Generic Workflow - VAPOR Python Cookbook

This shows an example workflow with vapor. We begin by creating a session and opening a dataset. You can have multiple sessions open at the same time.

import example_utils
from vapor import session, renderer, dataset, camera

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 1
----> 1 import example_utils
      2 from vapor import session, renderer, dataset, camera

File ~/work/vapor-python-cookbook/vapor-python-cookbook/notebooks/example_utils.py:20
     16     sys.path.append('..')
     19 from inspect import signature
---> 20 import numpy as np
     21 from math import sin
     23 def SampleFunctionOnRegularGrid(f, ext=None, shape=None):

ModuleNotFoundError: No module named 'numpy'

ses = session.Session()
data = example_utils.OpenExampleDataset(ses)

Dump the dataset metadata¶

print("Time Coordinate Variable Name:", data.GetTimeCoordVarName())
print("Coordinate Variable Names:", data.GetCoordVarNames())

print("Dimensions:")
for dim in data.GetDimensionNames():
    print(f"  {dim}:", data.GetDimensionLength(dim, 0))

print("Data Variables:")
for var in data.GetDataVarNames():
    print(f"  {var}")
    print(f"    Time Varying:", bool(data.IsTimeVarying(var)))
    print(f"    Dimensionality:", data.GetVarGeometryDim(var))
    print(f"    Coordinates:", data.GetVarCoordVars(var, True))
    print("     Data Range:", data.GetDataRange(var))

Render the first 2D variable as a pseudocolor¶

first_2d_var = data.GetDataVarNames(2)[0]
print(f"Rendering 2D variable {first_2d_var}")

ren = data.NewRenderer(renderer.TwoDDataRenderer)
ren.SetVariableName(first_2d_var)
ren.GetPrimaryTransferFunction().SetMinMapValue(-1)
ren.GetPrimaryTransferFunction().SetMaxMapValue(1)

ses.GetCamera().ViewAll()
ses.Show()

ses.DeleteRenderer(ren)

Render U10 and V10 as barbs over a map¶

# If your dataset is geo-referenced, this will automatically render a geographically correct map.
# map_ren = data.NewRenderer(renderer.ImageRenderer)

barbs = data.NewRenderer(renderer.BarbRenderer)
barbs.SetDimensions(2)
barbs.SetFieldVariableNames(['U10', 'V10'])
barbs.SetLineThickness(2)

ses.Show()
ses.DeleteRenderer(barbs)

Simulate and render a flow advection¶

flow = data.NewRenderer(renderer.FlowRenderer)
flow.SetFieldVariableNames(['U10', 'V10'])

ses.Show()

ses.DeleteRenderer(flow)

Volume render a 3D variable¶

volume = data.NewRenderer(renderer.VolumeRenderer)
volume.SetVariableName("V")

tf = volume.GetPrimaryTransferFunction()
tf.SetOpacityList([1, 0, 0, 1])

ses.GetCamera().ViewAll()
ses.Show()

# Show a colorbar for the volume rendering
tf.ShowMatPlotLibColorbar(label="V")

Scale the dataset Z axis¶

Vapor will automatically scale the Z axis of a dataset to produce reasonable results. This can be manually adjusted as shown below.

scales = data.GetTransform().GetScales()
print("Default dataset scaling =", scales);
scales[2] *= 0.3
data.GetTransform().SetScales(scales)
print("New dataset scaling =", data.GetTransform().GetScales());
ses.Show()

Add axis annotations to the rendering¶

annotations = ses.GetAxisAnnotations()
annotations.SetAxisAnnotationEnabled(True)
ses.Show()

Export the session for use in the Vapor GUI application¶

Sessions created in Python can be saved as a .vs3 file. These files can then be opened in the Vapor GUI application and explored interactively. Conversely, sessions created in the Vapor GUI can be loaded into Python with Session.Load(path)

Since this example uses a dynamically generated dataset, the session cannot be saved as it would point to a dataset that does not exist on disk. If you were using a physical dataset, this would work.

ses.Save("tutorial.vs3")