Seismic-Canvas is an interactive 3D visualization tool mainly designed for seismic data, at the same time also can be useful for any 3D data. This tool is based on VisPy, a Python library that leverages the computational power of GPUs through the OpenGL library.
Simply run the following command to install from PyPI:
pip install seismic_canvas
Add any number of slices to view the slices of your volume using seismic_canvas.volume_slices function. For example:
visual_nodes = volume_slices(volume,
x_pos=[370, 170, 570, 770], y_pos=810, z_pos=120, clim=(-2, 2))
Left click and drag to rotate the camera angle; right click and drag, or scroll mouse wheel, to zoom in and out. Hold Shift key, left click and drag to pan move. Press Space key to return to the initial view. Press S key to save a screenshot PNG file at any time. Press Esc key to close the window.
Most elements are draggable. Hold Ctrl key, the selectable visual nodes will be highlighted when your mouse hovers over them; left click and drag to move the highlighted visual node. The volume slices will update their contents in real-time during dragging. You can also press D key to toggle the dragging mode on/off.
Compatible to numpy memory map. For example, reading in a binary data file contatining a 3D seismic volume with size 210x920x825 (608MB), Seismic-Canvas takes ~700MB RAM (Windows PyQt5 backend).
volume = np.fromfile('./CostaRica_seismic.dat', '>f4').reshape(825, 920, 210)Instead, reading in the same file using numpy memory map, Seismic-Canvas takes only ~82MB RAM (Windows PyQt5 backend), and significantly reduces the launch time.
volume = np.memmap('./CostaRica_seismic.dat', dtype='>f4',
mode='r', shape=(825, 920, 210))When you drag and arrange everything on the canvas, press A key to print out a collection of useful parameters that can be used to reproduce the current canvas setting.
Seismic-Canvas depends on numpy and vispy Python libraries. The vispy library also depends on one of these backends to display a window on your OS: PyQt4/PySide, PyQt5/PySide2, glfw, pyglet, SDL, or wx. We recommend PyQt5 as it is the easiest to install and most compatible on different platforms.
It is also recommended to install PyOpenGL and matplotlib; they are optinal libraries that can enhance the visualization in various ways. For example, PyOpenGL allows for nice antialiased 3D line objects, and matplotlib helps render a much more useful colorbar than the vispy stock colorbar object.
See simple_demo.py for a simple demo. vispy/util/fetching.py will automatically download this example data.
Also try osv_F3_demo.py to check out the results from this research: xinwucwp/osv. You can download all the binary data volumes from this Google Drive link.
Using 2D markers to visualize a randomly generated well logs.
Using triangle mesh surfaces to visualize FaultSkins.
A dark themed example with a z-axis-up axis legend:
- Well log visualization
- Replace current colorbar with Matplotlib rendered colorbar
- Draw lines where slice planes intersect
