def test_typeclass_ndim_v03():
objects = [
C.Histogram(np.arange(6), np.arange(5)),
C.Points(np.arange(5)),
C.Histogram2d(np.arange(6), np.arange(7)),
C.Points(np.arange(12).reshape(3, 4))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt = R.TypeClasses.CheckNdimT(context.current_precision())(1, (0, 1))
R.SetOwnership(dt, False)
dt.dump()
print()
obj.add_typeclass(dt)
res = obj.process_types()
assert res
dt1 = R.TypeClasses.CheckNdimT(context.current_precision())(2, (-2, -1))
R.SetOwnership(dt1, False)
dt1.dump()
print()
obj.add_typeclass(dt1)
res = obj.process_types()
assert res
def test_typeclass_kind_v01():
objects = [
C.Histogram(np.arange(6), np.arange(5)),
C.Histogram2d(np.arange(6), np.arange(7))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt_points = R.TypeClasses.CheckKindT(context.current_precision())(1)
R.SetOwnership(dt_points, False)
dt_points.dump()
print()
dt_hist = R.TypeClasses.CheckKindT(context.current_precision())(2)
R.SetOwnership(dt_hist, False)
dt_hist.dump()
print()
obj.add_typeclass(dt_hist)
res = obj.process_types()
assert res
obj.add_typeclass(dt_points)
print('Exception expected: ', end='')
res = obj.process_types()
assert not res
def test_typeclass_passtype():
"""Last input has another edges"""
objects = [
C.Histogram2d(np.arange(4), np.arange(5)),
C.Histogram(np.arange(4)),
C.Points(np.arange(12).reshape(3,4))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
k = list(map(obj.add_input, outputs))
for i in range(5):
obj.add_output()
dt1 = R.TypeClasses.PassTypeT(context.current_precision())((0,), (0,1))
dt2 = R.TypeClasses.PassTypeT(context.current_precision())((1,), (2,-1))
R.SetOwnership(dt1, False)
R.SetOwnership(dt2, False)
dt1.dump(); print()
dt2.dump(); print()
obj.add_typeclass(dt1)
obj.add_typeclass(dt2)
res = obj.process_types();
assert res
obj.print()
dta = outputs[0].datatype()
dtb = outputs[1].datatype()
doutputs = obj.transformations.back().outputs
assert doutputs[0].datatype()==dta
assert doutputs[1].datatype()==dta
assert doutputs[2].datatype()==dtb
assert doutputs[3].datatype()==dtb
assert doutputs[4].datatype()==dtb
def test_typeclass_passeach_02():
"""Pass with step 2"""
objects = [
C.Histogram2d(np.arange(4), np.arange(5)),
C.Histogram(np.arange(4)),
C.Points(np.arange(20).reshape(4,5))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
i = list(map(obj.add_input, outputs))
i1 = list(map(obj.add_input, outputs))
for i in range(3):
obj.add_output()
dt1 = R.TypeClasses.PassEachTypeT(context.current_precision())((0,-1,2), (0,-1))
R.SetOwnership(dt1, False)
dt1.dump(); print()
obj.add_typeclass(dt1)
res = obj.process_types();
assert res
obj.print()
dta = outputs[0].datatype()
dtb = outputs[1].datatype()
dtc = outputs[2].datatype()
doutputs = obj.transformations.back().outputs
assert doutputs[0].datatype()==dta
assert doutputs[1].datatype()==dtc
assert doutputs[2].datatype()==dtb
def test_sumaxis_01(kind, axis):
"""Test ViewRear on Points (start, len)"""
size = 4
inp = np.arange(12.0).reshape(3, 4)
shouldbe = inp.sum(axis=axis)
xedges = np.arange(inp.shape[0] + 1)
yedges = np.arange(inp.shape[1] + 1)
edges = (xedges, yedges)
if kind == 'points':
Inp = C.Points(inp)
else:
Inp = C.Histogram2d(xedges, yedges, inp)
sum = C.SumAxis(axis, Inp)
sum.printtransformations()
res = sum.sumaxis.result.data()
print('Input', inp)
print('Result ({})'.format(axis), res)
print('Should be', shouldbe)
assert np.allclose(res, shouldbe, atol=0, rtol=0)
if kind == 'hist':
newedges = sum.sumaxis.result.datatype().edges
select = 1 if axis == 0 else 0
print('Original edges', edges)
print('New edges', newedges)
assert np.allclose(edges[select], newedges)
def test_histogram_v02_2d(tmp_path):
edgesx = np.logspace(0, 3, 6, base=2)
edgesy = np.linspace(0, 10, 20)
data = np.arange(1.0, (edgesx.size - 1) * (edgesy.size - 1) + 1,
dtype='d').reshape(edgesx.size - 1, edgesy.size - 1)
hist = C.Histogram2d(edgesx, edgesy, data)
res = hist.hist.hist()
edgesx_dt = np.array(hist.hist.hist.datatype().edgesNd[0])
edgesy_dt = np.array(hist.hist.hist.datatype().edgesNd[1])
# Plot
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X (column), log scale')
ax.set_ylabel('Y row')
ax.set_title('2d histogram example')
ax.set_xscale('log')
hist.hist.hist.plot_pcolor(colorbar=True)
suffix = 'histogram2d'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot(111, projection='3d')
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X (column)')
ax.set_ylabel('Y (row)')
ax.set_title('2d histogram example (3d)')
ax.azim -= 70
hist.hist.hist.plot_bar3d(cmap=True, colorbar=True)
suffix = 'histogram2d_3d'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
path = os.path.join(str(tmp_path), suffix + '_graph.png')
savegraph(hist.hist, path)
allure_attach_file(path)
plt.close()
# Test consistency
assert np.all(res == data)
assert np.all(edgesx == edgesx_dt)
assert np.all(edgesy == edgesy_dt)
def test_histogram_v02_TH2D(tmp_path):
rhist = R.TH2D('testhist', 'testhist', 20, 0, 10, 24, 0, 12)
xyg = R.TF2("xyg", "exp([0]*x)*exp([1]*y)", 0, 10, 0, 12)
xyg.SetParameter(0, -1 / 2.)
