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Cache harmonics sums for the as2 anomalous dimensions #179

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Cache harmonics sums for the as2 anomalous dimensions #179

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603ca1b
Init cache of different types of harmonic sums
niclaurenti Nov 29, 2022
aa4ffba
Change compute_new_sx_cache
niclaurenti Dec 2, 2022
859afd4
Change name of sx_cache_qed
niclaurenti Dec 2, 2022
342de1b
Add description for compute_new_sx_cache
niclaurenti Dec 2, 2022
9db86a0
Remove unnecessary functions
niclaurenti Dec 2, 2022
e9988dd
Fix docstring of compute_new_sx_cache
niclaurenti Dec 8, 2022
7a10274
Rename compute_new_sx_cache -> compute_additional_sx_cache
niclaurenti Dec 9, 2022
63d800a
Fix benchmarks/eko/benchmark_ad.py
niclaurenti Dec 9, 2022
963bc66
Merge branch 'feature/qed-solution' into feature/cache-harm-sums
niclaurenti Dec 9, 2022
fcf8744
Add additional_sx_cache to compute_cache
niclaurenti Dec 18, 2022
f0283bc
Fix benchmark_ad.py
niclaurenti Dec 19, 2022
9338c4c
Implement g3 as a function of Sm21
niclaurenti Dec 19, 2022
c08ac2e
Merge branch 'feature/qed-solution' into feature/cache-harm-sums
niclaurenti Dec 19, 2022
18b33c0
Merge branch 'feature/qed-solution' into feature/cache-harm-sums
niclaurenti Jan 9, 2023
4a583ab
Simplify test_init.py
niclaurenti Jan 10, 2023
8449d45
Refactor tests in test_init.py
niclaurenti Jan 10, 2023
018b121
REmove unnecessary parameters from test
niclaurenti Jan 10, 2023
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260 changes: 57 additions & 203 deletions tests/eko/evolution_operator/test_init.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,183 +28,40 @@ def test_quad_ker(monkeypatch):
monkeypatch.setattr(ns, "dispatcher", lambda *args: 1.0)
monkeypatch.setattr(qed_ns, "dispatcher", lambda *args: 1.0)
monkeypatch.setattr(s, "dispatcher", lambda *args: np.identity(2))
for is_log in [True, False]:
res_ns = quad_ker(
u=0,
order=(1, 0),
mode0=br.non_singlet_pids_map["ns+"],
mode1=0,
method="",
is_log=is_log,
logx=0.0,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_ns, 0.0)
res_ns = quad_ker(
u=0,
order=(3, 1),
mode0=br.non_singlet_pids_map["ns+u"],
mode1=0,
method="",
is_log=is_log,
logx=0.0,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_ns, 0.0)
res_s = quad_ker(
u=0,
order=(1, 0),
mode0=100,
mode1=100,
method="",
is_log=is_log,
logx=0.123,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_s, 1.0)
res_s = quad_ker(
u=0,
order=(1, 1),
mode0=100,
mode1=100,
method="iterate-exact",
is_log=is_log,
logx=0.123,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_s, 1.0)
res_s = quad_ker(
u=0,
order=(1, 0),
mode0=100,
mode1=21,
method="",
is_log=is_log,
logx=0.0,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_s, 0.0)
res_s = quad_ker(
u=0,
order=(1, 1),
mode0=100,
mode1=21,
method="iterate-exact",
is_log=is_log,
logx=0.0,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_s, 0.0)
res_v = quad_ker(
u=0,
order=(1, 1),
mode0=10200,
mode1=10200,
method="iterate-exact",
is_log=is_log,
logx=0.123,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_v, 1.0)
res_v = quad_ker(
u=0,
order=(1, 1),
mode0=10200,
mode1=10204,
method="iterate-exact",
is_log=is_log,
logx=0.123,
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_v, 0.0)
params = [
((1, 0), br.non_singlet_pids_map["ns+"], 0, "", 0.0, 0.0),
((3, 1), br.non_singlet_pids_map["ns+u"], 0, "", 0.0, 0.0),
((1, 0), 100, 100, "", 0.123, 1.0),
((1, 0), 100, 21, "", 0.0, 0.0),
((1, 1), 100, 100, "iterate-exact", 0.123, 1.0),
((1, 1), 100, 21, "iterate-exact", 0.123, 0.0),
((1, 1), 10200, 10200, "iterate-exact", 0.123, 1.0),
((1, 1), 10200, 10204, "iterate-exact", 0.123, 0.0),
]
for p in params:
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maybe better for order,mode0,mode1,bla,bla in params?

