def test_dtype_size(tmp_path):
filenames = ['dtype_16', 'dtype_32', 'dtype_64']
byte_sizes = [2, 4, 8]
dtypes = [np.float16, np.float32, np.float64]
hdf5_sizes = []
extent_kji = (1000, 100, 100)
a = np.random.random(extent_kji)
for filename, dtype in zip(filenames, dtypes):
epc = os.path.join(tmp_path, filename + '.epc')
h5_file = epc[:-4] + '.h5'
model = rq.new_model(epc)
grid = grr.RegularGrid(model, extent_kji=extent_kji)
grid.create_xml()
pc = rqp.PropertyCollection()
pc.set_support(support_uuid=grid.uuid, model=model)
pc.add_cached_array_to_imported_list(
cached_array=a,
source_info='random',
keyword='NTG',
property_kind='net to gross ratio',
indexable_element='cells',
uom='m3/m3')
pc.write_hdf5_for_imported_list(dtype=dtype)
model.store_epc()
model.h5_release()
hdf5_sizes.append(os.path.getsize(h5_file))
assert hdf5_sizes[0] < hdf5_sizes[1] < hdf5_sizes[2]
for i, (byte_size, hdf5_size) in enumerate(zip(byte_sizes, hdf5_sizes)):
array_size = byte_size * a.size
# following may need to be modified if using hdf5 compression
assert array_size < hdf5_size < array_size + 100000
def test_create_property_set_per_timestep_true(tmp_path):
# Arrange
current_filename = os.path.split(getsourcefile(lambda: 0))[0]
base_folder = os.path.dirname(os.path.dirname(current_filename))
ensemble_dir = f'{base_folder}/test_data/wren'
epc_file = f'{tmp_path}/test.epc'
no_parts_expected = [36, 21, 21, 21]
# Act
import_vdb_ensemble(epc_file,
ensemble_dir,
create_property_set_per_timestep=True)
model = rq.Model(epc_file)
grid = model.grid()
property_set_uuids = model.uuids(obj_type='PropertySet',
title='time index',
title_mode='contains')
# Assert
assert len(property_set_uuids) == 4
for uuid in property_set_uuids:
property_set_root = model.root_for_uuid(uuid=uuid)
title = rqet.citation_title_for_node(property_set_root).split()[-1]
property_set = rqp.PropertyCollection(
support=grid, property_set_root=property_set_root)
assert property_set.number_of_parts() == no_parts_expected[int(title)]
def _set_mesh_from_df(self):
"""Creates Mesh object; called before writing to hdf5 or creating xml."""
# note: actual data is stored in related Property if realization number is present, directly in Mesh otherwise
assert self.n_rows == len(self.df)
assert self.n_cols == len(self.df.columns)
if self.mesh is None:
origin = (0.0, 0.0, 0.0)
dxyz_dij = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]])
crs_uuids = self.model.uuids(obj_type='LocalDepth3dCrs')
if len(crs_uuids) == 0:
crs = rqc.Crs(self.model)
crs.create_xml()
crs_uuid = crs.uuid
else: # use any available crs
crs_uuid = crs_uuids[0]
if self.realization is None:
self.mesh = rqs.Mesh(self.model,
mesh_flavour='reg&z',
ni=self.n_cols,
nj=self.n_rows,
dxyz_dij=dxyz_dij,
origin=origin,
z_values=np.array(self.df),
crs_uuid=crs_uuid)
else:
self.mesh = rqs.Mesh(self.model,
mesh_flavour='regular',
ni=self.n_cols,
nj=self.n_rows,
dxyz_dij=dxyz_dij,
origin=origin,
crs_uuid=crs_uuid)
self.mesh.write_hdf5()
mesh_root = self.mesh.create_xml(title=self.title)
rqet.create_metadata_xml(mesh_root, {'dataframe': 'true'})
if self.realization is not None:
self.pc = rqp.PropertyCollection()
self.pc.set_support(support=self.mesh)
dataframe_pk_uuid = self.model.uuid(obj_type='PropertyKind',
title='dataframe')
if dataframe_pk_uuid is None:
dataframe_pk = rqp.PropertyKind(self.model,
title='dataframe',
example_uom='Euc')
dataframe_pk.create_xml()
dataframe_pk_uuid = dataframe_pk.uuid
self.pc.add_cached_array_to_imported_list(
np.array(self.df),
'dataframe',
self.title,
uom='Euc',
property_kind='dataframe',
local_property_kind_uuid=dataframe_pk_uuid,
realization=self.realization,
indexable_element='nodes')
self.pc.write_hdf5_for_imported_list()
self.pc.create_xml_for_imported_list_and_add_parts_to_model()
def test_create_property_set_per_realization_true(tmp_path):
# Arrange
current_filename = os.path.split(getsourcefile(lambda: 0))[0]
base_folder = os.path.dirname(os.path.dirname(current_filename))
ensemble_dir = f'{base_folder}/test_data/wren'
epc_file = f'{tmp_path}/test.epc'
# Act
import_vdb_ensemble(epc_file,
ensemble_dir,
create_property_set_per_realization=True)
model = rq.Model(epc_file)
grid = model.grid()
property_set_uuids = model.uuids(obj_type='PropertySet',
title='realization',
title_mode='contains')
# Assert
assert len(property_set_uuids) == 3
for uuid in property_set_uuids:
property_set_root = model.root_for_uuid(uuid=uuid)
property_set = rqp.PropertyCollection(
support=grid, property_set_root=property_set_root)
assert property_set.number_of_parts() == 46
def _create_grid_xml(grid,
ijk,
ext_uuid = None,
add_as_part = True,
add_relationships = True,
write_active = True,
write_geometry = True):
"""Function that returns an xml representation containing grid information"""
if grid.grid_representation and not write_geometry:
rqet.create_metadata_xml(node = ijk, extra_metadata = {'grid_flavour': grid.grid_representation})
ni_node = rqet.SubElement(ijk, ns['resqml2'] + 'Ni')
ni_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')
ni_node.text = str(grid.extent_kji[2])
nj_node = rqet.SubElement(ijk, ns['resqml2'] + 'Nj')
nj_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')
nj_node.text = str(grid.extent_kji[1])
nk_node = rqet.SubElement(ijk, ns['resqml2'] + 'Nk')
nk_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')
nk_node.text = str(grid.extent_kji[0])
if grid.k_gaps:
kg_node = rqet.SubElement(ijk, ns['resqml2'] + 'KGaps')
kg_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'KGaps')
kg_node.text = '\n'
kgc_node = rqet.SubElement(kg_node, ns['resqml2'] + 'Count')
kgc_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')
kgc_node.text = str(grid.k_gaps)
assert grid.k_gap_after_array.ndim == 1 and grid.k_gap_after_array.size == grid.nk - 1
kgal_node = rqet.SubElement(kg_node, ns['resqml2'] + 'GapAfterLayer')
