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Resolved merge conflict in tests

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This commit is contained in:
Simon Kuberski 2022-03-29 17:07:39 +02:00
commit be1eeccd8e
47 changed files with 9678 additions and 816 deletions
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1
tests/benchmark_test.py
View file

@ -1,7 +1,6 @@
import numpy as np
import pyerrors as pe
import pytest
import time
np.random.seed(0)

121
tests/correlators_test.py
View file

@ -24,6 +24,7 @@ def test_function_overloading():
for i, f in enumerate(fs):
t1 = f([corr_a, corr_b])
t1.gamma_method()
for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content):
t2 = f([o_a, o_b])
t2.gamma_method()
@ -98,6 +99,9 @@ def test_m_eff():
with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh')
with pytest.raises(Exception):
my_corr.m_eff('unkown_variant')
def test_reweighting():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
@ -131,6 +135,11 @@ def test_fit_correlator():
assert fit_res[0] == my_corr[0]
assert fit_res[1] == my_corr[1] - my_corr[0]
with pytest.raises(Exception):
my_corr.fit(f, "from 0 to 3")
with pytest.raises(Exception):
my_corr.fit(f, [0, 2, 3])
def test_plateau():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(1.042345, 0.008, 't')])
@ -172,13 +181,16 @@ def test_utility():
corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
corr = pe.correlators.Corr(corr_content)
corr.gamma_method()
corr.print()
corr.print([2, 4])
corr.show()
corr.show(comp=corr)
corr.dump('test_dump', datatype="pickle", path='.')
corr.dump('test_dump', datatype="pickle")
new_corr = pe.load_object('test_dump.p')
new_corr.gamma_method()
os.remove('test_dump.p')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
@ -188,6 +200,7 @@ def test_utility():
corr.dump('test_dump', datatype="json.gz", path='.')
corr.dump('test_dump', datatype="json.gz")
new_corr = pe.input.json.load_json('test_dump')
new_corr.gamma_method()
os.remove('test_dump.json.gz')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
@ -195,19 +208,27 @@ def test_utility():
assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
def test_prange():
corr_content = []
for t in range(8):
corr_content.append(pe.pseudo_Obs(2 + 10 ** (1.1 * t), 0.2, 't'))
corr = pe.correlators.Corr(corr_content)
corr.set_prange([2, 4])
with pytest.raises(Exception):
corr.set_prange([2])
with pytest.raises(Exception):
corr.set_prange([2, 2.3])
with pytest.raises(Exception):
corr.set_prange([4, 1])
def test_matrix_corr():
def _gen_corr(val):
corr_content = []
for t in range(16):
corr_content.append(pe.pseudo_Obs(val, 0.1, 't', 2000))
return pe.correlators.Corr(corr_content)
corr_aa = _gen_corr(1)
corr_ab = _gen_corr(0.5)
corr_ab = 0.5 * corr_aa
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
corr_mat.smearing(0, 0)
corr_mat.item(0, 0)
vec_0 = corr_mat.GEVP(0, 0)
vec_1 = corr_mat.GEVP(0, 0, state=1)
@ -221,6 +242,8 @@ def test_matrix_corr():
corr_mat.GEVP(0, 0, sorted_list="Eigenvalue")
corr_mat.GEVP(0, 0, sorted_list="Eigenvector")
corr_mat.matrix_symmetric()
with pytest.raises(Exception):
corr_mat.plottable()
@ -240,4 +263,82 @@ def test_matrix_corr():
corr_mat.plateau([2, 4])
with pytest.raises(Exception):
corr_o.smearing(0, 0)
corr_mat.hankel(3)
with pytest.raises(Exception):
corr_mat.fit(lambda x: x[0])
with pytest.raises(Exception):
corr_0.item(0, 0)
with pytest.raises(Exception):
corr_0.matrix_symmetric()
def test_hankel():
corr_content = []
for t in range(8):
exponent = 1.2
corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't'))
corr = pe.Corr(corr_content)
corr.Hankel(2)
corr.Hankel(6, periodic=True)
def test_thin():
c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren')
thin = c.thin()
thin.gamma_method()
thin.fit(lambda a, x: a[0] * x)
c.thin(offset=1)
c.thin(3, offset=1)
def test_corr_matrix_none_entries():
dim = 8
x = np.arange(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = [0.1] * dim
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
corr = pe.Corr(oy)
corr = corr.deriv()
pe.Corr(np.array([[corr, corr], [corr, corr]]))
def test_corr_vector_operations():
my_corr = _gen_corr(1.0)
my_vec = np.arange(1, 17)
my_corr + my_vec
my_corr - my_vec
my_corr * my_vec
my_corr / my_vec
assert np.all([o == 0 for o in ((my_corr + my_vec) - my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr - my_vec) + my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr * my_vec) / my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr / my_vec) * my_vec) - my_corr])
def test_spaghetti_plot():
corr = _gen_corr(12, 50)
corr += pe.pseudo_Obs(0.0, 0.1, 'another_ensemble')
corr += pe.cov_Obs(0.0, 0.01 ** 2, 'covobs')
corr.spaghetti_plot(True)
corr.spaghetti_plot(False)
def _gen_corr(val, samples=2000):
corr_content = []
for t in range(16):
corr_content.append(pe.pseudo_Obs(val, 0.1, 't', samples))
return pe.correlators.Corr(corr_content)

