xcube - An xarray-based EO data cube toolkit
xcube has been developed to generate, manipulate, analyse, and publish data cubes from EO data.
Overview
xcube is an open-source Python package and toolkit that has been developed to provide Earth observation (EO) data in an analysis-ready form to users. xcube achieves this by carefully converting EO data sources into self-contained data cubes that can be published in the cloud. The Python package is expanded and maintained by [Brockmann Consult GmbH](https://www.brockmann-consult.de)
Data Cube
The interpretation of the term data cube in the EO domain usually depends on the current context. It may refer to a data service such as Sentinel Hub, to some abstract API, or to a concrete set of spatial images that form a time-series.
This section briefly explains the specific concept of a data cube used in the xcube project - the xcube dataset.
xcube Dataset
Data Model
An xcube dataset contains one or more (geo-physical) data variables whose values are stored in cells of a common multi-dimensional, spatio-temporal grid. The dimensions are usually time, latitude, and longitude, however other dimensions may be present.
All xcube datasets are structured in the same way following a common data model. They are also self-describing by providing metadata for the cube and all cube’s variables following the CF conventions. For details regarding the common data model, please refer to the xcube Dataset Convention.
A xcube dataset’s in-memory representation in Python programs is an xarray.Dataset instance. Each dataset variable is represented by multi-dimensional xarray.DataArray that is arranged in non-overlapping, contiguous sub-regions called data chunks.
Data Chunks
Chunked variables allow for out-of-core computations of xcube dataset that don’t fit in a single computer’s RAM as data chunks can be processed independently from each other.
The way how dataset variables are sub-divided into smaller chunks - their chunking - has a substantial impact on processing performance and there is no single ideal chunking for all use cases. For time series analyses it is preferable to have chunks with a smaller spatial dimensions and larger time dimension, for spatial analyses and visualisation on using a map, the opposite is the case.
xcube provide tools for re-chunking of xcube datasets (xcube chunk, xcube level) and the xcube server (xcube serve) allows serving the same data cubes using different chunkings. For further reading have a look into the Chunking and Performance section of the xarray documentation.
Processing Model
When xcube datasets are opened, only the cube’s structure and its metadata are loaded into memory. The actual data arrays of variables are loaded on-demand only, and only for chunks intersecting the desired sub-region.
Operations that generate new data variables from existing ones will be chunked in the same way. Therefore, such operation chains generate a processing graph providing a deferred, concurrent execution model.
Data Format
For the external, physical representation of xcube datasets we usually use the Zarr format. Zarr takes full advantage of data chunks and supports parallel processing of chunks that may originate from the local file system or from remote cloud storage such as S3 and GCS.
Python Packages
The xcube package builds heavily on Python’s big data ecosystem for handling huge N-dimensional data arrays and exploiting cloud-based storage and processing resources. In particular, xcube’s in-memory data model is provided by xarray, the memory management and processing model is provided through dask, and the external format is provided by zarr. xarray, dask, and zarr have increased their popularity for big data solutions over the last couple of years, for creating scalable and efficient EO data solutions.
Toolkit
On top of xarray, dask, zarr, and other popular Python data science packages, xcube provides various higher-level tools to generate, manipulate, and publish xcube datasets:
CLI - access, generate, modify, and analyse xcube datasets using the
xcubetool;Python API - access, generate, modify, and analyse xcube datasets via Python programs and notebooks;
Web API and Server - access, analyse, visualize xcube datasets via an xcube server;
Viewer App – publish and visualise xcube datasets using maps and time-series charts.
Workflows
The basic use case is to generate an xcube dataset and deploy it so that your users can access it:
generate an xcube dataset from some EO data sources using the xcube gen tool with a specific input processor.
optimize the generated xcube dataset with respect to specific use cases using the xcube chunk tool.
optimize the generated xcube dataset by consolidating metadata and elimination of empty chunks using xcube optimize and xcube prune tools.
deploy the optimized xcube dataset(s) to some location (e.g. on AWS S3) where users can access them.
Then you can:
access, analyse, modify, transform, visualise the data using the Python API and xarray API through Python programs or JupyterLab, or
extract data points by coordinates from a cube using the xcube extract tool, or
resample the cube in time to generate temporal aggregations using the xcube resample tool.
Another way to provide the data to users is via the xcube server, that provides a RESTful API and a WMTS. The latter is used to visualise spatial subsets of xcube datasets efficiently at any zoom level. To provide optimal visualisation and data extraction performance through the xcube server, xcube datasets may be prepared beforehand. Steps 8 to 10 are optional.
verify a dataset to be published conforms with the xcube Dataset Convention using the xcube verify tool.
adjust your dataset chunking to be optimal for generating spatial image tiles and generate a multi-resolution image pyramid using the xcube chunk and xcube level tools.
create a dataset variant optimal for time series-extraction again using the xcube chunk tool.
configure xcube datasets and publish them through the xcube server using the xcube serve tool.
You may then use a WMTS-compatible client to visualise the datasets or develop your own xcube server client that will make use of the xcube’s REST API.
The easiest way to visualise your data is using the xcube Viewer App, a single-page web application that can be configured to work with xcube server URLs.
Examples
When you follow the examples section you can build your first tiny xcube dataset and view it in the xcube-viewer by using the xcube server. The examples section is still growing and improving :)
Have fun exploring xcube!
Warning
This chapter is a work in progress and currently less than a draft.
Generating an xcube dataset
In the following example a tiny demo xcube dataset is generated.
Analysed Sea Surface Temperature over the Global Ocean
Input data for this example is located in the xcube repository. The input files contain analysed sea surface temperature and sea surface temperature anomaly over the global ocean and are provided by Copernicus Marine Environment Monitoring Service. The data is described in a dedicated Product User Manual.
Before starting the example, you need to activate the xcube environment:
$ conda activate xcube
If you want to take a look at the input data you can use cli/xcube dump to print out the metadata of a selected input file:
$ xcube dump examples/gen/data/20170605120000-UKMO-L4_GHRSST-SSTfnd-OSTIAanom-GLOB-v02.0-fv02.0.nc
<xarray.Dataset>
Dimensions: (lat: 720, lon: 1440, time: 1)
Coordinates:
* lat (lat) float32 -89.875 -89.625 -89.375 ... 89.375 89.625 89.875
* lon (lon) float32 0.125 0.375 0.625 ... 359.375 359.625 359.875
* time (time) object 2017-06-05 12:00:00
Data variables:
sst_anomaly (time, lat, lon) float32 ...
analysed_sst (time, lat, lon) float32 ...
Attributes:
Conventions: CF-1.4
title: Global SST & Sea Ice Anomaly, L4 OSTIA, 0.25 ...
summary: A merged, multi-sensor L4 Foundation SST anom...
references: Donlon, C.J., Martin, M., Stark, J.D., Robert...
institution: UKMO
history: Created from sst:temperature regridded with a...
comment: WARNING Some applications are unable to prope...
license: These data are available free of charge under...
id: UKMO-L4LRfnd_GLOB-OSTIAanom
naming_authority: org.ghrsst
product_version: 2.0
uuid: 5c1665b7-06e8-499d-a281-857dcbfd07e2
gds_version_id: 2.0
netcdf_version_id: 3.6
date_created: 20170606T061737Z
start_time: 20170605T000000Z
time_coverage_start: 20170605T000000Z
stop_time: 20170606T000000Z
time_coverage_end: 20170606T000000Z
file_quality_level: 3
source: UKMO-L4HRfnd-GLOB-OSTIA
platform: Aqua, Envisat, NOAA-18, NOAA-19, MetOpA, MSG1...
sensor: AATSR, AMSR, AVHRR, AVHRR_GAC, SEVIRI, TMI
metadata_conventions: Unidata Observation Dataset v1.0
metadata_link: http://data.nodc.noaa.gov/NESDIS_DataCenters/...
keywords: Oceans > Ocean Temperature > Sea Surface Temp...
keywords_vocabulary: NASA Global Change Master Directory (GCMD) Sc...
standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Con...
westernmost_longitude: 0.0
easternmost_longitude: 360.0
southernmost_latitude: -90.0
northernmost_latitude: 90.0
spatial_resolution: 0.25 degree
geospatial_lat_units: degrees_north
geospatial_lat_resolution: 0.25 degree
geospatial_lon_units: degrees_east
geospatial_lon_resolution: 0.25 degree
acknowledgment: Please acknowledge the use of these data with...
creator_name: Met Office as part of CMEMS
creator_email: servicedesk.cmems@mercator-ocean.eu
creator_url: http://marine.copernicus.eu/
project: Group for High Resolution Sea Surface Tempera...
publisher_name: GHRSST Project Office
publisher_url: http://www.ghrsst.org
publisher_email: ghrsst-po@nceo.ac.uk
processing_level: L4
cdm_data_type: grid
Below an example xcube dataset will be created, which will contain the variable analysed_sst. The metadata for a specific variable can be viewed by:
$ xcube dump examples/gen/data/20170605120000-UKMO-L4_GHRSST-SSTfnd-OSTIAanom-GLOB-v02.0-fv02.0.nc --var analysed_sst
<xarray.DataArray 'analysed_sst' (time: 1, lat: 720, lon: 1440)>
[1036800 values with dtype=float32]
Coordinates:
* lat (lat) float32 -89.875 -89.625 -89.375 ... 89.375 89.625 89.875
* lon (lon) float32 0.125 0.375 0.625 0.875 ... 359.375 359.625 359.875
* time (time) object 2017-06-05 12:00:00
Attributes:
long_name: analysed sea surface temperature
standard_name: sea_surface_foundation_temperature
type: foundation
units: kelvin
valid_min: -300
valid_max: 4500
source: UKMO-L4HRfnd-GLOB-OSTIA
comment:
For creating a toy xcube dataset you can execute the command-line below. Please adjust the paths to your needs:
$ xcube gen -o "your/output/path/demo_SST_xcube.zarr" -c examples/gen/config_files/xcube_sst_demo_config.yml --sort examples/gen/data/*.nc
The configuration file specifies the input processor, which in this case is the default one.
The output size is 10240, 5632. The bounding box of the data cube is given by output_region in the configuration file.
The output format (output_writer_name) is defined as well.
The chunking of the dimensions can be set by the chunksizes attribute of the output_writer_params parameter,
and in the example configuration file the chunking is set for latitude and longitude. If the chunking is not set, a automatic chunking is applied.
The spatial resampling method (output_resampling) is set to ‘nearest’ and the configuration file contains only one
variable which will be included into the xcube dataset - ‘analysed-sst’.
The Analysed Sea Surface Temperature data set contains the variable already as needed. This means no pixel masking needs to be applied. However, this might differ depending on the input data. You can take a look at a configuration file which takes Sentinel-3 Ocean and Land Colour Instrument (OLCI) as input files, which is a bit more complex. The advantage of using pixel expressions is, that the generated cube contains only valid pixels and the user of the data cube does not have to worry about something like land-masking or invalid values. Furthermore, the generated data cube is spatially regular. This means the data are aligned on a common spatial grid and cover the same region. The time stamps are kept from the input data set.
Caution: If you have input data that has file names not only varying with the time stamp but with e.g. A and B as well,
you need to pass the input files in the desired order via a text file. Each line of the text file should contain the
path to one input file. If you pass the input files in a desired order, then do not use the parameter --sort within
the commandline interface.
Optimizing and pruning a xcube dataset
If you want to optimize your generated xcube dataset e.g. for publishing it in a xcube viewer via xcube serve you can use cli/xcube optimize:
$ xcube optimize demo_SST_xcube.zarr -C
By executing the command above, an optimized xcube dataset called demo_SST_xcube-optimized.zarr will be created.
You can take a look into the directory of the original xcube dataset and the optimized one, and you will notice that
a file called .zmetadata. .zmetadata contains the information stored in .zattrs and .zarray of each variable of the
xcube dataset and makes requests of metadata faster. The option -C optimizes coordinate variables by converting any
chunked arrays into single, non-chunked, contiguous arrays.
For deleting empty chunks cli/xcube prune can be used. It deletes all data files associated with empty (NaN-only) chunks of an xcube dataset, and is restricted to the ZARR format.
$ xcube prune demo_SST_xcube-optimized.zarr
The pruned xcube dataset is saved in place and does not need an output path. The size of the xcube dataset was 6,8 MB before pruning it and 6,5 MB afterwards. According to the output printed to the terminal, 30 block files were deleted.
The metadata of the xcube dataset can be viewed with cli/xcube dump as well:
$ xcube dump demo_SST_xcube-optimized.zarr
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 5632, lon: 10240, time: 3)
Coordinates:
* lat (lat) float64 62.67 62.66 62.66 62.66 ... 48.01 48.0 48.0
lat_bnds (lat, bnds) float64 dask.array<shape=(5632, 2), chunksize=(5632, 2)>
* lon (lon) float64 -16.0 -16.0 -15.99 -15.99 ... 10.66 10.66 10.67
lon_bnds (lon, bnds) float64 dask.array<shape=(10240, 2), chunksize=(10240, 2)>
* time (time) datetime64[ns] 2017-06-05T12:00:00 ... 2017-06-07T12:00:00
time_bnds (time, bnds) datetime64[ns] dask.array<shape=(3, 2), chunksize=(3, 2)>
Dimensions without coordinates: bnds
Data variables:
analysed_sst (time, lat, lon) float64 dask.array<shape=(3, 5632, 10240), chunksize=(1, 704, 640)>
Attributes:
acknowledgment: Data Cube produced based on data provided by ...
comment:
contributor_name:
contributor_role:
creator_email: info@brockmann-consult.de
creator_name: Brockmann Consult GmbH
creator_url: https://www.brockmann-consult.de
date_modified: 2019-09-25T08:50:32.169031
geospatial_lat_max: 62.666666666666664
geospatial_lat_min: 48.0
geospatial_lat_resolution: 0.002604166666666666
geospatial_lat_units: degrees_north
geospatial_lon_max: 10.666666666666664
geospatial_lon_min: -16.0
geospatial_lon_resolution: 0.0026041666666666665
geospatial_lon_units: degrees_east
history: xcube/reproj-snap-nc
id: demo-bc-sst-sns-l2c-v1
institution: Brockmann Consult GmbH
keywords:
license: terms and conditions of the DCS4COP data dist...
naming_authority: bc
processing_level: L2C
project: xcube
publisher_email: info@brockmann-consult.de
publisher_name: Brockmann Consult GmbH
publisher_url: https://www.brockmann-consult.de
references: https://dcs4cop.eu/
source: CMEMS Global SST & Sea Ice Anomaly Data Cube
standard_name_vocabulary:
summary:
time_coverage_end: 2017-06-08T00:00:00.000000000
time_coverage_start: 2017-06-05T00:00:00.000000000
title: CMEMS Global SST Anomaly Data Cube
The metadata for the variable analysed_sst can be viewed:
$ xcube dump demo_SST_xcube-optimized.zarr --var analysed_sst
<xarray.DataArray 'analysed_sst' (time: 3, lat: 5632, lon: 10240)>
dask.array<shape=(3, 5632, 10240), dtype=float64, chunksize=(1, 704, 640)>
Coordinates:
* lat (lat) float64 62.67 62.66 62.66 62.66 ... 48.01 48.01 48.0 48.0
* lon (lon) float64 -16.0 -16.0 -15.99 -15.99 ... 10.66 10.66 10.66 10.67
* time (time) datetime64[ns] 2017-06-05T12:00:00 ... 2017-06-07T12:00:00
Attributes:
comment:
long_name: analysed sea surface temperature
source: UKMO-L4HRfnd-GLOB-OSTIA
spatial_resampling: Nearest
standard_name: sea_surface_foundation_temperature
type: foundation
units: kelvin
valid_max: 4500
valid_min: -300
Warning
This chapter is a work in progress and currently less than a draft.
Publishing xcube datasets
This example demonstrates how to run an xcube server to publish existing xcube datasets.
Running the server
To run the server on default port 8080 using the demo configuration:
$ xcube serve --verbose -c examples/serve/demo/config.yml
To run the server using a particular xcube dataset path and styling information for a variable:
$ xcube serve --styles conc_chl=(0,20,"viridis") examples/serve/demo/cube-1-250-250.zarr
Test it
After starting the server, you can check the various functions provided by xcube Web API. To explore the functions, open
<base-url>/openapi.html.
xcube Viewer
xcube datasets published through xcube serve can be visualised using the xcube-viewer web application.
To do so, run xcube serve with the --open-viewer flag.
In order make this option usable, xcube-viewer must be installed and build:
Download and install yarn.
Download and build xcube-viewer:
$ git clone https://github.com/dcs4cop/xcube-viewer.git
$ cd xcube-viewer
$ yarn install
$ yarn build
Configure
xcube serveso it finds the xcube-viewer On Linux (please adjust path):
$ export XCUBE_VIEWER_PATH=/abs/path/to/xcube-viewer/build
On Windows (please adjust path):
> SET XCUBE_VIEWER_PATH=/abs/path/to/xcube-viewer/build
Then run
xcube serve --open-viewer:
$ xcube serve --open-viewer --styles conc_chl=(0,20,"viridis") examples/serve/demo/cube-1-250-250.zarr
Viewing the generated xcube dataset described in the example Generating an xcube dataset:
$ xcube serve --open-viewer --styles "analysed_sst=(280,290,'plasma')" demo_SST_xcube-optimized.zarr
In case you get an error message “cannot reach server” on the very bottom of the web app’s main window, refresh the page.
You can play around with the value range displayed in the viewer, here it is set to min=280K and max=290K. The colormap used for mapping can be modified as well and the colormaps provided by matplotlib can be used.
Other clients
There are example HTML pages for some tile server clients. They need to be run in
a web server. If you don’t have one, you can use Node’s httpserver:
$ npm install -g httpserver
After starting both the xcube server and web server, e.g. on port 9090:
$ httpserver -d -p 9090
you can run the client demos by following their links given below.
OpenLayers
Cesium
To run the Cesium Demo first
download Cesium and unpack the zip
into the xcube serve source directory so that there exists an
./Cesium-x.y.z sub-directory. You may have to adapt the Cesium version number
in the demo’s HTML file.
Installation
Prerequisites
xcube releases are packaged as conda packages in the conda-forge channel. It is recommended to install xcube into a conda environment using the mamba package manager, which will also automatically install and manage xcube’s dependencies. You can find installation instructions for mamba itself here, if you don’t already have it installed.
In addition to mamba, there are alternative package managers available for conda environments:
The original
condatool. When considering this tool, please note that package installation and management with conda may be significantly slower than with mamba.The
micromambatool, a minimalistic, self-contained version of mamba.
Overview of installation methods
There are two main ways to install the xcube package:
Install an official release from a conda-forge package, using the mamba package manager. This method is recommended for most users.
Use mamba to install only xcube’s dependencies, but not xcube itself. Then clone the xcube git repository and install directly from your local repository. You should use this method if you intend to participate in the development of xcube, or if you need to use features that are so new that they are not yet available in an officially release conda-forge package.
These methods are described in more detail in the following sections.
Installation from the conda-forge package
To install the latest release of xcube into a new conda environment called
xcube, run the following command.
mamba create --name xcube --channel conda-forge xcube
You can give the environment a different name by providing a different argument
to the --name option.
To install xcube into an existing, currently activated conda environment, use the following command.
mamba install --channel conda-forge xcube
Installation from the source code repository
First, clone the repository and create a conda environment from it:
git clone https://github.com/dcs4cop/xcube.git
cd xcube
mamba env create
From this point on, all instructions assume that your current directory is the root of the xcube repository.
The mamba env create command above creates an environment according to
the specifications in the environment.yml file in the repository, which
by default takes the name xcube. Then, to activate the environment and
install xcube from the repository:
mamba activate xcube
pip install --no-deps --editable .
The second command installs xcube in ‘editable mode’, meaning that it will
be run directly from the repository, and changes to the code in the repository
will take immediate effect without reinstallation. (As an alternative to
pip, the command python setup.py develop can be used, but this is
no longer recommended.
Among other things, pip has the advantage of allowing easy deinstallation of
installed packages.)
To update the install to the latest repository version and update the
environment to reflect to any changes in environment.yml:
mamba activate xcube
git pull --force
mamba env update -n xcube --file environment.yml --prune
To install pytest and run the unit test suite:
mamba install pytest
pytest
To analyse test coverage (after installing pytest as above):
pytest --cov=xcube
To produce an HTML coverage report:
pytest --cov-report html --cov=xcube
Docker
To start a demo using docker use the following commands
docker build -t [your name] .
docker run [your name]
docker run -d -p [host port]:8080 [your name]
Example 1:
docker build -t xcube:0.10.0 .
docker run xcube:0.10.0
This will create the docker container and list the functionality of the
xcube cli.
Example 2:
docker build -t xcube:0.10.0 .
docker run -d -p 8001:8080 xcube:0.10.0 "xcube serve -v --address 0.0.0.0 --port 8080 -c /home/xcube/examples/serve/demo/config.yml"
docker ps
This will start a service in the background which can be accessed through port 8001, as the startup of a service is configured as default behaviour.
CLI
The xcube command-line interface (CLI) is a single executable xcube with several sub-commands comprising functions ranging from xcube dataset generation, over analysis and manipulation, to dataset publication.
Common Arguments and Options
Most of the commands operate on inputs that are xcube datasets. Such inputs are consistently named
CUBE and provided as one or more command arguments. CUBE inputs may be a path into the
local file system or a path into some object storage bucket, e.g. in AWS S3.
Command inputs of other types are consistently called INPUT.
Many commands also output something, i.e. are writing files. The paths or names of such outputs are
consistently provided by the -o OUTPUT or --output OUTPUT option. As the output is an option,
there is usually a default value for it. If multiply file formats are supported, commands usually
provide a -f FORMAT or --format FORMAT option. If omitted, the format may be guessed from the
output’s name.
Cube generation
xcube gen
Synopsis
Generate xcube dataset.
$ xcube gen --help
Usage: xcube gen [OPTIONS] [INPUT]...
Generate xcube dataset. Data cubes may be created in one go or successively
for all given inputs. Each input is expected to provide a single time slice
which may be appended, inserted or which may replace an existing time slice
in the output dataset. The input paths may be one or more input files or a
pattern that may contain wildcards '?', '*', and '**'. The input paths can
also be passed as lines of a text file. To do so, provide exactly one input
file with ".txt" extension which contains the actual input paths to be used.
Options:
-P, --proc INPUT-PROCESSOR Input processor name. The available input
processor names and additional information
about input processors can be accessed by
calling xcube gen --info . Defaults to
"default", an input processor that can deal
with simple datasets whose variables have
dimensions ("lat", "lon") and conform with
the CF conventions.
-c, --config CONFIG xcube dataset configuration file in YAML
format. More than one config input file is
allowed.When passing several config files,
they are merged considering the order passed
via command line.
-o, --output OUTPUT Output path. Defaults to 'out.zarr'
-f, --format FORMAT Output format. Information about output
formats can be accessed by calling xcube gen
--info. If omitted, the format will be
guessed from the given output path.
-S, --size SIZE Output size in pixels using format
"<width>,<height>".
-R, --region REGION Output region using format "<lon-min>,<lat-
min>,<lon-max>,<lat-max>"
--variables, --vars VARIABLES Variables to be included in output. Comma-
separated list of names which may contain
wildcard characters "*" and "?".
--resampling [Average|Bilinear|Cubic|CubicSpline|Lanczos|Max|Median|Min|Mode|Nearest|Q1|Q3]
Fallback spatial resampling algorithm to be
used for all variables. Defaults to
'Nearest'. The choices for the resampling
algorithm are: ['Average', 'Bilinear',
'Cubic', 'CubicSpline', 'Lanczos', 'Max',
'Median', 'Min', 'Mode', 'Nearest', 'Q1',
'Q3']
-a, --append Deprecated. The command will now always
create, insert, replace, or append input
slices.
--prof Collect profiling information and dump
results after processing.
--no_sort The input file list will not be sorted
before creating the xcube dataset. If
--no_sort parameter is passed, the order of
the input list will be kept. This parameter
should be used for better performance,
provided that the input file list is in
correct order (continuous time).
-I, --info Displays additional information about format
options or about input processors.
--dry_run Just read and process inputs, but don't
produce any outputs.
--help Show this message and exit.
Below is the ouput of a xcube gen --info call showing five input processors installed via plugins.
$ xcube gen --info
input processors to be used with option --proc:
default Single-scene NetCDF/CF inputs in xcube standard format
rbins-seviri-highroc-scene-l2 RBINS SEVIRI HIGHROC single-scene Level-2 NetCDF inputs
rbins-seviri-highroc-daily-l2 RBINS SEVIRI HIGHROC daily Level-2 NetCDF inputs
snap-olci-highroc-l2 SNAP Sentinel-3 OLCI HIGHROC Level-2 NetCDF inputs
snap-olci-cyanoalert-l2 SNAP Sentinel-3 OLCI CyanoAlert Level-2 NetCDF inputs
vito-s2plus-l2 VITO Sentinel-2 Plus Level 2 NetCDF inputs
For more input processors use existing "xcube-gen-..." plugins from the github organisation DCS4COP or write own plugin.
Output formats to be used with option --format:
zarr (*.zarr) Zarr file format (http://zarr.readthedocs.io)
netcdf4 (*.nc) NetCDF-4 file format
csv (*.csv) CSV file format
mem (*.mem) In-memory dataset I/O
Configuration File
Configuration files passed to xcube gen via the -c, --config option use YAML format.
Multiple configuration files may be given. In this case all configurations are merged into a single one.
Parameter values will be overwritten by subsequent configurations if they are scalars. If
they are objects / mappings, their values will be deeply merged.
The following parameters can be used in the configuration files:
input_processorstrThe name of an input processor. See
-P, --procoption above.- Default:
The default value is
'default', xcube’s default input processor. It can ingest and process inputs thatuse an
EPSG:4326(or compatible) grid;have 1-D
lonandlatcoordinate variables using WGS84 coordinates and decimal degrees;have a decodable 1-D
timecoordinate or define the one of the following global attribute pairstime_coverage_startandtime_coverage_end,time_startandtime_endortime_stop;provide data variables with the dimensions
time,lat,lon, in this order.conform to the `CF Conventions`_.
output_size[int, int]The spatial dimension sizes of the output dataset given as number of grid cells in longitude and latitude direction (width and height).
output_region[float, float, float, float]The spatial extent of output datasets given as a bounding box [lat-min, lat-min, lon-max, lat-max] using decimal degrees.
output_variables[variable-definitions]The definition of variables that will be included in the output dataset. Each variable definition may be just a name or a mapping from a name to variable attributes. If it is just a name it must be the name of an existing variable either in the INPUT or in
processed_variables. If the variable definition is a mapping, some of the attributes affect the way how variables are processed. All but thenameattributes become variable metadata in the output.namestrThe new name of the variable in the output.
valid_pixel_expressionstrAn expression used to mask this variable, see Expressions. The expression identifies all valid pixels in each INPUT.
resamplingstrThe resampling method used. See
--resamplingoption above.
- Default:
By default, all variables in INPUT will occur in output.
processed_variables[variable-definitions]The definition of variables that will be produced or processed after reading each INPUT. The main purpose is to generate intermediate variables that can be referred to in the
expressionin other variable definitions inprocessed_variablesandvalid_pixel_expressionin variable definitions inoutput_variables. The following attributes are recognised:expressionstrAn expression used to produce this variable, see Expressions.
output_writer_namestrThe name of a supported output format. May be one of
'zarr','netcdf4','mem'.- Default:
'zarr'
output_writer_paramsstrA mapping that defines parameters that are passed to output writer denoted by
output_writer_name. Through theoutput_writer_paramsa packing of the variables may be defined. If not specified the default does not apply any packing which results in:_FillValue: nan dtype: dtype('float32')
and for coordinate variables
dtype: dtype('int64')
The user may specify a different packing variables, which might be useful for reducing the storage size of the datacubes. Currently it is only implemented for zarr format. This may be done by passing the parameters for packing as the following:
output_writer_params: packing: analysed_sst: scale_factor: 0.07324442274239326 add_offset: -300.0 dtype: 'uint16' _FillValue: 0.65535
Furthermore the compressor may be defined as well by, if not specified the default compressor (cname=’lz4’, clevel=5, shuffle=SHUFFLE, blocksize=0) is used.
output_writer_params: compressor: cname: 'zstd' clevel: 1 shuffle: 2
output_metadata[attribute-definitions]General metadata that will be present in the output dataset as global attributes. You can put any common CF attributes here.
Any attributes that are mappings will be “flattened” by concatenating the attribute names using the underscrore character. For example,:
publisher: name: "Brockmann Consult GmbH" url: "https://www.brockmann-consult.de"
will create the two entries:
publisher_name: "Brockmann Consult GmbH" publisher_url: "https://www.brockmann-consult.de"
Expressions
Expressions are plain text values of the expression and valid_pixel_expression attributes of the
variable definitions in the processed_variables and output_variables parameters.
The expression syntax is that of standard Python.
xcube gen uses expressions to produce new variables listed in processed_variables and to mask
variables by the valid_pixel_expression.
An expression may refer any variables in the INPUT datasets and any variables defined by the processed_variables
parameter. Expressions may make use of most of the standard Python operators
and may apply all numpy ufuncs to referred variables. Also most of the xarray.DataArray API
may be used on variables within an expression.
