_bins)
electronScaleFactorReco_RunBCDEF_bins = [np.array([10, 20, 45, 75, 100, 200]),
np.array([-2.500, -2.000, -1.566, -1.444, -1.000, -0.500, 0.000, 0.500, 1.000, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactorReco_RunBCDEF_counts = np.array([[0.9823, 0.9775, 0.9837, 0.9970, 0.9899],
[0.9886, 0.9816, 0.9817, 0.9970, 0.9899],
[1.0000, 0.9484, 0.9706, 1.0032, 1.0096],
[0.9765, 0.9693, 0.9755, 0.9959, 0.9847],
[0.9693, 0.9766, 0.9797, 0.9919, 0.9879],
[0.9693, 0.9704, 0.9797, 0.9919, 0.9939],
[0.9693, 0.9703, 0.9776, 0.9919, 0.9939],
[0.9693, 0.9724, 0.9787, 0.9919, 0.9879],
[0.9765, 0.9699, 0.9774, 0.9959, 0.9847],
[1.0000, 0.9580, 0.9639, 1.0032, 1.0096],
[0.9886, 0.9796, 0.9827, 0.9970, 0.9899],
[0.9823, 0.9796, 0.9837, 0.9970, 0.9899]])
self.h_electronScaleFactorReco_RunBCDEF = Hist2D.from_bincounts(electronScaleFactorReco_RunBCDEF_counts, electronScaleFactorReco_RunBCDEF_bins)
if self.year == 2018:
muonScaleFactor_RunABCD_bins = [np.array([20., 25., 30., 40., 50., 100.]),
np.array([0, 0.9, 1.2, 2.1, 2.4]),]
muonScaleFactor_RunABCD_counts = np.array([[0.9824, 1.0271, 0.9948, 0.9960, 0.9990],
[0.9784, 1.0110, 0.9906, 0.9949, 0.9970],
[1.0153, 0.9855, 1.0042, 1.0010, 1.0010],
[1.0511, 0.9913, 1.0103, 1.0041, 1.0030]])
self.h_muonScaleFactor_RunABCD = Hist2D.from_bincounts(muonScaleFactor_RunABCD_counts, muonScaleFactor_RunABCD_bins)
muonScaleFactor_Medium_bins = [np.array([20.,25.,30.,40.,50.,60.,100.]),
np.array([0,0.9,1.2,2.1,2.4]),]
muonScaleFactor_Medium_counts = np.array([[0.9916, 0.9951, 1.0004, 0.9980, 0.9965, 0.9989],
[1.0018, 0.9962, 0.9994, 0.9971, 0.9945, 0.9985],
[1.0031, 0.9935, 0.9981, 0.9960, 0.9939, 0.9957],
[0.9889, 0.9733, 0.9786, 0.9762, 0.9720, 0.9806]])
self.h_muonScaleFactor_Medium = Hist2D.from_bincounts(muonScaleFactor_Medium_counts, muonScaleFactor_Medium_bins)
electronScaleFactor_RunABCD_bins = [np.array([10, 20, 35, 50, 100, 200, 300]),
np.array([-2.500, -2.000, -1.566, -1.444, -0.800, 0.000, 0.800, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactor_RunABCD_counts = np.array([[1.3737, 1.0673, 0.9891, 0.9433, 0.9245, 0.9371],
[1.0453, 0.9401, 0.9352, 0.9310, 0.9250, 0.9500],
[1.3240, 0.9614, 0.9598, 0.9751, 0.9432, 0.8901],
[0.9262, 0.8841, 0.9237, 0.9311, 0.9448, 0.9460],
[0.8536, 0.8877, 0.9294, 0.9367, 0.9443, 0.9635],
[0.9133, 0.8955, 0.9346, 0.9417, 0.9626, 0.9709],
[0.9344, 0.8932, 0.9231, 0.9354, 0.9644, 0.8999],
[1.2237, 0.9316, 0.9421, 0.9530, 1.0114, 0.9646],
[1.0047, 0.9295, 0.9343, 0.9366, 0.8989, 0.9169],
[1.3372, 1.0471, 0.9709, 0.9212, 0.8736, 1.0113],])
self.h_electronScaleFactor_RunABCD = Hist2D.from_bincounts(electronScaleFactor_RunABCD_counts, electronScaleFactor_RunABCD_bins)
electronScaleFactorReco_RunABCD_bins = [np.array([10, 20, 45, 75, 100, 200]),
np.array([-2.500, -2.000, -1.566, -1.444, -1.000, -0.500, 0.000, 0.500, 1.000, 1.444, 1.566, 2.000, 2.500]),]
electronScaleFactorReco_RunABCD_counts = np.array([[1.0115, 0.9886, 0.9846, 1.0010, 1.0072],
[0.9724, 0.9908, 0.9908, 1.0061, 0.9919],
[1.4158, 0.9815, 0.9591, 1.0467, 0.9837],
[1.0163, 0.9875, 0.9887, 1.0051, 1.0010],
[0.9095, 0.9897, 0.9908, 1.0020, 1.0010],
[1.0000, 0.9856, 0.9887, 1.0061, 0.9869],
[1.0000, 0.9835, 0.9866, 1.0061, 0.9869],
[0.9095, 0.9866, 0.9887, 1.0020, 1.0010],
[1.0163, 0.9844, 0.9824, 1.0051, 1.0010],
[1.4158, 0.9848, 0.9727, 1.0467, 0.9837],
[0.9724, 0.9887, 0.9908, 1.0061, 0.9919],
[1.0115, 0.9918, 0.9857, 1.0010, 1.0072]])
self.h_electronScaleFactorReco_RunABCD = Hist2D.from_bincounts(electronScaleFactorReco_RunABCD_counts, electronScaleFactorReco_RunABCD_bins)
def test_sum():
h1 = Hist2D.from_bincounts([[2]], ([0, 1], [0, 1]))
h2 = Hist2D.from_bincounts([[3]], ([0, 1], [0, 1]))
h3 = Hist2D.from_bincounts([[4]], ([0, 1], [0, 1]))
assert (h1 + h2 + h3).integral == 9
assert sum([h1, h2, h3]).integral == 9
fig.text(0.0,
0.995,
'$\\bf{CMS}$ ETL',
fontsize=20,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
name = 'TOT'
fig.savefig(os.path.join(plot_dir, "{}.pdf".format(name)))
fig.savefig(os.path.join(plot_dir, "{}.png".format(name)))
fig, ax = plt.subplots(1, 1, figsize=(7, 7))
hit_matrix = Hist2D.from_bincounts(hits,
bins=(np.linspace(-0.5, 15.5, 17),
np.linspace(-0.5, 15.5, 17)))
hit_matrix.plot(logz=False, cmap="cividis")
ax.set_ylabel('Row')
ax.set_xlabel('Column')
fig.text(0.0,
0.995,
'$\\bf{CMS}$ ETL',
fontsize=20,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
name = 'hit_matrix'