24
tests/covobs_test.py
View file

@ -39,8 +39,8 @@ def test_covobs():
assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance(cl[0], cl[1]) == cov[1][0])
assert(np.isclose(pe.covariance([cl[0], cl[1]])[0, 1], cov[0][1]))
assert(np.isclose(pe.covariance([cl[0], cl[1]])[0, 1], cov[1][0]))
do = cl[0] * cl[1]
assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
@ -78,10 +78,22 @@ def test_covobs_init():
covobs = pe.cov_Obs(0.5, 0.002, 'test')
covobs = pe.cov_Obs([1, 2], [0.1, 0.2], 'test')
covobs = pe.cov_Obs([1, 2], np.array([0.1, 0.2]), 'test')
covobs = pe.cov_Obs([1, 2], [[0.1, 0.2], [0.1, 0.2]], 'test')
covobs = pe.cov_Obs([1, 2], np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
covobs = pe.cov_Obs([1, 2], [[0.21, 0.2], [0.2, 0.21]], 'test')
covobs = pe.cov_Obs([1, 2], np.array([[0.21, 0.2], [0.2, 0.21]]), 'test')
def test_covobs_covariance():
a = pe.cov_Obs(2.47, 0.03 ** 2, "Cov_obs 1")
b = pe.cov_Obs(-4.3, 0.335 ** 2, "Cov_obs 2")
x = [a + b, a - b]
[o.gamma_method() for o in x]
covariance = pe.covariance(x)
assert np.isclose(covariance[0, 0], covariance[1, 1])
assert np.isclose(covariance[0, 1], a.dvalue ** 2 - b.dvalue ** 2)
def test_covobs_exceptions():
with pytest.raises(Exception):
@ -92,3 +104,7 @@ def test_covobs_exceptions():
covobs = pe.cov_Obs([0.5, 0.1], np.array([[2, 1, 3], [1, 2, 3]]), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([0.5, 0.1], np.random.random((2, 2, 2)), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([1.5, 0.1], [[1., .2,], [.3, .5]] , 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([1.5, 0.1], [[8, 4,], [4, -2]] , 'test')

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tests/data/sfcf_test/data_a/data_a_r0.F_V0 Normal file
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tests/data/sfcf_test/data_a/data_a_r0.f_1 Normal file
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@ -0,0 +1,970 @@
[run]
version 2.1
date 2022-01-19 11:04:03 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /data_a_r0_n1
gauge_md5 1ea28326e4090996111a320b8372811d
param_name sfcf_unity_test.in
param_md5 d881e90d41188a33b8b0f1bd0bc53ea5
param_hash 686af5e712ee2902180f5428af94c6e7
data_name ./output_10519905/data_af_1
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quarks lquark lquark
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[run]
version 2.1
date 2022-01-19 11:04:05 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /data_a_r0_n2
gauge_md5 1ea28326e4090996111a320b8372811d
param_name sfcf_unity_test.in
param_md5 d881e90d41188a33b8b0f1bd0bc53ea5
param_hash 686af5e712ee2902180f5428af94c6e7
data_name ./output_10519905/data_af_1
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version 2.1
date 2022-01-19 11:04:07 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /data_a_r0_n3
gauge_md5 1ea28326e4090996111a320b8372811d
param_name sfcf_unity_test.in
param_md5 d881e90d41188a33b8b0f1bd0bc53ea5
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version 2.1
date 2022-01-19 11:04:09 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /data_a_r0_n4
gauge_md5 1ea28326e4090996111a320b8372811d
param_name sfcf_unity_test.in
param_md5 d881e90d41188a33b8b0f1bd0bc53ea5
param_hash 686af5e712ee2902180f5428af94c6e7
data_name ./output_10519905/data_af_1
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230
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@ -0,0 +1,230 @@
[run]
version 2.1
date 2022-01-19 11:04:00 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /unity
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194
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@ -0,0 +1,194 @@
[run]
version 2.1
date 2022-01-19 11:04:00 +0100
host r04n07.palma.wwu
dir /scratch/tmp/j_kuhl19
user j_kuhl19
gauge_name /unity
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80
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@ -0,0 +1,80 @@
[run]
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date 2022-01-19 11:04:00 +0100
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3273
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102
tests/data/sfcf_test/param/parameters_a Normal file
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@ -0,0 +1,102 @@
tsize 4
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csw +1.8228459568314139e+00
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errsq +1.0000000000000001e-18
nmax 4096
oqcd_solver_select -1
oqcd_solver_param 3844
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nmr 4
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nmx 2048
res +1.0000000000000000e-10
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[Solver 1]
solver CGNE
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[Solver 2]
solver SAP_GCR
nkv 24
isolv 1
nmr 4
ncy 5
nmx 2048
res +1.0000000000000000e-10
istop 0
[Solver 3]
solver CGNE_NO_EO
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[SAP]
bs 4 4 4 4
[Deflation subspace]
bs 4 4 4 4
Ns 20
[Deflation subspace generation]
kappa +1.3713790000000001e-01
mu +0.0000000000000000e+00
ninv 10
nmr 4
ncy 5
[Deflation projection]
nkv 18
nmx 2048
res +1.0000000000000000e-02
wf_offsets 2
+0.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +5.0000000000000000e-01
wf_basis 3
+0.0000000000000000e+00 +2.6666666666666665e+00
+1.0000000000000000e+00 +2.6666666666666665e+00
+0.0000000000000000e+00 +5.3333333333333330e+00
wf_coeff 3
+1.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +1.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +1.0000000000000000e+00
qr 1
lquark +1.3713790000000001e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 persistent
mrr 1
lquark lquark
crr 3
f_A
f_1
F_V0
sg 0
qs 0
mrs 0
crs 0
# param_hash 686af5e712ee2902180f5428af94c6e7

