class TestDeepPolarPBC(unittest.TestCase):
def setUp(self):
convert_pbtxt_to_pb(
str(tests_path / os.path.join("infer", "deeppolar.pbtxt")),
"deeppolar.pb")
self.dp = DeepPolar("deeppolar.pb")
self.coords = np.array([
12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, 3.36,
3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56
])
self.atype = [0, 1, 1, 0, 1, 1]
self.box = np.array([13., 0., 0., 0., 13., 0., 0., 0., 13.])
self.expected_d = np.array([
1.061407927405987051e-01, -3.569013342133873778e-01,
-2.862108976089940138e-02, -3.569013342133875444e-01,
1.304367268874677244e+00, 1.037647501453442256e-01,
-2.862108976089940138e-02, 1.037647501453441284e-01,
8.100521520762453409e-03, 1.236797829492216616e+00,
-3.717307430531632262e-01, 7.371515676976750919e-01,
-3.717307430531630041e-01, 1.127222682121889058e-01,
-2.239181552775717510e-01, 7.371515676976746478e-01,
-2.239181552775717787e-01, 4.448255365635306879e-01
])
def tearDown(self):
os.remove("deeppolar.pb")
def test_attrs(self):
self.assertEqual(self.dp.get_ntypes(), 2)
self.assertAlmostEqual(self.dp.get_rcut(), 6.0, places=default_places)
self.assertEqual(self.dp.get_type_map(), ['O', 'H'])
self.assertEqual(self.dp.get_sel_type(), [0])
def test_1frame_atm(self):
dd = self.dp.eval(self.coords, self.box, self.atype)
# check shape of the returns
nframes = 1
natoms = len(self.atype)
nsel = 2
self.assertEqual(dd.shape, (nframes, nsel, 9))
# check values
np.testing.assert_almost_equal(dd.ravel(), self.expected_d,
default_places)
def test_2frame_atm(self):
coords2 = np.concatenate((self.coords, self.coords))
box2 = np.concatenate((self.box, self.box))
dd = self.dp.eval(coords2, box2, self.atype)
# check shape of the returns
nframes = 2
natoms = len(self.atype)
nsel = 2
self.assertEqual(dd.shape, (nframes, nsel, 9))
# check values
expected_d = np.concatenate((self.expected_d, self.expected_d))
np.testing.assert_almost_equal(dd.ravel(), expected_d, default_places)
class TestDeepPolarNewPBC(unittest.TestCase):
def setUp(self):
convert_pbtxt_to_pb(
str(tests_path / os.path.join("infer", "deeppolar_new.pbtxt")),
"deeppolar_new.pb")
self.dp = DeepPolar("deeppolar_new.pb")
self.coords = np.array([
12.83, 2.56, 2.18, 12.09, 2.87, 2.74, 00.25, 3.32, 1.68, 3.36,
3.00, 1.81, 3.51, 2.51, 2.60, 4.27, 3.22, 1.56
])
self.nout = 9
self.atype = np.array([0, 1, 1, 0, 1, 1])
self.box = np.array([13., 0., 0., 0., 13., 0., 0., 0., 13.])
