def df(eclfiles: EclFiles,
initvectors: Optional[List[str]] = None) -> pd.DataFrame:
"""Main function for Python API users
Supports only COMPDAT information for now. Will
add a zone-name if a zonefile is found alongside
Returns:
pd.Dataframe with one row pr cell to well connection
"""
compdat_df = deck2dfs(eclfiles.get_ecldeck())["COMPDAT"]
compdat_df = unrolldf(compdat_df)
if initvectors:
compdat_df = merge_initvectors(eclfiles,
compdat_df,
initvectors,
ijknames=["I", "J", "K1"])
zonemap = eclfiles.get_zonemap()
if zonemap:
logger.info("Merging zonemap into compdat")
compdat_df = merge_zones(compdat_df, zonemap)
return compdat_df
def test_df_column_keys():
"""Test that we can slice the dataframe on columns"""
sumdf = summary.df(EclFiles(REEK), column_keys="FOPT")
assert set(sumdf.columns) == {"FOPT"}
assert set(sumdf.attrs["meta"].keys()) == {"FOPT"}
fop_cols = {
"FOPRS",
"FOPT",
"FOPRH",
"FOPTH",
"FOPRF",
"FOPR",
"FOPTS",
"FOPTF",
"FOPP",
}
sumdf = summary.df(EclFiles(REEK), column_keys="FOP*")
assert set(sumdf.columns) == fop_cols
assert set(sumdf.attrs["meta"].keys()) == fop_cols
sumdf = summary.df(EclFiles(REEK), column_keys=["FOP*"])
assert set(sumdf.columns) == fop_cols
assert set(sumdf.attrs["meta"].keys()) == fop_cols
sumdf = summary.df(EclFiles(REEK), column_keys=["FOPR", "FOPT"])
assert set(sumdf.columns) == {"FOPT", "FOPR"}
assert set(sumdf.attrs["meta"].keys()) == {"FOPT", "FOPR"}
sumdf_no_columns = summary.df(EclFiles(REEK), column_keys=["BOGUS"])
assert sumdf_no_columns.columns.empty
assert all(sumdf_no_columns.index == sumdf.index)
def test_ecldeck_to_satfunc_dframe():
"""Test that dataframes can be produced from a full Eclipse deck (the
example Reek case)"""
eclfiles = EclFiles(DATAFILE)
satdf = satfunc.df(eclfiles.get_ecldeck())
assert set(satdf["KEYWORD"]) == {"SWOF", "SGOF"}
assert set(satdf["SATNUM"]) == {1}
assert np.isclose(satdf["SW"].min(), 0.32)
assert np.isclose(satdf["SW"].max(), 1.0)
assert np.isclose(satdf["SG"].min(), 0.0)
assert np.isclose(satdf["SG"].max(), 1 - 0.32)
assert np.isclose(satdf["KRW"].min(), 0.0)
assert np.isclose(satdf["KRW"].max(), 1.0)
assert np.isclose(satdf["KROW"].min(), 0.0)
assert np.isclose(satdf["KROW"].max(), 1.0)
assert np.isclose(satdf["KROG"].min(), 0.0)
assert np.isclose(satdf["KROG"].max(), 1.0)
assert len(satdf) == 76
def test_df_column_keys():
"""Test that we can slice the dataframe on columns"""
sumdf = summary.df(EclFiles(DATAFILE), column_keys="FOPT")
assert set(sumdf.columns) == {"FOPT"}
assert set(sumdf.attrs["meta"].keys()) == {"FOPT"}
fop_cols = {
"FOPRS",
"FOPT",
"FOPRH",
"FOPTH",
"FOPRF",
"FOPR",
"FOPTS",
"FOPTF",
"FOPP",
}
sumdf = summary.df(EclFiles(DATAFILE), column_keys="FOP*")
assert set(sumdf.columns) == fop_cols
assert set(sumdf.attrs["meta"].keys()) == fop_cols
sumdf = summary.df(EclFiles(DATAFILE), column_keys=["FOP*"])
assert set(sumdf.columns) == fop_cols
assert set(sumdf.attrs["meta"].keys()) == fop_cols
sumdf = summary.df(EclFiles(DATAFILE), column_keys=["FOPR", "FOPT"])
assert set(sumdf.columns) == {"FOPT", "FOPR"}
assert set(sumdf.attrs["meta"].keys()) == {"FOPT", "FOPR"}
with pytest.raises(ValueError, match="No valid key"):
summary.df(EclFiles(DATAFILE), column_keys=["BOGUS"])
def test_density():
"""Test that DENSITY can be parsed from files and from strings"""
eclfiles = EclFiles(DATAFILE)
density_df = pvt.density_fromdeck(eclfiles.get_ecldeck())