102
tests/data/sfcf_test/param/parameters_c Normal file
View file

@ -0,0 +1,102 @@
tsize 4
l1 4
l2 4
l3 4
nf 3
beta +3.5522000000000000e+00
ct +1.0551857440459433e+00
cttilde +9.7457913405776697e-01
csw +1.8228459568314139e+00
isw 0
errsq +1.0000000000000001e-18
nmax 4096
oqcd_solver_select -1
oqcd_solver_param 3844
[Solver 0]
solver DFL_SAP_GCR
nkv 24
isolv 1
nmr 4
ncy 5
nmx 2048
res +1.0000000000000000e-10
istop 0
[Solver 1]
solver CGNE
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[Solver 2]
solver SAP_GCR
nkv 24
isolv 1
nmr 4
ncy 5
nmx 2048
res +1.0000000000000000e-10
istop 0
[Solver 3]
solver CGNE_NO_EO
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[SAP]
bs 4 4 4 4
[Deflation subspace]
bs 4 4 4 4
Ns 20
[Deflation subspace generation]
kappa +1.3713790000000001e-01
mu +0.0000000000000000e+00
ninv 10
nmr 4
ncy 5
[Deflation projection]
nkv 18
nmx 2048
res +1.0000000000000000e-02
wf_offsets 2
+0.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +5.0000000000000000e-01
wf_basis 3
+0.0000000000000000e+00 +2.6666666666666665e+00
+1.0000000000000000e+00 +2.6666666666666665e+00
+0.0000000000000000e+00 +5.3333333333333330e+00
wf_coeff 3
+1.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +1.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +1.0000000000000000e+00
qr 1
lquark +1.3713790000000001e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 persistent
mrr 1
lquark lquark
crr 3
f_A
f_1
F_V0
sg 0
qs 0
mrs 0
crs 0
# param_hash 686af5e712ee2902180f5428af94c6e7

102
tests/data/sfcf_test/param/parameters_o Normal file
View file

@ -0,0 +1,102 @@
tsize 4
l1 4
l2 4
l3 4
nf 3
beta +3.5522000000000000e+00
ct +1.0551857440459433e+00
cttilde +9.7457913405776697e-01
csw +1.8228459568314139e+00
isw 0
errsq +1.0000000000000001e-18
nmax 4096
oqcd_solver_select -1
oqcd_solver_param 3844
[Solver 0]
solver DFL_SAP_GCR
nkv 24
isolv 1
nmr 4
ncy 5
nmx 2048
res +1.0000000000000000e-10
istop 0
[Solver 1]
solver CGNE
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[Solver 2]
solver SAP_GCR
nkv 24
isolv 1
nmr 4
ncy 5
nmx 2048
res +1.0000000000000000e-10
istop 0
[Solver 3]
solver CGNE_NO_EO
nkv 0
isolv 0
nmr 0
ncy 0
nmx 4096
res +1.0000000000000000e-10
istop 0
[SAP]
bs 4 4 4 4
[Deflation subspace]
bs 4 4 4 4
Ns 20
[Deflation subspace generation]
kappa +1.3713790000000001e-01
mu +0.0000000000000000e+00
ninv 10
nmr 4
ncy 5
[Deflation projection]
nkv 18
nmx 2048
res +1.0000000000000000e-02
wf_offsets 2
+0.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +5.0000000000000000e-01
wf_basis 3
+0.0000000000000000e+00 +2.6666666666666665e+00
+1.0000000000000000e+00 +2.6666666666666665e+00
+0.0000000000000000e+00 +5.3333333333333330e+00
wf_coeff 3
+1.0000000000000000e+00 +0.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +1.0000000000000000e+00 +0.0000000000000000e+00
+0.0000000000000000e+00 +0.0000000000000000e+00 +1.0000000000000000e+00
qr 1
lquark +1.3713790000000001e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 +5.0000000000000000e-01 persistent
mrr 1
lquark lquark
crr 3
f_A
f_1
F_V0
sg 0
qs 0
mrs 0
crs 0
# param_hash 686af5e712ee2902180f5428af94c6e7