In order to utilise flagged variables, the syntax variable_name.flag_name can be used in expressions.
According to the CF Conventions,
flagged variables are variables whose metadata include the attributes flag_meanings and flag_values
and/or flag_masks. The flag_meanings attribute enumerates the allowed values for flag_name.
The flag attributes must be present in the variables of each INPUT.
Example
An example that uses a configuration file only:
$ xcube gen --config ./config.yml /data/eo-data/SST/2018/**/*.nc
An example that uses the default input processor and passes all other configuration via command-line options:
$ xcube gen -S 2000,1000 -R 0,50,5,52.5 --vars conc_chl,conc_tsm,kd489,c2rcc_flags,quality_flags -o hiroc-cube.zarr /data/eo-data/SST/2018/**/*.nc
Some input processors have been developed for specific EO data sources used within the DCS4COP project. They may serve as examples how to develop input processor plug-ins:
Python API
The related Python API function is xcube.core.gen.gen.gen_cube().
xcube grid
Attention
This tool will likely change in the near future.
Synopsis
Find spatial xcube dataset resolutions and adjust bounding boxes.
$ xcube grid --help
Usage: xcube grid [OPTIONS] COMMAND [ARGS]...
Find spatial xcube dataset resolutions and adjust bounding boxes.
We find suitable resolutions with respect to a possibly regional fixed
Earth grid and adjust regional spatial bounding boxes to that grid. We
also try to select the resolutions such that they are taken from a certain
level of a multi-resolution pyramid whose level resolutions increase by a
factor of two.
The graticule at a given resolution level L within the grid is given by
RES(L) = COVERAGE * HEIGHT(L)
HEIGHT(L) = HEIGHT_0 * 2 ^ L
LON(L, I) = LON_MIN + I * HEIGHT_0 * RES(L)
LAT(L, J) = LAT_MIN + J * HEIGHT_0 * RES(L)
With
RES: Grid resolution in degrees.
HEIGHT: Number of vertical grid cells for given level
HEIGHT_0: Number of vertical grid cells at lowest resolution level.
Let WIDTH and HEIGHT be the number of horizontal and vertical grid cells
of a global grid at a certain LEVEL with WIDTH * RES = 360 and HEIGHT *
RES = 180, then we also force HEIGHT = TILE * 2 ^ LEVEL.
Options:
--help Show this message and exit.
Commands:
abox Adjust a bounding box to a fixed Earth grid.
levels List levels for a resolution or a tile size.
res List resolutions close to a target resolution.
Example: Find suitable target resolution for a ~300m (Sentinel 3 OLCI FR resolution) fixed Earth grid within a deviation of 5%.
$ xcube grid res 300m -D 5%
TILE LEVEL HEIGHT INV_RES RES (deg) RES (m), DELTA_RES (%)
540 7 69120 384 0.0026041666666666665 289.9 -3.4
4140 4 66240 368 0.002717391304347826 302.5 0.8
8100 3 64800 360 0.002777777777777778 309.2 3.1
...
289.9m is close enough and provides 7 resolution levels, which is good. Its inverse resolution is 384, which is the fixed Earth grid identifier.
We want to see if the resolution pyramid also supports a resolution close to 10m (Sentinel 2 MSI resolution).
$ xcube grid levels 384 -m 6
LEVEL HEIGHT INV_RES RES (deg) RES (m)
0 540 3 0.3333333333333333 37106.5
1 1080 6 0.16666666666666666 18553.2
2 2160 12 0.08333333333333333 9276.6
...
11 1105920 6144 0.00016276041666666666 18.1
12 2211840 12288 8.138020833333333e-05 9.1
13 4423680 24576 4.0690104166666664e-05 4.5
This indicates we have a resolution of 9.1m at level 12.
Lets assume we have xcube dataset region with longitude from 0 to 5 degrees and latitudes from 50 to 52.5 degrees. What is the adjusted bounding box on a fixed Earth grid with the inverse resolution 384?
$ xcube grid abox 0,50,5,52.5 384
Orig. box coord. = 0.0,50.0,5.0,52.5
Adj. box coord. = 0.0,49.21875,5.625,53.4375
Orig. box WKT = POLYGON ((0.0 50.0, 5.0 50.0, 5.0 52.5, 0.0 52.5, 0.0 50.0))
Adj. box WKT = POLYGON ((0.0 49.21875, 5.625 49.21875, 5.625 53.4375, 0.0 53.4375, 0.0 49.21875))
Grid size = 2160 x 1620 cells
with
TILE = 540
LEVEL = 7
INV_RES = 384
RES (deg) = 0.0026041666666666665
RES (m) = 289.89450727414993
Note, to check bounding box WKTs, you can use the handy Wicket tool.
Cube computation
xcube compute
Synopsis
Compute a cube variable from other cube variables using a user-provided Python function.
$ xcube compute --help
Usage: xcube compute [OPTIONS] SCRIPT [CUBE]...
Compute a cube from one or more other cubes.
The command computes a cube variable from other cube variables in CUBEs
using a user-provided Python function in SCRIPT.
The SCRIPT must define a function named "compute":
def compute(*input_vars: numpy.ndarray,
input_params: Mapping[str, Any] = None,
dim_coords: Mapping[str, np.ndarray] = None,
dim_ranges: Mapping[str, Tuple[int, int]] = None) \
-> numpy.ndarray:
# Compute new numpy array from inputs
# output_array = ...
return output_array
where input_vars are numpy arrays (chunks) in the order given by VARIABLES
or given by the variable names returned by an optional "initialize" function
that my be defined in SCRIPT too, see below. input_params is a mapping of
parameter names to values according to PARAMS or the ones returned by the
aforesaid "initialize" function. dim_coords is a mapping from dimension name
to coordinate labels for the current chunk to be computed. dim_ranges is a
mapping from dimension name to index ranges into coordinate arrays of the
cube.
The SCRIPT may define a function named "initialize":
def initialize(input_cubes: Sequence[xr.Dataset],
input_var_names: Sequence[str],
input_params: Mapping[str, Any]) \
-> Tuple[Sequence[str], Mapping[str, Any]]:
# Compute new variable names and/or new parameters
# new_input_var_names = ...
# new_input_params = ...
return new_input_var_names, new_input_params
where input_cubes are the respective CUBEs, input_var_names the respective
VARIABLES, and input_params are the respective PARAMS. The "initialize"
function can be used to validate the data cubes, extract the desired
variables in desired order and to provide some extra processing parameters
passed to the "compute" function.
Note that if no input variable names are specified, no variables are passed
to the "compute" function.
The SCRIPT may also define a function named "finalize":
def finalize(output_cube: xr.Dataset,
input_params: Mapping[str, Any]) \
-> Optional[xr.Dataset]:
# Optionally modify output_cube and return it or return None
return output_cube
If defined, the "finalize" function will be called before the command writes
the new cube and then exists. The functions may perform a cleaning up or
perform side effects such as write the cube to some sink. If the functions
returns None, the CLI will *not* write any cube data.
Options:
--variables, --vars VARIABLES Comma-separated list of variable names.
-p, --params PARAMS Parameters passed as 'input_params' dict to
compute() and init() functions in SCRIPT.
-o, --output OUTPUT Output path. Defaults to 'out.zarr'
-f, --format FORMAT Output format.
-N, --name NAME Output variable's name.
-D, --dtype DTYPE Output variable's data type.
--help Show this message and exit.
Example
$ xcube compute s3-olci-cube.zarr ./algoithms/s3-olci-ndvi.py
with ./algoithms/s3-olci-ndvi.py being:
# TODO
Python API
The related Python API function is xcube.core.compute.compute_cube().
Cube inspection
xcube dump
Synopsis
Dump contents of a dataset.
$ xcube dump --help
Usage: xcube dump [OPTIONS] INPUT
Dump contents of an input dataset.
Options:
--variable, --var VARIABLE Name of a variable (multiple allowed).
-E, --encoding Dump also variable encoding information.
--help Show this message and exit.
Example
$ xcube dump xcube_cube.zarr
xcube verify
Synopsis
Perform cube verification.
$ xcube verify --help
Usage: xcube verify [OPTIONS] CUBE
Perform cube verification.
The tool verifies that CUBE
* defines the dimensions "time", "lat", "lon";
* has corresponding "time", "lat", "lon" coordinate variables and that they
are valid, e.g. 1-D, non-empty, using correct units;
* has valid bounds variables for "time", "lat", "lon" coordinate
variables, if any;
* has any data variables and that they are valid, e.g. min. 3-D, all have
same dimensions, have at least dimensions "time", "lat", "lon".
* spatial coordinates and their corresponding bounds (if exist) are equidistant
and monotonically increasing or decreasing.
If INPUT is a valid xcube dataset, the tool returns exit code 0. Otherwise a
violation report is written to stdout and the tool returns exit code 3.
Options:
--help Show this message and exit.
Python API
The related Python API functions are
Cube data extraction
xcube extract
Synopsis
Extract cube points.
$ xcube extract --help
Usage: xcube extract [OPTIONS] CUBE POINTS
Extract cube points.
Extracts data cells from CUBE at coordinates given in each POINTS record and
writes the resulting values to given output path and format.
POINTS must be a CSV file that provides at least the columns "lon", "lat",
and "time". The "lon" and "lat" columns provide a point's location in
decimal degrees. The "time" column provides a point's date or date-time. Its
format should preferably be ISO, but other formats may work as well.
Options:
-o, --output OUTPUT Output path. If omitted, output is written to stdout.
-f, --format FORMAT Output format. Currently, only 'csv' is supported.
-C, --coords Include cube cell coordinates in output.
-B, --bounds Include cube cell coordinate boundaries (if any) in
output.
-I, --indexes Include cube cell indexes in output.
-R, --refs Include point values as reference in output.
--help Show this message and exit.
Example
$ xcube extract xcube_cube.zarr -o point_data.csv -Cb --indexes --refs
Python API
Related Python API functions are
Cube manipulation
xcube chunk
Synopsis
(Re-)chunk xcube dataset.
$ xcube chunk --help
Usage: xcube chunk [OPTIONS] CUBE
(Re-)chunk xcube dataset. Changes the external chunking of all variables of
CUBE according to CHUNKS and writes the result to OUTPUT.
Note: There is a possibly more efficient way to (re-)chunk datasets through
the dedicated tool "rechunker", see https://rechunker.readthedocs.io.
Options:
-o, --output OUTPUT Output path. Defaults to 'out.zarr'
-f, --format FORMAT Format of the output. If not given, guessed from
OUTPUT.
-p, --params PARAMS Parameters specific for the output format. Comma-
separated list of <key>=<value> pairs.
-C, --chunks CHUNKS Chunk sizes for each dimension. Comma-separated list of
<dim>=<size> pairs, e.g. "time=1,lat=270,lon=270"
-q, --quiet Disable output of log messages to the console entirely.
Note, this will also suppress error and warning
messages.
-v, --verbose Enable output of log messages to the console. Has no
effect if --quiet/-q is used. May be given multiple
times to control the level of log messages, i.e., -v
refers to level INFO, -vv to DETAIL, -vvv to DEBUG,
-vvvv to TRACE. If omitted, the log level of the
console is WARNING.
--help Show this message and exit.
Example
$ xcube chunk input_not_chunked.zarr -o output_rechunked.zarr --chunks "time=1,lat=270,lon=270"
Python API
The related Python API function is xcube.core.chunk.chunk_dataset().
xcube level
Synopsis
Generate multi-resolution levels.
$ xcube level --help
Usage: xcube level [OPTIONS] INPUT
Generate multi-resolution levels.
Transform the given dataset by INPUT into the levels of a multi-level
pyramid with spatial resolution decreasing by a factor of two in both
spatial dimensions and write the result to directory OUTPUT.
INPUT may be an S3 object storage URL of the form "s3://<bucket>/<path>" or
"https://<endpoint>".
Options:
-o, --output OUTPUT Output path. If omitted, "INPUT.levels" will
be used. You can also use S3 object storage
URLs of the form "s3://<bucket>/<path>" or
"https://<endpoint>"
-L, --link Link the INPUT instead of converting it to a
level zero dataset. Use with care, as the
INPUT's internal spatial chunk sizes may be
inappropriate for imaging purposes.
-t, --tile-size TILE_SIZE Tile size, given as single integer number or
as <tile-width>,<tile-height>. If omitted,
the tile size will be derived from the
INPUT's internal spatial chunk sizes. If the
INPUT is not chunked, tile size will be 512.
-n, --num-levels-max NUM_LEVELS_MAX
Maximum number of levels to generate. If not
given, the number of levels will be derived
from spatial dimension and tile sizes.
-A, --agg-methods AGG_METHODS Aggregation method(s) to be used for data
variables. Either one of "first", "min",
"max", "mean", "median", "auto" or list of
assignments to individual variables using
the notation
"<var1>=<method1>,<var2>=<method2>,..."
Defaults to "first".
-r, --replace Whether to replace an existing dataset at
OUTPUT.
-a, --anon For S3 inputs or outputs, whether the access
is anonymous. By default, credentials are
required.
-q, --quiet Disable output of log messages to the
console entirely. Note, this will also
suppress error and warning messages.
-v, --verbose Enable output of log messages to the
console. Has no effect if --quiet/-q is
used. May be given multiple times to control
the level of log messages, i.e., -v refers
to level INFO, -vv to DETAIL, -vvv to DEBUG,
-vvvv to TRACE. If omitted, the log level of
the console is WARNING.
--help Show this message and exit.
Example
$ xcube level --link -t 720 data/cubes/test-cube.zarr
Python API
The related Python API functions are
xcube.core.level.compute_levels(),xcube.core.level.read_levels(), andxcube.core.level.write_levels().
xcube optimize
Synopsis
Optimize xcube dataset for faster access.
$ xcube optimize --help
Usage: xcube optimize [OPTIONS] CUBE
Optimize xcube dataset for faster access.
Reduces the number of metadata and coordinate data files in xcube dataset
given by CUBE. Consolidated cubes open much faster especially from remote
locations, e.g. in object storage, because obviously much less HTTP requests
are required to fetch initial cube meta information. That is, it merges all
metadata files into a single top-level JSON file ".zmetadata". Optionally,
it removes any chunking of coordinate variables so they comprise a single
binary data file instead of one file per data chunk. The primary usage of
this command is to optimize data cubes for cloud object storage. The command
currently works only for data cubes using ZARR format.
Options:
-o, --output OUTPUT Output path. The placeholder "<built-in function
input>" will be replaced by the input's filename
without extension (such as ".zarr"). Defaults to
"{input}-optimized.zarr".
-I, --in-place Optimize cube in place. Ignores output path.
-C, --coords Also optimize coordinate variables by converting any
chunked arrays into single, non-chunked, contiguous
arrays.
--help Show this message and exit.
Examples
Write an cube with consolidated metadata to cube-optimized.zarr:
$ xcube optimize ./cube.zarr
Write an optimized cube with consolidated metadata and consolidated coordinate variables to optimized/cube.zarr
(directory optimized must exist):
$ xcube optimize -C -o ./optimized/cube.zarr ./cube.zarr
Optimize a cube in-place with consolidated metadata and consolidated coordinate variables:
$ xcube optimize -IC ./cube.zarr
Python API
The related Python API function is xcube.core.optimize.optimize_dataset().
xcube patch
Synopsis
Patch and consolidate the metadata of a xcube dataset.
$ xcube patch --help
Usage: xcube patch [OPTIONS] DATASET
Patch and consolidate the metadata of a dataset.
DATASET can be either a local filesystem path or a URL. It must point to
either a Zarr dataset (*.zarr) or a xcube multi-level dataset (*.levels).
Additional storage options for a given protocol may be passed by the OPTIONS
option.
In METADATA, the special attribute value "__delete__" can be used to remove
that attribute from dataset or array metadata.
Options:
--metadata METADATA The metadata to be patched. Must be a JSON or YAML file
using Zarr consolidated metadata format.
--options OPTIONS Protocol-specific storage options (see fsspec). Must be
a JSON or YAML file.
-q, --quiet Disable output of log messages to the console entirely.
Note, this will also suppress error and warning
messages.
-v, --verbose Enable output of log messages to the console. Has no
effect if --quiet/-q is used. May be given multiple
times to control the level of log messages, i.e., -v
refers to level INFO, -vv to DETAIL, -vvv to DEBUG,
-vvvv to TRACE. If omitted, the log level of the
console is WARNING.
-d, --dry-run Do not change any data, just report what would have
been changed.
--help Show this message and exit.
Patch file example
Patch files use the Zarr Consolidated Metadata Format, v1.
For example, the following patch file (YAML) will delete the
global attribute TileSize and change the value of the
attribute long_name of variable conc_chl:
zarr_consolidated_format: 1
metadata:
.zattrs:
TileSize: __delete__
conc_chl/.zattrs:
long_name: Chlorophyll concentration
Storage options file example
Here is a storage options file for the “s3” protocol that provides credentials for AWS S3 access:
key: AJDKJCLSKKA
secret: kjkl456lkj45632k45j63l
Usage example
$ xcube patch s3://my-cubes-bucket/test.zarr --metadata patch.yml -v
xcube prune
Delete empty chunks.
Attention
This tool will likely be integrated into xcube optimize in the near future.
$ xcube prune --help
Usage: xcube prune [OPTIONS] DATASET
Delete empty chunks. Deletes all data files associated with empty (NaN-only)
chunks in given DATASET, which must have Zarr format.
Options:
-q, --quiet Disable output of log messages to the console entirely. Note,
this will also suppress error and warning messages.
-v, --verbose Enable output of log messages to the console. Has no effect
if --quiet/-q is used. May be given multiple times to control
the level of log messages, i.e., -v refers to level INFO, -vv
to DETAIL, -vvv to DEBUG, -vvvv to TRACE. If omitted, the log
level of the console is WARNING.
--dry-run Just read and process input, but don't produce any output.
--help Show this message and exit.
A related Python API function is xcube.core.optimize.get_empty_dataset_chunks().
xcube resample
Synopsis
Resample data along the time dimension.
$ xcube resample --help
Usage: xcube resample [OPTIONS] CUBE
Resample data along the time dimension.
Options:
-c, --config CONFIG xcube dataset configuration file in YAML
format. More than one config input file is
allowed.When passing several config files,
they are merged considering the order passed
via command line.
-o, --output OUTPUT Output path. Defaults to 'out.zarr'.
-f, --format [zarr|netcdf4|mem]
Output format. If omitted, format will be
guessed from output path.
--variables, --vars VARIABLES Comma-separated list of names of variables
to be included.
-M, --method TEXT Temporal resampling method. Available
downsampling methods are 'count', 'first',
'last', 'min', 'max', 'sum', 'prod', 'mean',
'median', 'std', 'var', the upsampling
methods are 'asfreq', 'ffill', 'bfill',
'pad', 'nearest', 'interpolate'. If the
upsampling method is 'interpolate', the
option '--kind' will be used, if given.
Other upsampling methods that select
existing values honour the '--tolerance'
option. Defaults to 'mean'.
-F, --frequency TEXT Temporal aggregation frequency. Use format
"<count><offset>" where <offset> is one of
'H', 'D', 'W', 'M', 'Q', 'Y'. Use 'all' to
aggregate all time steps included in the
dataset.Defaults to '1D'.
-O, --offset TEXT Offset used to adjust the resampled time
labels. Uses same syntax as frequency. Some
Pandas date offset strings are supported as
well.
-B, --base INTEGER For frequencies that evenly subdivide 1 day,
the origin of the aggregated intervals. For
example, for '24H' frequency, base could
range from 0 through 23. Defaults to 0.
-K, --kind TEXT Interpolation kind which will be used if
upsampling method is 'interpolation'. May be
one of 'zero', 'slinear', 'quadratic',
'cubic', 'linear', 'nearest', 'previous',
'next' where 'zero', 'slinear', 'quadratic',
'cubic' refer to a spline interpolation of
zeroth, first, second or third order;
'previous' and 'next' simply return the
previous or next value of the point. For
more info refer to
scipy.interpolate.interp1d(). Defaults to
'linear'.
-T, --tolerance TEXT Tolerance for selective upsampling methods.
Uses same syntax as frequency. If the time
delta exceeds the tolerance, fill values
(NaN) will be used. Defaults to the given
frequency.
-q, --quiet Disable output of log messages to the
console entirely. Note, this will also
suppress error and warning messages.
-v, --verbose Enable output of log messages to the
console. Has no effect if --quiet/-q is
used. May be given multiple times to control
the level of log messages, i.e., -v refers
to level INFO, -vv to DETAIL, -vvv to DEBUG,
-vvvv to TRACE. If omitted, the log level of
the console is WARNING.
--dry-run Just read and process inputs, but don't
produce any outputs.
--help Show this message and exit.
Examples
Upsampling example:
$ xcube resample --vars conc_chl,conc_tsm -F 12H -T 6H -M interpolation -K linear examples/serve/demo/cube.nc
Downsampling example:
$ xcube resample --vars conc_chl,conc_tsm -F 3D -M mean -M std -M count examples/serve/demo/cube.nc
Python API
The related Python API function is xcube.core.resample.resample_in_time().
xcube vars2dim
Synopsis
Convert cube variables into new dimension.
$ xcube vars2dim --help
Usage: xcube vars2dim [OPTIONS] CUBE
Convert cube variables into new dimension. Moves all variables of CUBE
into a single new variable <var-name> with a new dimension DIM-NAME and
writes the results to OUTPUT.
Options:
--variable, --var VARIABLE Name of the new variable that includes all
variables. Defaults to "data".
-D, --dim_name DIM-NAME Name of the new dimension into variables.
Defaults to "var".
-o, --output OUTPUT Output path. If omitted, 'INPUT-vars2dim.FORMAT'
will be used.
-f, --format FORMAT Format of the output. If not given, guessed from
OUTPUT.
--help Show this message and exit.
Python API
The related Python API function is xcube.core.vars2dim.vars_to_dim().
Cube conversion
Cube publication
xcube serve
Synopsis
Serve data cubes via web service.
xcube serve starts a light-weight web server that provides various services based on xcube datasets:
Catalogue services to query for xcube datasets and their variables and dimensions, and feature collections;
Tile map service, with some OGC WMTS 1.0 compatibility (REST and KVP APIs);
Dataset services to extract subsets like time-series and profiles for e.g. JavaScript clients.
$ xcube serve --help
Usage: xcube serve [OPTIONS] [PATHS...]
Run the xcube Server for the given configuration and/or the given raster
dataset paths given by PATHS.
Each of the PATHS arguments can point to a raster dataset such as a Zarr
directory (*.zarr), an xcube multi-level Zarr dataset (*.levels), a NetCDF
file (*.nc), or a GeoTIFF/COG file (*.tiff).
If one of PATHS is a directory that is not a dataset itself, it is scanned
for readable raster datasets.
The --show ASSET option can be used to inspect the current configuration of
the server. ASSET is one of:
apis outputs the list of APIs provided by the server
endpoints outputs the list of all endpoints provided by the server
openapi outputs the OpenAPI document representing this server
config outputs the effective server configuration
configschema outputs the JSON Schema for the server configuration
The ASSET may be suffixed by ".yaml" or ".json" forcing the respective
output format. The default format is YAML.
Note, if --show is provided, the ASSET will be shown and the program will
exit immediately.
Options:
--framework FRAMEWORK Web server framework. Defaults to "tornado"
-p, --port PORT Service port number. Defaults to 8080
-a, --address ADDRESS Service address. Defaults to "0.0.0.0".
-c, --config CONFIG Configuration YAML or JSON file. If
multiple configuration files are passed, they will be merged in
order.
--base-dir BASE_DIR Directory used to resolve relative paths in
CONFIG files. Defaults to the parent
directory of (last) CONFIG file.
--prefix URL_PREFIX Prefix path to be used for all endpoint
URLs. May include template variables, e.g.,
"api/{version}".
--revprefix REVERSE_URL_PREFIX Prefix path to be used for reverse endpoint
URLs that may be reported by server
responses. May include template variables,
e.g., "/proxy/{port}". Defaults to value of
URL_PREFIX.
--traceperf Whether to output extra performance logs.
--update-after TIME Check for server configuration updates every
TIME seconds.
--stop-after TIME Unconditionally stop service after TIME
seconds.
--show ASSET Show ASSET and exit. Possible values for
ASSET are 'apis', 'endpoints', 'openapi',
'config', 'configschema' optionally suffixed
by '.yaml' or '.json'.
--open-viewer After starting the server, open xcube Viewer
in a browser tab.
-q, --quiet Disable output of log messages to the
console entirely. Note, this will also
suppress error and warning messages.
-v, --verbose Enable output of log messages to the
console. Has no effect if --quiet/-q is
used. May be given multiple times to control
the level of log messages, i.e., -v refers
to level INFO, -vv to DETAIL, -vvv to DEBUG,
-vvvv to TRACE. If omitted, the log level of
the console is WARNING.
--help Show this message and exit.
Configuration File
The xcube server is used to configure the xcube datasets to be published.
xcube datasets are any datasets that
that comply to Unidata’s CDM and to the CF Conventions;
that can be opened with the xarray Python library;
that have variables that have the dimensions and shape (
lat,lon) or (time,lat,lon);that have 1D-coordinate variables corresponding to the dimensions;
that have their spatial grid defined in arbitrary spatial coordinate reference systems.
The xcube server supports xcube datasets stored as local NetCDF files, as well as Zarr directories in the local file system or remote object storage. Remote Zarr datasets must be stored AWS S3 compatible object storage.
As an example, here is the configuration of the demo server. The parts of the demo configuration file are explained in detail further down.
Some hints before, which are not addressed in the server demo configuration file.
To increase imaging performance, xcube datasets can be converted to multi-resolution pyramids using the
xcube level tool. In the configuration, the format must be set to 'levels'.
Leveled xcube datasets are configured this way:
Datasets:
- Identifier: my_multi_level_dataset
Title: My Multi-Level Dataset
FileSystem: file
Path: my_multi_level_dataset.levels
- ...
To increase time-series extraction performance, xcube datasets may be rechunked with larger chunk size in the time
dimension using the xcube chunk tool. In the xcube server configuration a hidden dataset is given,
and the it is referred to by the non-hidden, actual dataset using the TimeSeriesDataset setting:
Datasets:
- Identifier: my_dataset
Title: My Dataset
FileSystem: file
Path: my_dataset.zarr
TimeSeriesDataset: my_dataset_opt_for_ts
- Identifier: my_dataset_opt_for_ts
Title: My Dataset optimized for Time-Series
FileSystem: file
Path: my_ts_opt_dataset.zarr
Hidden: True
- ...
Server Demo Configuration File
The server configuration file consists of various parts, some of them are necessary others are optional. Here the demo configuration file used in the example is explained in detail.
The configuration file consists of five main parts authentication, dataset attribution, datasets, place groups and styles.
Authentication [optional]
In order to display data via xcube-viewer exclusively to registered and authorized users, the data served by xcube serve may be protected by adding Authentication to the server configuration. In order to ensure protection, an Authority and an Audience needs to be provided. Here authentication by Auth0 is used. Please note the trailing slash in the “Authority” URL.
Authentication:
Authority: https://xcube-dev.eu.auth0.com/
Audience: https://xcube-dev/api/
Example of OIDC configuration for Keycloak. Please note that there is no trailing slash in the “Authority” URL.
Authentication:
Authority: https://kc.brockmann-consult.de/auth/realms/AVL
Audience: avl-xc-api
Dataset Attribution [optional]
Dataset Attribution may be added to the server via DatasetAttribution.
DatasetAttribution:
- "© by Brockmann Consult GmbH 2020, contains modified Copernicus Data 2019, processed by ESA"
- "© by EU H2020 CyanoAlert project"
Base Directory [optional]
A typical xcube server configuration comprises many paths, and
relative paths of known configuration parameters are resolved against
the base_dir configuration parameter.
base_dir: s3://<bucket>/<path-to-your>/<resources>/
However, for values of
parameters passed to user functions that represent paths in user code,
this cannot be done automatically. For such situations, expressions
can be used. An expression is any string between "${"` and `"}" in a
configuration value. An expression can contain the variables
base_dir (a string), ctx the current server context
(type xcube.webapi.datasets.DatasetsContext), as well as the function
resolve_config_path(path) that is used to make a path absolut with
respect to base_dir and to normalize it. For example
Augmentation:
Path: augmentation/metadata.py
Function: metadata:update_metadata
InputParameters:
bands_config: ${resolve_config_path("../common/bands.yaml")}
Viewer Configuration [optional]
The xcube server endpoint /viewer/config/{*path} allows
for configuring the viewer accessible via endpoint /viewer.
The actual source for the configuration items is configured by xcube
server configuration using the new entry Viewer/Configuration/Path,
for example:
Viewer:
Configuration:
Path: s3://<bucket>/<viewer-config-dir-path>
Path [mandatory]
must be an absolute filesystem path or a S3 path as in the example above.
It points to a directory that is expected to contain the the viewer configuration file config.json
among other configuration resources, such as custom favicon.ico or logo.png.
The file config.json should conform to the
[configuration JSON Schema](https://github.com/dcs4cop/xcube-viewer/blob/master/src/resources/config.schema.json).
All its values are optional, if not provided,
[default values](https://github.com/dcs4cop/xcube-viewer/blob/master/src/resources/config.json)
are used instead.
