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Merged
EricEngle-NOAA merged 3 commits into from
May 7, 2025
Merged

190 interpolation issues with v251 #191

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3297a73
Update for _grib2io.py
EricEngle-NOAA May 7, 2025
a3415e0
Update _grib2io.py
EricEngle-NOAA May 7, 2025
a35933c
Added test_grib2_interp_to_stations_3darray.py
EricEngle-NOAA May 7, 2025
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156 changes: 113 additions & 43 deletions src/grib2io/_grib2io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1666,12 +1666,17 @@ def set_auto_nans(value: bool):
raise TypeError(f"Argument must be bool")


def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
method_options=None, num_threads=1):
def interpolate(a,
method: Union[int, str],
grid_def_in,
grid_def_out,
method_options=None,
num_threads=1,
):
"""
This is the module-level interpolation function.

This interfaces with the [NCEPLIBS-ip library](https://github.com/NOAA-EMC/NCEPLIBS-ip)
This interfaces with the 4-byte (32-bit float) [NCEPLIBS-ip library](https://github.com/NOAA-EMC/NCEPLIBS-ip)
through grib2io's internal iplib Cython extension module.

Parameters
Expand All @@ -1686,7 +1691,8 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
or 3-dimensional (:, ny, nx) where the 1st dimension represents another
spatial, temporal, or classification (i.e. ensemble members) dimension.
The function will properly flatten the (ny,nx) dimensions into (nx * ny)
acceptable for input into the interpolation subroutines.
acceptable for input into the interpolation subroutines. If needed, these
data will be converted to `np.float32`.
method
Interpolate method to use. This can either be an integer or string using
the following mapping:
Expand Down Expand Up @@ -1716,10 +1722,10 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
Returns
-------
interpolate
Returns a `numpy.ndarray` when scalar interpolation is performed or a
`tuple` of `numpy.ndarray`s when vector interpolation is performed with
the assumptions that 0-index is the interpolated u and 1-index is the
interpolated v.
Returns a `numpy.ndarray` of dtype `np.float32` when scalar interpolation
is performed or a `tuple` of `numpy.ndarray`s when vector interpolation is
performed with the assumptions that 0-index is the interpolated u and
1-index is the interpolated v.
"""

from . import iplib
Expand All @@ -1744,39 +1750,61 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
if method in {3,6}:
method_options[0:2] = -1

km = 1
mi = grid_def_in.npoints
mo = grid_def_out.npoints

# Adjust shape of input array(s)
a,newshp = _adjust_array_shape_for_interp(a,grid_def_in,grid_def_out)
a, newshp = _adjust_array_shape_for_interp(a, grid_def_in, grid_def_out)

# Call interpolation subroutines according to type of a.
if isinstance(a,np.ndarray):
# Scalar
km = a.shape[0]
if np.any(np.isnan(a)):
ibi = np.ones((km), dtype=np.int32)
li = np.where(np.isnan(a),0,1).astype(np.uint8)
else:
ibi = np.zeros((km), dtype=np.int32)
li = np.zeros(a.shape,dtype=np.uint8)
no,rlat,rlon,ibo,lo,go,iret = iplib.interpolate_scalar(method, method_options,
grid_def_in.gdtn, np.array(grid_def_in.gdt, dtype=np.int32),
grid_def_out.gdtn, np.array(grid_def_out.gdt, dtype=np.int32),
mi, mo, km, ibi, li, a)
no, rlat, rlon, ibo, lo, go, iret = iplib.interpolate_scalar(
method,
method_options,
grid_def_in.gdtn,
np.array(grid_def_in.gdt, dtype=np.int32),
grid_def_out.gdtn,
np.array(grid_def_out.gdt, dtype=np.int32),
mi,
mo,
km,
ibi,
li,
a,
)
out = np.where(lo==0,np.nan,go).reshape(newshp)
elif isinstance(a,tuple):
# Vector
km = a[0].shape[0]
if np.any(np.isnan(a)):
ibi = np.ones((km), dtype=np.int32)
li = np.where(np.isnan(a),0,1).astype(np.uint8)
else:
ibi = np.zeros((km), dtype=np.int32)
li = np.zeros(a[0].shape,dtype=np.uint8)
no,rlat,rlon,crot,srot,ibo,lo,uo,vo,iret = iplib.interpolate_vector(method, method_options,
grid_def_in.gdtn, np.array(grid_def_in.gdt, dtype=np.int32),
grid_def_out.gdtn, np.array(grid_def_out.gdt, dtype=np.int32),
mi, mo, km, ibi, li, a[0], a[1])
no, rlat, rlon, crot, srot, ibo, lo, uo, vo, iret = iplib.interpolate_vector(
method,
method_options,
grid_def_in.gdtn,
np.array(grid_def_in.gdt, dtype=np.int32),
grid_def_out.gdtn,
np.array(grid_def_out.gdt, dtype=np.int32),
mi,
mo,
km,
ibi,
li,
a[0],
a[1],
)
uo = np.where(lo==0,np.nan,uo).reshape(newshp)
vo = np.where(lo==0,np.nan,vo).reshape(newshp)
out = (uo,vo)
Expand All @@ -1789,12 +1817,19 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
return out


def interpolate_to_stations(a, method, grid_def_in, lats, lons,
method_options=None, num_threads=1):
def interpolate_to_stations(
a,
method: Union[int, str],
grid_def_in,
lats,
lons,
method_options=None,
num_threads=1
):
"""
Module-level interpolation function for interpolation to stations.