94
tests/fits_test.py
View file

@ -1,4 +1,5 @@
import autograd.numpy as np
import numpy as np
import autograd.numpy as anp
import math
import scipy.optimize
from scipy.odr import ODR, Model, RealData
@ -41,12 +42,12 @@ def test_least_squares():
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
return a * anp.exp(-b * x)
popt, pcov = scipy.optimize.curve_fit(f, x, y, sigma=[o.dvalue for o in oy], absolute_sigma=True)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
y = a[0] * anp.exp(-a[1] * x)
return y
out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
@ -60,7 +61,7 @@ def test_least_squares():
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(beta[0], beta[1]), pcov[0, 1], abs_tol=1e-3)
assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], pcov[0, 1], abs_tol=1e-3)
chi2_pyerrors = np.sum(((f(x, *[o.value for o in beta]) - y) / yerr) ** 2) / (len(x) - 2)
chi2_scipy = np.sum(((f(x, *popt) - y) / yerr) ** 2) / (len(x) - 2)
@ -81,7 +82,34 @@ def test_least_squares():
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], pcov[0, 1], abs_tol=1e-3)
def test_alternative_solvers():
dim = 192
x = np.arange(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
def func(a, x):
y = a[0] * anp.exp(-a[1] * x)
return y
chisquare_values = []
out = pe.least_squares(x, oy, func, method='migrad')
chisquare_values.append(out.chisquare)
out = pe.least_squares(x, oy, func, method='Powell')
chisquare_values.append(out.chisquare)
out = pe.least_squares(x, oy, func, method='Nelder-Mead')
chisquare_values.append(out.chisquare)
out = pe.least_squares(x, oy, func, method='Levenberg-Marquardt')
chisquare_values.append(out.chisquare)
chisquare_values = np.array(chisquare_values)
assert np.all(np.isclose(chisquare_values, chisquare_values[0]))
def test_correlated_fit():
@ -118,7 +146,7 @@ def test_correlated_fit():
return p[1] + p[0] * x
else:
def fitf(p, x):
return p[1] * np.exp(-p[0] * x)
return p[1] * anp.exp(-p[0] * x)
fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
@ -145,10 +173,10 @@ def test_total_least_squares():
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
return a * anp.exp(-b * x)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
y = a[0] * anp.exp(-a[1] * x)
return y
data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
@ -168,7 +196,7 @@ def test_total_least_squares():
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
assert math.isclose(pe.covariance(beta[0], beta[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
@ -191,7 +219,7 @@ def test_total_least_squares():
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
@ -206,7 +234,7 @@ def test_total_least_squares():
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
@ -291,6 +319,23 @@ def test_prior_fit():
fitp = pe.fits.least_squares([0, 1], y, f, priors=y, resplot=True, qqplot=True)
def test_correlated_fit_covobs():
x1 = pe.cov_Obs(1.01, 0.01 ** 2, 'test1')
x2 = pe.cov_Obs(2.01, 0.01 ** 2, 'test2')
x3 = pe.cov_Obs(2.99, 0.01 ** 2, 'test3')
[o.gamma_method() for o in [x1, x2, x3]]
def func(a, x):
return a[0] * x + a[1]
fit_res = pe.fits.least_squares(np.arange(1, 4), [x1, x2, x3], func, expected_chisquare=True)
assert np.isclose(fit_res.chisquare_by_dof, fit_res.chisquare_by_expected_chisquare)
fit_res_corr = pe.fits.least_squares(np.arange(1, 4), [x1, x2, x3], func, correlated_fit=True)
assert np.isclose(fit_res.chisquare_by_dof, fit_res_corr.chisquare_by_dof)
def test_error_band():
def f(a, x):
return a[0] + a[1] * x
@ -309,6 +354,33 @@ def test_error_band():
pe.fits.error_band(x, f, fitp.fit_parameters)
def test_fit_no_autograd():
dim = 10
x = np.arange(dim)
y = 2 * np.exp(-0.08 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
return y
with pytest.raises(Exception):
pe.least_squares(x, oy, func)
with pytest.raises(Exception):
pe.total_least_squares(oy, oy, func)
def test_singular_correlated_fit():
obs1 = pe.pseudo_Obs(1.0, 0.1, 'test')
with pytest.raises(Exception):
pe.fits.fit_lin([0, 1], [obs1, obs1], correlated_fit=True)
def test_ks_test():
def f(a, x):
y = a[0] + a[1] * x

115
tests/io_test.py → tests/json_io_test.py
View file

@ -4,7 +4,7 @@ import numpy as np
import pyerrors as pe
import pyerrors.input.json as jsonio
import pyerrors.input.dobs as dobsio
import pytest
def test_jsonio():
o = pe.pseudo_Obs(1.0, .2, 'one')
@ -231,3 +231,116 @@ def test_dobsio():
for j in range(len(or1)):
o = or1[j] - or2[j]
assert(o.is_zero())
def test_json_dict_io():
def check_dict_equality(d1, d2):
def dict_check_obs(d1, d2):
for k, v in d1.items():
if isinstance(v, dict):
v = dict_check_obs(v, d2[k])
elif isinstance(v, list) and all([isinstance(o, pe.Obs) for o in v]):
for i in range(len(v)):
assert((v[i] - d2[k][i]).is_zero())
elif isinstance(v, list):
v = list_check_obs(v, d2[k])
elif isinstance(v, pe.Obs):
assert((v - d2[k]).is_zero())
elif isinstance(v, pe.Corr):
for i in range(v.T):
assert((v[i] - d2[k][i]).is_zero())
elif isinstance(v, np.ndarray):
a1 = np.ravel(v)
a2 = np.ravel(d2[k])
for i in range(len(a1)):
assert((a1[i] - a2[i]).is_zero())
def list_check_obs(l1, l2):
for ei in range(len(l1)):
e = l1[ei]
if isinstance(e, list):
e = list_check_obs(e, l2[ei])
elif isinstance(e, list) and all([isinstance(o, pe.Obs) for o in e]):
for i in range(len(e)):
assert((e[i] - l2[ei][i]).is_zero())
elif isinstance(e, dict):
e = dict_check_obs(e, l2[ei])
elif isinstance(e, pe.Obs):
assert((e - l2[ei]).is_zero())
elif isinstance(e, pe.Corr):
for i in range(e.T):
assert((e[i] - l2[ei][i]).is_zero())
elif isinstance(e, np.ndarray):
a1 = np.ravel(e)
a2 = np.ravel(l2[ei])
for i in range(len(a1)):
assert((a1[i] - a2[i]).is_zero())
dict_check_obs(d1, d2)
return True
od = {
'l':
{
'a': pe.pseudo_Obs(1, .2, 'testa', samples=10),
'b': [pe.pseudo_Obs(1.1, .1, 'test', samples=10), pe.pseudo_Obs(1.2, .1, 'test', samples=10), pe.pseudo_Obs(1.3, .1, 'test', samples=10)],
'c': {
'd': 1,
'e': pe.pseudo_Obs(.2, .01, 'teste', samples=10),
'f': pe.Corr([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]),
'g': np.reshape(np.asarray([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]), (2, 2)),
}
},
's':
{
'a': 'Infor123',
'b': ['Some', 'list'],
'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'),
'se': None,
'sf': 1.2,
}
}
fname = 'test_rw'
desc = 'This is a random description'
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc, reps='|Test')
jsonio.dump_dict_to_json(od, fname, description=desc, reps='TEST')
nd = jsonio.load_json_dict(fname, full_output=True, reps='TEST')
with pytest.raises(Exception):
nd = jsonio.load_json_dict(fname, full_output=True)
jsonio.dump_dict_to_json(od, fname, description=desc)
nd = jsonio.load_json_dict(fname, full_output=True)
assert (desc == nd['description'])
assert(check_dict_equality(od, nd['obsdata']))
nd = jsonio.load_json_dict(fname, full_output=False)
assert(check_dict_equality(od, nd))
nl = jsonio.load_json(fname, full_output=True)
nl = jsonio.load_json(fname, full_output=False)
with pytest.raises(Exception):
jsonio.dump_dict_to_json(nl, fname, description=desc)
od['k'] = 'DICTOBS2'
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc)
od['k'] = ['DICTOBS2']
with pytest.raises(Exception):
jsonio.dump_dict_to_json(od, fname, description=desc)
os.remove(fname + '.json.gz')
def test_renorm_deriv_of_corr(tmp_path):
c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren')
c = c.deriv()
pe.input.json.dump_to_json(c, (tmp_path / 'test').as_posix())
recover = pe.input.json.load_json((tmp_path / 'test').as_posix())
assert np.all([o == 0 for o in (c - recover)[1:-1]])

