xarray
xarray supports direct serialization and IO to several file formats, from simple io.pickle files to the more flexible io.netcdf format (recommended).
python
import numpy as np import pandas as pd import xarray as xr
np.random.seed(123456)
The recommended way to store xarray data structures is netCDF, which is a binary file format for self-described datasets that originated in the geosciences. xarray is based on the netCDF data model, so netCDF files on disk directly correspond to :pyDataset objects (more accurately, a group in a netCDF file directly corresponds to a :pyDataset object. See io.netcdf_groups for more.)
NetCDF is supported on almost all platforms, and parsers exist for the vast majority of scientific programming languages. Recent versions of netCDF are based on the even more widely used HDF5 file-format.
Tip
If you aren't familiar with this data format, the netCDF FAQ is a good place to start.
Reading and writing netCDF files with xarray requires scipy or the netCDF4-Python library to be installed (the latter is required to read/write netCDF V4 files and use the compression options described below).
We can save a Dataset to disk using the :pyDataset.to_netcdf method:
python
- ds = xr.Dataset(
{"foo": (("x", "y"), np.random.rand(4, 5))}, coords={ "x": [10, 20, 30, 40], "y": pd.date_range("2000-01-01", periods=5), "z": ("x", list("abcd")), },
)
ds.to_netcdf("saved_on_disk.nc")
By default, the file is saved as netCDF4 (assuming netCDF4-Python is installed). You can control the format and engine used to write the file with the format and engine arguments.
Tip
Using the h5netcdf package by passing engine='h5netcdf' to :pyopen_dataset can sometimes be quicker than the default engine='netcdf4' that uses the netCDF4 package.
We can load netCDF files to create a new Dataset using :pyopen_dataset:
python
ds_disk = xr.open_dataset("saved_on_disk.nc") ds_disk
Similarly, a DataArray can be saved to disk using the :pyDataArray.to_netcdf method, and loaded from disk using the :pyopen_dataarray function. As netCDF files correspond to :pyDataset objects, these functions internally convert the DataArray to a Dataset before saving, and then convert back when loading, ensuring that the DataArray that is loaded is always exactly the same as the one that was saved.
A dataset can also be loaded or written to a specific group within a netCDF file. To load from a group, pass a group keyword argument to the open_dataset function. The group can be specified as a path-like string, e.g., to access subgroup 'bar' within group 'foo' pass '/foo/bar' as the group argument. When writing multiple groups in one file, pass mode='a' to to_netcdf to ensure that each call does not delete the file.
Data is always loaded lazily from netCDF files. You can manipulate, slice and subset Dataset and DataArray objects, and no array values are loaded into memory until you try to perform some sort of actual computation. For an example of how these lazy arrays work, see the OPeNDAP section below.
There may be minor differences in the :pyDataset object returned when reading a NetCDF file with different engines. For example, single-valued attributes are returned as scalars by the default engine=netcdf4, but as arrays of size (1,) when reading with engine=h5netcdf.
It is important to note that when you modify values of a Dataset, even one linked to files on disk, only the in-memory copy you are manipulating in xarray is modified: the original file on disk is never touched.
Tip
xarray's lazy loading of remote or on-disk datasets is often but not always desirable. Before performing computationally intense operations, it is often a good idea to load a Dataset (or DataArray) entirely into memory by invoking the :pyDataset.load method.
Datasets have a :pyDataset.close method to close the associated netCDF file. However, it's often cleaner to use a with statement:
python
# this automatically closes the dataset after use with xr.open_dataset("saved_on_disk.nc") as ds: print(ds.keys())
Although xarray provides reasonable support for incremental reads of files on disk, it does not support incremental writes, which can be a useful strategy for dealing with datasets too big to fit into memory. Instead, xarray integrates with dask.array (see dask), which provides a fully featured engine for streaming computation.
It is possible to append or overwrite netCDF variables using the mode='a' argument. When using this option, all variables in the dataset will be written to the original netCDF file, regardless if they exist in the original dataset.