kgal_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'BooleanHdf5Array')
kgal_node.text = '\n'
kgal_values = rqet.SubElement(kgal_node, ns['resqml2'] + 'Values')
kgal_values.set(ns['xsi'] + 'type', ns['eml'] + 'Hdf5Dataset')
kgal_values.text = '\n'
grid.model.create_hdf5_dataset_ref(ext_uuid, grid.uuid, 'GapAfterLayer', root = kgal_values)
if grid.stratigraphic_column_rank_uuid is not None and grid.stratigraphic_units is not None:
assert grid.model.type_of_uuid(
grid.stratigraphic_column_rank_uuid) == 'obj_StratigraphicColumnRankInterpretation'
strata_node = rqet.SubElement(ijk, ns['resqml2'] + 'IntervalStratigraphicUnits')
strata_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IntervalStratigraphicUnits')
strata_node.text = '\n'
ui_node = rqet.SubElement(strata_node, ns['resqml2'] + 'UnitIndices')
ui_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IntegerHdf5Array')
ui_node.text = '\n'
ui_null = rqet.SubElement(ui_node, ns['resqml2'] + 'NullValue')
ui_null.set(ns['xsi'] + 'type', ns['xsd'] + 'integer')
ui_null.text = '-1'
ui_values = rqet.SubElement(ui_node, ns['resqml2'] + 'Values')
ui_values.set(ns['xsi'] + 'type', ns['eml'] + 'Hdf5Dataset')
ui_values.text = '\n'
grid.model.create_hdf5_dataset_ref(ext_uuid, grid.uuid, 'unitIndices', root = ui_values)
grid.model.create_ref_node('StratigraphicOrganization',
grid.model.title(uuid = grid.stratigraphic_column_rank_uuid),
grid.stratigraphic_column_rank_uuid,
content_type = 'StratigraphicColumnRankInterpretation',
root = strata_node)
if grid.parent_window is not None:
pw_node = rqet.SubElement(ijk, ns['resqml2'] + 'ParentWindow')
pw_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IjkParentWindow')
pw_node.text = '\n'
assert grid.parent_grid_uuid is not None
parent_grid_root = grid.model.root(uuid = grid.parent_grid_uuid)
if parent_grid_root is None:
pg_title = 'ParentGrid'
else:
pg_title = rqet.citation_title_for_node(parent_grid_root)
grid.model.create_ref_node('ParentGrid',
pg_title,
grid.parent_grid_uuid,
content_type = 'obj_IjkGridRepresentation',
root = pw_node)
for axis in range(3):
regrid_node = rqet.SubElement(pw_node, 'KJI'[axis] + 'Regrid')
regrid_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'Regrid')
regrid_node.text = '\n'
if grid.is_refinement:
if grid.parent_window.within_coarse_box is None:
iiopg = 0 # InitialIndexOnParentGrid
else:
iiopg = grid.parent_window.within_coarse_box[0, axis]
else:
if grid.parent_window.within_fine_box is None:
iiopg = 0
else:
iiopg = grid.parent_window.within_fine_box[0, axis]
iiopg_node = rqet.SubElement(regrid_node, ns['resqml2'] + 'InitialIndexOnParentGrid')
iiopg_node.set(ns['xsi'] + 'type', ns['xsd'] + 'nonNegativeInteger')
iiopg_node.text = str(iiopg)
if grid.parent_window.fine_extent_kji[axis] == grid.parent_window.coarse_extent_kji[axis]:
continue # one-to-noe mapping
intervals_node = rqet.SubElement(regrid_node, ns['resqml2'] + 'Intervals')
intervals_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'Intervals')
intervals_node.text = '\n'
if grid.parent_window.constant_ratios[axis] is not None:
interval_count = 1
else:
if grid.is_refinement:
interval_count = grid.parent_window.coarse_extent_kji[axis]
else:
interval_count = grid.parent_window.fine_extent_kji[axis]
ic_node = rqet.SubElement(intervals_node, ns['resqml2'] + 'IntervalCount')
ic_node.set(ns['xsi'] + 'type', ns['xsd'] + 'positiveInteger')
ic_node.text = str(interval_count)
pcpi_node = rqet.SubElement(intervals_node, ns['resqml2'] + 'ParentCountPerInterval')
pcpi_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IntegerHdf5Array')
pcpi_node.text = '\n'
pcpi_values = rqet.SubElement(pcpi_node, ns['resqml2'] + 'Values')
pcpi_values.set(ns['xsi'] + 'type', ns['eml'] + 'Hdf5Dataset')
pcpi_values.text = '\n'
grid.model.create_hdf5_dataset_ref(ext_uuid,
grid.uuid,
'KJI'[axis] + 'Regrid/ParentCountPerInterval',
root = pcpi_values)
ccpi_node = rqet.SubElement(intervals_node, ns['resqml2'] + 'ChildCountPerInterval')
ccpi_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'IntegerHdf5Array')
ccpi_node.text = '\n'
ccpi_values = rqet.SubElement(ccpi_node, ns['resqml2'] + 'Values')
ccpi_values.set(ns['xsi'] + 'type', ns['eml'] + 'Hdf5Dataset')
ccpi_values.text = '\n'
grid.model.create_hdf5_dataset_ref(ext_uuid,
grid.uuid,
'KJI'[axis] + 'Regrid/ChildCountPerInterval',
root = ccpi_values)
if grid.is_refinement and not grid.parent_window.equal_proportions[axis]:
ccw_node = rqet.SubElement(intervals_node, ns['resqml2'] + 'ChildCellWeights')
ccw_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'DoubleHdf5Array')
ccw_node.text = rqet.null_xml_text
ccw_values_node = rqet.SubElement(ccw_node, ns['resqml2'] + 'Values')
ccw_values_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'Hdf5Dataset')
ccw_values_node.text = rqet.null_xml_text
grid.model.create_hdf5_dataset_ref(ext_uuid,
grid.uuid,
'KJI'[axis] + 'Regrid/ChildCellWeights',
root = ccw_values_node)
# todo: handle omit and cell overlap functionality as part of parent window refining or coarsening
if write_geometry:
__add_geometry_xml(ext_uuid, grid, ijk)
if add_as_part:
__add_as_part(add_relationships, ext_uuid, grid, ijk, write_geometry)
if (write_active and grid.active_property_uuid is not None and
grid.model.part(uuid = grid.active_property_uuid) is None):
# TODO: replace following with call to rprop.write_hdf5_and_create_xml_for_active_property()
active_collection = rprop.PropertyCollection()
active_collection.set_support(support = grid)
active_collection.create_xml(None,
None,
'ACTIVE',
'active',
p_uuid = grid.active_property_uuid,
discrete = True,
add_min_max = False,
find_local_property_kinds = True)
return ijk
def _write_hdf5_from_caches(grid,
file = None,
mode = 'a',
geometry = True,
imported_properties = None,
write_active = None,
stratigraphy = True,
expand_const_arrays = False):
"""Create or append to an hdf5 file.