self.expected_t = np.array([
1.936327241487292961e+00, 5.198696351735779264e-02,
3.888336625074450149e-03, 5.198696351735781346e-02,
1.764967784387830196e+00, -1.354658545697527347e-02,
3.888336625074451016e-03, -1.354658545697527000e-02,
1.939288409902199639e+00, 1.786740420980893029e+00,
4.868765294055640847e-02, -9.812132615180739481e-02,
4.868765294055640847e-02, 1.925999147066305373e+00,
2.895028407651457567e-02, -9.812132615180743644e-02,
2.895028407651457220e-02, 1.883109989034779996e+00
])
self.expected_f = np.array([
5.305178446980116092e-02, -1.127314829623577049e-02,
1.136493514861047216e-01, 5.598130220328862322e-05,
-4.352126938892845326e-02, -7.700608888887500170e-02,
-1.050015668789053697e-01, 5.882396336737016895e-02,
-3.723875897544067642e-02, -7.850322286760008650e-02,
7.279117637753844405e-02, -6.178451060078461732e-02,
3.404361490778949895e-01, 5.447934529195214842e-02,
-8.698375128815737101e-02, -2.100391251033939810e-01,
-1.313000673516965255e-01, 1.493637582671529240e-01,
-9.589318874236771317e-02, 6.285887854370801608e-02,
-1.824395427630142175e-01, -3.264267092869802683e-02,
3.637498661083633789e-02, 1.524859582123189172e-01,
1.442484990808054202e-01, -8.957992476622803069e-02,
3.076469140583825215e-02, 4.909822745881124717e-02,
-2.559151672032903835e-01, -1.522830913546814324e-01,
-2.885480042033320910e-02, 7.730841025065784966e-02,
1.553301391955271560e-01, -3.595606644821771475e-02,
1.689528165643162105e-01, -3.858154695988691516e-03,
5.018843026262573281e-02, 1.756005154318779349e-02,
3.489323893614350303e-02, -4.020411124876955428e-02,
2.218648284685413238e-02, -8.086177159691650476e-03,
-2.222392408702593067e-02, -3.825892777133557687e-02,
-1.689393838770965675e-02, -5.465804822761769627e-02,
-1.398775875506316768e-01, -1.165702490994514756e-01,
5.449067849718619572e-02, 1.588580450812354106e-01,
-8.209560373418453572e-02, 1.240697480360127003e-02,
-2.046806414931008622e-02, 1.887527294448937965e-01,
-9.589318874236771317e-02, 6.285887854370801608e-02,
-1.824395427630142175e-01, -3.264267092869802683e-02,
3.637498661083633789e-02, 1.524859582123189172e-01,
1.442484990808054202e-01, -8.957992476622803069e-02,
3.076469140583825215e-02, 4.909822745881124717e-02,
-2.559151672032903835e-01, -1.522830913546814324e-01,
-2.885480042033320910e-02, 7.730841025065784966e-02,
1.553301391955271560e-01, -3.595606644821771475e-02,
1.689528165643162105e-01, -3.858154695988691516e-03,
4.038746042068122599e-02, -2.549213597407858356e-01,
-1.131801705114504619e-01, 1.489732376295762606e-01,
2.734584831542113958e-01, -1.125511889088352951e-01,
-1.908551011160136424e-01, -2.400995606986339528e-02,
2.255650484976146619e-01, -2.185213968874370055e-02,
1.475333123369945709e-01, 9.584417756169674729e-02,
-1.576380405016522893e-02, -5.153693137796186430e-02,
-8.489897831367294867e-02, 3.911034680466508873e-02,
-9.052354830259493057e-02, -1.077888832535272776e-02,
-1.970229486427777510e-01, -6.538978166042377915e-02,
-1.570533119125729904e-01, 1.417940206277617798e-01,
-4.684714285705613573e-02, 6.070882964241105378e-02,
5.715183445260185735e-02, 1.138024049318459713e-01,
9.374622673558237473e-02, 3.096927839536914306e-02,
-9.232883741117139942e-02, -6.499836527010099951e-02,
2.839980861544661936e-02, 8.097497759757724123e-03,
1.006700103228213017e-01, -6.129199344840163821e-02,
8.266585923704758421e-02, -3.307338951814068478e-02,
5.018843026262574669e-02, 1.756005154318778308e-02,
3.489323893614350997e-02, -4.020411124876957509e-02,
2.218648284685414279e-02, -8.086177159691652211e-03,
-2.222392408702593067e-02, -3.825892777133557687e-02,