NetCDF groups are not supported as part of the :pyDataset data model. Instead, groups can be loaded individually as Dataset objects. To do so, pass a group keyword argument to the :pyopen_dataset function. The group can be specified as a path-like string, e.g., to access subgroup 'bar' within group 'foo' pass '/foo/bar' as the group argument. In a similar way, the group keyword argument can be given to the :pyDataset.to_netcdf method to write to a group in a netCDF file. When writing multiple groups in one file, pass mode='a' to :pyDataset.to_netcdf to ensure that each call does not delete the file.
NetCDF files follow some conventions for encoding datetime arrays (as numbers with a "units" attribute) and for packing and unpacking data (as described by the "scale_factor" and "add_offset" attributes). If the argument decode_cf=True (default) is given to :pyopen_dataset, xarray will attempt to automatically decode the values in the netCDF objects according to CF conventions. Sometimes this will fail, for example, if a variable has an invalid "units" or "calendar" attribute. For these cases, you can turn this decoding off manually.
You can view this encoding information (among others) in the :pyDataArray.encoding and :pyDataArray.encoding attributes:
In [1]: ds_disk["y"].encoding Out[1]: {'zlib': False, 'shuffle': False, 'complevel': 0, 'fletcher32': False, 'contiguous': True, 'chunksizes': None, 'source': 'saved_on_disk.nc', 'original_shape': (5,), 'dtype': dtype('int64'), 'units': 'days since 2000-01-01 00:00:00', 'calendar': 'proleptic_gregorian'}
In [9]: ds_disk.encoding Out[9]: {'unlimited_dims': set(), 'source': 'saved_on_disk.nc'}
Note that all operations that manipulate variables other than indexing will remove encoding information.
python
ds_disk.close()
NetCDF files are often encountered in collections, e.g., with different files corresponding to different model runs or one file per timestamp. xarray can straightforwardly combine such files into a single Dataset by making use of :pyconcat, :pymerge, :pycombine_nested and :pycombine_by_coords. For details on the difference between these functions see combining data.
Xarray includes support for manipulating datasets that don't fit into memory with dask. If you have dask installed, you can open multiple files simultaneously in parallel using :pyopen_mfdataset:
xr.open_mfdataset('my/files/*.nc', parallel=True)This function automatically concatenates and merges multiple files into a single xarray dataset. It is the recommended way to open multiple files with xarray. For more details on parallel reading, see combining.multi, dask.io and a blog post by Stephan Hoyer. :pyopen_mfdataset takes many kwargs that allow you to control its behaviour (for e.g. parallel, combine, compat, join, concat_dim). See its docstring for more details.
Note
A common use-case involves a dataset distributed across a large number of files with each file containing a large number of variables. Commonly, a few of these variables need to be concatenated along a dimension (say "time"), while the rest are equal across the datasets (ignoring floating point differences). The following command with suitable modifications (such as parallel=True) works well with such datasets:
xr.open_mfdataset('my/files/*.nc', concat_dim="time",
data_vars='minimal', coords='minimal', compat='override')This command concatenates variables along the "time" dimension, but only those that already contain the "time" dimension (data_vars='minimal', coords='minimal'). Variables that lack the "time" dimension are taken from the first dataset (compat='override').
Sometimes multi-file datasets are not conveniently organized for easy use of :pyopen_mfdataset. One can use the preprocess argument to provide a function that takes a dataset and returns a modified Dataset. :pyopen_mfdataset will call preprocess on every dataset (corresponding to each file) prior to combining them.
