def test_filedescriptors():
"""Test that filedescriptors are properly closed"""
fd_dir = Path("/proc/") / str(os.getpid()) / "fd"
if not fd_dir.exists():
print("Counting file descriptors on non-Linux not supported")
return
pre_fd_count = len(list(fd_dir.glob("*")))
eclfiles = EclFiles(EIGHTCELLS)
# No opened files yet:
assert len(list(fd_dir.glob("*"))) == pre_fd_count
eclfiles.close()
# No change, no files to close:
assert len(list(fd_dir.glob("*"))) == pre_fd_count
eclfiles.get_egrid()
# This should not leave any file descriptor open
assert len(list(fd_dir.glob("*"))) == pre_fd_count
eclfiles.get_initfile()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._initfile is not None
eclfiles.close()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._initfile is None
eclfiles.get_rstfile()
# Automatically closed by libecl
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._rstfile is not None
eclfiles.close()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._rstfile is None
eclfiles.get_eclsum()
assert len(list(fd_dir.glob("*"))) == pre_fd_count + 1
eclfiles.close()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
eclfiles.get_egridfile()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._egridfile is not None
eclfiles.close()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._egridfile is None
eclfiles.get_rftfile()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._rftfile is not None
eclfiles.close()
assert len(list(fd_dir.glob("*"))) == pre_fd_count
assert eclfiles._rftfile is None
eclfiles.get_ecldeck()
# This should not leave any file descriptor open
assert len(list(fd_dir.glob("*"))) == pre_fd_count
def df(eclfiles: EclFiles,
coords: bool = False,
pillars: bool = False) -> pd.DataFrame:
"""Produce a Pandas Dataframe with NNC information
A NNC is a pair of cells that are not next to each other
in the index space (I, J, K), and are associated to a
non-zero transmissibility.
Columns: I1, J1, K1 (first cell in cell pair)
I2, J2, K2 (second cell in cell pair), TRAN (transmissibility
between the two cells)
Args:
eclfiles: object that can serve EclFile and EclGrid
on demand
coords: Set to True if you want the midpoint of the two
connected cells to be computed and added to the columns
X, Y and Z.
pillars: Set to True if you want to filter to vertical
(along pillars) connections only.
Returns:
Empty if no NNC information found.
"""
egrid_file = eclfiles.get_egridfile()
egrid_grid = eclfiles.get_egrid()
init_file = eclfiles.get_initfile()
if not ("NNC1" in egrid_file and "NNC2" in egrid_file):
logger.warning("No NNC data in EGRID")
return pd.DataFrame()
if "TRANNNC" not in init_file:
logger.warning("No TRANNNC data in INIT (E300 parallell run?)")
return pd.DataFrame()
# Grid indices for first cell in cell pairs, into a vertical
# vector. The indices are "global" in libecl terms, and are
# 1-based (FORTRAN). Convert to zero-based before sending to get_ijk()
nnc1 = egrid_file["NNC1"][0].numpy_view().reshape(-1, 1)
logger.info(
"NNC1: len: %d, min: %d, max: %d (global indices)",
len(nnc1),
min(nnc1),
max(nnc1),
)
idx_cols1 = ["I1", "J1", "K1"]
nnc1_df = pd.DataFrame(
columns=idx_cols1,
data=[egrid_grid.get_ijk(global_index=int(x) - 1) for x in nnc1],
)
# Returned indices from get_ijk are zero-based, convert to 1-based indices
nnc1_df[idx_cols1] = nnc1_df[idx_cols1] + 1
# Grid indices for second cell in cell pairs
nnc2 = egrid_file["NNC2"][0].numpy_view().reshape(-1, 1)
logger.info(
"NNC2: len: %d, min: %d, max: %d (global indices)",
len(nnc2),
min(nnc2),
max(nnc2),
)
idx_cols2 = ["I2", "J2", "K2"]
nnc2_df = pd.DataFrame(
columns=idx_cols2,
data=[egrid_grid.get_ijk(global_index=int(x) - 1) for x in nnc2],
)
nnc2_df[idx_cols2] = nnc2_df[idx_cols2] + 1
# Obtain transmissibility value, corresponding to the cell pairs above.
tran = init_file["TRANNNC"][0].numpy_view().reshape(-1, 1)
logger.info(
"TRANNNC: len: %d, min: %f, max: %f, mean=%f",
len(tran),
min(tran),
max(tran),
tran.mean(),
)
tran_df = pd.DataFrame(columns=["TRAN"], data=tran)
nncdf = pd.concat([nnc1_df, nnc2_df, tran_df], axis=1)
if pillars:
nncdf = filter_vertical(nncdf)
if coords:
nncdf = add_nnc_coords(nncdf, eclfiles)
return nncdf