10
tests/linalg_test.py
View file

@ -314,6 +314,9 @@ def test_matrix_functions():
# Check determinant
assert pe.linalg.det(np.diag(np.diag(matrix))) == np.prod(np.diag(matrix))
with pytest.raises(Exception):
pe.linalg.det(5)
pe.linalg.pinv(matrix[:,:3])
@ -347,3 +350,10 @@ def test_complex_matrix_operations():
diff = ta * tb - 1
for (i, j), entry in np.ndenumerate(diff):
assert entry.is_zero()
def test_complex_matrix_real_entries():
my_mat = get_complex_matrix(4)
my_mat[0, 1] = 4
my_mat[2, 0] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
assert np.all((my_mat @ pe.linalg.inv(my_mat) - np.identity(4)) == 0)

205
tests/obs_test.py
View file

@ -1,7 +1,5 @@
import autograd.numpy as np
import os
import random
import string
import copy
import pyerrors as pe
import pytest
@ -53,6 +51,12 @@ def test_Obs_exceptions():
my_obs.gamma_method()
my_obs.details()
obs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
one = obs / obs
one.gamma_method()
with pytest.raises(Exception):
one.plot_piechart()
def test_dump():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
@ -88,6 +92,8 @@ def test_comparison():
assert test_obs2 != value2
assert test_obs1 != test_obs2
assert test_obs2 != test_obs1
assert +test_obs1 == test_obs1
assert -test_obs1 == 0 - test_obs1
def test_function_overloading():
@ -117,6 +123,10 @@ def test_function_overloading():
np.arctanh(1 / b)
np.sinc(1 / b)
b ** b
0.5 ** b
b ** 0.5
def test_overloading_vectorization():
a = np.random.randint(1, 100, 10)
@ -138,9 +148,8 @@ def test_overloading_vectorization():
assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
def test_gamma_method():
def test_gamma_method_standard_data():
for data in [np.tile([1, -1], 1000),
np.random.rand(100001),
np.zeros(1195),
np.sin(np.sqrt(2) * np.pi * np.arange(1812))]:
test_obs = pe.Obs([data], ['t'])
@ -244,17 +253,6 @@ def test_gamma_method_kwargs():
assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
def test_covariance_is_variance():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
def test_fft():
value = np.random.normal(5, 100)
dvalue = np.abs(np.random.normal(0, 5))
@ -266,22 +264,7 @@ def test_fft():
assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
test_obs1.gamma_method()
value2 = np.random.normal(5, 10)
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
def test_gamma_method():
def test_gamma_method_uncorrelated():
# Construct pseudo Obs with random shape
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
@ -304,14 +287,14 @@ def test_derived_observables():
d_Obs_fd = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs], num_grad=True)
d_Obs_fd.gamma_method()
assert d_Obs_ad.value == d_Obs_fd.value
assert d_Obs_ad == d_Obs_fd
assert np.abs(4.0 * np.sin(4.0) - d_Obs_ad.value) < 1000 * np.finfo(np.float64).eps * np.abs(d_Obs_ad.value)
assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
i_am_one.gamma_method()
assert i_am_one.value == 1.0
assert i_am_one == 1.0
assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
assert i_am_one.e_dvalue['t'] <= 2 * np.finfo(np.float64).eps
assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
@ -392,6 +375,11 @@ def test_cobs():
obs2 = pe.pseudo_Obs(-0.2, 0.03, 't')
my_cobs = pe.CObs(obs1, obs2)
assert +my_cobs == my_cobs
assert -my_cobs == 0 - my_cobs
my_cobs == my_cobs
str(my_cobs)
repr(my_cobs)
assert not (my_cobs + my_cobs.conjugate()).real.is_zero()
assert (my_cobs + my_cobs.conjugate()).imag.is_zero()
assert (my_cobs - my_cobs.conjugate()).real.is_zero()
@ -424,6 +412,23 @@ def test_cobs():
assert (other / my_cobs * my_cobs - other).is_zero()
def test_cobs_overloading():
obs = pe.pseudo_Obs(1.1, 0.1, 't')
cobs = pe.CObs(obs, obs)
cobs + obs
obs + cobs
cobs - obs
obs - cobs
cobs * obs
obs * cobs
cobs / obs
obs / cobs
def test_reweighting():
my_obs = pe.Obs([np.random.rand(1000)], ['t'])
assert not my_obs.reweighted
@ -591,6 +596,24 @@ def test_gamma_method_irregular():
assert((ae.e_tauint['a'] - 4 * ae.e_dtauint['a'] < ao.e_tauint['a']))
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
a = pe.pseudo_Obs(1, .1, 'a', samples=10)
a.idl['a'] = range(4, 15)
b = pe.pseudo_Obs(1, .1, 'a', samples=151)
b.idl['a'] = range(4, 608, 4)
ol = [a, b]
