def test_arrayview_comparison():
size = 5
a1 = N.arange(size, dtype=context.current_precision_short())
a2 = N.ascontiguousarray(a1[::-1])
a3 = N.arange(size + 1, dtype=context.current_precision_short())
view1a = R.arrayview(context.current_precision())(a1, a1.size)
view1b = R.arrayview(context.current_precision())(a1, a1.size)
view2 = R.arrayview(context.current_precision())(a2, a2.size)
view3 = R.arrayview(context.current_precision())(a3, a3.size)
assert view1a == view1b
assert not view1a != view1b
assert view1a != view2
assert not view1a == view2
assert view1a != view3
assert not view1a == view3
view4 = R.arrayview(context.current_precision())(view3.size())
view4 = view3
assert view3 == view4
assert not view3 != view4
view5 = R.arrayview(context.current_precision())(view3)
assert view3 == view5
assert not view3 != view5
def test_viewrear_hist_02_auto():
"""Test ViewRear on Histogram (determine start and length)"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size-1, dtype=context.current_precision_short())
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
print('Range', rng)
hist_main = C.Histogram(edges, arr)
start, len = rng
pedges = edges[start:start+len+1]
arr_sub = N.arange(start, start+len, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub, True)
view = C.ViewRear(hist_main);
hist_sub >> view.view.rear
res = view.view.result.data()
expect_edges = N.array(view.view.result.datatype().edges)
expect = arr.copy()
expect[start:start+len]=arr_sub
print('Result', res)
print('Expect', expect)
print('Result (edges)', edges)
print('Expect (edges)', expect_edges)
print()
assert (res==expect).all()
assert (edges==expect_edges).all()
assert (hist_main.single().data()==arr).all()
def test_histedges_offset_01():
size = 13
edges = N.arange(size, dtype='d')
hist_in = C.Histogram(edges)
arr = N.zeros(edges.size - 1, dtype=context.current_precision_short())
for offset in range(size - 1):
print('Offset', offset)
view = C.HistEdgesOffset(hist_in, offset)
edges_truncated = edges[offset:]
trans = view.histedges
points = trans.points.data()
points_truncated = trans.points_truncated.data()
hist_truncated_d = trans.hist_truncated.data()
hist_truncated = N.array(trans.hist_truncated.datatype().edges)
print(' Edges (expected)', edges)
print(' Edges truncated (expected)', edges_truncated)
print(' Points', points)
print(' Points truncated', points_truncated)
print(' Hist truncated', hist_truncated)
print(' Hist truncated data', hist_truncated_d)
assert (edges == points).all()
assert (edges_truncated == points_truncated).all()
assert (edges_truncated == hist_truncated).all()
assert (0.0 == hist_truncated_d).all()
def test_viewrear_points_04_auto_fromhist_edges():
"""Test ViewRear on Histogram (determine start and length)"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size, dtype=context.current_precision_short())
points_main = C.Points(arr)
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
print('Range', rng)
hist_main = C.Histogram(edges, arr[:-1])
start, len = rng
pedges = edges[start:start+len+1]
arr_sub = N.arange(start, start+len+1, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub[:-1], True)
points_sub=C.Points(arr_sub, True)
view = C.ViewRear()
view.determineOffset(hist_main, hist_sub, True)
points_main >> view.view.original
points_sub >> view.view.rear
res = view.view.result.data()
expect = arr.copy()
expect[start:start+len+1]=arr_sub
print('Result', res)
print('Expect', expect)
print()
assert (res==expect).all()
def test_viewrear_hist_04_auto_threshold():
"""Test ViewRear on Histogram (auto start and len), enable threshold"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size-1, dtype=context.current_precision_short())
