def snap_to_branch(self, branches, snap_method, maxdist=5):
"""Snap the geometries to the branch"""
geometry.find_nearest_branch(branches=branches,
geometries=self,
method=snap_method,
maxdist=maxdist)
def read_file(path,
hydamo_attribute,
index_col=None,
attribute_filter=None,
snap_to_branches=None,
keep_columns=None,
column_mapping=None,
z_coord=False
):
"""
Read any OGR supported feature-file to match hydamo-property.
A mask file can be specified to clip the selection.
Parameters
----------
path : str or Path
Path to the feature file
hydamo_attribute : HyDAMO property
property to map to (HyDAMO.branches, HyDAMO.crosssections)
index_col: str
column to be used as index (after optional mapping)
attribute_filter: dict
dict with lists or strings of the format {'column_name': [values to keep]}
snap_to_branches: dict
snap to a geodataframe with LineStrings. Keep only geometries that snap
to a LineString with a certain attribute value.
Format: {'branches': GeoDataFrame,
attribute_filter: {'column_name': [values to keep]}}
column_mapping: dict
dict for renaming input colunns to required columns
Result: GeoDataFrame matching the HyDAMO property
"""
gdf = gpd.read_file(path)
gdf.columns = gdf.columns.str.lower()
# filter by gdf_snap
if snap_to_branches:
distance = snap_to_branches["distance"]
branches = snap_to_branches["branches"]
find_nearest_branch(branches, gdf, maxdist=distance)
gdf = gdf.loc[gdf["branch_offset"].notna()]
gdf.loc[:, "hydromodel"] = gdf.apply(
(lambda x: branches.loc[x["branch_id"]]["hydromodel"]),
axis=1)
if snap_to_branches["attribute_filter"]:
snap_attribute_filter = {
key.lower(): value for key, value in snap_to_branches[
"attribute_filter"].items()}
gdf = _filter(gdf, snap_attribute_filter)
# filter by attribute
if attribute_filter:
attribute_filter = {
key.lower(): value for key, value in attribute_filter.items()}
gdf = _filter(gdf, attribute_filter)
# map to hydamo columns
if column_mapping:
column_mapping = {key.lower():value.lower() for key,value in column_mapping.items()}
gdf.rename(columns=column_mapping, inplace=True)
# drop all columns not needed
required_columns = getattr(hydamo,hydamo_attribute).required_columns.copy()
if keep_columns:
required_columns += [col.lower() for col in keep_columns]
if hydamo_attribute =='crosssections':
if z_coord:
required_columns += ['z', 'order']
drop_cols = [col for col in gdf.columns if not col in required_columns + ['geometry']]
if len(drop_cols) > 0:
gdf = gdf.drop(drop_cols, axis=1)
return gdf