If :pyopen_mfdataset does not meet your needs, other approaches are possible. The general pattern for parallel reading of multiple files using dask, modifying those datasets and then combining into a single Dataset is:
def modify(ds):
# modify ds here
return ds
# this is basically what open_mfdataset does
open_kwargs = dict(decode_cf=True, decode_times=False)
open_tasks = [dask.delayed(xr.open_dataset)(f, **open_kwargs) for f in file_names]
tasks = [dask.delayed(modify)(task) for task in open_tasks]
datasets = dask.compute(tasks) # get a list of xarray.Datasets
combined = xr.combine_nested(datasets) # or some combination of concat, mergeAs an example, here's how we could approximate MFDataset from the netCDF4 library:
from glob import glob
import xarray as xr
def read_netcdfs(files, dim):
# glob expands paths with * to a list of files, like the unix shell
paths = sorted(glob(files))
datasets = [xr.open_dataset(p) for p in paths]
combined = xr.concat(datasets, dim)
return combined
combined = read_netcdfs('/all/my/files/*.nc', dim='time')This function will work in many cases, but it's not very robust. First, it never closes files, which means it will fail if you need to load more than a few thousand files. Second, it assumes that you want all the data from each file and that it can all fit into memory. In many situations, you only need a small subset or an aggregated summary of the data from each file.
Here's a slightly more sophisticated example of how to remedy these deficiencies:
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 xr.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]
combined = xr.concat(datasets, dim)
return combined
# here we suppose we only care about the combined mean of each file;
# you might also use indexing operations like .sel to subset datasets
combined = read_netcdfs('/all/my/files/*.nc', dim='time',
transform_func=lambda ds: ds.mean())This pattern works well and is very robust. We've used similar code to process tens of thousands of files constituting 100s of GB of data.
Conversely, you can customize how xarray writes netCDF files on disk by providing explicit encodings for each dataset variable. The encoding argument takes a dictionary with variable names as keys and variable specific encodings as values. These encodings are saved as attributes on the netCDF variables on disk, which allows xarray to faithfully read encoded data back into memory.
It is important to note that using encodings is entirely optional: if you do not supply any of these encoding options, xarray will write data to disk using a default encoding, or the options in the encoding attribute, if set. This works perfectly fine in most cases, but encoding can be useful for additional control, especially for enabling compression.
In the file on disk, these encodings are saved as attributes on each variable, which allow xarray and other CF-compliant tools for working with netCDF files to correctly read the data.
These encoding options work on any version of the netCDF file format:
dtype: Any valid NumPy dtype or string convertable to a dtype, e.g.,'int16'or'float32'. This controls the type of the data written on disk._FillValue: Values ofNaNin xarray variables are remapped to this value when saved on disk. This is important when converting floating point with missing values to integers on disk, becauseNaNis not a valid value for integer dtypes. By default, variables with float types are attributed a_FillValueofNaNin the output file, unless explicitly disabled with an encoding{'_FillValue': None}.scale_factorandadd_offset: Used to convert from encoded data on disk to to the decoded data in memory, according to the formuladecoded = scale_factor * encoded + add_offset.
These parameters can be fruitfully combined to compress discretized data on disk. For example, to save the variable foo with a precision of 0.1 in 16-bit integers while converting NaN to -9999, we would use encoding={'foo': {'dtype': 'int16', 'scale_factor': 0.1, '_FillValue': -9999}}. Compression and decompression with such discretization is extremely fast.
xarray can write unicode strings to netCDF files in two ways:
- As variable length strings. This is only supported on netCDF4 (HDF5) files.
- By encoding strings into bytes, and writing encoded bytes as a character array. The default encoding is UTF-8.
By default, we use variable length strings for compatible files and fall-back to using encoded character arrays. Character arrays can be selected even for netCDF4 files by setting the dtype field in encoding to S1 (corresponding to NumPy's single-character bytes dtype).
If character arrays are used:
- The string encoding that was used is stored on disk in the
_Encodingattribute, which matches an ad-hoc convention adopted by the netCDF4-Python library. At the time of this writing (October 2017), a standard convention for indicating string encoding for character arrays in netCDF files was still under discussion. Technically, you can use any string encoding recognized by Python if you feel the need to deviate from UTF-8, by setting the_Encodingfield inencoding. But we don't recommend it. - The character dimension name can be specifed by the
char_dim_namefield of a variable'sencoding. If the name of the character dimension is not specified, the default isf'string{data.shape[-1]}'. When decoding character arrays from existing files, thechar_dim_nameis added to the variablesencodingto preserve if encoding happens, but the field can be edited by the user.