Writes datasets for the grid geometry (and parent grid mapping) and properties from cached arrays.
"""
# NB: when writing a new geometry, all arrays must be set up and exist as the appropriate attributes prior to calling this function
# if saving properties, active cell array should be added to imported_properties based on logical negation of inactive attribute
# xml is not created here for property objects
if write_active is None:
write_active = geometry
if geometry:
grid.cache_all_geometry_arrays()
if not file:
file = grid.model.h5_file_name()
h5_reg = rwh5.H5Register(grid.model)
if stratigraphy and grid.stratigraphic_units is not None:
h5_reg.register_dataset(grid.uuid, 'unitIndices', grid.stratigraphic_units, dtype = 'uint32')
if geometry:
__write_geometry(grid, h5_reg)
if write_active and grid.inactive is not None:
if imported_properties is None:
imported_properties = rprop.PropertyCollection()
imported_properties.set_support(support = grid)
else:
filtered_list = []
for entry in imported_properties.imported_list:
if (entry[2].upper() == 'ACTIVE' or entry[10] == 'active') and entry[14] == 'cells':
continue # keyword or property kind
filtered_list.append(entry)
imported_properties.imported_list = filtered_list # might have unintended side effects elsewhere
active_mask = np.logical_not(grid.inactive)
imported_properties.add_cached_array_to_imported_list(active_mask,
'active cell mask',
'ACTIVE',
discrete = True,
property_kind = 'active')
if imported_properties is not None and imported_properties.imported_list is not None:
for entry in imported_properties.imported_list:
tail = 'points_patch0' if entry[18] else 'values_patch0'
# expand constant arrays if required
if not hasattr(imported_properties, entry[3]) and expand_const_arrays and entry[17] is not None:
value = float(entry[17]) if isinstance(entry[17], str) else entry[17]
assert entry[14] == 'cells' and entry[15] == 1
imported_properties.__dict__[entry[3]] = np.full(grid.extent_kji, value)
if hasattr(imported_properties, entry[3]): # otherwise constant array not being expanded
h5_reg.register_dataset(entry[0], tail, imported_properties.__dict__[entry[3]])
if entry[10] == 'active':
grid.active_property_uuid = entry[0]
h5_reg.write(file, mode = mode)
def pinchout_connection_set(grid,
skip_inactive=True,
compute_transmissibility=False,
add_to_model=False,
realization=None):
"""Returns (and caches) a GridConnectionSet representing juxtaposition across pinched out cells.
arguments:
skip_inactive (boolean, default True): if True, then cell face pairs involving an inactive cell will
be omitted from the results
compute_transmissibilities (boolean, default False): if True, then transmissibilities will be computed
for the cell face pairs (unless already existing as a cached attribute of the grid)
add_to_model (boolean, default False): if True, the connection set is written to hdf5 and xml is created;
if compute_transmissibilty is True then the transmissibility property is also added
realization (int, optional): if present, is used as the realization number when adding transmissibility
property to model; ignored if compute_transmissibility is False
returns:
GridConnectionSet, numpy float array of shape (count,) transmissibilities (or None), where count is the
number of cell face pairs in the grid connection set, which contains entries for all juxtaposed K faces
separated logically by pinched out (zero thickness) cells; if there are no pinchouts (or no qualifying
connections) then (None, None) will be returned
"""
if not hasattr(grid, 'pgcs') or grid.pgcs_skip_inactive != skip_inactive:
grid.pgcs = rqf.pinchout_connection_set(grid,
skip_inactive=skip_inactive)
grid.pgcs_skip_inactive = skip_inactive
if grid.pgcs is None:
return None, None
new_tr = False
if compute_transmissibility and not hasattr(grid,
'array_pgcs_transmissibility'):
grid.array_pgcs_transmissibility = grid.pgcs.tr_property_array()
new_tr = True
tr = grid.array_pgcs_transmissibility if hasattr(
grid, 'array_pgcs_transmissibility') else None
if add_to_model:
if grid.model.uuid(uuid=grid.pgcs.uuid) is None:
grid.pgcs.write_hdf5()
grid.pgcs.create_xml()
if new_tr:
tr_pc = rprop.PropertyCollection()
tr_pc.set_support(support=grid.pgcs)
tr_pc.add_cached_array_to_imported_list(
tr,
'computed for faces across pinchouts',
'pinchout transmissibility',
discrete=False,
uom='m3.cP/(kPa.d)'
if grid.xy_units() == 'm' else 'bbl.cP/(psi.d)',
property_kind='transmissibility',
realization=realization,
indexable_element='faces',
count=1)
tr_pc.write_hdf5_for_imported_list()
tr_pc.create_xml_for_imported_list_and_add_parts_to_model()
return grid.pgcs, tr
def fault_connection_set(grid,
skip_inactive=True,
compute_transmissibility=False,
add_to_model=False,
realization=None,
inherit_features_from=None,
title='fault juxtaposition set'):
"""Returns (and caches) a GridConnectionSet representing juxtaposition across faces with split pillars.
