Read GSLib Table

Read any GSLib file as a table pyvist.Table

import os

import pooch

from PVGeo.grids import TableToTimeGrid
from PVGeo.gslib import GSLibReader

# points_url = 'http://www.trainingimages.org/uploads/3/4/7/0/34703305/sundarbans.zip'
url = "https://raw.githubusercontent.com/pyvista/vtk-data/master/Data/sundarbans.SGEMS.zip"
file_paths = pooch.retrieve(url=url, known_hash=None, processor=pooch.Unzip())
file_path = [f for f in file_paths if os.path.basename(f) == "sundarbans.SGEMS"][0]
file_path

'/home/runner/.cache/pooch/86487d9e94190c342742d91d20091f70-sundarbans.SGEMS.zip.unzip/sundarbans.SGEMS'

reader = GSLibReader()
table = reader.apply(file_path)
# Print the file header
print(reader.get_file_header())

1200 1750 1

table

Header	Data Arrays
Table Information N Rows 2100000 N Arrays 1	Name Type N Comp Min Max v float64 1 2.667e+00 2.550e+02

From inspecting the header, we realize that this dataset is gridded, so let’s use the PVGeo.grid.TableToTimeGrid filter to create a pyvista.ImageData of that dataset.

# 1200 x, 1750 y, 1 z, 1 t
grid = TableToTimeGrid(extent=(1200, 1750, 1, 1), order="F").apply(table)
grid

Header	Data Arrays
ImageData Information N Cells 2100000 N Points 4205902 X Bounds 0.000e+00, 1.200e+03 Y Bounds 0.000e+00, 1.750e+03 Z Bounds 0.000e+00, 1.000e+00 Dimensions 1201, 1751, 2 Spacing 1.000e+00, 1.000e+00, 1.000e+00 N Arrays 1	Name Field Type N Comp Min Max v Cells float64 1 2.667e+00 2.550e+02

grid.plot(cpos="xy")

Static Scene

Interactive Scene