Warning
Missing values in bytes or unicode string arrays (represented by NaN in xarray) are currently written to disk as empty strings ''. This means missing values will not be restored when data is loaded from disk. This behavior is likely to change in the future (1647). Unfortunately, explicitly setting a _FillValue for string arrays to handle missing values doesn't work yet either, though we also hope to fix this in the future.
zlib, complevel, fletcher32, continguous and chunksizes can be used for enabling netCDF4/HDF5's chunk based compression, as described in the documentation for createVariable for netCDF4-Python. This only works for netCDF4 files and thus requires using format='netCDF4' and either engine='netcdf4' or engine='h5netcdf'.
Chunk based gzip compression can yield impressive space savings, especially for sparse data, but it comes with significant performance overhead. HDF5 libraries can only read complete chunks back into memory, and maximum decompression speed is in the range of 50-100 MB/s. Worse, HDF5's compression and decompression currently cannot be parallelized with dask. For these reasons, we recommend trying discretization based compression (described above) first.
The units and calendar attributes control how xarray serializes datetime64 and timedelta64 arrays to datasets on disk as numeric values. The units encoding should be a string like 'days since 1900-01-01' for datetime64 data or a string like 'days' for timedelta64 data. calendar should be one of the calendar types supported by netCDF4-python: 'standard', 'gregorian', 'proleptic_gregorian' 'noleap', '365_day', '360_day', 'julian', 'all_leap', '366_day'.
By default, xarray uses the 'proleptic_gregorian' calendar and units of the smallest time difference between values, with a reference time of the first time value.
You can control the coordinates attribute written to disk by specifying DataArray.encoding["coordinates"]. If not specified, xarray automatically sets DataArray.encoding["coordinates"] to a space-delimited list of names of coordinate variables that share dimensions with the DataArray being written. This allows perfect roundtripping of xarray datasets but may not be desirable. When an xarray Dataset contains non-dimensional coordinates that do not share dimensions with any of the variables, these coordinate variable names are saved under a "global" "coordinates" attribute. This is not CF-compliant but again facilitates roundtripping of xarray datasets.
The library h5netcdf allows writing some dtypes (booleans, complex, ...) that aren't allowed in netCDF4 (see h5netcdf documentation). This feature is availabe through :pyDataArray.to_netcdf and :pyDataset.to_netcdf when used with engine="h5netcdf" and currently raises a warning unless invalid_netcdf=True is set:
python
# Writing complex valued data da = xr.DataArray([1.0 + 1.0j, 2.0 + 2.0j, 3.0 + 3.0j]) da.to_netcdf("complex.nc", engine="h5netcdf", invalid_netcdf=True)
# Reading it back xr.open_dataarray("complex.nc", engine="h5netcdf")
python
import os
os.remove("complex.nc")
Warning
Note that this produces a file that is likely to be not readable by other netCDF libraries!
The Iris tool allows easy reading of common meteorological and climate model formats (including GRIB and UK MetOffice PP files) into Cube objects which are in many ways very similar to DataArray objects, while enforcing a CF-compliant data model. If iris is installed, xarray can convert a DataArray into a Cube using :pyDataArray.to_iris:
python
- da = xr.DataArray(
np.random.rand(4, 5), dims=["x", "y"], coords=dict(x=[10, 20, 30, 40], y=pd.date_range("2000-01-01", periods=5)),
)
cube = da.to_iris() cube
Conversely, we can create a new DataArray object from a Cube using :pyDataArray.from_iris:
python
da_cube = xr.DataArray.from_iris(cube) da_cube
xarray includes support for OPeNDAP (via the netCDF4 library or Pydap), which lets us access large datasets over HTTP.