-1.689393838770965675e-02, -5.465804822761770321e-02,
-1.398775875506316491e-01, -1.165702490994514756e-01,
5.449067849718619572e-02, 1.588580450812354106e-01,
-8.209560373418453572e-02, 1.240697480360125615e-02,
-2.046806414931009316e-02, 1.887527294448937965e-01,
-1.970229486427777510e-01, -6.538978166042375140e-02,
-1.570533119125729626e-01, 1.417940206277618076e-01,
-4.684714285705613573e-02, 6.070882964241105378e-02,
5.715183445260184347e-02, 1.138024049318459713e-01,
9.374622673558236086e-02, 3.096927839536912919e-02,
-9.232883741117139942e-02, -6.499836527010102727e-02,
2.839980861544661589e-02, 8.097497759757731062e-03,
1.006700103228213017e-01, -6.129199344840162433e-02,
8.266585923704758421e-02, -3.307338951814066397e-02,
-3.078161564779093723e-02, -8.748776750553553111e-03,
-2.162930108693108394e-02, 2.135313622214399243e-02,
-8.845621737097757523e-03, 9.365293934359546560e-03,
8.562579091543631032e-03, 1.772751551871581607e-02,
1.573655414890783033e-02, -3.649820158632081230e-02,
-1.904914900326310223e-01, -1.076542087674599024e-01,
-5.186655049718805199e-02, 1.686765146765009937e-01,
-6.620206332305828001e-02, 8.923065241761217459e-02,
2.168185832506550753e-02, 1.703837250941818704e-01
])
self.expected_v = np.array([
-2.123013313652813774e-03, -2.646248889538913257e-04,
2.225254748021367093e-04, 9.843593195853941446e-04,
1.226963457840150472e-04, -1.031764725911038809e-04,
-8.467513732241481721e-04, -1.055440805151912256e-04,
8.875297679686559459e-05, 1.829118379697145316e-02,
2.302438731350108913e-03, -1.890198823577125386e-03,
3.300229266409118040e-02, -1.339230641165423293e-02,
-2.445540228188634868e-02, 5.127826101331301595e-02,
-2.458314752619149279e-02, -4.252530480245884925e-02,
9.733043787604266084e-02, -6.217238566516904152e-02,
3.767656091618994812e-02, 6.674680725588777973e-03,
4.245867422406505304e-02, -2.752200660186601699e-02,
-8.318636634138946995e-03, -2.738884420387305285e-02,
1.785195524121836741e-02, -3.151218435289559073e-03,
-3.927864338604547816e-04, 3.302976830190196104e-04,
1.387198082848713948e-06, 1.729085429046553641e-07,
-1.454003656243721975e-07, -4.056191292896940703e-05,
-5.055875832506090064e-06, 4.251531950061960394e-06,
7.087482338961141604e-02, -1.643445525800983908e-01,
2.668682182870234509e-01, 7.752581706917366366e-03,
-2.674714571946596939e-02, 4.308263417785011123e-02,
-9.385640612496094423e-03, 4.307848167667025635e-02,
-6.910099104451945806e-02, -1.822493611414978121e-01,
-4.510097387143227610e-02, 5.157836206906134952e-02,
-1.170389534066011428e-01, -2.858136680923874240e-02,
3.256883555835647648e-02, 1.336331160725280354e-01,
3.257484898923947853e-02, -3.710113093740719653e-02,
3.343993600586595179e-03, 4.168150663620683060e-04,
-3.505035785317401481e-04, -4.312491363797464269e-03,
-5.375343342977005178e-04, 4.520175083867039156e-04,
-5.045304632809267465e-04, -6.288764981405317546e-05,
5.288279643454484632e-05, 2.176577726533836937e-02,
-1.041710664445027849e-02, -1.802940684978692962e-02,
-3.097121964369356495e-02, 1.077096511204005125e-02,
2.079488766754130843e-02, -1.120464690158002596e-01,
4.736950869652114399e-02, 8.530900293808066359e-02,
3.029112757823893692e-02, 1.058529311156591879e-01,
-6.894903720238335088e-02, -5.089618157121258979e-02,
-6.973511953466600410e-02, 4.618114280030299196e-02,
1.143309394598741001e-02, 2.319568285212985151e-02,
-1.522637168466081138e-02, -1.535733649675188493e-03,
-1.914228911776438445e-04, 1.609692493993826663e-04,
-2.603290366421702733e-03, -3.244894507721100851e-04,
2.728661290583660171e-04, 6.938458118266074663e-04,
8.648503036932213837e-05, -7.272604826511198082e-05,
