def test_gridgeometry2df(mocker):
"""Test that dataframes are produced"""
eclfiles = EclFiles(REEK)
grid_geom = grid.gridgeometry2df(eclfiles)
assert isinstance(grid_geom, pd.DataFrame)
assert not grid_geom.empty
assert "I" in grid_geom
assert "J" in grid_geom
assert "K" in grid_geom
assert "X" in grid_geom
assert "Y" in grid_geom
assert "Z" in grid_geom
assert "Z_MIN" in grid_geom
assert "Z_MAX" in grid_geom
assert "VOLUME" in grid_geom
assert "ZONE" in grid_geom
assert "GLOBAL_INDEX" in grid_geom
# If at least one inactive cell, this will hold:
assert grid_geom["GLOBAL_INDEX"].max() > len(grid_geom)
assert (grid_geom["Z_MAX"] > grid_geom["Z_MIN"]).all()
with pytest.raises(TypeError, match="missing 1 required positional"):
grid.gridgeometry2df()
with pytest.raises(AttributeError):
# This error situation we don't really try to handle.
grid.gridgeometry2df(None)
with pytest.raises(ValueError, match="No EGRID file supplied"):
mocker.patch("ecl2df.eclfiles.EclFiles.get_egridfile", return_value=None)
grid.gridgeometry2df(eclfiles)
def test_gridzonemap():
"""Check that zonemap can be merged automatically be default, and also
that there is some API for supplying the zonemap directly as a dictionary"""
eclfiles = EclFiles(DATAFILE)
grid_geom = grid.gridgeometry2df(eclfiles, zonemap=None)
default_zonemap = grid_geom["ZONE"]
grid_no_zone = grid.gridgeometry2df(eclfiles, zonemap={})
assert "ZONE" not in grid_no_zone
assert (grid.df(eclfiles, zonemap=None)["ZONE"] == default_zonemap).all()
df_no_zone = grid.df(eclfiles, zonemap={})
assert "ZONE" not in df_no_zone
df_custom_zone = grid.gridgeometry2df(eclfiles, zonemap={1: "FIRSTLAYER"})
assert "ZONE" in df_custom_zone
assert set(
df_custom_zone[df_custom_zone["K"] == 1]["ZONE"].unique()) == set(
["FIRSTLAYER"])
assert len(df_custom_zone) == len(grid_no_zone)
df_bogus_zones = grid.gridgeometry2df(eclfiles,
zonemap={999999: "nonexistinglayer"})
assert pd.isnull(df_bogus_zones["ZONE"]).all()
# Test a custom "subzone" map via direct usage of merge_zone on an dataframe
# where ZONE already exists:
dframe = grid.df(eclfiles)
subzonemap = {1: "SUBZONE1", 2: "SUBZONE2"}
dframe = common.merge_zones(dframe,
subzonemap,
zoneheader="SUBZONE",
kname="K")
assert (dframe["ZONE"] == default_zonemap).all()
assert set(dframe[dframe["K"] == 1]["SUBZONE"].unique()) == set(
["SUBZONE1"])
assert set(dframe[dframe["K"] == 2]["SUBZONE"].unique()) == set(
["SUBZONE2"])
assert len(dframe) == len(grid_no_zone)
def test_mergegridframes():
"""Test that we can merge together data for the grid"""
eclfiles = EclFiles(DATAFILE)
init_df = grid.init2df(eclfiles)
grid_geom = grid.gridgeometry2df(eclfiles)
assert len(init_df) == len(grid_geom)
merged = grid.merge_gridframes(grid_geom, init_df, pd.DataFrame())
assert isinstance(merged, pd.DataFrame)
assert len(merged) == len(grid_geom)
# Check that PORV is sensible
assert (abs(sum(merged["PORO"] * merged["VOLUME"] - merged["PORV"])) /
sum(merged["PORV"]) < 0.00001)
def test_gridgeometry2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
grid_geom = grid.gridgeometry2df(eclfiles)
assert isinstance(grid_geom, pd.DataFrame)
assert not grid_geom.empty
assert "I" in grid_geom
assert "J" in grid_geom
assert "K" in grid_geom
assert "X" in grid_geom
assert "Y" in grid_geom
assert "Z" in grid_geom
assert "VOLUME" in grid_geom
assert "ZONE" in grid_geom
def test_gridgeometry2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
grid_geom = grid.gridgeometry2df(eclfiles)
assert isinstance(grid_geom, pd.DataFrame)
assert not grid_geom.empty
assert "I" in grid_geom
assert "J" in grid_geom
assert "K" in grid_geom
assert "X" in grid_geom
assert "Y" in grid_geom
assert "Z" in grid_geom
assert "VOLUME" in grid_geom
assert "ZONE" in grid_geom
assert "GLOBAL_INDEX" in grid_geom
# If at least one inactive cell, this will hold:
assert grid_geom["GLOBAL_INDEX"].max() > len(grid_geom)
def add_nnc_coords(nncdf, eclfiles):
"""Add columns X, Y and Z for the connection midpoint
This extracts x, y and z for (I1, J1, K1) and (I2, J2, K2)
and computes the average in each direction.
Arguments:
nncdf (DataFrame): With grid index columns (I1, J1, K1, I2, J2, K2)
eclfiles (EclFiles): Object used to fetch grid data from EGRID.
Returns:
DataFrame: Incoming dataframe augmented with the columns X, Y and Z.
"""
gridgeometry = grid.gridgeometry2df(eclfiles)
gnncdf = pd.merge(
nncdf,
gridgeometry,
how="left",
left_on=["I1", "J1", "K1"],
right_on=["I", "J", "K"],
)
gnncdf = pd.merge(
gnncdf,
gridgeometry,
how="left",
left_on=["I2", "J2", "K2"],
right_on=["I", "J", "K"],
suffixes=("", "_2"),
)
# Use pd.DataFrame.mean for averaging, since it can ignore
# NaN's. In case only one coordinate is NaN, we then get the other one.
# (NaN coordinates are potentially from zero-volume cells?)
gnncdf["X"] = gnncdf[["X", "X_2"]].mean(axis=1)
gnncdf["Y"] = gnncdf[["Y", "Y_2"]].mean(axis=1)
gnncdf["Z"] = gnncdf[["Z", "Z_2"]].mean(axis=1)
# Let go of the temporary columns we have in gnncdf
return gnncdf[list(nncdf.columns) + ["X", "Y", "Z"]]