For example, we can open a connection to GBs of weather data produced by the PRISM project, and hosted by IRI at Columbia:
- In [3]: remote_data = xr.open_dataset(
...: "http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods", ...: decode_times=False, ...: )
In [4]: remote_data Out[4]: <xarray.Dataset> Dimensions: (T: 1422, X: 1405, Y: 621) Coordinates: * X (X) float32 -125.0 -124.958 -124.917 -124.875 -124.833 -124.792 -124.75 ... * T (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 -772.5 -771.5 ... * Y (Y) float32 49.9167 49.875 49.8333 49.7917 49.75 49.7083 49.6667 49.625 ... Data variables: ppt (T, Y, X) float64 ... tdmean (T, Y, X) float64 ... tmax (T, Y, X) float64 ... tmin (T, Y, X) float64 ... Attributes: Conventions: IRIDL expires: 1375315200
Note
Like many real-world datasets, this dataset does not entirely follow CF conventions. Unexpected formats will usually cause xarray's automatic decoding to fail. The way to work around this is to either set decode_cf=False in open_dataset to turn off all use of CF conventions, or by only disabling the troublesome parser. In this case, we set decode_times=False because the time axis here provides the calendar attribute in a format that xarray does not expect (the integer 360 instead of a string like '360_day').
We can select and slice this data any number of times, and nothing is loaded over the network until we look at particular values:
In [4]: tmax = remote_data["tmax"][:500, ::3, ::3]
In [5]: tmax Out[5]: <xarray.DataArray 'tmax' (T: 500, Y: 207, X: 469)> [48541500 values with dtype=float64] Coordinates: * Y (Y) float32 49.9167 49.7917 49.6667 49.5417 49.4167 49.2917 ... * X (X) float32 -125.0 -124.875 -124.75 -124.625 -124.5 -124.375 ... * T (T) float32 -779.5 -778.5 -777.5 -776.5 -775.5 -774.5 -773.5 ... Attributes: pointwidth: 120 standard_name: air_temperature units: Celsius_scale expires: 1443657600
# the data is downloaded automatically when we make the plot In [6]: tmax[0].plot()
Some servers require authentication before we can access the data. For this purpose we can explicitly create a :pybackends.PydapDataStore and pass in a Requests session object. For example for HTTP Basic authentication:
import xarray as xr
import requests
session = requests.Session()
session.auth = ('username', 'password')
store = xr.backends.PydapDataStore.open('http://example.com/data',
session=session)
ds = xr.open_dataset(store)Pydap's cas module has functions that generate custom sessions for servers that use CAS single sign-on. For example, to connect to servers that require NASA's URS authentication:
import xarray as xr
from pydata.cas.urs import setup_session
ds_url = 'https://gpm1.gesdisc.eosdis.nasa.gov/opendap/hyrax/example.nc'
session = setup_session('username', 'password', check_url=ds_url)
store = xr.backends.PydapDataStore.open(ds_url, session=session)
ds = xr.open_dataset(store)The simplest way to serialize an xarray object is to use Python's built-in pickle module:
python
import pickle
# use the highest protocol (-1) because it is way faster than the default # text based pickle format pkl = pickle.dumps(ds, protocol=-1)
pickle.loads(pkl)
Pickling is important because it doesn't require any external libraries and lets you use xarray objects with Python modules like :pymultiprocessing or Dask <dask>. However, pickling is not recommended for long-term storage.
Restoring a pickle requires that the internal structure of the types for the pickled data remain unchanged. Because the internal design of xarray is still being refined, we make no guarantees (at this point) that objects pickled with this version of xarray will work in future versions.
Note
When pickling an object opened from a NetCDF file, the pickle file will contain a reference to the file on disk. If you want to store the actual array values, load it into memory first with :pyDataset.load or :pyDataset.compute.
We can convert a Dataset (or a DataArray) to a dict using :pyDataset.to_dict:
python
d = ds.to_dict() d
We can create a new xarray object from a dict using :pyDataset.from_dict:
python
ds_dict = xr.Dataset.from_dict(d) ds_dict
Dictionary support allows for flexible use of xarray objects. It doesn't require external libraries and dicts can easily be pickled, or converted to json, or geojson. All the values are converted to lists, so dicts might be quite large.