arguments:
skip_inactive (boolean, default True): if True, then cell face pairs involving an inactive cell will
be omitted from the results
compute_transmissibilities (boolean, default False): if True, then transmissibilities will be computed
for the cell face pairs (unless already existing as a cached attribute of the grid)
add_to_model (boolean, default False): if True, the connection set is written to hdf5 and xml is created;
if compute_transmissibilty is True then the transmissibility property is also added
realization (int, optional): if present, is used as the realization number when adding transmissibility
property to model; ignored if compute_transmissibility is False
inherit_features_from (GridConnectionSet, optional): if present, the features (named faults) are
inherited from this grid connection set based on a match of either cell face in a juxtaposed pair
title (string, default 'fault juxtaposition set'): the citation title to use if adding to model
returns:
GridConnectionSet, numpy float array of shape (count,) transmissibilities (or None), where count is the
number of cell face pairs in the grid connection set, which contains entries for all juxtaposed faces
with a split pillar as an edge; if the grid does not have split pillars (ie. is unfaulted) or there
are no qualifying connections, (None, None) is returned
"""
if not hasattr(grid, 'fgcs') or grid.fgcs_skip_inactive != skip_inactive:
grid.fgcs, grid.fgcs_fractional_area = rqtr.fault_connection_set(
grid, skip_inactive=skip_inactive)
grid.fgcs_skip_inactive = skip_inactive
if grid.fgcs is None:
return None, None
new_tr = False
if compute_transmissibility and not hasattr(grid,
'array_fgcs_transmissibility'):
grid.array_fgcs_transmissibility = grid.fgcs.tr_property_array(
grid.fgcs_fractional_area)
new_tr = True
tr = grid.array_fgcs_transmissibility if hasattr(
grid, 'array_fgcs_transmissibility') else None
if inherit_features_from is not None:
grid.fgcs.inherit_features(inherit_features_from)
grid.fgcs.clean_feature_list()
if add_to_model:
if grid.model.uuid(uuid=grid.fgcs.uuid) is None:
grid.fgcs.write_hdf5()
grid.fgcs.create_xml(title=title)
if new_tr:
tr_pc = rprop.PropertyCollection()
tr_pc.set_support(support=grid.fgcs)
tr_pc.add_cached_array_to_imported_list(
grid.array_fgcs_transmissibility,
'computed for faces with split pillars',
'fault transmissibility',
discrete=False,
uom='m3.cP/(kPa.d)'
if grid.xy_units() == 'm' else 'bbl.cP/(psi.d)',
property_kind='transmissibility',
realization=realization,
indexable_element='faces',
count=1)
tr_pc.write_hdf5_for_imported_list()
tr_pc.create_xml_for_imported_list_and_add_parts_to_model()
return grid.fgcs, tr
def test_gcs_property_inheritance(tmp_path):
epc = os.path.join(tmp_path, 'gcs_prop_inherit.epc')
model = rq.Model(epc,
new_epc=True,
create_basics=True,
create_hdf5_ext=True)
# create a grid
g = grr.RegularGrid(model, extent_kji=(5, 3, 3), dxyz=(10.0, 10.0, 1.0))
g.write_hdf5()
g.create_xml(title='unsplit grid')
# define an L shaped (in plan view) fault
j_faces = np.zeros((g.nk, g.nj - 1, g.ni), dtype=bool)
j_faces[:, 0, 1:] = True
i_faces = np.zeros((g.nk, g.nj, g.ni - 1), dtype=bool)
i_faces[:, 1:, 0] = True
gcs = rqf.GridConnectionSet(
model,
grid=g,
j_faces=j_faces,
i_faces=i_faces,
feature_name='L fault',
create_organizing_objects_where_needed=True,
create_transmissibility_multiplier_property=False)
# check that connection set has the right number of cell face pairs
assert gcs.count == g.nk * ((g.nj - 1) + (g.ni - 1))
# create a transmissibility multiplier property
tm = np.arange(gcs.count).astype(float)
if gcs.property_collection is None:
gcs.property_collection = rqp.PropertyCollection()
gcs.property_collection.set_support(support=gcs)
pc = gcs.property_collection
pc.add_cached_array_to_imported_list(
tm,
'unit test',
'TMULT',
uom='Euc', # actually a ratio of transmissibilities
property_kind='transmissibility multiplier',
local_property_kind_uuid=None,
realization=None,
indexable_element='faces')
# write gcs which should also write property collection and create a local property kind
gcs.write_hdf5()
gcs.create_xml(write_new_properties=True)
# check that a local property kind has materialised
pk_uuid = model.uuid(obj_type='PropertyKind',
title='transmissibility multiplier')
assert pk_uuid is not None
# check that we can create a surface object for the gcs
surf = gcs.surface()
assert surf is not None
t, _ = surf.triangles_and_points()
assert t.shape == (2 * gcs.count, 3)
# create a derived grid connection set using a layer range
thin_gcs, thin_indices = gcs.filtered_by_layer_range(min_k0=1,
max_k0=3,
return_indices=True)
assert thin_gcs is not None and thin_indices is not None
assert thin_gcs.count == 3 * ((g.nj - 1) + (g.ni - 1))
# inherit the transmissibility multiplier property
thin_gcs.inherit_properties_for_selected_indices(gcs, thin_indices)
thin_gcs.write_hdf5()
thin_gcs.create_xml() # by default will include write of new properties
# check that the inheritance has worked
assert thin_gcs.property_collection is not None and thin_gcs.property_collection.number_of_parts(
) > 0
thin_pc = thin_gcs.property_collection
tm_part = thin_pc.singleton(property_kind='transmissibility multiplier')
assert tm_part is not None
thin_tm = thin_pc.cached_part_array_ref(tm_part)
assert thin_tm is not None and thin_tm.ndim == 1
assert thin_tm.size == thin_gcs.count
assert_array_almost_equal(thin_tm, tm[thin_indices])
# check that get_combined...() method can execute using property collection
b_a, i_a, f_a = gcs.get_combined_fault_mask_index_value_arrays(
min_k=1, max_k=3, property_name='Transmissibility multiplier', ref_k=2)
assert b_a is not None and i_a is not None and f_a is not None
# check that transmissibility multiplier values have been sampled correctly from property array
assert f_a.shape == (g.nj, g.ni, 2, 2)
assert np.count_nonzero(
np.isnan(f_a)) == 4 * g.nj * g.ni - 2 * ((g.nj - 1) + (g.ni - 1))
assert np.nanmax(f_a) > np.nanmin(f_a)
restore = np.seterr(all='ignore')
assert np.all(np.logical_or(np.isnan(f_a), f_a >= np.nanmin(thin_tm)))
assert np.all(np.logical_or(np.isnan(f_a), f_a <= np.nanmax(thin_tm)))
np.seterr(**restore)
def test_property_collection(example_model_and_crs):
# Create a WellboreMarkerFrame object
# Load example model from a fixture
model, crs = example_model_and_crs
# Create a trajectory
well_name = 'Banoffee'
elevation = 100
datum = resqpy.well.MdDatum(parent_model=model,
crs_uuid=crs.uuid,
location=(0, 0, -elevation),
md_reference='kelly bushing')
mds = np.array([300.0, 310.0, 330.0])
zs = mds - elevation
source_dataframe = pd.DataFrame({
'MD': mds,
'X': [150.0, 165.0, 180.0],
'Y': [240.0, 260.0, 290.0],
'Z': zs,
})
trajectory = resqpy.well.Trajectory(parent_model=model,
data_frame=source_dataframe,
well_name=well_name,
md_datum=datum,
length_uom='m')
trajectory.write_hdf5()
trajectory.create_xml()
trajectory_uuid = trajectory.uuid
# Create features and interpretations
horizon_feature_1 = rqo.GeneticBoundaryFeature(
parent_model=model, kind='horizon', feature_name='horizon_feature_1')
horizon_feature_1.create_xml()
horizon_interp_1 = rqo.HorizonInterpretation(
parent_model=model,
title='horizon_interp_1',
genetic_boundary_feature=horizon_feature_1,
sequence_stratigraphy_surface='flooding',
boundary_relation_list=['conformable'])