o = (ol[0] - ol[1]) / (ol[1])
def test_covariance_is_variance():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.gamma_method()
assert np.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method()
assert np.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
@ -601,9 +624,9 @@ def test_covariance_symmetry():
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
cov_ab = pe.covariance([test_obs1, test_obs2])[0, 1]
cov_ba = pe.covariance([test_obs2, test_obs1])[0, 1]
assert np.isclose(cov_ab, cov_ba)
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
N = 100
@ -615,14 +638,82 @@ def test_covariance_symmetry():
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['t'], idl=[idx])
a.gamma_method()
assert np.isclose(a.dvalue**2, pe.covariance(a, a), atol=100, rtol=1e-4)
assert np.isclose(a.dvalue ** 2, pe.covariance([a, a])[0, 1], atol=100, rtol=1e-4)
cov_ab = pe.covariance(test_obs1, a)
cov_ba = pe.covariance(a, test_obs1)
cov_ab = pe.covariance([test_obs1, a])[0, 1]
cov_ba = pe.covariance([a, test_obs1])[0, 1]
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
def test_covariance_sum():
length = 2
t_fac = 0.4
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.99 * np.ones((length, length)) + 0.01 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
[o.gamma_method(S=0) for o in tt]
t_cov = pe.covariance(tt)
my_sum = tt[0] + tt[1]
my_sum.gamma_method(S=0)
e_cov = (my_sum.dvalue ** 2 - tt[0].dvalue ** 2 - tt[1].dvalue ** 2) / 2
assert np.isclose(e_cov, t_cov[0, 1])
def test_covariance_positive_semidefinite():
length = 64
t_fac = 1.5
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.99999 * np.ones((length, length)) + 0.00001 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
[o.gamma_method() for o in tt]
cov = pe.covariance(tt)
assert np.all(np.linalg.eigh(cov)[0] >= -1e-15)
def test_covariance_factorizing():
length = 2
t_fac = 1.5
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.75 * np.ones((length, length)) + 0.8 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
[o.gamma_method() for o in tt]
mt0 = -tt[0]
mt0.gamma_method()
assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
def test_covariance_alternation():
length = 12
t_fac = 2.5
tt1 = pe.misc.gen_correlated_data(np.zeros(length), -0.00001 * np.ones((length, length)) + 0.002 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=88)
tt2 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r0', tau=0.7 + t_fac * np.random.rand(length), samples=73)
tt3 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r1', tau=0.7 + t_fac * np.random.rand(length), samples=91)
tt = np.array(tt1) + (np.array(tt2) + np.array(tt3))
tt *= np.resize([1, -1], length)
[o.gamma_method() for o in tt]
cov = pe.covariance(tt, True)
assert np.all(np.linalg.eigh(cov)[0] > -1e-15)
def test_covariance_correlation():
test_obs = pe.pseudo_Obs(-4, 0.8, 'test', samples=784)
assert np.allclose(pe.covariance([test_obs, test_obs, test_obs], correlation=True), np.ones((3, 3)))
def test_covariance_rank_deficient():
obs = []
for i in range(5):
obs.append(pe.pseudo_Obs(1.0, 0.1, 'test', 5))
with pytest.warns(RuntimeWarning):
pe.covariance(obs)
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [])
@ -653,3 +744,39 @@ def test_import_jackknife():
my_jacks = my_obs.export_jackknife()
reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
assert my_obs == reconstructed_obs
def test_reduce_deltas():
idx_old = range(1, 101)
deltas = [float(i) for i in idx_old]
idl = [
range(2, 26, 2),
range(1, 101),
np.arange(1, 101),
[1, 2, 3, 5, 6, 7, 9, 12],
[7],
]
for idx_new in idl:
new = pe.obs._reduce_deltas(deltas, idx_old, idx_new)
print(new)
assert(np.alltrue([float(i) for i in idx_new] == new))
def test_merge_idx():
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
new_idx = pe.obs._merge_idx(idl)
assert(new_idx[-1] > new_idx[0])
for i in range(1, len(new_idx)):
assert(new_idx[i - 1] < new_idx[i])
def test_cobs_array():
cobs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']) * (1 + 2j)
np.identity(4) + cobs
cobs + np.identity(4)
np.identity(4) - cobs
cobs - np.identity(4)
np.identity(4) * cobs
cobs * np.identity(4)
np.identity(4) / cobs
cobs / np.ones((4, 4))

