def test_sparse_read():
direc = "tests/nastran_op4_data/"
fnames = glob(direc + "*.op4") + glob(direc + "*.other")
for fname in fnames:
m = op4.read(fname)
if fname.endswith("cdbin_ascii_sparse_nonbigmat.op4"):
# this has an all-zeros matrix that is written the same as
# the dense format (so the sparse form is not used by
# default)
del m["c2"]
m2 = op4.read(fname, sparse=None)
m3 = op4.read(fname, sparse=True)
m4 = op4.read(fname, sparse=False)
m5 = op4.read(fname, sparse=(None, sp.coo_matrix.tocsr))
if "bigmat" in fname:
for k, v in m.items():
assert sp.issparse(m2[k])
assert sp.isspmatrix_csr(m5[k])
assert np.all(m2[k].toarray() == v)
assert np.all(m5[k].toarray() == v)
else:
for k, v in m.items():
assert np.all(m2[k] == v)
assert np.all(m5[k] == v)
for k, v in m.items():
assert sp.issparse(m3[k])
assert np.all(m3[k].toarray() == v)
assert np.all(m4[k] == v)
def test_sparse_read():
direc = "tests/nastran_op4_data/"
fnames = glob(direc + "*.op4") + glob(direc + "*.other")
for fname in fnames:
m = op4.read(fname)
m2 = op4.read(fname, sparse=None)
m3 = op4.read(fname, sparse=True)
m4 = op4.read(fname, sparse=False)
m5 = op4.read(fname, sparse=(None, sp.coo_matrix.tocsr))
if "bigmat" in fname:
for k, v in m.items():
assert sp.issparse(m2[k])
assert sp.isspmatrix_csr(m5[k])
assert np.all(m2[k].toarray() == v)
assert np.all(m5[k].toarray() == v)
else:
for k, v in m.items():
assert np.all(m2[k] == v)
assert np.all(m5[k] == v)
for k, v in m.items():
assert sp.issparse(m3[k])
assert np.all(m3[k].toarray() == v)
assert np.all(m4[k] == v)
def test_asm2uset_2():
u, c, b = nastran.asm2uset("tests/nas2cam_extseout/reduced_bset_notall6.asm")
m = op4.read("tests/nas2cam_extseout/reduced_bset_notall6.op4")
assert u.shape[0] == 29
q = ~b
assert ((np.diag(m["maa"]) == 1.0) == q).all()
def write_read(m, binary, sparse):
f = tempfile.NamedTemporaryFile(delete=False)
name = f.name
f.close()
try:
op4.write(name, m, binary=binary, sparse=sparse)
m2 = op4.read(name, sparse=None)
finally:
os.remove(name)
return m2
def test_wtop4_2():
matfile = "tests/nastran_op4_data/r_c_rc.mat"
m = matlab.loadmat(matfile)
names = ["rmat", "cmat", "rcmat"]
mats = []
wtdct = {}
for nm in names:
mats.append(m[nm])
wtdct[nm] = m[nm]
# write(filename, names, matrices=None,
# binary=True, digits=16, endian='')
filenames = [
["tests/nastran_op4_data/temp_ascii.op4", False, ""],
["tests/nastran_op4_data/temp_le.op4", True, "<"],
["tests/nastran_op4_data/temp_be.op4", True, ">"],
]
for item in filenames:
filename = item[0]
binary = item[1]
endian = item[2]
op4.write(filename, names, mats, binary=binary, endian=endian)
names2, sizes, forms, mtypes = op4.dir(filename, verbose=False)
assert names2 == names
dct = op4.load(filename)
for nm in dct:
assert np.allclose(m[nm], dct[nm][0])
op4.save(filename, wtdct, binary=binary, endian=endian)
dct = op4.load(filename, into="dct")
for nm in dct:
assert np.allclose(m[nm], dct[nm][0])
dct = op4.read(filename)
for nm in dct:
assert np.allclose(m[nm], dct[nm])
op4.save(filename, wtdct, binary=binary, endian=endian)
dct = op4.read(filename, into="dct")
for nm in dct:
assert np.allclose(m[nm], dct[nm])
# clean up:
for item in filenames:
os.remove(item[0])
def test_large_sparse():
data = [2.3, 5, -100.4]
rows = [2, 500000, 3500000]
cols = [3750000, 500000, 4999999]
a = sp.csr_matrix((data, (rows, cols)), shape=(5000000, 5000000))
f = tempfile.NamedTemporaryFile(delete=False)
name = f.name
f.close()
try:
op4.write(name, dict(a=a))
