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gridded: dimensions and variable exclusion #227

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gridded: dimensions and variable exclusion #227

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b3ad4e8
First pass at #226 Added excelude_vars, and calculated profile_direct…
smwoodman Jul 9, 2025
296282e
Add depth back to 'exclude_vars'
smwoodman Jul 9, 2025
dc562db
look for grid_exclude flag, rather than exclude_vars argument
smwoodman Aug 5, 2025
0f54321
fix cf attribute
smwoodman Aug 5, 2025
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41 changes: 32 additions & 9 deletions pyglider/ncprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,6 +195,7 @@ def make_gridfiles(
depth_bins=None,
dz=1,
starttime='1970-01-01',
exclude_vars=None,
):
"""
Turn a timeseries netCDF file into a vertically gridded netCDF.
Expand All @@ -219,6 +220,14 @@ def make_gridfiles(
dz : float, default = 1
Vertical grid spacing in meters. Ignored if ``depth_bins`` is not None

starttime : str, default = '1970-01-01'
The minimum time of data that will be gridded. All data before this
time will be dropped

exclude_vars : list of strings, default empty list
Variable names from the timeseries that should not be gridded.
These variables will be excluded from the gridded netCDF file

Returns
-------
outname : str
Expand Down Expand Up @@ -274,6 +283,7 @@ def make_gridfiles(
dsout = xr.Dataset(
coords={'depth': ('depth', depths), 'profile': (xdimname, profiles)}
)
# dsout['profile'].attrs = ds.profile_index.attrs
dsout['depth'].attrs = {
'units': 'm',
'long_name': 'Depth',
Expand All @@ -285,14 +295,20 @@ def make_gridfiles(
}

# Bin by profile index, for the mean time, lat, and lon values for each profile
ds['time_1970'] = ds.temperature.copy()
ds['time_1970'] = ds.longitude.copy()
ds['time_1970'].values = ds.time.values.astype(np.float64)
for td in ('time_1970', 'longitude', 'latitude'):
td_lookup = {
'time_1970': 'mean',
'longitude': 'mean',
'latitude': 'mean',
'profile_direction': lambda x: stats.mode(x, keepdims=True)[0][0],
}
for td, bin_stat in td_lookup.items():
good = np.where(~np.isnan(ds[td]) & (ds['profile_index'] % 1 == 0))[0]
dat, xedges, binnumber = stats.binned_statistic(
ds['profile_index'].values[good],
ds[td].values[good],
statistic='mean',
statistic=bin_stat,
bins=[profile_bins],
)
if td == 'time_1970':
Expand All @@ -302,9 +318,12 @@ def make_gridfiles(
dsout[td] = (('time'), dat, ds[td].attrs)

# Bin by profile index, for the profile start (min) and end (max) times
profile_lookup = {'profile_time_start': "min", 'profile_time_end': "max"}
profile_time_lookup = {
'profile_time_start': "min",
'profile_time_end': "max"
}
good = np.where(~np.isnan(ds['time']) & (ds['profile_index'] % 1 == 0))[0]
for td, bin_stat in profile_lookup.items():
for td, bin_stat in profile_time_lookup.items():
_log.debug(f'td, bin_stat {td}, {bin_stat}')
dat, xedges, binnumber = stats.binned_statistic(
ds['profile_index'].values[good],
Expand All @@ -319,8 +338,12 @@ def make_gridfiles(
ds = ds.drop('time_1970')
_log.info(f'Done times!')

for k in ds.keys():
if k in ['time', 'profile', 'longitude', 'latitude', 'depth'] or 'time' in k:
if exclude_vars is None:
exclude_vars = []
exclude_vars += list(dsout.keys()) + ["depth"]
for k in ds.keys():
if (k in exclude_vars) or ('time' in k) or ('profile' in k):
_log.debug('Not gridding %s', k)
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@jklymak jklymak Aug 4, 2025

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This looks to be gridding longitude and latitude by deafult now? Or are they in the default exclude_vars?

I wonder if for non-coordinate data (eg temperature etc) we should really be setting whether to grid it or not in the yaml instead of specifying a list here. eg in the yaml say grid_exclude: True if we don't want it to be gridded, but we do want it in the time series?

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@smwoodman smwoodman Aug 4, 2025

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This looks to be gridding longitude and latitude by deafult now? Or are they in the default exclude_vars?

longitude and latitude (along with time, profile_direction, profile_time_start, and profile_time_end) are all guaranteed to be part of dsout. Thus, they're always added to exclude_vars in exclude_vars += list(dsout.keys()) + ["depth"]

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@smwoodman smwoodman Aug 4, 2025

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I wonder if for non-coordinate data (eg temperature etc) we should really be setting whether to grid it or not in the yaml instead of specifying a list here. eg in the yaml say grid_exclude: True if we don't want it to be gridded, but we do want it in the time series?

A grid_exclude boolean key feels totally reasonable to me. It's also more consistent with the function checking for the 'average_method' key. I'll update the pr now

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@jklymak jklymak Aug 5, 2025

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OK, this seems good. I was just wondering though if we wanted to allow the user to exclude a timeseries variable from being gridded at this stage by adding a property to the yaml. But maybe that can be a follow up?

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@jklymak jklymak Aug 20, 2025

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@smwoodman I was waiting to understand this...

continue
_log.info('Gridding %s', k)
good = np.where(~np.isnan(ds[k]) & (ds['profile_index'] % 1 == 0))[0]
Expand Down Expand Up @@ -372,10 +395,10 @@ def make_gridfiles(
dsout.attrs['time_coverage_end'] = dsout.attrs['time_coverage_end'][:19]
# fix standard_name so they don't overlap!
try:
dsout['waypoint_latitude'].attrs.pop('standard_name')
dsout['waypoint_longitude'].attrs.pop('standard_name')
dsout['profile_time_start'].attrs.pop('standard_name')
dsout['profile_time_end'].attrs.pop('standard_name')
dsout['waypoint_latitude'].attrs.pop('standard_name')
dsout['waypoint_longitude'].attrs.pop('standard_name')
except:
pass
# remove, so they can be encoded later:
Expand Down