/
nchilada_test.py
213 lines (195 loc) · 10.7 KB
/
nchilada_test.py
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import pynbody
import numpy as np
correct_pos_3000 = np.array([[4.80664825e+01, -8.99647751e+01,
3.74038162e+01],
[-5.44153690e+00,
1.38918352e+00, -4.13906145e+00],
[-2.14025784e+01, -
1.36437626e+01, -9.47246170e+00],
[4.10247002e+01,
3.61983910e+01, -2.48513794e+01],
[-5.81032515e+00,
7.98453445e+01, -7.39464235e+00],
[-6.44824982e-01, -
4.91317129e+00, -2.40600276e+00],
[7.13349762e+01,
8.73891678e+01, -1.09726463e+02],
[-3.53511566e+02, -1.00286308e+02,
2.75888123e+02],
[9.16142178e+00, -1.18060989e+01,
1.95731199e+00],
[1.93871933e+02,
3.49774742e+01, -3.19205170e+01],
[-3.21550280e-01,
9.50274229e-01, -4.75152826e+00],
[2.90328884e+01, 3.98563194e+01,
1.80117950e+01],
[8.80027390e+01,
3.49581116e+02, -9.87488022e+01],
[7.49165535e+00, -3.68064928e+00,
5.22999668e+00],
[-4.76786423e+01, -
8.92267323e+00, -3.53499756e+01],
[-5.13045835e+00,
1.15755057e+00, -1.24432411e+01],
[8.40043869e+01, -3.09200935e+01,
3.42498322e+01],
[8.57449875e+01, -
1.92537506e+02, -3.21286072e+02],
[3.55517745e-01, -4.25792408e+00,
2.54480147e+00],
[5.06873436e+01, -1.89251003e+01,
6.30320511e+01],
[-2.29010391e+01, -
2.98745537e+01, -6.39020443e-01],
[1.75614227e+02,
3.88110619e+01, -4.62935669e+02],
[-1.56760117e+02, -
1.05039883e+01, -2.15488834e+01],
[-1.48337769e+02, -5.88840103e+01,
5.87665253e+01],
[-2.57145252e+01, -
4.37019272e+01, -8.64454422e+01],
[5.60231543e+00, -5.63358154e+01,
4.04776115e+01],
[4.66480751e+01, -
1.87408848e+01, -9.87097397e+01],
[6.15837526e+00, -1.72479401e+01,
1.03489981e+01],
[2.07985783e+01, -1.86683559e+01,
4.93654976e+01],
[4.72650070e+01,
2.30763443e+02, -3.23870850e+02],
[-8.57861633e+01, -
1.02004997e+02, -6.86018219e+01],
[2.02718210e+00, 4.22350769e+01,
1.71071274e+02],
[-1.36086855e+01, -1.44415558e+02,
1.09305428e+02],
[5.34043312e+01, -
8.10125961e+01, -3.63210640e+01],
[-5.14458199e+01, -1.08560707e+02,
6.31101456e+01],
[-6.56126976e+00, 5.70295632e-01,
2.85579300e+01],
[5.27915688e+01, -1.28877045e+02,
1.09167343e+02],
[1.01338173e+02, -1.15229092e+01,
1.33744383e+01],
[-1.72840710e+01,
2.39051647e+01, -1.41212740e+01],
[8.56790314e+01, -5.56587563e+01,
2.17376709e+02],
[-8.52479858e+01, -
9.54728546e+01, -1.59210297e+02],
[-4.44902849e+00, -3.21001472e+01,
2.00425949e+01],
[2.36292999e+02, -1.47405457e+02,
1.10863857e+01],
[1.16263481e+02, -1.98876991e+01,
4.76252327e+01],
[-1.89394855e+01, -5.57372236e+00,
3.53287201e+01],
[3.44844589e+01, 2.32600460e+01,
9.82884674e+01],
[9.01199222e-01,
2.57520008e+00, -5.53647995e-01],
[3.16543064e+01,
5.56155300e+00, -4.71191940e+01],
[-9.48117828e+01, -7.04797745e+01,
1.47528107e+02],
[2.11919518e+01, -6.85180740e+01,
3.07458992e+01],
[-1.09558213e+00, -8.14043903e+00,
1.50014961e+00],
[-3.43446732e+00, -1.12701597e+01,
1.03842316e+01],
[-6.20061722e+01, -1.78703461e+02,
1.55959137e+02],
