issues

When opening a multi-file dataset with xray.open_mfdataset, also some variables are concatenated that do not have a time dimension.

My netCDF files contain a lot of those "static" variables (e.g. grid spacing etc.). netCDF4.MFDataset used to handle those as expected (i.e. did not concatenate them).

Is the different behaviour of xray.open_mfdataset intentional or due to a bug?

Note: I am using decode_times=False.

Example

python with xray.open_dataset(files[0], decode_times=False) as single: print single['dz']

<xray.DataArray 'dz' (z_t: 60)> array([ 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1000. , 1019.68078613, 1056.44836426, 1105.99511719, 1167.80700684, 1242.41333008, 1330.96777344, 1435.14099121, 1557.12585449, 1699.67956543, 1866.21240234, 2060.90234375, 2288.85205078, 2556.24707031, 2870.57495117, 3240.8371582 , 3677.77246094, 4194.03076172, 4804.22363281, 5524.75439453, 6373.19189453, 7366.94482422, 8520.89257812, 9843.65820312, 11332.46582031, 12967.19921875, 14705.34375 , 16480.70898438, 18209.13476562, 19802.234375 , 21185.95703125, 22316.50976562, 23186.49414062, 23819.44921875, 24257.21679688, 24546.77929688, 24731.01367188, 24844.328125 , 24911.97460938, 24951.29101562, 24973.59375 , 24985.9609375 , 24992.67382812, 24996.24414062, 24998.109375 ]) Coordinates: * z_t (z_t) float32 500.0 1500.0 2500.0 3500.0 4500.0 5500.0 6500.0 ... Attributes: long_name: thickness of layer k units: centimeters

python with xray.open_mfdataset(files, decode_times=False) as multiple: print multiple['dz']

<xray.DataArray 'dz' (time: 12, z_t: 60)> dask.array<concatenate-1156, shape=(12, 60), chunks=((1, 1, 1, ..., 1, 1), (60,)), dtype=float64> Coordinates: * z_t (z_t) float32 500.0 1500.0 2500.0 3500.0 4500.0 5500.0 6500.0 ... * time (time) float64 3.653e+04 3.656e+04 3.659e+04 3.662e+04 ... Attributes: long_name: thickness of layer k units: centimeters

Fixes #444

Fixes #451

Fixes #464 Fixes #438

The optional arguments concat_over and mode in xray.concat have been removed and replaced by data_vars and coords. The new arguments are both more easily understood and more robustly implemented.

This function allows for saving multiple datasets to disk simultaneously, which is useful when processing large datasets with dask.array. For example, to save a dataset too big to fit into memory to one file per year, we could write:

```

years, datasets = zip(*ds.groupby('time.year')) paths = ['%s.nc' % y for y in years] xray.save_mfdataset(datasets, paths) ```

Fixes #453

Fixes #443 Fixes #444

in http://xray.readthedocs.org/en/latest/io.html#combining-multiple-files there is a bit of code missing in the function read_netcdfs: instead of:

``` def read_netcdfs(files, dim, transform_func=None): def process_one_path(path): # use a context manager, to ensure the file gets closed after use with xray.open_dataset(path) as ds: # transform_func should do some sort of selection or # aggregation if transform_func is not None: ds = transform_func(ds) # load all data from the transformed dataset, to ensure we can # use it after closing each original file ds.load() return ds

paths = sorted(glob(files))
datasets = [process_one_path(p) for p in paths]
xray.concat(datasets, dim)

```

it should be:

``` def read_netcdfs(files, dim, transform_func=None): def process_one_path(path): # use a context manager, to ensure the file gets closed after use with xray.open_dataset(path) as ds: # transform_func should do some sort of selection or # aggregation if transform_func is not None: ds = transform_func(ds) # load all data from the transformed dataset, to ensure we can # use it after closing each original file ds.load() return ds

paths = sorted(glob(files))
datasets = [process_one_path(p) for p in paths]
dset = xray.concat(datasets, dim)
return dset

```