In [2]:

    

import xarray.plot as xplt
%matplotlib inline
from notebooks.Marco.use_case_9 import ect_load_ds, ect_adjust_geometry, ect_regional_subset


    

In [3]:

    

## Step 1 - Read two ECVs
sst_ds = ect_load_ds('sst')
oc_ds = ect_load_ds('oc')
# select one Variable from each ECV
chla_da = oc_ds.chlor_a
sst_da = sst_ds.analysed_sst


    

In [4]:

    

chla_da


    

    
Out[4]:





<xarray.DataArray 'chlor_a' (time: 12, lat: 4320, lon: 8640)>
dask.array<concate..., shape=(12, 4320, 8640), dtype=float64, chunksize=(1, 4320, 8640)>
Coordinates:
  * lon      (lon) float32 -179.979 -179.938 -179.896 -179.854 -179.813 ...
  * lat      (lat) float32 89.9792 89.9375 89.8958 89.8542 89.8125 89.7708 ...
  * time     (time) datetime64[ns] 2010-01-01 2010-02-01 2010-03-01 ...
Attributes:
    long_name: Chlorophyll-a concentration in seawater (not log-transformed), generated by SeaDAS using OC4v6 for SeaWiFS
    units: milligram m-3
    ancillary_variables: chlor_a_log10_rmsd,chlor_a_log10_bias
    grid_mapping: crs
    parameter_vocab_uri: http://vocab.ndg.nerc.ac.uk/term/P011/current/CHLTVOLU
    standard_name: mass_concentration_of_chlorophyll_a_in_sea_water
    units_nonstandard: mg m^-3

In [5]:

    

sst_da


    

    
Out[5]:





<xarray.DataArray 'analysed_sst' (time: 12, lat: 3600, lon: 7200)>
dask.array<concate..., shape=(12, 3600, 7200), dtype=float64, chunksize=(1, 3600, 7200)>
Coordinates:
  * lat      (lat) float32 -89.975 -89.925 -89.875 -89.825 -89.775 -89.725 ...
  * lon      (lon) float32 -179.975 -179.925 -179.875 -179.825 -179.775 ...
  * time     (time) datetime64[ns] 1947-05-12T09:58:14 2015-05-05T15:22:30 ...
Attributes:
    units: kelvin
    long_name: analysed sea surface temperature
    valid_min: -300
    valid_max: 4500
    standard_name: sea_water_temperature
    depth: 20 cm
    source: ATSR<1,2>-ESACCI-L3U-v1.0, AATSR-ESACCI-L3U-v1.0, AVHRR<12,14,15,16,17,18>_G-ESACCI-L2P-v1.0, AVHRRMTA-ESACCI-L2P-v1.0
    comment: SST analysis produced for ESA SST CCI project using the OSTIA system in reanalysis mode.

In [24]:

    

## Step 2 - Geometric Adjustments
# We make the OC data apply to spatial resolution of SST, because the latter is lower.
chla_da_low = ect_adjust_geometry(master=sst_da, slave=chla_da)
chla_da_low


    

    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-24-5b6192ad99b7> in <module>()
      1 ## Step 2 - Geometric Adjustments
      2 # We make the OC data apply to spatial resolution of SST, because the latter is lower.
----> 3 chla_da_low = ect_adjust_geometry(master=sst_da, slave=chla_da)
      4 chla_da

/home/marcoz/Projects/BC/CCI-Tools/sandbox/notebooks/Marco/use_case_9.py in ect_adjust_geometry(master, slave)
     36 
     37     # Help! This is soooo slow... few minutes on Norman's PC
---> 38     temp_da = slave.groupby('time').apply(resample)
     39     temp_lon = scipy.ndimage.zoom(slave.lon, [lon_factor])
     40     temp_lat = scipy.ndimage.zoom(slave.lat, [lat_factor])

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/xarray/core/groupby.py in apply(self, func, shortcut, **kwargs)
    381             grouped = self._iter_grouped()
    382         applied = (maybe_wrap_array(arr, func(arr, **kwargs)) for arr in grouped)
--> 383         combined = self._concat(applied, shortcut=shortcut)
    384         result = self._maybe_restore_empty_groups(combined)
    385         return result

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/xarray/core/groupby.py in _concat(self, applied, shortcut)
    393             combined = self._concat_shortcut(applied, concat_dim, positions)
    394         else:
--> 395             combined = concat(applied, concat_dim, positions=positions)
    396 
    397         if isinstance(combined, type(self.obj)):

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/xarray/core/combine.py in concat(objs, dim, data_vars, coords, compat, positions, indexers, mode, concat_over)
    112         raise TypeError('can only concatenate xarray Dataset and DataArray '
    113                         'objects, got %s' % type(first_obj))
--> 114     return f(objs, dim, data_vars, coords, compat, positions)
    115 
    116 

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/xarray/core/combine.py in _dataarray_concat(arrays, dim, data_vars, coords, compat, positions)
    281 def _dataarray_concat(arrays, dim, data_vars, coords, compat,
    282                       positions):
--> 283     arrays = list(arrays)
    284 
    285     if data_vars != 'all':

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/xarray/core/groupby.py in <genexpr>(.0)
    380         else:
    381             grouped = self._iter_grouped()
--> 382         applied = (maybe_wrap_array(arr, func(arr, **kwargs)) for arr in grouped)
    383         combined = self._concat(applied, shortcut=shortcut)
    384         result = self._maybe_restore_empty_groups(combined)

/home/marcoz/Projects/BC/CCI-Tools/sandbox/notebooks/Marco/use_case_9.py in resample(x)
     32 
     33     def resample(x):
---> 34         y = scipy.ndimage.zoom(x, [lat_factor, lon_factor])
     35         return xr.DataArray(y)
     36 

/home/marcoz/Projects/Anaconda/miniconda3/lib/python3.5/site-packages/scipy/ndimage/interpolation.py in zoom(input, zoom, output, order, mode, cval, prefilter)
    560     zoom = numpy.asarray(zoom, dtype=numpy.float64)
    561     zoom = numpy.ascontiguousarray(zoom)
--> 562     _nd_image.zoom_shift(filtered, zoom, None, output, order, mode, cval)
    563     return return_value
    564 

KeyboardInterrupt: 

In [14]:

    

## Step 3 - Spatial Subsetting
lat_slice = slice(30., 45.)
lon_slice = slice(-60., -45.)

chla_da_sub = ect_regional_subset(chla_da_low, lat_slice=lat_slice, lon_slice=lon_slice)
sst_da_sub = ect_regional_subset(sst_da, lat_slice=lat_slice, lon_slice=lon_slice)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-14-ac899d157c78> in <module>()
      3 lon_slice = slice(-60., -45.)
      4 
----> 5 chla_da_sub = ect_regional_subset(chla_da_low, lat_slice=lat_slice, lon_slice=lon_slice)
      6 sst_da_sub = ect_regional_subset(sst_da, lat_slice=lat_slice, lon_slice=lon_slice)

NameError: name 'chla_da_low' is not defined

In [10]:

    

xplt.imshow(oc_ds.chlor_a.isel(time=0), vmax=0.3)


    

    
Out[10]:





<matplotlib.image.AxesImage at 0x7fd2f00953c8>






    

In [11]:

    

xplt.imshow(sst.isel(time=0))


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x7f2c0dde65c0>