/usr/lib/python3/dist-packages/gsw/utility.py is in python3-gsw 3.2.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Functions not specific to the TEOS-10 realm of variables.
"""
import numpy as np
from . import _gsw_ufuncs
from ._utilities import match_args_return, indexer
@match_args_return
def pchip_interp(x, y, xi, axis=0):
"""
Interpolate using Piecewise Cubic Hermite Interpolating Polynomial
This is a shape-preserving algorithm; it does not introduce new local
extrema. The implementation in C that is wrapped here is largely taken
from the scipy implementation,
https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.PchipInterpolator.html.
Points outside the range of the interpolation table are filled using the
end values in the table. (In contrast,
scipy.interpolate.pchip_interpolate() extrapolates using the end
polynomials.)
Parameters
----------
x, y : array-like
Interpolation table x and y; n-dimensional, must be broadcastable to
the same dimensions.
xi : array-like
One-dimensional array of new x values.
axis : int, optional, default is 0
Axis along which xi is taken.
Returns
-------
yi : array
Values of y interpolated to xi along the specified axis.
"""
xi = np.array(xi, dtype=float, copy=False, order='C', ndmin=1)
if xi.ndim > 1:
raise ValueError('xi must be no more than 1-dimensional')
nxi = xi.size
x, y = np.broadcast_arrays(x, y)
shape0 = x.shape
out_shape = list(x.shape)
out_shape[axis] = nxi
yi = np.empty(out_shape, dtype=float)
yi.fill(np.nan)
goodmask = ~(np.isnan(x) | np.isnan(y))
order = 'F' if y.flags.fortran else 'C'
for ind in indexer(y.shape, axis, order=order):
igood = goodmask[ind]
# If p_ref is below the deepest value, skip the profile.
xgood = x[ind][igood]
ygood = y[ind][igood]
yi[ind][igood] = _gsw_ufuncs.util_pchip_interp(xgood, ygood, xi)
return yi
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