99
tests/openQCD_in_test.py Normal file
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@ -0,0 +1,99 @@
import os
import numpy as np
import pyerrors as pe
import pytest
def test_rwms():
path = './tests//data/openqcd_test/'
prefix = 'sfqcd'
postfix = '.rwms'
# sfqcd-1.6: Trajectories instead of confignumbers are printed to file.
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix)
repname = list(rwfo[0].idl.keys())[0]
assert(rwfo[0].idl[repname] == range(1, 13))
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[1], r_stop=[12])
assert(rwfo[0].idl[repname] == range(1, 13))
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[3], r_stop=[8])
assert(rwfo[0].idl[repname] == range(3, 9))
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[2], r_stop=[6])
assert(rwfo[0].idl[repname] == range(2, 7))
rwfs = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[1], r_stop=[12], r_step=2)
assert(rwfs[0].idl[repname] == range(1, 12, 2))
rwfs = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[2], r_stop=[12], r_step=2)
assert(rwfs[0].idl[repname] == range(2, 13, 2))
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix)
assert((rwfo[0].r_values[repname] + rwfo[0].deltas[repname][1]) == (rwfs[0].r_values[repname] + rwfs[0].deltas[repname][0]))
o = pe.pseudo_Obs(1., .01, repname, samples=12)
pe.reweight(rwfo[0], [o])
o = pe.pseudo_Obs(1., .01, repname, samples=6)
pe.reweight(rwfo[0], [o])
o.idl[repname] = range(2, 13, 2)
pe.reweight(rwfo[0], [o])
pe.reweight(rwfs[0], [o])
files = ['openqcd2r1.ms1.dat']
names = ['openqcd2|r1']
# TM with 2 Hasenbusch factors and 2 sources each + RHMC with one source, openQCD 2.0
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='2.0', files=files, names=names)
assert(len(rwfo) == 2)
assert(rwfo[0].value == 0.9999974970236312)
assert(rwfo[1].value == 1.184681251089919)
repname = list(rwfo[0].idl.keys())[0]
assert(rwfo[0].idl[repname] == range(1, 10))
rwfo = pe.input.openQCD.read_rwms(path, prefix, version='2.0', files=files, names=names, r_start=[1], r_stop=[8], print_err=True)
assert(rwfo[0].idl[repname] == range(1, 9))
# t0
prefix = 'openqcd'
t0 = pe.input.openQCD.extract_t0(path, prefix, dtr_read=3, xmin=0, spatial_extent=4)
files = ['openqcd2r1.ms.dat']
names = ['openqcd2|r1']
t0 = pe.input.openQCD.extract_t0(path, '', dtr_read=3, xmin=0, spatial_extent=4, files=files, names=names, fit_range=2)
t0 = pe.input.openQCD.extract_t0(path, prefix, dtr_read=3, xmin=0, spatial_extent=4, r_start=[1])
repname = list(rwfo[0].idl.keys())[0]
assert(t0.idl[repname] == range(1, 10))
t0 = pe.input.openQCD.extract_t0(path, prefix, dtr_read=3, xmin=0, spatial_extent=4, r_start=[2], r_stop=[8])
repname = list(rwfo[0].idl.keys())[0]
assert(t0.idl[repname] == range(2, 9))
t0 = pe.input.openQCD.extract_t0(path, prefix, dtr_read=3, xmin=0, spatial_extent=4, fit_range=2, plaquette=True, assume_thermalization=True)
pe.input.openQCD.extract_t0(path, '', dtr_read=3, xmin=0, spatial_extent=4, files=files, names=names, fit_range=2, plot_fit=True)
def test_Qtop():
path = './tests//data/openqcd_test/'
prefix = 'sfqcd'
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.3, version='sfqcd')
repname = list(qtop.idl.keys())[0]
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.3, version='sfqcd', Zeuthen_flow=True, L=4)
qi = pe.input.openQCD.read_qtop(path, prefix, c=0.3, version='sfqcd', integer_charge=True)
for conf in range(len(qi.idl[repname])):
assert(0 == qi.r_values[repname] + qi.deltas[repname][conf])
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.0, version='sfqcd')
assert (np.isclose(-4.572999e-02, qtop.r_values[repname] + qtop.deltas[repname][0]))
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.28, version='sfqcd')
assert (np.isclose(3.786893e-02, qtop.r_values[repname] + qtop.deltas[repname][0]))
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.08, version='sfqcd', Zeuthen_flow=True)
assert (np.isclose(3.653140e-02, qtop.r_values[repname] + qtop.deltas[repname][1]))
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.40, version='sfqcd', Zeuthen_flow=True)
assert (np.isclose(2.745865e-01, qtop.r_values[repname] + qtop.deltas[repname][1]))
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.40, version='sfqcd', Zeuthen_flow=True, r_start=[2])
assert(qtop.idl[repname] == range(2, 7))
assert (np.isclose(2.745865e-01, qtop.r_values[repname] + qtop.deltas[repname][0]))
qtop = pe.input.openQCD.read_qtop(path, prefix, c=0.40, version='sfqcd', Zeuthen_flow=True, r_stop=[5])
assert(qtop.idl[repname] == range(1, 6))
names = ['sfqcd|r1']
files = ['sfqcdr1.gfms.dat']
qs = pe.input.openQCD.read_qtop_sector(path, '', 0.3, target=0, Zeuthen_flow=True, version='sfqcd')
assert((pe.input.openQCD.qtop_projection(qi, target=0) - qs).is_zero())

12
tests/roots_test.py
View file

@ -30,3 +30,15 @@ def test_root_linear_idl():
difference = my_obs - my_root
assert difference.is_zero()
def test_root_no_autograd():
def root_function(x, d):
return x - np.log(np.exp(d))
value = np.random.normal(0, 100)
my_obs = pe.pseudo_Obs(value, 0.1, 't')
with pytest.raises(Exception):
my_root = pe.roots.find_root(my_obs, root_function)