To export just the dataset schema without the data itself, use the data=False option:
python
ds.to_dict(data=False)
This can be useful for generating indices of dataset contents to expose to search indices or other automated data discovery tools.
python
import os
os.remove("saved_on_disk.nc")
GeoTIFFs and other gridded raster datasets can be opened using rasterio, if rasterio is installed. Here is an example of how to use :pyopen_rasterio to read one of rasterio's test files:
In [7]: rio = xr.open_rasterio("RGB.byte.tif")
In [8]: rio Out[8]: <xarray.DataArray (band: 3, y: 718, x: 791)> [1703814 values with dtype=uint8] Coordinates: * band (band) int64 1 2 3 * y (y) float64 2.827e+06 2.826e+06 2.826e+06 2.826e+06 2.826e+06 ... * x (x) float64 1.021e+05 1.024e+05 1.027e+05 1.03e+05 1.033e+05 ... Attributes: res: (300.0379266750948, 300.041782729805) transform: (300.0379266750948, 0.0, 101985.0, 0.0, -300.041782729805, 28... is_tiled: 0 crs: +init=epsg:32618
The x and y coordinates are generated out of the file's metadata (bounds, width, height), and they can be understood as cartesian coordinates defined in the file's projection provided by the crs attribute. crs is a PROJ4 string which can be parsed by e.g. pyproj or rasterio. See /examples/visualization_gallery.ipynb#Parsing-rasterio-geocoordinates for an example of how to convert these to longitudes and latitudes.
Warning
This feature has been added in xarray v0.9.6 and should still be considered experimental. Please report any bugs you may find on xarray's github repository.
Additionally, you can use rioxarray for reading in GeoTiff, netCDF or other GDAL readable raster data using rasterio as well as for exporting to a geoTIFF. rioxarray can also handle geospatial related tasks such as re-projecting and clipping.
In [1]: import rioxarray
In [2]: rds = rioxarray.open_rasterio("RGB.byte.tif")
In [3]: rds Out[3]: <xarray.DataArray (band: 3, y: 718, x: 791)> [1703814 values with dtype=uint8] Coordinates: * band (band) int64 1 2 3 * y (y) float64 2.827e+06 2.826e+06 ... 2.612e+06 2.612e+06 * x (x) float64 1.021e+05 1.024e+05 ... 3.389e+05 3.392e+05 spatial_ref int64 0 Attributes: STATISTICS_MAXIMUM: 255 STATISTICS_MEAN: 29.947726688477 STATISTICS_MINIMUM: 0 STATISTICS_STDDEV: 52.340921626611 transform: (300.0379266750948, 0.0, 101985.0, 0.0, -300.0417827... _FillValue: 0.0 scale_factor: 1.0 add_offset: 0.0 grid_mapping: spatial_ref
In [4]: rds.rio.crs Out[4]: CRS.from_epsg(32618)
In [5]: rds4326 = rds.rio.reproject("epsg:4326")
In [6]: rds4326.rio.crs Out[6]: CRS.from_epsg(4326)
In [7]: rds4326.rio.to_raster("RGB.byte.4326.tif")
Zarr is a Python package that provides an implementation of chunked, compressed, N-dimensional arrays. Zarr has the ability to store arrays in a range of ways, including in memory, in files, and in cloud-based object storage such as Amazon S3 and Google Cloud Storage. Xarray's Zarr backend allows xarray to leverage these capabilities, including the ability to store and analyze datasets far too large fit onto disk (particularly in combination with dask <dask>).
Warning
Zarr support is still an experimental feature. Please report any bugs or unexepected behavior via github issues.
Xarray can't open just any zarr dataset, because xarray requires special metadata (attributes) describing the dataset dimensions and coordinates. At this time, xarray can only open zarr datasets that have been written by xarray. For implementation details, see zarr_encoding.
To write a dataset with zarr, we use the :pyDataset.to_zarr method.
To write to a local directory, we pass a path to a directory:
python
! rm -rf path/to/directory.zarr
python
- ds = xr.Dataset(
{"foo": (("x", "y"), np.random.rand(4, 5))}, coords={ "x": [10, 20, 30, 40], "y": pd.date_range("2000-01-01", periods=5), "z": ("x", list("abcd")), },
) ds.to_zarr("path/to/directory.zarr")
(The suffix .zarr is optional--just a reminder that a zarr store lives there.) If the directory does not exist, it will be created. If a zarr store is already present at that path, an error will be raised, preventing it from being overwritten. To override this behavior and overwrite an existing store, add mode='w' when invoking :py~Dataset.to_zarr.
