def point_to_lines_dict(self, points_dict):
"""build lines dictionary from points dictionary
Args:
points_dict(dict()): points dictionary
Returns:
lines_dict(dict()): build lines between same well points
"""
lines_dict = {}
for bore_id in points_dict:
poly = PVGeo.points_to_poly_data(points_dict[bore_id])
lines_dict["{0}".format(
bore_id)] = PVGeo.filters.AddCellConnToPoints(
nearest_nbr=True).apply(poly)
# notice that the building of the lines need to follow the nearest neighbourhood search
return lines_dict
################################################################################ # Let's go ahead and load a simple file that has XYZ coordinates and a boolean # array for fault presence. This point cloud makes some sort of regular grid, # but we have forgotten the deatials of the cell spacings and local coordinate # rotations. # # We will read in this data with ``pandas`` and send it to the # :func:`PVGeo.points_to_poly_data` helper to create a :class:`vista.PolyData` # object (essentially a point cloud). points = pd.read_csv(fault_file) print(points[0:2]) ################################################################################ vtkpoints = PVGeo.points_to_poly_data(points) print(vtkpoints) ################################################################################ # Note that we have a :class:`vista.PolyData` object now which allows us to do # all types of immediate plotting of our data. First, lets threshold our points # as the point cloud has a bunch of zeros and ones throughout the dataspace to # describe the presence of a fault. # # To threshold the points, we call the threshold filter directly on our data # object and pass the thresholding value. We can then plot the result by # calling the plot function. (Note: change the notebook parameter to # ``False`` for an interactive window) vtkpoints.plot(clim=[0, 1], point_size=1) ################################################################################
###############################################################################
temp_grid = temperature_data['kriged_temperature_model']
temp_grid_cropped = temp_grid.clip_box(gdc19.get_roi_bounds(), invert=False)
# Remove values above topography
temp_grid_no_topo = PVGeo.grids.ExtractTopography(
remove=True, # remove the inactive cells
tolerance=10.0 # buffer around the topo surface
).apply(temp_grid_cropped, topo)
temp_roi = temp_grid_no_topo.threshold([175., 225.])
###############################################################################
well_locs = pd.read_csv(gdc19.get_well_path('well_location_from_earth_model.csv'))
well_locs = PVGeo.points_to_poly_data(well_locs[['x', 'y', 'z (land surface)']].values).clip_box(
gdc19.get_roi_bounds(), invert=False)
WELLS = gdc19.load_well_db()
proposed = PVGeo.filters.AddCellConnToPoints().apply(WELLS.pop('well_new2'))#vtki.MultiBlock()
well_5832 = PVGeo.filters.AddCellConnToPoints().apply(WELLS.pop('well_5832'))
#well_5832.set_active_scalar('ECGR')
well_Acord1 = PVGeo.filters.AddCellConnToPoints().apply(WELLS.pop('well_Acord1'))
#well_Acord1 = WELLS.set_active_scalar('GR_SPLICE (GAPI)')
###############################################################################
# load the gravity model
gf = gdc19.get_gravity_path('forge_inverse_problem/RESULT_THRESHED.vtu')
grav_model = vtki.read(gf)
grav_model.active_scalar_name = 'Magnitude'
def read_surface_verts(filename, grid=False):
surf = pd.read_csv(filename)
if grid:
return grid_surface(surf.values)
return PVGeo.points_to_poly_data(surf.values)