horizon_interp_1.create_xml()
woc_feature_1 = rqo.FluidBoundaryFeature(parent_model=model,
kind='water oil contact',
feature_name='woc_1')
# fluid boundary feature does not have an associated interpretation
woc_feature_1.create_xml()
fault_feature_1 = rqo.TectonicBoundaryFeature(
parent_model=model, kind='fault', feature_name='fault_feature_1')
fault_feature_1.create_xml()
fault_interp_1 = rqo.FaultInterpretation(
parent_model=model,
title='fault_interp_1',
tectonic_boundary_feature=fault_feature_1,
is_normal=True,
maximum_throw=15)
fault_interp_1.create_xml()
df = pd.DataFrame({
'MD': [400.0, 410.0, 430.0],
'Boundary_Feature_Type': ['horizon', 'water oil contact', 'fault'],
'Marker_Citation_Title':
['marker_horizon_1', 'marker_woc_1', 'marker_fault_1'],
'Interp_Citation_Title': ['horizon_interp_1', None, 'fault_interp_1'],
})
# Create a wellbore marker frame from a dataframe
wellbore_marker_frame = resqpy.well.WellboreMarkerFrame.from_dataframe(
parent_model=model,
dataframe=df,
trajectory_uuid=trajectory_uuid,
title='WBF1',
originator='Human',
extra_metadata={'target_reservoir': 'treacle'})
wellbore_marker_frame.write_hdf5()
wellbore_marker_frame.create_xml()
# create a property collection for the wellbore marker frame and add a couple of properties
pc = rqp.PropertyCollection(support=wellbore_marker_frame)
assert pc is not None
node_prop = np.array([123.45, -456.78, 987.65])
pc.add_cached_array_to_imported_list(node_prop,
source_info='unit test',
keyword='node prop',
uom='m',
property_kind='length',
indexable_element='nodes')
interval_prop = np.array([3, 1], dtype=int)
pc.add_cached_array_to_imported_list(interval_prop,
source_info='unit test',
keyword='interval prop',
discrete=True,
null_value=-1,
property_kind='discrete',
indexable_element='intervals')
pc.write_hdf5_for_imported_list()
pc.create_xml_for_imported_list_and_add_parts_to_model()
del pc
# reload the property collection
pc = rqp.PropertyCollection(support=wellbore_marker_frame)
node_prop_part = model.part(obj_type='ContinuousProperty',
title='node prop')
interval_prop_part = model.part(obj_type='DiscreteProperty',
title='interval prop')
# check the property arrays
assert pc is not None
assert pc.number_of_parts() == 2
assert node_prop_part is not None
assert interval_prop_part is not None
assert node_prop_part in pc.parts()
assert interval_prop_part in pc.parts()
assert_array_almost_equal(pc.cached_part_array_ref(node_prop_part),
[123.45, -456.78, 987.65])
int_prop_array = pc.cached_part_array_ref(interval_prop_part)
assert int_prop_array.size == 2
assert np.all(int_prop_array == (3, 1))
def grid_columns_property_from_gcs_property(model,
gcs_property_uuid,
null_value=np.nan,
title=None,
multiple_handling='default'):
"""Derives a new grid columns property (map) from a single-grid gcs property using values for k faces.
arguments:
model (Model): the model in which the existing objects are to be found and the new property added
gcs_property_uuid (UUID): the uuid of the existing grid connection set property
null_value (float or int, default NaN): the value to use in columns where no K faces are present in the gcs
title (str, optional): the title for the new grid property; defaults to that of the gcs property
multiple_handling (str, default 'mean'): one of 'default', 'mean', 'min', 'max', 'min_k', 'max_k', 'exception';
determines how a value is generated when more than one K face is present in the gcs for a column
returns:
uuid of the newly created Property (RESQML ContinuousProperty, DiscreteProperty, CategoricalProperty or
PointsProperty)
notes:
the grid connection set which is the support for gcs_property must involve only one grid;
the resulting columns grid property is of the same class as the original gcs property;
the write_hdf() and create_xml() methods are called by this function, for the new property,
which is added to the model;
the default multiple handling mode is mean for continuous data, any for discrete (inc categorical);
in the case of discrete (including categorical) data, a null_value of NaN will be changed to -1
"""
assert multiple_handling in [
'default', 'mean', 'min', 'max', 'any', 'exception'
]
assert gcs_property_uuid is not None and bu.is_uuid(gcs_property_uuid)
gcs_property = rqp.Property(model, uuid=gcs_property_uuid)
assert gcs_property is not None
support_uuid = gcs_property.collection.support_uuid
assert support_uuid is not None
assert model.type_of_uuid(
support_uuid, strip_obj=True) == 'GridConnectionSetRepresentation'
gcs = rqf.GridConnectionSet(model, uuid=support_uuid)
gcs_prop_array = gcs_property.array_ref()
if gcs_property.is_continuous():
dtype = float
if multiple_handling == 'default':
multiple_handling = 'mean'
else:
dtype = int
if null_value == np.nan:
null_value = -1
elif type(null_value) is float:
null_value = int(null_value)
if multiple_handling == 'default':
multiple_handling = 'any'
assert multiple_handling != 'mean', 'mean specified as multiple handling for non-continuous property'
assert gcs.number_of_grids(
) == 1, 'only single grid gcs supported for grid columns property derivation'
grid = gcs.grid_list[0]
if gcs_property.is_points():
map_shape = (grid.nj, grid.ni, 3)
else:
map_shape = (grid.nj, grid.ni)
assert gcs_property.count() == 1
map = np.full(map_shape, null_value, dtype=dtype)
count_per_col = np.zeros((grid.nj, grid.ni), dtype=int)
cells_and_faces = gcs.list_of_cell_face_pairs_for_feature_index(None)
assert cells_and_faces is not None and len(cells_and_faces) == 2
cell_pairs, face_pairs = cells_and_faces
if cell_pairs is None or len(cell_pairs) == 0:
log.warning(
'no faces found for grid connection set property {gcs_property.title}'
)
return None
assert len(cell_pairs) == len(face_pairs)
assert len(cell_pairs) == len(gcs_prop_array)
for index in range(len(cell_pairs)):
if face_pairs[index, 0, 0] != 0 or face_pairs[index, 1, 0] != 0:
continue # not a K face
col_j, col_i = cell_pairs[index, 0,
1:] # assume paired cell is in same column!
if count_per_col[col_j, col_i] == 0 or multiple_handling == 'any':
map[col_j, col_i] = gcs_prop_array[index]
elif multiple_handling == 'mean':
map[col_j,
col_i] = (((map[col_j, col_i] * count_per_col[col_j, col_i]) +
gcs_prop_array[index]) /
float(count_per_col[col_j, col_i] + 1))
elif multiple_handling == 'min':
if gcs_prop_array[index] < map[col_j, col_i]:
map[col_j, col_i] = gcs_prop_array[index]
elif multiple_handling == 'max':
if gcs_prop_array[index] > map[col_j, col_i]:
map[col_j, col_i] = gcs_prop_array[index]
else:
raise ValueError(
'multiple grid connection set K faces found for column')
count_per_col[col_j, col_i] += 1
# create an empty PropertyCollection and add the map data as a new property
time_index = gcs_property.time_index()
pc = rqp.PropertyCollection()
pc.set_support(support=grid)
pc.add_cached_array_to_imported_list(
map,
source_info=f'{gcs.title} property {gcs_property.title} map',
keyword=title if title else gcs_property.title,
discrete=not gcs_property.is_continuous(),
uom=gcs_property.uom(),
time_index=time_index,
null_value=None if gcs_property.is_continuous() else null_value,
property_kind=gcs_property.property_kind(),
local_property_kind_uuid=gcs_property.local_property_kind_uuid(),
facet_type=gcs_property.facet_type(),
facet=gcs_property.facet(),
realization=None, # do we want to preserve the realisation number?