assert len(density_df) == 1
assert "PVTNUM" in density_df
assert "OILDENSITY" in density_df
assert "WATERDENSITY" in density_df
assert "GASDENSITY" in density_df
dframe_via_string = pvt.density_fromdeck(pvt.df2ecl_density(density_df))
pd.testing.assert_frame_equal(dframe_via_string, density_df)
two_pvtnum_deck = """DENSITY
860 999.04 1.1427 /
800 950 1.05
/
"""
density_df = pvt.density_fromdeck(EclFiles.str2deck(two_pvtnum_deck))
# (a warning will be printed that we cannot guess)
assert len(density_df) == 1
density_df = pvt.density_fromdeck(two_pvtnum_deck)
assert "PVTNUM" in density_df
assert density_df["PVTNUM"].max() == 2
assert density_df["PVTNUM"].min() == 1
assert "OILDENSITY" in density_df
dframe_via_string = pvt.density_fromdeck(pvt.df2ecl_density(density_df))
pd.testing.assert_frame_equal(dframe_via_string, density_df)
# Test emtpy data:
inc = pvt.df2ecl_density(pvt.df(""))
assert "No data" in inc
assert pvt.df(inc).empty
def test_decks():
"""Test some string decks"""
deckstr = """
OIL
WATER
GAS
EQUIL
2000 200 2200 /
"""
deck = EclFiles.str2deck(deckstr)
df = equil.deck2df(deck)
assert df["OWC"].values == 2200
assert len(df) == 1
assert "IGNORE1" not in df
deckstr = """
OIL
WATER
EQUIL
2000 200 2200 /
"""
deck = EclFiles.str2deck(deckstr)
df = equil.deck2df(deck)
assert df["OWC"].values == 2200
assert len(df) == 1
assert "IGNORE1" not in df
deckstr = """
GAS
WATER
EQUIL
2000 200 2200 /
"""
deck = EclFiles.str2deck(deckstr)
df = equil.deck2df(deck)
assert df["GWC"].values == 2200
assert "OWC" not in df
assert len(df) == 1
assert "IGNORE2" not in df
deckstr = """
GAS
OIL
EQUIL
2000 200 2200 1 2100 3 /
"""
deck = EclFiles.str2deck(deckstr)
df = equil.deck2df(deck)
assert df["GOC"].values == 2100
assert "GWC" not in df
assert "OWC" not in df
assert len(df) == 1
assert "IGNORE2" not in df
def test_wcon2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
wcondf = wcon.df(eclfiles.get_ecldeck())
assert not wcondf.empty
assert "DATE" in wcondf # for all data
assert "KEYWORD" in wcondf
for col in wcondf.columns:
assert col == col.upper()
def test_satfunc_roundtrip():
"""Test that we can produce a SATNUM dataframe from the Reek case, convert
it back to an include file, and then reinterpret it to the same"""
eclfiles = EclFiles(DATAFILE)
satdf = satfunc.df(eclfiles.get_ecldeck())
inc = satfunc.df2ecl(satdf)
df_from_inc = satfunc.df(inc)
pd.testing.assert_frame_equal(
satdf.sort_values(["SATNUM", "KEYWORD"]),
df_from_inc.sort_values(["SATNUM", "KEYWORD"]),
)
def test_multiple_parameters():
"""Test what happens when we have duplicate parameter files"""
eclfiles = EclFiles(DATAFILE)
parametersjson = Path(eclfiles.get_path()) / "parameters.json"
parameterstxt = Path(eclfiles.get_path()).parent / "parameters.txt"
parameterstxt.write_text("FOO 1\nBAR 4", encoding="utf-8")
parametersjson.write_text(json.dumps({"BAR": 5, "COM": 6}), encoding="utf-8")
param_dict = load_all(find_parameter_files(eclfiles))
assert len(param_dict) == 3
assert param_dict["BAR"] == 5 # json has precedence over txt
parametersjson.unlink()
parameterstxt.unlink()
def test_faults2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
faultsdf = faults.df(eclfiles.get_ecldeck())
assert "NAME" in faultsdf
assert "I" in faultsdf
assert "J" in faultsdf
assert "K" in faultsdf
assert "FACE" in faultsdf
assert not faultsdf.empty
def test_multiple_parameters():
"""Test what happens when we have duplicate parameter files"""