To store variable length strings, convert them to object arrays first with dtype=object.
To read back a zarr dataset that has been created this way, we use the :pyopen_zarr method:
python
ds_zarr = xr.open_zarr("path/to/directory.zarr") ds_zarr
It is possible to read and write xarray datasets directly from / to cloud storage buckets using zarr. This example uses the gcsfs package to provide an interface to Google Cloud Storage.
From v0.16.2: general fsspec URLs are parsed and the store set up for you automatically when reading, such that you can open a dataset in a single call. You should include any arguments to the storage backend as the key storage_options, part of backend_kwargs.
ds_gcs = xr.open_dataset(
"gcs://<bucket-name>/path.zarr",
backend_kwargs={
"storage_options": {"project": "<project-name>", "token": None}
},
engine="zarr",
)This also works with open_mfdataset, allowing you to pass a list of paths or a URL to be interpreted as a glob string.
For older versions, and for writing, you must explicitly set up a MutableMapping instance and pass this, as follows:
import gcsfs
fs = gcsfs.GCSFileSystem(project="<project-name>", token=None)
gcsmap = gcsfs.mapping.GCSMap("<bucket-name>", gcs=fs, check=True, create=False)
# write to the bucket
ds.to_zarr(store=gcsmap)
# read it back
ds_gcs = xr.open_zarr(gcsmap)(or use the utility function fsspec.get_mapper()).
There are many different options for compression and filtering possible with zarr. These are described in the zarr documentation. These options can be passed to the to_zarr method as variable encoding. For example:
python
! rm -rf foo.zarr
python
import zarr
compressor = zarr.Blosc(cname="zstd", clevel=3, shuffle=2) ds.to_zarr("foo.zarr", encoding={"foo": {"compressor": compressor}})
Note
Not all native zarr compression and filtering options have been tested with xarray.
Xarray needs to read all of the zarr metadata when it opens a dataset. In some storage mediums, such as with cloud object storage (e.g. amazon S3), this can introduce significant overhead, because two separate HTTP calls to the object store must be made for each variable in the dataset. With version 2.3, zarr will support a feature called consolidated metadata, which allows all metadata for the entire dataset to be stored with a single key (by default called .zmetadata). This can drastically speed up opening the store. (For more information on this feature, consult the zarr docs.)
If you have zarr version 2.3 or greater, xarray can write and read stores with consolidated metadata. To write consolidated metadata, pass the consolidated=True option to the :pyDataset.to_zarr method:
ds.to_zarr('foo.zarr', consolidated=True)To read a consolidated store, pass the consolidated=True option to :pyopen_zarr:
ds = xr.open_zarr('foo.zarr', consolidated=True)Xarray can't perform consolidation on pre-existing zarr datasets. This should be done directly from zarr, as described in the zarr docs.
Xarray supports several ways of incrementally writing variables to a Zarr store. These options are useful for scenarios when it is infeasible or undesirable to write your entire dataset at once.
Tip
If you can load all of your data into a single Dataset using dask, a single call to to_zarr() will write all of your data in parallel.
Warning
Alignment of coordinates is currently not checked when modifying an existing Zarr store. It is up to the user to ensure that coordinates are consistent.
To add or overwrite entire variables, simply call :py~Dataset.to_zarr with mode='a' on a Dataset containing the new variables, passing in an existing Zarr store or path to a Zarr store.