indexable_element='columns',
points=gcs_property.is_points())
time_series_uuid = pc.write_hdf5_for_imported_list()
string_lookup_uuid = gcs_property.string_lookup_uuid()
time_series_uuid = None if time_index is None else gcs_property.time_series_uuid(
)
new_uuids = pc.create_xml_for_imported_list_and_add_parts_to_model(
time_series_uuid=time_series_uuid,
string_lookup_uuid=string_lookup_uuid)
assert len(new_uuids) == 1
return new_uuids[0]
def from_unsplit_grid(cls,
parent_model,
grid_uuid,
inherit_properties = True,
title = None,
extra_metadata = {},
write_active = None):
"""Creates a new (unstructured) HexaGrid from an existing resqpy unsplit (IJK) Grid without K gaps.
arguments:
parent_model (model.Model object): the model which this grid is part of
grid_uuid (uuid.UUID): the uuid of an IjkGridRepresentation from which the hexa grid will be created
inherit_properties (boolean, default True): if True, properties will be created for the new grid
title (str, optional): citation title for the new grid
extra_metadata (dict, optional): dictionary of extra metadata items to add to the grid
write_active (boolean, optional): if True (or None and inactive property is established) then an
active cell property is created (in addition to any inherited properties)
returns:
a newly created HexaGrid object
note:
this method includes the writing of hdf5 data, creation of xml for the new grid and adding it as a part
"""
import resqpy.grid as grr
# establish existing IJK grid
ijk_grid = grr.Grid(parent_model, uuid = grid_uuid, find_properties = inherit_properties)
assert ijk_grid is not None
assert not ijk_grid.has_split_coordinate_lines, 'IJK grid has split coordinate lines (faults)'
assert not ijk_grid.k_gaps, 'IJK grid has K gaps'
ijk_grid.cache_all_geometry_arrays()
ijk_points = ijk_grid.points_ref(masked = False)
if title is None:
title = ijk_grid.title
# make empty unstructured hexa grid
hexa_grid = cls(parent_model, title = title, extra_metadata = extra_metadata)
# derive hexa grid attributes from ijk grid
hexa_grid.crs_uuid = ijk_grid.crs_uuid
hexa_grid.set_cell_count(ijk_grid.cell_count())
if ijk_grid.inactive is not None:
hexa_grid.inactive = ijk_grid.inactive.reshape((hexa_grid.cell_count,))
hexa_grid.all_inactive = np.all(hexa_grid.inactive)
if hexa_grid.all_inactive:
log.warning(f'all cells marked as inactive for unstructured hexa grid {hexa_grid.title}')
else:
hexa_grid.all_inactive = False
# inherit points (nodes) in IJK grid order, ie. K cycling fastest, then I, then J
hexa_grid.points_cached = ijk_points.reshape((-1, 3))
# setup faces per cell
# ordering of faces (in nodes per face): all K faces, then all J faces, then all I faces
# within J faces, ordering is all of J- faces for J = 0 first, then increasing planes in J
# similarly for I faces
nk_plus_1 = ijk_grid.nk + 1
nj_plus_1 = ijk_grid.nj + 1
ni_plus_1 = ijk_grid.ni + 1
k_face_count = nk_plus_1 * ijk_grid.nj * ijk_grid.ni
j_face_count = ijk_grid.nk * nj_plus_1 * ijk_grid.ni
i_face_count = ijk_grid.nk * ijk_grid.nj * ni_plus_1
kj_face_count = k_face_count + j_face_count
hexa_grid.face_count = k_face_count + j_face_count + i_face_count
hexa_grid.faces_per_cell_cl = 6 * (1 + np.arange(hexa_grid.cell_count, dtype = int)) # 6 faces per cell
hexa_grid.faces_per_cell = np.empty(6 * hexa_grid.cell_count, dtype = int)
arange = np.arange(hexa_grid.cell_count, dtype = int)
hexa_grid.faces_per_cell[0::6] = arange # K- faces
hexa_grid.faces_per_cell[1::6] = ijk_grid.nj * ijk_grid.ni + arange # K+ faces
nki = ijk_grid.nk * ijk_grid.ni
nkj = ijk_grid.nk * ijk_grid.nj
# todo: vectorise following for loop
for cell in range(hexa_grid.cell_count):
k, j, i = ijk_grid.denaturalized_cell_index(cell)
j_minus_face = k_face_count + nki * j + ijk_grid.ni * k + i
hexa_grid.faces_per_cell[6 * cell + 2] = j_minus_face # J- face
hexa_grid.faces_per_cell[6 * cell + 3] = j_minus_face + nki # J+ face
i_minus_face = kj_face_count + nkj * i + ijk_grid.nj * k + j
hexa_grid.faces_per_cell[6 * cell + 4] = i_minus_face # I- face
hexa_grid.faces_per_cell[6 * cell + 5] = i_minus_face + nkj # I+ face
# setup nodes per face, clockwise when viewed from negative side of face if ijk handedness matches xyz handedness
# ordering of nodes in points array is as for the IJK grid
hexa_grid.node_count = hexa_grid.points_cached.shape[0]
assert hexa_grid.node_count == (ijk_grid.nk + 1) * (ijk_grid.nj + 1) * (ijk_grid.ni + 1)
hexa_grid.nodes_per_face_cl = 4 * (1 + np.arange(hexa_grid.face_count, dtype = int)) # 4 nodes per face
hexa_grid.nodes_per_face = np.empty(4 * hexa_grid.face_count, dtype = int)
# todo: vectorise for loops
# K faces
face_base = 0
for k in range(nk_plus_1):
for j in range(ijk_grid.nj):
for i in range(ijk_grid.ni):
hexa_grid.nodes_per_face[face_base] = (k * nj_plus_1 + j) * ni_plus_1 + i # ip 0, jp 0
hexa_grid.nodes_per_face[face_base + 1] = (k * nj_plus_1 + j + 1) * ni_plus_1 + i # ip 0, jp 1
hexa_grid.nodes_per_face[face_base + 2] = (k * nj_plus_1 + j + 1) * ni_plus_1 + i + 1 # ip 1, jp 1
hexa_grid.nodes_per_face[face_base + 3] = (k * nj_plus_1 + j) * ni_plus_1 + i + 1 # ip 1, jp 0
face_base += 4
# J faces
assert face_base == 4 * k_face_count
for j in range(nj_plus_1):
for k in range(ijk_grid.nk):
for i in range(ijk_grid.ni):
hexa_grid.nodes_per_face[face_base] = (k * nj_plus_1 + j) * ni_plus_1 + i # ip 0, kp 0
hexa_grid.nodes_per_face[face_base + 1] = (k * nj_plus_1 + j) * ni_plus_1 + i + 1 # ip 1, kp 0
hexa_grid.nodes_per_face[face_base +
2] = ((k + 1) * nj_plus_1 + j) * ni_plus_1 + i + 1 # ip 1, kp 1
hexa_grid.nodes_per_face[face_base + 3] = ((k + 1) * nj_plus_1 + j) * ni_plus_1 + i # ip 0, kp 1
face_base += 4
# I faces
assert face_base == 4 * kj_face_count
for i in range(ni_plus_1):
for k in range(ijk_grid.nk):
for j in range(ijk_grid.nj):
hexa_grid.nodes_per_face[face_base] = (k * nj_plus_1 + j) * ni_plus_1 + i # jp 0, kp 0
hexa_grid.nodes_per_face[face_base + 1] = ((k + 1) * nj_plus_1 + j) * ni_plus_1 + i # jp 0, kp 1
hexa_grid.nodes_per_face[face_base +
2] = ((k + 1) * nj_plus_1 + j + 1) * ni_plus_1 + i # jp 1, kp 1
hexa_grid.nodes_per_face[face_base + 3] = (k * nj_plus_1 + j + 1) * ni_plus_1 + i # jp 1, kp 0
face_base += 4
assert face_base == 4 * hexa_grid.face_count
