diff --git a/404.html b/404.html index e5d015bdb..b6a745ca2 100644 --- a/404.html +++ b/404.html @@ -8,13 +8,13 @@ - - + +

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- + diff --git a/about.html b/about.html index a09130011..c65449dc7 100644 --- a/about.html +++ b/about.html @@ -8,8 +8,8 @@ - - + +
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Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands ["),t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("near-infrared")]),e._v("] and 3 timesteps ["),t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")]),e._v("], displayed here in an orderly, timeseries-like manner:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(303),alt:"Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)."}}),e._v(" "),t("figcaption",[e._v("An examplary raster datacube with 4 dimensions: x, y, bands and time.")])]),e._v(" "),t("p",[e._v("It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(304),alt:"Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)."}}),e._v(" "),t("figcaption",[e._v("This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")])]),e._v(" "),t("p",[e._v("A vector datacube on the other hand could look like this:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(305),alt:"An examplary vector datacube with 3 dimensions: 2 geometries are given for the `geometries` dimension, along with 3 timesteps for the temporal dimension `time` and 4 bands in the `bands` dimension."}}),e._v(" "),t("figcaption",[e._v("An examplary vector datacube with 3 dimensions: 2 geometries are given for the "),t("code",[e._v("geometries")]),e._v(" dimension, along with 3 timesteps for the temporal dimension "),t("code",[e._v("time")]),e._v(" and 4 bands in the "),t("code",[e._v("bands")]),e._v(" dimension.")])]),e._v(" "),t("p",[t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Vector datacubes"),t("OutboundLink")],1),e._v(" and raster datacubes are common cases of datacubes in the EO domain.\nA raster datacube has at least two spatial dimensions (usually named "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(") and a vector datacube has at least one geometry dimension (usually named "),t("code",[e._v("geometry")]),e._v(').\nThe purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named '),t("code",[e._v("t")]),e._v(").")]),e._v(" "),t("p",[e._v("The following additional information are usually available for datacubes:")]),e._v(" "),t("ul",[t("li",[e._v("the dimensions (see "),t("a",{attrs:{href:"#dimensions"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a sampling method (see "),t("a",{attrs:{href:"#values-in-a-datacube"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a unit for the values")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("h2",{attrs:{id:"dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#dimensions"}},[e._v("#")]),e._v(" Dimensions")]),e._v(" "),t("p",[e._v("A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(", and the temporal dimension "),t("code",[e._v("t")]),e._v(". Furthermore, it has a "),t("code",[e._v("bands")]),e._v(" dimension, extending into the realm of "),t("em",[e._v("what kind of information")]),e._v(" is contained in the cube.")]),e._v(" "),t("p",[e._v("The following properties are usually available for dimensions:")]),e._v(" "),t("ul",[t("li",[e._v("name")]),e._v(" "),t("li",[e._v("type (potential types include: spatial (raster or vector data), temporal and other data such as bands)")]),e._v(" "),t("li",[e._v("axis (for spatial dimensions) / number")]),e._v(" "),t("li",[e._v("labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)")]),e._v(" "),t("li",[e._v("reference system / projection")]),e._v(" "),t("li",[e._v("resolution / step size")]),e._v(" "),t("li",[e._v("unit for the labels (either explicitly specified or implicitly provided by the reference system)")]),e._v(" "),t("li",[e._v("additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("p",[e._v("Here is an overview of the dimensions contained in our example raster datacube above:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("#")]),e._v(" "),t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("resolution")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("1")]),e._v(" "),t("td",[t("code",[e._v("x")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("466380")]),e._v(", "),t("code",[e._v("466580")]),e._v(", "),t("code",[e._v("466780")]),e._v(", "),t("code",[e._v("466980")]),e._v(", "),t("code",[e._v("467180")]),e._v(", "),t("code",[e._v("467380")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("2")]),e._v(" "),t("td",[t("code",[e._v("y")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("7167130")]),e._v(", "),t("code",[e._v("7166930")]),e._v(", "),t("code",[e._v("7166730")]),e._v(", "),t("code",[e._v("7166530")]),e._v(", "),t("code",[e._v("7166330")]),e._v(", "),t("code",[e._v("7166130")]),e._v(", "),t("code",[e._v("7165930")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("3")]),e._v(" "),t("td",[t("code",[e._v("bands")])]),e._v(" "),t("td",[e._v("bands")]),e._v(" "),t("td",[t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("nir")])]),e._v(" "),t("td",[e._v("4 bands")]),e._v(" "),t("td",[e._v("-")])]),e._v(" "),t("tr",[t("td",[e._v("4")]),e._v(" "),t("td",[t("code",[e._v("t")])]),e._v(" "),t("td",[e._v("temporal")]),e._v(" "),t("td",[t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")])]),e._v(" "),t("td",[e._v("12 days")]),e._v(" "),t("td",[e._v("Gregorian calendar / UTC")])])])]),e._v(" "),t("p",[e._v('Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example.\nFor example, temporal labels are usually encoded as '),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/ISO_8601",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISO 8601"),t("OutboundLink")],1),e._v(" compatible dates and/or times and similarly geometries can be encoded as "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry",target:"_blank",rel:"noopener noreferrer"}},[e._v("Well-known Text (WKT)"),t("OutboundLink")],1),e._v(" or be represented by their IDs.")]),e._v(" "),t("p",[e._v("Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually "),t("code",[e._v("bands")]),e._v("), retain the order in which they have been defined in metadata or processes (e.g. through "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), with new labels simply being appended to the existing labels.")]),e._v(" "),t("p",[e._v("A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[t("code",[e._v("geometry")])]),e._v(" "),t("td",[e._v("vector")]),e._v(" "),t("td",[t("code",[e._v("POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6))")]),e._v(", "),t("code",[e._v("POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5))")])]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/4326",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:4326"),t("OutboundLink")],1)])])])]),e._v(" "),t("p",[e._v("A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.\nIt is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).\nEmpty geometries (such as GeoJSON features with a "),t("code",[e._v("null")]),e._v(" geometry or GeoJSON geometries with an empty coordinates array) are allowed and can sometimes also be the result of certain vector operations such as a negative buffer.")]),e._v(" "),t("h3",{attrs:{id:"applying-processes-on-dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes-on-dimensions"}},[e._v("#")]),e._v(" Applying Processes on Dimensions")]),e._v(" "),t("p",[e._v('Some processes are typically applied "along a dimension". You can imagine said dimension as an arrow and whatever is happening as a parallel process to that arrow. It simply means: "we focus on '),t("em",[e._v("this")]),e._v(' dimension right now".')]),e._v(" "),t("h3",{attrs:{id:"resolution"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resolution"}},[e._v("#")]),e._v(" Resolution")]),e._v(" "),t("p",[e._v("The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.")]),e._v(" "),t("h3",{attrs:{id:"coordinate-reference-system-as-a-dimension"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#coordinate-reference-system-as-a-dimension"}},[e._v("#")]),e._v(" Coordinate Reference System as a Dimension")]),e._v(" "),t("p",[e._v("In the example above, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimension values have a "),t("em",[e._v("unique")]),e._v(" relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would "),t("em",[e._v("have")]),e._v(" to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to "),t("em",[e._v("look")]),e._v(" at the result, meaning it is available in a single coordinate reference system.")]),e._v(" "),t("p",[e._v("Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one "),t("em",[e._v("single")]),e._v(" data cube containing all tiles that has an "),t("em",[e._v("additional dimension")]),e._v(" with the coordinate reference system. In that data cube, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" no longer point to a unique world coordinate, because identical "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" coordinate pairs occur in each UTM zone. Now, only the combination ("),t("em",[e._v("x")]),e._v(", "),t("em",[e._v("y")]),e._v(", "),t("em",[e._v("crs")]),e._v(") has a unique relationship to the world coordinates.")]),e._v(" "),t("p",[e._v("On such a "),t("em",[e._v("crs-dimensioned data cube")]),e._v(", several operations make perfect sense, such as "),t("code",[e._v("apply")]),e._v(" or "),t("code",[e._v("reduce_dimension")]),e._v(" on spectral and/or temporal dimensions. A simple reduction over the "),t("code",[e._v("crs")]),e._v(" dimension, using "),t("em",[e._v("sum")]),e._v(" or "),t("em",[e._v("mean")]),e._v(' would typically not make sense. The "reduction" (removal) of the '),t("code",[e._v("crs")]),e._v(" dimension that is meaningful involves the resampling/warping of all sub-cubes for the "),t("code",[e._v("crs")]),e._v(" dimension to a single, common target coordinate reference system.")]),e._v(" "),t("h2",{attrs:{id:"values-in-a-datacube"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#values-in-a-datacube"}},[e._v("#")]),e._v(" Values in a datacube")]),e._v(" "),t("p",[e._v("openEO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, "),t("code",[e._v("(x, y)")]),e._v(" refers to a unique location, that changes to "),t("code",[e._v("(x, y, CRS)")]),e._v(" when "),t("code",[e._v("(x, y)")]),e._v(" values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Be Careful with Data Types")]),e._v(" "),t("p",[e._v("As stated above, datacubes only contain scalar values. However, implementations may differ in their ability to handle or convert them. Implementations may also not allow mixing data types in a datacube. For example, returning a boolean value for a reducer on a numerical datacube may result in an error on some back-ends. The recommendation is to not change the data type of values in a datacube unless the back-end supports it explicitly.")])]),e._v(" "),t("p",[e._v("Data cube values can be sampled in two different ways. The values are either area or point samples.")]),e._v(" "),t("ul",[t("li",[e._v("Area sampling aggregates measurements over defined regions, i.e. the grid cells for raster data or polygons/lines for vector data.")]),e._v(" "),t("li",[e._v("Point sampling collects data at specific locations, providing detailed information for specific points.")])]),e._v(" "),t("h2",{attrs:{id:"processes-on-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes-on-datacubes"}},[e._v("#")]),e._v(" Processes on Datacubes")]),e._v(" "),t("p",[e._v("In the following part, the basic processes for manipulating datacubes are introduced.")]),e._v(" "),t("h3",{attrs:{id:"filter"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#filter"}},[e._v("#")]),e._v(" Filter")]),e._v(" "),t("p",[e._v("When filtering data (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_temporal")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Filtering vegetarian options from [corn, potato, pig] returns [corn, potato].\n"}},[t("code",[e._v("filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]")])])])]),e._v(" "),t("p",[e._v("In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval "),t("code",[e._v('["2020-10-15", "2020-10-27"]')]),e._v(". The result is a cube with only the rasters for the timestep that lies within that interval ("),t("code",[e._v('"2020-10-25"')]),e._v(") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band "),t("code",[e._v('["nir"]')]),e._v(" in the middle and a specific spatial region "),t("code",[e._v("[Polygon(...)]")]),e._v(" on the right.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(306),alt:"Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area."}}),e._v(" "),t("figcaption",[e._v("Filtering the sample datacube. It is displayed at the top with dimensions labels. Filtered results are shown at the bottom.")])]),e._v(" "),t("h3",{attrs:{id:"apply"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#apply"}},[e._v("#")]),e._v(" Apply")]),e._v(" "),t("p",[e._v("The "),t("code",[e._v("apply*")]),e._v(" functions (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#apply",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_neighborhood",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_neighborhood")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_dimension")]),t("OutboundLink")],1),e._v(") employ a process on the datacube that calculates new pixel values for each pixel, based on "),t("code",[e._v("n")]),e._v(" other pixels. Please note that several programming languages use the name "),t("code",[e._v("map")]),e._v(" instead of "),t("code",[e._v("apply")]),e._v(", but they describe the same type of function.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Applying the process 'cook' to [corn, potato, pig] returns [popcorn, fries, meat]."}},[t("code",[e._v("apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]")])])])]),e._v(" "),t("p",[e._v("For the case "),t("code",[e._v("n = 1")]),e._v(" this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the "),t("code",[e._v("absolute()")]),e._v(" function, calculating the absolute value of the input pixel value.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(307),alt:"Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0."}}),e._v(" "),t("figcaption",[e._v("Applying an unary process. Only the pixel itself is considered for calculating the new pixel value.")])]),e._v(" "),t("p",[e._v("If "),t("code",[e._v("n")]),e._v(" is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.")]),e._v(" "),t("p",[e._v("In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_kernel",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_kernel")]),t("OutboundLink")],1),e._v("). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(308),alt:"Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance."}}),e._v(" "),t("figcaption",[e._v("Applying a spatial kernel. For calculating each new pixel value, the defined weighted neighbourhood is used.")])]),e._v(" "),t("p",[e._v("Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (t"),t("sub",[e._v("n-1")]),e._v(", t"),t("sub",[e._v("n")]),e._v(" and t"),t("sub",[e._v("n+1")]),e._v("). No padding was added which is why we observe edge effects (NA values are returned for t"),t("sub",[e._v("1")]),e._v(" and t"),t("sub",[e._v("5")]),e._v(", because their temporal neighbourhood is missing input timesteps).")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(309),alt:"Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike."}}),e._v(" "),t("figcaption",[e._v("Applying a moving average (temporal smoothing) by averaging the direct temporal neighbourhoods of pixels. No padding is used, which leads to edge effects.")])]),e._v(" "),t("p",[e._v("Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension ("),t("code",[e._v("n")]),e._v(" equals the complete dimension). If a process is applied along the "),t("code",[e._v("time")]),e._v(" dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the "),t("code",[e._v("spatial")]),e._v(" dimensions (e.g. a "),t("code",[e._v("mean")]),e._v("), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to "),t("a",{attrs:{href:"#reduce"}},[e._v("reduce")]),e._v(". In the image below, a "),t("code",[e._v("mean")]),e._v(" is applied to the "),t("code",[e._v("time")]),e._v(" dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(310),alt:"Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged."}}),e._v(" "),t("figcaption",[e._v("Applying a mean to the temporal dimension.")])]),e._v(" "),t("h3",{attrs:{id:"resample"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resample"}},[e._v("#")]),e._v(" Resample")]),e._v(" "),t("p",[e._v("In a resampling processes (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_temporal")]),t("OutboundLink")],1),e._v("), the "),t("em",[e._v("layout")]),e._v(" of a certain dimension is changed into another "),t("em",[e._v("layout")]),e._v(", most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be "),t("em",[e._v("upscaled")]),e._v(" or "),t("em",[e._v("downscaled")]),e._v(" (also called "),t("em",[e._v("upsampling")]),e._v(" and "),t("em",[e._v("downsampling")]),e._v("), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Downscaling a raster image (to infinity) returns one pixel."}},[t("code",[e._v("resample(🖼️, downscale) => 🟦")])])]),e._v(" "),t("p",[t("span",{attrs:{title:"Reprojecting a globe results into a map."}},[t("code",[e._v("resample(🌍, reproject) => 🗺️")])])])]),e._v(" "),t("p",[e._v("The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(311),alt:"Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps."}}),e._v(" "),t("figcaption",[e._v("Temporal downsampling is seen on the left, upsampling on the right. The temporal layout that the cubes are resampled to is displayed in the middle.")])]),e._v(" "),t("p",[e._v("The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(312),alt:"Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added)."}}),e._v(" "),t("figcaption",[e._v("Spatial resampling. The geometry which the input cube is resampled to is displayed in the middle. The output cube then contains the same information, but in the resampled spatial layout.")])]),e._v(" "),t("h3",{attrs:{id:"reduce"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reduce"}},[e._v("#")]),e._v(" Reduce")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://processes.openeo.org/#reduce_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("reduce_dimension")]),t("OutboundLink")],1),e._v(" process "),t("em",[e._v("collapses")]),e._v(" a whole dimension of the datacube. It does so by using some sort of "),t("strong",[e._v("reducer")]),e._v(", which is a function that calculates a single result from an amount of values, as e.g. "),t("code",[e._v("mean()")]),e._v(", "),t("code",[e._v("min()")]),e._v(" and "),t("code",[e._v("max()")]),e._v(" are. For example we can reduce the time dimension ("),t("code",[e._v("t")]),e._v(") of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(" dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Reducing [mixed greens, cucumber, tomato, onion] returns a salad."}},[t("code",[e._v("reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗")])])])]),e._v(" "),t("p",[e._v("Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(313),alt:"Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile."}}),e._v(" "),t("figcaption",[e._v("Overview of reducing the sample datacube. Dimensions that are reduced are crossed out in the result images. Their information value has been compressed by a reducer function.")])]),e._v(" "),t("h3",{attrs:{id:"aggregate"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#aggregate"}},[e._v("#")]),e._v(" Aggregate")]),e._v(" "),t("p",[e._v("An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:")]),e._v(" "),t("ol",[t("li",[e._v("Grouping via a grouping variable, i.e. spatial geometries or temporal intervals")]),e._v(" "),t("li",[e._v("Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month")])]),e._v(" "),t("p",[e._v("While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure "),t("code",[e._v("reduce")]),e._v(", the dimensions along which the reducer function is applied still exist after the operation.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Aggregating different family representations with 3, 2 and 4 members with the function 'countFamilyMembers' returns [3, 2, 4]."}},[t("code",[e._v("aggregate(👪 👩‍👦 👨‍👩‍👦‍👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]")])])])]),e._v(" "),t("p",[e._v("A temporal aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_temporal")]),t("OutboundLink")],1),e._v(") is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function ("),t("code",[e._v("mean")]),e._v(", "),t("code",[e._v("max")]),e._v(", etc.) and assigned to the given new labels.")]),e._v(" "),t("p",[e._v("A spatial aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_spatial")]),t("OutboundLink")],1),e._v(") works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(314),alt:"Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps."}}),e._v(" "),t("figcaption",[e._v("When aggregating spatially, pixels are grouped (cut out) based on geometries, and then collapsed with a reducer function. A vector datacube is returned (as shown with dimensions names and labels). Please note that this visualization shows aggregation on a data cube with four dimensions, but "),t("code",[e._v("aggregate_spatial")]),e._v(" specifically can only handle data cubes with three dimensions as of now.")])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[13],{310:function(e,t,a){e.exports=a.p+"assets/img/dc_timeseries.c2c7a902.png"},311:function(e,t,a){e.exports=a.p+"assets/img/dc_flat.01dc91de.png"},312:function(e,t,a){e.exports=a.p+"assets/img/vector.201588fe.png"},313:function(e,t,a){e.exports=a.p+"assets/img/dc_filter.bdafa6d0.png"},314:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_unary.f69f840c.png"},315:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_kernel.4013a72c.png"},316:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_ts.4f9449f6.png"},317:function(e,t,a){e.exports=a.p+"assets/img/dc_apply_dim_ts.c895aa81.png"},318:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_time.987e797c.png"},319:function(e,t,a){e.exports=a.p+"assets/img/dc_resample_space.417f814b.png"},320:function(e,t,a){e.exports=a.p+"assets/img/dc_reduce.872f16c2.png"},321:function(e,t,a){e.exports=a.p+"assets/img/dc_aggregate_space.ae550945.png"},513:function(e,t,a){"use strict";a.r(t);var s=a(4),i=Object(s.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#datacubes"}},[e._v("#")]),e._v(" Datacubes")]),e._v(" "),t("h2",{attrs:{id:"what-are-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#what-are-datacubes"}},[e._v("#")]),e._v(" What are Datacubes?")]),e._v(" "),t("p",[e._v("Data is represented as datacubes in openEO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands ["),t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("near-infrared")]),e._v("] and 3 timesteps ["),t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")]),e._v("], displayed here in an orderly, timeseries-like manner:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(310),alt:"Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)."}}),e._v(" "),t("figcaption",[e._v("An examplary raster datacube with 4 dimensions: x, y, bands and time.")])]),e._v(" "),t("p",[e._v("It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(311),alt:"Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)."}}),e._v(" "),t("figcaption",[e._v("This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")])]),e._v(" "),t("p",[e._v("A vector datacube on the other hand could look like this:")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(312),alt:"An examplary vector datacube with 3 dimensions: 2 geometries are given for the `geometries` dimension, along with 3 timesteps for the temporal dimension `time` and 4 bands in the `bands` dimension."}}),e._v(" "),t("figcaption",[e._v("An examplary vector datacube with 3 dimensions: 2 geometries are given for the "),t("code",[e._v("geometries")]),e._v(" dimension, along with 3 timesteps for the temporal dimension "),t("code",[e._v("time")]),e._v(" and 4 bands in the "),t("code",[e._v("bands")]),e._v(" dimension.")])]),e._v(" "),t("p",[t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("Vector datacubes"),t("OutboundLink")],1),e._v(" and raster datacubes are common cases of datacubes in the EO domain.\nA raster datacube has at least two spatial dimensions (usually named "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(") and a vector datacube has at least one geometry dimension (usually named "),t("code",[e._v("geometry")]),e._v(').\nThe purpose of these distinctions is simply to make it easier to describe "special" cases of datacubes, but you can also define other types such as a temporal datacube that has at least one temporal dimension (usually named '),t("code",[e._v("t")]),e._v(").")]),e._v(" "),t("p",[e._v("The following additional information are usually available for datacubes:")]),e._v(" "),t("ul",[t("li",[e._v("the dimensions (see "),t("a",{attrs:{href:"#dimensions"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a sampling method (see "),t("a",{attrs:{href:"#values-in-a-datacube"}},[e._v("below")]),e._v(")")]),e._v(" "),t("li",[e._v("a unit for the values")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("h2",{attrs:{id:"dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#dimensions"}},[e._v("#")]),e._v(" Dimensions")]),e._v(" "),t("p",[e._v("A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(", and the temporal dimension "),t("code",[e._v("t")]),e._v(". Furthermore, it has a "),t("code",[e._v("bands")]),e._v(" dimension, extending into the realm of "),t("em",[e._v("what kind of information")]),e._v(" is contained in the cube.")]),e._v(" "),t("p",[e._v("The following properties are usually available for dimensions:")]),e._v(" "),t("ul",[t("li",[e._v("name")]),e._v(" "),t("li",[e._v("type (potential types include: spatial (raster or vector data), temporal and other data such as bands)")]),e._v(" "),t("li",[e._v("axis (for spatial dimensions) / number")]),e._v(" "),t("li",[e._v("labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)")]),e._v(" "),t("li",[e._v("reference system / projection")]),e._v(" "),t("li",[e._v("resolution / step size")]),e._v(" "),t("li",[e._v("unit for the labels (either explicitly specified or implicitly provided by the reference system)")]),e._v(" "),t("li",[e._v("additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)")])]),e._v(" "),t("p",[e._v("All these information are usually provided through the datacube metadata.")]),e._v(" "),t("p",[e._v("Here is an overview of the dimensions contained in our example raster datacube above:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("#")]),e._v(" "),t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("resolution")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[e._v("1")]),e._v(" "),t("td",[t("code",[e._v("x")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("466380")]),e._v(", "),t("code",[e._v("466580")]),e._v(", "),t("code",[e._v("466780")]),e._v(", "),t("code",[e._v("466980")]),e._v(", "),t("code",[e._v("467180")]),e._v(", "),t("code",[e._v("467380")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("2")]),e._v(" "),t("td",[t("code",[e._v("y")])]),e._v(" "),t("td",[e._v("spatial")]),e._v(" "),t("td",[t("code",[e._v("7167130")]),e._v(", "),t("code",[e._v("7166930")]),e._v(", "),t("code",[e._v("7166730")]),e._v(", "),t("code",[e._v("7166530")]),e._v(", "),t("code",[e._v("7166330")]),e._v(", "),t("code",[e._v("7166130")]),e._v(", "),t("code",[e._v("7165930")])]),e._v(" "),t("td",[e._v("200m")]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/32627",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:32627"),t("OutboundLink")],1)])]),e._v(" "),t("tr",[t("td",[e._v("3")]),e._v(" "),t("td",[t("code",[e._v("bands")])]),e._v(" "),t("td",[e._v("bands")]),e._v(" "),t("td",[t("code",[e._v("blue")]),e._v(", "),t("code",[e._v("green")]),e._v(", "),t("code",[e._v("red")]),e._v(", "),t("code",[e._v("nir")])]),e._v(" "),t("td",[e._v("4 bands")]),e._v(" "),t("td",[e._v("-")])]),e._v(" "),t("tr",[t("td",[e._v("4")]),e._v(" "),t("td",[t("code",[e._v("t")])]),e._v(" "),t("td",[e._v("temporal")]),e._v(" "),t("td",[t("code",[e._v("2020-10-01")]),e._v(", "),t("code",[e._v("2020-10-13")]),e._v(", "),t("code",[e._v("2020-10-25")])]),e._v(" "),t("td",[e._v("12 days")]),e._v(" "),t("td",[e._v("Gregorian calendar / UTC")])])])]),e._v(" "),t("p",[e._v('Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example.\nFor example, temporal labels are usually encoded as '),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/ISO_8601",target:"_blank",rel:"noopener noreferrer"}},[e._v("ISO 8601"),t("OutboundLink")],1),e._v(" compatible dates and/or times and similarly geometries can be encoded as "),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry",target:"_blank",rel:"noopener noreferrer"}},[e._v("Well-known Text (WKT)"),t("OutboundLink")],1),e._v(" or be represented by their IDs.")]),e._v(" "),t("p",[e._v("Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually "),t("code",[e._v("bands")]),e._v("), retain the order in which they have been defined in metadata or processes (e.g. through "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), with new labels simply being appended to the existing labels.")]),e._v(" "),t("p",[e._v("A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:")]),e._v(" "),t("table",[t("thead",[t("tr",[t("th",[e._v("name")]),e._v(" "),t("th",[e._v("type")]),e._v(" "),t("th",[e._v("labels")]),e._v(" "),t("th",[e._v("reference system")])])]),e._v(" "),t("tbody",[t("tr",[t("td",[t("code",[e._v("geometry")])]),e._v(" "),t("td",[e._v("vector")]),e._v(" "),t("td",[t("code",[e._v("POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6))")]),e._v(", "),t("code",[e._v("POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5))")])]),e._v(" "),t("td",[t("a",{attrs:{href:"https://epsg.io/4326",target:"_blank",rel:"noopener noreferrer"}},[e._v("EPSG:4326"),t("OutboundLink")],1)])])])]),e._v(" "),t("p",[e._v("A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.