a2 = op4.read(name, sparse=None)
finally:
os.remove(name)
assert sp.issparse(a2["a"])
a2 = a2["a"].tocsr()
for i, j, v in zip(rows, cols, data):
assert np.allclose(a2[i, j], v)
def test_wtop4_single_2():
matfile = "tests/nastran_op4_data/r_c_rc.mat"
m = matlab.loadmat(matfile)
name = "rmat"
mat = m[name]
# write(filename, names, matrices=None,
# binary=True, digits=16, endian='')
filenames = [["tests/nastran_op4_data/temp_ascii.op4", False, ""]]
for item in filenames:
filename = item[0]
binary = item[1]
endian = item[2]
op4.write(filename, name, mat, binary=binary, endian=endian)
dct = op4.read(filename)
for nm in dct:
assert np.allclose(m[nm], dct[nm])
# clean up:
for item in filenames:
os.remove(item[0])
def test_premature_eof_warning():
a = np.random.randn(10, 10)
f = tempfile.NamedTemporaryFile(delete=False)
fname = f.name
f.close()
try:
op4.write(fname, {"a": a})
bufr = open(fname, "rb").read()
open(fname, "wb").write(bufr[:-6])
with assert_warns(RuntimeWarning) as cm:
a2 = op4.read(fname)
assert cm.warnings[0].message.args[0].startswith("Premature end-of-file")
finally:
os.remove(fname)
assert (a2["a"] == a).all()
def test_large_rows_dense():
fname = "tests/nastran_op4_data/x100000.op4"
m = op4.read(fname)
x = np.zeros((100000, 1))
x[45678] = 1.0
assert np.allclose(m["x"], x)
def test_sparse_write():
fnames = [
"tests/nastran_op4_data/cdbin.op4",
"tests/nastran_op4_data/rs.op4",
"tests/nastran_op4_data/r_c_rc.op4",
"tests/nastran_op4_data/double_bigmat_le.op4",
"tests/nastran_op4_data/double_nonbigmat_be.op4",
"tests/nastran_op4_data/single_dense_be.op4",
# the nx nastran v9 version of the following files does not
# make NR negative ... not really a big deal, but the tests
# below would need to be changed. So, just using the pyYeti
# versions directly (nastran read them fine)
"tests/nastran_op4_data/big_bigmat_ascii.op4",
"tests/nastran_op4_data/big_bigmat_binary.op4",
"tests/nastran_op4_data/big_dense_ascii.op4",
"tests/nastran_op4_data/big_dense_binary.op4",
]
for fname in fnames:
for rd_sparse in (True, False, None):
m = op4.read(fname, sparse=rd_sparse)
m2 = write_read(m, binary=True, sparse="dense")
m3 = write_read(m, binary=False, sparse="dense")
m4 = write_read(m, binary=False, sparse="nonbigmat")
m5 = write_read(m, binary=True, sparse="nonbigmat")
m6 = write_read(m, binary=False, sparse="bigmat")
m7 = write_read(m, binary=True, sparse="bigmat")
m8 = write_read(m, binary=False, sparse="auto")
m9 = write_read(m, binary=True, sparse="auto")
for k, v in m.items():
if rd_sparse:
assert sp.issparse(v)
elif rd_sparse is None and "bigmat" in fname:
if "nonbigmat" in fname and not sp.issparse(v):
# v could be dense if all zeros
assert np.all(v == 0.0)
else:
assert sp.issparse(v)
va = v.toarray() if sp.issparse(v) else v
assert np.all(va == m2[k])
assert np.all(va == m3[k])
if sp.issparse(m4[k]):
assert np.all(va == m4[k].A)
else:
assert np.allclose(va, m4[k])
assert np.all(m4[k] == 0.0)
if sp.issparse(m5[k]):
assert np.all(va == m5[k].A)
else:
assert np.allclose(va, m5[k])
assert np.all(m5[k] == 0.0)
assert sp.issparse(m6[k])
assert np.all(va == m6[k].A)
assert sp.issparse(m7[k])
assert np.all(va == m7[k].A)
if rd_sparse or (rd_sparse is None and sp.issparse(v)):
assert sp.issparse(m8[k])
assert np.all(va == m8[k].A)
assert sp.issparse(m9[k])
assert np.all(va == m9[k].A)
else:
assert np.all(va == m8[k])
assert np.all(va == m9[k])
def test_cglf_moment_signs():
pth = os.path.dirname(inspect.getfile(cb))
pth = os.path.join(pth, "..")