Datasets [mandatory]
In order to publish selected xcube datasets via xcube serve,
the datasets need to be described in the server configuration.
Remotely stored xcube Datasets
The following configuration snippet demonstrates how to publish static (persistent) xcube datasets stored in S3-compatible object storage:
Datasets:
- Identifier: remote
Title: Remote OLCI L2C cube for region SNS
BoundingBox: [0.0, 50, 5.0, 52.5]
FileSystem: s3
Endpoint: "https://s3.eu-central-1.amazonaws.com"
Path: xcube-examples/OLCI-SNS-RAW-CUBE-2.zarr
Region: eu-central-1
Anonymous: true
Style: default
ChunkCacheSize: 250M
PlaceGroups:
- PlaceGroupRef: inside-cube
- PlaceGroupRef: outside-cube
AccessControl:
RequiredScopes:
- read:datasets
The above example of how to specify a xcube dataset to be served above is using a datacube stored in an S3 bucket within the Amazon Cloud. Please have a closer look at the parameter Anonymous: true. This means, the datasets permissions are set to public read in your source s3 bucket. If you have a dataset that is not public-read, set Anonymous: false. Furthermore, you need to have valid credentials on the machine where the server runs. Credentials may be saved either in a file called .aws/credentials with content like below:
[default]aws_access_key_id=AKIAIOSFODNN7EXAMPLEaws_secret_access_key=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Or they may be exported as environment variables AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID.
Further down an example for a locally stored xcube datasets will be given, as well as an example of dynamic xcube datasets.
Identifier [mandatory] is a unique ID for each xcube dataset, it is ment for machine-to-machine interaction and therefore does not have to be a fancy human-readable name.
Title [optional] should be understandable for humans. This title that will be displayed within the viewer for the dataset selection. If omitted, the key title from the dataset metadata will be used. If that is missing too, the identifier will be used.
BoundingBox [optional] may be set in order to restrict the region which is served from a certain datacube. The notation of the BoundingBox is [lon_min,lat_min,lon_max,lat_max].
FileSystem [mandatory] is set to “s3” which lets xcube serve know, that the datacube is located in the cloud.
Endpoint [mandatory] contains information about the cloud provider endpoint, this will differ if you use a different cloud provider.
Path [mandatory] leads to the specific location of the datacube. The particular datacube is stored in an OpenTelecomCloud S3 bucket called “xcube-examples” and the datacube is called “OLCI-SNS-RAW-CUBE-2.zarr”.
Region [optional] is the region where the specified cloud provider is operating.
Styles [optional] influence the visualization of the xucbe dataset in the xcube viewer if specified in the server configuration file. The usage of Styles is described in section styles.
PlaceGroups [optional] allow to associate places (e.g. polygons or point-location) with a particular xcube dataset. Several different place groups may be connected to a xcube dataset, these different place groups are distinguished by the PlaceGroupRef. The configuration of PlaceGroups is described in section place groups.
AccessControl [optional] can only be used when providing authentication. Datasets may be protected by configuring the RequiredScopes entry whose value is a list of required scopes, e.g. “read:datasets”.
Variables [optional] enforces the order of variables reported by xcube server. Is a list of wildcard patterns that determines the order of variables and the subset of variables to be reported.
The following example reports only variables whose name starts with “conc_”:
Datasets:
- Path: demo.zarr
Variables:
- "conc_*"
The next example reports all variables but ensures that conc_chl
and conc_tsm are the first ones:
Datasets:
- Path: demo.zarr
Variables:
- conc_chl
- conc_tsm
- "*"
Locally stored xcube Datasets
The following configuration snippet demonstrates how to publish static (persistent) xcube datasets stored in the local filesystem:
- Identifier: local
Title: Local OLCI L2C cube for region SNS
BoundingBox: [0.0, 50, 5.0, 52.5]
FileSystem: file
Path: cube-1-250-250.zarr
Style: default
TimeSeriesDataset: local_ts
Augmentation:
Path: compute_extra_vars.py
Function: compute_variables
InputParameters:
factor_chl: 0.2
factor_tsm: 0.7
PlaceGroups:
- PlaceGroupRef: inside-cube
- PlaceGroupRef: outside-cube
AccessControl:
IsSubstitute: true
Most of the configuration of locally stored datasets is equal to the configuration of remotely stored xcube datasets.
FileSystem [mandatory] is set to “file” which lets xcube serve know, that the datacube is locally stored.
TimeSeriesDataset [optional] is not bound to local datasets, this parameter may be used for remotely stored datasets as well. By using this parameter a time optimized datacube will be used for generating the time series. The configuration of this time optimized datacube is shown below. By adding Hidden with true to the dataset configuration, the time optimized datacube will not appear among the displayed datasets in xcube viewer.
# Will not appear at all, because it is a "hidden" resource
- Identifier: local_ts
Title: 'local' optimized for time-series
BoundingBox: [0.0, 50, 5.0, 52.5]
FileSystem: file
Path: cube-5-100-200.zarr
Hidden: true
Style: default
Augmentation [optional] augments data cubes by new variables computed on-the-fly, the generation of the on-the-fly variables depends on the implementation of the python module specified in the Path within the Augmentation configuration.
AccessControl [optional] can only be used when providing authentication. By passing the IsSubstitute flag a dataset disappears for authorized requests. This might be useful for showing a demo dataset in the viewer for user who are not logged in.
Dynamic xcube Datasets
There is the possibility to define dynamic xcube datasets that are computed on-the-fly. Given here is an example that obtains daily or weekly averages of an xcube dataset named “local”.
- Identifier: local_1w
Title: OLCI weekly L3 cube for region SNS computed from local L2C cube
BoundingBox: [0.0, 50, 5.0, 52.5]
FileSystem: memory
Path: resample_in_time.py
Function: compute_dataset
InputDatasets: ["local"]
InputParameters:
period: 1W
incl_stdev: True
Style: default
PlaceGroups:
- PlaceGroupRef: inside-cube
- PlaceGroupRef: outside-cube
AccessControl:
IsSubstitute: True
FileSystem [mandatory] must be “memory” for dynamically generated datasets.
Path [mandatory] points to a Python module. Can be a Python file, a package, or a Zip file.
Function [mandatory, mutually exclusive with Class]
references a function in the Python file given by Path. Must be suffixed
by colon-separated module name, if Path references a package or Zip file.
The function receives one or more datasets of type xarray.Dataset
as defined by InputDatasets and optional keyword-arguments as
given by InputParameters, if any. It must return a new xarray.Dataset
with same spatial coordinates as the inputs.
If “resample_in_time.py” is compressed among any other modules in a zip archive, the original module name
must be indicated by the prefix to the function name:
Path: modules.zip
Function: resample_in_time:compute_dataset
InputDatasets: ["local"]
Class [mandatory, mutually exclusive with Function]
references a callable in the Python file given by Path. Must be suffixed
by colon-separated module name, if Path references a package or Zip file.
The callable is either a class derived from
xcube.core.mldataset.MultiLevelDataset or a function that returns
an instance of xcube.core.mldataset.MultiLevelDataset.
The callable receives one or more datasets of type
xcube.core.mldataset.MultiLevelDataset as defined by InputDatasets
and optional keyword-arguments as given by InputParameters, if any.
InputDatasets [mandatory] specifies the input datasets passed to Function or Class.
InputParameters [mandatory] specifies optional keyword arguments passed to Function or Class. In the example, InputParameters defines which kind of resampling should be performed.
Again, the dataset may be associated with place groups.
Place Groups [optional]
Place groups are specified in a similar manner compared to specifying datasets within a server. Place groups may be stored e.g. in shapefiles or a geoJson.
PlaceGroups:
- Identifier: outside-cube
Title: Points outside the cube
Path: places/outside-cube.geojson
PropertyMapping:
image: ${resolve_config_path("images/outside-cube/${ID}.jpg")}
Identifier [mandatory] is a unique ID for each place group, it is the one xcube serve uses to associate a place group to a particular dataset.
Title [mandatory] should be understandable for humans and this is the title that will be displayed within the viewer for the place selection if the selected xcube dataset contains a place group.
Path [mandatory] defines where the file storing the place group is located. Please note that the paths within the example config are relative.
PropertyMapping [mandatory]
determines which information contained within the place group should be used for selecting a certain location of the given place group.
This depends very strongly of the data used. In the above example, the image URL is determined by a feature’s ID property.
Property Mappings
The entry PropertyMapping is used to map a set of well-known properties (or roles) to the actual properties provided by a place feature in a place group. For example, the well-known properties are used to in xcube viewer to display information about the currently selected place. The possible well-known properties are:
label: The property that provides a label for the place, if any. Defaults to to case-insensitive nameslabel,title,name,idin xcube viewer.color: The property that provides a place’s color. Defaults to the case-insensitive namecolorin xcube viewer.image: The property that provides a place’s image URL, if any. Defaults to case-insensitive namesimage,img,picture,picin xcube viewer.description: The property that provides a place’s description text, if any. Defaults to case-insensitive namesdescription,desc,abstract,commentin xcube viewer.
In the following example, a place’s label is provided by the place feature’s NAME property,
while an image is provided by the place feature’s IMG_URL property:
PlaceGroups:
Identifier: my_group
...
PropertyMapping:
label: NAME
image: IMG_URL
The values on the right side may either be feature property names or contain them as placeholders in the form
${PROPERTY}.
Styles [optional]
Within the Styles section, colorbars may be defined which should be used initially for a certain variable of a dataset, as well as the value ranges. For xcube viewer version 0.3.0 or higher the colorbars and the value ranges may be adjusted by the user within the xcube viewer.
Styles:
- Identifier: default
ColorMappings:
conc_chl:
ColorBar: plasma
ValueRange: [0., 24.]
conc_tsm:
ColorBar: PuBuGn
ValueRange: [0., 100.]
kd489:
ColorBar: jet
ValueRange: [0., 6.]
rgb:
Red:
Variable: conc_chl
ValueRange: [0., 24.]
Green:
Variable: conc_tsm
ValueRange: [0., 100.]
Blue:
Variable: kd489
ValueRange: [0., 6.]
The ColorMapping may be specified for each variable of the datasets to be served. If not specified, xcube server will try to extract default values from attributes of dataset variables. The default value ranges are determined by:
xcube-specific variable attributes
color_value_minandcolor_value_max;The CF variable attributes
valid_min,valid_maxorvalid_range.Or otherwise, the value range
[0, 1]is assumed.
The colorbar name can be set using the
xcube-specific variable attribute
color_bar_name;Otherwise, the default colorbar name will be
viridis.
The special name rgb may be used to generate an RGB-image from any other three dataset variables used for the individual Red, Green and Blue channels of the resulting image. An example is shown in the configuration above.
Colormaps may be reversed by using name suffix “_r”. They also can have alpha blending indicated by name suffix “_alpha”. Both, reversed and alpha blending is possible as well and can be configured by name suffix “_r_alpha”.
Styles:
- Identifier: default
ColorMappings:
conc_chl:
ColorBar: plasma_r_alpha
ValueRange: [0., 24.]
Example
xcube serve --port 8080 --config ./examples/serve/demo/config.yml --verbose
xcube Server: WMTS, catalogue, data access, tile, feature, time-series services for xarray-enabled data cubes, version 0.2.0
[I 190924 17:08:54 service:228] configuration file 'D:\\Projects\\xcube\\examples\\serve\\demo\\config.yml' successfully loaded
[I 190924 17:08:54 service:158] service running, listening on localhost:8080, try http://localhost:8080/datasets
[I 190924 17:08:54 service:159] press CTRL+C to stop service
Server Demo Configuration File for DataStores
The server configuration file consists of various parts, some of them are necessary, others are optional. Here the demo stores configuration file used in the example stores is explained in detail.
This configuration file differs only in one part compared to section Server Demo Configuration File: data stores. The other main parts (authentication, dataset attribution, place groups, and styles) can be used in combination with data stores.
DataStores [mandatory]
Datasets, which are stored in the same location, may be configured in the configuration file using DataStores.
DataStores:
- Identifier: edc
StoreId: s3
StoreParams:
root: xcube-dcfs/edc-xc-viewer-data
max_depth: 1
storage_options:
anon: true
# client_kwargs:
# endpoint_url: https://s3.eu-central-1.amazonaws.com
Datasets:
- Path: "*2.zarr"
Style: default
# ChunkCacheSize: 1G
Identifier [mandatory] is a unique ID for each DataStore.
StoreID [mandatory] can be file for locally stored datasets and s3 for datasets located in the cloud.
Datasets [optional] if not specified, every dataset in the indicated location supported by xcube will be read and served by xcube serve. In order to filter certain datasets you can list Paths that shall be served by xcube serve. Path may contain wildcards. Each Dataset entry may have Styles and PlaceGroups associated with them, the same way as described in section Server Demo Configuration File.
Example Stores
xcube serve --port 8080 --config ./examples/serve/demo/config-with-stores.yml --verbose
xcube Server: WMTS, catalogue, data access, tile, feature, time-series services for xarray-enabled data cubes, version
[I 190924 17:08:54 service:228] configuration file 'D:\\Projects\\xcube\\examples\\serve\\demo\\config.yml' successfully loaded
[I 190924 17:08:54 service:158] service running, listening on localhost:8080, try http://localhost:8080/datasets
[I 190924 17:08:54 service:159] press CTRL+C to stop service
Example Azure Blob Storage filesystem Stores
xcube server includes support for Azure Blob Storage filesystem by a data store abfs. This enables access to data cubes (.zarr or .levels) in Azure blob storage as shown here:
DataStores:
- Identifier: siec
StoreId: abfs
StoreParams:
root: my_blob_container
max_depth: 1
storage_options:
anon: true
account_name: "xxx"
account_key': "xxx"
# or
# connection_string: "xxx"
Datasets:
- Path: "*.levels"
Style: default
Web API
The xcube server has a dedicated self describing Web API Documentation. After starting the server, you can check the
various functions provided by xcube Web API. To explore the functions, open <base-url>/openapi.html.
The xcube server implements the OGC WMTS RESTful and KVP architectural styles of the OGC WMTS 1.0.0 specification. The following operations are supported:
GetCapabilities:
/xcube/wmts/1.0.0/WMTSCapabilities.xmlGetTile:
/xcube/wmts/1.0.0/tile/{DatasetName}/{VarName}/{TileMatrix}/{TileCol}/{TileRow}.pngGetFeatureInfo: in progress
Python API
Data Store Framework
Functions
- xcube.core.store.new_data_store(data_store_id: str, extension_registry: ExtensionRegistry | None = None, **data_store_params) DataStore | MutableDataStore[source]
Create a new data store instance for given data_store_id and data_store_params.
- Parameters:
data_store_id (
str) – A data store identifier.extension_registry – Optional extension registry. If not given, the global extension registry will be used.
data_store_params – Data store specific parameters.
- Returns:
A new data store instance
- xcube.core.store.new_fs_data_store(protocol: str, root: str = '', max_depth: int | None = 1, read_only: bool = False, includes: Sequence[str] | None = None, excludes: Sequence[str] | None = None, storage_options: Dict[str, Any] = None) FsDataStore[source]
Create a new instance of a filesystem-based data store.
The data store is capable of filtering the data identifiers reported by
get_data_ids(). For this purpose the optional keywords excludes and includes are used which can both take the form of a wildcard pattern or a sequence of wildcard patterns:excludes: if given and if any pattern matches the identifier, the identifier is not reported.includes: if not given or if any pattern matches the identifier, the identifier is reported.
- Return type:
FsDataStore- Parameters:
protocol (
str) – The filesystem protocol, for example “file”, “s3”, “memory”.root (
str) – Root or base directory. Defaults to “”.max_depth – Maximum recursion depth. None means limitless. Defaults to 1.
read_only (
bool) – Whether this is a read-only store. Defaults to False.includes – Optional sequence of wildcards that include certain filesystem paths. Affects the data identifiers (paths) returned by get_data_ids(). By default, all paths are included.
excludes – Optional sequence of wildcards that exclude certain filesystem paths. Affects the data identifiers (paths) returned by get_data_ids(). By default, no paths are excluded.
storage_options – Options specific to the underlying filesystem identified by protocol. Used to instantiate the filesystem.
- Returns:
A new data store instance of type :class:FsDataStore.
- xcube.core.store.find_data_store_extensions(predicate: Callable[[Extension], bool] = None, extension_registry: ExtensionRegistry | None = None) List[Extension][source]
Find data store extensions using the optional filter function predicate.
- Parameters:
predicate – An optional filter function.
extension_registry – Optional extension registry. If not given, the global extension registry will be used.
- Returns:
List of data store extensions.
- xcube.core.store.get_data_store_class(data_store_id: str, extension_registry: ExtensionRegistry | None = None) Type[DataStore] | Type[MutableDataStore][source]
Get the class for the data store identified by data_store_id.
- Parameters:
data_store_id (
str) – A data store identifier.extension_registry – Optional extension registry. If not given, the global extension registry will be used.
- Returns:
The class for the data store.
- xcube.core.store.get_data_store_params_schema(data_store_id: str, extension_registry: ExtensionRegistry | None = None) JsonObjectSchema[source]
Get the JSON schema for instantiating a new data store identified by data_store_id.
- Return type:
JsonObjectSchema- Parameters:
data_store_id (
str) – A data store identifier.extension_registry – Optional extension registry. If not given, the global extension registry will be used.
- Returns:
The JSON schema for the data store’s parameters.
Classes
- class xcube.core.store.DataStore[source]
A data store represents a collection of data resources that can be enumerated, queried, and opened in order to obtain in-memory representations of the data. The same data resource may be made available using different data types. Therefore, many methods allow specifying a data_type parameter.
A store implementation may use any existing openers/writers, or define its own, or not use any openers/writers at all.
Store implementers should follow the conventions outlined in https://xcube.readthedocs.io/en/latest/storeconv.html .
The :class:DataStore is an abstract base class that both read-only and mutable data stores must implement.
- classmethod get_data_store_params_schema() JsonObjectSchema[source]
Get descriptions of parameters that must or can be used to instantiate a new DataStore object. Parameters are named and described by the properties of the returned JSON object schema. The default implementation returns JSON object schema that can have any properties.
- abstract classmethod get_data_types() Tuple[str, ...][source]
Get alias names for all data types supported by this store. The first entry in the tuple represents this store’s default data type.
- Returns:
The tuple of supported data types.
- abstract get_data_types_for_data(data_id: str) Tuple[str, ...][source]
Get alias names for of data types that are supported by this store for the given data_id.
- Parameters:
data_id (
str) – An identifier of data that is provided by this store- Returns:
A tuple of data types that apply to the given data_id.
- Raises:
DataStoreError – If an error occurs.
- abstract get_data_ids(data_type: str | None | type | DataType = None, include_attrs: Container[str] = None) Iterator[str] | Iterator[Tuple[str, Dict[str, Any]]][source]
Get an iterator over the data resource identifiers for the given type data_type. If data_type is omitted, all data resource identifiers are returned.
If a store implementation supports only a single data type, it should verify that data_type is either None or compatible with the supported data type.
If include_attrs is provided, it must be a sequence of names of metadata attributes. The store will then return extra metadata for each returned data resource identifier according to the names of the metadata attributes as tuples (data_id, attrs).
Hence, the type of the returned iterator items depends on the value of include_attrs:
If include_attrs is None (the default), the method returns an iterator of dataset identifiers data_id of type str.
If include_attrs is a sequence of attribute names, even an empty one, the method returns an iterator of tuples (data_id, attrs) of type Tuple[str, Dict], where attrs is a dictionary filled according to the names in include_attrs. If a store cannot provide a given attribute, it should simply ignore it. This may even yield to an empty dictionary for a given data_id.
The individual attributes do not have to exist in the dataset’s metadata, they may also be generated on-the-fly. An example for a generic attribute name is “title”. A store should try to resolve
include_attrs=["title"]by returning items such as("ESACCI-L4_GHRSST-SSTdepth-OSTIA-GLOB_CDR2.1-v02.0-fv01.0.zarr", {"title": "Level-4 GHRSST Analysed Sea Surface Temperature"}).- Parameters:
data_type – If given, only data identifiers that are available as this type are returned. If this is omitted, all available data identifiers are returned.
include_attrs – A sequence of names of attributes to be returned for each dataset identifier. If given, the store will attempt to provide the set of requested dataset attributes in addition to the data ids. (added in xcube 0.8.0)
- Returns:
An iterator over the identifiers and titles of data resources provided by this data store.
- Raises:
DataStoreError – If an error occurs.
- list_data_ids(data_type: str | None | type | DataType = None, include_attrs: Container[str] = None) List[str] | List[Tuple[str, Dict[str, Any]]][source]
Convenience version of get_data_ids() that returns a list rather than an iterator.
- Parameters:
data_type – If given, only data identifiers that are available as this type are returned. If this is omitted, all available data identifiers are returned.
include_attrs – A sequence of names of attributes to be returned for each dataset identifier. If given, the store will attempt to provide the set of requested dataset attributes in addition to the data ids. (added in xcube 0.8.0)
- Returns:
A list comprising the identifiers and titles of data resources provided by this data store.
- Raises:
DataStoreError – If an error occurs.
- abstract has_data(data_id: str, data_type: str | None | type | DataType = None) bool[source]
Check if the data resource given by data_id is available in this store.
- Return type:
bool- Parameters:
data_id (
str) – A data identifierdata_type – An optional data type. If given, it will also be checked whether the data is available as the specified type. May be given as type alias name, as a type, or as a :class:DataType instance.
- Returns:
True, if the data resource is available in this store, False otherwise.
- abstract describe_data(data_id: str, data_type: str | None | type | DataType = None) DataDescriptor[source]
Get the descriptor for the data resource given by data_id.
Raises a :class:DataStoreError if data_id does not exist in this store or the data is not available as the specified data_type.
- Return type:
- Parameters:
data_id (
str) – An identifier of data provided by this storedata_type – If given, the descriptor of the data will describe the data as specified by the data type. May be given as type alias name, as a type, or as a :class:DataType instance.
:return a data-type specific data descriptor :raise DataStoreError: If an error occurs.
- abstract get_data_opener_ids(data_id: str = None, data_type: str | None | type | DataType = None) Tuple[str, ...][source]
Get identifiers of data openers that can be used to open data resources from this store.
If data_id is given, data accessors are restricted to the ones that can open the identified data resource. Raises if data_id does not exist in this store.
If data_type is given, only openers that are compatible with this data type are returned.
If a store implementation supports only a single data type, it should verify that data_type is either None or equal to that single data type.
- Parameters:
data_id (
str) – An optional data resource identifier that is known to exist in this data store.data_type – An optional data type that is known to be supported by this data store. May be given as type alias name, as a type, or as a :class:DataType instance.
- Returns:
A tuple of identifiers of data openers that can be used to open data resources.
- Raises:
DataStoreError – If an error occurs.
- abstract get_open_data_params_schema(data_id: str = None, opener_id: str = None) JsonObjectSchema[source]
Get the schema for the parameters passed as open_params to :meth:open_data(data_id, open_params).
If data_id is given, the returned schema will be tailored to the constraints implied by the identified data resource. Some openers might not support this, therefore data_id is optional, and if it is omitted, the returned schema will be less restrictive. If given, the method raises if data_id does not exist in this store.
If opener_id is given, the returned schema will be tailored to the constraints implied by the identified opener. Some openers might not support this, therefore opener_id is optional, and if it is omitted, the returned schema will be less restrictive.
For maximum compatibility of stores, it is strongly encouraged to apply the following conventions on parameter names, types, and their interpretation.
Let P be the value of an optional, data constraining open parameter, then it should be interpreted as follows:
Given here are names, types, and descriptions of common, constraining open parameters for gridded datasets. Note, whether any of these is optional or mandatory depends on the individual data store. A store may also define other open parameters or support only a subset of the following. Note all parameters may be optional, the Python-types given here refer to _given_, non-Null parameters:
variable_names: List[str]: Included data variables. Available coordinate variables will be auto-included for any dimension of the data variables.bbox: Tuple[float, float, float, float]: Spatial coverage as xmin, ymin, xmax, ymax.crs: str: Spatial CRS, e.g. “EPSG:4326” or OGC CRS URI.spatial_res: float: Spatial resolution in coordinates of the spatial CRS.time_range: Tuple[Optional[str], Optional[str]]: Time range interval in UTC date/time units using ISO format. Start or end time may be missing which means everything until available start or end time.``time_period: str`: Pandas-compatible period/frequency string, e.g. “8D”, “2W”.
E.g. applied to an optional variable_names parameter, this means
variable_names is None - include all data variables
variable_names == [] - do not include data variables (schema only)
variable_names == [“<var_1>”, “<var_2>”, …] only include data variables named “<var_1>”, “<var_2>”, …
- Return type:
JsonObjectSchema- Parameters:
data_id (
str) – An optional data identifier that is known to exist in this data store.opener_id (
str) – An optional data opener identifier.
- Returns:
The schema for the parameters in open_params.
- Raises:
DataStoreError – If an error occurs.
- abstract open_data(data_id: str, opener_id: str = None, **open_params) Any[source]
Open the data given by the data resource identifier data_id using the supplied open_params.
The data type of the return value depends on the data opener used to open the data resource.
If opener_id is given, the identified data opener will be used to open the data resource and open_params must comply with the schema of the opener’s parameters. Note that some store implementations may not support using different openers or just support a single one.
Raises if data_id does not exist in this store.
- Return type:
Any- Parameters:
data_id (
str) – The data identifier that is known to exist in this data store.opener_id (
str) – An optional data opener identifier.open_params – Opener-specific parameters.
- Returns:
An in-memory representation of the data resources identified by data_id and open_params.
- Raises:
DataStoreError – If an error occurs.
- class xcube.core.store.MutableDataStore[source]
A mutable data store is a data store that also allows for adding, updating, and removing data resources.
MutableDataStore is an abstract base class that any mutable data store must implement.
- abstract get_data_writer_ids(data_type: str | None | type | DataType = None) Tuple[str, ...][source]
Get identifiers of data writers that can be used to write data resources to this store.
If data_type is given, only writers that support this data type are returned.
If a store implementation supports only a single data type, it should verify that data_type is either None or equal to that single data type.
- Parameters:
data_type – An optional data type specifier that is known to be supported by this data store. May be given as type alias name, as a type, or as a :class:DataType instance.
- Returns:
A tuple of identifiers of data writers that can be used to write data resources.
- Raises:
DataStoreError – If an error occurs.
- abstract get_write_data_params_schema(writer_id: str = None) JsonObjectSchema[source]
Get the schema for the parameters passed as write_params to :meth:write_data(data, data_id, open_params).
If writer_id is given, the returned schema will be tailored to the constraints implied by the identified writer. Some writers might not support this, therefore writer_id is optional, and if it is omitted, the returned schema will be less restrictive.
Given here is a pseudo-code implementation for stores that support multiple writers and where the store has common parameters with the writer:
store_params_schema = self.get_data_store_params_schema() writer_params_schema = get_writer(writer_id).get_write_data_params_schema() return subtract_param_schemas(writer_params_schema, store_params_schema)
- Return type:
JsonObjectSchema- Parameters:
writer_id (
str) – An optional data writer identifier.- Returns:
The schema for the parameters in write_params.
- Raises:
DataStoreError – If an error occurs.
- abstract write_data(data: Any, data_id: str = None, writer_id: str = None, replace: bool = False, **write_params) str[source]
- Write a data in-memory instance using the supplied data_id
and write_params.
If data identifier data_id is not given, a writer-specific default will be generated, used, and returned.
If writer_id is given, the identified data writer will be used to write the data resource and write_params must comply with the schema of writers’s parameters. Note that some store implementations may not support using different writers or just support a single one.
Given here is a pseudo-code implementation for stores that support multiple writers:
writer_id = writer_id or self.gen_data_id() path = self.resolve_data_id_to_path(data_id) write_params = add_params(self.get_data_store_params(), write_params) get_writer(writer_id).write_data(data, path, **write_params) self.register_data(data_id, data)
Raises if data_id does not exist in this store.
- Return type:
str- Parameters:
data (
Any) – The data in-memory instance to be written.data_id (
str) – An optional data identifier that is known to be unique in this data store.writer_id (
str) – An optional data writer identifier.replace (
bool) – Whether to replace an existing data resource.write_params – Writer-specific parameters.
- Returns:
The data identifier used to write the data.
- Raises:
DataStoreError – If an error occurs.
- abstract delete_data(data_id: str, **delete_params)[source]
Delete the data resource identified by data_id.
Typically, an implementation would delete the data resource from the physical storage and also remove any registered metadata from an associated database.
Raises if data_id does not exist in this store.
- Parameters:
data_id (
str) – An data identifier that is known to exist in this data store.- Raises:
DataStoreError – If an error occurs.
- abstract register_data(data_id: str, data: Any)[source]
Register the in-memory representation of a data resource data using the given data resource identifier data_id.
This method can be used to register data resources that are already physically stored in the data store, but are not yet searchable or otherwise accessible by the given data_id.
Typically, an implementation would extract metadata from data and store it in a store-specific database. An implementation should just store the metadata of data. It should not write data.
- Parameters:
data_id (
str) – A data resource identifier that is known to be unique in this data store.data (
Any) – An in-memory representation of a data resource.
- Raises:
DataStoreError – If an error occurs.
- abstract deregister_data(data_id: str)[source]
De-register a data resource identified by data_id from this data store.