\nIt is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).\nEmpty geometries (such as GeoJSON features with a "),t("code",[e._v("null")]),e._v(" geometry or GeoJSON geometries with an empty coordinates array) are allowed and can sometimes also be the result of certain vector operations such as a negative buffer.")]),e._v(" "),t("h3",{attrs:{id:"applying-processes-on-dimensions"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes-on-dimensions"}},[e._v("#")]),e._v(" Applying Processes on Dimensions")]),e._v(" "),t("p",[e._v('Some processes are typically applied "along a dimension". You can imagine said dimension as an arrow and whatever is happening as a parallel process to that arrow. It simply means: "we focus on '),t("em",[e._v("this")]),e._v(' dimension right now".')]),e._v(" "),t("h3",{attrs:{id:"resolution"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resolution"}},[e._v("#")]),e._v(" Resolution")]),e._v(" "),t("p",[e._v("The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.")]),e._v(" "),t("h3",{attrs:{id:"coordinate-reference-system-as-a-dimension"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#coordinate-reference-system-as-a-dimension"}},[e._v("#")]),e._v(" Coordinate Reference System as a Dimension")]),e._v(" "),t("p",[e._v("In the example above, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimension values have a "),t("em",[e._v("unique")]),e._v(" relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would "),t("em",[e._v("have")]),e._v(" to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to "),t("em",[e._v("look")]),e._v(" at the result, meaning it is available in a single coordinate reference system.")]),e._v(" "),t("p",[e._v("Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one "),t("em",[e._v("single")]),e._v(" data cube containing all tiles that has an "),t("em",[e._v("additional dimension")]),e._v(" with the coordinate reference system. In that data cube, "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" no longer point to a unique world coordinate, because identical "),t("em",[e._v("x")]),e._v(" and "),t("em",[e._v("y")]),e._v(" coordinate pairs occur in each UTM zone. Now, only the combination ("),t("em",[e._v("x")]),e._v(", "),t("em",[e._v("y")]),e._v(", "),t("em",[e._v("crs")]),e._v(") has a unique relationship to the world coordinates.")]),e._v(" "),t("p",[e._v("On such a "),t("em",[e._v("crs-dimensioned data cube")]),e._v(", several operations make perfect sense, such as "),t("code",[e._v("apply")]),e._v(" or "),t("code",[e._v("reduce_dimension")]),e._v(" on spectral and/or temporal dimensions. A simple reduction over the "),t("code",[e._v("crs")]),e._v(" dimension, using "),t("em",[e._v("sum")]),e._v(" or "),t("em",[e._v("mean")]),e._v(' would typically not make sense. The "reduction" (removal) of the '),t("code",[e._v("crs")]),e._v(" dimension that is meaningful involves the resampling/warping of all sub-cubes for the "),t("code",[e._v("crs")]),e._v(" dimension to a single, common target coordinate reference system.")]),e._v(" "),t("h2",{attrs:{id:"values-in-a-datacube"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#values-in-a-datacube"}},[e._v("#")]),e._v(" Values in a datacube")]),e._v(" "),t("p",[e._v("openEO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, "),t("code",[e._v("(x, y)")]),e._v(" refers to a unique location, that changes to "),t("code",[e._v("(x, y, CRS)")]),e._v(" when "),t("code",[e._v("(x, y)")]),e._v(" values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Be Careful with Data Types")]),e._v(" "),t("p",[e._v("As stated above, datacubes only contain scalar values. However, implementations may differ in their ability to handle or convert them. Implementations may also not allow mixing data types in a datacube. For example, returning a boolean value for a reducer on a numerical datacube may result in an error on some back-ends. The recommendation is to not change the data type of values in a datacube unless the back-end supports it explicitly.")])]),e._v(" "),t("p",[e._v("Data cube values can be sampled in two different ways. The values are either area or point samples.")]),e._v(" "),t("ul",[t("li",[e._v("Area sampling aggregates measurements over defined regions, i.e. the grid cells for raster data or polygons/lines for vector data.")]),e._v(" "),t("li",[e._v("Point sampling collects data at specific locations, providing detailed information for specific points.")])]),e._v(" "),t("h2",{attrs:{id:"processes-on-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes-on-datacubes"}},[e._v("#")]),e._v(" Processes on Datacubes")]),e._v(" "),t("p",[e._v("In the following part, the basic processes for manipulating datacubes are introduced.")]),e._v(" "),t("h3",{attrs:{id:"filter"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#filter"}},[e._v("#")]),e._v(" Filter")]),e._v(" "),t("p",[e._v("When filtering data (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_temporal")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#filter_bands",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("filter_bands")]),t("OutboundLink")],1),e._v("), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Filtering vegetarian options from [corn, potato, pig] returns [corn, potato].\n"}},[t("code",[e._v("filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]")])])])]),e._v(" "),t("p",[e._v("In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval "),t("code",[e._v('["2020-10-15", "2020-10-27"]')]),e._v(". The result is a cube with only the rasters for the timestep that lies within that interval ("),t("code",[e._v('"2020-10-25"')]),e._v(") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band "),t("code",[e._v('["nir"]')]),e._v(" in the middle and a specific spatial region "),t("code",[e._v("[Polygon(...)]")]),e._v(" on the right.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(313),alt:"Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area."}}),e._v(" "),t("figcaption",[e._v("Filtering the sample datacube. It is displayed at the top with dimensions labels. Filtered results are shown at the bottom.")])]),e._v(" "),t("h3",{attrs:{id:"apply"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#apply"}},[e._v("#")]),e._v(" Apply")]),e._v(" "),t("p",[e._v("The "),t("code",[e._v("apply*")]),e._v(" functions (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#apply",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_neighborhood",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_neighborhood")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_dimension")]),t("OutboundLink")],1),e._v(") employ a process on the datacube that calculates new pixel values for each pixel, based on "),t("code",[e._v("n")]),e._v(" other pixels. Please note that several programming languages use the name "),t("code",[e._v("map")]),e._v(" instead of "),t("code",[e._v("apply")]),e._v(", but they describe the same type of function.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Applying the process 'cook' to [corn, potato, pig] returns [popcorn, fries, meat]."}},[t("code",[e._v("apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]")])])])]),e._v(" "),t("p",[e._v("For the case "),t("code",[e._v("n = 1")]),e._v(" this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the "),t("code",[e._v("absolute()")]),e._v(" function, calculating the absolute value of the input pixel value.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(314),alt:"Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0."}}),e._v(" "),t("figcaption",[e._v("Applying an unary process. Only the pixel itself is considered for calculating the new pixel value.")])]),e._v(" "),t("p",[e._v("If "),t("code",[e._v("n")]),e._v(" is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.")]),e._v(" "),t("p",[e._v("In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via "),t("a",{attrs:{href:"https://processes.openeo.org/#apply_kernel",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("apply_kernel")]),t("OutboundLink")],1),e._v("). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(315),alt:"Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance."}}),e._v(" "),t("figcaption",[e._v("Applying a spatial kernel. For calculating each new pixel value, the defined weighted neighbourhood is used.")])]),e._v(" "),t("p",[e._v("Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (t"),t("sub",[e._v("n-1")]),e._v(", t"),t("sub",[e._v("n")]),e._v(" and t"),t("sub",[e._v("n+1")]),e._v("). No padding was added which is why we observe edge effects (NA values are returned for t"),t("sub",[e._v("1")]),e._v(" and t"),t("sub",[e._v("5")]),e._v(", because their temporal neighbourhood is missing input timesteps).")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(316),alt:"Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike."}}),e._v(" "),t("figcaption",[e._v("Applying a moving average (temporal smoothing) by averaging the direct temporal neighbourhoods of pixels. No padding is used, which leads to edge effects.")])]),e._v(" "),t("p",[e._v("Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension ("),t("code",[e._v("n")]),e._v(" equals the complete dimension). If a process is applied along the "),t("code",[e._v("time")]),e._v(" dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the "),t("code",[e._v("spatial")]),e._v(" dimensions (e.g. a "),t("code",[e._v("mean")]),e._v("), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to "),t("a",{attrs:{href:"#reduce"}},[e._v("reduce")]),e._v(". In the image below, a "),t("code",[e._v("mean")]),e._v(" is applied to the "),t("code",[e._v("time")]),e._v(" dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(317),alt:"Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged."}}),e._v(" "),t("figcaption",[e._v("Applying a mean to the temporal dimension.")])]),e._v(" "),t("h3",{attrs:{id:"resample"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#resample"}},[e._v("#")]),e._v(" Resample")]),e._v(" "),t("p",[e._v("In a resampling processes (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_spatial")]),t("OutboundLink")],1),e._v(", "),t("a",{attrs:{href:"https://processes.openeo.org/#resample_cube_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("resample_cube_temporal")]),t("OutboundLink")],1),e._v("), the "),t("em",[e._v("layout")]),e._v(" of a certain dimension is changed into another "),t("em",[e._v("layout")]),e._v(", most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be "),t("em",[e._v("upscaled")]),e._v(" or "),t("em",[e._v("downscaled")]),e._v(" (also called "),t("em",[e._v("upsampling")]),e._v(" and "),t("em",[e._v("downsampling")]),e._v("), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Downscaling a raster image (to infinity) returns one pixel."}},[t("code",[e._v("resample(🖼️, downscale) => 🟦")])])]),e._v(" "),t("p",[t("span",{attrs:{title:"Reprojecting a globe results into a map."}},[t("code",[e._v("resample(🌍, reproject) => 🗺️")])])])]),e._v(" "),t("p",[e._v("The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(318),alt:"Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps."}}),e._v(" "),t("figcaption",[e._v("Temporal downsampling is seen on the left, upsampling on the right. The temporal layout that the cubes are resampled to is displayed in the middle.")])]),e._v(" "),t("p",[e._v("The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(319),alt:"Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added)."}}),e._v(" "),t("figcaption",[e._v("Spatial resampling. The geometry which the input cube is resampled to is displayed in the middle. The output cube then contains the same information, but in the resampled spatial layout.")])]),e._v(" "),t("h3",{attrs:{id:"reduce"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#reduce"}},[e._v("#")]),e._v(" Reduce")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"https://processes.openeo.org/#reduce_dimension",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("reduce_dimension")]),t("OutboundLink")],1),e._v(" process "),t("em",[e._v("collapses")]),e._v(" a whole dimension of the datacube. It does so by using some sort of "),t("strong",[e._v("reducer")]),e._v(", which is a function that calculates a single result from an amount of values, as e.g. "),t("code",[e._v("mean()")]),e._v(", "),t("code",[e._v("min()")]),e._v(" and "),t("code",[e._v("max()")]),e._v(" are. For example we can reduce the time dimension ("),t("code",[e._v("t")]),e._v(") of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the "),t("code",[e._v("x")]),e._v(" and "),t("code",[e._v("y")]),e._v(" dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Reducing [mixed greens, cucumber, tomato, onion] returns a salad."}},[t("code",[e._v("reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗")])])])]),e._v(" "),t("p",[e._v("Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(320),alt:"Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile."}}),e._v(" "),t("figcaption",[e._v("Overview of reducing the sample datacube. Dimensions that are reduced are crossed out in the result images. Their information value has been compressed by a reducer function.")])]),e._v(" "),t("h3",{attrs:{id:"aggregate"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#aggregate"}},[e._v("#")]),e._v(" Aggregate")]),e._v(" "),t("p",[e._v("An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:")]),e._v(" "),t("ol",[t("li",[e._v("Grouping via a grouping variable, i.e. spatial geometries or temporal intervals")]),e._v(" "),t("li",[e._v("Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month")])]),e._v(" "),t("p",[e._v("While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure "),t("code",[e._v("reduce")]),e._v(", the dimensions along which the reducer function is applied still exist after the operation.")]),e._v(" "),t("div",{staticClass:"custom-block tip"},[t("p",{staticClass:"custom-block-title"},[e._v("Simplified")]),e._v(" "),t("p",[t("span",{attrs:{title:"Aggregating different family representations with 3, 2 and 4 members with the function 'countFamilyMembers' returns [3, 2, 4]."}},[t("code",[e._v("aggregate(👪 👩‍👦 👨‍👩‍👦‍👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]")])])])]),e._v(" "),t("p",[e._v("A temporal aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_temporal",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_temporal")]),t("OutboundLink")],1),e._v(") is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function ("),t("code",[e._v("mean")]),e._v(", "),t("code",[e._v("max")]),e._v(", etc.) and assigned to the given new labels.")]),e._v(" "),t("p",[e._v("A spatial aggregation (e.g. "),t("a",{attrs:{href:"https://processes.openeo.org/#aggregate_spatial",target:"_blank",rel:"noopener noreferrer"}},[t("code",[e._v("aggregate_spatial")]),t("OutboundLink")],1),e._v(") works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.")]),e._v(" "),t("figure",[t("img",{attrs:{src:a(321),alt:"Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps."}}),e._v(" "),t("figcaption",[e._v("When aggregating spatially, pixels are grouped (cut out) based on geometries, and then collapsed with a reducer function. A vector datacube is returned (as shown with dimensions names and labels). Please note that this visualization shows aggregation on a data cube with four dimensions, but "),t("code",[e._v("aggregate_spatial")]),e._v(" specifically can only handle data cubes with three dimensions as of now.")])])])}),[],!1,null,null,null);t.default=i.exports}}]); \ No newline at end of file diff --git a/assets/js/15.f548d8e3.js b/assets/js/15.4231ee89.js similarity index 99% rename from assets/js/15.f548d8e3.js rename to assets/js/15.4231ee89.js index c39008d4f..46b20b2eb 100644 --- a/assets/js/15.f548d8e3.js +++ b/assets/js/15.4231ee89.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[15],{325:function(e,t,o){e.exports=o.p+"assets/img/qgis_button.2b5f39a5.png"},326:function(e,t,o){e.exports=o.p+"assets/img/login_dialog.424c4b4e.png"},327:function(e,t,o){e.exports=o.p+"assets/img/backend_info.a00e7a1d.png"},328:function(e,t,o){e.exports=o.p+"assets/img/jobs.fef9e692.png"},329:function(e,t,o){e.exports=o.p+"assets/img/adapt_job.64ea4b55.png"},330:function(e,t,o){e.exports=o.p+"assets/img/edit_spatial.23df03fa.png"},331:function(e,t,o){e.exports=o.p+"assets/img/edit_temporal.609c87e2.png"},332:function(e,t,o){e.exports=o.p+"assets/img/edit_bands.d9ed77a5.png"},333:function(e,t,o){e.exports=o.p+"assets/img/edit_subpg.c7fbfd4d.png"},334:function(e,t,o){e.exports=o.p+"assets/img/services.a958fd61.png"},335:function(e,t,o){e.exports=o.p+"assets/img/services_create.0881f2cd.png"},533:function(e,t,o){"use strict";o.r(t);var n=o(4),a=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"qgis-plugin"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#qgis-plugin"}},[e._v("#")]),e._v(" QGIS Plugin")]),e._v(" "),t("p",[e._v("The openEO QGIS plugin enables exploring openEO backends as well as execute and modify your jobs at an backend\nwithin QGIS. The plugin can load the results of a job execution to the QGIS map, so that the user can make further\nanalyses and visualisation steps.")]),e._v(" "),t("h2",{attrs:{id:"installation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[e._v("#")]),e._v(" Installation")]),e._v(" "),t("p",[e._v("There are two different versions of the QGIS Plugin available:")]),e._v(" "),t("ol",[t("li",[e._v("The "),t("strong",[e._v("latest stable version")]),e._v(' is accessible in the plugin\nmanager of QGIS (Plugins -> Manage and Install Plugins). Note that you have to activate the option to show experimental\nplugins (Settings -> Show also experimental plugins). After that, you can search for\n"OpenEO", install and activate the plugin.')]),e._v(" "),t("li",[e._v("The "),t("strong",[e._v("latest version")]),e._v(" is accessible at the\n"),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-qgis-plugin",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repository"),t("OutboundLink")],1),e._v('. First, download the repository as zip archive.\nNow, open the plugin manager of QGIS (Plugins -> Manage and Install Plugins) and select "Install from ZIP".\nThere you can browse for the downloaded zip archive, install and activate it.')])]),e._v(" "),t("p",[e._v("This tutorial shows the capabilities of version 0.9.2. Make sure you have installed at least that version by checking in\nthe plugin manager.")]),e._v(" "),t("p",[e._v("After successfully installing and activating the plugin, the openEO logo should be part of the toolbar:")]),e._v(" "),t("p",[t("img",{attrs:{src:o(325),alt:"QGIS Plugin Icon"}})]),e._v(" "),t("p",[e._v("Simply click on the icon to start the plugin.")]),e._v(" "),t("h2",{attrs:{id:"connecting"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#connecting"}},[e._v("#")]),e._v(" Connecting")]),e._v(" "),t("p",[e._v("The first window you see when starting the plugin is a login dialog, since you can only access your jobs at the backend\nif you are authenticated. The backend dropdown menu contains the available openEO backends from\nthe "),t("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Hub"),t("OutboundLink")],1),e._v('. By default, the plugin chooses the latest compatible version from the\nselected provider. If you want to connect to a specific version of a backend, you need to uncheck\nthe checkbox labeled "Only Latest versions". You can also manually type a backend url into the selection\nfield, in case it is not registered at our hub yet.')]),e._v(" "),t("p",[t("img",{attrs:{src:o(326),alt:"QGIS Login Dialog"}})]),e._v(" "),t("p",[e._v("Now that you have chosen a backend, you can log in with your username and password provided by the backend.\nNote that the QGIS plugin only supports Basic authentication and not "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" yet.\nBackends that do not support this authentication can not be accessed via this plugin at the moment.")]),e._v(" "),t("p",[e._v("If logged in successfully, the main window of the plugin appears with three tabs ("),t("strong",[e._v("Backend Info")]),e._v(", "),t("strong",[e._v("Jobs")]),e._v(", "),t("strong",[e._v("Services")]),e._v(").\nThe following sections will go through the capabilities of these tabs.")]),e._v(" "),t("h2",{attrs:{id:"exploring-a-backend"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-backend"}},[e._v("#")]),e._v(" Exploring a backend")]),e._v(" "),t("p",[e._v("The first tab shows information about the backend and the available collections and processes.\nOn the top, there is a basic description about the backend you are connected to.\nNext, you can choose a collection in the drop down menu to see a description of that dataset.")]),e._v(" "),t("p",[t("img",{attrs:{src:o(327),alt:"QGIS Backend Info"}})]),e._v(" "),t("p",[e._v("On the bottom, you can use the drop down menu to choose one of the available processes from the backend.\nAfter selection, the parameters of the process are listed in the table below. The first column contains the\nparameter name, which is required if bold and otherwise optional.\nThe second column shows the type of the parameter (e.g. number) and the third column a short description.\nTo learn more about the process, you can click on the info button next to the drop down menu\n(marked red in the figure above). The return type of the process is written next to the info box. If you want to get an\noverview of all processes of openEO you might visit the "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[e._v("official processes page")]),e._v(", but note that not all backends\nwill implement all processes listed there, whereas only available processes of the backend are in the QGIS plugin.")],1),e._v(" "),t("h2",{attrs:{id:"job-management"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#job-management"}},[e._v("#")]),e._v(" Job Management")]),e._v(" "),t("p",[e._v('The "Jobs" tab lists all of your '),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[e._v("jobs")]),e._v("\nat the backend. In openEO a job describes what the backend needs to process\n(e.g. by defining a "),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[e._v("process graph")]),e._v(") as well as\nadditional meta-information (e.g. title, creation date). The first two columns show the title and the creation date of\nthe job. The other elements of the table are numbered in the screenshot and described in the following.")],1),e._v(" "),t("p",[t("img",{attrs:{src:o(328),alt:"QGIS Jobs"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Status")]),e._v(' - Shows the current lifecycle state of the job. The first status of a job is "created",\nif it got created at the backend, but not executed yet. After the job gets executed it will be\n"queued" if the backend plans to executes it, but has not started yet. If the execution started but hasn\'t finished yet,\nit has the status of "running". Last but not least it will have either "canceled", if it was stopped during execution,\n"finished" if the execution was successfully or "error", if an error occurred during execution.')]),e._v(" "),t("li",[t("strong",[e._v("Execute")]),e._v(' - By clicking on the "Play" button, the plugin will start the execution of the job at the backend.\nAlready finished jobs are executed again. The icon of the button will change if the job is in the "running" state,\nso that it looks like a "Stop" button, which will cancel the job execution.')]),e._v(" "),t("li",[t("strong",[e._v("Display")]),e._v(' - By clicking on the "Eye" button, the plugin downloads the resulting image(s) of the job from the backend\nand creates a new layer in QGIS named after the title and the creation date of the image. Note that if the result\nis not geo referenced, you might get a dialog from QGIS to choose a coordinate reference system, therefore it is\nrecommended to choose GeoTiff as job output format if available at the backend.')]),e._v(" "),t("li",[t("strong",[e._v("Save")]),e._v(' - By clicking on the "Download" button, you can navigate to the folder where you want to store the\nresulting image(s) of the job and the plugin will download it there.')]),e._v(" "),t("li",[t("strong",[e._v("Adapt")]),e._v(' - By clicking on the "Pencil" button a new dialog appears, where you can adapt the job title and\ndescription as well as the processes and extent of the job. How this works in detail is described in the following sections.')]),e._v(" "),t("li",[t("strong",[e._v("Information")]),e._v(' - By clicking on the "Info" button a window with additional information about the job appears.\nIt contains information like a description, a cost plan etc., of course only if this information is provided by the\nbackend. If the backend provides a logging output, it is also part of this info window.')]),e._v(" "),t("li",[t("strong",[e._v("Delete")]),e._v(' - By clicking on the "Trash" button the job gets deleted from the backend, after being asked if you are\nreally sure about it.')]),e._v(" "),t("li",[t("strong",[e._v("Load Job from openEO Hub")]),e._v(" - At the moment it is recommended to not use this feature, since the exchange of\nprocess graphs in the OpenEO Hub is under development.")]),e._v(" "),t("li",[t("strong",[e._v("Create new Job in Web Editor")]),e._v(" - This button opens the "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(" in your\nsystem browser, so that you can create a new job using it. This is the recommended way for creating a new job, since it has\na graphical and therefore more convenient way of doing so.")]),e._v(" "),t("li",[t("strong",[e._v("Create new Job")]),e._v(" - You can create a job from scratch using this button.\nMore information in the next sections.")]),e._v(" "),t("li",[t("strong",[e._v("Refresh Table")]),e._v(" - This button refreshes the list of jobs manually, in general the list will refresh every few\nseconds. If you do not want to automatically refresh the list (it might cause some lags if the connection to the\nbackend is low) you can do so by deactivating the checkbox next to the refresh button.")]),e._v(" "),t("li",[t("strong",[e._v("User Manual")]),e._v(" - This button opens your browser with this introduction page.")])]),e._v(" "),t("h3",{attrs:{id:"creating-new-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#creating-new-jobs"}},[e._v("#")]),e._v(" Creating new Jobs")]),e._v(" "),t("p",[e._v("There are three approaches to create a new job using this plugin, depending on whether you want to start from scratch or just\nchange existing jobs:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("From scratch with web editor")]),e._v(" - As described in the job table overview above, there is a button (no. 9) that opens\nyour web browser with the "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(". It is the recommended way to create a new job\nfrom scratch, since it has a very convenient graphical user interface. It also shows the process graph in a neat graph\nbased manner for a better understanding of how processes are connected.")]),e._v(" "),t("li",[t("strong",[e._v("From scratch with QGIS")]),e._v(' - If you want to use the QGIS plugin, you can create a new job using the\nbutton (no. 10) of the job overview dialog. It opens the job adaption window (more details in the section below).\nIn the beginning the job has already a "load_collection" process as a starting point.')]),e._v(" "),t("li",[t("strong",[e._v("From existing Job")]),e._v(" - You can create a new job by adapting\none of your existing jobs at the backend. You can e.g. create a new job with a different spatial extent, or\nadding a few processes to the original one. To do so, you need to click on the edit button in the job overview window (no. 5)")])]),e._v(" "),t("h3",{attrs:{id:"adapting-existing-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#adapting-existing-jobs"}},[e._v("#")]),e._v(" Adapting existing Jobs")]),e._v(" "),t("p",[e._v("In this section you'll get an overview of the job adaption workflow using the job adaption window.\nHow you get there was described in the previous section.")]),e._v(" "),t("p",[t("img",{attrs:{src:o(329),alt:"QGIS Adapting Job"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Job Title")]),e._v(" - Here you can write the title of the job. It is recommended to choose it in a way that\nmakes you identify the job in a list more easily.")]),e._v(" "),t("li",[t("strong",[e._v("Job Description")]),e._v(" - You can describe the job in more detail in this field (e.g. what is calculated and how).")]),e._v(" "),t("li",[t("strong",[e._v("Process Graph Table")]),e._v(" - In the process graph table you can see a list of all processes used by the job.\nThe first column contains the identifier of the process node, so a unique identifier of the process throughout the\nwhole process graph (e.g. loadcollection1). Most openEO clients generate these identifiers automatically. We define a\nprocess node (or just node) as one element of the process graph, so a single executed process."),t("br"),e._v('\nIn the second column you can see the name/identifier of the process (e.g. load_collection), which needs to be one of the\nprocesses that the backend provides. The third column shows the node identifier of the predecessor node.