-2.609239945314979423e-02, 1.142603664459106681e-02,
-2.051406106454568487e-02, 5.779549344910496142e-03,
-3.860615030463052100e-02, 6.168332781226748551e-02,
2.068839156841529789e-02, -7.643723474881176927e-02,
1.229844977392647865e-01, -3.554667688747349674e-02,
-8.262665730398828859e-03, 9.285295046969522723e-03,
1.497274901467501862e-01, 3.666859638982037511e-02,
-4.181688913175674732e-02, -3.257377626487627069e-03,
-8.171909213273372040e-04, 9.379633299917983094e-04,
5.408910405506226968e-04, 6.741984641424190365e-05,
-5.669396175743082354e-05, 4.696290607396237790e-04,
5.853733334998140626e-05, -4.922457577157541143e-05,
-5.350269144276139158e-03, -6.668890718077903363e-04,
5.607930831110977251e-04, 3.013271000130106694e-02,
-1.241570117891089425e-02, -2.255430712666738058e-02,
-1.643158253499693577e-02, 6.876116339617440766e-03,
1.242585434168311936e-02, 2.120265775977717496e-03,
-2.988284987993197143e-03, -4.123302560925387432e-03,
3.528008965720315360e-02, -1.132921329184741026e-02,
6.435692645130823564e-03, -2.115291124444698342e-02,
-2.971050496327276927e-02, 1.966236467455729359e-02,
-2.194244461519655187e-02, -1.469000955331024871e-02,
1.000316933044766328e-02, -2.208576023807404254e-03,
-2.752899293131040766e-04, 2.314938041951108548e-04,
-5.840262773118632192e-04, -7.279647649213021596e-05,
6.121521886838239123e-05, -1.263538670848133802e-03,
-1.574949051482092536e-04, 1.324388975109944740e-04,
8.955566031735841259e-03, -2.660296383100100095e-02,
4.296567375352825652e-02, 2.380373596470350059e-02,
-7.784355459714024927e-02, 1.255004729498893912e-01,
-1.824501349606120690e-02, 3.948761180940744964e-02,
-6.423389834199008663e-02, 1.038606825469970407e-02,
2.616819816765628484e-03, -3.006960935423359793e-03,
-1.864007491704058883e-02, -4.504736174636920880e-03,
5.118497771104377897e-03, 1.680266347982039554e-01,
4.105963063126880086e-02, -4.679634408112137711e-02,
3.343993600586595179e-03, 4.168150663620683060e-04,
-3.505035785317401481e-04, -4.312491363797464269e-03,
-5.375343342977005178e-04, 4.520175083867039156e-04,
-5.045304632809267465e-04, -6.288764981405317546e-05,
5.288279643454484632e-05, 2.176577726533836937e-02,
-1.041710664445027849e-02, -1.802940684978692962e-02,
-3.097121964369356495e-02, 1.077096511204005125e-02,
2.079488766754130843e-02, -1.120464690158002596e-01,
4.736950869652114399e-02, 8.530900293808066359e-02,
3.029112757823893692e-02, 1.058529311156591879e-01,
-6.894903720238335088e-02, -5.089618157121258979e-02,
-6.973511953466600410e-02, 4.618114280030299196e-02,
1.143309394598741001e-02, 2.319568285212985151e-02,
-1.522637168466081138e-02, -1.535733649675188493e-03,
-1.914228911776438445e-04, 1.609692493993826663e-04,
-2.603290366421702733e-03, -3.244894507721100851e-04,
2.728661290583660171e-04, 6.938458118266074663e-04,
8.648503036932213837e-05, -7.272604826511198082e-05,
-2.609239945314979423e-02, 1.142603664459106681e-02,
-2.051406106454568487e-02, 5.779549344910496142e-03,
-3.860615030463052100e-02, 6.168332781226748551e-02,
2.068839156841529789e-02, -7.643723474881176927e-02,
1.229844977392647865e-01, -3.554667688747349674e-02,
-8.262665730398828859e-03, 9.285295046969522723e-03,
1.497274901467501862e-01, 3.666859638982037511e-02,
-4.181688913175674732e-02, -3.257377626487627069e-03,
-8.171909213273372040e-04, 9.379633299917983094e-04,
1.097257666720985849e-03, 1.367686610077148478e-04,
-1.150100103928514269e-04, -3.252401295559594844e-03,
-4.053984617694676175e-04, 3.409032519425078027e-04,
-1.217154259382106555e-04, -1.517132787898375553e-05,
1.275770753460001047e-05, -1.104423096905816498e-01,
4.615651100464009809e-02, 8.344619780982527601e-02,
