issue_comments: 204340656

This data as json

html_url issue_url id node_id user created_at updated_at author_association body reactions performed_via_github_app issue
https://github.com/pydata/xarray/pull/814#issuecomment-204340656 https://api.github.com/repos/pydata/xarray/issues/814 204340656 MDEyOklzc3VlQ29tbWVudDIwNDM0MDY1Ng== 6215361 2016-04-01T10:21:23Z 2016-04-01T10:21:23Z NONE

docker pull nparley/xarray_test is a python 2.7 environment with Iris etc. which mirrors the travis CONDA_ENV=py27-cdat+iris+pynio.

It doesn't do everything, but it's a good first go. Here is an example:

``` python

import xarray import iris nc_file = 'E1_north_america.nc' cube = iris.load_cube(nc_file) darray = xarray.open_dataset(nc_file)['air_temperature'] print(darray) <xarray.DataArray 'air_temperature' (time: 240, latitude: 37, longitude: 49)> [435120 values with dtype=float32] Coordinates: * time (time) datetime64[ns] 1860-06-01 1861-06-01 ... * latitude (latitude) float32 15.0 16.25 17.5 18.75 20.0 ... * longitude (longitude) float32 225.0 226.875 228.75 ... forecast_period (time) timedelta64[ns] 449 days 18:00:00 ... forecast_reference_time datetime64[ns] 1859-09-01T06:00:00 height float64 1.5 Attributes: standard_name: air_temperature units: K Model scenario: E1 ukmo__um_stash_source: m01s03i236 source: Data from Met Office Unified Model 6.05 cell_methods: time: mean (interval: 6 hour) print(xarray.DataArray.from_iris(cube)) <xarray.DataArray u'air_temperature' (time: 240, latitude: 37, longitude: 49)> [435120 values with dtype=float32] Coordinates: * time (time) datetime64[ns] 1860-06-01 1861-06-01 ... * latitude (latitude) float32 15.0 16.25 17.5 18.75 20.0 ... * longitude (longitude) float32 225.0 226.875 228.75 ... forecast_reference_time datetime64[ns] 1859-09-01T06:00:00 height float64 1.5 forecast_period (time) timedelta64[ns] 449 days 18:00:00 ... Attributes: Model scenario: E1 STASH: m01s03i236 Conventions: CF-1.5 source: Data from Met Office Unified Model 6.05 standard_name: air_temperature units: K print(cube) air_temperature / (K) (time: 240; latitude: 37; longitude: 49) Dimension coordinates: time x - - latitude - x - longitude - - x Auxiliary coordinates: forecast_period x - - Scalar coordinates: forecast_reference_time: 1859-09-01 06:00:00 height: 1.5 m Attributes: Conventions: CF-1.5 Model scenario: E1 STASH: m01s03i236 source: Data from Met Office Unified Model 6.05 Cell methods: mean: time (6 hour print(darray.to_iris()) air_temperature / (K) (time: 240; latitude: 37; longitude: 49) Dimension coordinates: time x - - latitude - x - longitude - - x Auxiliary coordinates: forecast_period x - - Scalar coordinates: forecast_reference_time: 1859-09-01 06:00:00 height: 1.5 m Attributes: Model scenario: E1 source: Data from Met Office Unified Model 6.05 ukmo__um_stash_source: m01s03i236 ```

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   145140657