# set cell face is right handed
# todo: check Energistics documents for meaning of cell face is right handed
# here the assumption is clockwise ordering of nodes viewed from within cell means 'right handed'
hexa_grid.cell_face_is_right_handed = np.zeros(6 * hexa_grid.cell_count,
dtype = bool) # initially set to left handed
# if IJK grid's ijk handedness matches the xyz handedness, then set +ve faces to right handed; else -ve faces
if ijk_grid.off_handed():
hexa_grid.cell_face_is_right_handed[0::2] = True # negative faces are right handed
else:
hexa_grid.cell_face_is_right_handed[1::2] = True # positive faces are right handed
hexa_grid.write_hdf5(write_active = write_active)
hexa_grid.create_xml(write_active = write_active)
if inherit_properties:
ijk_pc = ijk_grid.extract_property_collection()
hexa_pc = rqp.PropertyCollection(support = hexa_grid)
for part in ijk_pc.parts():
count = ijk_pc.count_for_part(part)
hexa_part_shape = (hexa_grid.cell_count,) if count == 1 else (hexa_grid.cell_count, count)
hexa_pc.add_cached_array_to_imported_list(
ijk_pc.cached_part_array_ref(part).reshape(hexa_part_shape),
'inherited from grid ' + str(ijk_grid.title),
ijk_pc.citation_title_for_part(part),
discrete = not ijk_pc.continuous_for_part(part),
uom = ijk_pc.uom_for_part(part),
time_index = ijk_pc.time_index_for_part(part),
null_value = ijk_pc.null_value_for_part(part),
property_kind = ijk_pc.property_kind_for_part(part),
local_property_kind_uuid = ijk_pc.local_property_kind_uuid(part),
facet_type = ijk_pc.facet_type_for_part(part),
facet = ijk_pc.facet_for_part(part),
realization = ijk_pc.realization_for_part(part),
indexable_element = ijk_pc.indexable_for_part(part),
count = count,
const_value = ijk_pc.constant_value_for_part(part))
# todo: patch min & max values if present in ijk part
hexa_pc.write_hdf5_for_imported_list()
hexa_pc.create_xml_for_imported_list_and_add_parts_to_model(
support_uuid = hexa_grid.uuid,
time_series_uuid = ijk_pc.time_series_uuid_for_part(part),
string_lookup_uuid = ijk_pc.string_lookup_uuid_for_part(part),
extra_metadata = ijk_pc.extra_metadata_for_part(part))
return hexa_grid
def __init__(
self,
model,
support_root=None, # deprecated
uuid=None,
df=None,
uom_list=None,
realization=None,
title='dataframe',
column_lookup_uuid=None,
uom_lookup_uuid=None,
extra_metadata=None):
"""Create a new Dataframe object from either a previously stored property or a pandas dataframe.
arguments:
model (model.Model): the model to which the new Dataframe will be attached
support_root (lxml.Element, DEPRECATED): use uuid instead
uuid (uuid.UUID, optional): the uuid of an existing Grid2dRepresentation
object acting as support for a dataframe property (or holding the dataframe as z values)
df (pandas.DataFrame, optional): a dataframe from which the new Dataframe is to be created;
if both uuid (or support_root) and df are supplied, realization must not be None and a new
realization property will be created
uom_list (list of str, optional): a list holding the units of measure for each
column; if present, length of list must match number of columns in df; ignored if
uuid or support_root is not None
realization (int, optional): if present, the realization number of the RESQML property
holding the dataframe
title (str, default 'dataframe'): used as the citation title for the Mesh (and property);
ignored if uuid or support_root is not None
column_lookup_uuid (uuid, optional): if present, the uuid of a string lookup table holding
the column names; if present, the contents and order of the table must match the columns
in the dataframe; if absent, a new lookup table will be created; ignored if support_root
is not None
uom_lookup_uuid (uuid, optional): if present, the uuid of a string lookup table holding
the units of measure for each column; if None and uom_list is present, a new table
will be created; if both uom_list and uom_lookup_uuid are present, their contents
must match; ignored if support_root is not None
extra_metadata (dict, optional): if present, a dictionary of extra metadata items, str: str;
ignored if uuid (or support_root) is not None
returns:
a newly created Dataframe object
notes:
when initialising from an existing RESQML object, the supporting mesh and its property should
have been originally created using this class; when working with ensembles, each object of this
class will only handle the data for one realization, though they may share a common support_root
"""
assert uuid is not None or support_root is not None or df is not None
assert (uuid is None and
support_root is None) or df is None or realization is not None
if uuid is None:
if support_root is not None:
warnings.warn(
"support_root parameter is deprecated, use uuid instead",
DeprecationWarning)
uuid = rqet.uuid_for_part_root(support_root)
else:
support_root = model.root_for_uuid(uuid)
self.model = model
self.df = None
self.n_rows = self.n_cols = 0
self.uom_list = None
self.realization = realization
self.title = title
self.mesh = None # only generated when needed for write_hdf5(), create_xml()
self.pc = None # property collection; only generated when needed for write_hdf5(), create_xml()
self.column_lookup_uuid = column_lookup_uuid
self.column_lookup = None # string lookup table mapping column index (0 based) to column name
self.uom_lookup_uuid = uom_lookup_uuid
self.uom_lookup = None # string lookup table mapping column index (0 based) to uom
self.extra_metadata = extra_metadata
if uuid is not None:
assert rqet.node_type(support_root) == 'obj_Grid2dRepresentation'
self.mesh = rqs.Mesh(self.model, uuid=uuid)
self.extra_metadata = self.mesh.extra_metadata
assert 'dataframe' in self.extra_metadata and self.extra_metadata[
'dataframe'] == 'true'
self.title = self.mesh.title
self.n_rows, self.n_cols = self.mesh.nj, self.mesh.ni
cl_uuid = self.model.uuid(obj_type='StringTableLookup',
related_uuid=uuid,
title='dataframe columns')
assert cl_uuid is not None, 'column name lookup table not found for dataframe'
self.column_lookup = rqp.StringLookup(self.model, uuid=cl_uuid)
self.column_lookup_uuid = self.column_lookup.uuid
assert self.column_lookup.length() == self.n_cols
ul_uuid = self.model.uuid(obj_type='StringTableLookup',
related_uuid=uuid,
title='dataframe units')
if ul_uuid is not None:
self.uom_lookup = rqp.StringLookup(self.model, uuid=ul_uuid)
self.uom_lookup_uuid = self.uom_lookup.uuid
self.uom_list = self.uom_lookup.get_list()
da = self.mesh.full_array_ref(
)[...,
2] # dataframe data as 2D numpy array, defaulting to z values in mesh
