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pandas-based avoidance for non-nanosecond datetime value conversion t… #152

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pandas-based avoidance for non-nanosecond datetime value conversion t… #152

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7 changes: 4 additions & 3 deletions ecco_v4_py/ecco_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

from __future__ import division, print_function
import numpy as np
import pandas as pd
import xarray as xr
import datetime
import dateutil
Expand Down Expand Up @@ -303,7 +304,7 @@ def make_time_bounds_and_center_times_from_ecco_dataset(ecco_dataset, \
time_bnds = time_bnds.T

# make time bounds dataset
if 'time' not in ecco_dataset.dims.keys():
if 'time' not in ecco_dataset.dims:
ecco_dataset = ecco_dataset.expand_dims(dim='time')

#print ('-- tb shape ', time_bnds.shape)
Expand Down Expand Up @@ -388,7 +389,7 @@ def make_time_bounds_from_ds64(rec_avg_end, output_freq_code):
def extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(dt64):
"""

Extract separate fields for year, monday, day, hour, min, sec from
Extract separate fields for year, month, day, hour, min, sec from
a datetime64 object, or an array-like object of datetime64 objects

Parameters
Expand Down Expand Up @@ -420,7 +421,7 @@ def extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(dt64):
xdates = extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(xr.DataArray(dt64))
return tuple([x.values for x in xdates])
elif isinstance(dt64,np.datetime64):
xdates = extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(xr.DataArray(np.array([dt64])))
xdates = extract_yyyy_mm_dd_hh_mm_ss_from_datetime64(xr.DataArray(pd.to_datetime(dt64)))
return tuple([int(x.values) for x in xdates])

#%%
Expand Down