for is_log in [True, False]:
res_ns = quad_ker(
u=0,
order=p[0],
mode0=p[1],
mode1=p[2],
method=p[3],
is_log=is_log,
logx=p[4],
areas=np.zeros(3),
as1=1,
as0=2,
as_raw=1,
aem_list=[0.00058],
alphaem_running=False,
nf=3,
L=0,
ev_op_iterations=0,
ev_op_max_order=(0, 0),
sv_mode=1,
is_threshold=False,
)
np.testing.assert_allclose(res_ns, p[5])
for label in [(br.non_singlet_pids_map["ns+"], 0), (100, 100)]:
for sv in [2, 3]:
res_sv = quad_ker(
Expand Down Expand Up @@ -287,14 +144,23 @@ def test_quad_ker(monkeypatch):
np.testing.assert_allclose(res_ns, 0.0)


class FakeCoupling:
def __init__(self):
self.alphaem_running = None
self.q2_ref = 0.0

def a(self, scale_to=None, fact_scale=None, nf_to=None):
return (0.1, 0.01)

def compute_aem_as(self, aem_ref, as_from, as_to, nf):
return aem_ref


fake_managers = {"couplings": FakeCoupling()}


class TestOperator:
def test_labels(self, theory_ffns, operator_card, tmp_path):
tcard: TheoryCard = theory_ffns(3)
tcard.xif = 2.0
ocard: OperatorCard = operator_card
ocard.configs.scvar_method = ScaleVariationsMethod.EXPONENTIATED
r = eko.runner.legacy.Runner(tcard, ocard, path=tmp_path / "eko.tar")
g = r.op_grid
def test_labels(self):
o = Operator(
dict(
order=(3, 0),
Expand All @@ -303,7 +169,7 @@ def test_labels(self, theory_ffns, operator_card, tmp_path):
n_integration_cores=1,
ModSV=None,
),
g.managers,
fake_managers,
3,
1,
2,
Expand All @@ -317,20 +183,14 @@ def test_labels(self, theory_ffns, operator_card, tmp_path):
n_integration_cores=1,
ModSV=None,
),
g.managers,
fake_managers,
3,
1,
2,
)
assert sorted(o.labels) == []

def test_labels_qed(self, theory_ffns, operator_card, tmp_path):
tcard: TheoryCard = theory_ffns(3)
tcard.xif = 2.0
ocard: OperatorCard = operator_card
ocard.configs.scvar_method = ScaleVariationsMethod.EXPONENTIATED
r = eko.runner.legacy.Runner(tcard, ocard, path=tmp_path / "eko.tar")
g = r.op_grid
def test_labels_qed(self):
o = Operator(
dict(
order=(3, 1),
Expand All @@ -340,7 +200,7 @@ def test_labels_qed(self, theory_ffns, operator_card, tmp_path):
ModSV=None,
ev_op_iterations=1,
),
g.managers,
fake_managers,
3,
1,
2,
Expand All @@ -355,24 +215,18 @@ def test_labels_qed(self, theory_ffns, operator_card, tmp_path):
ModSV=None,
ev_op_iterations=1,
),
g.managers,
fake_managers,
3,
1,
2,
)
assert sorted(o.labels) == []

def test_n_pools(self, theory_ffns, operator_card, tmp_path):
def test_n_pools(self):
excluded_cores = 3
# make sure we actually have more the those cores (e.g. on github we don't)
if os.cpu_count() <= excluded_cores:
return
tcard: TheoryCard = theory_ffns(3)
tcard.xif = 2.0
ocard: OperatorCard = operator_card
ocard.configs.scvar_method = ScaleVariationsMethod.EXPONENTIATED
r = eko.runner.legacy.Runner(tcard, ocard, path=tmp_path / "eko.tar")
g = r.op_grid
o = Operator(
dict(
order=(2, 0),
Expand All @@ -381,7 +235,7 @@ def test_n_pools(self, theory_ffns, operator_card, tmp_path):
n_integration_cores=-excluded_cores,
ModSV=None,
),
g.managers,
fake_managers,
3,
1,
10,
Expand Down