\nFor example, "loadcollection1" in the second row means that the output of the node with id "loadcollection1" is the input\nof the node with id "reducedimension1". In other words the process of the second row uses the output of the process of\nthe first row and is therefore executed after the first process.')]),e._v(" "),t("li",[t("strong",[e._v("Process Edit")]),e._v(' - When pressing on the "pencil" button, the process of the same row is loaded into the process\ntable on the right (see no. 6). On this table you can adapt the arguments of the chosen process and therefore configure\nits behaviour.')]),e._v(" "),t("li",[t("strong",[e._v("Delete Process")]),e._v(' - Clicking on the "trash" button deletes the selected process from the process graph.\nNote that other processes, which have this process as predecessor might fail after the deletion.')]),e._v(" "),t("li",[t("strong",[e._v("Undo Changes")]),e._v(" - Press this button if you want to undo all your changes on the job.")]),e._v(" "),t("li",[t("strong",[e._v("Raw Graph")]),e._v(' - Press this button to see the raw process graph (JSON format) in a pop up window. Changes to the\nprocess graph are also applied if you click "Apply". Therefore, you can also copy and paste here a complete process graph'),t("br"),e._v("\ninto the QGIS plugin.")]),e._v(" "),t("li",[t("strong",[e._v("Add Process")]),e._v(' - If you want to add a new process to the process graph you can select one at the drop down menu\n(see "Select a process") and press the button labeled "Add Process". The text field on the right will be the new node id\nand it is autogenerated after selecting a process, but can also be manually defined by typing it into the text field. Note\nthat it has to be unique within the job, otherwise the plugin shows an error message.\nOnce added, the id can not be modified.')]),e._v(" "),t("li",[t("strong",[e._v("Process Table")]),e._v(" - In this table you see all arguments of the currently selected process, the expected type and\nthe current value. If the name of the argument is bold, it is a required argument, hence it must be set.\nYou can edit the values of the arguments directly in the text field of the value column. For some arguments the plugin\nprovides helper function in the fourth column of the process table (e.g. drop down menu of all available collections in\nthe first row)")]),e._v(" "),t("li",[t("strong",[e._v("Process Argument Helper")]),e._v(" - For some arguments of a process, the QGIS Plugin provides helper functions to make the\nadaption of the arguments easier. This is the case, if the field of the fourth column in the process table is\nnot empty.")]),e._v(" "),t("li",[t("strong",[e._v("Result Process")]),e._v(' - This checkbox needs to be checked on the last process of the process graph. It tells the\nbackend that the result of the checked process is the output of the job (usually a "save_result" process). Note that\nonly one process of the process graph can be a result node. If you create a sub process graph in the process graph\n(e.g. when using apply or reduce), it also needs to have exactly one resulting process.')]),e._v(" "),t("li",[t("strong",[e._v("Clone Process")]),e._v(' - When clicking on this button, a clone of the currently selected node gets created and loaded\ninto the process table on the right. Note that clicking on this button does not add it to the job already, so you need to\npress on the "Add Process" button to do so.')]),e._v(" "),t("li",[t("strong",[e._v("Save Process")]),e._v(" - Changes on the process arguments are not stored in the process graph until this button is pressed.\nYou can see if there are unsaved changes if this button is enabled, otherwise it is disabled (see screenshot).")]),e._v(" "),t("li",[t("strong",[e._v("Manage Job")]),e._v(' - In this section of the adaption dialog, there are three options to handle changes on the job.\nFirst, by pressing on "Adapt Job" the selected job will be adapted to the changes you did and no new job will be created.\nSecond, by pressing on "Create New Job" a new job will be created at the backend, so if you have selected a job before\nfor the adaption, it will stay unchanged and your changes will just be applied to the newly created job.\nThird, the "Cancel" button closes the job adaption dialog with your changes having no effect. The same behaviour applies\nby closing the dialog.')])]),e._v(" "),t("h4",{attrs:{id:"process-argument-helper"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#process-argument-helper"}},[e._v("#")]),e._v(" Process Argument Helper")]),e._v(" "),t("p",[e._v("For some process arguments the QGIS plugin provides helper functions for a more convenient editing.\nThis section provides a list of the available helper functions and how to use them:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Collection Id")]),e._v(' - Whenever an argument expects a collection id (e.g. "load_collection" process), the plugin shows a\ndrop down menu with all available collections of the backend. There you can select one and it will be put into the\narguments value.')]),e._v(" "),t("li",[t("strong",[e._v("Spatial Extent")]),e._v(" - Edit buttons of arguments which expect a spatial extent (e.g. geojson, bounding-box) open a\nspatial extent dialog:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(330),alt:"QGIS Edit spatial extent"}})]),e._v(" "),t("p",[e._v('On the bottom you can edit the spatial extent manually in the text box. In the drop down menu on the top you can choose\nhow to select the spatial extent. You can get the extent of the current layer, the extent of a shape file, the extent of\nthe current QGIS map canvas, by drawing a rectangle on the QGIS map canvas or by drawing a polygon on the QGIS map\ncanvas. The drawings start after clicking on the "Draw Extent" button, so that your curser becomes a cross to draw on\nthe map. After pressing the "OK" button, the extent string in the text field is set to the process argument and the\nwindow closes.')]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Temporal Extent")]),e._v(" - If an argument expects a start and end date, the edit button opens a calendar dialog:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(331),alt:"QGIS Edit temporal extent"}})]),e._v(" "),t("p",[e._v("The left calendar sets the start date and the right calendar the end date.")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Band selection")]),e._v(" - If an argument expects a selection of bands, the edit button opens the following dialog to choose\nfrom the bands available at the selected collection. Note that it always lists all bands of the collection even if some\nbands are filtered by previous processes and therefore not available in the datacube anymore.")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(332),alt:"QGIS Edit band selection"}})]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Process Graph Node")]),e._v(' - If an argument expects a raster data cube, a drop down menu will appear on the edit column.\nThere you can choose from existing process graph nodes (e.g. "loadcollection1") It uses the output of the given node\nidentifier as input of the process argument.')]),e._v(" "),t("li",[t("strong",[e._v("Sub Process Graph")]),e._v(" - Some processes need a whole (sub-)process graph as input value (e.g. apply or reduce\nprocesses). When clicking on the edit button, the plugin opens a new adaption window, which looks similar to the job\nadaption window, but shows the sub process graph:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(333),alt:"QGIS Edit sub process graph"}})]),e._v(" "),t("p",[e._v('In this window you can adapt the sub process graph in the same way as the original process graph. After pressing on\n"Adapt", the changes are written to the original job. Process arguments, which do not have special editing fields,\nhave a "Proc-Param" button. If pressed it will forward the input data of the parent node\n(so the one with the subgraph as attribute) to the selected argument (see screenshot "data" argument). Note that the\nsub process graph also needs to have at least one result node.')]),e._v(" "),t("h2",{attrs:{id:"service-management"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#service-management"}},[e._v("#")]),e._v(" Service Management")]),e._v(" "),t("p",[e._v('The "Services" tab lists all of your '),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[e._v("secondary web services")]),e._v("\nat the backend. In openEO a secondary service has a process graph like a job, but depending on the service type it is\npossible for the user to change the viewing extent dynamically. The first two columns shows the title, the description\nand the date and time it got created at the backend.\nThe other elements of this window are numbered at the screenshot below and described in the following.")],1),e._v(" "),t("p",[t("img",{attrs:{src:o(334),alt:"QGIS Services"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Display")]),e._v(' - When clicking on the "Eye", the secondary service gets loaded into a new QGIS layer, named after the\nservice. QGIS now fetches the result of the processing into the current extent of the QGIS map canvas. Note that it will\nload the whole screen at once, so it might take a while.')]),e._v(" "),t("li",[t("strong",[e._v("Information")]),e._v(" - Clicking on the Infobox, shows detailed information about the service.")]),e._v(" "),t("li",[t("strong",[e._v("Delete")]),e._v(" - Clicking on the delete button removes the service from the backend.")]),e._v(" "),t("li",[t("strong",[e._v("Create from job")]),e._v(' - By clicking on this button you can create a new service from one of your jobs. It will create a\nservice with the same process graph as the selected job, but the extent will be dynamically loaded via the service. By\nclicking on the button a new dialog pops up. On the creation dialog you can set a title and a description text, but note\nthat they are optional. Nevertheless, you have to select a job and choose a type of secondary service. The drop down\nmenu of the service types contains only the ones supported by your backend. By clicking on "Create Service" the\nplugin creates the service on the backend, so you should be able to see it on the table. Note that the list of jobs\ncontains only successfully executed jobs (e.g. with "finished" status). By clicking on "Raw Graph"\n'),t("img",{attrs:{src:o(335),alt:"QGIS Create Services from job"}})]),e._v(" "),t("li",[t("strong",[e._v("Create from web editor")]),e._v(" - Similarly to the job table overview, this button opens your web browser with\nthe "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(". There you can create new services via the editor or from your jobs.")]),e._v(" "),t("li",[t("strong",[e._v("Refresh Table")]),e._v(" - Similar to the jobs table, this button refreshes the list of services manually, in general the list will refresh every few\nseconds. If you do not want to automatically refresh the list (it might cause some lags if the connection to the\nbackend is low) you can do so by deactivating the checkbox next to the refresh button.")]),e._v(" "),t("li",[t("strong",[e._v("Create from web editor")]),e._v(" - This button opens your browser with this introduction page.")])]),e._v(" "),t("h2",{attrs:{id:"additional-information"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#additional-information"}},[e._v("#")]),e._v(" Additional Information")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-qgis-plugin",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github Repository"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://plugins.qgis.org/plugins/openeo-qgis-plugin-master/",target:"_blank",rel:"noopener noreferrer"}},[e._v("QGIS Plugin Page"),t("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[15],{325:function(e,t,o){e.exports=o.p+"assets/img/qgis_button.2b5f39a5.png"},326:function(e,t,o){e.exports=o.p+"assets/img/login_dialog.424c4b4e.png"},327:function(e,t,o){e.exports=o.p+"assets/img/backend_info.a00e7a1d.png"},328:function(e,t,o){e.exports=o.p+"assets/img/jobs.fef9e692.png"},329:function(e,t,o){e.exports=o.p+"assets/img/adapt_job.64ea4b55.png"},330:function(e,t,o){e.exports=o.p+"assets/img/edit_spatial.23df03fa.png"},331:function(e,t,o){e.exports=o.p+"assets/img/edit_temporal.609c87e2.png"},332:function(e,t,o){e.exports=o.p+"assets/img/edit_bands.d9ed77a5.png"},333:function(e,t,o){e.exports=o.p+"assets/img/edit_subpg.c7fbfd4d.png"},334:function(e,t,o){e.exports=o.p+"assets/img/services.a958fd61.png"},335:function(e,t,o){e.exports=o.p+"assets/img/services_create.0881f2cd.png"},532:function(e,t,o){"use strict";o.r(t);var n=o(4),a=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"qgis-plugin"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#qgis-plugin"}},[e._v("#")]),e._v(" QGIS Plugin")]),e._v(" "),t("p",[e._v("The openEO QGIS plugin enables exploring openEO backends as well as execute and modify your jobs at an backend\nwithin QGIS. The plugin can load the results of a job execution to the QGIS map, so that the user can make further\nanalyses and visualisation steps.")]),e._v(" "),t("h2",{attrs:{id:"installation"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[e._v("#")]),e._v(" Installation")]),e._v(" "),t("p",[e._v("There are two different versions of the QGIS Plugin available:")]),e._v(" "),t("ol",[t("li",[e._v("The "),t("strong",[e._v("latest stable version")]),e._v(' is accessible in the plugin\nmanager of QGIS (Plugins -> Manage and Install Plugins). Note that you have to activate the option to show experimental\nplugins (Settings -> Show also experimental plugins). After that, you can search for\n"OpenEO", install and activate the plugin.')]),e._v(" "),t("li",[e._v("The "),t("strong",[e._v("latest version")]),e._v(" is accessible at the\n"),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-qgis-plugin",target:"_blank",rel:"noopener noreferrer"}},[e._v("GitHub repository"),t("OutboundLink")],1),e._v('. First, download the repository as zip archive.\nNow, open the plugin manager of QGIS (Plugins -> Manage and Install Plugins) and select "Install from ZIP".\nThere you can browse for the downloaded zip archive, install and activate it.')])]),e._v(" "),t("p",[e._v("This tutorial shows the capabilities of version 0.9.2. Make sure you have installed at least that version by checking in\nthe plugin manager.")]),e._v(" "),t("p",[e._v("After successfully installing and activating the plugin, the openEO logo should be part of the toolbar:")]),e._v(" "),t("p",[t("img",{attrs:{src:o(325),alt:"QGIS Plugin Icon"}})]),e._v(" "),t("p",[e._v("Simply click on the icon to start the plugin.")]),e._v(" "),t("h2",{attrs:{id:"connecting"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#connecting"}},[e._v("#")]),e._v(" Connecting")]),e._v(" "),t("p",[e._v("The first window you see when starting the plugin is a login dialog, since you can only access your jobs at the backend\nif you are authenticated. The backend dropdown menu contains the available openEO backends from\nthe "),t("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO Hub"),t("OutboundLink")],1),e._v('. By default, the plugin chooses the latest compatible version from the\nselected provider. If you want to connect to a specific version of a backend, you need to uncheck\nthe checkbox labeled "Only Latest versions". You can also manually type a backend url into the selection\nfield, in case it is not registered at our hub yet.')]),e._v(" "),t("p",[t("img",{attrs:{src:o(326),alt:"QGIS Login Dialog"}})]),e._v(" "),t("p",[e._v("Now that you have chosen a backend, you can log in with your username and password provided by the backend.\nNote that the QGIS plugin only supports Basic authentication and not "),t("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenID Connect"),t("OutboundLink")],1),e._v(" yet.\nBackends that do not support this authentication can not be accessed via this plugin at the moment.")]),e._v(" "),t("p",[e._v("If logged in successfully, the main window of the plugin appears with three tabs ("),t("strong",[e._v("Backend Info")]),e._v(", "),t("strong",[e._v("Jobs")]),e._v(", "),t("strong",[e._v("Services")]),e._v(").\nThe following sections will go through the capabilities of these tabs.")]),e._v(" "),t("h2",{attrs:{id:"exploring-a-backend"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-backend"}},[e._v("#")]),e._v(" Exploring a backend")]),e._v(" "),t("p",[e._v("The first tab shows information about the backend and the available collections and processes.\nOn the top, there is a basic description about the backend you are connected to.\nNext, you can choose a collection in the drop down menu to see a description of that dataset.")]),e._v(" "),t("p",[t("img",{attrs:{src:o(327),alt:"QGIS Backend Info"}})]),e._v(" "),t("p",[e._v("On the bottom, you can use the drop down menu to choose one of the available processes from the backend.\nAfter selection, the parameters of the process are listed in the table below. The first column contains the\nparameter name, which is required if bold and otherwise optional.\nThe second column shows the type of the parameter (e.g. number) and the third column a short description.\nTo learn more about the process, you can click on the info button next to the drop down menu\n(marked red in the figure above). The return type of the process is written next to the info box. If you want to get an\noverview of all processes of openEO you might visit the "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[e._v("official processes page")]),e._v(", but note that not all backends\nwill implement all processes listed there, whereas only available processes of the backend are in the QGIS plugin.")],1),e._v(" "),t("h2",{attrs:{id:"job-management"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#job-management"}},[e._v("#")]),e._v(" Job Management")]),e._v(" "),t("p",[e._v('The "Jobs" tab lists all of your '),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[e._v("jobs")]),e._v("\nat the backend. In openEO a job describes what the backend needs to process\n(e.g. by defining a "),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[e._v("process graph")]),e._v(") as well as\nadditional meta-information (e.g. title, creation date). The first two columns show the title and the creation date of\nthe job. The other elements of the table are numbered in the screenshot and described in the following.")],1),e._v(" "),t("p",[t("img",{attrs:{src:o(328),alt:"QGIS Jobs"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Status")]),e._v(' - Shows the current lifecycle state of the job. The first status of a job is "created",\nif it got created at the backend, but not executed yet. After the job gets executed it will be\n"queued" if the backend plans to executes it, but has not started yet. If the execution started but hasn\'t finished yet,\nit has the status of "running". Last but not least it will have either "canceled", if it was stopped during execution,\n"finished" if the execution was successfully or "error", if an error occurred during execution.')]),e._v(" "),t("li",[t("strong",[e._v("Execute")]),e._v(' - By clicking on the "Play" button, the plugin will start the execution of the job at the backend.\nAlready finished jobs are executed again. The icon of the button will change if the job is in the "running" state,\nso that it looks like a "Stop" button, which will cancel the job execution.')]),e._v(" "),t("li",[t("strong",[e._v("Display")]),e._v(' - By clicking on the "Eye" button, the plugin downloads the resulting image(s) of the job from the backend\nand creates a new layer in QGIS named after the title and the creation date of the image. Note that if the result\nis not geo referenced, you might get a dialog from QGIS to choose a coordinate reference system, therefore it is\nrecommended to choose GeoTiff as job output format if available at the backend.')]),e._v(" "),t("li",[t("strong",[e._v("Save")]),e._v(' - By clicking on the "Download" button, you can navigate to the folder where you want to store the\nresulting image(s) of the job and the plugin will download it there.')]),e._v(" "),t("li",[t("strong",[e._v("Adapt")]),e._v(' - By clicking on the "Pencil" button a new dialog appears, where you can adapt the job title and\ndescription as well as the processes and extent of the job. How this works in detail is described in the following sections.')]),e._v(" "),t("li",[t("strong",[e._v("Information")]),e._v(' - By clicking on the "Info" button a window with additional information about the job appears.\nIt contains information like a description, a cost plan etc., of course only if this information is provided by the\nbackend. If the backend provides a logging output, it is also part of this info window.')]),e._v(" "),t("li",[t("strong",[e._v("Delete")]),e._v(' - By clicking on the "Trash" button the job gets deleted from the backend, after being asked if you are\nreally sure about it.')]),e._v(" "),t("li",[t("strong",[e._v("Load Job from openEO Hub")]),e._v(" - At the moment it is recommended to not use this feature, since the exchange of\nprocess graphs in the OpenEO Hub is under development.")]),e._v(" "),t("li",[t("strong",[e._v("Create new Job in Web Editor")]),e._v(" - This button opens the "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(" in your\nsystem browser, so that you can create a new job using it. This is the recommended way for creating a new job, since it has\na graphical and therefore more convenient way of doing so.")]),e._v(" "),t("li",[t("strong",[e._v("Create new Job")]),e._v(" - You can create a job from scratch using this button.\nMore information in the next sections.")]),e._v(" "),t("li",[t("strong",[e._v("Refresh Table")]),e._v(" - This button refreshes the list of jobs manually, in general the list will refresh every few\nseconds. If you do not want to automatically refresh the list (it might cause some lags if the connection to the\nbackend is low) you can do so by deactivating the checkbox next to the refresh button.")]),e._v(" "),t("li",[t("strong",[e._v("User Manual")]),e._v(" - This button opens your browser with this introduction page.")])]),e._v(" "),t("h3",{attrs:{id:"creating-new-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#creating-new-jobs"}},[e._v("#")]),e._v(" Creating new Jobs")]),e._v(" "),t("p",[e._v("There are three approaches to create a new job using this plugin, depending on whether you want to start from scratch or just\nchange existing jobs:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("From scratch with web editor")]),e._v(" - As described in the job table overview above, there is a button (no. 9) that opens\nyour web browser with the "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(". It is the recommended way to create a new job\nfrom scratch, since it has a very convenient graphical user interface. It also shows the process graph in a neat graph\nbased manner for a better understanding of how processes are connected.")]),e._v(" "),t("li",[t("strong",[e._v("From scratch with QGIS")]),e._v(' - If you want to use the QGIS plugin, you can create a new job using the\nbutton (no. 10) of the job overview dialog. It opens the job adaption window (more details in the section below).\nIn the beginning the job has already a "load_collection" process as a starting point.')]),e._v(" "),t("li",[t("strong",[e._v("From existing Job")]),e._v(" - You can create a new job by adapting\none of your existing jobs at the backend. You can e.g. create a new job with a different spatial extent, or\nadding a few processes to the original one. To do so, you need to click on the edit button in the job overview window (no. 5)")])]),e._v(" "),t("h3",{attrs:{id:"adapting-existing-jobs"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#adapting-existing-jobs"}},[e._v("#")]),e._v(" Adapting existing Jobs")]),e._v(" "),t("p",[e._v("In this section you'll get an overview of the job adaption workflow using the job adaption window.\nHow you get there was described in the previous section.")]),e._v(" "),t("p",[t("img",{attrs:{src:o(329),alt:"QGIS Adapting Job"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Job Title")]),e._v(" - Here you can write the title of the job. It is recommended to choose it in a way that\nmakes you identify the job in a list more easily.")]),e._v(" "),t("li",[t("strong",[e._v("Job Description")]),e._v(" - You can describe the job in more detail in this field (e.g. what is calculated and how).")]),e._v(" "),t("li",[t("strong",[e._v("Process Graph Table")]),e._v(" - In the process graph table you can see a list of all processes used by the job.\nThe first column contains the identifier of the process node, so a unique identifier of the process throughout the\nwhole process graph (e.g. loadcollection1). Most openEO clients generate these identifiers automatically. We define a\nprocess node (or just node) as one element of the process graph, so a single executed process."),t("br"),e._v('\nIn the second column you can see the name/identifier of the process (e.g. load_collection), which needs to be one of the\nprocesses that the backend provides. The third column shows the node identifier of the predecessor node.\nFor example, "loadcollection1" in the second row means that the output of the node with id "loadcollection1" is the input\nof the node with id "reducedimension1". In other words the process of the second row uses the output of the process of\nthe first row and is therefore executed after the first process.')]),e._v(" "),t("li",[t("strong",[e._v("Process Edit")]),e._v(' - When pressing on the "pencil" button, the process of the same row is loaded into the process\ntable on the right (see no. 6). On this table you can adapt the arguments of the chosen process and therefore configure\nits behaviour.')]),e._v(" "),t("li",[t("strong",[e._v("Delete Process")]),e._v(' - Clicking on the "trash" button deletes the selected process from the process graph.\nNote that other processes, which have this process as predecessor might fail after the deletion.')]),e._v(" "),t("li",[t("strong",[e._v("Undo Changes")]),e._v(" - Press this button if you want to undo all your changes on the job.")]),e._v(" "),t("li",[t("strong",[e._v("Raw Graph")]),e._v(' - Press this button to see the raw process graph (JSON format) in a pop up window. Changes to the\nprocess graph are also applied if you click "Apply". Therefore, you can also copy and paste here a complete process graph'),t("br"),e._v("\ninto the QGIS plugin.")]),e._v(" "),t("li",[t("strong",[e._v("Add Process")]),e._v(' - If you want to add a new process to the process graph you can select one at the drop down menu\n(see "Select a process") and press the button labeled "Add Process". The text field on the right will be the new node id\nand it is autogenerated after selecting a process, but can also be manually defined by typing it into the text field. Note\nthat it has to be unique within the job, otherwise the plugin shows an error message.\nOnce added, the id can not be modified.')]),e._v(" "),t("li",[t("strong",[e._v("Process Table")]),e._v(" - In this table you see all arguments of the currently selected process, the expected type and\nthe current value. If the name of the argument is bold, it is a required argument, hence it must be set.\nYou can edit the values of the arguments directly in the text field of the value column. For some arguments the plugin\nprovides helper function in the fourth column of the process table (e.g. drop down menu of all available collections in\nthe first row)")]),e._v(" "),t("li",[t("strong",[e._v("Process Argument Helper")]),e._v(" - For some arguments of a process, the QGIS Plugin provides helper functions to make the\nadaption of the arguments easier. This is the case, if the field of the fourth column in the process table is\nnot empty.")]),e._v(" "),t("li",[t("strong",[e._v("Result Process")]),e._v(' - This checkbox needs to be checked on the last process of the process graph. It tells the\nbackend that the result of the checked process is the output of the job (usually a "save_result" process). Note that\nonly one process of the process graph can be a result node. If you create a sub process graph in the process graph\n(e.g. when using apply or reduce), it also needs to have exactly one resulting process.')]),e._v(" "),t("li",[t("strong",[e._v("Clone Process")]),e._v(' - When clicking on this button, a clone of the currently selected node gets created and loaded\ninto the process table on the right. Note that clicking on this button does not add it to the job already, so you need to\npress on the "Add Process" button to do so.')]),e._v(" "),t("li",[t("strong",[e._v("Save Process")]),e._v(" - Changes on the process arguments are not stored in the process graph until this button is pressed.\nYou can see if there are unsaved changes if this button is enabled, otherwise it is disabled (see screenshot).")]),e._v(" "),t("li",[t("strong",[e._v("Manage Job")]),e._v(' - In this section of the adaption dialog, there are three options to handle changes on the job.\nFirst, by pressing on "Adapt Job" the selected job will be adapted to the changes you did and no new job will be created.\nSecond, by pressing on "Create New Job" a new job will be created at the backend, so if you have selected a job before\nfor the adaption, it will stay unchanged and your changes will just be applied to the newly created job.\nThird, the "Cancel" button closes the job adaption dialog with your changes having no effect. The same behaviour applies\nby closing the dialog.')])]),e._v(" "),t("h4",{attrs:{id:"process-argument-helper"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#process-argument-helper"}},[e._v("#")]),e._v(" Process Argument Helper")]),e._v(" "),t("p",[e._v("For some process arguments the QGIS plugin provides helper functions for a more convenient editing.