def test_eclcompress():
"""Test that we can compress string using Eclipse style
run-length encoding"""
assert common.runlength_eclcompress("") == ""
assert common.runlength_eclcompress(" ") == ""
assert common.runlength_eclcompress("1 2") == "1 2"
assert common.runlength_eclcompress("1 2", sep=" ") == "1 2"
assert common.runlength_eclcompress("1 2", sep=" ") == "1 2"
assert common.runlength_eclcompress("1") == "1"
assert common.runlength_eclcompress("1 1") == "2*1"
assert common.runlength_eclcompress("1 1 1") == "3*1"
assert common.runlength_eclcompress("1 1 1") == "3*1"
assert common.runlength_eclcompress("1 \n 1 1 2") == "3*1 2"
def df2ecl(grid_df,
keywords,
eclfiles=None,
dtype=None,
filename=None,
nocomments=False):
"""
Write an include file with grid data keyword, like PERMX, PORO,
FIPNUM etc, for the GRID section of the Eclipse deck.
Output (returned as string and optionally written to file) will then
contain f.ex::
PERMX
3.3 4.1 500.1 8543.0 1223.0 5022.0
411.455 4433.9
/
if the grid contains 8 cells (inactive and active).
Args:
grid_df (pd.DataFrame). Dataframe with the keyword for which
we want to export data, and also the a column with GLOBAL_INDEX.
Without GLOBAL_INDEX, the output will likely be invalid.
The grid can contain both active and inactive cells.
keywords (str or list of str): The keyword(s) to export, with one
value for every cell.
eclfiles (EclFiles): If provided, the total cell count for the grid
will be requested from this object. If not, it will be *guessed*
from the maximum number of GLOBAL_INDEX, which can be under-estimated
in the corner-case that the last cells are inactive.
dtype (float or int-class): If provided, the columns which are
outputted are converted to int or float. Dataframe columns
read from CSV files easily gets the wrong type, while Eclipse
might require some data to be strictly integer.
filename (str): If provided, the string produced will also to be
written to this filename.
nocomments (bool): Set to True to avoid any comments being written. Defaults
to False.
"""
if isinstance(keywords, str):
keywords = [keywords]
if isinstance(dtype, str):
if dtype.startswith("int"):
dtype = int
elif dtype.startswith("float"):
dtype = float
else:
raise ValueError("Wrong dtype argument {}".format(dtype))
# Figure out the total number of cells for which we need to export data for:
global_size = None
active_cells = None
if eclfiles is not None:
if eclfiles.get_egrid() is not None:
global_size = eclfiles.get_egrid().get_global_size()
active_cells = eclfiles.get_egrid().getNumActive()
if "GLOBAL_INDEX" not in grid_df:
logger.warning(("Global index not found in grid dataframe. "
"Assumes all cells are active"))
# Drop NaN rows for columns to be used (triggerd by stacked
# dates and no global index, unlikely)
# Also copy dataframe to avoid side-effects on incoming data.
grid_df = grid_df.dropna(
axis="rows",
subset=[keyword for keyword in keywords if keyword in grid_df])
grid_df["GLOBAL_INDEX"] = grid_df.index
if global_size is None:
global_size = int(grid_df["GLOBAL_INDEX"].max() + 1)
active_cells = len(grid_df[grid_df.index >= 0])
logger.warning("Global grid size estimated to %s", str(global_size))
ecl2df_header = ("Output file printed by " + "ecl2df.grid " + __version__ +
"\n" + " at " + str(datetime.datetime.now()))
string = ""
if not nocomments:
string += common.comment_formatter(ecl2df_header)
string += "\n"
# If we have NaNs in the dataframe, we will be more careful (costs memory)
if grid_df.isna().any().any():
grid_df = grid_df.dropna(
axis="rows",
subset=[keyword for keyword in keywords if keyword in grid_df])
for keyword in keywords:
if keyword not in grid_df.columns:
raise ValueError(
"Keyword {} not found in grid dataframe".format(keyword))
vector = np.zeros(global_size)
vector[grid_df["GLOBAL_INDEX"].astype(int).values] = grid_df[keyword]
if dtype == int:
vector = vector.astype(int)
if dtype == float:
vector = vector.astype(float)
if len(vector) != global_size:
logger.warning(
("Mismatch between dumped vector length "
"%d from df2ecl and assumed grid size %d"),
len(vector),
global_size,
)
logger.warning("Data will be dumped, but may error in simulator")
strvector = " ".join([str(x) for x in vector])
strvector = common.runlength_eclcompress(strvector)
string += keyword + "\n"
indent = " " * 5
string += "\n".join(
textwrap.wrap(strvector,
initial_indent=indent,
subsequent_indent=indent,
width=70))
string += "\n/"
if not nocomments:
string += " -- {}: {} active cells, {} total cell count\n".format(
keyword, active_cells, global_size)
string += "\n"
if filename is not None:
# Make directory if not present:
filenamedir = os.path.dirname(filename)
if filenamedir and not os.path.exists(filenamedir):
os.makedirs(filenamedir)
with open(filename, "w") as file_handle:
file_handle.write(string)
return string