To resize and then append values along an existing dimension in a store, set append_dim. This is a good option if data always arives in a particular order, e.g., for time-stepping a simulation:
python
! rm -rf path/to/directory.zarr
python
- ds1 = xr.Dataset(
{"foo": (("x", "y", "t"), np.random.rand(4, 5, 2))}, coords={ "x": [10, 20, 30, 40], "y": [1, 2, 3, 4, 5], "t": pd.date_range("2001-01-01", periods=2), },
) ds1.to_zarr("path/to/directory.zarr") ds2 = xr.Dataset( {"foo": (("x", "y", "t"), np.random.rand(4, 5, 2))}, coords={ "x": [10, 20, 30, 40], "y": [1, 2, 3, 4, 5], "t": pd.date_range("2001-01-03", periods=2), }, ) ds2.to_zarr("path/to/directory.zarr", append_dim="t")
Finally, you can use region to write to limited regions of existing arrays in an existing Zarr store. This is a good option for writing data in parallel from independent processes.
To scale this up to writing large datasets, the first step is creating an initial Zarr store without writing all of its array data. This can be done by first creating a Dataset with dummy values stored in dask <dask>, and then calling to_zarr with compute=False to write only metadata (including attrs) to Zarr:
python
! rm -rf path/to/directory.zarr
python
import dask.array
# The values of this dask array are entirely irrelevant; only the dtype, # shape and chunks are used dummies = dask.array.zeros(30, chunks=10) ds = xr.Dataset({"foo": ("x", dummies)}) path = "path/to/directory.zarr" # Now we write the metadata without computing any array values ds.to_zarr(path, compute=False, consolidated=True)
Now, a Zarr store with the correct variable shapes and attributes exists that can be filled out by subsequent calls to to_zarr. The region provides a mapping from dimension names to Python slice objects indicating where the data should be written (in index space, not coordinate space), e.g.,
python
# For convenience, we'll slice a single dataset, but in the real use-case # we would create them separately, possibly even from separate processes. ds = xr.Dataset({"foo": ("x", np.arange(30))}) ds.isel(x=slice(0, 10)).to_zarr(path, region={"x": slice(0, 10)}) ds.isel(x=slice(10, 20)).to_zarr(path, region={"x": slice(10, 20)}) ds.isel(x=slice(20, 30)).to_zarr(path, region={"x": slice(20, 30)})
Concurrent writes with region are safe as long as they modify distinct chunks in the underlying Zarr arrays (or use an appropriate lock).
As a safety check to make it harder to inadvertently override existing values, if you set region then all variables included in a Dataset must have dimensions included in region. Other variables (typically coordinates) need to be explicitly dropped and/or written in a separate calls to to_zarr with mode='a'.
python
import shutil
shutil.rmtree("foo.zarr") shutil.rmtree("path/to/directory.zarr")
xarray supports reading GRIB files via ECMWF cfgrib python driver, if it is installed. To open a GRIB file supply engine='cfgrib' to :pyopen_dataset:
In [1]: ds_grib = xr.open_dataset("example.grib", engine="cfgrib")
We recommend installing cfgrib via conda:
conda install -c conda-forge cfgribxarray can also read GRIB, HDF4 and other file formats supported by PyNIO, if PyNIO is installed. To use PyNIO to read such files, supply engine='pynio' to :pyopen_dataset.
We recommend installing PyNIO via conda:
conda install -c conda-forge pynioWarning
PyNIO is no longer actively maintained and conflicts with netcdf4 > 1.5.3. The PyNIO backend may be moved outside of xarray in the future.
xarray can also read CAMx, BPCH, ARL PACKED BIT, and many other file formats supported by PseudoNetCDF, if PseudoNetCDF is installed. PseudoNetCDF can also provide Climate Forecasting Conventions to CMAQ files. In addition, PseudoNetCDF can automatically register custom readers that subclass PseudoNetCDF.PseudoNetCDFFile. PseudoNetCDF can identify readers either heuristically, or by a format specified via a key in backend_kwargs.
To use PseudoNetCDF to read such files, supply engine='pseudonetcdf' to :pyopen_dataset.
Add backend_kwargs={'format': '<format name>'} where <format name> options are listed on the PseudoNetCDF page.
For more options (tabular formats and CSV files in particular), consider exporting your objects to pandas and using its broad range of IO tools. For CSV files, one might also consider xarray_extras.
More formats are supported by extension libraries:
- xarray-mongodb: Store xarray objects on MongoDB