#!/usr/bin/env python
# coding: utf-8

# ## Tutorial on how to use `timestaps` in Field construction

# In[1]:


from parcels import Field
from glob import glob
import numpy as np


# Some NetCDF files, such as for example those from the [World Ocean Atlas](https://www.nodc.noaa.gov/OC5/woa18/), have time calendars that can't be parsed by `xarray`. These result in a `ValueError: unable to decode time units`, for example when the calendar is in 'months since' a particular date.
# 
# In these cases, a workaround in Parcels is to use the `timestamps` argument in `Field` (or `FieldSet`) creation. Here, we show how this works for example temperature data from the World Ocean Atlas in the Pacific Ocean

# The following cell would give an error, since the calendar of the World Ocean Atlas data is in "months since 1955-01-01 00:00:00"

# In[2]:


# tempfield = Field.from_netcdf(glob('WOA_data/woa18_decav_*_04.nc'), 't_an', 
#                               {'lon': 'lon', 'lat': 'lat', 'time': 'time'})


# However, we can create our own numpy array of timestamps associated with each of the 12 snapshots in the netcdf file

# In[3]:


timestamps = np.expand_dims(np.array([np.datetime64('2001-%.2d-15' %m) for m in range(1,13)]), axis=1)


# And then we can add the `timestamps` as an extra argument

# In[4]:


tempfield = Field.from_netcdf(glob('WOA_data/woa18_decav_*_04.nc'), 't_an', 
                              {'lon': 'lon', 'lat': 'lat', 'time': 'time'}, 
                              timestamps=timestamps)


# Note, by the way, that adding the `time_periodic=True` argument to `Field.from_netcdf()` will also mean that the climatology can be cycled for multiple years.