[-7.22693024e+01, -
4.85273781e+01, -3.17128677e+01],
[1.80612230e+00, -
2.49209857e+00, -5.65673053e-01],
[7.25562210e+01, 5.13925858e+01,
1.36459608e+01],
[9.43354034e+01, -
1.44577225e+02, -3.63367615e+01],
[-9.77776184e+01, -1.39123398e+02,
1.63941727e+02],
[-5.67525139e+01,
3.34907951e+01, -1.75442715e+01],
[-2.68184853e+00,
2.11684442e+00, -4.06608544e-02],
[-2.96782818e+01, 8.53648376e+01,
4.66984673e+01],
[-1.54433002e+01, -9.27590256e+01,
2.82807068e+02],
[-1.11955597e+02,
3.67757835e+01, -9.62517242e+01],
[-2.50878549e+00,
4.25698608e-01, -9.43133712e-01],
[6.53796315e-01,
2.00569868e-01, -2.51112208e-02],
[-1.64453244e+00,
2.01585084e-01, -9.04290155e-02],
[-1.18329549e+00, -
2.19713300e-02, -4.17537242e-01],
[-6.43875718e-01,
2.19385475e-01, -4.35074180e-01],
[-2.17796063e+00, -
5.66213965e-01, -2.24855438e-01],
[-2.85428286e+00, -
6.99547052e-01, -7.77058899e-02],
[-9.91025925e-01, -3.35044384e-01,
1.01516500e-01],
[1.48876154e+00,
1.09505355e+00, -4.32662398e-01],
[5.53907633e-01,
5.82947791e-01, -3.85044366e-01],
[5.44333506e+00,
4.35375547e+00, -1.00743210e+00],
[-8.23547661e-01,
1.96279678e-02, -5.96994758e-01],
[-9.66986239e-01, -
2.12422982e-02, -1.02104858e-01],
[-9.48075593e-01, 2.02855930e-01, -7.51522779e-02]])
def setup():
global f
f = pynbody.load("testdata/nchilada_test/12M.00001")
def test_get():
global correct_pos_3000, x_pos_3000
current = f['pos'][2998::3000]
x_pos_3000 = current
assert (np.abs(current - correct_pos_3000).mean() < 1.e-6)
def test_get_gas():
correct = [ 1.19859905e-07, 8.66461534e-08,
1.36909285e-03, 2.11048416e-07, 2.63381509e-07,
2.98710262e-07, 1.08983599e-07, 1.07130404e-07,
7.49599457e-01, 2.05589359e-07, 1.20924241e-07,
1.72308305e-07, 1.82599763e-07, 1.91588853e-07,
1.55311682e-07, 2.24381495e-07, 7.48850703e-01,
3.63184469e-08, 9.44389882e-08, 1.55873153e-07,
1.49604929e-07, 1.38858326e-07, 1.57812593e-07,
1.15718933e-07, 7.49949515e-01, 7.47042358e-01,
1.99594979e-07, 2.19145448e-07, 6.16430157e-07,
4.46814852e-07]
current = f.gas['HI'][2998::3000]
assert (np.abs(current - correct).sum() < 1.e-9)
def test_array_completion_unit_sanity():
del f['pos']
f.gas['pos']
assert 'pos' not in f
f.gas['pos'].convert_units("Mpc")
f.star['pos'].convert_units("pc")
assert (np.abs(f['pos'][2998::3000] - x_pos_3000).mean() < 1.e-6)
def test_partial_loading():
f_f = pynbody.load("testdata/nchilada_test/12M.00001")
test_ptcls = [11634, 24181, 26275, 37336, 37795, 38040, 38280, 38327,
38524, 39349, 46758, 48892, 52160, 53267, 53745, 68970,
78073, 83777, 86865, 93492, 94596, 96567, 99713, 106100,
107856, 111036, 111830, 112560, 115082, 117111, 117444, 117667,
123604, 123665, 124911, 132957, 138551, 154869, 158919, 182131,
184252, 190498, 197946, 198288, 204526, 221720, 226375, 226915,
229959, 231778] # randomly generated sample
f_p = pynbody.load("testdata/nchilada_test/12M.00001", take=test_ptcls)
assert((f_p['pos'] == f_f['pos'][test_ptcls]).all())