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6)]
for rng in ranges:
hist_main = C.Histogram(edges, arr)
start, len = rng
pedges = edges[start:start+len+1].copy()
print('Original edges', pedges)
pedges[0] = 0.5*(pedges[:2].sum())
pedges[-1] = 0.5*(pedges[-2:].sum())
print('Modified edges', pedges)
arr_sub = N.arange(start, start+len, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub, True)
view = C.ViewRear(hist_main);
view.allowThreshold()
hist_sub >> view.view.rear
res = view.view.result.data()
expect_edges = N.array(view.view.result.datatype().edges)
expect = arr.copy()
expect[start:start+len]=arr_sub
print('Range', rng)
print('Result', res)
print('Expect', expect)
print('Result (edges)', edges)
print('Expect (edges)', expect_edges)
print()
assert (res==expect).all()
assert (edges==expect_edges).all()
assert (hist_main.single().data()==arr).all()
def test_jacobian_v01():
ns = env.globalns("test_jacobian_v01")
names = ['zero', 'one', 'two', 'three', 'four', 'five']
values = N.arange(len(names), dtype=context.current_precision_short())
jac = C.Jacobian()
for name, value in zip(names, values):
if value:
par = ns.defparameter(name, central=value, relsigma=0.1)
else:
par = ns.defparameter(name, central=value, sigma=0.1)
if name == 'five':
break
jac.append(par)
five = par
with ns:
va = C.VarArray(names)
va.vararray.points >> jac.jacobian.func
vars = va.vararray.points.data()
print('Python array:', values.shape, values)
print('Array:', vars.shape, vars)
res = jac.jacobian.jacobian.data()
print('Jacobian:', res.shape, res)
req = N.eye(len(names), len(names) - 1)
assert N.allclose(res, req, atol=1.e-12)
t = jac.jacobian
tf1 = t.getTaintflag()
tf0 = va.vararray.getTaintflag()
assert not tf0.tainted()
assert not tf1.tainted()
assert tf1.frozen()
five.set(12.0)
assert tf0.tainted()
assert not tf1.tainted()
assert tf1.frozen()
t.unfreeze()
assert tf0.tainted()
assert tf1.tainted()
assert not tf1.frozen()
res = jac.jacobian.jacobian.data()
assert N.allclose(res, req, atol=1.e-12)
assert not tf0.tainted()
assert not tf1.tainted()
assert tf1.frozen()
def test_arrayview_vector():
size = 5
from gna.constructors import stdvector
a1 = N.arange(size, dtype=context.current_precision_short())
v1 = stdvector(a1)
view1 = R.arrayview(context.current_precision())(a1, size)
assert view1 == v1
assert not view1 != v1
def test_points_v02():
arr = N.arange(12, dtype=context.current_precision_short())
points = C.Points(arr)
identity = C.Identity()
points >> identity
out = identity.single()
for i in range(arr.size):
points.set(arr, i)
assert (out.data() == arr[:i]).all()
def test_viewrear_points_01_start_len(function_name):
"""Test ViewRear on Points (start, len)"""
arr = N.zeros(12, dtype=context.current_precision_short())
ns = env.env.globalns(function_name)
names=[]
for i in range(arr.size):
name='val_%02i'%i
names.append(name)
ns.defparameter( name, central=i, fixed=True, label='Value %i'%i )
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
start, len = rng
pview = arr[start:start+len]
points = C.Points(arr)
view = C.ViewRear(points, start, len);
cnames = names[start:start+len]
with ns:
vararray = C.VarArray(cnames)
vararray >> view.view.rear
print('Range', rng)
for ichange, iname in enumerate(['']+cnames, -1):
if iname:
print(' Change', ichange)
par=ns[iname]
par.set(par.value()+1.0)
res0 = vararray.single().data()
res = view.view.result.data()
expect = arr.copy()
for i in range(start, start+len):
expect[i] = ns[names[i]].value()
expect0 = []
for i in range(start, start+len):
expect0.append(ns[names[i]].value())
print(' Result 0', res0)
print(' Expect 0', expect0)
print(' Result', res)
print(' Expect', expect)
print(' Original data', points.single().data())