This method can be used to de-register data resources so it will be no longer searchable or otherwise accessible by the given data_id.
Typically, an implementation would extract metadata from data and store it in a store-specific database. An implementation should only remove a data resource’s metadata. It should not delete data from its physical storage space.
Raises if data_id does not exist in this store.
- Parameters:
data_id (
str) – A data resource identifier that is known to exist in this data store.- Raises:
DataStoreError – If an error occurs.
- class xcube.core.store.DataOpener[source]
An interface that specifies a parameterized open_data() operation.
Possible open parameters are implementation-specific and are described by a JSON Schema.
Note this interface uses the term “opener” to underline the expected laziness of the operation. For example, when a xarray.Dataset is returned from a Zarr directory, the actual data is represented by Dask arrays and will be loaded only on-demand.
- abstract get_open_data_params_schema(data_id: str = None) JsonObjectSchema[source]
Get the schema for the parameters passed as open_params to :meth:open_data(data_id, open_params). If data_id is given, the returned schema will be tailored to the constraints implied by the identified data resource. Some openers might not support this, therefore data_id is optional, and if it is omitted, the returned schema will be less restrictive.
- Return type:
JsonObjectSchema- Parameters:
data_id (
str) – An optional data resource identifier.- Returns:
The schema for the parameters in open_params.
- Raises:
DataStoreError – If an error occurs.
- abstract open_data(data_id: str, **open_params) Any[source]
Open the data resource given by the data resource identifier data_id using the supplied open_params.
Raises if data_id does not exist.
- Return type:
Any- Parameters:
data_id (
str) – The data resource identifier.open_params – Opener-specific parameters.
- Returns:
An xarray.Dataset instance.
- Raises:
DataStoreError – If an error occurs.
- class xcube.core.store.DataSearcher[source]
Allow searching data in a data store.
- abstract classmethod get_search_params_schema(data_type: str | None | type | DataType = None) JsonObjectSchema[source]
Get the schema for the parameters that can be passed as search_params to :meth:search_data(). Parameters are named and described by the properties of the returned JSON object schema.
- Return type:
JsonObjectSchema- Parameters:
data_type – If given, the search parameters will be tailored to search for data for the given data_type.
- Returns:
A JSON object schema whose properties describe this store’s search parameters.
- abstract search_data(data_type: str | None | type | DataType = None, **search_params) Iterator[DataDescriptor][source]
Search this store for data resources. If data_type is given, the search is restricted to data resources of that type.
Returns an iterator over the search results which are returned as :class:DataDescriptor objects.
If a store implementation supports only a single data type, it should verify that data_type is either None or compatible with the supported data type specifier.
- Parameters:
data_type – An optional data type that is known to be supported by this data store.
search_params – The search parameters.
- Returns:
An iterator of data descriptors for the found data resources.
- Raises:
DataStoreError – If an error occurs.
- class xcube.core.store.DataWriter[source]
An interface that specifies a parameterized write_data() operation.
Possible write parameters are implementation-specific and are described by a JSON Schema.
- abstract get_write_data_params_schema() JsonObjectSchema[source]
Get the schema for the parameters passed as write_params to :meth:write_data(data resource, data_id, open_params).
- Returns:
The schema for the parameters in write_params.
- Raises:
DataStoreError – If an error occurs.
- abstract write_data(data: Any, data_id: str, replace: bool = False, **write_params) str[source]
Write a data resource using the supplied data_id and write_params.
- Return type:
str- Parameters:
data (
Any) – The data resource’s in-memory representation to be written.data_id (
str) – A unique data resource identifier.replace (
bool) – Whether to replace an existing data resource.write_params – Writer-specific parameters.
- Returns:
The data resource identifier used to write the data resource.
- Raises:
DataStoreError – If an error occurs.
- class xcube.core.store.DataStoreError(message: str)[source]
Raised on error in any of the data store, opener, or writer methods.
- Parameters:
message – The error message.
- class xcube.core.store.DataDescriptor(data_id: str, data_type: str | None | type | DataType, *, crs: str = None, bbox: Tuple[float, float, float, float] = None, time_range: Tuple[str | None, str | None] = None, time_period: str = None, open_params_schema: JsonObjectSchema = None, **additional_properties)[source]
A generic descriptor for any data. Also serves as a base class for more specific data descriptors.
- Parameters:
data_id – An identifier for the data
data_type – A type specifier for the data
crs – A coordinate reference system identifier, as an EPSG, PROJ or WKT string
bbox – A bounding box of the data
time_range – Start and end time delimiting this data’s temporal extent
time_period – The data’s periodicity if it is evenly temporally resolved.
open_params_schema – A JSON schema describing the parameters that may be used to open this data.
- class xcube.core.store.DatasetDescriptor(data_id: str, *, data_type: str | None | type | DataType = 'dataset', crs: str = None, bbox: Tuple[float, float, float, float] = None, time_range: Tuple[str | None, str | None] = None, time_period: str = None, spatial_res: float = None, dims: Mapping[str, int] = None, coords: Mapping[str, VariableDescriptor] = None, data_vars: Mapping[str, VariableDescriptor] = None, attrs: Mapping[Hashable, any] = None, open_params_schema: JsonObjectSchema = None, **additional_properties)[source]
A descriptor for a gridded, N-dimensional dataset represented by xarray.Dataset. Comprises a description of the data variables contained in the dataset.
Regrading time_range and time_period parameters, please refer to https://github.com/dcs4cop/xcube/blob/master/docs/source/storeconv.md#date-time-and-duration-specifications
- Parameters:
data_id – An identifier for the data
data_type – The data type of the data described
crs – A coordinate reference system identifier, as an EPSG, PROJ or WKT string
bbox – A bounding box of the data
time_range – Start and end time delimiting this data’s temporal extent
time_period – The data’s periodicity if it is evenly temporally resolved
spatial_res – The spatial extent of a pixel in crs units
dims – A mapping of the dataset’s dimensions to their sizes
coords – mapping of the dataset’s data coordinate names to instances of :class:VariableDescriptor
data_vars – A mapping of the dataset’s variable names to instances of :class:VariableDescriptor
attrs – A mapping containing arbitrary attributes of the dataset
open_params_schema – A JSON schema describing the parameters that may be used to open this data
- class xcube.core.store.MultiLevelDatasetDescriptor(data_id: str, num_levels: int, *, data_type: str | None | type | DataType = 'mldataset', **kwargs)[source]
A descriptor for a gridded, N-dimensional, multi-level, multi-resolution dataset represented by xcube.core.mldataset.MultiLevelDataset.
- Parameters:
data_id – An identifier of the multi-level dataset
num_levels – The number of levels of this multi-level dataset
data_type – A type specifier for the multi-level dataset
- class xcube.core.store.DatasetDescriptor(data_id: str, *, data_type: str | None | type | DataType = 'dataset', crs: str = None, bbox: Tuple[float, float, float, float] = None, time_range: Tuple[str | None, str | None] = None, time_period: str = None, spatial_res: float = None, dims: Mapping[str, int] = None, coords: Mapping[str, VariableDescriptor] = None, data_vars: Mapping[str, VariableDescriptor] = None, attrs: Mapping[Hashable, any] = None, open_params_schema: JsonObjectSchema = None, **additional_properties)[source]
A descriptor for a gridded, N-dimensional dataset represented by xarray.Dataset. Comprises a description of the data variables contained in the dataset.
Regrading time_range and time_period parameters, please refer to https://github.com/dcs4cop/xcube/blob/master/docs/source/storeconv.md#date-time-and-duration-specifications
- Parameters:
data_id – An identifier for the data
data_type – The data type of the data described
crs – A coordinate reference system identifier, as an EPSG, PROJ or WKT string
bbox – A bounding box of the data
time_range – Start and end time delimiting this data’s temporal extent
time_period – The data’s periodicity if it is evenly temporally resolved
spatial_res – The spatial extent of a pixel in crs units
dims – A mapping of the dataset’s dimensions to their sizes
coords – mapping of the dataset’s data coordinate names to instances of :class:VariableDescriptor
data_vars – A mapping of the dataset’s variable names to instances of :class:VariableDescriptor
attrs – A mapping containing arbitrary attributes of the dataset
open_params_schema – A JSON schema describing the parameters that may be used to open this data
- class xcube.core.store.VariableDescriptor(name: str, dtype: str, dims: Sequence[str], *, chunks: Sequence[int] = None, attrs: Mapping[Hashable, any] = None, **additional_properties)[source]
A descriptor for dataset variable represented by xarray.DataArray instances. They are part of dataset descriptor for an gridded, N-dimensional dataset represented by xarray.Dataset.
- Parameters:
name – The variable name
dtype – The data type of the variable.
dims – A list of the names of the variable’s dimensions.
chunks – A list of the chunk sizes of the variable’s dimensions
attrs – A mapping containing arbitrary attributes of the variable
- class xcube.core.store.GeoDataFrameDescriptor(data_id: str, *, data_type: str | None | type | DataType = 'geodataframe', feature_schema: JsonObjectSchema = None, **kwargs)[source]
A descriptor for a geo-vector dataset represented by a geopandas.GeoDataFrame instance.
- Parameters:
data_id – An identifier of the geopandas.GeoDataFrame
feature_schema – A schema describing the properties of the vector data
kwargs – Parameters passed to super :class:DataDescriptor
Cube generation
- xcube.core.gen.gen.gen_cube(input_paths: Sequence[str] = None, input_processor_name: str = None, input_processor_params: Dict = None, input_reader_name: str = None, input_reader_params: Dict[str, Any] = None, output_region: Tuple[float, float, float, float] = None, output_size: Tuple[int, int] = [512, 512], output_resampling: str = 'Nearest', output_path: str = 'out.zarr', output_writer_name: str = None, output_writer_params: Dict[str, Any] = None, output_metadata: Dict[str, Any] = None, output_variables: List[Tuple[str, Dict[str, Any] | None]] = None, processed_variables: List[Tuple[str, Dict[str, Any] | None]] = None, profile_mode: bool = False, no_sort_mode: bool = False, append_mode: bool = None, dry_run: bool = False, monitor: Callable[[...], None] = None) bool[source]
Generate a xcube dataset from one or more input files.
- Return type:
bool- Parameters:
no_sort_mode (
bool) –input_paths – The input paths.
input_processor_name (
str) – Name of a registered input processor (xcube.core.gen.inputprocessor.InputProcessor) to be used to transform the inputs.input_processor_params – Parameters to be passed to the input processor.
input_reader_name (
str) – Name of a registered input reader (xcube.core.util.dsio.DatasetIO).input_reader_params – Parameters passed to the input reader.
output_region – Output region as tuple of floats: (lon_min, lat_min, lon_max, lat_max).
output_size – The spatial dimensions of the output as tuple of ints: (width, height).
output_resampling (
str) – The resampling method for the output.output_path (
str) – The output directory.output_writer_name (
str) – Name of an output writer (xcube.core.util.dsio.DatasetIO) used to write the cube.output_writer_params – Parameters passed to the output writer.
output_metadata – Extra metadata passed to output cube.
output_variables – Output variables.
processed_variables – Processed variables computed on-the-fly.
profile_mode (
bool) – Whether profiling should be enabled.append_mode (
bool) – Deprecated. The function will always either insert, replace, or append new time slices.dry_run (
bool) – Doesn’t write any data. For testing.monitor – A progress monitor.
- Returns:
True for success.
- xcube.core.new.new_cube(title='Test Cube', width=360, height=180, x_name='lon', y_name='lat', x_dtype='float64', y_dtype=None, x_units='degrees_east', y_units='degrees_north', x_res=1.0, y_res=None, x_start=-180.0, y_start=-90.0, inverse_y=False, time_name='time', time_dtype='datetime64[s]', time_units='seconds since 1970-01-01T00:00:00', time_calendar='proleptic_gregorian', time_periods=5, time_freq='D', time_start='2010-01-01T00:00:00', use_cftime=False, drop_bounds=False, variables=None, crs=None, crs_name=None, time_encoding_dtype='int64')[source]
Create a new empty cube. Useful for creating cubes templates with predefined coordinate variables and metadata. The function is also heavily used by xcube’s unit tests.
The values of the variables dictionary can be either constants, array-like objects, or functions that compute their return value from passed coordinate indexes. The expected signature is::
def my_func(time: int, y: int, x: int) -> Union[bool, int, float]
- Parameters:
title (
str) – A title. Defaults to ‘Test Cube’.width (
int) – Horizontal number of grid cells. Defaults to 360.height (
int) – Vertical number of grid cells. Defaults to 180.x_name (
str) – Name of the x coordinate variable. Defaults to ‘lon’.y_name (
str) – Name of the y coordinate variable. Defaults to ‘lat’.x_dtype (
str) – Data type of x coordinates. Defaults to ‘float64’.y_dtype – Data type of y coordinates. Defaults to ‘float64’.
x_units (
str) – Units of the x coordinates. Defaults to ‘degrees_east’.y_units (
str) – Units of the y coordinates. Defaults to ‘degrees_north’.x_start (
float) – Minimum x value. Defaults to -180.y_start (
float) – Minimum y value. Defaults to -90.x_res (
float) – Spatial resolution in x-direction. Defaults to 1.0.y_res – Spatial resolution in y-direction. Defaults to 1.0.
inverse_y (
bool) – Whether to create an inverse y axis. Defaults to False.time_name (
str) – Name of the time coordinate variable. Defaults to ‘time’.time_periods (
int) – Number of time steps. Defaults to 5.time_freq (
str) – Duration of each time step. Defaults to `1D’.time_start (
str) – First time value. Defaults to ‘2010-01-01T00:00:00’.time_dtype (
str) – Numpy data type for time coordinates. Defaults to ‘datetime64[s]’. If used, parameter ‘use_cftime’ must be False.time_units (
str) – Units for time coordinates. Defaults to ‘seconds since 1970-01-01T00:00:00’.time_calendar (
str) – Calender for time coordinates. Defaults to ‘proleptic_gregorian’.use_cftime (
bool) – If True, the time will be given as data types according to the ‘cftime’ package. If used, the time_calendar parameter must be also be given with an appropriate value such as ‘gregorian’ or ‘julian’. If used, parameter ‘time_dtype’ must be None.drop_bounds (
bool) – If True, coordinate bounds variables are not created. Defaults to False.variables – Dictionary of data variables to be added. None by default.
crs – pyproj-compatible CRS string or instance of pyproj.CRS or None
crs_name – Name of the variable that will hold the CRS information. Ignored, if crs is not given.
time_encoding_dtype (
str) – data type used to encode the time variable when serializing the dataset
- Returns:
A cube instance
Cube computation
- xcube.core.compute.compute_cube(cube_func: ~typing.Callable[[...], ~xarray.core.dataarray.DataArray | ~numpy.ndarray | ~typing.Sequence[~xarray.core.dataarray.DataArray | ~numpy.ndarray]], *input_cubes: ~xarray.core.dataset.Dataset, input_cube_schema: ~xcube.core.schema.CubeSchema = None, input_var_names: ~typing.Sequence[str] = None, input_params: ~typing.Dict[str, ~typing.Any] = None, output_var_name: str = 'output', output_var_dtype: ~typing.Any = <class 'numpy.float64'>, output_var_attrs: ~typing.Dict[str, ~typing.Any] = None, vectorize: bool = None, cube_asserted: bool = False) Dataset[source]
Compute a new output data cube with a single variable named output_var_name from variables named input_var_names contained in zero, one, or more input data cubes in input_cubes using a cube factory function cube_func.
For a more detailed description of the function usage, please refer to :func:compute_dataset.
- Return type:
- Parameters:
cube_func – The cube factory function.
input_cubes (
Dataset) – An optional sequence of input cube datasets, must be provided if input_cube_schema is not.input_cube_schema (
CubeSchema) – An optional input cube schema,
must be provided if input_cubes is not. Will be ignored if input_cubes is provided. :param input_var_names: A sequence of variable names :param input_params: Optional dictionary with processing parameters passed to cube_func. :param output_var_name: Optional name of the output variable, defaults to
'output'. :param output_var_dtype: Optional numpy datatype of the output variable, defaults to'float32'. :param output_var_attrs: Optional metadata attributes for the output variable. :param vectorize: Whether all input_cubes have the same variables which are concatenated and passed as vectorsto cube_func. Not implemented yet.
- Parameters:
cube_asserted – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A new dataset that contains the computed output variable.
- xcube.core.evaluate.evaluate_dataset(dataset: Dataset, processed_variables: List[Tuple[str, Dict[str, Any] | None]] = None, errors: str = 'raise') Dataset[source]
Compute new variables or mask existing variables in dataset by the evaluation of Python expressions, that may refer to other existing or new variables. Returns a new dataset that contains the old and new variables, where both may bew now masked.
Expressions may be given by attributes of existing variables in dataset or passed a via the processed_variables argument which is a sequence of variable name / attributes tuples.
Two types of expression attributes are recognized in the attributes:
The attribute
expressiongenerates a new variable computed from its attribute value.The attribute
valid_pixel_expressionmasks out invalid variable values.
In both cases the attribuite value must be a string that forms a valid Python expression that can reference any other preceding variables by name. The expression can also reference any flags defined by another variable according the their CF attributes
flag_meaningandflag_values.Invalid variable values may be masked out using the value the
valid_pixel_expressionattribute whose value should form a Boolean Python expression. In case, the expression returns zero or false, the value of the_FillValueattribute or NaN will be used in the new variable.Other attributes will be stored as variable metadata as-is.
- Return type:
- Parameters:
dataset (
Dataset) – A dataset.processed_variables – Optional list of variable name-attributes pairs that will processed in the given order.
errors (
str) – How to deal with errors while evaluating expressions. May be be one of “raise”, “warn”, or “ignore”.
- Returns:
new dataset with computed variables
Cube data extraction
- xcube.core.extract.get_cube_values_for_points(cube: Dataset, points: Dataset | DataFrame | Mapping[str, ndarray | Array | DataArray | Series | Sequence[int | float]], var_names: Sequence[str] = None, include_coords: bool = False, include_bounds: bool = False, include_indexes: bool = False, index_name_pattern: str = '{name}_index', include_refs: bool = False, ref_name_pattern: str = '{name}_ref', method: str = 'nearest', cube_asserted: bool = False) Dataset[source]
Extract values from cube variables at given coordinates in points.
Returns a new dataset with values of variables from cube selected by the coordinate columns provided in points. All variables will be 1-D and have the same order as the rows in points.
- Return type:
- Parameters:
cube (
Dataset) – The cube dataset.points – Dictionary that maps dimension name to coordinate arrays.
var_names – An optional list of names of data variables in cube whose values shall be extracted.
include_coords (
bool) – Whether to include the cube coordinates for each point in return value.include_bounds (
bool) – Whether to include the cube coordinate boundaries (if any) for each point in return value.include_indexes (
bool) – Whether to include computed indexes into the cube for each point in return value.index_name_pattern (
str) – A naming pattern for the computed index columns. Must include “{name}” which will be replaced by the index’ dimension name.include_refs (
bool) – Whether to include point (reference) values from points in return value.ref_name_pattern (
str) – A naming pattern for the computed point data columns. Must include “{name}” which will be replaced by the point’s attribute name.method (
str) – “nearest” or “linear”.cube_asserted (
bool) – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A new data frame whose columns are values from cube variables at given points.
- xcube.core.extract.get_cube_point_indexes(cube: ~xarray.core.dataset.Dataset, points: ~xarray.core.dataset.Dataset | ~pandas.core.frame.DataFrame | ~typing.Mapping[str, ~numpy.ndarray | ~dask.array.core.Array | ~xarray.core.dataarray.DataArray | ~pandas.core.series.Series | ~typing.Sequence[int | float]], dim_name_mapping: ~typing.Mapping[str, str] = None, index_name_pattern: str = '{name}_index', index_dtype=<class 'numpy.float64'>, cube_asserted: bool = False) Dataset[source]
Get indexes of given point coordinates points into the given dataset.
- Return type:
- Parameters:
cube (
Dataset) – The cube dataset.points – A mapping from column names to column data arrays, which must all have the same length.
dim_name_mapping – A mapping from dimension names in cube to column names in points.
index_name_pattern (
str) – A naming pattern for the computed indexes columns. Must include “{name}” which will be replaced by the dimension name.index_dtype – Numpy data type for the indexes. If it is a floating point type (default), then indexes will contain fractions, which may be used for interpolation. For out-of-range coordinates in points, indexes will be -1 if index_dtype is an integer type, and NaN, if index_dtype is a floating point types.
cube_asserted (
bool) – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A dataset containing the index columns.
- xcube.core.extract.get_cube_values_for_indexes(cube: Dataset, indexes: Dataset | DataFrame | Mapping[str, Any], include_coords: bool = False, include_bounds: bool = False, data_var_names: Sequence[str] = None, index_name_pattern: str = '{name}_index', method: str = 'nearest', cube_asserted: bool = False) Dataset[source]
Get values from the cube at given indexes.
- Return type:
- Parameters:
cube (
Dataset) – A cube dataset.indexes – A mapping from column names to index and fraction arrays for all cube dimensions.
include_coords (
bool) – Whether to include the cube coordinates for each point in return value.include_bounds (
bool) – Whether to include the cube coordinate boundaries (if any) for each point in return value.data_var_names – An optional list of names of data variables in cube whose values shall be extracted.
index_name_pattern (
str) – A naming pattern for the computed indexes columns. Must include “{name}” which will be replaced by the dimension name.method (
str) – “nearest” or “linear”.cube_asserted (
bool) – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A new data frame whose columns are values from cube variables at given indexes.
- xcube.core.extract.get_dataset_indexes(dataset: ~xarray.core.dataset.Dataset, coord_var_name: str, coord_values: ~numpy.ndarray | ~dask.array.core.Array | ~xarray.core.dataarray.DataArray | ~pandas.core.series.Series | ~typing.Sequence[int | float], index_dtype=<class 'numpy.float64'>) DataArray | ndarray[source]
Compute the indexes and their fractions into a coordinate variable coord_var_name of a dataset for the given coordinate values coord_values.
The coordinate variable’s labels must be monotonic increasing or decreasing, otherwise the result will be nonsense.
For any value in coord_values that is out of the bounds of the coordinate variable’s values, the index depends on the value of index_dtype. If index_dtype is an integer type, invalid indexes are encoded as -1 while for floating point types, NaN will be used.
Returns a tuple of indexes as int64 array and fractions as float64 array.
- Parameters:
dataset (
Dataset) – A cube dataset.coord_var_name (
str) – Name of a coordinate variable.coord_values – Array-like coordinate values.
index_dtype – Numpy data type for the indexes. If it is floating point type (default), then indexes contain fractions, which may be used for interpolation. If dtype is an integer type out-of-range coordinates are indicated by index -1, and NaN if it is is a floating point type.
- Returns:
The indexes and their fractions as a tuple of numpy int64 and float64 arrays.
- xcube.core.timeseries.get_time_series(cube: Dataset, grid_mapping: GridMapping | None = None, geometry: BaseGeometry | Dict[str, Any] | str | Sequence[float | int] | None = None, var_names: Sequence[str] | None = None, start_date: datetime64 | str | None = None, end_date: datetime64 | str | None = None, agg_methods: str | Sequence[str] | AbstractSet[str] = 'mean', use_groupby: bool = False, cube_asserted: bool | None = None) Dataset | None[source]
Get a time series dataset from a data cube.
geometry may be provided as a (shapely) geometry object, a valid GeoJSON object, a valid WKT string, a sequence of box coordinates (x1, y1, x2, y2), or point coordinates (x, y). If geometry covers an area, i.e. is not a point, the function aggregates the variables to compute a mean value and if desired, the number of valid observations and the standard deviation.
start_date and end_date may be provided as a numpy.datetime64 or an ISO datetime string.
Returns a time-series dataset whose data variables have a time dimension but no longer have spatial dimensions, hence the resulting dataset’s variables will only have N-2 dimensions. A global attribute
max_number_of_observationswill be set to the maximum number of observations that could have been made in each time step. If the given geometry does not overlap the cube’s boundaries, or if not output variables remain, the function returnsNone.- Parameters:
cube (
Dataset) – The xcube datasetgrid_mapping – Grid mapping of cube.
geometry – Optional geometry
var_names – Optional sequence of names of variables to be included.
start_date – Optional start date.
end_date – Optional end date.
agg_methods – Aggregation methods. May be single string or sequence of strings. Possible values are ‘mean’, ‘median’, ‘min’, ‘max’, ‘std’, ‘count’. Defaults to ‘mean’. Ignored if geometry is a point.
use_groupby (
bool) – Use group-by operation. May increase or decrease runtime performance and/or memory consumption.cube_asserted – Deprecated and ignored since xcube 0.11.0. No replacement.
Cube Resampling
- xcube.core.resampling.affine_transform_dataset(dataset: Dataset, source_gm: GridMapping, target_gm: GridMapping, var_configs: Mapping[Hashable, Mapping[str, Any]] = None, encode_cf: bool = True, gm_name: str | None = None, reuse_coords: bool = False) Dataset[source]
Resample dataset according to an affine transformation.
The affine transformation will be applied only if the CRS of source_gm and the CRS of target_gm are both geographic or equal. Otherwise, a
ValueErrorwill be raised.- Return type:
- Parameters:
dataset (
Dataset) – The source datasetsource_gm (
GridMapping) – Source grid mapping of dataset. Must be regular. Must have same CRS as target_gm.target_gm (
GridMapping) – Target grid mapping. Must be regular. Must have same CRS as source_gm.var_configs – Optional resampling configurations for individual variables.
encode_cf (
bool) – Whether to encode the target grid mapping into the resampled dataset in a CF-compliant way. Defaults toTrue.gm_name – Name for the grid mapping variable. Defaults to “crs”. Used only if encode_cf is
True.reuse_coords (
bool) – Whether to either reuse target coordinate arrays from target_gm or to compute new ones.
- Returns:
The resampled target dataset.
- xcube.core.resampling.resample_ndimage(image: ~numpy.ndarray | ~dask.array.core.Array, scale: float | ~typing.Tuple[float, float] = 1, offset: float | ~typing.Tuple[float, float] = None, shape: int | ~typing.Tuple[int, int] = None, chunks: ~typing.Sequence[int] = None, spline_order: int = 1, aggregator: ~typing.Callable[[~numpy.ndarray | ~dask.array.core.Array], ~numpy.ndarray | ~dask.array.core.Array] | None = <function nanmean>, recover_nan: bool = False) Array[source]
- xcube.core.resampling.encode_grid_mapping(ds: Dataset, gm: GridMapping, gm_name: str | None = None, force: bool | None = None) Dataset[source]
Encode the given grid mapping gm into a copy of ds in a CF-compliant way and return the dataset copy. The function removes any existing grid mappings.
If the CRS of gm is geographic and the spatial dimension and coordinate names are “lat”, “lon” and force is
False, or force isNoneand no former grid mapping was encoded in ds, then nothing else is done and the dataset copy is returned without further action.Otherwise, for every spatial data variable with dims=(…, y, x), the function sets the attribute “grid_mapping” to gm_name. The grid mapping CRS is encoded in a new 0-D variable named gm_name.
- Return type:
- Parameters:
ds (
Dataset) – The dataset.gm (
GridMapping) – The dataset’s grid mapping.gm_name – Name for the grid mapping variable. Defaults to “crs”.
force – Whether to force encoding of grid mapping even if CRS is geographic and spatial dimension names are “lon”, “lat”. Optional value, if not provided, force will be assumed
Trueif a former grid mapping was encoded in ds.
- Returns:
A copy of ds with gm encoded into it.
- xcube.core.resampling.rectify_dataset(source_ds: Dataset, *, var_names: str | Sequence[str] = None, source_gm: GridMapping = None, xy_var_names: Tuple[str, str] = None, target_gm: GridMapping = None, encode_cf: bool = True, gm_name: str | None = None, tile_size: int | Tuple[int, int] = None, is_j_axis_up: bool = None, output_ij_names: Tuple[str, str] = None, compute_subset: bool = True, uv_delta: float = 0.001) Dataset | None[source]
Reproject dataset source_ds using its per-pixel x,y coordinates or the given source_gm.
The function expects source_ds or the given source_gm to have either one- or two-dimensional coordinate variables that provide spatial x,y coordinates for every data variable with the same spatial dimensions.
For example, a dataset may comprise variables with spatial dimensions
var(..., y_dim, x_dim), then one the function expects coordinates to be provided in two forms:One-dimensional
x_var(x_dim)andy_var(y_dim)(coordinate) variables.Two-dimensional
x_var(y_dim, x_dim)andy_var(y_dim, x_dim)(coordinate) variables.
If target_gm is given and it defines a tile size or tile_size is given, and the number of tiles is greater than one in the output’s x- or y-direction, then the returned dataset will be composed of lazy, chunked dask arrays. Otherwise, the returned dataset will be composed of ordinary numpy arrays.