xyg.SetParameter(1, -1 / 8.)
R.gDirectory.Add(xyg)
rhist.FillRandom('xyg', 10000)
hist = C.Histogram2d(rhist)
buf = rhist.get_buffer().T
res = hist.hist.hist()
# Plot
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X label')
ax.set_ylabel('Y label')
ax.set_title('ROOT histogram')
rhist.pcolorfast(colorbar=True)
suffix = 'histogram2d'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X label')
ax.set_ylabel('Y label')
ax.set_title('GNA histogram')
hist.hist.hist.plot_pcolorfast(colorbar=True)
suffix = 'histogram2d'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
path = os.path.join(str(tmp_path), suffix + '_graph.png')
savegraph(hist.hist, path)
allure_attach_file(path)
plt.close()
# Test consistency
assert np.all(buf == res)
def test_typeclass_same_v04():
"""Last input has another edges"""
objects=[C.Histogram2d(np.arange(4), np.arange(5)) for i in range(5)]
objects.append(C.Histogram2d(np.arange(1,5), np.arange(5)))
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt = R.TypeClasses.CheckSameTypesT(context.current_precision())((1,-1), 'shape')
R.SetOwnership(dt, False)
dt.dump(); print()
obj.add_typeclass(dt)
res = obj.process_types();
assert res
dt1 = R.TypeClasses.CheckSameTypesT(context.current_precision())((1,-1),)
R.SetOwnership(dt1, False)
dt1.dump(); print()
obj.add_typeclass(dt1)
print('Exception expected: ',end='')
res = obj.process_types();
assert not res
from gna.bindings import common
from matplotlib import pyplot as plt
# Create numpy array for data points
xmin, ymin = 10, 20
nbinsx, nbinsy = 40, 50
edgesx = np.linspace(xmin, xmin + nbinsx * 0.5, nbinsx + 1)
edgesy = np.linspace(ymin, ymin + nbinsy * 0.5, nbinsy + 1)
# Create fake data array
cx = (edgesx[1:] + edgesx[:-1]) * 0.5
cy = (edgesy[1:] + edgesy[:-1]) * 0.5
X, Y = np.meshgrid(cx, cy, indexing='ij')
narray = np.exp(-0.5 * (X - 15.0)**2 / 10.0**2 - 0.5 * (Y - 30.0)**2 / 3.0**2)
# Create a histogram instance with data, stored in `narray`
# and edges, stored in `edges`
hist = C.Histogram2d(edgesx, edgesy, narray)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('pcolorfast')
ax.minorticks_on()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
hist.hist.hist.plot_pcolorfast(colorbar=True)
savefig(tutorial_image_name('png', suffix='pcolorfast'))
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('imshow')
R.gDirectory.Add(xyg)
roothist2d.FillRandom('xyg', 10000)
# Fill TMatrixD with
for i, (i1, i2) in enumerate(
I.product(range(rootmatrix.GetNrows()), range(rootmatrix.GetNcols()))):
rootmatrix[i1, i2] = i
# Create Points
p1 = C.Points(roothist1d)
p2 = C.Points(roothist2d)
p3 = C.Points(rootmatrix)
# Create Histograms
h1d = C.Histogram(roothist1d)
h2d = C.Histogram2d(roothist2d)
# Check p1
print('Points from TH1D (underflow/overflow are ignored)')
roothist1d.Print('all')
print(p1.points.points())
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X axis')
ax.set_ylabel('Entries')
ax.set_title('Points and TH1 comparison')
ns.printparameters(labels=True) print() # Define binning and integration orders x_nbins = 20 x_edges = np.linspace(-np.pi, np.pi, x_nbins + 1, dtype='d') x_widths = x_edges[1:] - x_edges[:-1] x_orders = 4 y_nbins = 30 y_edges = np.linspace(0, 2.0 * np.pi, y_nbins + 1, dtype='d') y_widths = y_edges[1:] - y_edges[:-1] y_orders = 3 # Initialize histogram hist = C.Histogram2d(x_edges, y_edges) # Initialize integrator integrator = R.Integrator2GL(x_nbins, x_orders, y_nbins, y_orders) integrator.points.edges(hist.hist.hist) int_points = integrator.points # Create integrable: a*sin(x) + b*cos(k*x) arg_t = C.WeightedSum(['a', 'b'], [int_points.xmesh, int_points.ymesh]) sin_t = R.Sin(arg_t.sum.sum) # integrator.add_input(sint_t.sin.result) integrator.hist.f(sin_t.sin.result) X, Y = integrator.points.xmesh.data(), integrator.points.ymesh.data() integrator.print()