pth = os.path.join(pth, "tests")
pth = os.path.join(pth, "cla_test_data")
se = 101
uset, coords = nastran.bulk2uset(os.path.join(pth, "outboard.asm"))
dct = op4.read(os.path.join(pth, "outboard.op4"))
maa = dct["mxx"]
kaa = dct["kxx"]
atm = dct["mug1"]
ltm = dct["mef1"]
pch = os.path.join(pth, "outboard.pch")
atm_labels = [
"Grid {:4d}-{:1d}".format(grid, dof) for grid, dof in nastran.rddtipch(pch)
]
ltm_labels = [
"CBAR {:4d}-{:1d}".format(cbar, arg)
for cbar, arg in nastran.rddtipch(pch, "tef1")
]
nb = uset.shape[0]
nq = maa.shape[0] - nb
bset = np.arange(nb)
qset = np.arange(nq) + nb
ref = [600.0, 150.0, 150.0]
g = 9806.65
# use addgrid to get coordinate transformations from lv to sc:
cid = [1, 0, 0]
A = [0, 0, 0]
# define sc in terms of lv coords:
# (all drawn out by hand)
BC = [
[[0, 0, 1.0], [1.0, 0, 0]], # lv x is up
[[0, 0, -1.0], [0, 1.0, 0]], # lv y is up
[[-1.0, 0, 0.0], [0, 0, 1.0]], # lv z is up
[[0, 0, -1.0], [-1.0, 0, 0]], # lv x is down
[[0, 0, 1.0], [0, -1.0, 0]], # lv y is down
[[0, -1.0, 0], [0, 0, -1.0]], # lv z is down
]
Ts = []
nets = []
rb = n2p.rbgeom_uset(uset, ref)
rbcglfa = []
for bc in BC:
CI = n2p.mkusetcoordinfo([cid, A, *bc], None, {})
T = CI[2:]
Ts.append(T)
net = cb.mk_net_drms(maa, kaa, bset, uset=uset, ref=ref, g=g, sccoord=T)
nets.append(net)
rba = net.cglfa[:, :24] @ rb
rbcglfa.append(rba)
# sc rows:
assert np.all(np.sign(rba[1, [1, 5]]) == np.sign(rba[3, [1, 5]]))
assert np.all(np.sign(rba[2, [2, 4]]) == np.sign(rba[4, [2, 4]]))
# lv rows:
assert np.all(np.sign(rba[6, [1, 5]]) == np.sign(rba[8, [1, 5]]))
assert np.all(np.sign(rba[7, [2, 4]]) == np.sign(rba[9, [2, 4]]))
wh_sc = nets[0].weight_sc * nets[0].height_sc
wh_lv = nets[0].weight_lv * nets[0].height_lv
n = nets[0].cgatm_sc.shape[1]
# x is down:
cgdrm = np.vstack(
(
# 5 s/c rows
nets[0].cgatm_sc[:3],
-nets[0].ifltma_sc[5] / wh_sc,
nets[0].ifltma_sc[4] / wh_sc,
# 5 l/v rows
nets[0].cgatm_lv[:3],
-nets[0].ifltma_lv[5] / wh_lv,
nets[0].ifltma_lv[4] / wh_lv,
# 4 RSS rows ... filled in during data recovery
np.zeros((4, n)),
)
)
assert np.allclose(cgdrm, nets[0].cglfa)
def test_sparse_write():
fnames = [
"tests/nastran_op4_data/cdbin.op4",
"tests/nastran_op4_data/rs.op4",
"tests/nastran_op4_data/r_c_rc.op4",
"tests/nastran_op4_data/double_bigmat_le.op4",
"tests/nastran_op4_data/double_nonbigmat_be.op4",
"tests/nastran_op4_data/single_dense_be.op4",
]
for fname in fnames:
for rd_sparse in (True, False, None):
m = op4.read(fname, sparse=rd_sparse)
m2 = write_read(m, binary=True, sparse="dense")
m3 = write_read(m, binary=False, sparse="dense")
m4 = write_read(m, binary=False, sparse="nonbigmat")
m5 = write_read(m, binary=True, sparse="nonbigmat")
m6 = write_read(m, binary=False, sparse="bigmat")
m7 = write_read(m, binary=True, sparse="bigmat")
m8 = write_read(m, binary=False, sparse="auto")
m9 = write_read(m, binary=True, sparse="auto")
for k, v in m.items():
if rd_sparse:
assert sp.issparse(v)
elif rd_sparse is None and "bigmat" in fname:
if "nonbigmat" in fname and not sp.issparse(v):
# v could be dense if all zeros
assert np.all(v == 0.0)
else:
assert sp.issparse(v)
va = v.toarray() if sp.issparse(v) else v
assert np.all(va == m2[k])
assert np.all(va == m3[k])
if sp.issparse(m4[k]):
assert np.all(va == m4[k].A)
else:
assert np.allclose(va, m4[k])
assert np.all(m4[k] == 0.0)
if sp.issparse(m5[k]):
assert np.all(va == m5[k].A)
else:
assert np.allclose(va, m5[k])
assert np.all(m5[k] == 0.0)
assert sp.issparse(m6[k])
assert np.all(va == m6[k].A)
assert sp.issparse(m7[k])
assert np.all(va == m7[k].A)
if rd_sparse or (rd_sparse is None and sp.issparse(v)):
assert sp.issparse(m8[k])
assert np.all(va == m8[k].A)
assert sp.issparse(m9[k])
assert np.all(va == m9[k].A)
else:
assert np.all(va == m8[k])
assert np.all(va == m9[k])