\nThis section provides a list of the available helper functions and how to use them:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Collection Id")]),e._v(' - Whenever an argument expects a collection id (e.g. "load_collection" process), the plugin shows a\ndrop down menu with all available collections of the backend. There you can select one and it will be put into the\narguments value.')]),e._v(" "),t("li",[t("strong",[e._v("Spatial Extent")]),e._v(" - Edit buttons of arguments which expect a spatial extent (e.g. geojson, bounding-box) open a\nspatial extent dialog:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(330),alt:"QGIS Edit spatial extent"}})]),e._v(" "),t("p",[e._v('On the bottom you can edit the spatial extent manually in the text box. In the drop down menu on the top you can choose\nhow to select the spatial extent. You can get the extent of the current layer, the extent of a shape file, the extent of\nthe current QGIS map canvas, by drawing a rectangle on the QGIS map canvas or by drawing a polygon on the QGIS map\ncanvas. The drawings start after clicking on the "Draw Extent" button, so that your curser becomes a cross to draw on\nthe map. After pressing the "OK" button, the extent string in the text field is set to the process argument and the\nwindow closes.')]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Temporal Extent")]),e._v(" - If an argument expects a start and end date, the edit button opens a calendar dialog:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(331),alt:"QGIS Edit temporal extent"}})]),e._v(" "),t("p",[e._v("The left calendar sets the start date and the right calendar the end date.")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Band selection")]),e._v(" - If an argument expects a selection of bands, the edit button opens the following dialog to choose\nfrom the bands available at the selected collection. Note that it always lists all bands of the collection even if some\nbands are filtered by previous processes and therefore not available in the datacube anymore.")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(332),alt:"QGIS Edit band selection"}})]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("Process Graph Node")]),e._v(' - If an argument expects a raster data cube, a drop down menu will appear on the edit column.\nThere you can choose from existing process graph nodes (e.g. "loadcollection1") It uses the output of the given node\nidentifier as input of the process argument.')]),e._v(" "),t("li",[t("strong",[e._v("Sub Process Graph")]),e._v(" - Some processes need a whole (sub-)process graph as input value (e.g. apply or reduce\nprocesses). When clicking on the edit button, the plugin opens a new adaption window, which looks similar to the job\nadaption window, but shows the sub process graph:")])]),e._v(" "),t("p",[t("img",{attrs:{src:o(333),alt:"QGIS Edit sub process graph"}})]),e._v(" "),t("p",[e._v('In this window you can adapt the sub process graph in the same way as the original process graph. After pressing on\n"Adapt", the changes are written to the original job. Process arguments, which do not have special editing fields,\nhave a "Proc-Param" button. If pressed it will forward the input data of the parent node\n(so the one with the subgraph as attribute) to the selected argument (see screenshot "data" argument). Note that the\nsub process graph also needs to have at least one result node.')]),e._v(" "),t("h2",{attrs:{id:"service-management"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#service-management"}},[e._v("#")]),e._v(" Service Management")]),e._v(" "),t("p",[e._v('The "Services" tab lists all of your '),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[e._v("secondary web services")]),e._v("\nat the backend. In openEO a secondary service has a process graph like a job, but depending on the service type it is\npossible for the user to change the viewing extent dynamically. The first two columns shows the title, the description\nand the date and time it got created at the backend.\nThe other elements of this window are numbered at the screenshot below and described in the following.")],1),e._v(" "),t("p",[t("img",{attrs:{src:o(334),alt:"QGIS Services"}})]),e._v(" "),t("ol",[t("li",[t("strong",[e._v("Display")]),e._v(' - When clicking on the "Eye", the secondary service gets loaded into a new QGIS layer, named after the\nservice. QGIS now fetches the result of the processing into the current extent of the QGIS map canvas. Note that it will\nload the whole screen at once, so it might take a while.')]),e._v(" "),t("li",[t("strong",[e._v("Information")]),e._v(" - Clicking on the Infobox, shows detailed information about the service.")]),e._v(" "),t("li",[t("strong",[e._v("Delete")]),e._v(" - Clicking on the delete button removes the service from the backend.")]),e._v(" "),t("li",[t("strong",[e._v("Create from job")]),e._v(' - By clicking on this button you can create a new service from one of your jobs. It will create a\nservice with the same process graph as the selected job, but the extent will be dynamically loaded via the service. By\nclicking on the button a new dialog pops up. On the creation dialog you can set a title and a description text, but note\nthat they are optional. Nevertheless, you have to select a job and choose a type of secondary service. The drop down\nmenu of the service types contains only the ones supported by your backend. By clicking on "Create Service" the\nplugin creates the service on the backend, so you should be able to see it on the table. Note that the list of jobs\ncontains only successfully executed jobs (e.g. with "finished" status). By clicking on "Raw Graph"\n'),t("img",{attrs:{src:o(335),alt:"QGIS Create Services from job"}})]),e._v(" "),t("li",[t("strong",[e._v("Create from web editor")]),e._v(" - Similarly to the job table overview, this button opens your web browser with\nthe "),t("a",{attrs:{href:"https://editor.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO web editor"),t("OutboundLink")],1),e._v(". There you can create new services via the editor or from your jobs.")]),e._v(" "),t("li",[t("strong",[e._v("Refresh Table")]),e._v(" - Similar to the jobs table, this button refreshes the list of services manually, in general the list will refresh every few\nseconds. If you do not want to automatically refresh the list (it might cause some lags if the connection to the\nbackend is low) you can do so by deactivating the checkbox next to the refresh button.")]),e._v(" "),t("li",[t("strong",[e._v("Create from web editor")]),e._v(" - This button opens your browser with this introduction page.")])]),e._v(" "),t("h2",{attrs:{id:"additional-information"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#additional-information"}},[e._v("#")]),e._v(" Additional Information")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-qgis-plugin",target:"_blank",rel:"noopener noreferrer"}},[e._v("Github Repository"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://plugins.qgis.org/plugins/openeo-qgis-plugin-master/",target:"_blank",rel:"noopener noreferrer"}},[e._v("QGIS Plugin Page"),t("OutboundLink")],1)])])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/18.131835dc.js b/assets/js/18.2f2caa18.js similarity index 99% rename from assets/js/18.131835dc.js rename to assets/js/18.2f2caa18.js index d02a97994..193b22f22 100644 --- a/assets/js/18.131835dc.js +++ b/assets/js/18.2f2caa18.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[18],{315:function(t,s,a){t.exports=a.p+"assets/img/urk_scaling.1f27c164.jpg"},316:function(t,s,a){t.exports=a.p+"assets/img/urk.e6564348.jpg"},317:function(t,s,a){t.exports=a.p+"assets/img/pellworm_248.29ad280d.jpg"},318:function(t,s,a){t.exports=a.p+"assets/img/pellworm_ndvi.36c775d8.jpg"},319:function(t,s,a){t.exports=a.p+"assets/img/pellworm_threshold.be97c121.jpg"},320:function(t,s,a){t.exports=a.p+"assets/img/pellworm_s1.acf74ce7.jpg"},321:function(t,s,a){t.exports=a.p+"assets/img/pellworm_kernels.d12dda41.jpg"},515:function(t,s,a){"use strict";a.r(s);var n=a(4),e=Object(n.a)({},(function(){var t=this,s=t._self._c;return s("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[s("h1",{attrs:{id:"openeo-cookbook"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#openeo-cookbook"}},[t._v("#")]),t._v(" openEO Cookbook")]),t._v(" "),s("p",[t._v("This is the openEO cookbook that you can refer to to get a first idea on how to solve problems with openEO in the three client languages Python, R and JavaScript. It describes how to implement simple use cases in a pragmatic way.")]),t._v(" "),s("p",[t._v("Please refer to the getting started guides for "),s("RouterLink",{attrs:{to:"/documentation/1.0/javascript/"}},[t._v("JavaScript")]),t._v(", "),s("RouterLink",{attrs:{to:"/documentation/1.0/python/"}},[t._v("Python")]),t._v(" and "),s("RouterLink",{attrs:{to:"/documentation/1.0/r/"}},[t._v("R")]),t._v(" if you have never worked with one of the openEO client libraries before. 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For that, we load and filter a collection (a datacube) of satellite data and calculate the temporal mean of that data. Different steps (e.g. a linear scaling) are done to prepare for the data to be output in one of the formats: Raster or text data.")]),t._v(" "),s("p",[t._v("Throughout this guide, code examples for all three client languages are given. Select your preferred language with the code switcher on the right-hand side to set all examples to that language.")]),t._v(" "),s("h3",{attrs:{id:"connecting-to-a-back-end"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#connecting-to-a-back-end"}},[t._v("#")]),t._v(" Connecting to a back-end")]),t._v(" "),s("p",[t._v("Click the link below to see how to connect to a back-end (via OpenID Connect). You can call the connection object "),s("code",[t._v("con")]),t._v(" as it is done in all following code, to avoid confusion throughout the rest of the tutorials.")]),t._v(" "),s("CodeSwitcher",{scopedSlots:t._u([{key:"py",fn:function(){return[s("p",[s("RouterLink",{attrs:{to:"/documentation/1.0/python/#openid-connect-authentication"}},[t._v("Getting started: Authentication")])],1)]},proxy:!0},{key:"r",fn:function(){return[s("p",[s("RouterLink",{attrs:{to:"/documentation/1.0/r/#openid-connect-authentication"}},[t._v("Getting started: Authentication")])],1)]},proxy:!0},{key:"js",fn:function(){return[s("p",[s("RouterLink",{attrs:{to:"/documentation/1.0/javascript/#openid-connect-authentication"}},[t._v("Getting started: Authentication")])],1)]},proxy:!0}])}),t._v(" "),s("p",[t._v("In R and JavaScript it is very useful to assign a graph-building helper object to a variable, to easily access all openEO processes and add them to the process graph that you will be building. 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In Python, it also helps to import a helper object, even though we'll need it less often.")]),t._v(" "),s("CodeSwitcher",{scopedSlots:t._u([{key:"py",fn:function(){return[s("div",{staticClass:"language-python extra-class"},[s("pre",{pre:!0,attrs:{class:"language-python"}},[s("code",[s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# import ProcessBuilder functions")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("from")]),t._v(" openeo"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("processes "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" ProcessBuilder\n")])])]),s("p",[s("strong",[t._v("Note:")]),t._v(" Many functions in "),s("em",[t._v("child processes")]),t._v(" (see below), are instances of this "),s("code",[t._v("ProcessBuilder")]),t._v(" import.")])]},proxy:!0},{key:"r",fn:function(){return[s("div",{staticClass:"language-r extra-class"},[s("pre",{pre:!0,attrs:{class:"language-r"}},[s("code",[s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# assign the graph-building helper object to "p" for easy access to all openEO processes, see > ?processes()')]),t._v("\np "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("<-")]),t._v(" processes"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),s("p",[s("strong",[t._v("Note:")]),t._v(" In all R code, "),s("code",[t._v("p")]),t._v(" is used to select openEO processes.")])]},proxy:!0},{key:"js",fn:function(){return[s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('// assign the graph-building helper object to "builder" for easy access to all openEO processes')]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" builder "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("buildProcess")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[s("strong",[t._v("Note:")]),t._v(" In all JavaScript code, "),s("code",[t._v("builder")]),t._v(" is used to select openEO processes.")])]},proxy:!0}])}),t._v(" "),s("h3",{attrs:{id:"input-load-collection"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#input-load-collection"}},[t._v("#")]),t._v(" Input: "),s("code",[t._v("load_collection")])]),t._v(" "),s("p",[t._v("Before loading a collection, we need to find out the exact name of a collection we want to use (back-end-specific, see references "),s("a",{attrs:{href:"#openeo-cookbook"}},[t._v("at the top")]),t._v("). We assign the spatial and temporal extent to variables, so that we can re-use them on other collections we might want to load. Let's look for a Sentinel 2 (preprocessed level 2A preferably) collection and load the green, red and a near-infrared band (bands 3, 4 and 8).")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Collection and Band Names")]),t._v(" "),s("p",[t._v("The names of collections and bands differ between back-ends. So always check the collection description for the correct names. 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RGB = red, nir and blue"}}),t._v(" "),s("figcaption",[t._v("This is a false color image of our area of interest for this chapter. The bands 4, 8 and 2 have been assigned to the RGB channels without any further processing. The surrounding area of the island Pellworm is shown at low tide.\n")])]),t._v(" "),s("h3",{attrs:{id:"bandmath"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#bandmath"}},[t._v("#")]),t._v(" Bandmath")]),t._v(" "),s("p",[t._v("Bandmath refers to computations that involve multiple bands, like indices (Normalized Difference Vegetation Index, Normalized Burn Ratio etc.).")]),t._v(" "),s("p",[t._v("In openEO, this goes along with using for example "),s("code",[t._v("reduce_dimension")]),t._v(" over the "),s("code",[t._v("bands")]),t._v(" dimension. In this process, a new pixel value is calculated from the values of different bands using a given formula (the actual bandmath), to then eliminate the "),s("code",[t._v("bands")]),t._v(" dimension alltogether. If e.g. a cube contains a "),s("code",[t._v("red")]),t._v(" and a "),s("code",[t._v("nir")]),t._v(" band in its "),s("code",[t._v("bands")]),t._v(" dimension and we reduce said dimension with a formula for the NDVI, that cube afterwards contains NDVI values, but no "),s("code",[t._v("bands")]),t._v(" dimension anymore.")]),t._v(" "),s("p",[t._v("In the following we'll observe a thorough explanation of how to calculate an NDVI. That section covers different ways (depending on the client) to set up such a process in openEO. Afterwards, we'll see how an EVI is computed in a quicker, less thorough example.")]),t._v(" "),s("h4",{attrs:{id:"example-1-ndvi"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#example-1-ndvi"}},[t._v("#")]),t._v(" Example 1: NDVI")]),t._v(" "),s("p",[t._v("As mentioned above, bandmath is about reducing the "),s("code",[t._v("bands")]),t._v(" dimension with a child process (a formula) that gives us the desired output. In "),s("a",{attrs:{href:"#chapter-1"}},[t._v("chapter one")]),t._v(", we already saw different ways of defining a child process: a) We learned how to use a simple "),s("code",[t._v("mean")]),t._v(" to "),s("RouterLink",{attrs:{to:"/documentation/1.0/cookbook/#temporal-mean-reduce-dimension"}},[t._v("reduce the time dimension")]),t._v(", and b) we saw how to access and use the openEO defined "),s("code",[t._v("linear_scale_range")]),t._v(" function "),s("RouterLink",{attrs:{to:"/documentation/1.0/cookbook/#scale-all-pixels-linearly-apply-linear-scale-range"}},[t._v("to scale all pixels linearly")]),t._v(".")],1),t._v(" "),s("p",[t._v("In this chapter, we'll reiterate these techniques and discuss some subtleties and alternatives. It is up to you to choose one that works best. Special to the NDVI is, given that it is "),s("em",[t._v("the")]),t._v(" most common use case, that openEO has predefined processes that cover the math for us and only need to be given the correct input. There even is a function "),s("code",[t._v("ndvi")]),t._v(" that masks all details and only takes a datacube and the bandnames as input. Further down we'll dig into what this "),s("code",[t._v("ndvi")]),t._v(" process actually masks, how we can reproduce that manually, and thus how we could formulate other bandmath functions, apart from an NDVI.")]),t._v(" "),s("p",[t._v("By using the openEO defined "),s("strong",[s("code",[t._v("ndvi")]),t._v(" function")]),t._v(", calculating an NDVI becomes pretty straightforward. The process can detect the red and near-infrared band based on metadata and doesn't need bandname input (but band names can be passed to calculate other normalized differences). Also, by supplying the optional parameter "),s("code",[t._v("target_band")]),t._v(" we can decide if we want to get a reduced cube with NDVI values and no "),s("code",[t._v("bands")]),t._v(" dimension as result, or if we want to append the new NDVI band to the existing bands of our input cube. If we choose the latter, we only need to supply a name for that new NDVI band.")]),t._v(" "),s("CodeSwitcher",{scopedSlots:t._u([{key:"py",fn:function(){return[s("div",{staticClass:"language-python extra-class"},[s("pre",{pre:!0,attrs:{class:"language-python"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("ndvi"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# cube_s2_ndvi_bands = cube_s2.ndvi(nir = "B08", red = "B04", target_band = "NDVI")')]),t._v("\n")])])])]},proxy:!0},{key:"r",fn:function(){return[s("div",{staticClass:"language-r extra-class"},[s("pre",{pre:!0,attrs:{class:"language-r"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("<-")]),t._v(" p"),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("ndvi"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# cube_s2_nvdi_bands <- p$ndvi(data = cube_s2, nir = "B08", red = "B04", target_band = "NDVI")')]),t._v("\n")])])])]},proxy:!0},{key:"js",fn:function(){return[s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" builder"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("ndvi")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('// cube_s2_ndvi_bands = builder.ndvi(cube_s2, "B08", "B04", "NDVI")')]),t._v("\n")])])])]},proxy:!0}])}),t._v(" "),s("p",[t._v("For the purpose of understanding, we'll stick with our NDVI example to explore different ways of defining bandmath in openEO. 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This is usually done by providing a boolean mask that detemines which pixels to replace with a no-data value, and which to leave as they are. In the following we will consider two cases: a) Creating a mask from classification classes and b) creating a mask by thresholding an image.")]),t._v(" "),s("h4",{attrs:{id:"mask-out-specific-values"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#mask-out-specific-values"}},[t._v("#")]),t._v(" Mask Out Specific Values")]),t._v(" "),s("p",[t._v("In some cases we want to mask our data with other data. A common example is to mask out clouds, which optical satellites can not see through. Some Sentinel 2 collections provide the "),s("code",[t._v("SCL")]),t._v(" classification band (see the table of classes "),s("a",{attrs:{href:"https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm",target:"_blank",rel:"noopener noreferrer"}},[t._v("at the bottom here"),s("OutboundLink")],1),t._v("), providing a representation coded into land cover classes. In this classification, vegetation is coded as "),s("code",[t._v("4")]),t._v(" and non-vegetation as "),s("code",[t._v("5")]),t._v(", while e.g. clouds are coded as "),s("code",[t._v("8")]),t._v(" to "),s("code",[t._v("10")]),t._v(".")]),t._v(" "),s("p",[t._v("In the following, we're building a mask using some logical operations as a child process while reducing the dimension "),s("code",[t._v("bands")]),t._v(". As explained at "),s("a",{attrs:{href:"#example-1-ndvi"}},[t._v("Example 1: NDVI")]),t._v(" this is done to be left with a cube that has no dimension "),s("code",[t._v("bands")]),t._v(". In R and JavaScript, we write a function "),s("code",[t._v("filter_")]),t._v(" that turns the values of the "),s("code",[t._v("SCL")]),t._v(" band into "),s("code",[t._v("0")]),t._v("s (for (non-) vegetation) and "),s("code",[t._v("1")]),t._v("s (for all others), and ignores all other bands. During "),s("code",[t._v("mask")]),t._v(", all "),s("code",[t._v("1")]),t._v("s (i.e. "),s("code",[t._v("TRUE")]),t._v(") pixels will be replaced, and as we don't define a replacement they will simply be set to "),s("code",[t._v("null")]),t._v(". 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This could be useful to have a look at the vegetation in the area, without being distracted by all other NDVI values. For this example we reuse the NDVI cube "),s("code",[t._v("cube_s2_ndvi")]),t._v(" that was calculated in the "),s("a",{attrs:{href:"#example-1-ndvi"}},[t._v("NDVI bandmath-example")]),t._v(", thus for this to work you must include said code into your script.")]),t._v(" "),s("p",[t._v("If you look closely, you'll notice that this time we're not using "),s("code",[t._v("reduce_dimension")]),t._v(" to construct our masking cube (in contrast to "),s("a",{attrs:{href:"#mask-out-specific-values"}},[t._v("Mask out Specific Values")]),t._v("). In R and JavaScript we use "),s("code",[t._v("apply")]),t._v(" instead, and in the python client no "),s("code",[t._v(".band()")]),t._v(" is necessary anymore. 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In "),s("a",{attrs:{href:"#chapter-1"}},[t._v("chapter one")]),t._v(", we already saw different ways of defining a child process: a) We learned how to use a simple "),s("code",[t._v("mean")]),t._v(" to "),s("RouterLink",{attrs:{to:"/documentation/1.0/cookbook/#temporal-mean-reduce-dimension"}},[t._v("reduce the time dimension")]),t._v(", and b) we saw how to access and use the openEO defined "),s("code",[t._v("linear_scale_range")]),t._v(" function "),s("RouterLink",{attrs:{to:"/documentation/1.0/cookbook/#scale-all-pixels-linearly-apply-linear-scale-range"}},[t._v("to scale all pixels linearly")]),t._v(".")],1),t._v(" "),s("p",[t._v("In this chapter, we'll reiterate these techniques and discuss some subtleties and alternatives. It is up to you to choose one that works best. Special to the NDVI is, given that it is "),s("em",[t._v("the")]),t._v(" most common use case, that openEO has predefined processes that cover the math for us and only need to be given the correct input. There even is a function "),s("code",[t._v("ndvi")]),t._v(" that masks all details and only takes a datacube and the bandnames as input. Further down we'll dig into what this "),s("code",[t._v("ndvi")]),t._v(" process actually masks, how we can reproduce that manually, and thus how we could formulate other bandmath functions, apart from an NDVI.")]),t._v(" "),s("p",[t._v("By using the openEO defined "),s("strong",[s("code",[t._v("ndvi")]),t._v(" function")]),t._v(", calculating an NDVI becomes pretty straightforward. The process can detect the red and near-infrared band based on metadata and doesn't need bandname input (but band names can be passed to calculate other normalized differences). Also, by supplying the optional parameter "),s("code",[t._v("target_band")]),t._v(" we can decide if we want to get a reduced cube with NDVI values and no "),s("code",[t._v("bands")]),t._v(" dimension as result, or if we want to append the new NDVI band to the existing bands of our input cube. If we choose the latter, we only need to supply a name for that new NDVI band.")]),t._v(" "),s("CodeSwitcher",{scopedSlots:t._u([{key:"py",fn:function(){return[s("div",{staticClass:"language-python extra-class"},[s("pre",{pre:!0,attrs:{class:"language-python"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("ndvi"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# cube_s2_ndvi_bands = cube_s2.ndvi(nir = "B08", red = "B04", target_band = "NDVI")')]),t._v("\n")])])])]},proxy:!0},{key:"r",fn:function(){return[s("div",{staticClass:"language-r extra-class"},[s("pre",{pre:!0,attrs:{class:"language-r"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("<-")]),t._v(" p"),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("ndvi"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# cube_s2_nvdi_bands <- p$ndvi(data = cube_s2, nir = "B08", red = "B04", target_band = "NDVI")')]),t._v("\n")])])])]},proxy:!0},{key:"js",fn:function(){return[s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("cube_s2_ndvi "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" builder"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("ndvi")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("cube_s2"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// or name bands explicitly + append the result band to the existing cube")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v('// cube_s2_ndvi_bands = builder.ndvi(cube_s2, "B08", "B04", "NDVI")')]),t._v("\n")])])])]},proxy:!0}])}),t._v(" "),s("p",[t._v("For the purpose of understanding, we'll stick with our NDVI example to explore different ways of defining bandmath in openEO. That gives you the knowledge to define other processes, not just NDVIs. The first method is available in all clients: "),s("strong",[t._v("Defining a function")]),t._v(" and passing it to a process, e.g. "),s("code",[t._v("reduce_dimension")]),t._v(". If available, other possibilities are discussed afterwards.")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Important: Check Band Indices and Bandnames!")]),t._v(" "),s("p",[t._v("Be careful when handling the names or array indices of bands. While names differ across back-ends, indices can be mixed up easily when some other band is deleted from the input collection. Python and JavaScript have 0-based indices, R indices are 1-based. 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This is usually done by providing a boolean mask that detemines which pixels to replace with a no-data value, and which to leave as they are. In the following we will consider two cases: a) Creating a mask from classification classes and b) creating a mask by thresholding an image.")]),t._v(" "),s("h4",{attrs:{id:"mask-out-specific-values"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#mask-out-specific-values"}},[t._v("#")]),t._v(" Mask Out Specific Values")]),t._v(" "),s("p",[t._v("In some cases we want to mask our data with other data. A common example is to mask out clouds, which optical satellites can not see through. Some Sentinel 2 collections provide the "),s("code",[t._v("SCL")]),t._v(" classification band (see the table of classes "),s("a",{attrs:{href:"https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm",target:"_blank",rel:"noopener noreferrer"}},[t._v("at the bottom here"),s("OutboundLink")],1),t._v("), providing a representation coded into land cover classes. In this classification, vegetation is coded as "),s("code",[t._v("4")]),t._v(" and non-vegetation as "),s("code",[t._v("5")]),t._v(", while e.g. clouds are coded as "),s("code",[t._v("8")]),t._v(" to "),s("code",[t._v("10")]),t._v(".")]),t._v(" "),s("p",[t._v("In the following, we're building a mask using some logical operations as a child process while reducing the dimension "),s("code",[t._v("bands")]),t._v(". As explained at "),s("a",{attrs:{href:"#example-1-ndvi"}},[t._v("Example 1: NDVI")]),t._v(" this is done to be left with a cube that has no dimension "),s("code",[t._v("bands")]),t._v(". In R and JavaScript, we write a function "),s("code",[t._v("filter_")]),t._v(" that turns the values of the "),s("code",[t._v("SCL")]),t._v(" band into "),s("code",[t._v("0")]),t._v("s (for (non-) vegetation) and "),s("code",[t._v("1")]),t._v("s (for all others), and ignores all other bands. During "),s("code",[t._v("mask")]),t._v(", all "),s("code",[t._v("1")]),t._v("s (i.e. "),s("code",[t._v("TRUE")]),t._v(") pixels will be replaced, and as we don't define a replacement they will simply be set to "),s("code",[t._v("null")]),t._v(". 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If this first example "Mask Out Specific Values" doesn\'t work for you, that could be because we are trying to apply a mask (a cube with dimensions '),s("code",[t._v("x")]),t._v(", "),s("code",[t._v("y")]),t._v(", "),s("code",[t._v("t")]),t._v(") to the input data (a cube with dimensions "),s("code",[t._v("x")]),t._v(", "),s("code",[t._v("y")]),t._v(", "),s("code",[t._v("t")]),t._v(", "),s("code",[t._v("bands")]),t._v(") and thus, cubes with different dimensionality. If you encounter such a problem, try applying the first mask to the NDVI cube (with dimensions "),s("code",[t._v("x")]),t._v(", "),s("code",[t._v("y")]),t._v(", "),s("code",[t._v("t")]),t._v(') as it is shown in the second example "Thresholds".')]),t._v(" "),s("h4",{attrs:{id:"thresholds"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#thresholds"}},[t._v("#")]),t._v(" Thresholds")]),t._v(" "),s("p",[t._v("In this scenario we want an image that contains all NDVI values above 0.3, and holds no-data values otherwise. This could be useful to have a look at the vegetation in the area, without being distracted by all other NDVI values. For this example we reuse the NDVI cube "),s("code",[t._v("cube_s2_ndvi")]),t._v(" that was calculated in the "),s("a",{attrs:{href:"#example-1-ndvi"}},[t._v("NDVI bandmath-example")]),t._v(", thus for this to work you must include said code into your script.")]),t._v(" "),s("p",[t._v("If you look closely, you'll notice that this time we're not using "),s("code",[t._v("reduce_dimension")]),t._v(" to construct our masking cube (in contrast to "),s("a",{attrs:{href:"#mask-out-specific-values"}},[t._v("Mask out Specific Values")]),t._v("). In R and JavaScript we use "),s("code",[t._v("apply")]),t._v(" instead, and in the python client no "),s("code",[t._v(".band()")]),t._v(" is necessary anymore. 