Interfaces with the [NCEPLIBS-ip library](https://github.com/NOAA-EMC/NCEPLIBS-ip)
Interfaces with the 4-byte (32-bit float) [NCEPLIBS-ip library](https://github.com/NOAA-EMC/NCEPLIBS-ip)
via grib2io's iplib Cython exntension module. It supports scalar and
vector interpolation according to the type of object a.

Expand All @@ -1810,7 +1845,8 @@ def interpolate_to_stations(a, method, grid_def_in, lats, lons,
or 3-dimensional (:, ny, nx) where the 1st dimension represents another
spatial, temporal, or classification (i.e. ensemble members) dimension.
The function will properly flatten the (ny,nx) dimensions into (nx * ny)
acceptable for input into the interpolation subroutines.
acceptable for input into the interpolation subroutines. If needed, these
data will be converted to `np.float32`.
method
Interpolate method to use. This can either be an integer or string using
the following mapping:
Expand Down Expand Up @@ -1842,9 +1878,10 @@ def interpolate_to_stations(a, method, grid_def_in, lats, lons,
Returns
-------
interpolate_to_stations
Returns a `numpy.ndarray` when scalar interpolation is performed or a
`tuple` of `numpy.ndarray`s when vector interpolation is performed with
the assumptions that 0-index is the interpolated u and 1-index is the
Returns a `numpy.ndarray` of dtype `np.float32` when scalar
interpolation is performed or a `tuple` of `numpy.ndarray`s
when vector interpolation is performed with the assumptions
that 0-index is the interpolated u and 1-index is the
interpolated v.
"""
from . import iplib
Expand Down Expand Up @@ -1885,12 +1922,11 @@ def interpolate_to_stations(a, method, grid_def_in, lats, lons,
if nlats != nlons:
raise ValueError('Station lats and lons must be same size.')

km = 1
mi = grid_def_in.npoints
mo = nlats

# Adjust shape of input array(s)
a,newshp = _adjust_array_shape_for_interp_stations(a,grid_def_in,mo)
a, newshp = _adjust_array_shape_for_interp_stations(a, grid_def_in, mo)

# Use gdtn = -1 for stations and an empty template array
gdtn = -1
Expand All @@ -1899,25 +1935,49 @@ def interpolate_to_stations(a, method, grid_def_in, lats, lons,
# Call interpolation subroutines according to type of a.
if isinstance(a,np.ndarray):
# Scalar
km = a.shape[0]
ibi = np.zeros((km), dtype=np.int32)
li = np.zeros(a.shape,dtype=np.uint8)
no,rlat,rlon,ibo,lo,go,iret = iplib.interpolate_scalar(method, method_options,
grid_def_in.gdtn, np.array(grid_def_in.gdt, dtype=np.int32),
gdtn, gdt,
mi, mo, km, ibi, li, a,
lats=np.array(lats, dtype=np.float32),
lons=np.array(lons, dtype=np.float32))
no, rlat, rlon, ibo, lo, go, iret = iplib.interpolate_scalar(
method,
method_options,
grid_def_in.gdtn,
np.array(grid_def_in.gdt, dtype=np.int32),
gdtn,
gdt,
mi,
mo,
km,
ibi,
li,
a,
lats=np.array(lats, dtype=np.float32),
lons=np.array(lons, dtype=np.float32),
)
out = go.reshape(newshp)

elif isinstance(a,tuple):
# Vector
km = a[0].shape[0]
ibi = np.zeros((km), dtype=np.int32)
li = np.zeros(a[0].shape,dtype=np.uint8)
no,rlat,rlon,crot,srot,ibo,lo,uo,vo,iret = iplib.interpolate_vector(method, method_options,
grid_def_in.gdtn, np.array(grid_def_in.gdt, dtype=np.int32),
gdtn, gdt,
mi, mo, km, ibi, li, a[0], a[1],
lats=np.array(lats, dtype=np.float32),
lons=np.array(lons, dtype=np.float32))
no, rlat, rlon, crot, srot, ibo, lo, uo, vo, iret = iplib.interpolate_vector(
method,
method_options,
grid_def_in.gdtn,
np.array(grid_def_in.gdt, dtype=np.int32),
gdtn,
gdt,
mi,
mo,
km,
ibi,
li,
a[0],
a[1],
lats=np.array(lats, dtype=np.float32),
lons=np.array(lons, dtype=np.float32),
)
out = (uo.reshape(newshp),vo.reshape(newshp))

try:
Expand Down Expand Up @@ -1969,7 +2029,7 @@ def _adjust_array_shape_for_interp(a, grid_def_in, grid_def_out):
"""
Adjust shape of input data array for interpolation to grids.