- Parameters:
source_ds (
Dataset) – Source dataset grid mapping.var_names – Optional variable name or sequence of variable names.
source_gm (
GridMapping) – Source dataset grid mapping.xy_var_names – Optional tuple of the x- and y-coordinate variables in source_ds. Ignored if source_gm is given.
target_gm (
GridMapping) – Optional target geometry. If not given, output geometry will be computed to spatially fit dataset and to retain its spatial resolution.encode_cf (
bool) – Whether to encode the target grid mapping into the resampled dataset in a CF-compliant way. Defaults toTrue.gm_name – Name for the grid mapping variable. Defaults to “crs”. Used only if encode_cf is
True.tile_size – Optional tile size for the output.
is_j_axis_up (
bool) – Whether y coordinates are increasing with positive image j axis.output_ij_names – If given, a tuple of variable names in which to store the computed source pixel coordinates in the returned output.
compute_subset (
bool) – Whether to compute a spatial subset from dataset using output_geom. If set, the function may returnNonein case there is no overlap.uv_delta (
float) – A normalized value that is used to determine whether x,y coordinates in the output are contained in the triangles defined by the input x,y coordinates. The higher this value, the more inaccurate the rectification will be.
- Returns:
a reprojected dataset, or None if the requested output does not intersect with dataset.
- xcube.core.resampling.resample_in_space(dataset: Dataset, source_gm: GridMapping = None, target_gm: GridMapping = None, var_configs: Mapping[Hashable, Mapping[str, Any]] = None, encode_cf: bool = True, gm_name: str | None = None)[source]
Resample a dataset in the spatial dimensions.
If the source grid mapping source_gm is not given, it is derived from dataset:
source_gm = GridMapping.from_dataset(dataset).If the target grid mapping target_gm is not given, it is derived from source_gm:
target_gm = source_gm.to_regular().If source_gm is almost equal to target_gm, this function is a no-op and dataset is returned unchanged.
Otherwise the function computes a spatially resampled version of dataset and returns it.
Using var_configs, the resampling of individual variables can be configured. If given, var_configs must be a mapping from variable names to configuration dictionaries which can have the following properties:
spline_order(int) - The order of spline polynomialsused for interpolating. It is used for upsampling only. Possible values are 0 to 5. Default is 1 (bi-linear) for floating point variables, and 0 (= nearest neighbor) for integer and bool variables.
aggregator(str) - An optional aggregatingfunction. It is used for downsampling only. Examples are numpy.nanmean, numpy.nanmin, numpy.nanmax. Default is numpy.nanmean for floating point variables, and None (= nearest neighbor) for integer and bool variables.
recover_nan(bool) - whether a special algorithmshall be used that is able to recover values that would otherwise yield NaN during resampling. Default is True for floating point variables, and False for integer and bool variables.
Note that var_configs is only used if the resampling involves an affine transformation. This is true if the CRS of source_gm and CRS of target_gm are equal and one of two cases is given:
source_gm is regular. In this case the resampling is the affine transformation. and the result is returned directly.
source_gm is not regular and has a lower resolution than target_cm. In this case dataset is downsampled first using an affine transformation. Then the result is rectified.
In all other cases, no affine transformation is applied and the resampling is a direct rectification.
- Parameters:
dataset (
Dataset) – The source dataset.source_gm (
GridMapping) – The source grid mapping.target_gm (
GridMapping) – The target grid mapping. Must be regular.var_configs – Optional resampling configurations for individual variables.
encode_cf (
bool) – Whether to encode the target grid mapping into the resampled dataset in a CF-compliant way. Defaults toTrue.gm_name – Name for the grid mapping variable. Defaults to “crs”. Used only if encode_cf is
True.
- Returns:
The spatially resampled dataset.
- xcube.core.resampling.resample_in_time(dataset: Dataset, frequency: str, method: str | Sequence[str], offset=None, base=None, tolerance=None, interp_kind=None, time_chunk_size=None, var_names: Sequence[str] = None, metadata: Dict[str, Any] = None, cube_asserted: bool = False) Dataset[source]
Resample a dataset in the time dimension.
The argument method may be one or a sequence of
'all','any','argmax','argmin','count','first','last','max','min','mean','median','percentile_<p>','std','sum','var'.In value
'percentile_<p>'is a placeholder, where'<p>'must be replaced by an integer percentage value, e.g.'percentile_90'is the 90%-percentile.Important note: As of xarray 0.14 and dask 2.8, the methods
'median'and'percentile_<p>'` cannot be used if the variables in *cube* comprise chunked dask arrays. In this case, use the ``compute()orload()method to convert dask arrays into numpy arrays.- Return type:
- Parameters:
dataset (
Dataset) – The xcube dataset.frequency (
str) – Temporal aggregation frequency. Use format “<count><offset>” where <offset> is one of ‘H’, ‘D’, ‘W’, ‘M’, ‘Q’, ‘Y’.method – Resampling method or sequence of resampling methods.
offset – Offset used to adjust the resampled time labels. Uses same syntax as frequency.
base – Deprecated since xcube 1.0.4. No longer used as of pandas 2.0.
time_chunk_size – If not None, the chunk size to be used for the “time” dimension.
var_names – Variable names to include.
tolerance – Time tolerance for selective upsampling methods. Defaults to frequency.
interp_kind – Kind of interpolation if method is ‘interpolation’.
metadata – Output metadata.
cube_asserted (
bool) – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A new xcube dataset resampled in time.
Cube Manipulation
- xcube.core.vars2dim.vars_to_dim(cube: Dataset, dim_name: str = 'var', var_name='data', cube_asserted: bool = False)[source]
Convert data variables into a dimension.
- Parameters:
cube (
Dataset) – The xcube dataset.dim_name (
str) – The name of the new dimension and coordinate variable. Defaults to ‘var’.var_name (
str) – The name of the new, single data variable. Defaults to ‘data’.cube_asserted (
bool) – If False, cube will be verified, otherwise it is expected to be a valid cube.
- Returns:
A new xcube dataset with data variables turned into a new dimension.
- xcube.core.chunk.chunk_dataset(dataset: Dataset, chunk_sizes: Dict[str, int] = None, format_name: str = None) Dataset[source]
Chunk dataset using chunk_sizes and optionally update encodings for given format_name.
- xcube.core.unchunk.unchunk_dataset(dataset_path: str, var_names: Sequence[str] = None, coords_only: bool = False)[source]
Unchunk dataset variables in-place.
- Parameters:
dataset_path (
str) – Path to ZARR dataset directory.var_names – Optional list of variable names.
coords_only (
bool) – Un-chunk coordinate variables only.
- xcube.core.optimize.optimize_dataset(input_path: str, output_path: str = None, in_place: bool = False, unchunk_coords: bool | str | ~typing.Sequence[str] = False, exception_type: ~typing.Type[Exception] = <class 'ValueError'>)[source]
Optimize a dataset for faster access.
Reduces the number of metadata and coordinate data files in xcube dataset given by given by dataset_path. Consolidated cubes open much faster from remote locations, e.g. in object storage, because obviously much less HTTP requests are required to fetch initial cube meta information. That is, it merges all metadata files into a single top-level JSON file “.zmetadata”.
If unchunk_coords is given, it also removes any chunking of coordinate variables so they comprise a single binary data file instead of one file per data chunk. The primary usage of this function is to optimize data cubes for cloud object storage. The function currently works only for data cubes using Zarr format. unchunk_coords can be a name, or list of names of the coordinate variable(s) to be consolidated. If boolean
Trueis used, coordinate all variables will be consolidated.- Parameters:
input_path (
str) – Path to input dataset with ZARR format.output_path (
str) – Path to output dataset with ZARR format. May contain “{input}” template string, which is replaced by the input path’s file name without file name extension.in_place (
bool) – Whether to modify the dataset in place. If False, a copy is made and output_path must be given.unchunk_coords – The name of a coordinate variable or a list of coordinate variables whose chunks should be consolidated. Pass
Trueto consolidate chunks of all coordinate variables.exception_type – Type of exception to be used on value errors.
Cube Subsetting
- xcube.core.select.select_variables_subset(dataset: Dataset, var_names: Collection[str] | None = None) Dataset[source]
Select data variable from given dataset and create new dataset.
- xcube.core.geom.clip_dataset_by_geometry(dataset: Dataset, geometry: BaseGeometry | Dict[str, Any] | str | Sequence[float | int], save_geometry_wkt: str | bool = False) Dataset | None[source]
Spatially clip a dataset according to the bounding box of a given geometry.
- Parameters:
dataset (
Dataset) – The datasetgeometry – A geometry-like object, see py:function:convert_geometry.
save_geometry_wkt – If the value is a string, the effective intersection geometry is stored as a Geometry WKT string in the global attribute named by save_geometry. If the value is True, the name “geometry_wkt” is used.
- Returns:
The dataset spatial subset, or None if the bounding box of the dataset has a no or a zero area intersection with the bounding box of the geometry.
Cube Masking
- xcube.core.geom.mask_dataset_by_geometry(dataset: Dataset, geometry: BaseGeometry | Dict[str, Any] | str | Sequence[float | int], tile_size: int | Tuple[int, int] = None, excluded_vars: Sequence[str] = None, no_clip: bool = False, all_touched: bool = False, save_geometry_mask: str | bool = False, save_geometry_wkt: str | bool = False) Dataset | None[source]
Mask a dataset according to the given geometry. The cells of variables of the returned dataset will have NaN-values where their spatial coordinates are not intersecting the given geometry.
- Parameters:
dataset (
Dataset) – The datasetgeometry – A geometry-like object, see py:function:convert_geometry.
tile_size – If given, the unconditional spatial chunk sizes in x- and y-direction in pixels. May be given as integer scalar or x,y-pair of integers.
excluded_vars – Optional sequence of names of data variables that should not be masked (but still may be clipped).
no_clip (
bool) – If True, the function will not clip the dataset before masking, this is, the returned dataset will have the same dimension size as the given dataset.all_touched (
bool) – If True, all pixels intersected by geometry outlines will be included in the mask. If False, only pixels whose center is within the polygon or that are selected by Bresenham’s line algorithm will be included in the mask. The default value is set to False.save_geometry_mask – If the value is a string, the effective geometry mask array is stored as a 2D data variable named by save_geometry_mask. If the value is True, the name “geometry_mask” is used.
save_geometry_wkt – If the value is a string, the effective intersection geometry is stored as a Geometry WKT string in the global attribute named by save_geometry. If the value is True, the name “geometry_wkt” is used.
- Returns:
The dataset spatial subset, or None if the bounding box of the dataset has a no or a zero area intersection with the bounding box of the geometry.
- class xcube.core.maskset.MaskSet(flag_var: DataArray)[source]
A set of mask variables derived from a variable flag_var with the following CF attributes:
One or both of flag_masks and flag_values
flag_meanings (always required)
See https://cfconventions.org/Data/cf-conventions/cf-conventions-1.9/cf-conventions.html#flags for details on the use of these attributes.
Each mask is represented by an xarray.DataArray, has the name of the flag, is of type numpy.unit8, and has the dimensions of the given flag_var.
- Parameters:
flag_var – an xarray.DataArray that defines flag values. The CF attributes flag_meanings and one or both of flag_masks and flag_values are expected to exist and be valid.
- classmethod get_mask_sets(dataset: Dataset) Dict[str, MaskSet][source]
For each “flag” variable in given dataset, turn it into a
MaskSet, store it in a dictionary.- Parameters:
dataset (
Dataset) – The dataset- Returns:
A mapping of flag names to
MaskSet. Will be empty if there are no flag variables in dataset.
Rasterisation of Features
- xcube.core.geom.rasterize_features(dataset: Dataset, features: pandas.geodataframe.GeoDataFrame | Sequence[Mapping[str, Any]], feature_props: Sequence[str], var_props: Dict[str, Mapping[str, Mapping[str, Any]]] = None, tile_size: int | Tuple[int, int] = None, all_touched: bool = False, in_place: bool = False) Dataset | None[source]
Rasterize feature properties given by feature_props of vector-data features as new variables into dataset.
dataset must have two spatial 1-D coordinates, either
lonandlatin degrees, reprojected coordinates,xandy, or similar.feature_props is a sequence of names of feature properties that must exists in each feature of features.
features may be passed as pandas.GeoDataFrame`` or as an iterable of GeoJSON features.
Using the optional var_props, the properties of newly created variables from feature properties can be specified. It is a mapping of feature property names to mappings of variable properties. Here is an example variable properties mapping::
{- ‘name’: ‘land_class’, # (str) - the variable’s name,
# default is the feature property name;
- ‘dtype’ np.int16, # (str|np.dtype) - the variable’s dtype,
# default is np.float64;
- ‘fill_value’: -999, # (bool|int|float|np.nparray) -
# the variable’s fill value, # default is np.nan;
- ‘attrs’: {}, # (Mapping[str, Any]) -
# the variable’s fill value, default is {};
- ‘converter’: int, # (Callable[[Any], Any]) -
# a converter function used to convert # from property feature value to variable # value, default is float. # Deprecated, no longer used.
}
Note that newly created variables will have data type np.float64 because np.nan is used to encode missing values. fill_value and dtype are used to encode the variables when persisting the data.
Currently, the coordinates of the geometries in the given features must use the same CRS as the given dataset.
- Parameters:
dataset (
Dataset) – The xarray dataset.features – A
geopandas.GeoDataFrameinstance or a sequence of GeoJSON features.feature_props – Sequence of names of numeric feature properties to be rasterized.
var_props – Optional mapping of feature property name to a name or a 5-tuple (name, dtype, fill_value, attributes, converter) for the new variable.
tile_size – If given, the unconditional spatial chunk sizes in x- and y-direction in pixels. May be given as integer scalar or x,y-pair of integers.
all_touched (
bool) – If True, all pixels intersected by a feature’s geometry outlines will be included. If False, only pixels whose center is within the feature polygon or that are selected by Bresenham’s line algorithm will be included in the mask. The default is False.in_place (
bool) – Whether to add new variables to dataset. If False, a copy will be created and returned.
- Returns:
dataset with rasterized feature_property
Cube Metadata
- xcube.core.update.update_dataset_attrs(dataset: Dataset, global_attrs: Dict[str, Any] = None, update_existing: bool = False, in_place: bool = False) Dataset[source]
Update spatio-temporal CF/THREDDS attributes given dataset according to spatio-temporal coordinate variables time, lat, and lon.
- Return type:
- Parameters:
dataset (
Dataset) – The dataset.global_attrs – Optional global attributes.
update_existing (
bool) – IfTrue, any existing attributes will be updated.in_place (
bool) – IfTrue, dataset will be modified in place and returned.
- Returns:
A new dataset, if in_place if
False(default), else the passed and modified dataset.
- xcube.core.update.update_dataset_spatial_attrs(dataset: Dataset, update_existing: bool = False, in_place: bool = False) Dataset[source]
Update spatial CF/THREDDS attributes of given dataset.
- Return type:
- Parameters:
dataset (
Dataset) – The dataset.update_existing (
bool) – IfTrue, any existing attributes will be updated.in_place (
bool) – IfTrue, dataset will be modified in place and returned.
- Returns:
A new dataset, if in_place if
False(default), else the passed and modified dataset.
- xcube.core.update.update_dataset_temporal_attrs(dataset: Dataset, update_existing: bool = False, in_place: bool = False) Dataset[source]
Update temporal CF/THREDDS attributes of given dataset.
- Return type:
- Parameters:
dataset (
Dataset) – The dataset.update_existing (
bool) – IfTrue, any existing attributes will be updated.in_place (
bool) – IfTrue, dataset will be modified in place and returned.
- Returns:
A new dataset, if in_place is
False(default), else the passed and modified dataset.
Cube verification
- xcube.core.verify.assert_cube(dataset: Dataset, name=None) Dataset[source]
Assert that the given dataset is a valid xcube dataset.
- xcube.core.verify.verify_cube(dataset: Dataset) List[str][source]
Verify the given dataset for being a valid xcube dataset.
The tool verifies that dataset * defines two spatial x,y coordinate variables, that are 1D, non-empty, using correct units; * defines a time coordinate variables, that are 1D, non-empty, using correct units; * has valid bounds variables for spatial x,y and time coordinate variables, if any; * has any data variables and that they are valid, e.g. min. 3-D, all have
same dimensions, have at least the dimensions dim(time), dim(y), dim(x) in that order.
Returns a list of issues, which is empty if dataset is a valid xcube dataset.
- Parameters:
dataset (
Dataset) – A dataset to be verified.- Returns:
List of issues or empty list.
Multi-Resolution Datasets
- class xcube.core.mldataset.MultiLevelDataset[source]
A multi-level dataset of decreasing spatial resolutions (a multi-resolution pyramid).
The pyramid level at index zero provides the original spatial dimensions. The size of the spatial dimensions in subsequent levels is computed by the formula
size[index + 1] = (size[index] + 1) // 2withsize[index]being the maximum size of the spatial dimensions at level zero.Any dataset chunks are assumed to be the same in all levels. Usually, the number of chunks is one in one of the spatial dimensions of the highest level.
- abstract property grid_mapping: GridMapping
- Returns:
the CF-conformal grid mapping
- property resolutions: Sequence[Tuple[float, float]]
- Returns:
the x,y resolutions for each level given in the spatial units of the dataset’s CRS (i.e.
self.grid_mapping.crs).
- property avg_resolutions: Sequence[float]
- Returns:
the average x,y resolutions for each level given in the spatial units of the dataset’s CRS (i.e.
self.grid_mapping.crs).
- property datasets: Sequence[Dataset]
Get datasets for all levels.
Calling this method will trigger any lazy dataset instantiation.
- Returns:
the datasets for all levels.
- abstract get_dataset(index: int) Dataset[source]
- Return type:
- Parameters:
index (
int) – the level index- Returns:
the dataset for the level at index.
- apply(function: Callable[[Dataset, Dict[str, Any]], Dataset], kwargs: Dict[str, Any] = None, ds_id: str = None) MultiLevelDataset[source]
Apply function to all level datasets and return a new multi-level dataset.
- class xcube.core.mldataset.BaseMultiLevelDataset(base_dataset: Dataset, grid_mapping: GridMapping | None = None, num_levels: int | None = None, agg_methods: None | str | Mapping[str, None | str] = 'first', ds_id: str | None = None)[source]
A multi-level dataset whose level datasets are created by down-sampling a base dataset.
- Parameters:
base_dataset – The base dataset for the level at index zero.
grid_mapping – Optional grid mapping for base_dataset.
num_levels – Optional number of levels.
ds_id – Optional dataset identifier.
agg_methods – Optional aggregation methods. May be given as string or as mapping from variable name pattern to aggregation method. Valid aggregation methods are None, “first”, “min”, “max”, “mean”, “median”. If None, the default, “first” is used for integer variables and “mean” for floating point variables.
- class xcube.core.mldataset.CombinedMultiLevelDataset(ml_datasets: Sequence[MultiLevelDataset], ds_id: str | None = None, combiner_function: Callable | None = None, combiner_params: Dict[str, Any] | None = None)[source]
A multi-level dataset that is a combination of other multi-level datasets.
- Parameters:
ml_datasets – The multi-level datasets to be combined. At least two must be provided.
ds_id – Optional dataset identifier.
combiner_function – An optional function used to combine the datasets, for example
xarray.merge. If given, it receives a list of datasets (xarray.Datasetinstances) and combiner_params as keyword arguments. If not given orNoneis passed, a copy of the first dataset is made, which is then subsequently updated by the remaining datasets usingxarray.Dataset.update().combiner_params – Parameters to the combiner_function passed as keyword arguments.
- class xcube.core.mldataset.ComputedMultiLevelDataset(script_path: str, callable_name: str, input_ml_dataset_ids: ~typing.Sequence[str], input_ml_dataset_getter: ~typing.Callable[[str], ~xcube.core.mldataset.abc.MultiLevelDataset], input_parameters: ~typing.Mapping[str, ~typing.Any] | None = None, ds_id: str = '', exception_type: type = <class 'ValueError'>)[source]
A multi-level dataset whose level datasets are computed by a user function.
The script can import other Python modules located in the same directory as script_path.
- class xcube.core.mldataset.FsMultiLevelDataset(path: str, fs: AbstractFileSystem | None = None, fs_root: str | None = None, fs_kwargs: Mapping[str, Any] | None = None, cache_size: int | None = None, consolidate: bool | None = None, **zarr_kwargs)[source]
- class xcube.core.mldataset.IdentityMultiLevelDataset(ml_dataset: MultiLevelDataset, ds_id: str = None)[source]
The identity.
- class xcube.core.mldataset.LazyMultiLevelDataset(grid_mapping: GridMapping | None = None, num_levels: int | None = None, ds_id: str | None = None, parameters: Mapping[str, Any] | None = None)[source]
A multi-level dataset where each level dataset is lazily retrieved, i.e. read or computed by the abstract method
get_dataset_lazily(index, **kwargs).- Parameters:
ds_id – Optional dataset identifier.
parameters – Optional keyword arguments that will be passed to the
get_dataset_lazilymethod.
- property grid_mapping: GridMapping
- Returns:
the CF-conformal grid mapping
- property lock: RLock
Get the reentrant lock used by this object to synchronize lazy instantiation of properties.
- get_dataset(index: int) Dataset[source]
Get or compute the dataset for the level at given index.
- Return type:
- Parameters:
index (
int) – the level index- Returns:
the dataset for the level at index.
- set_dataset(index: int, level_dataset: Dataset)[source]
Set the dataset for the level at given index.
Callers need to ensure that the given level_dataset has the correct spatial dimension sizes for the given level at index.
- Parameters:
index (
int) – the level indexlevel_dataset (
Dataset) – the dataset for the level at index.
Zarr Store
- class xcube.core.zarrstore.ZarrStoreHolder(dataset: Dataset)[source]
Represents a xarray dataset property
zarr_store.It is used to permanently associate a dataset with its Zarr store, which would otherwise not be possible.
In xcube server, we use the new property to expose datasets via the S3 emulation API.
For that concept to work, datasets must be associated with their Zarr stores explicitly. Therefore, the xcube data store framework sets the Zarr stores of datasets after opening them
xr.open_zarr():`python dataset = xr.open_zarr(zarr_store, **open_params) dataset.zarr_store.set(zarr_store) `Note, that the dataset may change after the Zarr store has been set, so that the dataset and its Zarr store are no longer in sync. This may be an issue and limit the application of the new property.
- Parameters:
dataset – The xarray dataset that is associated with a Zarr store.
- get() MutableMapping[source]
Get the Zarr store of a dataset. If no Zarr store has been set, the method will use
GenericZarrStore.from_dataset()to create and set one.- Returns:
The Zarr store.
- set(zarr_store: MutableMapping) None[source]
Set the Zarr store of a dataset. :type zarr_store:
MutableMapping:param zarr_store: The Zarr store.
- class xcube.core.zarrstore.GenericZarrStore(*arrays: GenericArray | Dict[str, Any], attrs: Dict[str, Any] | None = None, array_defaults: GenericArray | Dict[str, Any] | None = None)[source]
A Zarr store that maintains generic arrays in a flat, top-level hierarchy. The root of the store is a Zarr group conforming to the Zarr spec v2.
It is designed to serve as a Zarr store for xarray datasets that compute their data arrays dynamically.
See class
GenericArrayfor specifying the arrays’ properties.The array data of this store’s arrays are either retrieved from static (numpy) arrays or from a callable that provides the array’s data chunks as bytes or numpy arrays.
- Parameters:
arrays – Arrays to be added. Typically, these will be instances of
GenericArray.attrs – Optional attributes of the top-level group. If given, it must be JSON serializable.
array_defaults – Optional array defaults for array properties not passed to
add_array. Typically, this will be an instance ofGenericArray.
- Array
alias of
GenericArray
- add_array(array: GenericArray | Dict[str, Any] | None = None, **array_kwargs) None[source]
Add a new array to this store.
- Parameters:
array – Optional array properties. Typically, this will be an instance of
GenericArray.array_kwargs – Keyword arguments form for the properties of
GenericArray.
- listdir(path: str = '') List[str][source]
List a store path. :type path:
str:param path: The path. :return: List of sorted directory entries.
- rmdir(path: str = '') None[source]
The general form removes store paths. This implementation can remove entire arrays only. :type path:
str:param path: The array’s name.
- rename(src_path: str, dst_path: str) None[source]
The general form renames store paths. This implementation can rename arrays only.
- Parameters:
src_path (
str) – Source array name.dst_path (
str) – Target array name.
- classmethod from_dataset(dataset: Dataset, array_defaults: GenericArray | Dict[str, Any] | None = None) GenericZarrStore[source]
Create a Zarr store for given dataset. to the dataset’s attributes. The following array_defaults properties can be provided (other properties are prescribed by the dataset):
fill_value- defaults to Nonecompressor- defaults to Nonefilters- defaults to Noneorder- defaults to “C”chunk_encoding- defaults to “bytes”
- Parameters:
dataset (
Dataset) – The datasetarray_defaults – Array default values.
- Returns:
A new Zarr store instance.
- class xcube.core.zarrstore.GenericArray(array: Dict[str, any] | None = None, name: str | None = None, get_data: Callable[[Tuple[int]], bytes | ndarray] | None = None, get_data_params: Dict[str, Any] | None = None, data: ndarray | None = None, dtype: str | dtype | None = None, dims: str | Sequence[str] | None = None, shape: Sequence[int] | None = None, chunks: Sequence[int] | None = None, fill_value: bool | int | float | str | None = None, compressor: Codec | None = None, filters: Sequence[Codec] | None = None, order: str | None = None, attrs: Dict[str, Any] | None = None, on_close: Callable[[Dict[str, Any]], None] | None = None, chunk_encoding: str | None = None, **kwargs)[source]
Represent a generic array in the
GenericZarrStoreas dictionary of properties.Although all properties of this class are optional, some of them are mandatory when added to the
GenericZarrStore.When added to the store using
GenericZarrStore.add_array(), the array name and dims must always be present. Other mandatory properties depend on the data and get_data properties, which are mutually exclusive:get_data is called for a requested data chunk of an array. It must return a bytes object or a numpy nd-array and is passed the chunk index, the chunk shape, and this array info dictionary. get_data requires the following properties to be present too: name, dims, dtype, shape. chunks is optional and defaults to shape.
data must be a bytes object or a numpy nd-array. data requires the following properties to be present too: name, dims. chunks must be same as shape.
The function get_data receives only keyword-arguments which comprises the ones passed by get_data_params, if any, and two special ones which may occur in the signature of get_data:
The keyword argument chunk_info, if given, provides a dictionary that holds information about the current chunk: -
index: tuple[int, ...]- the chunk’s index -shape: tuple[int, ...]- the chunk’s shape -slices: tuple[slice, ...]- the chunk’s array slicesThe keyword argument array_info, if given, provides a dictionary that holds information about the overall array. It contains all array properties passed to the constructor of
GenericArrayplus -ndim: int- number of dimensions -num_chunks: tuple[int, ...]- number of chunks in every dimension
GenericZarrStorewill convert a Numpy array returned by get_data or given by data into a bytes object. It will also be compressed, if a compressor is given. It is important that the array chunks always See also https://zarr.readthedocs.io/en/stable/spec/v2.html#chunksNote that if the value of a named keyword argument is None, it will not be stored.
- Parameters:
array – Optional array info dictionary
name – Optional array name
data – Optional array data. Mutually exclusive with get_data. Must be a bytes object or a numpy array.
get_data – Optional array data chunk getter. Mutually exclusive with data. Called for a requested data chunk of an array. Must return a bytes object or a numpy array.
get_data_params – Optional keyword-arguments passed to get_data.
dtype – Optional array data type. Either a string using syntax of the Zarr spec or a
numpy.dtype. For string encoded data types, see https://zarr.readthedocs.io/en/stable/spec/v2.html#data-type-encodingdims – Optional sequence of dimension names.
shape – Optional sequence of shape sizes for each dimension.
chunks – Optional sequence of chunk sizes for each dimension.
fill_value – Optional fill value, see https://zarr.readthedocs.io/en/stable/spec/v2.html#fill-value-encoding
compressor – Optional compressor. If given, it must be an instance of
numcodecs.abc.Codec.filters – Optional sequence of filters, see https://zarr.readthedocs.io/en/stable/spec/v2.html#filters.
order – Optional array endian ordering. If given, must be “C” or “F”. Defaults to “C”.
attrs – Optional array attributes. If given, must be JSON-serializable.
on_close – Optional array close handler. Called if the store is closed.
chunk_encoding – Optional encoding type of the chunk data returned for the array. Can be “bytes” (the default) or “ndarray” for array chunks that are numpy.ndarray instances.
kwargs – Other keyword arguments passed directly to the dictionary constructor.
- finalize() GenericArray[source]
Normalize and validate array properties and return a valid array info dictionary to be stored in the GenericZarrStore.
- class xcube.core.zarrstore.CachedZarrStore(store: MutableMapping, cache: MutableMapping)[source]
A read-only Zarr store that is faster than store because it uses a writable cache store.
The cache store is assumed to read values for a given key much faster than store.
Note that iterating keys and containment checks are performed on store only.
- Parameters:
store – A Zarr store that is known to be slow in reading values.
cache – A writable Zarr store that can read values faster than store.
- class xcube.core.zarrstore.DiagnosticZarrStore(store: MutableMapping)[source]
A diagnostic Zarr store used for testing and investigating behaviour of Zarr and xarray’s Zarr backend.
- Parameters:
store – Wrapped Zarr store.