existing_pc = rqp.PropertyCollection(support=self.mesh)
existing_count = 0 if existing_pc is None else existing_pc.number_of_parts(
)
if df is None: # existing dara, either in mesh or property
if existing_count > 0: # use property data instead of z values
if existing_count == 1:
if self.realization is not None:
assert existing_pc.realization_for_part(
existing_pc.singleton()) == self.realization
else:
assert self.realization is not None, 'no realization specified when accessing ensemble dataframe'
da = existing_pc.single_array_ref(
realization=self.realization)
assert da is not None and da.ndim == 2 and da.shape == (
self.n_rows, self.n_cols)
else:
assert realization is None
self.df = pd.DataFrame(da,
columns=self.column_lookup.get_list())
else: # both support_root and df supplied: add a new realisation
if existing_count > 0:
assert existing_pc.singleton(
realization=self.realization
) is None, 'dataframe realization already exists'
self.df = df.copy()
assert len(self.df) == self.n_rows
assert len(self.df.columns) == self.n_rows
else:
assert df is not None, 'no dataframe (or support root) provided when instantiating DataFrame object'
self.df = df.copy()
# todo: check data type of columns – restrict to numerical data
self.n_rows = len(self.df)
self.n_cols = len(self.df.columns)
if column_lookup_uuid is not None:
self.column_lookup = rqp.StringLookup(self.model,
uuid=column_lookup_uuid)
assert self.column_lookup is not None
assert self.column_lookup.length() == self.n_cols
assert all(self.df.columns == self.column_lookup.get_list()
) # exact match of column names required!
if uom_lookup_uuid is not None:
self.uom_lookup = rqp.StringLookup(self.model,
uuid=uom_lookup_uuid)
assert self.uom_lookup is not None
if uom_list is not None:
assert len(uom_list) == self.n_cols
self.uom_list = uom_list.copy()
if self.uom_lookup is not None:
assert self.uom_list == self.uom_lookup.get_list()
elif self.uom_lookup is not None:
self.uom_list = self.uom_lookup.get_list()
def add_one_blocked_well_property(epc_file,
a,
property_kind,
blocked_well_uuid,
source_info='imported',
title=None,
discrete=False,
uom=None,
time_index=None,
time_series_uuid=None,
string_lookup_uuid=None,
null_value=None,
indexable_element='cells',
facet_type=None,
facet=None,
realization=None,
local_property_kind_uuid=None,
count_per_element=1,
points=False,
extra_metadata={},
new_epc_file=None):
"""Adds a blocked well property from a numpy array to an existing resqml dataset.
arguments:
epc_file (string): file name to load model resqml model from (and rewrite to if new_epc_file is None)
a (1D numpy array): the blocked well property array to be added to the model
property_kind (string): the resqml property kind
blocked_well_uuid (uuid object or string): the uuid of the blocked well to which the property relates
source_info (string): typically the name of a file from which the array has been read but can be any
information regarding the source of the data
title (string): this will be used as the citation title when a part is generated for the array
discrete (boolean, default False): if True, the array should contain integer (or boolean) data; if False, float
uom (string, default None): the resqml units of measure for the data; not relevant to discrete data
time_index (integer, default None): if not None, the time index to be used when creating a part for the array
time_series_uuid (uuid object or string, default None): required if time_index is not None
string_lookup_uuid (uuid object or string, optional): required if the array is to be stored as a categorical
property; set to None for non-categorical discrete data; only relevant if discrete is True
null_value (int, default None): if present, this is used in the metadata to indicate that this value
is to be interpreted as a null value wherever it appears in the data (use for discrete data only)
indexable_element (string, default 'cells'): the indexable element in the supporting representation (the blocked well);
valid values are 'cells', 'intervals' (which includes unblocked intervals), or 'nodes'
facet_type (string): resqml facet type, or None
facet (string): resqml facet, or None
realization (int): realization number, or None
local_property_kind_uuid (uuid.UUID or string): uuid of local property kind, or None
count_per_element (int, default 1): the number of values per indexable element; if greater than one then this
must be the fastest cycling axis in the cached array, ie last index; if greater than 1 then a must be a 2D array
points (bool, default False): if True, this is a points property with an extra dimension of extent 3
extra_metadata (dict, optional): any items in this dictionary are added as extra metadata to the new
property
new_epc_file (string, optional): if None, the source epc_file is extended with the new property object; if present,
a new epc file (& associated h5 file) is created to contain a copy of the blocked well (and dependencies) and
the new property
returns:
uuid.UUID of newly created property object
"""
if new_epc_file and epc_file and (
(new_epc_file == epc_file) or
(os.path.exists(new_epc_file) and os.path.exists(epc_file)
and os.path.samefile(new_epc_file, epc_file))):
new_epc_file = None
# open up model and establish grid object
model = rq.Model(epc_file)
blocked_well = rqw.BlockedWell(model, uuid=blocked_well_uuid)
assert blocked_well is not None, f'no blocked well object found with uuid {blocked_well_uuid}'
if not discrete:
string_lookup_uuid = None
# create an empty property collection and add the new array to its 'imported' list
bwpc = rqp.PropertyCollection()
bwpc.set_support(support=blocked_well, model=model)
bwpc.add_cached_array_to_imported_list(
a,
source_info,
title,
discrete=discrete,
uom=uom,
time_index=time_index,
null_value=null_value,
property_kind=property_kind,
local_property_kind_uuid=local_property_kind_uuid,
facet_type=facet_type,
facet=facet,
realization=realization,
indexable_element=indexable_element,
count=count_per_element,
points=points)
bwpc.write_hdf5_for_imported_list()
uuid_list = bwpc.create_xml_for_imported_list_and_add_parts_to_model(
time_series_uuid=time_series_uuid,
string_lookup_uuid=string_lookup_uuid,
property_kind_uuid=local_property_kind_uuid,
extra_metadata=extra_metadata)
assert len(uuid_list) == 1
model.store_epc()
return uuid_list[0]