-1.998235369855275168e-01, 8.508819942125579738e-02,
1.528709647298205909e-01, 8.333302476347614896e-02,
-3.488524142655123617e-02, -6.303339769808283255e-02,
-7.468341447282240975e-02, -1.443673498458480642e-01,
9.485360739696327426e-02, -2.685004652445167612e-04,
-1.702408228533323561e-02, 1.097613894113106531e-02,
9.496752299747332482e-02, 1.714581306702349373e-01,
-1.128066531362114239e-01, -2.109671824413435984e-03,
-2.629619271223545066e-04, 2.211270750801623281e-04,
1.011694656468142307e-02, 1.261035832424879221e-03,
-1.060416495448196581e-03, 2.326027531269699879e-04,
2.899297772687444119e-05, -2.438045854305356789e-05,
-9.775618976121780001e-04, 7.897148922927013995e-03,
-1.259878571596698138e-02, -5.534571406250721713e-03,
2.552681480358522451e-02, -4.094434810336724379e-02,
-1.258721457759937913e-02, 4.161890111720080443e-02,
-6.708566706120022705e-02, 3.521744971093632853e-02,
8.557787631933998912e-03, -9.738493960065902622e-03,
-8.446926488038911107e-02, -2.017604402799078392e-02,
2.285024948138817888e-02, -9.755577915095828626e-03,
-2.364722966186930900e-03, 2.689144780896026744e-03,
8.392348196279006065e-05, 1.046071729847805219e-05,
-8.796512273720217211e-06, -2.967282659264359589e-03,
-3.698595949224694123e-04, 3.110182957302592738e-04,
-1.688223115474902841e-03, -2.104300767164184042e-04,
1.769525645115341121e-04, -1.040849854787611189e-01,
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1.968691296265078980e-02, 4.764576998712748319e-03,
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3.728680895816388619e-02, -4.242975875503233324e-02
])
self.expected_gt = self.expected_t.reshape(
-1, self.nout).sum(0).reshape(-1)
self.expected_gv = self.expected_v.reshape(1, self.nout, 6,
9).sum(-2).reshape(-1)
def tearDown(self):
os.remove("deeppolar_new.pb")
def test_attrs(self):
self.assertEqual(self.dp.get_ntypes(), 2)
self.assertAlmostEqual(self.dp.get_rcut(), 6.0, places=default_places)
self.assertEqual(self.dp.get_type_map(), ['O', 'H'])
self.assertEqual(self.dp.get_sel_type(), [0])
def test_1frame_old(self):
gt = self.dp.eval(self.coords, self.box, self.atype, atomic=False)
# check shape of the returns
nframes = 1
self.assertEqual(gt.shape, (nframes, self.nout))
# check values
np.testing.assert_almost_equal(gt.ravel(), self.expected_gt,
default_places)
def test_1frame_old_atm(self):
at = self.dp.eval(self.coords, self.box, self.atype)
# check shape of the returns
nframes = 1
natoms = len(self.atype)
nsel = 2
self.assertEqual(at.shape, (nframes, nsel, self.nout))
# check values
np.testing.assert_almost_equal(at.ravel(), self.expected_t,
default_places)
def test_2frame_old_atm(self):
coords2 = np.concatenate((self.coords, self.coords))
box2 = np.concatenate((self.box, self.box))
at = self.dp.eval(coords2, box2, self.atype)
# check shape of the returns
nframes = 2
natoms = len(self.atype)
nsel = 2
self.assertEqual(at.shape, (nframes, nsel, self.nout))
# check values
expected_d = np.concatenate((self.expected_t, self.expected_t))
np.testing.assert_almost_equal(at.ravel(), expected_d, default_places)
def test_1frame_full(self):
gt, ff, vv = self.dp.eval_full(self.coords,
self.box,
self.atype,
atomic=False)
# check shape of the returns
nframes = 1
natoms = len(self.atype)
self.assertEqual(gt.shape, (nframes, self.nout))
self.assertEqual(ff.shape, (nframes, self.nout, natoms, 3))
self.assertEqual(vv.shape, (nframes, self.nout, 9))
# check values
np.testing.assert_almost_equal(ff.ravel(), self.expected_f,
default_places)
np.testing.assert_almost_equal(gt.ravel(), self.expected_gt,
default_places)
np.testing.assert_almost_equal(vv.ravel(), self.expected_gv,