Utilities
- class xcube.core.gridmapping.GridMapping(size: int | Tuple[int, int], tile_size: int | Tuple[int, int] | None, xy_bbox: Tuple[int | float, int | float, int | float, int | float], xy_res: int | float | Tuple[int | float, int | float], crs: CRS, xy_var_names: Tuple[str, str], xy_dim_names: Tuple[str, str], is_regular: bool | None, is_lon_360: bool | None, is_j_axis_up: bool | None, x_coords: DataArray | None = None, y_coords: DataArray | None = None, xy_coords: DataArray | None = None)[source]
An abstract base class for grid mappings that define an image grid and a transformation from image pixel coordinates to spatial Earth coordinates defined in a well-known coordinate reference system (CRS).
This class cannot be instantiated directly. Use one of its factory methods to create instances:
:meth:regular()
:meth:from_dataset()
:meth:from_coords()
Some instance methods can be used to derive new instances:
:meth:derive()
:meth:scale()
:meth:transform()
:meth:to_regular()
This class is thread-safe.
- derive(xy_var_names: Tuple[str, str] = None, xy_dim_names: Tuple[str, str] = None, tile_size: int | Tuple[int, int] = None, is_j_axis_up: bool = None)[source]
Derive a new grid mapping from this one with some properties changed.
- Parameters:
xy_var_names – The new x-, and y-variable names.
xy_dim_names – The new x-, and y-dimension names.
tile_size – The new tile size
is_j_axis_up (
bool) – Whether j-axis points up.
- Returns:
A new, derived grid mapping.
- scale(xy_scale: int | float | Tuple[int | float, int | float], tile_size: int | Tuple[int, int] = None) GridMapping[source]
Derive a scaled version of this regular grid mapping.
Scaling factors lower than one correspond to up-scaling (pixels sizes decrease, image size increases).
Scaling factors larger than one correspond to down-scaling. (pixels sizes increase, image size decreases).
- Parameters:
xy_scale – The x-, and y-scaling factors. May be a single number or tuple.
tile_size – The new tile size
- Returns:
A new, scaled grid mapping.
- property x_coords
The 1D or 2D x-coordinate array of shape (width,) or (height, width).
- property y_coords
The 1D or 2D y-coordinate array of shape (width,) or (height, width).
- property xy_coords: DataArray
The x,y coordinates as data array of shape (2, height, width). Coordinates are given in units of the CRS.
- property xy_var_names: Tuple[str, str]
The variable names of the x,y coordinates as tuple (x_var_name, y_var_name).
- property xy_dim_names: Tuple[str, str]
The dimension names of the x,y coordinates as tuple (x_dim_name, y_dim_name).
- property crs: CRS
The coordinate reference system.
- property is_lon_360: bool | None
Check whether x_max is greater than 180 degrees. Effectively tests whether the range x_min, x_max crosses the anti-meridian at 180 degrees. Works only for geographical coordinate reference systems.
- property is_regular: bool | None
Do the x,y coordinates for a regular grid? A regular grid has a constant delta in both x- and y-directions of the x- and y-coordinates. :return None, if this property cannot be determined,
True or False otherwise.
- property is_j_axis_up: bool | None
Does the positive image j-axis point up? By default, the positive image j-axis points down.
- :return None, if this property cannot be determined,
True or False otherwise.
- property ij_to_xy_transform: Tuple[Tuple[int | float, int | float, int | float], Tuple[int | float, int | float, int | float]]
The affine transformation matrix from image to CRS coordinates. Defined only for grid mappings with rectified x,y coordinates.
- property xy_to_ij_transform: Tuple[Tuple[int | float, int | float, int | float], Tuple[int | float, int | float, int | float]]
The affine transformation matrix from CRS to image coordinates. Defined only for grid mappings with rectified x,y coordinates.
- ij_transform_to(other: GridMapping) Tuple[Tuple[int | float, int | float, int | float], Tuple[int | float, int | float, int | float]][source]
Get the affine transformation matrix that transforms image coordinates of other into image coordinates of this image geometry.
Defined only for grid mappings with rectified x,y coordinates.
- Parameters:
other – The other image geometry
- Returns:
Affine transformation matrix
- ij_transform_from(other: GridMapping) Tuple[Tuple[int | float, int | float, int | float], Tuple[int | float, int | float, int | float]][source]
Get the affine transformation matrix that transforms image coordinates of this image geometry to image coordinates of other.
Defined only for grid mappings with rectified x,y coordinates.
- Parameters:
other – The other image geometry
- Returns:
Affine transformation matrix
- ij_bbox_from_xy_bbox(xy_bbox: Tuple[float, float, float, float], xy_border: float = 0.0, ij_border: int = 0) Tuple[int, int, int, int][source]
Compute bounding box in i,j pixel coordinates given a bounding box xy_bbox in x,y coordinates.
- Parameters:
xy_bbox – Box (x_min, y_min, x_max, y_max) given in the same CS as x and y.
xy_border (
float) – If non-zero, grows the bounding box xy_bbox before using it for comparisons. Defaults to 0.ij_border (
int) – If non-zero, grows the returned i,j bounding box and clips it to size. Defaults to 0.
- Returns:
Bounding box in (i_min, j_min, i_max, j_max) in pixel coordinates. Returns
(-1, -1, -1, -1)if xy_bbox isn’t intersecting any of the x,y coordinates.
- ij_bboxes_from_xy_bboxes(xy_bboxes: ndarray, xy_border: float = 0.0, ij_border: int = 0, ij_bboxes: ndarray = None) ndarray[source]
Compute bounding boxes in pixel coordinates given bounding boxes xy_bboxes [[x_min, y_min, x_max, y_max], …] in x,y coordinates.
The returned array in i,j pixel coordinates has the same shape as xy_bboxes. The value ranges in the returned array [[i_min, j_min, i_max, j_max], ..]] are:
i_min from 0 to width-1, i_max from 1 to width;
j_min from 0 to height-1, j_max from 1 to height;
so the i,j pixel coordinates can be used as array index slices.
- Return type:
- Parameters:
xy_bboxes (
ndarray) – Numpy array of x,y bounding boxes [[x_min, y_min, x_max, y_max], …] given in the same CS as x and y.xy_border (
float) – If non-zero, grows the bounding box xy_bbox before using it for comparisons. Defaults to 0.ij_border (
int) – If non-zero, grows the returned i,j bounding box and clips it to size. Defaults to 0.ij_bboxes (
ndarray) – Numpy array of pixel i,j bounding boxes [[x_min, y_min, x_max, y_max], …]. If given, must have same shape as xy_bboxes.
- Returns:
Bounding boxes in [[i_min, j_min, i_max, j_max], ..]] in pixel coordinates.
- to_dataset_attrs() Mapping[str, Any][source]
Get spatial dataset attributes as recommended by https://wiki.esipfed.org/Attribute_Convention_for_Data_Discovery_1-3#Recommended
- Returns:
dictionary with dataset coordinate attributes.
- to_coords(xy_var_names: Tuple[str, str] = None, xy_dim_names: Tuple[str, str] = None, exclude_bounds: bool = False, reuse_coords: bool = False) Mapping[str, DataArray][source]
Get CF-compliant axis coordinate variables and cell boundary coordinate variables.
Defined only for grid mappings with regular x,y coordinates.
- Parameters:
xy_var_names – Optional coordinate variable names (x_var_name, y_var_name).
xy_dim_names – Optional coordinate dimensions names (x_dim_name, y_dim_name).
exclude_bounds (
bool) – If True, do not create bounds coordinates. Defaults to False.reuse_coords (
bool) – Whether to either reuse target coordinate arrays from target_gm or to compute new ones.
- Returns:
dictionary with coordinate variables
- transform(crs: str | CRS, *, tile_size: int | Tuple[int, int] = None, xy_var_names: Tuple[str, str] = None, tolerance: float = 1e-05) GridMapping[source]
Transform this grid mapping so it uses the given spatial coordinate reference system into another crs.
- Parameters:
crs – The new spatial coordinate reference system.
tile_size – Optional new tile size.
xy_var_names – Optional new coordinate names.
tolerance (
float) – Absolute tolerance used when comparing coordinates with each other. Must be in the units of the crs and must be greater zero.
- Returns:
A new grid mapping that uses crs.
- classmethod regular(size: int | Tuple[int, int], xy_min: Tuple[float, float], xy_res: float | Tuple[float, float], crs: str | CRS, *, tile_size: int | Tuple[int, int] = None, is_j_axis_up: bool = False) GridMapping[source]
Create a new regular grid mapping.
- Parameters:
size – Size in pixels.
xy_min – Minimum x- and y-coordinates.
xy_res – Resolution in x- and y-directions.
crs – Spatial coordinate reference system.
tile_size – Optional tile size.
is_j_axis_up (
bool) – Whether positive j-axis points up. Defaults to false.
- Returns:
A new regular grid mapping.
- to_regular(tile_size: int | Tuple[int, int] = None, is_j_axis_up: bool = False) GridMapping[source]
Transform this grid mapping into one that is regular.
- Parameters:
tile_size – Optional tile size.
is_j_axis_up (
bool) – Whether positive j-axis points up. Defaults to false.
- Returns:
A new regular grid mapping or this grid mapping, if it is already regular.
- classmethod from_dataset(dataset: Dataset, *, crs: str | CRS = None, tile_size: int | Tuple[int, int] = None, prefer_is_regular: bool = True, prefer_crs: str | CRS = None, emit_warnings: bool = False, tolerance: float = 1e-05) GridMapping[source]
Create a grid mapping for the given dataset.
- Parameters:
dataset (
Dataset) – The dataset.crs – Optional spatial coordinate reference system.
tile_size – Optional tile size
prefer_is_regular (
bool) – Whether to prefer a regular grid mapping if multiple found. Default is True.prefer_crs – The preferred CRS of a grid mapping if multiple found.
emit_warnings (
bool) – Whether to emit warning for non-CF compliant datasets.tolerance (
float) – Absolute tolerance used when comparing coordinates with each other. Must be in the units of the crs and must be greater zero.
- Returns:
a new grid mapping instance.
- classmethod from_coords(x_coords: DataArray, y_coords: DataArray, crs: str | CRS, *, tile_size: int | Tuple[int, int] = None, tolerance: float = 1e-05) GridMapping[source]
Create a grid mapping from given x- and y-coordinates x_coords, y_coords and spatial coordinate reference system crs.
- Parameters:
x_coords (
DataArray) – The x-coordinates.y_coords (
DataArray) – The y-coordinates.crs – The spatial coordinate reference system.
tile_size – Optional tile size.
tolerance (
float) – Absolute tolerance used when comparing coordinates with each other. Must be in the units of the crs and must be greater zero.
- Returns:
A new grid mapping.
- is_close(other: GridMapping, tolerance: float = 1e-05) bool[source]
Tests whether this grid mapping is close to other.
- Return type:
bool- Parameters:
other – The other grid mapping.
tolerance (
float) – Absolute tolerance used when comparing coordinates with each other. Must be in the units of the crs and must be greater zero.
- Returns:
True, if so, False otherwise.
- xcube.core.geom.convert_geometry(geometry: BaseGeometry | Dict[str, Any] | str | Sequence[float | int] | None) BaseGeometry | None[source]
Convert a geometry-like object into a shapely geometry object (
shapely.geometry.BaseGeometry).A geometry-like object may be any shapely geometry object, * a dictionary that can be serialized to valid GeoJSON, * a WKT string, * a box given by a string of the form “<x1>,<y1>,<x2>,<y2>”
or by a sequence of four numbers x1, y1, x2, y2,
a point by a string of the form “<x>,<y>” or by a sequence of two numbers x, y.
Handling of geometries crossing the anti-meridian:
If box coordinates are given, it is allowed to pass x1, x2 where x1 > x2, which is interpreted as a box crossing the anti-meridian. In this case the function splits the box along the anti-meridian and returns a multi-polygon.
In all other cases, 2D geometries are assumed to _not cross the anti-meridian at all_.
- Parameters:
geometry – A geometry-like object
- Returns:
Shapely geometry object or None.
- class xcube.core.schema.CubeSchema(shape: Sequence[int], coords: Mapping[str, DataArray], x_name: str = 'lon', y_name: str = 'lat', time_name: str = 'time', dims: Sequence[str] = None, chunks: Sequence[int] = None)[source]
A schema that can be used to create new xcube datasets. The given shape, dims, and chunks, coords apply to all data variables.
- Parameters:
shape – A tuple of dimension sizes.
coords – A dictionary of coordinate variables. Must have values for all dims.
dims – A sequence of dimension names. Defaults to
('time', 'lat', 'lon').chunks – A tuple of chunk sizes in each dimension.
- classmethod new(cube: Dataset) CubeSchema[source]
Create a cube schema from given cube.
- xcube.util.dask.new_cluster(provider: str = 'coiled', name: str | None = None, software: str | None = None, n_workers: int = 4, resource_tags: Dict[str, str] | None = None, account: str = None, region: str = 'eu-central-1', **kwargs) Cluster[source]
Create a new Dask cluster.
Cloud resource tags can be specified in an environment variable XCUBE_DASK_CLUSTER_TAGS in the format
tag_1=value_1:tag_2=value_2:...:tag_n=value_n. In case of conflicts, tags specified inresource_tagswill override tags specified by the environment variable.The cluster provider account name can be specified in an environment variable
XCUBE_DASK_CLUSTER_ACCOUNT. If theaccountargument is given tonew_cluster, it will override the value from the environment variable.- Return type:
Cluster- Parameters:
provider (
str) – identifier of the provider to use. Currently, only ‘coiled’ is supported.name – name to use as an identifier for the cluster
software – identifier for the software environment to be used.
n_workers (
int) – number of workers in the clusterresource_tags – tags to apply to the cloud resources forming the cluster
account (
str) – cluster provider account name**kwargs –
further named arguments will be passed on to the cluster creation function
region (
str) – default region where workers of the cluster will be deployed set to eu-central-1
Plugin Development
- class xcube.util.extension.ExtensionRegistry[source]
A registry of extensions. Typically used by plugins to register extensions.
- has_extension(point: str, name: str) bool[source]
Test if an extension with given point and name is registered.
- Return type:
bool- Parameters:
point (
str) – extension point identifiername (
str) – extension name
- Returns:
True, if extension exists
- get_extension(point: str, name: str) Extension | None[source]
Get registered extension for given point and name.
- Parameters:
point (
str) – extension point identifiername (
str) – extension name
- Returns:
the extension or None, if no such exists
- get_component(point: str, name: str) Any[source]
Get extension component for given point and name. Raises a ValueError if no such extension exists.
- Return type:
Any- Parameters:
point (
str) – extension point identifiername (
str) – extension name
- Returns:
extension component
- find_extensions(point: str, predicate: Callable[[Extension], bool] = None) List[Extension][source]
Find extensions for point and optional filter function predicate.
The filter function is called with an extension and should return a truth value to indicate a match or mismatch.
- Parameters:
point (
str) – extension point identifierpredicate – optional filter function
- Returns:
list of matching extensions
- find_components(point: str, predicate: Callable[[Extension], bool] = None) List[Any][source]
Find extension components for point and optional filter function predicate.
The filter function is called with an extension and should return a truth value to indicate a match or mismatch.
- Parameters:
point (
str) – extension point identifierpredicate – optional filter function
- Returns:
list of matching extension components
- add_extension(point: str, name: str, component: Any = None, loader: Callable[[Extension], Any] = None, **metadata) Extension[source]
Register an extension component or an extension component loader for the given extension point, name, and additional metadata.
Either component or loader must be specified, but not both.
A given loader must be a callable with one positional argument extension of type
Extensionand is expected to return the actual extension component, which may be of any type. The loader will only be called once and only when the actual extension component is requested for the first time. Consider using the functionimport_component()to create a loader that lazily imports a component from a module and optionally executes it.- Return type:
- Parameters:
point (
str) – extension point identifiername (
str) – extension namecomponent (
Any) – extension componentloader – extension component loader function
metadata – extension metadata
- Returns:
a registered extension
- class xcube.util.extension.Extension(point: str, name: str, component: Any = None, loader: Callable[[Extension], Any] = None, **metadata)[source]
An extension that provides a component of any type.
Extensions are registered in a
ExtensionRegistry.Extension objects are not meant to be instantiated directly. Instead,
ExtensionRegistry.add_extension()is used to register extensions.- Parameters:
point – extension point identifier
name – extension name
component – extension component
loader – extension component loader function
metadata – extension metadata
- xcube.util.extension.import_component(spec: str, transform: Callable[[Any, Extension], Any] = None, call: bool = False, call_args: Sequence[Any] = None, call_kwargs: Mapping[str, Any] = None) Callable[[Extension], Any][source]
Return a component loader that imports a module or module component from spec. To import a module, spec should be the fully qualified module name. To import a component, spec must also append the component name to the fully qualified module name separated by a color (“:”) character.
An optional transform callable my be used to transform the imported component. If given, a new component is computed:
component = transform(component, extension)
If the call flag is set, the component is expected to be a callable which will be called using the given call_args and call_kwargs to produce a new component:
component = component(*call_kwargs, **call_kwargs)
Finally, the component is returned.
- Parameters:
spec (
str) – String of the form “module_path” or “module_path:component_name”transform – callable that takes two positional arguments, the imported component and the extension of type
Extensioncall (
bool) – Whether to finally call the component with given call_args and call_kwargscall_args – arguments passed to a callable component if call flag is set
call_kwargs – keyword arguments passed to callable component if call flag is set
- Returns:
a component loader
- xcube.constants.EXTENSION_POINT_INPUT_PROCESSORS = 'xcube.core.gen.iproc'
The extension point identifier for input processor extensions
- xcube.constants.EXTENSION_POINT_DATASET_IOS = 'xcube.core.dsio'
The extension point identifier for dataset I/O extensions
- xcube.constants.EXTENSION_POINT_CLI_COMMANDS = 'xcube.cli'
The extension point identifier for CLI command extensions
- xcube.util.plugin.get_extension_registry() ExtensionRegistry[source]
Get populated extension registry.
Web API and Server
xcube’s RESTful web API is used to publish data cubes to clients. Using the API, clients can
List configured xcube datasets;
Get xcube dataset details including metadata, coordinate data, and metadata about all included variables;
Get cube data;
Extract time-series statistics from any variable given any geometry;
Get spatial image tiles from any variable;
Browse datasets and retrieve dataset data and metadata using the STAC API;
Get places (GeoJSON features including vector data) that can be associated with xcube datasets;
Perform compute operations on datasets, with the results calculated on demand and presented as new, dynamically generated datasets;
Retrieve coverages backed by datasets using the OGC API - Coverages API.
Later versions of API will also allow for xcube dataset management including generation, modification, and deletion of xcube datasets.
The complete description of all available functions is provided via openapi.html after starting the server locally. Please check out Publishing xcube datasets to learn how to do access it.
The web API is provided through the xcube server which is started using the xcube serve CLI command.
Viewer App
The xcube viewer app is a simple, single-page web application to be used with the xcube server.
Demo
To test the viewer app, you can use the xcube viewer demo. This is our Brockmann Consult Demo xcube viewer. Via the viewer’s settings it is possible to change the xcube server url which is used for displaying data. To do so open the viewer’s settings panels, select “Server”. A “Select Server” panel is opened, click the “+” button to add a new server. Here is demo server that you may add for testing:
Euro Data Cube Server (
https://edc-api.brockmann-consult.de/api) has integrated amongst others a data cube with global essential climate variables (ECVs) variables from the ESA Earth System Data Lab Project. To access the Euro Data Cube viewer directly please visit https://edc-viewer.brockmann-consult.de .
Functionality
The xcube viewer functionality is described exemplary using the xcube viewer demo. The viewer visualizes data from the xcube datasets on top of a basemap. For zooming use the buttons in the top right corner of the map window or the zooming function of your computer mouse. A scale for the map is located in the lower right corner and in the upper left corner a corresponding legend to the mapped data of the data cube is available.
A xcube viewer may hold several xcube datasets which you can select via the drop-down menu Dataset. The viewed area automatically adjusts to a selected xcube dataset, meaning that if a newly selected dataset is located in a different region, the correct region is displayed on the map.
If more than one variable is available within a selected xcube dataset, you may change the variable by using the drop-down menu Variable.
To see metadata for a dataset click on the info-icon on the right-hand side. Besides the dataset metadata you will see the metadata for the selected variable.
To obtain a time series set a point marker on the map and then select the graph-icon next to the Variables drop-down menu. You can select a different date by clicking into the time series graph on a value of interest. The data displayed in the viewer changes accordingly to the newly selected date.
The current date is preserved when you select a different variable and the data of the variable is mapped for the date.
To generate a time series for the newly selected variable press the time series-icon again.
You may place multiple points on the map and you can generate time series for them. This allows a comparison between two locations. The color of the points corresponds to the color of the graph in the time series. You can find the coordinates of the point markers visualized in the time series beneath the graphs.
To delete a created location use the remove-icon next to the Place drop-down menu. Not only point location may be selected via the viewer, you can draw polygons and circular areas by using the icons on the right-hand side of the Place drop-down menu as well. You can visualize time series for areas, too.
In order to change the date for the data display use the calendar or step through the time line with the arrows on the right-hand side of the calendar.
When a time series is displayed two time-line tools are visible, the upper one for selecting the date displayed on the map of the viewer and the lower one may be used to narrow the time frame displayed in the time series graph. Just above the graph of the time series on the right-hand side is an x-icon for removing the time series from the view and to left of it is an icon which sets the time series back to the whole time extent.
To adjust the default settings select the Settings-icon on the very top right corner. There you have the possibility to change the server url, in order to view data which is available via a different server. You can choose a different language - if available - as well as set your preferences of displaying data and graph of the time series.
To see the map settings please scroll down in the settings window. There you can adjust the base map, switch the displayed projection between Geographic and Mercator. You can also choose to turn image smoothing on and to view the dataset boundaries.
On the very bottom of the Settings pop-up window you can see information about the viewer and server version.
Back to the general view, if you would like to change the value ranges of the displayed variable you can do it by clicking into the area of the legend where the value ticks are located or you can enter the desired values in the Minimum and/or Maximum text field.
You can change the color mapping as well by clicking into the color range of the legend. There you can also decide to hide lower values and it is possible to adjust the opacity.
The xcube viewer app is constantly evolving and enhancements are added, therefore please be aware that the above described features may not always be completely up-to-date.
Build and Deploy
You can also build and deploy your own viewer instance. In the latter case, visit the xcube-viewer repository on GitHub and follow the instructions provides in the related README file.
Data Access
In xcube, data cubes are raster datasets that are basically a collection of N-dimensional geo-physical variables represented by xarray.Dataset Python objects (see also xcube Dataset Convention). Data cubes may be provided by a variety of sources and may be stored using different data formats. In the simplest case you have a NetCDF file or a Zarr directory in your local filesystem that already represents a data cube. Data cubes may also be stored on AWS S3 or Google Cloud Storage using the Zarr format. Sometimes a set of NetCDF or GeoTIFF files in some storage must first be concatenated to form a data cube. In other cases, data cubes can be generated on-the-fly by suitable requests to some cloud-hosted data API such as the ESA Climate Data Centre or Sentinel Hub.
Data Store Framework
The xcube data store framework provides a simple and consistent Python interface that is used to open xarray.Dataset and other data objects from data stores which abstract away the individual data sources, protocols, formats and hides involved data processing steps. For example, the following two lines open a data cube from the ESA Climate Data Centre comprising the essential climate variable Sea Surface Temperature (SST):
store = new_data_store("cciodp")
cube = store.open_data("esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.1-1.r1")
Often, and in the example above, data stores create data cube views on a given data source. That is, the actual data arrays are subdivided into chunks and each chunk is fetched from the source in a “lazy” manner. In such cases, the xarray.Dataset’s variables are backed by Dask arrays. This allows data cubes to be virtually of any size.
Data stores can provide the data using different Python in-memory representations or data types. The most common representation for a data cube is an xarray.Dataset instance, multi-resolution data cubes would be represented as a xcube MultiLevelDataset instance (see also xcube Multi-Level Dataset Convention). Vector data is usually provided as an instance of geopandas.GeoDataFrame.
Data stores can also be writable. All read-only data stores share the same
functional interface share the same functional interface and so do writable
data stores. Of course, different data stores will have different
configuration parameters. Also, the parameters passed to the open_data()
method, or respectively the write_data() method, may change based on the
store’s capabilities.
Depending on what is offered by a given data store, also the parameters
passed to the open_data() method may change.
The xcube data store framework is exported from the xcube.core.store package, see also its API reference.
The DataStore abstract base class is the primary user interface for accessing data in xcube. The most important operations of a data store are:
list_data_ids()- enumerate the datasets of a data store by returning their data identifiers;describe_data(data_id)- describe a given dataset in terms of its metadata by returning a specific DataDescriptor, e.g., a DatasetDescriptor;search_data(...)- search for datasets in the data store and return a DataDescriptor iterator;open_data(data_id, ...)- open a given dataset and return, e.g., an xarray.Dataset instance.
The MutableDataStore abstract base class represents a writable data store and extends DataStore by the following operations:
write_data(dataset, data_id, ...)- write a dataset to the data store;delete_data(data_id)- delete a dataset from the data store;
Above, the ellipses ... are used to indicate store-specific parameters
that are passed as keyword-arguments. For a given data store instance,
it is not obvious what are parameters are allowed. Therefore, data stores
provide a programmatic way to describe the allowed parameters for the
operations of a given data store by the means of a parameter schema:
get_open_data_params_schema()- describes parameters ofopen_data();get_search_data_params_schema()- describes parameters ofsearch_data();get_write_data_params_schema()- describes parameters ofwrite_data().
All operations return an instance of a JSON Object Schema. The JSON object’s properties describe the set of allowed and required parameters as well as the type and value range of each parameter. The schemas are also used internally to validate the parameters passed by the user.
xcube comes with a predefined set of writable, filesystem-based data stores, Since data stores are xcube extensions, additional data stores can be added by xcube plugins. The data store framework provides a number of global functions that can be used to access the available data stores:
find_data_store_extensions() -> list[Extension]- get a list of xcube data store extensions;new_data_store(store_id, ...) -> DataStore- instantiate a data store with store-specific parameters;get_data_store_params_schema(store_id) -> Schema- describe the store-specific parameters that must/can be passed tonew_data_store()as JSON Object Schema.
The following example outputs all installed data stores:
from xcube.core.store import find_data_store_extensions
for ex in find_data_store_extensions():
store_id = ex.name
store_md = ex.metadata
print(store_id, "-", store_md.get("description"))
If one of the installed data stores is, e.g. sentinelhub, you could
further introspect its specific parameters and datasets as shown in the
following example:
from xcube.core.store import get_data_store_params_schema
from xcube.core.store import new_data_store
store_schema = get_data_store_params_schema("sentinelhub")
store = new_data_store("sentinelhub",
# The following parameters are specific to the
# "sentinelhub" data store.
# Refer to the store_schema.
client_id="YOURID",
client_secret="YOURSECRET",
num_retries=250,
enable_warnings=True)
data_ids = store.list_data_ids()
# Among others, we find "S2L2A" in data_ids
open_schema = store.get_open_data_params_schema("S2L2A")
cube = store.open_data("S2L2A",
# The following parameters are specific to
# "sentinelhub" datasets, such as "S2L2A".
# Refer to the open_schema.
variable_names=["B03", "B06", "B8A"],
bbox=[9, 53, 20, 62],
spatial_res=0.025,
crs="WGS-84",
time_range=["2022-01-01", "2022-01-05"],
time_period="1D")
Available Data Stores
This sections lists briefly the official data stores available for xcube. We provide the store identifier, list the store parameters, and list the common parameters used to open data cubes, i.e., xarray.Dataset instances.
Note that some data stores the open parameters may differ by from dataset to dataset depending on the actual dataset layout, coordinate references system or data type. Some data stores may also provide vector data.
For every data store we also provide a dedicated example Notebook that demonstrates its specific usage in examples/notebooks/datastores.
Filesystem-based data stores
The following filesystem-based data stores are available in xcube:
"file"for the local filesystem;"s3"for AWS S3 compatible object storage;"abfs"for Azure blob storage;"memory"for mimicking an in-memory filesystem.
All filesystem-based data store have the following parameters:
root: str- The root directory of the store in the filesystem. Defaults to''.max_depth: int- Maximum directory traversal depth. Defaults to1.read_only: bool- Whether this store is read-only. Defaults toFalse.includes: list[str]- A list of paths to include into the store. May contain wildcards*and?. Defaults toUNDEFINED.excludes: list[str]- A list of paths to exclude from the store. May contain wildcards*and?. Defaults toUNDEFINED.storage_options: dict[str, any]- Filesystem-specific options.
The parameter storage_options is filesystem-specific. Valid
storage_options for all filesystem data stores are:
use_listings_cache: boollistings_expiry_time: floatmax_paths: intskip_instance_cache: boolasynchronous: bool
The following storage_options can be used for the file data store:
auto_mkdirs: bool- Whether, when opening a file, the directory containing it should be created (if it doesn’t already exist).
The following storage_options can be used for the s3 data store:
anon: bool- Whether to anonymously connect to AWS S3.key: str- AWS access key identifier.secret: str- AWS secret access key.token: str- Session token.use_ssl: bool- Whether to use SSL in connections to S3; may be faster without, but insecure. Defaults toTrue.requester_pays: bool- If “RequesterPays” buckets are supported. Defaults toFalse.s3_additional_kwargs: dict- parameters that are used when calling S3 API methods. Typically, used for things like “ServerSideEncryption”.client_kwargs: dict- Parameters for the botocore client.