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a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"glossary"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#glossary"}},[e._v("#")]),e._v(" Glossary")]),e._v(" "),a("p",[e._v("This glossary introduces the major technical terms used in the openEO project.")]),e._v(" "),a("h2",{attrs:{id:"general-terms"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#general-terms"}},[e._v("#")]),e._v(" General terms")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("EO")]),e._v(": Earth observation")]),e._v(" "),a("li",[a("strong",[e._v("API")]),e._v(": application programming interface ("),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Application_programming_interface",target:"_blank",rel:"noopener noreferrer"}},[e._v("wikipedia"),a("OutboundLink")],1),e._v("); a communication protocol between client and back-end")]),e._v(" "),a("li",[a("strong",[e._v("client")]),e._v(": software tool or environment that end-users directly interact with, e.g. R (RStudio), Python (Jupyter notebook), and JavaScript (web browser); R and Python are two major data science platforms; JavaScript is a major language for web development")]),e._v(" "),a("li",[a("strong",[e._v("(cloud) back-end")]),e._v(": server; computer infrastructure (one or more physical computers or virtual machines) used for storing EO data and processing it")]),e._v(" "),a("li",[a("strong",[e._v("big Earth observation cloud back-end")]),e._v(": server infrastructure where industry and researchers analyse large amounts of EO data")])]),e._v(" "),a("h2",{attrs:{id:"processes-and-process-graphs"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#processes-and-process-graphs"}},[e._v("#")]),e._v(" Processes and process graphs")]),e._v(" "),a("p",[e._v("The terms "),a("em",[e._v("process")]),e._v(" and "),a("em",[e._v("process graph")]),e._v(" have specific meanings in the openEO API specification.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process")]),e._v(" is an operation provided by the back end that performs a specific task on a set of parameters and returns a result. An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process graph")]),e._v(" chains specific process calls together. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually. In a process graph, processes need to be specific, i.e. concrete values for input parameters need to be specified. These arguments can again be process graphs, scalar values, arrays or objects.")]),e._v(" "),a("h2",{attrs:{id:"eo-data-collections"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),a("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),a("strong",[e._v("granule")]),e._v(" (sometimes also called "),a("em",[e._v("item")]),e._v(" or "),a("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),a("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),a("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),a("li",[a("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),a("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),a("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),a("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) data cube. All further processing is then applied to the data cube on the back-end.")]),e._v(" "),a("h2",{attrs:{id:"spatial-data-cubes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#spatial-data-cubes"}},[e._v("#")]),e._v(" Spatial data cubes")]),e._v(" "),a("p",[e._v("A "),a("em",[e._v("spatiotemporal")]),e._v(" data cube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as "),a("em",[e._v("spatial")]),e._v(" data cubes in short.\nSpecial cases are raster and vector data cubes.")]),e._v(" "),a("p",[e._v("The figure below shows the data of\na four-dimensional (8 x 8 x 2 x 2) raster data cube, with dimension names\nand values:")]),e._v(" "),a("table",[a("thead",[a("tr",[a("th",[e._v("#")]),e._v(" "),a("th",[e._v("dimension name")]),e._v(" "),a("th",[e._v("dimension values")])])]),e._v(" "),a("tbody",[a("tr",[a("td",[e._v("1")]),e._v(" "),a("td",[e._v("x")]),e._v(" "),a("td",[e._v("288790.5, 288819, 288847.5, 288876, 288904.5, 288933, 288961.5, 288990")])]),e._v(" "),a("tr",[a("td",[e._v("2")]),e._v(" "),a("td",[e._v("y")]),e._v(" "),a("td",[e._v("9120747, 9120718, 9120690, 9120661, 9120633, 9120604, 9120576, 9120547")])]),e._v(" "),a("tr",[a("td",[e._v("3")]),e._v(" "),a("td",[e._v("band")]),e._v(" "),a("td",[a("code",[e._v("red")]),e._v(", "),a("code",[e._v("green")])])]),e._v(" "),a("tr",[a("td",[e._v("4")]),e._v(" "),a("td",[e._v("time")]),e._v(" "),a("td",[a("code",[e._v("2018-02-10")]),e._v(", "),a("code",[e._v("2018-02-17")])])])])]),e._v(" "),a("p",[e._v("dimensions x and time are aligned along the x-axis; y and band are aligned along the y-axis.")]),e._v(" "),a("p",[a("img",{attrs:{src:t(302),alt:"Data cube example"}})]),e._v(" "),a("p",[e._v("Data cubes as defined here have a "),a("em",[e._v("single value")]),e._v(" (scalar) for each\nunique combination of dimension values. The value pointed to by\narrows corresponds to the combination of x=288847.5 (red arrow),\ny=9120661 (yellow arrow), band=red (blue arrow), time=2018-02-17 (green arrow),\nand its value is 84 (brown arrow).")]),e._v(" "),a("p",[e._v("If the data concerns grayscale imagery, we could call this "),a("em",[e._v("single")]),e._v("\nvalue a "),a("em",[e._v("pixel value")]),e._v(". One should keep in mind that it is "),a("em",[e._v("never")]),e._v("\na tuple of, say, "),a("code",[e._v("{red, green, blue}")]),e._v(' values. "Cell value of a\nsingle raster layer" would be a better analogy; '),a("em",[e._v("data cube cell\nvalue")]),e._v(" may be a good compromise.")]),e._v(" "),a("p",[e._v("A data cube stores some additional properties per dimension such as:")]),e._v(" "),a("ul",[a("li",[e._v("name")]),e._v(" "),a("li",[e._v("axis / number")]),e._v(" "),a("li",[e._v("type")]),e._v(" "),a("li",[e._v("extents "),a("em",[e._v("or")]),e._v(" nominal dimension values")]),e._v(" "),a("li",[e._v("reference systems / projections")]),e._v(" "),a("li",[e._v("resolutions")])]),e._v(" "),a("p",[e._v("Having these properties available allows to easily resample from one data cube to another for example.")]),e._v(" "),a("h3",{attrs:{id:"apply-processes-that-do-not-change-dimensions"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#apply-processes-that-do-not-change-dimensions"}},[e._v("#")]),e._v(" "),a("code",[e._v("apply")]),e._v(": processes that do not change dimensions")]),e._v(" "),a("p",[e._v("Math process that does not reduce or change anything to the array\ndimensions. The process "),a("code",[e._v("apply")]),e._v(" can be used to apply unary functions\nsuch as "),a("code",[e._v("abs")]),e._v(" or "),a("code",[e._v("sqrt")]),e._v(" to all values in a data cube.")]),e._v(" "),a("p",[e._v("The process "),a("code",[e._v("apply_dimension")]),e._v(" applies (maps) an n-ary function to a particular\ndimension. An example along the time dimension is to apply a moving\naverage filter to implement temporal smoothing.\nAn example of "),a("code",[e._v("apply_dimension")]),e._v(" to the spatial dimensions\nis to do a historgram stretch for every spatial (grayscale) image\nof an image time series.")]),e._v(" "),a("h3",{attrs:{id:"filter-subsetting-dimensions-by-dimension-value-selection"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#filter-subsetting-dimensions-by-dimension-value-selection"}},[e._v("#")]),e._v(" "),a("code",[e._v("filter")]),e._v(": subsetting dimensions by dimension value selection")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("filter")]),e._v(" process makes a cube smaller by selecting specific\nvalue ranges for a particular dimension.")]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a band filter that selects the "),a("code",[e._v("red")]),e._v(" band")]),e._v(" "),a("li",[e._v('a bounding box filter "crops" the collection to a spatial extent')])]),e._v(" "),a("h3",{attrs:{id:"reduce-removing-dimensions-entirely-by-computation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#reduce-removing-dimensions-entirely-by-computation"}},[e._v("#")]),e._v(" "),a("code",[e._v("reduce")]),e._v(": removing dimensions entirely by computation")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("reduce")]),e._v(' process removes a dimension by "rolling up" or summarizing\nthe values along that dimension to a single value.\nFor example: eliminate the time dimension by taking the '),a("code",[e._v("mean")]),e._v(" along that dimension.\nAnother example is taking the "),a("code",[e._v("sum")]),e._v(" or "),a("code",[e._v("max")]),e._v(" along the band dimension.")]),e._v(" "),a("h3",{attrs:{id:"aggregate-reducing-resolution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#aggregate-reducing-resolution"}},[e._v("#")]),e._v(" "),a("code",[e._v("aggregate")]),e._v(": reducing resolution")]),e._v(" "),a("p",[e._v("Aggregation computes new values from sets of values that are "),a("em",[e._v("uniquely")]),e._v(" assigned to groups. It involves a grouping predicate (e.g. monthly, 100 m x 100 m grid cells, or a set of non-overlapping spatial polygons), and an reducer (e.g., "),a("code",[e._v("mean")]),e._v(") that computes one or more new values from the original ones.")]),e._v(" "),a("p",[e._v("In effect, "),a("code",[e._v("aggregate")]),e._v(" combines the following three steps:")]),e._v(" "),a("ul",[a("li",[a("em",[e._v("split")]),e._v(" the data cube in groups, based on dimension constraints (time intervals, band groups, spatial polygons)")]),e._v(" "),a("li",[a("em",[e._v("apply")]),e._v(" a reducer to each group (similar to the "),a("code",[e._v("reduce")]),e._v(" process, but reducing a group rather than an entire dimension)")]),e._v(" "),a("li",[a("em",[e._v("combine")]),e._v(" the result to a new data cube, with some dimensions having reduced resolution (or e.g. raster to vector converted)")])]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a weekly time series may be aggregated to monthly values by computing the "),a("code",[e._v("mean")]),e._v(" for all values in a month (grouping predicate: months)")]),e._v(" "),a("li",[a("em",[e._v("spatial")]),e._v(" aggregation involves computing e.g. "),a("em",[e._v("mean")]),e._v(" pixel values on a 100 x 100 m grid, from 10 m x 10 m pixels, where each original pixel is assigned uniquely to a larger pixel (grouping predicate: 100 m x 100 m grid cells)")])]),e._v(" "),a("h3",{attrs:{id:"resample-changing-data-cube-geometry"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resample-changing-data-cube-geometry"}},[e._v("#")]),e._v(" "),a("code",[e._v("resample")]),e._v(": changing data cube geometry")]),e._v(" "),a("p",[e._v("Resampling considers the case where we have data at one resolution and coordinate reference system, and need values at another. In case we have values at a 100 m x 100 m grid and need values at a 10 m x 10 m grid, the original values will be reused many times, and may be simply assigned to the nearest high resolution grid cells (nearest neighbor method), or may be interpolated using various methods (e.g. by bilinear interpolation). This is often called "),a("em",[e._v("upsampling")]),e._v(" or "),a("em",[e._v("upscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("Resampling from finer to coarser grid is a special case of aggregation often called "),a("em",[e._v("downsampling")]),e._v(" or "),a("em",[e._v("downscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("When the target grid or time series has a lower resolution (larger grid cells) or lower frequency (longer time intervals) than the source grid, aggregation might be used for resampling. For example, if the resolutions are similar, (e.g. the source collection provides 10 day intervals and the target needs values for 16 day intervals), then some form of interpolation may be more appropriate than aggregation as defined here.")]),e._v(" "),a("h2",{attrs:{id:"user-defined-function-udf"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),a("p",[e._v("The abbreviation "),a("strong",[e._v("UDF")]),e._v(" stands for "),a("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),a("RouterLink",{attrs:{to:"/documentation/0.4/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),a("h2",{attrs:{id:"data-processing-modes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),a("p",[e._v("Process graphs can be processed in three different ways:")]),e._v(" "),a("ol",[a("li",[e._v("Results can be pre-computed by creating a "),a("em",[a("strong",[e._v("batch job")])]),e._v(" using "),a("code",[e._v("POST /jobs")]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until "),a("code",[e._v("POST /jobs/{job_id}/results")]),e._v(" has been called. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),a("li",[e._v("A more dynamic way of processing and accessing data is to create a "),a("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),a("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),a("OutboundLink")],1),e._v(" (Web Coverage Service) or "),a("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),a("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),a("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),a("li",[e._v("Process graphs can also be executed "),a("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously) by sending the process graph to "),a("code",[e._v("POST /result")]),e._v(". Results are delivered with the request itself and no job is created. Only lightweight computations, for example small previews, should be executed using this approach as timeouts are to be expected for "),a("a",{attrs:{href:"https://www.pubnub.com/blog/2014-12-01-http-long-polling/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long-polling HTTP requests"),a("OutboundLink")],1),e._v(".")])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[42],{302:function(e,a,t){e.exports=t.p+"assets/img/datacube-example.bab870f7.png"},508:function(e,a,t){"use strict";t.r(a);var s=t(4),o=Object(s.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"glossary"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#glossary"}},[e._v("#")]),e._v(" Glossary")]),e._v(" "),a("p",[e._v("This glossary introduces the major technical terms used in the openEO project.")]),e._v(" "),a("h2",{attrs:{id:"general-terms"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#general-terms"}},[e._v("#")]),e._v(" General terms")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("EO")]),e._v(": Earth observation")]),e._v(" "),a("li",[a("strong",[e._v("API")]),e._v(": application programming interface ("),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Application_programming_interface",target:"_blank",rel:"noopener noreferrer"}},[e._v("wikipedia"),a("OutboundLink")],1),e._v("); a communication protocol between client and back-end")]),e._v(" "),a("li",[a("strong",[e._v("client")]),e._v(": software tool or environment that end-users directly interact with, e.g. R (RStudio), Python (Jupyter notebook), and JavaScript (web browser); R and Python are two major data science platforms; JavaScript is a major language for web development")]),e._v(" "),a("li",[a("strong",[e._v("(cloud) back-end")]),e._v(": server; computer infrastructure (one or more physical computers or virtual machines) used for storing EO data and processing it")]),e._v(" "),a("li",[a("strong",[e._v("big Earth observation cloud back-end")]),e._v(": server infrastructure where industry and researchers analyse large amounts of EO data")])]),e._v(" "),a("h2",{attrs:{id:"processes-and-process-graphs"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#processes-and-process-graphs"}},[e._v("#")]),e._v(" Processes and process graphs")]),e._v(" "),a("p",[e._v("The terms "),a("em",[e._v("process")]),e._v(" and "),a("em",[e._v("process graph")]),e._v(" have specific meanings in the openEO API specification.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process")]),e._v(" is an operation provided by the back end that performs a specific task on a set of parameters and returns a result. An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),a("p",[e._v("A "),a("strong",[e._v("process graph")]),e._v(" chains specific process calls together. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually. In a process graph, processes need to be specific, i.e. concrete values for input parameters need to be specified. These arguments can again be process graphs, scalar values, arrays or objects.")]),e._v(" "),a("h2",{attrs:{id:"eo-data-collections"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),a("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),a("strong",[e._v("granule")]),e._v(" (sometimes also called "),a("em",[e._v("item")]),e._v(" or "),a("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),a("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),a("p",[e._v("The "),a("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),a("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),a("li",[a("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),a("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),a("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),a("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) data cube. All further processing is then applied to the data cube on the back-end.")]),e._v(" "),a("h2",{attrs:{id:"spatial-data-cubes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#spatial-data-cubes"}},[e._v("#")]),e._v(" Spatial data cubes")]),e._v(" "),a("p",[e._v("A "),a("em",[e._v("spatiotemporal")]),e._v(" data cube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as "),a("em",[e._v("spatial")]),e._v(" data cubes in short.\nSpecial cases are raster and vector data cubes.")]),e._v(" "),a("p",[e._v("The figure below shows the data of\na four-dimensional (8 x 8 x 2 x 2) raster data cube, with dimension names\nand values:")]),e._v(" "),a("table",[a("thead",[a("tr",[a("th",[e._v("#")]),e._v(" "),a("th",[e._v("dimension name")]),e._v(" "),a("th",[e._v("dimension values")])])]),e._v(" "),a("tbody",[a("tr",[a("td",[e._v("1")]),e._v(" "),a("td",[e._v("x")]),e._v(" "),a("td",[e._v("288790.5, 288819, 288847.5, 288876, 288904.5, 288933, 288961.5, 288990")])]),e._v(" "),a("tr",[a("td",[e._v("2")]),e._v(" "),a("td",[e._v("y")]),e._v(" "),a("td",[e._v("9120747, 9120718, 9120690, 9120661, 9120633, 9120604, 9120576, 9120547")])]),e._v(" "),a("tr",[a("td",[e._v("3")]),e._v(" "),a("td",[e._v("band")]),e._v(" "),a("td",[a("code",[e._v("red")]),e._v(", "),a("code",[e._v("green")])])]),e._v(" "),a("tr",[a("td",[e._v("4")]),e._v(" "),a("td",[e._v("time")]),e._v(" "),a("td",[a("code",[e._v("2018-02-10")]),e._v(", "),a("code",[e._v("2018-02-17")])])])])]),e._v(" "),a("p",[e._v("dimensions x and time are aligned along the x-axis; y and band are aligned along the y-axis.")]),e._v(" "),a("p",[a("img",{attrs:{src:t(302),alt:"Data cube example"}})]),e._v(" "),a("p",[e._v("Data cubes as defined here have a "),a("em",[e._v("single value")]),e._v(" (scalar) for each\nunique combination of dimension values. The value pointed to by\narrows corresponds to the combination of x=288847.5 (red arrow),\ny=9120661 (yellow arrow), band=red (blue arrow), time=2018-02-17 (green arrow),\nand its value is 84 (brown arrow).")]),e._v(" "),a("p",[e._v("If the data concerns grayscale imagery, we could call this "),a("em",[e._v("single")]),e._v("\nvalue a "),a("em",[e._v("pixel value")]),e._v(". One should keep in mind that it is "),a("em",[e._v("never")]),e._v("\na tuple of, say, "),a("code",[e._v("{red, green, blue}")]),e._v(' values. "Cell value of a\nsingle raster layer" would be a better analogy; '),a("em",[e._v("data cube cell\nvalue")]),e._v(" may be a good compromise.")]),e._v(" "),a("p",[e._v("A data cube stores some additional properties per dimension such as:")]),e._v(" "),a("ul",[a("li",[e._v("name")]),e._v(" "),a("li",[e._v("axis / number")]),e._v(" "),a("li",[e._v("type")]),e._v(" "),a("li",[e._v("extents "),a("em",[e._v("or")]),e._v(" nominal dimension values")]),e._v(" "),a("li",[e._v("reference systems / projections")]),e._v(" "),a("li",[e._v("resolutions")])]),e._v(" "),a("p",[e._v("Having these properties available allows to easily resample from one data cube to another for example.")]),e._v(" "),a("h3",{attrs:{id:"apply-processes-that-do-not-change-dimensions"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#apply-processes-that-do-not-change-dimensions"}},[e._v("#")]),e._v(" "),a("code",[e._v("apply")]),e._v(": processes that do not change dimensions")]),e._v(" "),a("p",[e._v("Math process that does not reduce or change anything to the array\ndimensions. The process "),a("code",[e._v("apply")]),e._v(" can be used to apply unary functions\nsuch as "),a("code",[e._v("abs")]),e._v(" or "),a("code",[e._v("sqrt")]),e._v(" to all values in a data cube.")]),e._v(" "),a("p",[e._v("The process "),a("code",[e._v("apply_dimension")]),e._v(" applies (maps) an n-ary function to a particular\ndimension. An example along the time dimension is to apply a moving\naverage filter to implement temporal smoothing.\nAn example of "),a("code",[e._v("apply_dimension")]),e._v(" to the spatial dimensions\nis to do a historgram stretch for every spatial (grayscale) image\nof an image time series.")]),e._v(" "),a("h3",{attrs:{id:"filter-subsetting-dimensions-by-dimension-value-selection"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#filter-subsetting-dimensions-by-dimension-value-selection"}},[e._v("#")]),e._v(" "),a("code",[e._v("filter")]),e._v(": subsetting dimensions by dimension value selection")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("filter")]),e._v(" process makes a cube smaller by selecting specific\nvalue ranges for a particular dimension.")]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a band filter that selects the "),a("code",[e._v("red")]),e._v(" band")]),e._v(" "),a("li",[e._v('a bounding box filter "crops" the collection to a spatial extent')])]),e._v(" "),a("h3",{attrs:{id:"reduce-removing-dimensions-entirely-by-computation"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#reduce-removing-dimensions-entirely-by-computation"}},[e._v("#")]),e._v(" "),a("code",[e._v("reduce")]),e._v(": removing dimensions entirely by computation")]),e._v(" "),a("p",[e._v("The "),a("code",[e._v("reduce")]),e._v(' process removes a dimension by "rolling up" or summarizing\nthe values along that dimension to a single value.\nFor example: eliminate the time dimension by taking the '),a("code",[e._v("mean")]),e._v(" along that dimension.\nAnother example is taking the "),a("code",[e._v("sum")]),e._v(" or "),a("code",[e._v("max")]),e._v(" along the band dimension.")]),e._v(" "),a("h3",{attrs:{id:"aggregate-reducing-resolution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#aggregate-reducing-resolution"}},[e._v("#")]),e._v(" "),a("code",[e._v("aggregate")]),e._v(": reducing resolution")]),e._v(" "),a("p",[e._v("Aggregation computes new values from sets of values that are "),a("em",[e._v("uniquely")]),e._v(" assigned to groups. It involves a grouping predicate (e.g. monthly, 100 m x 100 m grid cells, or a set of non-overlapping spatial polygons), and an reducer (e.g., "),a("code",[e._v("mean")]),e._v(") that computes one or more new values from the original ones.")]),e._v(" "),a("p",[e._v("In effect, "),a("code",[e._v("aggregate")]),e._v(" combines the following three steps:")]),e._v(" "),a("ul",[a("li",[a("em",[e._v("split")]),e._v(" the data cube in groups, based on dimension constraints (time intervals, band groups, spatial polygons)")]),e._v(" "),a("li",[a("em",[e._v("apply")]),e._v(" a reducer to each group (similar to the "),a("code",[e._v("reduce")]),e._v(" process, but reducing a group rather than an entire dimension)")]),e._v(" "),a("li",[a("em",[e._v("combine")]),e._v(" the result to a new data cube, with some dimensions having reduced resolution (or e.g. raster to vector converted)")])]),e._v(" "),a("p",[e._v("Examples:")]),e._v(" "),a("ul",[a("li",[e._v("a weekly time series may be aggregated to monthly values by computing the "),a("code",[e._v("mean")]),e._v(" for all values in a month (grouping predicate: months)")]),e._v(" "),a("li",[a("em",[e._v("spatial")]),e._v(" aggregation involves computing e.g. "),a("em",[e._v("mean")]),e._v(" pixel values on a 100 x 100 m grid, from 10 m x 10 m pixels, where each original pixel is assigned uniquely to a larger pixel (grouping predicate: 100 m x 100 m grid cells)")])]),e._v(" "),a("h3",{attrs:{id:"resample-changing-data-cube-geometry"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#resample-changing-data-cube-geometry"}},[e._v("#")]),e._v(" "),a("code",[e._v("resample")]),e._v(": changing data cube geometry")]),e._v(" "),a("p",[e._v("Resampling considers the case where we have data at one resolution and coordinate reference system, and need values at another. In case we have values at a 100 m x 100 m grid and need values at a 10 m x 10 m grid, the original values will be reused many times, and may be simply assigned to the nearest high resolution grid cells (nearest neighbor method), or may be interpolated using various methods (e.g. by bilinear interpolation). This is often called "),a("em",[e._v("upsampling")]),e._v(" or "),a("em",[e._v("upscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("Resampling from finer to coarser grid is a special case of aggregation often called "),a("em",[e._v("downsampling")]),e._v(" or "),a("em",[e._v("downscaling")]),e._v(".")]),e._v(" "),a("p",[e._v("When the target grid or time series has a lower resolution (larger grid cells) or lower frequency (longer time intervals) than the source grid, aggregation might be used for resampling. For example, if the resolutions are similar, (e.g. the source collection provides 10 day intervals and the target needs values for 16 day intervals), then some form of interpolation may be more appropriate than aggregation as defined here.")]),e._v(" "),a("h2",{attrs:{id:"user-defined-function-udf"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),a("p",[e._v("The abbreviation "),a("strong",[e._v("UDF")]),e._v(" stands for "),a("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),a("RouterLink",{attrs:{to:"/documentation/0.4/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),a("h2",{attrs:{id:"data-processing-modes"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),a("p",[e._v("Process graphs can be processed in three different ways:")]),e._v(" "),a("ol",[a("li",[e._v("Results can be pre-computed by creating a "),a("em",[a("strong",[e._v("batch job")])]),e._v(" using "),a("code",[e._v("POST /jobs")]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until "),a("code",[e._v("POST /jobs/{job_id}/results")]),e._v(" has been called. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),a("li",[e._v("A more dynamic way of processing and accessing data is to create a "),a("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),a("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),a("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),a("OutboundLink")],1),e._v(" (Web Coverage Service) or "),a("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),a("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),a("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),a("li",[e._v("Process graphs can also be executed "),a("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously) by sending the process graph to "),a("code",[e._v("POST /result")]),e._v(". Results are delivered with the request itself and no job is created. Only lightweight computations, for example small previews, should be executed using this approach as timeouts are to be expected for "),a("a",{attrs:{href:"https://www.pubnub.com/blog/2014-12-01-http-long-polling/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long-polling HTTP requests"),a("OutboundLink")],1),e._v(".")])])])}),[],!1,null,null,null);a.default=o.exports}}]); \ No newline at end of file diff --git a/assets/js/43.4be98c2f.js b/assets/js/43.3e1dcc31.js similarity index 98% rename from assets/js/43.4be98c2f.js rename to assets/js/43.3e1dcc31.js index d66985d3c..430ca3e6d 100644 --- a/assets/js/43.4be98c2f.js +++ b/assets/js/43.3e1dcc31.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[43],{322:function(e,t,a){e.exports=a.p+"assets/img/arch.e75ccb9a.png"},518:function(e,t,a){"use strict";a.r(t);var i=a(4),n=Object(i.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"architecture"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#architecture"}},[e._v("#")]),e._v(" Architecture")]),e._v(" "),t("p",[e._v("The openEO API defines a language for how clients communicate to back-ends in order to analyze large Earth observation datasets. 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The basic service's metadata (capabilities) can be accessed via")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" info "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("capabilities")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("This allows to request a couple of "),s("a",{attrs:{href:"https://open-eo.github.io/openeo-js-client/latest/Capabilities.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("different information"),s("OutboundLink")],1),t._v(", like API version, description, related links or the billing plans. 