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Fastest way to get min / max NetCDF variable using Python?

My usual method for extracting min/maxvariable data values ​​from a NetCDF file is a slow order value when switching to the python netCDF4 module compared to scipy.io.netcdf.

I work with relatively large ocean model output files (from ROMS) with several depth levels over a given map area (Hawaii). When they were in NetCDF-3, I used scipy.io.netcdf.

Now that these files are in NetCDF-4 ("Classic"), I can no longer use it scipy.io.netcdfand instead switched to using the netCDF4 Python module. However, slowness is worrying, and I wondered if there is a more efficient method to extract a range of variable data (minimum and maximum data values)?

Here is my NetCDF-3 method using scipy:

import scipy.io.netcdf
netcdf = scipy.io.netcdf.netcdf_file(file)
var = netcdf.variables['sea_water_potential_temperature']
min = var.data.min()
max = var.data.max()

Here is my NetCDF-4 method using netCDF4:

import netCDF4
netcdf = netCDF4.Dataset(file)
var = netcdf.variables['sea_water_potential_temperature']
var_array = var.data.flatten()
min = var_array.data.min()
max = var_array.data.max()

The significant difference is that I have to smooth the data array in netCDF4 first , and this operation seems to slow down.

Is there a better / faster way?

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hpaulj , nco ncwa, subprocess. OPeNDAP , , .

, , , .

, ​​ nco.

def ncwa(path, fnames, var, op_type, times=None, lons=None, lats=None):
    '''Perform arithmetic operations on netCDF file or OPeNDAP data

    Args
    ----
    path: str
        prefix
    fnames: str or iterable
        Names of file(s) to perform operation on
    op_type: str
        ncwa arithmetic operation to perform. Available operations are:
        avg,mabs,mebs,mibs,min,max,ttl,sqravg,avgsqr,sqrt,rms,rmssdn
    times: tuple
        Minimum and maximum timestamps within which to perform the operation
    lons: tuple
        Minimum and maximum longitudes within which to perform the operation
    lats: tuple
        Minimum and maximum latitudes within which to perform the operation

    Returns
    -------
    result: float
        Result of the operation on the selected data

    Note
    ----
    Adapted from the OPeNDAP examples in the NCO documentation:
    http://nco.sourceforge.net/nco.html#OPeNDAP
    '''
    import os
    import netCDF4
    import numpy
    import subprocess

    output = 'tmp_output.nc'

    # Concatenate subprocess command
    cmd = ['ncwa']
    cmd.extend(['-y', '{}'.format(op_type)])
    if times:
        cmd.extend(['-d', 'time,{},{}'.format(times[0], times[1])])
    if lons:
        cmd.extend(['-d', 'lon,{},{}'.format(lons[0], lons[1])])
    if lats:
        cmd.extend(['-d', 'lat,{},{}'.format(lats[0], lats[1])])
    cmd.extend(['-p', path])
    cmd.extend(numpy.atleast_1d(fnames).tolist())
    cmd.append(output)

    # Run cmd and check for errors
    subprocess.run(cmd, stdout=subprocess.PIPE, check=True)

    # Load, read, close data and delete temp .nc file
    data = netCDF4.Dataset(output)
    result = float(data[var][:])
    data.close()
    os.remove(output)

    return result

path = 'https://ecowatch.ncddc.noaa.gov/thredds/dodsC/hycom/hycom_reg6_agg/'
fname = 'HYCOM_Region_6_Aggregation_best.ncd'

times = (0.0, 48.0)
lons = (201.5, 205.5)
lats = (18.5, 22.5)

smax = ncwa(path, fname, 'salinity', 'max', times, lons, lats)
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