def extract_z(gdf: gpd.geodataframe.GeoDataFrame,
dem: Union[np.ndarray, rasterio.io.DatasetReader],
inplace: bool = False,
**kwargs) -> gpd.geodataframe.GeoDataFrame:
"""
Extracting altitude values from digital elevation model
Args:
gdf - gpd.geodataframe.GeoDataFrame containing x,y values
dem - rasterio.io.DatasetReader containing the z values
inplace - bool - default False -> copy of the current gdf is created
Kwargs:
extent - list containing the extent of the np.ndarray, must be provided in the same CRS as the gdf
Return:
gdf - gpd.geodataframe.GeoDataFrame containing x,y,z values obtained from a DEM
"""
# Input object must be a GeoDataFrame
if not isinstance(gdf, gpd.geodataframe.GeoDataFrame):
raise TypeError('Loaded object is not a GeoDataFrame')
# Create deep copy of gdf
if not inplace:
gdf = gdf.copy(deep=True)
# Input object must be a np.ndarray or a rasterio.io.DatasetReader
if not isinstance(dem, (np.ndarray, rasterio.io.DatasetReader)):
raise TypeError(
'Loaded object is not a np.ndarray or rasterio.io.DatasetReader')
# The GeoDataFrame must not contain a Z-column
if pd.Series(['Z']).isin(gdf.columns).all():
raise ValueError('Data already contains Z-values')
# Extracting z values from a DEM loaded with Rasterio
if isinstance(dem, rasterio.io.DatasetReader):
try:
if gdf.crs == dem.crs:
if np.logical_not(
pd.Series(['X', 'Y']).isin(gdf.columns).all()):
gdf = extract_xy(gdf)
gdf['Z'] = [
z[0] for z in dem.sample(gdf[['X', 'Y']].to_numpy())
]
else:
crs_old = gdf.crs
gdf = gdf.to_crs(crs=dem.crs)
gdf = extract_xy(gdf)
gdf['Z'] = [
z[0] for z in dem.sample(gdf[['X', 'Y']].to_numpy())
]
gdf = gdf.to_crs(crs=crs_old)
del gdf['X']
del gdf['Y']
gdf = extract_xy(gdf)
except IndexError:
raise ValueError(
'One or more points are located outside the boundaries of the raster'
)
# Extracting z values from a DEM as np.ndarray
else:
if np.logical_not(pd.Series(['X', 'Y']).isin(gdf.columns).all()):
gdf = extract_xy(gdf)
extent = kwargs.get('extent', None)
assert extent is not None, 'Extent of array is needed to extract Z values'
gdf['Z'] = [
sample(dem, extent, gdf[['X', 'Y']].values.tolist()[i])
for i, point in enumerate(gdf[['X', 'Y']].values.tolist())
]
# Convert dip and azimuth columns to floats
if pd.Series(['dip']).isin(gdf.columns).all():
gdf['dip'] = gdf['dip'].astype(float)
if pd.Series(['azimuth']).isin(gdf.columns).all():
gdf['azimuth'] = gdf['azimuth'].astype(float)
# Convert formation column to string
if pd.Series(['formation']).isin(gdf.columns).all():
gdf['formation'] = gdf['formation'].astype(str)
return gdf
def extract_coordinates(gdf: gpd.geodataframe.GeoDataFrame,
dem: Union[np.ndarray, rasterio.io.DatasetReader,
type(None)] = None,
inplace: bool = False,
**kwargs) -> gpd.geodataframe.GeoDataFrame:
"""
Extract x,y and z coordinates from a GeoDataFrame
Args:
gdf - gpd.geodataframe.GeoDataFrame containing Points or LineStrings
dem - rasterio.io.DatasetReader containing the z values
Kwargs:
extent - list containing the extent of the np.ndarray, must be provided in the same CRS as the gdf
Return:
gdf - gpd.geodataframe.GeoDataFrame containing x, y and z values
"""
# Input object must be a GeoDataFrame
if not isinstance(gdf, gpd.geodataframe.GeoDataFrame):
raise TypeError('Loaded object is not a GeoDataFrame')
# Create deep copy of gdf
if not inplace:
gdf = gdf.copy(deep=True)
# Checking if Z is in GeoDataFrame
if np.logical_not(pd.Series(['Z']).isin(gdf.columns).all()):
# Checking if dem is not None
if dem is None:
raise ValueError('DEM is missing')
# Checking if DEM is of type np.ndarray or rasterio object
if not isinstance(dem, (np.ndarray, rasterio.io.DatasetReader)):
raise TypeError(
'Loaded object is not a np.ndarray or Rasterio object')
extent = kwargs.get('extent', None)
# Checking if X and Y column already exist in gdf
if np.logical_not(pd.Series(['X', 'Y']).isin(gdf.columns).all()):
if isinstance(dem, np.ndarray):
gdf = extract_z(gdf, dem, extent=extent)
# Extract XYZ values if dem is rasterio object
else:
# Extract XYZ values if CRSs are matching
if gdf.crs == dem.crs:
gdf = extract_z(gdf, dem)
# Convert gdf before XYZ values extraction
else:
crs_old = gdf.crs
gdf = gdf.to_crs(crs=dem.crs)
gdf.rename(columns={'X': 'X1', 'Y': 'Y1'})
gdf = extract_z(extract_xy(gdf), dem)
gdf = gdf.to_crs(crs=crs_old)
del gdf['X']
del gdf['Y']
gdf.rename(columns={'X1': 'X', 'Y1': 'Y'})
else:
# Extract XYZ values if dem is of type np.ndarray
if isinstance(dem, np.ndarray):
gdf = extract_z(extract_xy(gdf), dem, extent=extent)
# Extract XYZ values if dem is rasterio object
else:
# Extract XYZ values if CRSs are matching
if gdf.crs == dem.crs:
gdf = extract_z(extract_xy(gdf), dem)
# Convert gdf before XYZ values extraction
else:
crs_old = gdf.crs
gdf = gdf.to_crs(crs=dem.crs)
gdf = extract_z(extract_xy(gdf), dem)
gdf = gdf.to_crs(crs=crs_old)
del gdf['X']
del gdf['Y']
gdf = extract_xy(gdf)
else:
# Checking if X and Y column already exist in gdf
if np.logical_not(pd.Series(['X', 'Y']).isin(gdf.columns).all()):
gdf = extract_xy(gdf, inplace=inplace)
# Convert dip and azimuth columns to floats
if pd.Series(['dip']).isin(gdf.columns).all():
gdf['dip'] = gdf['dip'].astype(float)
if pd.Series(['azimuth']).isin(gdf.columns).all():
gdf['azimuth'] = gdf['azimuth'].astype(float)
# Convert formation column to string
if pd.Series(['formation']).isin(gdf.columns).all():
gdf['formation'] = gdf['formation'].astype(str)
return gdf