print(' Original data (expect)', arr)
assert (res==expect).all()
assert (res0==expect0).all()
assert (res[start:start+len]==res0).all()
assert (points.single().data()==arr).all()
print()
def test_vararray_preallocated_v01(function_name):
ns = env.globalns(function_name)
names = ['zero', 'one', 'two', 'three', 'four', 'five']
values = N.arange(len(names), dtype=context.current_precision_short())
variables = R.vector('variable<%s>' % context.current_precision())()
with context.allocator(100) as allocator:
for name, value in zip(names, values):
par = ns.defparameter(name, central=value, relsigma=0.1)
variables.push_back(par.getVariable())
with ns:
vsum = C.VarSum(names, 'sum', ns=ns)
vsum_var = ns['sum'].get()
variables.push_back(vsum_var.getVariable())
vprod = C.VarProduct(names, 'product', ns=ns)
vprod_var = ns['product'].get()
variables.push_back(vprod_var.getVariable())
va = C.VarArrayPreallocated(variables)
pool = allocator.view()
res = va.vararray.points.data()
values_all = N.zeros(shape=values.size + 2, dtype=values.dtype)
values_all[:-2] = values
values_all[-2] = values_all[:-2].sum()
values_all[-1] = values_all[:-2].prod()
print('Python array:', values_all)
print('VarArray (preallocated):', res)
print('Pool:', pool)
assert (values_all == res).all()
assert (values_all == pool).all()
assert (res == pool).all()
for i, (val, name) in enumerate(enumerate(names, 2)):
ns[name].set(val)
values_all[i] = val
values_all[-2] = values_all[:-2].sum()
values_all[-1] = values_all[:-2].prod()
res = va.vararray.points.data()
print('Iteration', i)
print(' Python array:', values_all)
print(' VarArray (preallocated):', res)
assert (values_all == res).all()
assert (values_all == pool).all()
assert (res == pool).all()
def test_arrayview_constructor_view():
a = N.arange(5, dtype=context.current_precision_short())
view = R.arrayview(context.current_precision())(a, a.size)
assert view.size() == a.size
assert not view.isOwner()
vview = view.view()
print('Python/C++', a, vview)
assert (a == vview).all()
assert view == view
assert not view != view
def normalizedconvolution_prepare(fcn, weights, scale=1.0, nsname=None):
fcn = N.ascontiguousarray(fcn, dtype=context.current_precision_short())
weights = N.ascontiguousarray(weights,
dtype=context.current_precision_short())
fcn_p, weights_p = C.Points(fcn), C.Points(weights)
if nsname is not None:
ns = env.globalns(nsname)
ns.defparameter('scale', central=scale, fixed=True)
with ns:
nc = C.NormalizedConvolution('scale')
else:
scale = 1.0
nc = C.NormalizedConvolution()
fcn_p >> nc.normconvolution.fcn
weights_p >> nc.normconvolution.weights
res = nc.normconvolution.result.data()
prod = nc.normconvolution.product.data()
product_e = fcn * weights
res_e = scale * product_e.sum() / weights.sum()
print('Scale', scale)
print('Function', fcn)
print('Weights', weights)
print('Product', prod)
print('Product expected', product_e)
print('Result', res)
print('Result expected', res_e)
assert (prod == product_e).all()
assert (res == res_e).all()
print()
def test_arrayview_constructor():
size = 5
import ctypes
san_size = ctypes.c_int(5)
a = N.arange(size, dtype=context.current_precision_short())
print("in failing test! Precision is {}".format(
context.current_precision()))
view = R.arrayview[context.current_precision()](size)
assert view.size() == size
assert view.isOwner()
for i, v in enumerate(a):
view[i] = v
vview = view.view()
print('C++', vview)
assert (a == vview).all()
def Points(array, *args, **kwargs):
"""Convert array/TH1/TH2 to Points"""
if isinstance(array, R.TObject):
if not hasattr(array, 'get_buffer'):
raise Exception(
'Only TH1D/TH2D/TH1F/TH2F/TMatrixD/TMatrixF may be converted to Points'
)
if isinstance(array, R.TH2):
