def test_filters(self, data_grid):
"""Test filters as argument"""
data_grid["filters"] = {
"reek_sim_facies2.roff": {
"include": ["FINESAND", "COARSESAND"],
}
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert ["FINESAND", "COARSESAND"] == list(pdf.dataframe["FACIES"].unique())
assert pdf.dataframe["PORO"].mean() == pytest.approx(0.2374, abs=0.001)
data_grid["filters"] = {
"reek_sim_facies2.roff": {
"exclude": "FINESAND",
}
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert "FINESAND" not in list(pdf.dataframe["FACIES"].unique())
data_grid["filters"] = {
"reek_sim_poro.roff": {
"range": [0.15, 0.25],
}
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert pdf.dataframe["PORO"].mean() == pytest.approx(0.2027, abs=0.001)
assert pdf.dataframe["PORO"].min() > 0.15
assert pdf.dataframe["PORO"].max() < 0.25
def _extract_statistics(
self, dtype: str, data: dict, project: Optional[object], source: str
):
"""Create dataframe from properties and extract statistics"""
QCC.verbosity = data.get("verbosity", 0)
QCC.print_info("Starting run...")
# Create Property dataframe from input (using XTGeo)
property_data = (
GridProps2df(project=project, data=data, xtgdata=self._xtgdata)
if dtype == "grid"
else WellLogs2df(
project=project,
data=data,
xtgdata=self._xtgdata,
blockedwells=dtype == "bwells",
)
)
# Compute statistics
stats = PropertyAggregation(property_data)
self._set_dataframe_id_and_class_attributes(
stats,
source=source,
run_id=data.get("name", source),
)
return stats.dataframe
def test_gridprops(self, data_grid):
"""Test creating property dataframe from grid properties"""
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert pdf.dataframe["PORO"].mean() == pytest.approx(0.1677, abs=0.001)
assert pdf.dataframe["PORO"].max() == pytest.approx(0.3613, abs=0.001)
assert set(pdf.dataframe.columns) == set(
["PORO", "PERM", "ZONE", "FACIES"])
def test_codenames(self, data_grid):
"""Test modifying codenames on selectors"""
data_grid["selectors"] = {
"ZONE": {
"name": "reek_sim_zone.roff",
"codes": {
1: "TOP",
2: "MID"
}
},
"FACIES": {
"name": "reek_sim_facies2.roff",
"codes": {
1: "SAND",
2: "SAND"
},
},
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert set(["TOP", "MID", "Below_Low_reek"]) == {
x
for x in list(pdf.dataframe["ZONE"].unique()) if x is not None
}
assert set(["SAND", "SHALE"]) == {
x
for x in list(pdf.dataframe["FACIES"].unique()) if x is not None
}
def test_selector_filters(self, data_grid):
"""Test filters on selector"""
data_grid["selectors"] = {
"FACIES": {"name": "reek_sim_facies2.roff", "include": "FINESAND"},
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert ["FINESAND"] == list(pdf.dataframe["FACIES"].unique())
# test exclude values using list
data_grid["selectors"] = {
"FACIES": {
"name": "reek_sim_facies2.roff",
"exclude": ["FINESAND", "SHALE"],
},
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert "FINESAND" not in list(pdf.dataframe["FACIES"].unique())
assert "SHALE" not in list(pdf.dataframe["FACIES"].unique())
def test_filters_and_selector_filters(self, data_grid):
"""
Test filters on both selector and as separate argument
Wanted behaviour is to ignore the filter on the selector
"""
data_grid["selectors"] = {
"FACIES": {"name": "reek_sim_facies2.roff", "exclude": "FINESAND"},
}
data_grid["filters"] = {
"reek_sim_facies2.roff": {
"include": ["FINESAND", "COARSESAND"],
}
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert ["FINESAND", "COARSESAND"] == list(pdf.dataframe["FACIES"].unique())
assert pdf.dataframe["PORO"].mean() == pytest.approx(0.2374, abs=0.001)
def test_filters_and_property_filters(self, data_grid):
"""
Test filters on both properties and as separate argument.
Wanted behaviour is to ignore the filter on the property
"""
data_grid["properties"] = {
"PORO": {"name": "reek_sim_poro.roff", "range": [0.2, 0.4]},
}
data_grid["filters"] = {
"reek_sim_poro.roff": {
"range": [0.15, 0.25],
}
}
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert pdf.dataframe["PORO"].mean() == pytest.approx(0.2027, abs=0.001)
assert pdf.dataframe["PORO"].min() > 0.15
assert pdf.dataframe["PORO"].max() < 0.25
def test_props_and_selectors_as_list(self, data_grid):
"""Test"""
data_grid["properties"] = ["reek_sim_poro.roff", "reek_sim_permx.roff"]
data_grid["selectors"] = [
"reek_sim_zone.roff", "reek_sim_facies2.roff"
]
pdf = GridProps2df(data=data_grid, project=None, xtgdata=QCData())
assert pdf.dataframe["reek_sim_poro.roff"].mean() == pytest.approx(
0.1677, abs=0.001)
assert pdf.dataframe["reek_sim_poro.roff"].max() == pytest.approx(
0.3613, abs=0.001)
assert set(pdf.dataframe.columns) == set([
"reek_sim_poro.roff",
"reek_sim_permx.roff",
"reek_sim_zone.roff",
"reek_sim_facies2.roff",
])