124
tests/sfcf_in_test.py Normal file
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@ -0,0 +1,124 @@
import os
import sys
import inspect
import pyerrors as pe
import pyerrors.input.sfcf as sfin
import shutil
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
def build_test_environment(env_type, cfgs, reps):
if env_type == "o":
for i in range(2,cfgs+1):
shutil.copytree("tests/data/sfcf_test/data_o/test_r0/cfg1","tests/data/sfcf_test/data_o/test_r0/cfg"+str(i))
for i in range(1,reps):
shutil.copytree("tests/data/sfcf_test/data_o/test_r0", "tests/data/sfcf_test/data_o/test_r"+str(i))
elif env_type == "c":
for i in range(2,cfgs+1):
shutil.copy("tests/data/sfcf_test/data_c/data_c_r0/data_c_r0_n1","tests/data/sfcf_test/data_c/data_c_r0/data_c_r0_n"+str(i))
for i in range(1,reps):
os.mkdir("tests/data/sfcf_test/data_c/data_c_r"+str(i))
for j in range(1,cfgs+1):
shutil.copy("tests/data/sfcf_test/data_c/data_c_r0/data_c_r0_n1","tests/data/sfcf_test/data_c/data_c_r"+str(i)+"/data_c_r"+str(i)+"_n"+str(j))
def clean_test_environment(env_type, cfgs, reps):
if env_type == "o":
for i in range(1,reps):
shutil.rmtree("tests/data/sfcf_test/data_o/test_r"+str(i))
for i in range(2,cfgs+1):
shutil.rmtree("tests/data/sfcf_test/data_o/test_r0/cfg"+str(i))
elif env_type == "c":
for i in range(1,reps):
shutil.rmtree("tests/data/sfcf_test/data_c/data_c_r"+str(i))
for i in range(2,cfgs+1):
os.remove("tests/data/sfcf_test/data_c/data_c_r0/data_c_r0_n"+str(i))
def test_o_bb():
build_test_environment("o",5,3)
f_1 = sfin.read_sfcf("tests/data/sfcf_test/data_o", "test", "f_1",quarks="lquark lquark", wf = 0, wf2=0, version = "2.0", corr_type="bb")
print(f_1)
clean_test_environment("o",5,3)
assert len(f_1) == 1
assert f_1[0].value == 351.1941525454502
def test_o_bi():
build_test_environment("o",5,3)
f_A = sfin.read_sfcf("tests/data/sfcf_test/data_o", "test", "f_A",quarks="lquark lquark", wf = 0, version = "2.0")
print(f_A)
clean_test_environment("o",5,3)
assert len(f_A) == 3
assert f_A[0].value == 65.4711887279723
assert f_A[1].value == 1.0447210336915187
assert f_A[2].value == -41.025094911185185
def test_o_bib():
build_test_environment("o",5,3)
f_V0 = sfin.read_sfcf("tests/data/sfcf_test/data_o", "test", "F_V0",quarks="lquark lquark", wf = 0, wf2 = 0, version = "2.0", corr_type="bib")
print(f_V0)
clean_test_environment("o",5,3)
assert len(f_V0) == 3
assert f_V0[0] == 683.6776090085115
assert f_V0[1] == 661.3188585582334
assert f_V0[2] == 683.6776090081005
def test_c_bb():
build_test_environment("c",5,3)
f_1 = sfin.read_sfcf("tests/data/sfcf_test/data_c", "data_c", "f_1", quarks="lquark lquark", wf = 0, wf2=0, version = "2.0c", corr_type="bb")
print(f_1)
clean_test_environment("c",5,3)
assert len(f_1) == 1
assert f_1[0].value == 351.1941525454502
def test_c_bi():
build_test_environment("c",5,3)
f_A = sfin.read_sfcf("tests/data/sfcf_test/data_c", "data_c", "f_A", quarks="lquark lquark", wf = 0, version = "2.0c")
print(f_A)
clean_test_environment("c",5,3)
assert len(f_A) == 3
assert f_A[0].value == 65.4711887279723
assert f_A[1].value == 1.0447210336915187
assert f_A[2].value == -41.025094911185185
def test_c_bib():
build_test_environment("c",5,3)
f_V0 = sfin.read_sfcf("tests/data/sfcf_test/data_c", "data_c", "F_V0",quarks="lquark lquark", wf = 0, wf2 = 0, version = "2.0c", corr_type="bib")
print(f_V0)
clean_test_environment("c",5,3)
assert len(f_V0) == 3
assert f_V0[0] == 683.6776090085115
assert f_V0[1] == 661.3188585582334
assert f_V0[2] == 683.6776090081005
def test_a_bb():
build_test_environment("a",5,3)
f_1 = sfin.read_sfcf("tests/data/sfcf_test/data_a", "data_a", "f_1", quarks="lquark lquark", wf = 0, wf2=0, version = "2.0a", corr_type="bb")
print(f_1)
clean_test_environment("a",5,3)
assert len(f_1) == 1
assert f_1[0].value == 351.1941525454502
def test_a_bi():
build_test_environment("a",5,3)
f_A = sfin.read_sfcf("tests/data/sfcf_test/data_a", "data_a", "f_A", quarks="lquark lquark", wf = 0, version = "2.0a")
print(f_A)
clean_test_environment("a",5,3)
assert len(f_A) == 3
assert f_A[0].value == 65.4711887279723
assert f_A[1].value == 1.0447210336915187
assert f_A[2].value == -41.02509491118518
def test_a_bib():
build_test_environment("a",5,3)
f_V0 = sfin.read_sfcf("tests/data/sfcf_test/data_a", "data_a", "F_V0",quarks="lquark lquark", wf = 0, wf2 = 0, version = "2.0a", corr_type="bib")
print(f_V0)
clean_test_environment("a",5,3)
assert len(f_V0) == 3
assert f_V0[0] == 683.6776090085115
assert f_V0[1] == 661.3188585582334
assert f_V0[2] == 683.6776090081005