eclfiles = EclFiles(DATAFILE)
parametersjson = os.path.join(eclfiles.get_path(), "parameters.json")
parameterstxt = os.path.join(eclfiles.get_path(), os.pardir, "parameters.txt")
with open(parameterstxt, "w") as pfile:
pfile.write("FOO 1\nBAR 4")
with open(parametersjson, "w") as pfile:
pfile.write(json.dumps({"BAR": 5, "COM": 6}))
param_dict = load_all(find_parameter_files(eclfiles))
assert len(param_dict) == 3
assert param_dict["BAR"] == 5 # json has precedence over txt
os.unlink(parametersjson)
os.unlink(parameterstxt)
def test_df2ecl_order():
"""Test that we can control the keyword order in generated
strings by the list supplied in keywords argument"""
eclfiles = EclFiles(DATAFILE)
satdf = satfunc.df(eclfiles.get_ecldeck())
swof_sgof = satfunc.df2ecl(satdf, keywords=["SWOF", "SGOF"])
assert swof_sgof.find("SWOF") < swof_sgof.find("SGOF")
sgof_swof = satfunc.df2ecl(satdf, keywords=["SGOF", "SWOF"])
assert sgof_swof.find("SGOF") < sgof_swof.find("SWOF")
only_swof = satfunc.df2ecl(satdf, keywords=["SWOF"])
assert "SGOF" not in only_swof
only_sgof = satfunc.df2ecl(satdf, keywords="SGOF")
assert "SWOF" not in only_sgof
def test_eightcells_dataset():
"""Test Eightcells dataset"""
eclfiles = EclFiles(EIGHTCELLS)
gruptree_df = gruptree.df(eclfiles.get_ecldeck())
expected_dframe = pd.DataFrame(
[
["2000-01-01", "FIELD", "GRUPTREE", np.nan],
["2000-01-01", "OP1", "WELSPECS", "OPS"],
["2000-01-01", "OPS", "GRUPTREE", "FIELD"],
],
columns=["DATE", "CHILD", "KEYWORD", "PARENT"],
)
expected_dframe["DATE"] = pd.to_datetime(expected_dframe["DATE"])
pd.testing.assert_frame_equal(gruptree_df, expected_dframe, check_dtype=False)
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_subvectors():
"""Test that we can ask for a few vectors only"""
eclfiles = EclFiles(EIGHTCELLS)
init_df = grid.init2df(eclfiles, "PORO")
assert "PORO" in init_df
assert "PERMX" not in init_df
assert "PORV" not in init_df
init_df = grid.init2df(eclfiles, "P*")
assert "PORO" in init_df
assert "PERMX" in init_df
assert "PVTNUM" in init_df
assert "SATNUM" not in init_df
init_df = grid.init2df(eclfiles, ["P*"])
assert "PORO" in init_df
assert "PERMX" in init_df
assert "PVTNUM" in init_df
assert "SATNUM" not in init_df
init_df = grid.init2df(eclfiles, ["P*", "*NUM"])
assert "PORO" in init_df
assert "PERMX" in init_df
assert "PVTNUM" in init_df
assert "SATNUM" in init_df
assert "MULTZ" not in init_df
def test_summary2df_dates():
"""Test that we have some API possibilities with ISO dates"""
eclfiles = EclFiles(DATAFILE)
sumdf = summary.df(
eclfiles,
start_date=datetime.date(2002, 1, 2),
end_date="2002-03-01",
time_index="daily",
datetime=True,
)
assert sumdf.index.name == "DATE"
assert sumdf.index.dtype == "datetime64[ns]" or sumdf.index.dtype == "datetime64"
assert len(sumdf) == 59
assert str(sumdf.index.values[0])[0:10] == "2002-01-02"
assert sumdf.index.values[0] == np.datetime64("2002-01-02")
assert sumdf.index.values[-1] == np.datetime64("2002-03-01")
sumdf = summary.df(eclfiles, time_index="last", datetime=True)
assert len(sumdf) == 1
assert sumdf.index.values[0] == np.datetime64("2003-01-02")
# Leave this test for the datetime=False behaviour:
sumdf = summary.df(eclfiles, time_index="first")
assert len(sumdf) == 1
assert str(sumdf.index.values[0]) == "2000-01-01"
def test_reek_dataset():
"""Test Reek dataset. It contains no CPI data and should return
an empty dataframe.