array = array.get_buffer().T
else:
array = array.get_buffer()
array = np.ascontiguousarray(array,
dtype=context.current_precision_short())
if len(array.shape) > 2:
raise Exception('Can convert only 1- and 2- dimensional arrays')
s = array_to_stdvector_size_t(array.shape)
return R.GNA.GNAObjectTemplates.PointsT(context.current_precision())(
array.ravel(order='F'), s, *args, **kwargs)
def test_object_variables(function_name):
ns = env.globalns(function_name)
names = [ 'zero', 'one', 'two', 'three', 'four', 'five' ]
values = N.arange(len(names), dtype=context.current_precision_short())
for name, value in zip(names, values):
ns.defparameter(name, central=value, relsigma=0.1)
with ns:
va = C.VarArray(names)
for i, (name, val_true) in enumerate(zip(names, values)):
var = va.variables[i].getVariable()
assert name==var.name()
val = var.cast().value()
assert val==val_true
for i, (name, val_true) in enumerate(zip(names, values)):
ns[name].set(val_true)
var=va.variables[i].getVariable()
val=var.cast().value()
assert val==val_true
def test_vararray_v01(function_name):
ns = env.globalns(function_name)
names = ['zero', 'one', 'two', 'three', 'four', 'five']
values = N.arange(len(names), dtype=context.current_precision_short())
for name, value in zip(names, values):
ns.defparameter(name, central=value, relsigma=0.1)
with ns:
va = C.VarArray(names)
res = va.vararray.points.data()
print('Python array:', values)
print('Array:', res)
assert N.allclose(values, res)
for i, (val, name) in enumerate(enumerate(names, 2)):
ns[name].set(val)
values[i] = val
res = va.vararray.points.data()
assert N.allclose(values, res)
def test_histedges_offset_02():
size = 13
for edges_offset in (0, 1.5):
edges = N.arange(size, dtype='d') + edges_offset
hist_in = C.Histogram(edges)
arr = N.zeros(edges.size - 1, dtype=context.current_precision_short())
for offset in range(size - 1):
threshold = edges[offset] + 0.6
print('Offset', offset)
print('Threshold', threshold)
view = C.HistEdgesOffset(hist_in, threshold)
edges_truncated = edges[offset:]
edges_threshold = edges_truncated.copy()
edges_threshold[0] = threshold
edges_offset = edges_threshold.copy()
edges_offset -= threshold
trans = view.histedges
points = trans.points.data()
points_truncated = trans.points_truncated.data()
points_threshold = trans.points_threshold.data()
points_offset = trans.points_offset.data()
hist_truncated_d = trans.hist_truncated.data()
hist_truncated = N.array(trans.hist_truncated.datatype().edges)
hist_threshold_d = trans.hist_threshold.data()
hist_threshold = N.array(trans.hist_threshold.datatype().edges)
hist_offset_d = trans.hist_offset.data()
hist_offset = N.array(trans.hist_offset.datatype().edges)
print(' Edges (expected)', edges)
print(' Edges truncated (expected)', edges_truncated)
print(' Edges threshold (expected)', edges_threshold)
print(' Edges offset (expected)', edges_offset)
print(' Points', points)
print(' Points truncated', points_truncated)
print(' Points threshold', points_threshold)
print(' Points offset', points_offset)
print(' Hist truncated', hist_truncated)
print(' Hist truncated data', hist_truncated_d)
print(' Hist threshold', hist_threshold)
print(' Hist threshold data', hist_threshold_d)
print(' Hist offset', hist_offset)
print(' Hist offset data', hist_offset_d)
assert (edges == points).all()
assert (edges_truncated == points_truncated).all()
assert (edges_threshold == points_threshold).all()
assert (edges_offset == points_offset).all()
assert (edges_truncated == hist_truncated).all()
assert (0.0 == hist_truncated_d).all()
assert (edges_threshold == hist_threshold).all()
assert (0.0 == hist_threshold_d).all()
assert (edges_offset == hist_offset).all()
assert (0.0 == hist_offset_d).all()