default_places)
def test_1frame_full_atm(self):
gt, ff, vv, at, av = self.dp.eval_full(self.coords,
self.box,
self.atype,
atomic=True)
# print the values
for dd in (at, ff, av):
print("\n\n")
print(", ".join(f"{i:.18e}" for i in dd.reshape(-1)))
print("\n\n")
# check shape of the returns
nframes = 1
natoms = len(self.atype)
nsel = 2
self.assertEqual(gt.shape, (nframes, self.nout))
self.assertEqual(ff.shape, (nframes, self.nout, natoms, 3))
self.assertEqual(vv.shape, (nframes, self.nout, 9))
self.assertEqual(at.shape, (nframes, nsel, self.nout))
self.assertEqual(av.shape, (nframes, self.nout, natoms, 9))
# check values
np.testing.assert_almost_equal(ff.reshape([-1]),
self.expected_f.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(at.reshape([-1]),
self.expected_t.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(av.reshape([-1]),
self.expected_v.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(gt.reshape([-1]),
self.expected_gt.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(vv.reshape([-1]),
self.expected_gv.reshape([-1]),
decimal=default_places)
def test_1frame_full_atm_shuffle(self):
i_sf = [2, 1, 3, 0, 5, 4]
isel_sf = [1, 0]
gt, ff, vv, at, av = self.dp.eval_full(self.coords.reshape(
-1, 3)[i_sf].reshape(-1),
self.box,
self.atype[i_sf],
atomic=True)
# check shape of the returns
nframes = 1
natoms = len(self.atype)
nsel = 2
self.assertEqual(gt.shape, (nframes, self.nout))
self.assertEqual(ff.shape, (nframes, self.nout, natoms, 3))
self.assertEqual(vv.shape, (nframes, self.nout, 9))
self.assertEqual(at.shape, (nframes, nsel, self.nout))
self.assertEqual(av.shape, (nframes, self.nout, natoms, 9))
# recover the shuffled result
nff = np.empty_like(ff)
nav = np.empty_like(av)
nat = np.empty_like(at)
nff[:, :, i_sf] = ff
nav[:, :, i_sf] = av
nat[:, isel_sf] = at
# check values
np.testing.assert_almost_equal(nff.reshape([-1]),
self.expected_f.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(nat.reshape([-1]),
self.expected_t.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(nav.reshape([-1]),
self.expected_v.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(gt.reshape([-1]),
self.expected_gt.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(vv.reshape([-1]),
self.expected_gv.reshape([-1]),
decimal=default_places)
def test_2frame_full_atm(self):
coords2 = np.concatenate((self.coords, self.coords))
box2 = np.concatenate((self.box, self.box))
gt, ff, vv, at, av = self.dp.eval_full(coords2,
box2,
self.atype,
atomic=True)
# check shape of the returns
nframes = 2
natoms = len(self.atype)
nsel = 2
self.assertEqual(gt.shape, (nframes, self.nout))
self.assertEqual(ff.shape, (nframes, self.nout, natoms, 3))
self.assertEqual(vv.shape, (nframes, self.nout, 9))
self.assertEqual(at.shape, (nframes, nsel, self.nout))
self.assertEqual(av.shape, (nframes, self.nout, natoms, 9))
# check values
expected_f = np.tile(self.expected_f.reshape(-1), nframes)
expected_t = np.tile(self.expected_t.reshape(-1), nframes)
expected_v = np.tile(self.expected_v.reshape(-1), nframes)
expected_gt = np.tile(self.expected_gt.reshape(-1), nframes)
expected_gv = np.tile(self.expected_gv.reshape(-1), nframes)
np.testing.assert_almost_equal(ff.reshape([-1]),
expected_f.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(at.reshape([-1]),
expected_t.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(av.reshape([-1]),
expected_v.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(gt.reshape([-1]),
expected_gt.reshape([-1]),
decimal=default_places)
np.testing.assert_almost_equal(vv.reshape([-1]),
expected_gv.reshape([-1]),
decimal=default_places)