The following storage_options can be used for the abfs data store:
anon: bool- Whether to anonymously connect to Azure Blob Storage.account_name: str- Azure storage account name.account_key: str- Azure storage account key.connection_string: str- Connection string for Azure blob storage.
All filesystem data stores can open datasets from various data formats. Datasets in Zarr, GeoTIFF / COG, or NetCDF format will be provided either by xarray.Dataset or xcube MultiLevelDataset instances. Datasets stored in GeoJSON or ESRI Shapefile will yield geopandas.GeoDataFrame instances.
Common parameters for opening xarray.Dataset instances:
cache_size: int- Defaults toUNDEFINED.group: str- Group path. (a.k.a. path in zarr terminology.). Defaults toUNDEFINED.chunks: dict[str, int | str]- Optional chunk sizes along each dimension. Chunk size values may be None, “auto” or an integer value. Defaults toUNDEFINED.decode_cf: bool- Whether to decode these variables, assuming they were saved according to CF conventions. Defaults toTrue.mask_and_scale: bool- If True, replace array values equal to attribute “_FillValue” with NaN. Use “scale_factor” and “add_offset” attributes to compute actual values.. Defaults toTrue.decode_times: bool- If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.. Defaults toTrue.decode_coords: bool- If True, decode the “coordinates” attribute to identify coordinates in the resulting dataset. Defaults toTrue.drop_variables: list[str]- List of names of variables to be dropped. Defaults toUNDEFINED.consolidated: bool- Whether to open the store using Zarr’s consolidated metadata capability. Only works for stores that have already been consolidated. Defaults toFalse.log_access: bool- Defaults toFalse.
ESA Climate Data Centre cciodp
The data store cciodp provides the datasets of
the ESA Climate Data Centre.
This data store is provided by the xcube plugin xcube-cci.
You can install it using conda install -c conda-forge xcube-cci.
Data store parameters:
endpoint_url: str- Defaults to'https://archive.opensearch.ceda.ac.uk/opensearch/request'.endpoint_description_url: str- Defaults to'https://archive.opensearch.ceda.ac.uk/opensearch/description.xml?parentIdentifier=cci'.enable_warnings: bool- Whether to output warnings. Defaults toFalse.num_retries: int- Number of retries when requesting data fails. Defaults to200.retry_backoff_max: int- Defaults to40.retry_backoff_base: float- Defaults to1.001.
Common parameters for opening xarray.Dataset instances:
variable_names: list[str]- List of variable names. Defaults to all.bbox: (float, float, float, float)- Bounding box in geographical coordinates.time_range: (str, str)- Time range.normalize_data: bool- Whether to normalize and sanitize the data. Defaults toTrue.
ESA Climate Data Centre ccizarr
A subset of the datasets of the cciodp store have been made available
using the Zarr format using the data store ccizarr. It provides
much better data access performance.
It has no dedicated data store parameters.
Its common dataset open parameters for opening xarray.Dataset instances are the same as for the filesystem-based data stores described above.
ESA SMOS
A data store for ESA SMOS data is currently under development and will be released soon.
This data store is provided by the xcube plugin xcube-smos.
Once available, you will be able to install it using
conda install -c conda-forge xcube-smos.
Copernicus Climate Data Store cds
The data store cds provides datasets of the Copernicus Climate Data Store.
This data store is provided by the xcube plugin xcube-cds.
You can install it using conda install -c conda-forge xcube-cds.
Data store parameters:
cds_api_key: str- User API key for Copernicus Climate Data Store.endpoint_url: str- API endpoint URL.num_retries: int- Defaults to200.normalize_names: bool- Defaults toFalse.
Common parameters for opening xarray.Dataset instances:
bbox: (float, float, float, float)- Bounding box in geographical coordinates.time_range: (str, str)- Time range.variable_names: list[str]- List of names of variables to be included. Defaults to all.spatial_res: float- Spatial resolution. Defaults to0.1.
Copernicus Marine Service cmems
The data store cmems provides datasets of the Copernicus Marine Service.
This data store is provided by the xcube plugin xcube-cmems.
You can install it using conda install -c conda-forge xcube-cmems.
Data store parameters:
cmems_username: str- CMEMS API usernamecmems_password: str- CMEMS API passwordcas_url: str- Defaults to'https://cmems-cas.cls.fr/cas/login'.csw_url: str- Defaults to'https://cmems-catalog-ro.cls.fr/geonetwork/srv/eng/csw-MYOCEAN-CORE-PRODUCTS?'.databases: str- One of['nrt', 'my'].server: str- Defaults to'cmems-du.eu/thredds/dodsC/'.
Common parameters for opening xarray.Dataset instances:
variable_names: list[str]- List of variable names.time_range: [str, str]- Time range.
Sentinel Hub API
The data store sentinelhub provides the datasets of the
Sentinel Hub API.
This data store is provided by the xcube plugin xcube-sh.
You can install it using conda install -c conda-forge xcube-sh.
Data store parameters:
client_id: str- Sentinel Hub API client identifierclient_secret: str- Sentinel Hub API client secretapi_url: str- Sentinel Hub API URL. Defaults to'https://services.sentinel-hub.com'.oauth2_url: str- Sentinel Hub API authorisation URL. Defaults to'https://services.sentinel-hub.com/oauth'.enable_warnings: bool- Whether to output warnings. Defaults toFalse.error_policy: str- Policy for errors while requesting data. Defaults to'fail'.num_retries: int- Number of retries when requesting data fails. Defaults to200.retry_backoff_max: int- Defaults to40.retry_backoff_base: number- Defaults to1.001.
Common parameters for opening xarray.Dataset instances:
bbox: (float, float, float, float)- Bounding box in coordinate units ofcrs. Required.crs: str- Defaults to'WGS84'.time_range: (str, str)- Time range. Required.variable_names: list[str]- List of variable names. Defaults to all.variable_fill_values: list[float]- List of fill values according tovariable_namesvariable_sample_types: list[str]- List of sample types according tovariable_namesvariable_units: list[str]- List of sample units according tovariable_namestile_size: (int, int)- Defaults to(1000, 1000).spatial_res: float- Required.upsampling: str- Defaults to'NEAREST'.downsampling: str- Defaults to'NEAREST'.mosaicking_order: str- Defaults to'mostRecent'.time_period: str- Defaults to'1D'.time_tolerance: str- Defaults to'10M'.collection_id: str- Name of the collection.four_d: bool- Defaults toFalse.extra_search_params: dict- Extra search parameters passed to a catalogue query.max_cache_size: int- Maximum chunk cache size in bytes.
Developing new data stores
Implementing the data store
New data stores can be developed by implementing the xcube DataStore interface for read-only data store, or the MutableDataStore interface for writable data stores, and should follow the xcube Data Store Conventions.
If a data store supports combinations of Python data types, external storages
types, and/or data formats it should consider the following design pattern:
Here, we implement a dedicated DataOpener for a suitable combination of
supported Python data types, external storages types, and/or data formats.
The DataStore, which implements the DataOpener interface delegates
to specialized DataOpener implementations based on the open
parameters passed to the open_data() method. The same holds for the
DataWriter implementations for a MutableDataStore.
New data stores that are backed by some cloud-based data API can make use the xcube GenericZarrStore to implement the lazy fetching of data array chunks from the API.
Registering the data store
To register the new data store with xcube, it must be provided as
a Python package. Based on the package’s name there are to ways
to register it with xcube. If your package name matches the pattern
xcube_*, then you would need to provide a function init_plugin()
in the package’s plugin module (hence {package}.plugin.init_plugin()).
Alternatively, the package can have any name, but then it must register
a setuptools entry point in the slot “xcube_plugins”. In this case the
function init_plugin() can also be placed anywhere in your code.
If you use setup.cfg:
[options.entry_points]
xcube_plugins =
{your_name} = {your_package}.plugin:init_plugin
If you are (still) using setup.py:
from setuptools import setup
setup(
# ...,
entry_points={
'xcube_plugins': [
'{your_name} = {your_package}.plugin:init_plugin',
]
}
)
The function init_plugin will be implemented as follows:
from xcube.constants import EXTENSION_POINT_DATA_OPENERS
from xcube.constants import EXTENSION_POINT_DATA_STORES
from xcube.constants import EXTENSION_POINT_DATA_WRITERS
from xcube.util import extension
def init_plugin(ext_registry: extension.ExtensionRegistry):
# register your DataStore extension
ext_registry.add_extension(
loader=extension.import_component(
'{your_package}.store:{YourStoreClass}'),
point=EXTENSION_POINT_DATA_STORES,
name="{your_store_id}",
description='{your store description}'
)
# register any extra DataOpener (EXTENSION_POINT_DATA_OPENERS)
# or DataWriter (EXTENSION_POINT_DATA_WRITERS) extensions (optional)
ext_registry.add_extension(
loader=extension.import_component(
'{your_package}.opener:{YourOpenerClass}'),
point=EXTENSION_POINT_DATA_OPENERS,
name="{your_opener_id}",
description='{your opener description}'
)
The xcube generator
Introduction
The generator is an xcube feature which allows users to create, manipulate, and write xcube datasets according to a supplied configuration. The same configuration can be used to generate a dataset on the user’s local computer or remotely, using an online server.
The generator offers two main user interfaces: A Python API, configured using Python objects; and a command-line interface, configured using YAML or JSON files. The Python and file-based configurations have the same structure and are interconvertible.
The online generator service interfaces with the xcube client via a well-defined REST API; it is also possible for third-party clients to make use of this API directly, though it is expected that the Python and command-line interfaces will be more convenient in most cases.
Further documentation
This document aims to provide a brief overview of the generation process and the available configuration options. More details are available in other documents and in the code itself:
Probably the most thorough documentation is available in the Jupyter demo notebooks in the xcube repository. These can be run in any JupyterLab environment containing an xcube installation. They combine explanation with interactive worked examples to demonstrate practical usage of the generator in typical use cases.
For the Python API in particular, the xcube API documentation is generated from the docstrings included in the code itself and serves as a detailed low-level reference for individual Python classes and methods. The docstrings can also be read from a Python environment (e.g. using the
?postfix in IPython or JupyterLab) or, of course, by browsing the source code itself.For the YAML/JSON configuration syntax used with the command-line interface, there are several examples available in the examples/gen2/configs subdirectory of the xcube repository.
For the REST API underlying the Python and command-line interfaces, there is a formal definition on SwaggerHub, and one of the example notebooks demonstrates its usage with the Python requests library.
The generation process
The usual cube generation process is as follows:
The generator opens the input data store using the store identifier and parameters in the input configuration.
The generator reads from the input store the data specified in the cube configuration and uses them to create a data cube, often with additional manipulation steps such as resampling the data.
If an optional code configuration has been given, the user-supplied code is run on the created data cube, potentially modifying it.
The generator writes the generated cube to the data store specified in the output configuration.
Invoking the generator from a Python environment
The configurations for the various parts of the generator are used to
initialize a GeneratorRequest, which is then passed to
xcube.core.gen2.generator.CubeGenerator.generate_cube. The
generate_cube method returns a cube reference which can be used to
open the cube from the output data store.
The generator can also be directly invoked with a configuration file
from a Python environment, using the
xcube.core.gen2.generator.CubeGenerator.from_file method.
Invoking the generator from the command line
The generator can be invoked from the command line using the
xcube gen2 subcommand. (Note: the subcommand xcube gen invokes
an earlier, deprecated generator feature which is not compatible with
the generator framework described here.)
Configuration syntax
All Python configuration classes are defined in the xcube.core.gen2
package, except for CodeConfig, which is in xcube.core.byoa.
The types in the parameter tables are given in an ad-hoc, semi-formal
notation whose corresponding Python and JSON representations should be
obvious. For the formal Python type definitions, see the signatures of
the __init__ methods of the configuration classes; for the formal
JSON type definitions, see the JSON schemata (in JSON Schema
format) produced by the get_schema
methods of the configuration classes.
Remote generator service configuration
The command-line interface allows a service configuration for the
remote generator service to be provided as a YAML or JSON file. This
file defines the endpoint and access credentials for an online generator
service. If it is provided, the specified remote service will be used to
generate the cube. If it is omitted, the cube will be generated locally.
The configuration file defines three values: endpoint_url,
client_id, and client_secret. A typical service configuration
YAML file might look as follows:
endpoint_url: "https://xcube-gen.brockmann-consult.de/api/v2/"
client_id: "93da366d7c39517865e4f141ddf1dd81"
client_secret: "d2l0aG91dCByZXN0cmljdGlvbiwgaW5jbHVkaW5nIHd"
Store configuration
In the command-line interface, an additional YAML or JSON file containing one or more store configurations may be supplied. A store configuration encapsulates a data store ID and an associated set of store parameters, which can then be referenced by an associated store configuration identifier. This identifier can be used in the input configuration, as described below. A typical YAML store configuration might look as follows:
sentinelhub_eu:
title: SENTINEL Hub (Central Europe)
description: Datasets from the SENTINEL Hub API deployment in Central Europe
store_id: sentinelhub
store_params:
api_url: https://services.sentinel-hub.com
client_id: myid123
client_secret: 0c5892208a0a82f1599df026b5e19017
cds:
title: C3S Climate Data Store (CDS)
description: Selected datasets from the Copernicus CDS API
store_id: cds
store_params:
normalize_names: true
num_retries: 3
my_data_bucket:
title: S3 output bucket
description: An S3 bucket for output data sets
store_id: s3
store_params:
root: cube-outputs
storage_options:
key: qwerty12345
secret: 7ff889c0aea254d5e00440858289b85c
client_kwargs:
endpoint_url: https://my-endpoint.some-domain.org/
Input configuration
The input configuration defines the data store from which data for the cube are to be read, and any additional parameters which this data store requires.
The Python configuration object is InputConfig; the corresponding
YAML configuration section is input_configs.
Parameter |
Required? |
Type |
Description |
|---|---|---|---|
|
N |
str |
Identifier for the data store |
|
N |
str |
Identifier for the data opener |
|
Y |
str |
Identifier for the dataset |
|
N |
map(str→ |
Parameters for the data store |
|
N |
map(str→ |
Parameters for the data opener |
store_id is a string identifier for a particular xcube data store,
defined by the data store itself. If a store configuration file has been
supplied (see above), a store configuration identifier can also be
supplied here in place of a ‘plain’ store identifier. Store
configuration identifiers must be prefixed by an @ symbol. If a
store configuration identifier is supplied in place of a store
identifier, store_params values will be supplied from the predefined
store configuration and can be omitted from the input configuration.
data_id is a string identifier for the dataset within a particular
store.
The format and content of the store_params and open_params
dictionaries is defined by the individual store or opener.
The generator service does not yet provide a remote interface to list
available data stores, datasets, and store parameters (i.e. allowed
values for the parameters in the table above). In a local xcube Python
environment, you can list the currently available store identifiers with
the expression
list(map(lambda e: e.name, xcube.core.store.find_data_store_extensions())).
You can create a local store object for an identifier store_id with
xcube.core.store.get_data_store_instance(store_id).store. The store
object provides methods list_data_ids,
get_data_store_params_schema, and get_open_data_params_schema to
describe the allowed values for the corresponding parameters. Note that
the available stores and datasets on a remote xcube generator server may
not be the same as those available in your local xcube environment.
Cube configuration
This configuration element defines the characteristics of the cube that
should be generated. The Python configuration class is called
CubeConfig, and the YAML section cube_config. All parameters are
optional and will be filled in with defaults if omitted; the default
values are dependent on the data store and dataset.
Parameter |
Type |
Units/Description |
|---|---|---|
|
[str, …] |
Available variables are data store dependent. |
|
str |
PROJ string, JSON string with PROJ parameters, CRS WKT string, or authority string |
|
[float, float, float, float] |
Bounding-box
( |
|
float or [float, float] |
CRS-dependent, usually degrees |
|
int or [int, int] |
pixels |
|
str or [str, str] |
ISO 8601 subset |
|
str |
integer + unit |
|
map(str→null/int) |
maps variable names to chunk sizes |
The crs parameter string is interpreted using `CRS.from_string
in the pyproj
package <https://pyproj4.github.io/pyproj/dev/api/crs/crs.html#pyproj.crs.CRS.from_string>`__
and therefore accepts the same specifiers.
time_range specified the start and end of the requested time range.
can be specified either as a date in the format YYYY-MM-DD or as a
date and time in the format YYYY-MM-DD HH:MM:SS. If the time is
omitted, it is taken to be 00:00:00 (the start of the day) for the
start specifier and 24:00:00 (the end of the day) for the specifier.
The end specifier may be omitted; in this case the current time is used.
time_period specified the duration of a single time step in the
requested cube, which determines the temporal resolution. It consists of
an integer denoting the number of time units, followed by single
upper-case letter denoting the time unit. Valid time unit specifiers are
D (day), W (week), M (month), and Y (year). Examples of time_period
values: 1Y (one year), 2M (two months), 10D (ten days).
The value of the chunks mapping determines how the generated data is
chunked for storage. The chunking has no effect on the data itself, but
can have a dramatic impact on data access speeds in different scenarios.
The value of chunks is structured a map from variable names
(corresponding to those specified by the variable_names parameter)
to chunk sizes.
Code configuration
The code configuration supports multiple ways to define a dataset processor – fundamentally, a Python function which takes a dataset and returns a processed version of the input dataset. Since the code configuration can work directly with instantiated Python objects (which can’t be stored in a YAML file), there are some differences in code configuration between the Python API and the YAML format.
Parameter |
Type |
Units/description |
|---|---|---|
|
Callable |
Function to be called to process the datacube. Only available via Python API |
|
str (non-empty) |
A reference to a
Python class or
function, in the
format
|
|
map(str→ |
Parameters to be passed to the specified callable |
|
str (non-empty) |
An inline snippet of Python code |
|
FileSet (Python) / map (YAML) |
A bundle of Python modules or packages (see details below) |
|
boolean |
If set, indicates
that |
All parameters are optional (as is the entire code configuration itself). The three parameters marked † are mutually exclusive: at most one of them may be given.
_callable provides the dataset processor directly and is only
available in the Python API. It must be either a function or a class.
If a function, it takes a
Datasetand optional additional named parameters, and returns aDataset. Any additional parameters are supplied in thecallable_paramsparameter of the code configuration.If an object, it must implement a method
process_dataset, which is treated like the function described above, and may optionally implement a class methodget_process_params_schema, which returns aJsonObjectSchemadescribing the additional parameters. For convenience and clarity, the object may extend the abstract base classDatasetProcessor, which declares both these methods.
callable_ref is a string with the structure
<module>:<function_or_class>, and specifies the function or class to
call when inline_code or file_set is provided. The specified
function or class is handled like the _callable parameter described
above.
callable_params specifies a dictionary of named parameters which are
passed to the processor function or method.
inline_code is a string containing Python source code. If supplied,
it should contain the definition of a function or object as described
for the _callable parameter. The module and class identifiers for
the callable in the inline code snippet should be specified in
callable_ref parameter.
file_set specifies a set of files which should be read from an
fsspec file system and
which contain a definition of a dataset processor. As with
inline_code, the parameter callable_ref should also be supplied
to tell the generator which class or function in the file set is the
actual processor. The parameters of file_set are identical with
those of the constructor of the corresponding Python FileSet class,
and are as follows:
Parameter |
Type |
Description |
|---|---|---|
|
str |
fsspec-compatible root path specifier |
|
str |
optional sub-path to append to main path |
|
[str] |
include files matching any of these patterns |
|
[str] |
exclude files matching any of these patterns |
|
map(str→ |
FS-specific parameters (passed to fsspec) |
Output configuration
This configuration element determines where the generated cube should be
written to. The Python configuration class is called OutputConfig,
and the YAML section output_config.
Parameter |
Type |
Units/description |
|---|---|---|
|
str |
Identifier of output store |
|
str |
Identifier of data writer |
|
str |
Identifier under which to write the cube |
|
map(str→ |
Store-dependent parameters for output store |
|
map(str→ |
Writer-dependent parameters for output writer |
|
bool |
If true, replace any existing data with the same identifier. |
xcube Dataset Convention
Version 1.0 Draft, last updated April 28, 2023
Introduction
The purpose of this document is to define a convention for how geospatial data cubes accessed and generated by xcube should look. The goal of the convention is to ease access to data, make it compliant to existing data standards, make it comprehensive and understandable, and minimize the effort to ingest the data into users’ processing, visualisation, and analysis code. In short, the goal is to make xcube datasets analysis-ready. The intention is neither to invent any new data formats nor to try to establish new “standards”. Instead, the xcube conventions solely build on existing, popular, well-established data formats and data standards.
This convention applies to gridded (array) datasets only.
In this document, the verbs must, should, and may have a special meaning when used to describe conventions. A must is a mandatory requirement that must be fulfilled, should is optional but always recommended, and may is optional and recommended in cases (like good to have).
Table of Contents
Data Model and Format
The data model for xcube gridded datasets is a subset of the Unidata Common Data Model (CDM). The CDM has originally been established for NetCDF (Network Common Data Form) but is implemented also for the OPenDAP protocol and the HDF and Zarr formats. Other common gridded data models can be easily translated into the CDM, for example GeoTIFF.
xcube gridded datasets must fully comply to the Climate and Forecast Metadata Conventions, CF Conventions for short. The following sections in this chapter describe how these conventions are tailored and applied to xcube datasets. By default, xcube gridded datasets are made available in the Zarr format v2 as this format is ideal for Cloud storage, e.g. Object Storage such as AWS S3, and can be easily converted to other formats such as NetCDF or TIFF. We consider the popular xarray Python package to be the primary in-memory representation for xcube gridded datasets. The xarray.Dataset data model represents a subset of the CDM, interprets CF compliant data correctly, and we will follow this simplified CDM model for xcube gridded datasets in both structure and terminology:
A dataset is a container for variables. A dataset has metadata in form of a key-value mapping (global attributes).
A variable has N dimensions in a prescribed order, and each dimension has a name and a size. A variable contains the actual gridded data in the form of an N-dimensional data array that has the shape of the dimension sizes. The data type of the array should be numeric, however also text strings and complex types are allowed. A variable has metadata in form of a key-value mapping (variable attributes). There are two types of variables, coordinates and data variables.
Coordinates usually contain the labels for a dimension. For example, the coordinate
loncould have a 1-D array that contains 3600 longitude values for the dimensionlonof size 3600. However, coordinates may also be 2-D. For example. a dataset using satellite geometry may provide itsloncoordinate as a 2-D array for the dimensionsxandy(for which co-ordinates should also exist).All variables that are not coordinates are data variables. They provide the actual dataset’s data. For each of the dimensions used by a data variable, a corresponding coordinate must exist. For example, for a data variable
NDVIwith dimensionstime,lat,lon, the coordinatestime,lat,lonmust exist too.
Metadata
Global and variable metadata must follow the recommendations of the ESIP Attribute Convention for Data Discovery v1.3. If other metadata attributes are used, they should be covered by the CF Conventions v1.8.
Spatial Reference
The spatial dimensions of a data variable must be the innermost dimensions,
namely lat, lon for geographic (EPGS:4326, WGS-84) grids, and y, x
for other grids – in this order.
Datasets that use a geographic (EPGS:4326, WGS-84) grid must provide the
1-D coordinates lat for dimension lat and lon for dimension lon.
Datasets that use a non-geographic grid must provide the 1-D coordinates
y for dimension y and x for dimension x.
In case the grid refers to a known spatial reference system (projected CRS), the dataset must make use of the CRS encoding described in the CF Conventions on Grid Mapping, i.e. add a variable
crs.In case the grid is referring to satellite viewing geometry, the dataset must provide 2-D coordinates
latandlonboth having the dimensionsy,x– in exactly this order, and apply the CF Conventions on 2-D Lat and Lon.
It is expected that the 1-D coordinates have a uniform spacing, i.e. there
should be a unique linear mapping between coordinate values and the image grid.
Ideally, the spacing should also be the same in x- and y-direction.
Note, it is always a good practice to add geographic coordinates to
non-geographic, projected grids. Therefore, a dataset may also provide the 2-D
coordinates lat and lon in this case. Spatial coordinates must follow
the CF Conventions on Coordinates.
Temporal Reference
The temporal dimension of a data variables should be named time. It should be
the variable’s outermost dimension. A corresponding coordinate named time
must exist and the CF Conventions on the Time Coordinate must be applied.
It is recommended to use the unit seconds since 1970.01.01.
Data variables may contain other dimensions in between the temporal dimension
and the spatial dimensions, i.e. a variable’s dimensions may be
time, …,lat, lon (geographic CRS), or time, …, y, x
(non-geographic CRS) – both in exactly this order, where the ellipsis … refers
to such extra dimensions. If there is a requirement to store individual time
stamps per pixel value, this could still be done by adding a variable to the
dataset which would then be e.g. pixel_time with dimensions
time, lat, and lon.
Encoding of Units
All variables that represent quantities must set the attributeunits according to the CF Convention on Units.
Even if the quantity is dimensionless, the units attribute must be set
to indicate its dimensionless-ness by using the value "1".
Encoding of Flags
For data variables that are encoded as flags using named bits or bit masks/ranges, we strictly follow the CF Convention on Flags.
Encoding of Missing Values
According to the CF Convention on Missing Data, missing data should be
indicated by the variable attribute _FillValue.
However, Zarr does not define or interpret (decode) array attributes at all.
The Zarr equivalent of the CF attribute _FillValue is the array property
fill_value (not an attribute). fill_value can and should be set for all
data types including integers, also because it is given in raw units,
that is, before scaling_factor and add_offset are applied (by xarray).
Zarr’s fill_value has the advantage that data array chunks comprising
only fill_value values can and should be dropped entirely. This can reduce
number of chunks dramatically and improve data access performance a lot for
many no-data chunks. In our case we should use fill_value to indicate
that a data cube’s grid cell does not contain a value at all,
it does not exist, it does not make sense, it had no input, etc.
However, when reading a Zarr with Python xarray using decode_cf=True
(the new default), xarray will also encode variable attribute
_FillValue and missing_value and mask the data accordingly. It will
unfortunately ignore valid_min, valid_max, and valid_range.
Missing values shall therefore be indicated by the Zarr array property
fill_value. In addition valid_min, valid_max, valid_range
variable attributes may be given, but they will not be decoded. However,
applications might still find them useful, e.g. xcube Viewer uses them,
if provided as value range for mapping color bars.
Encoding of Scale/Offset Values
Scale and offset encoding of bands / variables follows the
CF Convention on Packed Data. In practice, affected variables must have
attributes scaling_factor (default is 1) and add_offset (with default 0).
Multi-Resolution Datasets
Spatial multi-resolution datasets are described in detail in a dedicated document xcube Multi-Resolution Datasets.
Multi-Band Datasets
Individual spectral bands of a dataset should be stored as variables.
For example, this is the case for multi-band products, like Sentinel-2,
where each wavelength will be represented by a separate variable named
B01, B02, etc.
Metadata Consolidation
Datasets using Zarr format should provide consolidated dataset metadata in their root directories. This allows reading the metadata of datasets with many variables more efficiently, especially when data is stored in Object Storage and every metadata file would need to fetched via an individual HTTP request.
The consolidated metadata file should be named .zmetadata,
which is also the default name used by the Zarr format.
The format of the consolidated metadata must be JSON. It is a JSON object using the following structure:
{
"zarr_consolidated_format": 1,
"metadata": {
".zattrs": {…},
".zgroup": {…},
"band_1/.zarray": {…},
"band_1/.zattrs": {…},
"band_2/.zarray": {…},
"band_2/.zattrs": {…},
…
}
}
Zip Archives
Zarr datasets may also be provided as Zip archives. Such Zip archives
should contain the contents of the archived Zarr directory in their root.
In other words, the keys of the entries in such an archive should not
have a common prefix as is often the case when directories are zipped
for convenience. The name of a zipped Zarr dataset should be the original
Zarr dataset name plus the .zip extension. For example:
Desired:
${dataset-name}.zarr.zip/
.zarray
.zgroup
.zmetadata
band_1/
time/
…
Zipping the Zarr datasets this way allows opening the Zarr datasets directly
from the Zip, e.g., for xarray this is
xarray.open_zarr("./dataset.zarr.zip").
Dataset Examples
TODO (forman)
Global WGS-84
Projected CRS
Satellite Viewing Geometry
Multi-resolution
Common data store conventions
This document is a work in progress.
The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in RFC 2119.
Useful references related to this document include:
The JSON Schema Specification and the book Understanding JSON Schema
xcube Issue #330 (‘Establish common data store conventions’)
The existing xcube store plugins xcube-sh, xcube-cci, and xcube-cds
The
xcube.util.jsonschemasource code
Naming Identifiers
This section explains various identifiers used by the xcube data store framework and defines their format.
In the data store framework, identifiers are used to denote data sources,
data stores, and data accessors.
Data store, data opener, and data writer identifiers are used to register the
component as extension in a package’s plugin.py. Identifiers MUST be
unambiguous in the scope of the data store.
They SHOULD be unambiguous across the entirety of data stores.
There are no further restrictions for data source and data store identifiers.
A data accessor identifier MUST correspond to the following scheme:
<data_type>:<format>:<storage>[:<version>]
<data_type> identifies the in-memory data type to represent the data,
e.g., dataset (or xarray.Dataset), geodataframe
(or geopandas.GeoDataFrame).