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punctuation"}},[t._v(";")]),t._v("\n")])])]),s("h3",{attrs:{id:"collections"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),s("p",[t._v("Collections represent the basic data the back-end provides (e.g. Sentinel 2 collection).\nCollections are used as input data for job executions ("),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("more info on collections")]),t._v(").\nWith the following code snippet you can print all 400+ available collection names and their summary.")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Available Collections:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" response "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listCollections")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nresponse"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("collections"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("collection")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("collection"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("id"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v(": ")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("collection"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("summary"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")])]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("To get detailed information about a single collection, you can pass any of the collection IDs requested earlier to "),s("code",[t._v("describeCollection")]),t._v(" and get a full object of "),s("a",{attrs:{href:"https://github.com/radiantearth/stac-spec/tree/v1.0.0/collection-spec/collection-spec.md",target:"_blank",rel:"noopener noreferrer"}},[t._v("STAC compliant Collection metadata"),s("OutboundLink")],1),t._v(" back.\nIn this example we request information about the Sentinel-2 Level 1C data from Google:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("describeCollection")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S2"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("The collections descriptions returned by "),s("code",[t._v("listCollections")]),t._v(" are usually not complete. To get the full set of metadata you should always use "),s("code",[t._v("describeCollection")]),t._v(".")]),t._v(" "),s("h3",{attrs:{id:"processes"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),s("p",[t._v("Processes in openEO are small tasks that can be applied on (EO) data.\nThe input of a process might be the output of another process, so that several connected processes form a new (user-defined) process itself.\nTherefore, a process resembles the smallest unit of task descriptions in openEO ("),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[t._v("more details on processes")]),t._v(").\nWith the following code snippet you can print all available process IDs and their summaries.")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token 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punctuation"}},[t._v(";")]),t._v("\nresponse"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("processes"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("process")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("process"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("id"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v(": ")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("process"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("summary"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")])]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("In contrast to the collections, the process descriptions returned by "),s("code",[t._v("listProcesses")]),t._v(" are complete.\nThere's no need to call "),s("code",[t._v("describeProcess")]),t._v(" to get the full set of metadata.\n"),s("code",[t._v("describeProcess")]),t._v(" is just a convenience function to get a single process from "),s("code",[t._v("listProcesses")]),t._v(".\nIn this example we request the process specification for the "),s("code",[t._v("apply")]),t._v(" process:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("describeProcess")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"apply"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("For a graphical overview of the openEO processes, there is an "),s("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("online documentation")]),t._v(" for general process descriptions and the "),s("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),s("OutboundLink")],1),t._v(" for back-end specific process descriptions.")],1),t._v(" "),s("h2",{attrs:{id:"authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),s("p",[t._v("In the code snippets above, authentication is usually not necessary, since we only fetch general information about the back-end.\nTo run your own jobs at the back-end or to access job results, you need to authenticate at the back-end.")]),t._v(" "),s("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out.\nYou can also have a look at the "),s("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),s("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),s("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),s("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due to better security mechanisms implemented in the OpenID Connect protocol.")])]),t._v(" "),s("h3",{attrs:{id:"basic-authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#basic-authentication"}},[t._v("#")]),t._v(" Basic Authentication")]),t._v(" "),s("p",[t._v("The Basic authentication method is a common way of authenticate HTTP requests given username and password.")]),t._v(" "),s("p",[t._v("The following code snippet shows how to log in via Basic authentication:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token 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"),s("code",[t._v("authenticateBasic")]),t._v(" method, you are logged into the back-end with your account.\nThis means, that every call that comes after that via the "),s("code",[t._v("con")]),t._v(" variable is executed by your user account.")]),t._v(" "),s("h3",{attrs:{id:"openid-connect-authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),s("p",[t._v("For Google Earth Engine, only "),s("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),s("p",[t._v("The OIDC ("),s("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect"),s("OutboundLink")],1),t._v(") authentication can be used to authenticate via an external service given a client ID.")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("Important")]),t._v(" "),s("p",[t._v("If you have included the library using HTML "),s("code",[t._v("script")]),t._v(" tags, then you need to include the following OIDC client before the openEO client:")]),t._v(" "),s("div",{staticClass:"language-html extra-class"},[s("pre",{pre:!0,attrs:{class:"language-html"}},[s("code",[s("span",{pre:!0,attrs:{class:"token tag"}},[s("span",{pre:!0,attrs:{class:"token tag"}},[s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("<")]),t._v("script")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token attr-name"}},[t._v("src")]),s("span",{pre:!0,attrs:{class:"token attr-value"}},[s("span",{pre:!0,attrs:{class:"token punctuation attr-equals"}},[t._v("=")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v('"')]),t._v("https://cdn.jsdelivr.net/npm/oidc-client@1/lib/oidc-client.min.js"),s("span",{pre:!0,attrs:{class:"token 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process")]),t._v(" "),s("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.\nFirst we need to create a user-defined process and for that a process builder is the easiest method.")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" builder "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("buildProcess")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("With the builder, a "),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#spatial-datacubes"}},[t._v("datacube")]),t._v(" can be initialized by selecting a collection from the back-end with the process "),s("code",[t._v("load_collection")]),t._v(":")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" datacube "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" builder"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("load_collection")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S1_GRD"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token 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datacube, giving it the title "),s("code",[t._v("Example Title")]),t._v(".")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" job "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("createJob")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("result"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Example Title"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("The "),s("code",[t._v("createJob")]),t._v(" method sends all necessary information to the back-end and creates a new job, which gets returned.\nAfter this, the job is just created, but has not started the execution at the back-end yet.\nIt needs to be queued for processing explicitly:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("startJob")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("Now the execution of the job can be monitored by requesting the job status and the log files every once in a while (30 seconds in this example):")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("let")]),t._v(" stopFn "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("monitorJob")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" logs")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token 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punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("30")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("The monitoring stops automatically once the job has finished, was canceled or errored out.\nBut with the return value of the "),s("code",[t._v("monitorJob")]),t._v(" function, you can also stop monitoring the job manually:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token function"}},[t._v("stopFn")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("When the job is finished, calling "),s("code",[t._v("listResults")]),t._v(" gets you the URLs to the results.")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" urls "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listResults")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),s("p",[t._v("This only works if the job execution has finished.\nWe recommend to use "),s("code",[t._v("listResults")]),t._v(" in combination with "),s("code",[t._v("monitorJob")]),t._v(", for example as follows:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("let")]),t._v(" stopFn "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("monitorJob")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("async")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" logs")]),s("span",{pre:!0,attrs:{class:"token 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punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listResults")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n urls"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("url")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation 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punctuation"}},[t._v(";")]),t._v("\n")])])])]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),s("p",[t._v("There's also the method "),s("code",[t._v("downloadResults")]),t._v(" to download the results directly.\nUnfortunately, you can only download files from a Node.js environment where file access to your local drive is possible.\nIn a Browser environment, it is also an option to download the STAC Item or Collection for the results using the "),s("code",[t._v("getResultsAsStac")]),t._v(" method and point a "),s("a",{attrs:{href:"https://stacindex.org/ecosystem?category=Client",target:"_blank",rel:"noopener noreferrer"}},[t._v("STAC client"),s("OutboundLink")],1),t._v(" to it for downloading.")])]),t._v(" "),s("p",[t._v("Now you know the general workflow of job executions.")]),t._v(" "),s("h2",{attrs:{id:"full-example"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#full-example"}},[t._v("#")]),t._v(" Full Example")]),t._v(" "),s("p",[t._v("In this chapter we will show a full example of an earth observation use case using the JavaScript client in a Node.js environment and the Google Earth Engine back-end.\nInstead of batch job processing, we compute the image synchronously. Synchronous processing means the result is directly returned in the response, which usually works only for smaller amounts of data.")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Use Case")]),t._v(" "),s("p",[t._v("We want to produce a monthly RGB composite of Sentinel 1 backscatter data over the area of Vienna, Austria for three\nmonths in 2017. This can be used for classification and crop monitoring.")])]),t._v(" "),s("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),s("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),s("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// Make the client available to the Node.js script")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// Also include the Formula library for simple math expressions")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("const")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" OpenEO"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" Formula "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("require")]),s("span",{pre:!0,attrs:{class:"token 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punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("error")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("error")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("error"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("Now the resulting PNG file of the RGB backscatter composite is stored as "),s("code",[t._v("result.png")]),t._v(" in the node.JS working directory and should look as follows:")]),t._v(" "),s("p",[s("img",{attrs:{src:a(193),alt:"RGB composite"}})]),t._v(" "),s("h2",{attrs:{id:"user-defined-functions"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions"}},[t._v("#")]),t._v(" User Defined Functions")]),t._v(" "),s("p",[t._v("If your use case can not be accomplished with the "),s("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("default processes")]),t._v(" of openEO, you can define a "),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#user-defined-function-udf"}},[t._v("user defined function")]),t._v(".\nUnfortunately, you can only create Python and R functions at the moment.\nTherefore, this guide doesn't get into detail.\nFor more information check out the Python or R tutorials on UDFs.")],1),t._v(" "),s("h2",{attrs:{id:"additional-information"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#additional-information"}},[t._v("#")]),t._v(" Additional Information")]),t._v(" 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Depending on whether you are directly working in Node.js or are just using a Node.js build tool, the import can be different. 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You can print some of these information to the console as follows:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"API Version: "')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" info"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("apiVersion")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nconsole"),s("span",{pre:!0,attrs:{class:"token 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punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("link")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation 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punctuation"}},[t._v(";")]),t._v("\n")])])]),s("h3",{attrs:{id:"collections"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),s("p",[t._v("Collections represent the basic data the back-end provides (e.g. Sentinel 2 collection).\nCollections are used as input data for job executions ("),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("more info on collections")]),t._v(").\nWith the following code snippet you can print all 400+ available collection names and their summary.")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Available Collections:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" response "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listCollections")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nresponse"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("collections"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("collection")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("collection"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("id"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v(": ")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("collection"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("summary"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")])]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("To get detailed information about a single collection, you can pass any of the collection IDs requested earlier to "),s("code",[t._v("describeCollection")]),t._v(" and get a full object of "),s("a",{attrs:{href:"https://github.com/radiantearth/stac-spec/tree/v1.0.0/collection-spec/collection-spec.md",target:"_blank",rel:"noopener noreferrer"}},[t._v("STAC compliant Collection metadata"),s("OutboundLink")],1),t._v(" back.\nIn this example we request information about the Sentinel-2 Level 1C data from Google:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("describeCollection")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S2"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("The collections descriptions returned by "),s("code",[t._v("listCollections")]),t._v(" are usually not complete. To get the full set of metadata you should always use "),s("code",[t._v("describeCollection")]),t._v(".")]),t._v(" "),s("h3",{attrs:{id:"processes"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),s("p",[t._v("Processes in openEO are small tasks that can be applied on (EO) data.\nThe input of a process might be the output of another process, so that several connected processes form a new (user-defined) process itself.\nTherefore, a process resembles the smallest unit of task descriptions in openEO ("),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[t._v("more details on processes")]),t._v(").\nWith the following code snippet you can print all available process IDs and their summaries.")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Available Collections:"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" response "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listProcesses")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\nresponse"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("processes"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("forEach")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token parameter"}},[t._v("process")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=>")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v("\n console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token template-string"}},[s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("process"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("id"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v(": ")]),s("span",{pre:!0,attrs:{class:"token interpolation"}},[s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("${")]),t._v("process"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("summary"),s("span",{pre:!0,attrs:{class:"token interpolation-punctuation punctuation"}},[t._v("}")])]),s("span",{pre:!0,attrs:{class:"token template-punctuation string"}},[t._v("`")])]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("In contrast to the collections, the process descriptions returned by "),s("code",[t._v("listProcesses")]),t._v(" are complete.\nThere's no need to call "),s("code",[t._v("describeProcess")]),t._v(" to get the full set of metadata.\n"),s("code",[t._v("describeProcess")]),t._v(" is just a convenience function to get a single process from "),s("code",[t._v("listProcesses")]),t._v(".\nIn this example we request the process specification for the "),s("code",[t._v("apply")]),t._v(" process:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[t._v("console"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("log")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("describeProcess")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"apply"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("For a graphical overview of the openEO processes, there is an "),s("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("online documentation")]),t._v(" for general process descriptions and the "),s("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),s("OutboundLink")],1),t._v(" for back-end specific process descriptions.")],1),t._v(" "),s("h2",{attrs:{id:"authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),s("p",[t._v("In the code snippets above, authentication is usually not necessary, since we only fetch general information about the back-end.\nTo run your own jobs at the back-end or to access job results, you need to authenticate at the back-end.")]),t._v(" "),s("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out.\nYou can also have a look at the "),s("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),s("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),s("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),s("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due to better security mechanisms implemented in the OpenID Connect protocol.")])]),t._v(" "),s("h3",{attrs:{id:"basic-authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#basic-authentication"}},[t._v("#")]),t._v(" Basic Authentication")]),t._v(" "),s("p",[t._v("The Basic authentication method is a common way of authenticate HTTP requests given username and password.")]),t._v(" "),s("p",[t._v("The following code snippet shows how to log in via Basic authentication:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token 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"),s("code",[t._v("authenticateBasic")]),t._v(" method, you are logged into the back-end with your account.\nThis means, that every call that comes after that via the "),s("code",[t._v("con")]),t._v(" variable is executed by your user account.")]),t._v(" "),s("h3",{attrs:{id:"openid-connect-authentication"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),s("p",[t._v("For Google Earth Engine, only "),s("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),s("p",[t._v("The OIDC ("),s("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect"),s("OutboundLink")],1),t._v(") authentication can be used to authenticate via an external service given a client ID.")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("Important")]),t._v(" "),s("p",[t._v("If you have included the library using HTML "),s("code",[t._v("script")]),t._v(" tags, then you need to include the following OIDC client before the openEO client:")]),t._v(" "),s("div",{staticClass:"language-html extra-class"},[s("pre",{pre:!0,attrs:{class:"language-html"}},[s("code",[s("span",{pre:!0,attrs:{class:"token tag"}},[s("span",{pre:!0,attrs:{class:"token tag"}},[s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("<")]),t._v("script")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token attr-name"}},[t._v("src")]),s("span",{pre:!0,attrs:{class:"token attr-value"}},[s("span",{pre:!0,attrs:{class:"token punctuation attr-equals"}},[t._v("=")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v('"')]),t._v("https://cdn.jsdelivr.net/npm/oidc-client@1/lib/oidc-client.min.js"),s("span",{pre:!0,attrs:{class:"token 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process")]),t._v(" "),s("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.\nFirst we need to create a user-defined process and for that a process builder is the easiest method.")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" builder "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" con"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("buildProcess")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("With the builder, a "),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#spatial-datacubes"}},[t._v("datacube")]),t._v(" can be initialized by selecting a collection from the back-end with the process "),s("code",[t._v("load_collection")]),t._v(":")],1),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" datacube "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" builder"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("load_collection")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S1_GRD"')]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n "),s("span",{pre:!0,attrs:{class:"token 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sends all necessary information to the back-end and creates a new job, which gets returned.\nAfter this, the job is just created, but has not started the execution at the back-end yet.\nIt needs to be queued for processing explicitly:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("startJob")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("Now the execution of the job can be monitored by requesting the job status and the log files every once in a while (30 seconds in this example):")]),t._v(" "),s("div",{staticClass:"language-js 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punctuation"}},[t._v(";")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token number"}},[t._v("30")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("The monitoring stops automatically once the job has finished, was canceled or errored out.\nBut with the return value of the "),s("code",[t._v("monitorJob")]),t._v(" function, you can also stop monitoring the job manually:")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token function"}},[t._v("stopFn")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("p",[t._v("When the job is finished, calling "),s("code",[t._v("listResults")]),t._v(" gets you the URLs to the results.")]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("var")]),t._v(" urls "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("await")]),t._v(" job"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("listResults")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(";")]),t._v("\n")])])]),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),s("p",[t._v("This only works 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Example")]),t._v(" "),s("p",[t._v("In this chapter we will show a full example of an earth observation use case using the JavaScript client in a Node.js environment and the Google Earth Engine back-end.\nInstead of batch job processing, we compute the image synchronously. Synchronous processing means the result is directly returned in the response, which usually works only for smaller amounts of data.")]),t._v(" "),s("div",{staticClass:"custom-block tip"},[s("p",{staticClass:"custom-block-title"},[t._v("Use Case")]),t._v(" "),s("p",[t._v("We want to produce a monthly RGB composite of Sentinel 1 backscatter data over the area of Vienna, Austria for three\nmonths in 2017. This can be used for classification and crop monitoring.")])]),t._v(" "),s("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),s("div",{staticClass:"custom-block warning"},[s("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),s("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),s("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),s("div",{staticClass:"language-js extra-class"},[s("pre",{pre:!0,attrs:{class:"language-js"}},[s("code",[s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// Make the client available to the Node.js script")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token comment"}},[t._v("// Also include the Formula library for simple math expressions")]),t._v("\n"),s("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("const")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" OpenEO"),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" Formula "),s("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),s("span",{pre:!0,attrs:{class:"token function"}},[t._v("require")]),s("span",{pre:!0,attrs:{class:"token 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"),s("h2",{attrs:{id:"user-defined-functions"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions"}},[t._v("#")]),t._v(" User Defined Functions")]),t._v(" "),s("p",[t._v("If your use case can not be accomplished with the "),s("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("default processes")]),t._v(" of openEO, you can define a "),s("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#user-defined-function-udf"}},[t._v("user defined function")]),t._v(".\nUnfortunately, you can only create Python and R functions at the moment.\nTherefore, this guide doesn't get into detail.\nFor more information check out the Python or R tutorials on UDFs.")],1),t._v(" "),s("h2",{attrs:{id:"additional-information"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#additional-information"}},[t._v("#")]),t._v(" Additional Information")]),t._v(" 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-(window.webpackJsonp=window.webpackJsonp||[]).push([[47],{193:function(t,e,a){t.exports=a.p+"assets/img/getting-started-result-example.7820ee84.jpg"},532:function(t,e,a){"use strict";a.r(e);var n=a(4),s=Object(n.