"""
eclfiles = EclFiles(REEK)
wellconnstatus_df = wellconnstatus.df(eclfiles)
assert wellconnstatus_df.empty
def test_pvtw():
"""Test that PVTW can be parsed from a string"""
deck = """PVTW
327.3 1.03 4.51E-005 0.25 0 /"""
pvtw_df = pvt.pvtw_fromdeck(EclFiles.str2deck(deck))
pd.testing.assert_frame_equal(
pvtw_df,
pd.DataFrame(
columns=[
"PRESSURE",
"VOLUMEFACTOR",
"COMPRESSIBILITY",
"VISCOSITY",
"VISCOSIBILITY",
"PVTNUM",
],
data=[[327.3, 1.03, 4.51e-005, 0.25, 0.0, 1]],
),
check_like=True,
)
deck = """PVTW
327.3 1.03 4.51E-005 0.25 0 /
300 1 0.0001 0.2 /"""
pvtw_df = pvt.pvtw_fromdeck(
deck) # Must give string, not deck, for NTPVT guessing
assert len(pvtw_df) == 2
# Test emtpy data:
inc = pvt.df2ecl_pvtw(pvt.df(""))
assert "No data" in inc
assert pvt.df(inc).empty
def test_pillars():
"""Test that we can build a dataframe of pillar statistics"""
eclfiles = EclFiles(DATAFILE)
pillars_df = pillars.df(eclfiles)
assert "PILLAR" in pillars_df
assert "VOLUME" in pillars_df
assert "PORV" in pillars_df
assert "PERMX" in pillars_df
assert "X" in pillars_df
assert "Y" in pillars_df
assert "PORO" in pillars_df
assert "OILVOL" not in pillars_df
assert "FIPNUM" not in pillars_df
assert "EQLNUM" not in pillars_df
assert "OWC" not in pillars_df
assert "GOC" not in pillars_df
assert len(pillars_df) == 2560
pillars_df = pillars.df(eclfiles, region="FIPNUM")
assert "FIPNUM" in pillars_df
assert len(pillars_df["FIPNUM"].unique()) == 6
assert "OILVOL" not in pillars_df
pillars_df = pillars.df(eclfiles, rstdates="first")
firstdate = str(grid.dates2rstindices(eclfiles, "first")[1][0])
assert "OILVOL@" + firstdate in pillars_df
assert "GASVOL@" + firstdate in pillars_df
assert "WATVOL@" + firstdate in pillars_df
pillars_df = pillars.df(eclfiles, rstdates="last", soilcutoff=0.2, sgascutoff=0.2)
lastdate = str(grid.dates2rstindices(eclfiles, "last")[1][0])
assert "OWC@" + lastdate in pillars_df
assert "GOC@" + lastdate not in pillars_df # Because the dataset has no GAS...