<format> identifies the data format that may be accessed,
e.g., zarr, netcdf, geojson.
<storage> identifies the kind of storage or data provision the
accessor can access. Example values are file (the local file system),
s3 (AWS S3-compatible object storage), or sentinelhub
(the Sentinel Hub API), or cciodp (the ESA CCI Open Data Portal API).
The <version> finally is an optional notifier
about a data accessor’s version. The version MUST follow the
Semantic Versioning.
Examples for valid data accessors identifiers are:
dataset:netcdf:filedataset:zarr:sentinelhubgeodataframe:geojson:filegeodataframe:shapefile:cciodp:0.4.1
Open Parameters
This section aims to provide an overview of the interface defined by an xcube data store or opener in its open parameters schema, and how this schema may be used by a UI generator to automatically construct a user interface for a data opener.
Specification of open parameters
Every implementation of the xcube.core.store.DataOpener or
xcube.core.store.DataStore abstract base classes MUST implement the
get_open_data_params_schema method in order to provide a description of the
allowed arguments to open_data for each dataset supported by the
DataOpener or DataStore. The description is provided as a
JsonObjectSchema object corresponding to a JSON
Schema. The intention is that this description
should be full and detailed enough to allow the automatic construction of a
user interface for access to the available datasets. Note that, under this
system:
Every dataset provided by an opener can support a different set of open parameters.
The schema does not allow the representation of interdependencies between values of open parameters within a dataset. For instance, the following interdependencies between two open parameters sensor_type and variables would not be representable in an open parameters schema:
sensor_type: A or B
variables: [temperature, humidity] for sensor type A; [temperature, pressure] for sensor type B
To work around some of the restrictions of point (2) above, a dataset MAY be
presented by the opener as multiple “virtual” datasets with different
parameter schemas. For instance, the hypothetical dataset described above MAY
be offered not as a single dataset envdata but as two datasets
envdata:sensor-a (with a fixed sensor_type of A) and envdata:sensor-b,
(with a fixed sensor_type of B), offering different sets of permitted
variables.
Sometimes, the interdependencies between parameters are too complex to
be fully represented by splitting datasets in this manner. In these cases:
The JSON Schema SHOULD describe the smallest possible superset of the allowed parameter combinations.
The additional restrictions on parameter combinations MUST be clearly documented.
If illegal parameter combinations are supplied, the opener MUST raise an exception with an informative error message, and the user interface SHOULD present this message clearly to the user.
Common parameters
While an opener is free to define any open parameters for any of its datasets,
there are some common parameters which are likely to be used by the majority
of datasets. Furthermore, there are some parameters which are fundamental for
the description of a dataset and therefore MUST be included in a schema
(these parameters are denoted explicitly in the list below). In case that an
opener does not support varying values of one of these parameters, a constant
value must defined. This may be achieved by the JSON schema’s const property
or by an enum property value whose is a one-element array.
Any dataset requiring the specification of these parameters MUST
use the standard parameter names, syntax, and semantics defined below, in
order to keep the interface consistent. For instance, if a dataset allows a
time aggregation period to be specified, it MUST use the time_period
parameter with the format described below rather than some other alternative
name and/or format. Below, the parameters are described with their Python type
annotations.
variable_names: List[str]
A list of the identifiers of the requested variables. This parameter MUST be included in an opener parameters schema.bbox: Union[str,Tuple[float, float, float, float]]The bounding box for the requested data, in the order xmin, ymin, xmax, ymax. Must be given in the units of the specified spatial coordinate reference systemcrs. This parameter MUST be included in an opener parameters schema.crs: str
The identifier for the spatial coordinate reference system of geographic data.spatial_res: float
The requested spatial resolution (x and y) of the returned data. Must be given in the units of the specified spatial coordinate reference systemcrs. This parameter MUST be included in an opener parameters schema.time_range: Tuple[Optional[str], Optional[str]]
The requested time range for the data to be returned. The first member of the tuple is the start time; the second is the end time. See section ‘Date, time, and duration specifications’. This parameter MUST be included in an opener parameters schema. If a date without a time is given as the start time, it is interpeted as 00:00 on the specified date. If a date without a time is given as the end time, it is interpreted as 24:00 on the specified date (identical with 00:00 on the date following the specified date). If the end time is specified asNone, it is interpreted as the current time.time_period: str
The requested temporal aggregation period for the data. See section ‘Date, time, and duration specifications’. This parameter MUST be included in an opener parameters schema.force_cube: bool
Whether to return results as a specification-compliant xcube. If a store supports this parameter and if a dataset is opened with this parameter set toTrue, the store MUST return a specification-compliant xcube dataset. If this parameter is not supported or if a dataset is opened with this parameter set toFalse, the caller MUST NOT assume that the returned data conform to the xcube specification.
Semantics of list-valued parameters
The variables parameter takes as its value a list, with no duplicated members
and the values of its members drawn from a predefined set. The values of this
parameter, and other parameters whose values also follow such a format, are
interpreted by xcube as a restriction, much like a bounding box or time
range. That is:
By default (if the parameter is omitted or if a
Nonevalue is supplied for it), all the possible member values MUST be included in the list. In the case ofvariables, this will result in a dataset containing all the available variables.If a list containing some of the possible members is given, a dataset corresponding to those members only MUST be returned. In the case of
variables, this will result in a dataset containing only the requested variables.A special case of the above: if an empty list is supplied, a dataset containing no data MUST be returned – but with the requested spatial and temporal dimensions.
Date, time, and duration specifications
In the common parameter time_range, times can be specified using the
standard JSON Schema formats date-time or date. Any additional time or
date parameters supported by an xcube opener dataset SHOULD also use these
formats, unless there is some good reason to prefer a different format.
The formats are described in the JSON Schema Validation 2019
draft,
which adopts definitions from RFC 3339 Section
5.6. The JSON Schema
date-time format corresponds to RFC 3339’s date-time production, and JSON
Schema’s date format to RFC 3339’s full-date production. These formats are
subsets of the widely adopted ISO
8601 format.
The date format corresponds to the pattern YYYY-MM-DD (four-digit year –
month – day), for example 1995-08-20. The date-time format consists of a
date (in the date format), a time (in HH:MM:SS format), and timezone (Z
for UTC, or +HH:MM or -HH:MM format). The date and time are separated by
the letter T. Examples of date-time format include 1961-03-23T12:22:45Z
and 2018-04-01T21:12:00+08:00. Fractions of a second MAY also be included,
but are unlikely to be relevant for xcube openers.
The format for durations, as used for aggregation period, does not conform to the syntax defined for this purpose in the ISO 8601 standard (which is also quoted as Appendix A of RFC 3339). Instead, the required format is a small subset of the pandas time series frequency syntax, defined by the following regular expression:
^([1-9][0-9]*)?[HDWMY]$
That is: an optional positive integer followed by one of the letters H (hour), D (day), W (week), M (month), and Y (year). The letter specifies the time unit and the integer specifies the number of units. If the integer is omitted, 1 is assumed.
Time limits: an extension to the JSON Schema
JSON Schema itself does not offer a way to impose time limits on a string
schema with the date or date-time format. This is a problem for xcube
generator UI creation, since it might be reasonably expected that a UI will
show and enforce such limits. The xcube opener API therefore defines an
unofficial extension to the JSON string schema: a JsonStringSchema object
(as returned as part of a JsonSchema by a call to
get_open_data_params_schema) MAY, if it has a format property with a value
of date or date-time, also have one or both of the properties
min_datetime and max_datetime. These properties must also conform to the
date or date-time format. xcube provides a dedicated JsonDatetimeSchema
for this purpose. Internally, it extends JsonStringSchema by adding the
required properties to the JSON string schema.
Generating a UI from a schema
With the addition of the time limits extension described above, the JSON
Schema returned by get_open_data_params_schema is expected to be extensive
and detailed enough to fully describe a UI for cube generation.
Order of properties in a schema
Sub-elements of a JsonObjectSchema are passed to the constructor using the
properties parameter with type signature Mapping[str, JsonSchema]. Openers
SHOULD provide an ordered mapping as the value of properties, with the
elements placed in an order suitable for presentation in a UI, and UI
generators SHOULD lay out the UI in the provided order, with the exception of
the common parameters discussed below. Note that the CPython dict object
preserves the insertion order of its elements as of Python 3.6, and that this
behaviour is officially guaranteed as of Python 3.7, so additional classes
like OrderedDict are no longer necessary to fulfil this requirement.
Special handling of common parameters
Any of the common parameters listed above SHOULD, if present, be recognized
and handled specially. They SHOULD be presented in a consistent position
(e.g. at the top of the page for a web GUI), in a consistent order, and with
user-friendly labels and tooltips even if the title and description
annotations (see below) are absent. The UI generator MAY provide special
representations for these parameters, for instance an interactive map for the
bbox parameter.
An opener MAY provide title, description, and/or examples annotations
for any of the common parameters, and a UI generator MAY choose to use any of
these to supplement or modify its standard presentation of the common
parameters.
Schema annotations (title, description, examples, and default)
For JSON Schemas describing parameters other than the common parameters, an
opener SHOULD provide the title and description annotations. A UI
generator SHOULD make use of these annotations, for example by taking the
label for a UI control from title and the tooltip from description. The
opener and UI generator MAY additionally make use of the examples annotation
to record and display example values for a parameter. If a sensible default
value can be envisaged, the opener SHOULD record this default as the value of
the default annotation and the UI generator SHOULD set the default value in
the UI accordingly. If the title annotation is absent, the UI generator
SHOULD use the key corresponding to the parameter’s schema in the parent
schema as a fallback.
Generalized conversion of parameter schemas
For parameters other than the common parameters, the UI can be generated automatically from the schema structure. In the case of a GUI, a one-to-one conversion of values of JSON Schema properties into GUI elements will generally be fairly straightforward. For instance:
A schema of type
booleancan be represented as a checkbox.A schema of type
stringwithout restrictions on allowed items can be represented as an editable text field.A schema of type
stringwith anenumkeyword giving a list of allowed values can be represented as a drop-down menu.A schema of type
stringwith the keyword setting"format": "date"can be represented as a specialized date selector.A schema of type
arraywith the keyword setting"uniqueItems": trueand anitemskeyword giving a fixed list of allowed values can be represented as a list of checkboxes.
xcube Developer Guide
Version 0.2, draft
IMPORTANT NOTE: Any changes to this doc must be reviewed by dev-team through pull requests.
Table of Contents
Versioning
We adhere to PEP-440.
Therefore, the xcube software version uses the format
<major>.<minor>.<micro> for released versions and
<major>.<minor>.<micro>.dev<n> for versions in development.
<major>is increased for major enhancements. CLI / API changes may introduce incompatibilities with former version.<minor>is increased for new features and and minor enhancements. CLI / API changes are backward compatible with former version.<micro>is increased for bug fixes and micro enhancements. CLI / API changes are backward compatible with former version.<n>is increased whenever the team (internally) deploys new builds of a development snapshot.
The current software version is in xcube/version.py.
Coding Style
We follow PEP-8, including its recommendation of PEP-484 syntax for type hints.
Updating code style in the existing codebase
A significant portion of the existing codebase does not adhere to our current code style guidelines. It is of course a goal to bring these parts into conformance with the style guide, but major style changes should not be bundled into pull requests focused on other improvements or bug fixes, because they obscure the significant code changes and make reviews difficult. Large-scale style and formatting updates should instead be made via dedicated pull requests.
Line length
As recommended in PEP-8, all lines should be limited to a maximum of 79 characters, including docstrings and comments.
Quotation marks for string literals
In general, single quotation marks should always be used for string literals. Double quotation marks should only be used if there is a compelling reason to do so in a particular case.
Main Packages
xcube.core- Hosts core API functions. Code in here should be maintained w.r.t. backward compatibility. Therefore think twice before adding new or change existing core API.xcube.cli- Hosts CLI commands. CLI command implementations should be lightweight. Move implementation code either intocoreorutil.
CLI commands must be maintained w.r.t. backward compatibility. Therefore think twice before adding new or change existing CLI commands.xcube.webapi- Hosts Web API functions. Web API command implementations should be lightweight. Move implementation code either intocoreorutil.
Web API interface must be maintained w.r.t. backward compatibility. Therefore think twice before adding new or change existing web API.xcube.util- Mainly implementation helpers. Comprises classes and functions that are used bycli,core,webapiin order to maximize modularisation and testability but to minimize code duplication.
The code in here must not be dependent on any ofcli,core,webapi. The code in here may change often and in any way as desired by code implementing thecli,core,webapipackages.
The following sections will guide you through extending or changing the main packages that form xcube’s public interface.
Package xcube.cli
Checklist
Make sure your change
is covered by unit-tests (package
test/cli);is reflected by the CLI’s doc-strings and tools documentation (currently in
README.md);follows existing xcube CLI conventions;
follows our code style conventions;
is reflected in API and WebAPI, if desired;
is reflected in
CHANGES.md.
Hints
Make sure your new CLI command is in line with the others commands regarding command name, option names, as well as metavar arguments names. The CLI command name shall ideally be a verb.
Avoid introducing new option arguments if similar options are already in use for existing commands.
In the following common arguments and options are listed.
Input argument:
@click.argument('input')
If input argument is restricted to an xcube dataset:
@click.argument('cube')
Output (directory) option:
@click.option('--output', '-o', metavar='OUTPUT',
help='Output directory. If omitted, "INPUT.levels" will be used.')
Output format:
@click.option('--format', '-f', metavar='FORMAT', type=click.Choice(['zarr', 'netcdf']),
help="Format of the output. If not given, guessed from OUTPUT.")
Output parameters:
@click.option('--param', '-p', metavar='PARAM', multiple=True,
help="Parameter specific for the output format. Multiple allowed.")
Variable names:
@click.option('--variable',--var', metavar='VARIABLE', multiple=True,
help="Name of a variable. Multiple allowed.")
For parsing CLI inputs, use helper functions that are already in use.
In the CLI command implementation code, raise
click.ClickException(message) with a clear message for users.
Common xcube CLI options like -f for FORMAT should be lower case
letters and specific xcube CLI options like -S for SIZE in xcube gen
are recommended to be uppercase letters.
Extensively validate CLI inputs to avoid that API functions raise
ValueError, TypeError, etc. Such errors and their message texts are
usually hard to understand by users. They are actually dedicated to
to developers, not CLI users.
There is a global option --traceback flag that user can set to dump
stack traces. You don’t need to print stack traces from your code.
Package xcube.core
Checklist
Make sure your change
is covered by unit-tests (package
test/core);is covered by API documentation;
follows existing xcube API conventions;
follows our code style conventions;
is reflected in xarray extension class
xcube.core.xarray.DatasetAccessor;is reflected in CLI and WebAPI if desired;
is reflected in
CHANGES.md.
Hints
Create new module in xcube.core and add your functions.
For any functions added make sure naming is in line with other API.
Add clear doc-string to the new API. Use Sphinx RST format.
Decide if your API methods requires xcube datasets as
inputs, if so, name the primary dataset argument cube and add a
keyword parameter cube_asserted: bool = False.
Otherwise name the primary dataset argument dataset.
Reflect the fact, that a certain API method or function operates only
on datasets that conform with the xcube dataset specifications by
using cube in its name rather than dataset. For example
compute_dataset can operate on any xarray datasets, while
get_cube_values_for_points expects a xcube dataset as input or
read_cube ensures it will return valid xcube datasets only.
In the implementation, if not cube_asserted,
we must assert and verify the cube is a cube.
Pass True to cube_asserted argument of other API called later on:
from xcube.core.verify import assert_cube
def frombosify_cube(cube: xr.Dataset, ..., cube_asserted: bool = False):
if not cube_asserted:
assert_cube(cube)
...
result = bibosify_cube(cube, ..., cube_asserted=True)
...
If import xcube.core.xarray is imported in client code, any xarray.Dataset
object will have an extra property xcube whose interface is defined
by the class xcube.core.xarray.DatasetAccessor. This class is an
xarray extension
that is used to reflect xcube.core functions and make it directly
applicable to the xarray.Dataset object.
Therefore any xcube API shall be reflected in this extension class.
Package xcube.webapi
Checklist
Make sure your change
is covered by unit-tests (package
test/webapi);is covered by Web API specification and documentation (currently in
webapi/res/openapi.yml);follows existing xcube Web API conventions;
follows our code style conventions;
is reflected in CLI and API, if desired;
is reflected in
CHANGES.md.
Hints
The Web API is defined in
webapi.appwhich defines mapping from resource URLs to handlersAll handlers are implemented in
webapi.handlers. Handler code just delegates to dedicated controllers.All controllers are implemented in
webapi.controllers.*. They might further delegate intocore.*
Development Process
Make sure there is an issue ticket for your code change work item.
Select issue; priorities are as follows:
“urgent” and (”important” and “bug”)
“urgent” and (”important” or “bug”)
“urgent”
“important” and “bug”
“important” or “bug”
others
Make sure issue is assigned to you; if unclear, agree with team first.
Add issue label “in progress”.
Clone the GitHub repository to your local machine, if you haven’t already done so.
Check out the
masterbranch and make sure it’s up to date (git pull).Create a development branch named
"<developer>-<issue>-<title>"(see below).Develop, having in mind the checklists and implementation hints above.
In your first commit, refer the issue so it will appear as link in the issue history.
It is useful to push your first commit to the GitHub repository at once and create a draft pull request for your branch, to make it easier to find online. Also, it will run continuous integration and check the compatibility of your branch with the main branch.
Develop, test, and push to the remote branch as desired.
In your last commit, utilize checklists above. (You can include the line “closes #
<issue>” in your commit message to auto-close the issue once the PR is merged.)
When your branch is ready to merge (as determined by the checklist), either create a pull request, or, if you had created a draft in the previous step, remove its draft status and invite one or more reviewers. Exactly who should review depends on how potentially dangerous your changes are, who’s available, who knows that part of the codebase well, etc. – if unsure, discuss with the team. You can also mention other team members with the
@usernamesyntax in a PR comment to make sure that they’re aware of the PR even if they don’t need to review it.Wait for reviewers to review your PR and respond to comments and suggestions. Reviewers can also commit to your branch with additional changes.
Once all reviewers have given an “accept” review, the PR should be merged. The last reviewer to give an “accept” should perform the merge; if they forget, the PR author should merge.
Remove issue label “in progress”.
Delete the feature branch.
If the PR completely solves the associated issue: close the issue, if it wasn’t already automatically closed by a keyword in the pull request description, and make sure that there is a link from the issue to the PR (e.g. by adding a “fixed by PR #123” comment on the issue).
If the PR is only partly solving an issue:
Make sure the issue contains a to-do list (checkboxes) to complete the issue.
Do not include the line “closes #
<issue>” in your last commit message.Add “relates to issue#” in PR.
Make sure to check the corresponding to-do items (checkboxes) after the PR is merged.
Remove issue label “in progress”.
Leave issue open.
Branches and Releases
Target Branch
The master branch contains latest developments,
including new features and fixes.
Its software version string is always <major>.<minor>.<micro>.dev<n>.
The branch is used to generate major, minor, or maintenance releases.
That is, either <major>, <minor>, or <fix> is increased.
Before a release, the last thing we do is to remove the .dev<n> suffix,
after a release, the first thing we do is to increase the micro version
and add the .dev<n> suffix.
Development Branches
Development branches should be named <developer>-<issue>-<title> where
<developer>is the github name of the code author<issue>is the number of the issue in the github issue tracker that is targeted by the work on this branch<title>is either the name of the issue or an abbreviated version of it
Release Process
Release on GitHub
This describes the release process for xcube. For a plugin release,
you need to adjust the paths accordingly.
Check issues in progress, close any open issues that have been fixed.
Make sure that all unit tests pass and that test coverage is 100% (or as near to 100% as practicable).
In
xcube/version.pyremove the.devsuffix from version name.Make sure
CHANGES.mdis complete. Remove the suffix(in development)from the last version headline.Push changes to either master or a new maintenance branch (see above).
Await results from Travis CI and ReadTheDocs builds. If broken, fix.
Go to xcube/releases and press button “Draft a new Release”.
Tag version is:
v${version}(with a “v” prefix)Release title is:
${version}(without a “v” prefix)Paste latest changes from
CHANGES.mdinto field “Describe this release”Press “Publish release” button
After the release on GitHub, rebase sources, if the branch was
master, create a new maintenance branch (see above)In
xcube/version.pyincrease version number and append a.dev0suffix to the version name so that it is still PEP-440 compatible.In
CHANGES.mdadd a new version headline and attach(in development)to it.Push changes to either master or a new maintenance branch (see above).
Activate new doc version on ReadTheDocs.
Go through the same procedure for all xcube plugin packages dependent on this version of xcube.
Release on Conda-Forge
These instructions are based on the documentation at conda-forge.
Conda-forge packages are produced from a github feedstock repository belonging
to the conda-forge organization. A repository’s feedstock is usually located at
https://github.com/conda-forge/<repo-name>-feedstock, e.g.,
https://github.com/conda-forge/xcube-feedstock.
Usually, a conda-forge bot will create a feedstock pull request automatically for each new GitHub release, and the maintainers only need to merge it (potentially after some manual improvements). The manual procedure below is only needed if there isn’t time to wait for the bot, or if an additional conda-forge build has to be done without a new GitHub release. The package is updated by
forking the repository
creating a new branch for the changes
creating a pull request to merge this branch into conda-forge’s feedstock repository (this is done automatically if the build number is 0).
In detail, the steps are:
Fork
https://github.com/conda-forge/<repo-name>-feedstockin your personal GitHub account.Clone the repository locally and create a new branch. The name of the branch is not strictly prescribed, but it’s sensible to choose an informative name like
update_0_5_3.In case the build number is 0, a bot will render the feedstock during the pull request. Otherwise, conduct the following steps: Rerender the feedstock using conda-smithy. This updates common conda-forge feedstock files. It’s probably easiest to install conda-smithy in a fresh environment for this:
conda install -c conda-forge conda-smithyconda smithy rerender -c autoUpdate
recipe/meta.yamlfor the new version. Mainly this will involve the following steps:Update the value of the version variable (or, if the version number has not changed, increment the build number).
If the version number has changed, ensure that the build number is set to 0.
Update the sha256 hash of the source archive prepared by GitHub.
If the dependencies have changed, update the list of dependencies in the
-runsubsection to match those in the environment.yml file.
Commit the changes and push them to GitHub. A pull request at the feedstock repository on conda-forge will be automatically created by a bot if the build number is 0. If it is higher, you will have to create the pull request yourself.
Once conda-forge’s automated checks have passed, merge the pull request.
Delete your personal fork of the
xcube-feedstock-repository.
Once the pull request has been merged, the updated package should usually become available from conda-forge within a couple of hours.
TODO: Describe deployment of xcube Docker image after release
If any changes apply to xcube serve and the xcube Web API:
Make sure changes are reflected in xcube/webapi/res/openapi.yml.
If there are changes, sync xcube/webapi/res/openapi.yml with
xcube Web API docs on SwaggerHub.
Check if changes affect the xcube-viewer code. If so make sure changes are reflected in xcube-viewer code and test viewer with latest xcube Web API. Then release a new xcube viewer.
xcube Viewer
Cd into viewer project directory (
.../xcube-viewer/.).Remove the
-devsuffix fromversionproperty inpackage.json.Remove the
-devsuffix fromversionconstant insrc/config.ts.Make sure
CHANGES.mdis complete. Remove the suffix(in development)from the last version headline.Build the app and test the build using a local http-server, e.g.:
$ npm install -g http-server $ cd build $ http-server -p 3000 -c-1
Push changes to either master or a new maintenance branch (see above).
Goto xcube-viewer/releases and press button “Draft a new Release”.
Tag version is:
v${version}(with a “v” prefix).Release title is:
${version}.Paste latest changes from
CHANGES.mdinto field “Describe this release”.Press “Publish release” button.
After the release on GitHub, if the branch was
master, create a new maintenance branch (see above).Increase
versionproperty andversionconstant inpackage.jsonandsrc/config.tsand append-dev.0suffix to version name so it is SemVer compatible.In
CHANGES.mdadd a new version headline and attach(in development)to it.Push changes to either master or a new maintenance branch (see above).
Deploy builds of
masterbranches to related web content providers.
Plugins
xcube’s functionality can be extended by plugins. A plugin contributes extensions to specific extension points defined by xcube. Plugins are detected and dynamically loaded, once the available extensions need to be inquired.
Installing Plugins
Plugins are installed by simply installing the plugin’s package into xcube’s Python environment.
In order to be detected by xcube, an plugin package’s name must either start with xcube_
or the plugin package’s setup.py file must specify an entry point in the group
xcube_plugins. Details are provided below in section plugin_development.
Available Plugins
SENTINEL Hub
The xcube_sh plugin adds support for the SENTINEL Hub Cloud API. It extends xcube by a new Python API
function xcube_sh.cube.open_cube to create data cubes from SENTINEL Hub on-the-fly. It also
adds a new CLI command xcube sh gen to generate and write data cubes created from SENTINEL Hub
into the file system.
ESA CCI Open Data Portal
The xcube_cci plugin provides support for the ESA CCI Open Data Portal.
Copernicus Climate Data Store
The xcube_cds plugin provides support for the Copernicus Climate Data Store.
Cube Generation
xcube’s GitHub organisation currently hosts a few plugins that add new input processor extensions (see below) to xcube’s data cube generation tool xcube gen. They are very specific but are a good starting point for developing your own input processors:
xcube_gen_bc - adds new input processors for specific Ocean Colour Earth Observation products derived from the Sentinel-3 OLCI measurements.
xcube_gen_rbins - adds new input processors for specific Ocean Colour Earth Observation products derived from the SEVIRI measurements.
xcube_gen_vito - adds new input processors for specific Ocean Colour Earth Observation products derived from the Sentinel-2 MSI measurements.
Plugin Development
Plugin Definition
An xcube plugin is a Python package that is installed in xcube’s Python environment. xcube can detect plugins either
by naming convention (more simple);
by entry point (more flexible).
By naming convention: Any Python package named xcube_<name> that defines a plugin initializer function
named init_plugin either defined in xcube_<name>/plugin.py (preferred) or xcube_<name>/__init__.py
is an xcube plugin.
By entry point: Any Python package installed using Setuptools that
defines a non-empty entry point group xcube_plugins is an xcube plugin. An entry point in the
xcube_plugins group has the format <name> = <fully-qualified-module-path>:<init-func-name>,
and therefore specifies where plugin initializer function named <init-func-name> is found.
As an example, refer to the xcube standard plugin definitions in xcube’s
setup.py file.
For more information on Setuptools entry points refer to section Creating and discovering plugins in the Python Packing User Guide and Dynamic Discovery of Services and Plugins in the Setuptools documentation.
Initializer Function
xcube plugins are initialized using a dedicated function that has a single extension registry argument
of type xcube.util.extension.ExtensionRegistry, that is used by plugins’s to register their extensions
to xcube. By convention, this function is called init_plugin, however, when using entry points,
it can have any name. As an example, here is the initializer function of the SENTINEL Hub plugin
xcube_sh/plugin.py::
from xcube.constants import EXTENSION_POINT_CLI_COMMANDS
from xcube.util import extension
def init_plugin(ext_registry: extension.ExtensionRegistry):
"""xcube SentinelHub extensions"""
ext_registry.add_extension(loader=extension.import_component('xcube_sh.cli:cli'),
point=EXTENSION_POINT_CLI_COMMANDS,
name='sh_cli')
Extension Points and Extensions
When a plugin is loaded, it adds its extensions to predefined extension points defined by xcube. xcube defines the following extension points:
xcube.core.gen.iproc: input processor extensionsxcube.core.dsio: dataset I/O extensionsxcube.cli: Command-line interface (CLI) extensions
An extension is added to the extension registry’s add_extension method. The extension registry is
passed to the plugin initializer function as its only argument.
Input Processor Extensions
Input processors are used the xcube gen CLI command and gen_cube API function.
An input processor is responsible for processing individual time slices after they have been
opened from their sources and before they are appended to or inserted into the data cube
to be generated. New input processors are usually programmed to support the characteristics
of specific xcube gen inputs, mostly specific Earth Observation data products.
By default, xcube uses a standard input processor named default that expects inputs
to be individual NetCDF files that conform to the CF-convention. Every file is expected
to contain a single spatial image with dimensions lat and lon and the time
is expected to be given as global attributes.
If your input files do not conform with the default expectations, you can extend xcube
and write your own input processor. An input processor is an implementation of the
xcube.core.gen.iproc.InputProcessor or xcube.core.gen.iproc.XYInputProcessor
class.
As an example take a look at the implementation of the default input processor
xcube.core.gen.iproc.DefaultInputProcessor or the various input processor plugins mentioned above.
The extension point identifier is defined by the constant xcube.constants.EXTENSION_POINT_INPUT_PROCESSORS.
Dataset I/O Extensions
More coming soon…
The extension point identifier is defined by the constant xcube.constants.EXTENSION_POINT_DATASET_IOS.
CLI Extensions
CLI extensions enhance the xcube command-line tool by new sub-commands.
The xcube CLI is implemented using the click library, therefore the extension
components must be click commands or command groups.
The extension point identifier is defined by the constant xcube.constants.EXTENSION_POINT_CLI_COMMANDS.