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"python-client"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#python-client"}},[t._v("#")]),t._v(" Python Client")]),t._v(" "),e("p",[t._v("This Getting Started guide will give you just a simple overview of the capabilities of the openEO Python client library.\nMore in-depth information can be found in its "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h2",{attrs:{id:"installation"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[t._v("#")]),t._v(" Installation")]),t._v(" "),e("p",[t._v("The openEO Python client library is available on "),e("a",{attrs:{href:"https://pypi.org/project/openeo/",target:"_blank",rel:"noopener noreferrer"}},[t._v("PyPI"),e("OutboundLink")],1),t._v("\nand can easily be installed with a tool like "),e("code",[t._v("pip")]),t._v(", for example:")]),t._v(" "),e("div",{staticClass:"language-shell script extra-class"},[e("pre",{pre:!0,attrs:{class:"language-shell"}},[e("code",[t._v("pip "),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("install")]),t._v(" openeo\n")])])]),e("p",[t._v("The client library is also available on "),e("a",{attrs:{href:"https://anaconda.org/conda-forge/openeo",target:"_blank",rel:"noopener noreferrer"}},[t._v("Conda Forge"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("p",[t._v("It's recommended to work in a virtual environment of some kind ("),e("code",[t._v("venv")]),t._v(", "),e("code",[t._v("conda")]),t._v(", ...),\ncontaining Python 3.6 or higher.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("For more details, alternative installation procedures or troubleshooting tips:\nsee the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/installation.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("official "),e("code",[t._v("openeo")]),t._v(" package installation documentation"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h2",{attrs:{id:"exploring-a-back-end"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-back-end"}},[t._v("#")]),t._v(" Exploring a back-end")]),t._v(" "),e("p",[t._v("If you do not know an openEO back-end that you want to connect to yet, you can have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(", to find all known back-ends with information on their capabilities.")]),t._v(" "),e("p",[t._v("For this tutorial we will use the openEO instance of Google Earth Engine, which is available at "),e("code",[t._v("https://earthengine.openeo.org")]),t._v(".\nNote that the code snippets in this guide work the same way for the other back-ends listed in the openEO Hub. Just the collection identifier and band names might differ.")]),t._v(" "),e("p",[t._v("First we need to establish a connection to the back-end.")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\nconnection "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#module-openeo.rest.connection",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Connection")]),t._v(" object"),e("OutboundLink")],1),t._v("\nis your central gateway to")]),t._v(" "),e("ul",[e("li",[t._v("list data collections, available processes, file formats and other capabilities of the back-end")]),t._v(" "),e("li",[t._v("start building your openEO algorithm from the desired data on the back-end")]),t._v(" "),e("li",[t._v("execute and monitor (batch) jobs on the back-end")]),t._v(" "),e("li",[t._v("etc.")])]),t._v(" "),e("h3",{attrs:{id:"collections"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("p",[t._v('The EO data available at a back-end is organised in so-called collections.\nFor example, a back-end might provide fundamental satellite collections like "Sentinel 1" or "Sentinel 2",\nor preprocessed collections like "NDVI".\nCollections are used as input data for your openEO jobs.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v('More information on how openEO "collections" relate to terminology used in other systems can be found in\n('),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("the openEO glossary")]),t._v(").")],1)]),t._v(" "),e("p",[t._v("Let's list all available collections on the back-end,\nusing "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.list_collections",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("list_collections")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("print")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("connection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("list_collections"),e("span",{pre:!0,attrs:{class:"token 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noreferrer"}},[e("code",[t._v("describe_collection")]),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("Programmatically listing collections is just a very simple usage example of the Python client.\nIn reality, you probably want to look up or inspect available collections in a web based overview such as the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("p",[t._v('Processes in openEO are operations that can be applied on (EO) data\n(e.g. calculate the mean of an array, or mask out observations outside a given polygon).\nThe output of one process can be used as the input of another process,\nand by doing so, multiple processes can be connected that way in a larger "process graph":\na new (user-defined) processes that implements a certain algorithm.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Check "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[t._v("the openEO glossary")]),t._v("\nfor more details on pre-defined, user-defined processes and process graphs.")],1)]),t._v(" "),e("p",[t._v("Let's list the (pre-defined) processes available on the back-end\nwith "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.list_processes",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("list_processes")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token 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the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" for back-end specific process descriptions\nor browse the "),e("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("reference specifications of openEO processes"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above we did not need to log in\nsince we just queried publicly available back-end information.\nHowever, to run non-trivial processing queries one has to authenticate\nso that permissions, resource usage, etc. can be managed properly.")]),t._v(" "),e("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out.\nYou can also have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.")]),t._v(" "),e("p",[t._v("A detailed description of why and how to use the authentication methods is on the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/auth.html#authentication-and-account-management",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),e("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),e("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due 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"),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.authenticate_basic",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("authenticate_basic")]),e("OutboundLink")],1),t._v(" method, you are logged into the back-end with your account.")]),t._v(" "),e("p",[t._v("This means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h3",{attrs:{id:"openid-connect-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("For Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),e("p",[t._v("The OIDC 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noreferrer"}},[t._v("load_collection"),e("OutboundLink")],1),t._v("\nto avoid loading too much data upfront.")])]),t._v(" "),e("h3",{attrs:{id:"applying-processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes"}},[t._v("#")]),t._v(" Applying processes")]),t._v(" "),e("p",[t._v("By applying an openEO process on a datacube, we create a new datacube object that represents the manipulated data.\nThe standard way to do this with the Python client is to call the appropriate "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Datacube")]),t._v(" object"),e("OutboundLink")],1),t._v(" method.\nThe most common or popular openEO processes have a dedicated "),e("code",[t._v("Datacube")]),t._v(" method (e.g. "),e("code",[t._v("mask")]),t._v(", "),e("code",[t._v("aggregate_spatial")]),t._v(", "),e("code",[t._v("filter_bbox")]),t._v(", ...).\nOther processes without a dedicated method can still be applied in a generic way.\nAn on top of that, there are also some convenience methods that implement\nopenEO processes is a compact, Pythonic interface.")]),t._v(" "),e("p",[t._v("For example, the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.min_time",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("min_time")]),e("OutboundLink")],1),t._v(" method\nimplements a "),e("code",[t._v("reduce_dimension")]),t._v(" process along the temporal dimension, using the "),e("code",[t._v("min")]),t._v(" process as reducer function:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("min_time"),e("span",{pre:!0,attrs:{class:"token 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noreferrer"}},[e("code",[t._v("ndvi")]),t._v(" process"),e("OutboundLink")],1),t._v(' to the datacube with the arguments of "data", "nir" and "red" (This example assumes a datacube with bands '),e("code",[t._v("B8")]),t._v(" and "),e("code",[t._v("B4")]),t._v(").")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Still unsure on how to make use of processes with the Python client?\nVisit the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/processes.html#working-with-processes",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation on working with processes"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"defining-output-format"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#defining-output-format"}},[t._v("#")]),t._v(" Defining output format")]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("result "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"GTiff"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("h2",{attrs:{id:"execution"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#execution"}},[t._v("#")]),t._v(" Execution")]),t._v(" "),e("p",[t._v("It's important to note that all the datacube processes we applied up to this point\nare not actually executed yet, neither locally nor remotely on the back-end.\nWe just built an abstract representation of the algorithm (input data and processing chain),\nencapsulated in a local "),e("code",[t._v("Datacube")]),t._v(" object (e.g. the "),e("code",[t._v("result")]),t._v(" variable above).\nTo trigger an actual execution (on the back-end) we have to explicitly send this representation\nto the back-end.")]),t._v(" "),e("p",[t._v("openEO defines "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[t._v("several processing modes")]),t._v(",\nbut for this introduction we'll focus on batch jobs, which is a good default choice.")],1),t._v(" "),e("h3",{attrs:{id:"batch-job-execution"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-execution"}},[t._v("#")]),t._v(" Batch job execution")]),t._v(" "),e("p",[t._v("The "),e("code",[t._v("result")]),t._v(" datacube object we built above describes the desired input collections, processing steps and output format.\nWe can now just send this description to the back-end to create a batch job with the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.create_job",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("create_job")]),t._v(" method"),e("OutboundLink")],1),t._v(" like this:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Creating a new job at the back-end by sending the datacube information.")]),t._v("\njob "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The batch job, which is referenced by the returned "),e("code",[t._v("job")]),t._v(" object, is just created at the back-end,\nit is not started yet.\nTo start the job and let your Python script wait until the job has finished then\ndownload it automatically, you can use the "),e("code",[t._v("start_and_wait")]),t._v(" method.")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Starts the job and waits until it finished to download the result.")]),t._v("\njob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("start_and_wait"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("get_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token 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This can be used for classification and crop monitoring.")])]),t._v(" "),e("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# First, we connect to the back-end and authenticate ourselves via Basic authentication. 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There we define that ")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# the result should be a GeoTiff file.")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# We also set, which band should be used for "red", "green" and "blue" color in the options.')]),t._v("\n\nRGB "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" RGB"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token builtin"}},[t._v("format")]),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"GTIFF-THUMB"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# With the last process we have finished the datacube definition and can create and start the job at the back-end.")]),t._v("\n\njob "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" RGB"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("start_and_wait"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("download_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Now the resulting GTiff file of the RGB backscatter composite is in your current directory.")]),t._v(" "),e("p",[e("img",{attrs:{src:a(193),alt:"RGB composite"}})]),t._v(" "),e("p",[t._v("The "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client/blob/c21b928ab5f4b4561bb07b7c4a934f0ea9b4f0b8/examples/gee_uc1_temp.py",target:"_blank",rel:"noopener noreferrer"}},[t._v("source code"),e("OutboundLink")],1),t._v(" of this example can be found on GitHub.")]),t._v(" "),e("h2",{attrs:{id:"user-defined-functions"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions"}},[t._v("#")]),t._v(" User Defined Functions")]),t._v(" "),e("p",[t._v("If your use case can not be accomplished with the "),e("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("default processes")]),t._v(" of openEO, you can define a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#user-defined-function-udf"}},[t._v("user defined function")]),t._v(".\nTherefore, you can create a Python function that will be executed at the back-end and functions as a process in your process graph.")],1),t._v(" "),e("p",[t._v("Detailed information about Python UDFs can be found in the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/udf.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation"),e("OutboundLink")],1),t._v(" as well as examples in the "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client/tree/master/examples/udf",target:"_blank",rel:"noopener noreferrer"}},[t._v("Python client repository"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h2",{attrs:{id:"additional-information"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#additional-information"}},[t._v("#")]),t._v(" Additional Information")]),t._v(" "),e("p",[t._v("Additional information and resources about the openEO Python Client Library:")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client/tree/master/examples",target:"_blank",rel:"noopener noreferrer"}},[t._v("Example scripts"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client/tree/master/examples/notebooks",target:"_blank",rel:"noopener noreferrer"}},[t._v("Example Jupyter Notebooks"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Official openEO Python Client Library Documentation"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client",target:"_blank",rel:"noopener noreferrer"}},[t._v("Repository on GitHub"),e("OutboundLink")],1)])])])}),[],!1,null,null,null);e.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[47],{193:function(t,e,a){t.exports=a.p+"assets/img/getting-started-result-example.7820ee84.jpg"},530:function(t,e,a){"use strict";a.r(e);var n=a(4),s=Object(n.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"python-client"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#python-client"}},[t._v("#")]),t._v(" Python Client")]),t._v(" "),e("p",[t._v("This Getting Started guide will give you just a simple overview of the capabilities of the openEO Python client library.\nMore in-depth information can be found in its "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h2",{attrs:{id:"installation"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[t._v("#")]),t._v(" Installation")]),t._v(" "),e("p",[t._v("The openEO Python client library is available on "),e("a",{attrs:{href:"https://pypi.org/project/openeo/",target:"_blank",rel:"noopener noreferrer"}},[t._v("PyPI"),e("OutboundLink")],1),t._v("\nand can easily be installed with a tool like "),e("code",[t._v("pip")]),t._v(", for example:")]),t._v(" "),e("div",{staticClass:"language-shell script extra-class"},[e("pre",{pre:!0,attrs:{class:"language-shell"}},[e("code",[t._v("pip "),e("span",{pre:!0,attrs:{class:"token function"}},[t._v("install")]),t._v(" openeo\n")])])]),e("p",[t._v("The client library is also available on "),e("a",{attrs:{href:"https://anaconda.org/conda-forge/openeo",target:"_blank",rel:"noopener noreferrer"}},[t._v("Conda Forge"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("p",[t._v("It's recommended to work in a virtual environment of some kind ("),e("code",[t._v("venv")]),t._v(", "),e("code",[t._v("conda")]),t._v(", ...),\ncontaining Python 3.6 or higher.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("For more details, alternative installation procedures or troubleshooting tips:\nsee the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/installation.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("official "),e("code",[t._v("openeo")]),t._v(" package installation documentation"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h2",{attrs:{id:"exploring-a-back-end"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-back-end"}},[t._v("#")]),t._v(" Exploring a back-end")]),t._v(" "),e("p",[t._v("If you do not know an openEO back-end that you want to connect to yet, you can have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(", to find all known back-ends with information on their capabilities.")]),t._v(" "),e("p",[t._v("For this tutorial we will use the openEO instance of Google Earth Engine, which is available at "),e("code",[t._v("https://earthengine.openeo.org")]),t._v(".\nNote that the code snippets in this guide work the same way for the other back-ends listed in the openEO Hub. Just the collection identifier and band names might differ.")]),t._v(" "),e("p",[t._v("First we need to establish a connection to the back-end.")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\nconnection "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#module-openeo.rest.connection",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Connection")]),t._v(" object"),e("OutboundLink")],1),t._v("\nis your central gateway to")]),t._v(" "),e("ul",[e("li",[t._v("list data collections, available processes, file formats and other capabilities of the back-end")]),t._v(" "),e("li",[t._v("start building your openEO algorithm from the desired data on the back-end")]),t._v(" "),e("li",[t._v("execute and monitor (batch) jobs on the back-end")]),t._v(" "),e("li",[t._v("etc.")])]),t._v(" "),e("h3",{attrs:{id:"collections"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("p",[t._v('The EO data available at a back-end is organised in so-called collections.\nFor example, a back-end might provide fundamental satellite collections like "Sentinel 1" or "Sentinel 2",\nor preprocessed collections like "NDVI".\nCollections are used as input data for your openEO jobs.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v('More information on how openEO "collections" relate to terminology used in other systems can be found in\n('),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("the openEO glossary")]),t._v(").")],1)]),t._v(" "),e("p",[t._v("Let's list all available collections on the back-end,\nusing "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.list_collections",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("list_collections")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("print")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("connection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("list_collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("which returns list of collection metadata dictionaries, e.g. something like:")]),t._v(" "),e("div",{staticClass:"language- extra-class"},[e("pre",{pre:!0,attrs:{class:"language-text"}},[e("code",[t._v("[{'id': 'AGERA5', 'title': 'ECMWF AGERA5 meteo dataset', 'description': 'Daily surface meteorolociga datal ...', ...},\n {'id': 'SENTINEL2_L2A_SENTINELHUB', 'title': 'Sentinel-2 top of canopy', ...},\n {'id': 'SENTINEL1_GRD', ...},\n ...]\n")])])]),e("p",[t._v("This listing includes basic metadata for each collection.\nIf necessary, a more detailed metadata listing for a given collection can be obtained with\n"),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.describe_collection",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("describe_collection")]),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("Programmatically listing collections is just a very simple usage example of the Python client.\nIn reality, you probably want to look up or inspect available collections in a web based overview such as the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("p",[t._v('Processes in openEO are operations that can be applied on (EO) data\n(e.g. calculate the mean of an array, or mask out observations outside a given polygon).\nThe output of one process can be used as the input of another process,\nand by doing so, multiple processes can be connected that way in a larger "process graph":\na new (user-defined) processes that implements a certain algorithm.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Check "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[t._v("the openEO glossary")]),t._v("\nfor more details on pre-defined, user-defined processes and process graphs.")],1)]),t._v(" "),e("p",[t._v("Let's list the (pre-defined) processes available on the back-end\nwith "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.list_processes",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("list_processes")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("print")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("connection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("list_processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("which returns a list of dictionaries describing the process (including expected arguments and return type), e.g.:")]),t._v(" "),e("div",{staticClass:"language- extra-class"},[e("pre",{pre:!0,attrs:{class:"language-text"}},[e("code",[t._v("[{'id': 'absolute', 'summary': 'Absolute value', 'description': 'Computes the absolute value of a real number `x`, which is th...', \n {'id': 'mean', 'summary': 'Arithmetic mean(average)', ...}\n ...]\n")])])]),e("p",[t._v("Like with collections, instead of programmatic exploration you'll probably prefer a web-based overview such as the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" for back-end specific process descriptions\nor browse the "),e("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("reference specifications of openEO processes"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above we did not need to log in\nsince we just queried publicly available back-end information.\nHowever, to run non-trivial processing queries one has to authenticate\nso that permissions, resource usage, etc. can be managed properly.")]),t._v(" "),e("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out.\nYou can also have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.")]),t._v(" "),e("p",[t._v("A detailed description of why and how to use the authentication methods is on the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/auth.html#authentication-and-account-management",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation"),e("OutboundLink")],1),t._v(".")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),e("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),e("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due to better security mechanisms implemented in the OpenID Connect protocol.")])]),t._v(" "),e("h3",{attrs:{id:"basic-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#basic-authentication"}},[t._v("#")]),t._v(" Basic Authentication")]),t._v(" "),e("p",[t._v("The Basic authentication method is a common way of authenticate HTTP requests given username and password.")]),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via Basic authentication:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("print")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Authenticate with Basic authentication"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nconnection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("authenticate_basic"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"username"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"password"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("You can get username and password here: "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-earthengine-driver#demo",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/Open-EO/openeo-earthengine-driver#demo"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("After successfully calling the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.authenticate_basic",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("authenticate_basic")]),e("OutboundLink")],1),t._v(" method, you are logged into the back-end with your account.")]),t._v(" "),e("p",[t._v("This means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h3",{attrs:{id:"openid-connect-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("For Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),e("p",[t._v("The OIDC ("),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect"),e("OutboundLink")],1),t._v(") authentication can be used to authenticate via an external service given a client ID.\nThe following code snippet shows how to log in via OIDC authentication:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("print")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Authenticate with OIDC authentication"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nconnection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("authenticate_oidc"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Calling this method opens your system web browser, with which you can authenticate yourself on the back-end authentication system.\nAfter that the website will give you the instructions to go back to the python client, where your connection has logged your account in.\nThis means that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h2",{attrs:{id:"working-with-datacube"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#working-with-datacube"}},[t._v("#")]),t._v(" Working with Datacube")]),t._v(" "),e("p",[t._v("Now that we know how to discover the capabilities of the back-end and how to authenticate,\nlet's do some real work and process some EO data in a batch job.\nWe'll build the desired algorithm by working on so-called \"Datacubes\",\nwhich is the central concept in openEO to represent EO data,\nas "),e("RouterLink",{attrs:{to:"/documentation/1.0/datacubes.html"}},[t._v("discussed in great detail here")]),t._v(".")],1),t._v(" "),e("h3",{attrs:{id:"creating-a-datacube"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-datacube"}},[t._v("#")]),t._v(" Creating a Datacube")]),t._v(" "),e("p",[t._v("The first step is loading the desired slice of a data collection\nwith "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.connection.Connection.load_collection",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Connection.load_collection")]),e("OutboundLink")],1),t._v(":")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token 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tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("You can also filter the datacube step by step or at a later stage by using the following filter methods:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("filter_bbox"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("west"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" south"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" 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noreferrer"}},[t._v("load_collection"),e("OutboundLink")],1),t._v("\nto avoid loading too much data upfront.")])]),t._v(" "),e("h3",{attrs:{id:"applying-processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#applying-processes"}},[t._v("#")]),t._v(" Applying processes")]),t._v(" "),e("p",[t._v("By applying an openEO process on a datacube, we create a new datacube object that represents the manipulated data.\nThe standard way to do this with the Python client is to call the appropriate "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("Datacube")]),t._v(" object"),e("OutboundLink")],1),t._v(" method.\nThe most common or popular openEO processes have a dedicated "),e("code",[t._v("Datacube")]),t._v(" method (e.g. "),e("code",[t._v("mask")]),t._v(", "),e("code",[t._v("aggregate_spatial")]),t._v(", "),e("code",[t._v("filter_bbox")]),t._v(", ...).\nOther processes without a dedicated method can still be applied in a generic way.