Returned array will conform to the dimensionality used in the NCEPLIBS-ip
Returned array will conform to the dimensionality and type used in the NCEPLIBS-ip
interpolation subroutine arguments for grids.

Parameters
Expand Down Expand Up @@ -2001,6 +2061,11 @@ def _adjust_array_shape_for_interp(a, grid_def_in, grid_def_out):
return (a0,a1),newshp

if isinstance(a,np.ndarray):

# Change type if needed.
if a.dtype != np.float32:
a = a.astype(np.float32)

if len(a.shape) == 2 and a.shape == grid_def_in.shape:
newshp = (grid_def_out.ny,grid_def_out.nx)
a = np.expand_dims(a.flatten(),axis=0)
Expand All @@ -2010,14 +2075,14 @@ def _adjust_array_shape_for_interp(a, grid_def_in, grid_def_out):
else:
raise ValueError("Input array shape must be either (ny,nx) or (:,ny,nx).")

return a,newshp
return a, newshp


def _adjust_array_shape_for_interp_stations(a,grid_def_in,nstations):
"""
Adjust shape of input data array for interpolation to stations.

Returned array will conform to the dimensionality used in the NCEPLIBS-ip
Returned array will conform to the dimensionality and type used in the NCEPLIBS-ip
interpolation subroutine arguments for grids.

Parameters
Expand Down Expand Up @@ -2049,6 +2114,11 @@ def _adjust_array_shape_for_interp_stations(a,grid_def_in,nstations):
return (a0,a1),newshp

if isinstance(a,np.ndarray):

# Change type if needed.
if a.dtype != np.float32:
a = a.astype(np.float32)

if len(a.shape) == 2 and a.shape == grid_def_in.shape:
newshp = (nstations)
a = np.expand_dims(a.flatten(),axis=0)
Expand All @@ -2058,4 +2128,4 @@ def _adjust_array_shape_for_interp_stations(a,grid_def_in,nstations):
else:
raise ValueError("Input array shape must be either (ny,nx) or (:,ny,nx).")

return a,newshp
return a, newshp
61 changes: 61 additions & 0 deletions tests/test_grib2_interp_to_stations_3darray.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,61 @@
import pytest
import numpy as np
import datetime
import grib2io

# Output grid (NBM 2.5km CONUS Grid)
gdtn_out = 30
gdt_out = np.array(
[1, 0, 6371200, 255, 255, 255, 255, 2345, 1597, 19229000, 233723400, 48,
25000000, 265000000, 2539703, 2539703, 0, 80, 25000000, 25000000,
-90000000, 0],
dtype=np.int32
)
grid_def_out = grib2io.Grib2GridDef(gdtn_out, gdt_out)

# Test stations
# KPHL, KPIT, KMFL, KORD, KDEN, KSFO
station_lats = [ 39.8605, 40.4846, 25.7542, 41.9602, 39.8466, 37.6196]
station_lons = [ -75.2708, -80.2145, -80.3839, -87.9316,-104.6562,-122.3656]

def test_interp_to_stations_3darray(request):
data = request.config.rootdir / 'tests' / 'input_data'
with grib2io.open(data / 'gfs.t00z.pgrb2.1p00.f024') as f:
msg = f.select(shortName='TMP', level='2 m above ground')[0]

# Interpolate
newmsg = msg.interpolate('bilinear', grid_def_out, num_threads=1)

# Build 3D arrays of lats and lons
n = 5
ny, nx = newmsg.ny, newmsg.nx
glats = np.zeros((n, ny, nx), dtype=np.float32)
glons = np.zeros((n, ny, nx), dtype=np.float32)
for i in range(n):
glats[i] = newmsg.lats
glons[i] = newmsg.lons

# Interpolate lats and lons to stations using neighbor
slats = grib2io.interpolate_to_stations(glats, 'neighbor', newmsg.griddef, station_lats, station_lons)
slons = grib2io.interpolate_to_stations(glons, 'neighbor', newmsg.griddef, station_lats, station_lons)

# ...
latdiff = []
londiff = []
for s in range(len(station_lats)):
latdiff.append(np.unique(np.abs(slats[:, s]-station_lats[s])))
londiff.append(np.unique(np.abs(slons[:, s]-station_lons[s])))
latdiff = np.array(latdiff).flatten()
londiff = np.array(londiff).flatten()

rtol = 1e-3
np.testing.assert_allclose(
latdiff,
np.array([3.1509399e-03, 9.8762512e-03, 2.3097992e-03, 5.6610107e-03, 1.0345459e-02, 4.5776367e-05], dtype=np.float32),
rtol=rtol,
)
np.testing.assert_allclose(
londiff,
np.array([0.00893402, 0.0038681 , 0.00794983, 0.00233459, 0.00022888, 0.00444794], dtype=np.float32),
rtol=rtol,
)
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