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lucduron avatar lucduron commented on May 30, 2024

Dear Keith,

We developed some years ago a CLI script to extract a specific layer or compute the max/min/mean over the vertical:
https://github.com/CNR-Engineering/PyTelTools/blob/01335992e76c1c0be71ec37a6d0774e4c7ccf5e9/cli/slf_3d_to_2d.py

In pyteltools/slf/misc.py there is the computation of the weights for the mean:

PyTelTools/pyteltools/slf/misc.py

Lines 685 to 697 in 0133599

# Compute dimensionless layer ponderations for mean operation
weight = None
if self.operation == MEAN:
diff_upper = z - np.roll(z, 1, axis=0)
diff_upper[0, :] = 0.0
diff_lower = np.roll(z, -1, axis=0) - z
diff_lower[-1, :] = 0.0
diff = (diff_upper + diff_lower) / 2
diff_sum = diff.sum(axis=0)
with np.errstate(divide='ignore', invalid='ignore'):
weight = diff / diff_sum
# weight.shape = (nb_planes, nb_nodes_2d)
# weight.sum(axis=0) = array([ 1., 1., 1., ..., 1., 1., 1.], dtype=float32)

The depth-averaged is then computed as:

PyTelTools/pyteltools/slf/misc.py

Lines 704 to 705 in 0133599

var_3d = weight * computed_values[var]
vars_2d[i, :] = var_3d.sum(axis=0)

Hope it helps,
Luc

from pyteltools.

krober10nd avatar krober10nd commented on May 30, 2024

Thanks! That worked

from pyteltools.

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