def test_datenormalization():
"""Test normalization of dates, where
dates can be ensured to be on dategrid boundaries"""
start = datetime.date(1997, 11, 5)
end = datetime.date(2020, 3, 2)
assert normalize_dates(start, end, "monthly") == (
datetime.date(1997, 11, 1),
datetime.date(2020, 4, 1),
)
assert normalize_dates(start, end, "yearly") == (
datetime.date(1997, 1, 1),
datetime.date(2021, 1, 1),
)
# Check it does not touch already aligned dates
assert normalize_dates(datetime.date(1997, 11,
1), datetime.date(2020, 4, 1),
"monthly") == (datetime.date(1997, 11, 1),
datetime.date(2020, 4, 1))
assert normalize_dates(datetime.date(1997, 1,
1), datetime.date(2021, 1, 1),
"yearly") == (datetime.date(1997, 1, 1),
datetime.date(2021, 1, 1))
# Check that we normalize correctly with get_smry():
# realization-0 here has its last summary date at 2003-01-02
eclfiles = EclFiles(DATAFILE)
daily = summary.df(eclfiles, column_keys="FOPT", time_index="daily")
assert str(daily.index[-1]) == "2003-01-02"
monthly = summary.df(eclfiles, column_keys="FOPT", time_index="monthly")
assert str(monthly.index[-1]) == "2003-02-01"
yearly = summary.df(eclfiles, column_keys="FOPT", time_index="yearly")
assert str(yearly.index[-1]) == "2004-01-01"
def test_str2df():
"""Test when we send in a string directly"""
schstr = """
GRUPTREE
'OPWEST' 'OP' /
'OP' 'FIELD' /
'FIELD' 'AREA' /
'AREA' 'NORTHSEA' /
/
WELSPECS
'OP1' 'OPWEST' 41 125 1759.74 'OIL' 0.0 'STD' 'SHUT' 'YES' 0 'SEG' /
/
"""
deck = EclFiles.str2deck(schstr)
grupdf = gruptree.df(deck)
assert grupdf.dropna().empty # the DATE is empty
# This is only available if GRUPNET is also there
assert "TERMINAL_PRESSURE" not in grupdf
withstart = gruptree.gruptree2df(deck, startdate="2019-01-01")
assert not withstart.dropna().empty
assert len(withstart) == 5
def test_gruptree2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
grupdf = gruptree.df(eclfiles.get_ecldeck())
assert not grupdf.empty
assert len(grupdf["DATE"].unique()) == 5
assert len(grupdf["CHILD"].unique()) == 10
assert len(grupdf["PARENT"].unique()) == 3
assert set(grupdf["KEYWORD"].unique()) == set(["GRUPTREE", "WELSPECS"])
grupdfnowells = gruptree.df(eclfiles.get_ecldeck(), welspecs=False)
assert len(grupdfnowells["KEYWORD"].unique()) == 1
assert grupdf["PARENT"].unique()[0] == "FIELD"
assert grupdf["KEYWORD"].unique()[0] == "GRUPTREE"
def test_merge_initvectors():
eclfiles = EclFiles(REEK)
assert grid.merge_initvectors(eclfiles, pd.DataFrame(), []).empty
foo_df = pd.DataFrame([{"FOO": 1}])
pd.testing.assert_frame_equal(grid.merge_initvectors(eclfiles, foo_df, []), foo_df)
with pytest.raises(ValueError, match="All of the columns"):
grid.merge_initvectors(eclfiles, foo_df, ["NONEXISTING"])
minimal_df = pd.DataFrame([{"I": 10, "J": 11, "K": 12}])
with pytest.raises(KeyError):
grid.merge_initvectors(eclfiles, minimal_df, ["NONEXISTING"])
withporo = grid.merge_initvectors(eclfiles, minimal_df, ["PORO"])
pd.testing.assert_frame_equal(
withporo, minimal_df.assign(PORO=0.221848), check_dtype=False
)
with pytest.raises(ValueError):
# ijknames must be length 3
grid.merge_initvectors(
eclfiles, minimal_df, ["PORO"], ijknames=["I", "J", "K", "L"]
)
with pytest.raises(ValueError):
grid.merge_initvectors(eclfiles, minimal_df, ["PORO"], ijknames=["I", "J"])
with pytest.raises(ValueError, match="All of the columns"):
grid.merge_initvectors(eclfiles, minimal_df, ["PORO"], ijknames=["A", "B", "C"])
def test_df2ecl_editnnc(tmpdir):
"""Test generation of EDITNNC keyword"""
eclfiles = EclFiles(DATAFILE)
nncdf = nnc.df(eclfiles)
tmpdir.chdir()
nncdf["TRANM"] = 2
editnnc = nnc.df2ecl_editnnc(nncdf, filename="editnnc.inc")
editnnc_fromfile = "".join(open("editnnc.inc").readlines())
assert editnnc == editnnc_fromfile
assert "EDITNNC" in editnnc
assert editnnc.count("/") == len(nncdf) + 1
assert "avg multiplier" in editnnc
# Fails when columns are missing
with pytest.raises((KeyError, ValueError)):
nnc.df2ecl_editnnc(nncdf[["I1", "I2"]])
editnnc = nnc.df2ecl_editnnc(nncdf, nocomments=True)
assert "avg multiplier" not in editnnc
# Test compatibility with trans module:
trans_df = trans.df(eclfiles, addnnc=True)
editnnc = nnc.df2ecl_editnnc(trans_df.assign(TRANM=0.3))
assert "avg multiplier 0.3" in editnnc or "avg multiplier 0.29999" in editnnc
print(nnc.df2ecl_editnnc(nnc.df(eclfiles).head(4).assign(TRANM=0.1)))
def test_smry_meta():
"""Test obtaining metadata dictionary for summary vectors from an EclSum object"""
meta = smry_meta(EclFiles(DATAFILE))
assert isinstance(meta, dict)
assert "FOPT" in meta
assert "FOPTH" in meta
assert meta["FOPT"]["unit"] == "SM3"
assert meta["FOPR"]["unit"] == "SM3/DAY"
assert meta["FOPT"]["is_total"]
assert not meta["FOPR"]["is_total"]
assert not meta["FOPT"]["is_rate"]
assert meta["FOPR"]["is_rate"]
assert not meta["FOPT"]["is_historical"]
assert meta["FOPTH"]["is_historical"]
assert meta["WOPR:OP_1"]["wgname"] == "OP_1"
assert meta["WOPR:OP_1"]["keyword"] == "WOPR"
if "wgname" in meta["FOPT"]:
# Not enforced yet to have None fields actually included
assert meta["FOPT"]["wgname"] is None
# Can create dataframes like this:
meta_df = pd.DataFrame.from_dict(meta, orient="index")
hist_keys = meta_df[meta_df["is_historical"]].index
assert all([key.split(":")[0].endswith("H") for key in hist_keys])
def test_wconinje():
"""Test WCONINJE parsing and column names"""
wconstr = """
WCONINJE
'FOO' 0 1 /
/
"""
deck = EclFiles.str2deck(wconstr)
wconinje_df = wcon.df(deck)
pd.testing.assert_frame_equal(
wconinje_df,
pd.DataFrame([{
"WELL": "FOO",
"TYPE": "0",
"STATUS": "1",
"CMODE": None,
"RATE": None,
"RESV": None,
"BHP": 6895,
"THP": None,
"VFP_TABLE": 0,
"VAPOIL_C": 0,
"GAS_STEAM_RATIO": 0,
"SURFACE_OIL_FRACTION": 0,
"SURFACE_WATER_FRACTION": 0,
"SURFACE_GAS_FRACTION": 0,
"OIL_STEAM_RATIO": 0,
"DATE": None,
"KEYWORD": "WCONINJE",
}]),
)
def test_wconhist():
"""Test WCONHIST parsing and column names"""
wconstr = """
WCONHIST
'FOO' 0 1 /
/
"""
deck = EclFiles.str2deck(wconstr)
wconhist_df = wcon.df(deck)
pd.testing.assert_frame_equal(
wconhist_df,
pd.DataFrame([{
"WELL": "FOO",
"STATUS": "0",
"CMODE": "1",
"ORAT": 0,
"WRAT": 0,
"GRAT": 0,
"VFP_TABLE": 0,
"ALQ": 0,
"THP": 0,
"BHP": 0,
"NGLRAT": 0,
"DATE": None,
"KEYWORD": "WCONHIST",
}]),
)
def test_tstep():
"""Test that we support the TSTEP keyword"""
schstr = """
DATES
1 MAY 2001 /
/
WCONHIST
'OP1' 1000 /
/
TSTEP
1 /
WCONHIST
'OP1' 2000 /
/
TSTEP
2 3 /
WCONHIST
'OP1' 3000 /
/
"""
deck = EclFiles.str2deck(schstr)
wcondf = wcon.df(deck)
dates = [str(x) for x in wcondf["DATE"].unique()]
assert len(dates) == 3
assert "2001-05-01" in dates
assert "2001-05-02" in dates
assert "2001-05-07" in dates
def test_rft2df():
"""Test that dataframes are produced"""
eclfiles = EclFiles(DATAFILE)
rftdf = rft.rft2df(eclfiles)
assert "ZONE" in rftdf
assert not rftdf.empty
assert not rftdf.columns.empty
def test_nnc2df_coords():
"""Test that we are able to add coordinates"""
eclfiles = EclFiles(DATAFILE)
gnncdf = nnc.df(eclfiles, coords=True)
assert not gnncdf.empty
assert "X" in gnncdf
assert "Y" in gnncdf
assert "Z" in gnncdf