\nAn on top of that, there are also some convenience methods that implement\nopenEO processes is a compact, Pythonic interface.")]),t._v(" "),e("p",[t._v("For example, the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.min_time",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("min_time")]),e("OutboundLink")],1),t._v(" method\nimplements a "),e("code",[t._v("reduce_dimension")]),t._v(" process along the temporal dimension, using the "),e("code",[t._v("min")]),t._v(" process as reducer function:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("min_time"),e("span",{pre:!0,attrs:{class:"token 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noreferrer"}},[e("code",[t._v("ndvi")]),t._v(" process"),e("OutboundLink")],1),t._v(' to the datacube with the arguments of "data", "nir" and "red" (This example assumes a datacube with bands '),e("code",[t._v("B8")]),t._v(" and "),e("code",[t._v("B4")]),t._v(").")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Still unsure on how to make use of processes with the Python client?\nVisit the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/processes.html#working-with-processes",target:"_blank",rel:"noopener noreferrer"}},[t._v("official documentation on working with processes"),e("OutboundLink")],1),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"defining-output-format"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#defining-output-format"}},[t._v("#")]),t._v(" Defining output format")]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[t._v("result "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"GTiff"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("h2",{attrs:{id:"execution"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#execution"}},[t._v("#")]),t._v(" Execution")]),t._v(" "),e("p",[t._v("It's important to note that all the datacube processes we applied up to this point\nare not actually executed yet, neither locally nor remotely on the back-end.\nWe just built an abstract representation of the algorithm (input data and processing chain),\nencapsulated in a local "),e("code",[t._v("Datacube")]),t._v(" object (e.g. the "),e("code",[t._v("result")]),t._v(" variable above).\nTo trigger an actual execution (on the back-end) we have to explicitly send this representation\nto the back-end.")]),t._v(" "),e("p",[t._v("openEO defines "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[t._v("several processing modes")]),t._v(",\nbut for this introduction we'll focus on batch jobs, which is a good default choice.")],1),t._v(" "),e("h3",{attrs:{id:"batch-job-execution"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-execution"}},[t._v("#")]),t._v(" Batch job execution")]),t._v(" "),e("p",[t._v("The "),e("code",[t._v("result")]),t._v(" datacube object we built above describes the desired input collections, processing steps and output format.\nWe can now just send this description to the back-end to create a batch job with the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/api.html#openeo.rest.datacube.DataCube.create_job",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("create_job")]),t._v(" method"),e("OutboundLink")],1),t._v(" like this:")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Creating a new job at the back-end by sending the datacube information.")]),t._v("\njob "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The batch job, which is referenced by the returned "),e("code",[t._v("job")]),t._v(" object, is just created at the back-end,\nit is not started yet.\nTo start the job and let your Python script wait until the job has finished then\ndownload it automatically, you can use the "),e("code",[t._v("start_and_wait")]),t._v(" method.")]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# Starts the job and waits until it finished to download the result.")]),t._v("\njob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("start_and_wait"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njob"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("get_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(".")]),t._v("download_files"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"output"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v('When everything completes successfully, the processing result will be downloaded as a GeoTIFF file\nin a folder "output".')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("The official openEO Python Client documentation has more information\non "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/basics.html#managing-jobs-in-openeo",target:"_blank",rel:"noopener noreferrer"}},[t._v("batch job basics"),e("OutboundLink")],1),t._v("\nor "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/batch_jobs.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("more detailed batch job (result) 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This can be used for classification and crop monitoring.")])]),t._v(" "),e("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),e("div",{staticClass:"language-python extra-class"},[e("pre",{pre:!0,attrs:{class:"language-python"}},[e("code",[e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("import")]),t._v(" openeo\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# First, we connect to the back-end and authenticate ourselves via Basic authentication. 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\ No newline at end of file diff --git a/assets/js/48.44794e44.js b/assets/js/48.aa42c484.js similarity index 99% rename from assets/js/48.44794e44.js rename to assets/js/48.aa42c484.js index 2d8b6054a..033f68f43 100644 --- a/assets/js/48.44794e44.js +++ b/assets/js/48.aa42c484.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[48],{193:function(t,e,a){t.exports=a.p+"assets/img/getting-started-result-example.7820ee84.jpg"},534:function(t,e,a){"use strict";a.r(e);var s=a(4),n=Object(s.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"r-client"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#r-client"}},[t._v("#")]),t._v(" R Client")]),t._v(" "),e("h2",{attrs:{id:"useful-links"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#useful-links"}},[t._v("#")]),t._v(" Useful links")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Documentation"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Vignettes"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client",target:"_blank",rel:"noopener noreferrer"}},[t._v("Code Repository"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("for function documentation, use R's "),e("code",[t._v("?")]),t._v(" function or see the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("online documentation"),e("OutboundLink")],1)])]),t._v(" "),e("h2",{attrs:{id:"installation"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[t._v("#")]),t._v(" Installation")]),t._v(" "),e("p",[t._v("Before you install the R client module into your R environment, please make sure that you have at least R version 3.6. Older versions might also work, but were not tested.")]),t._v(" "),e("p",[t._v("Stable releases can be installed from CRAN:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("install.packages"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"openeo"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Installing the development version")]),t._v(" "),e("p",[t._v("If you want to install the development version, you can install from GitHub. It may contain more features, but may also be unstable.")]),t._v(" "),e("p",[t._v("You need to have the package 'devtools' installed. If it is not installed use "),e("code",[t._v('install.packages("devtools")')]),t._v(".")]),t._v(" "),e("p",[t._v("Now you can use "),e("code",[t._v("install_github")]),t._v(" from the devtools package to install the development version:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("devtools"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("::")]),t._v("install_github"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("repo"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Open-EO/openeo-r-client"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dependencies"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("TRUE")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" ref"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"develop"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("If this gives you an error, something went wrong with the installation so please check the requirements again.")])]),t._v(" "),e("p",[t._v("If you have troubles installing the package, feel free to "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" or leave an issue at the "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub project"),e("OutboundLink")],1),t._v(".")],1),t._v(" "),e("p",[t._v("Now that the installation was successfully finished, we can load the package and connect to openEO compliant back-ends.\nIn the following chapters we quickly walk through the main features of the R client.")]),t._v(" "),e("h2",{attrs:{id:"exploring-a-back-end"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-back-end"}},[t._v("#")]),t._v(" Exploring a back-end")]),t._v(" "),e("p",[t._v("If you do not know an openEO back-end that you want to connect to yet, you can have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(", to find all known back-ends with information on their capabilities.")]),t._v(" "),e("p",[t._v("For this tutorial we will use the openEO instance of Google Earth Engine, which is available at "),e("code",[t._v("https://earthengine.openeo.org")]),t._v(".\nNote that the code snippets in this guide work the same way for the other back-ends listed in the openEO Hub. Just the collection identifier and band names might differ.")]),t._v(" "),e("p",[t._v("First we need to establish a connection to the back-end.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\ngee "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("host "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The object stored as variable "),e("code",[t._v("gee")]),t._v(" is a connection and resembles the "),e("code",[t._v("OpenEOClient")]),t._v(" - an object, that bundles all information and functions to interact with the openEO back-end. It can be used explicitly in all of the functions to determine which connection has to be used (usually parameter "),e("code",[t._v("con")]),t._v("). If only one connection is active, then you can omit the parameter, because the last active connection is always stored in a package environment and used if no specific connection was present (see "),e("code",[t._v("?active_connection")]),t._v(" in the package documentation).")]),t._v(" "),e("p",[t._v("The capabilities of the back-end and the collections are generally publicly available, unless the data collections are proprietary and licensing issues prevent the back-end provider from publishing the collection. For the publicly available information you do not need to have an account on the back-end for reading them.")]),t._v(" "),e("h3",{attrs:{id:"collections"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("p",[t._v("Collections represent the basic data the back-end provides (e.g. Sentinel 1 collection) and are therefore often used as input data for job executions ("),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("more info on collections")]),t._v(").\nWith the following code snippet you can get all available collection names and their description. The collection list and its entries have their own implementations of the "),e("code",[t._v("print")]),t._v(" function. The collection list object is coerced into a "),e("code",[t._v("data.frame")]),t._v(" only for printing purposes and the collection for the collection some key information are printed.")],1),t._v(" "),e("p",[t._v("To get the collection list can be indexed by the collections ID to get the more details about the overview information. With the "),e("code",[t._v("describe_collection")]),t._v(" function you can get an even more detailed information about the collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get the collection list")]),t._v("\ncollections "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# print an overview of the available collections (printed as data.frame or tibble)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# to print more of the reduced overview metadata")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("`COPERNICUS"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("/")]),t._v("S2`"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# Dictionary of the full metadata of the "COPERNICUS/S2" collection (dict)')]),t._v("\ns2 "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" describe_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S2"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the collection entry from the list, e.g. collections$`COPERNICUS/S2`")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("s2"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("In general all metadata objects are based on lists, so you can use "),e("code",[t._v("str()")]),t._v(" to get the structure of the list and address fields by the "),e("code",[t._v("$")]),t._v(" operator.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("If the package is used with RStudio the metadata can also be nicely rendered as a web page in the viewer panel by running "),e("code",[t._v('collection_viewer(x="COPERNICUS/S2")')]),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("p",[t._v("Processes in openEO are tasks that can be applied to (EO) data.\nThe input of a process might be the output of another process, so that several connected processes form a new (user-defined) process itself.\nTherefore, a process resembles the smallest unit of task descriptions in openEO ("),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#processes"}},[t._v("more details on processes")]),t._v(").\nThe following code snippet shows how to get the available processes.")],1),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes with full metadata")]),t._v("\nprocesses "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# List of available openEO processes by identifiers (string)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("names"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# print metadata of the process with ID "load_collection"')]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("list_processes()")]),t._v(" method returns a list of process metadata objects that the back-end provides.\nEach process list entry is a more complex list object (called 'ProcessInfo') and contains the process identifier and additional metadata about the process, such as expected arguments and return types.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("As for the collection, processes can also be rendered as a web page in the viewer panel, if RStudio is used. In order to open the viewer use "),e("code",[t._v("process_viewer()")]),t._v(" with either a particular process ("),e("code",[t._v('process_viewer("load_collection")')]),t._v(") or you can pass on all processes ("),e("code",[t._v("process_viewer(processes)")]),t._v("). When all processes are chosen, there is also a search bar and a category tree.")])]),t._v(" "),e("p",[t._v("For other graphical overviews of the openEO processes, there is an "),e("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("online documentation")]),t._v(" for general process descriptions and the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" for back-end specific process descriptions.")],1),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above, authentication is usually not necessary, since we only fetch general information about the back-end.\nTo run your own jobs at the back-end or to access job results, you need to authenticate at the back-end.")]),t._v(" "),e("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out and how to obtain login credentials for yourself and your 'openeo' installation in R.")]),t._v(" "),e("p",[t._v("You can also have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.\nFor Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),e("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),e("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due to better security mechanisms implemented in the OpenID Connect protocol.")])]),t._v(" "),e("h3",{attrs:{id:"basic-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#basic-authentication"}},[t._v("#")]),t._v(" Basic Authentication")]),t._v(" "),e("p",[t._v("The Basic authentication method is a common way of authenticate HTTP requests given username and password.")]),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via Basic authentication:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("login_type"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"basic"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n user"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"user"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n password"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"password"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("You can get username and password here: "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-earthengine-driver#demo",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/Open-EO/openeo-earthengine-driver#demo"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("After successfully calling the "),e("code",[t._v("login")]),t._v(' method with the "basic" login type, you are logged into the back-end with your account.\nThis means, that every call that comes after that via the connection variable is executed by your user account.')]),t._v(" "),e("h3",{attrs:{id:"openid-connect-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("For Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),e("p",[t._v("The OIDC ("),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect"),e("OutboundLink")],1),t._v(") authentication can be used to authenticate via an external service.")]),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication if the back-end supports the simplified authentication method:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication if the simplified authentication method doesn't work and you need to provide a client ID and secret:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get supported OIDC providers which the back-end supports")]),t._v("\noidc_providers "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_oidc_providers"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nlogin"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("provider "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" oidc_providers"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("some_provider"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n config "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n client_id"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"..."')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n secret "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"..."')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Calling this method opens your system web browser, with which you can authenticate yourself on the back-end authentication system.\nAfter that the website will give you the instructions to go back to the R client, where your connection has logged your account in.\nThis means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h2",{attrs:{id:"creating-a-user-defined-process"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-user-defined-process"}},[t._v("#")]),t._v(" Creating a (user-defined) process")]),t._v(" "),e("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.")]),t._v(" "),e("p",[t._v("First we need to create a process builder object that carries all the available predefined openEO processes of the connected back-end as attached R functions with the parameters stated in the process metadata.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("p "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The functions of the builder return process nodes, which represent a particular result in the workflow. As one of the first steps we need to select the source data collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n id "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S1_GRD"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n spatial_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("west "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" south "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" east "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.65")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" north "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.35")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n temporal_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-03-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-04-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n bands"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VV"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VH"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("This results in a process node that represents a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#spatial-datacubes"}},[e("code",[t._v("datacube")])]),t._v(' and contains the "COPERNICUS/S1_GRD" data restricted to the given spatial extent, the given temporal extent and the given bands .')],1),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Sample Data Retrieval")]),t._v(" "),e("p",[t._v("In order to get a better understanding about the processing mechanisms and the data structures used in openEO Platform, it helps to check the actual data from time to time.\nThe function "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/reference/get_sample.html",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("get_sample")]),e("OutboundLink")],1),t._v(" aids the user in downloading data for a very small spatial extent. It is automatically loaded into R so that you can directly inspect it with "),e("code",[t._v("stars")]),t._v('.\nRead the vignette on "'),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/sample_data.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Sample Data Retrieval"),e("OutboundLink")],1),t._v('" for more details.')])]),t._v(" "),e("p",[t._v("Having the input data ready, we want to apply a process on the datacube.\nTherefore, we can call the process directly on the datacube object, which then returns a datacube with the process applied.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("min_reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("function")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("context"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" \n return"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("min"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n\nreduced "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("reduce_dimension"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" min_reducer"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dimension"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"t"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The datacube is now reduced by the time dimension named "),e("code",[t._v("t")]),t._v(', by taking the minimum value of the timeseries values. Now the datacube has no time dimension left. Other so called "reducer" processes exist, e.g. for computing maximum and mean values.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Everything applied to the datacube at this point is neither executed locally on your machine nor executed on the back-end.\nIt just defines the input data and process chain the back-end needs to apply when it sends the datacube to the back-end and executes it there.\nHow this can be done is the topic of the next chapter.")])]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("formats "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_file_formats"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nresult "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" reduced"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" format "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" formats"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("output"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("`GTIFF"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("ZIP`"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The first line retrieves the back-ends offered input and output formats. The second line creates the result node, which stores the data as a zipped GeoTiff.")]),t._v(" "),e("h2",{attrs:{id:"batch-job-management"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-management"}},[t._v("#")]),t._v(" Batch Job Management")]),t._v(" "),e("p",[t._v("After you have finished working on your (user-defined) process, we can now send it to the back-end and start the execution.\nIn openEO, an execution of a (user-defined) process is called a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[t._v("(batch) job")]),t._v(".\nTherefore, we need to create a job at the back-end using our datacube, giving it the title "),e("code",[t._v("Example Title")]),t._v(".")],1),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("graph"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("title "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Example Title"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("create_job")]),t._v(" method sends all necessary information to the back-end and creates a new job, which gets returned.\nAfter this, the job is just created, but has not started the execution at the back-end yet.\nIt needs to be queued for processing explicitly:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("start_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("After the job was executed, status updates can be fetched by using the "),e("code",[t._v("list_jobs()")]),t._v(" function. This function returns a list of job descriptions, which can be indexed with the jobs ID to limit the search results. But remember that only "),e("code",[t._v("list_jobs()")]),t._v(" refreshes this list. So, to monitor a job you have to iteratively call the job ("),e("code",[t._v("describe_job()")]),t._v(") or the job list "),e("code",[t._v("list_jobs()")]),t._v(".")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("jobs "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_jobs"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njobs "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# printed as a tibble or data.frame, but the object is a list")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the job id (in this example 'cZ2ND0Z5nhBFNQFq') as index to get a particular job overview")]),t._v("\njobs"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("cZ2ND0Z5nhBFNQFq\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# alternatively request detailed information about the job")]),t._v("\ndescribe_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("When the job is finished, calling "),e("code",[t._v("download_results()")]),t._v(" will download the results of a job. Using "),e("code",[t._v("list_results()")]),t._v(" will return an overview about the created files and their download link or it states the error message, in case of an error.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# list the processed results")]),t._v("\nlist_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# download all the files into a folder on the file system")]),t._v("\ndownload_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" folder "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"/some/folder/on/filesystem"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("The printing behavior and the actual data structure might differ!")])]),t._v(" "),e("p",[t._v("Now you know the general workflow of job executions.")]),t._v(" "),e("h2",{attrs:{id:"full-example"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#full-example"}},[t._v("#")]),t._v(" Full Example")]),t._v(" "),e("p",[t._v("In this chapter we will show a full example of an earth observation use case using the R client and the Google Earth Engine back-end.\nInstead of batch job processing, we compute the image synchronously. Synchronous processing means the result is directly returned in the response, which usually works only for smaller amounts of data.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Use Case")]),t._v(" "),e("p",[t._v("We want to produce a monthly RGB composite of Sentinel 1 backscatter data over the area of Vienna, Austria for three\nmonths in 2017. This can be used for classification and crop monitoring.")])]),t._v(" "),e("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nlibrary"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("tibble"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nuser "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"group7"')]),t._v("\npwd "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"test123"')]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# connect to the back-end and login either via explicit call of login, or use your credentials in the connect function")]),t._v("\ngee "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("host "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("user "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" user"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("password "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" pwd"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("login_type "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"basic"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get the process collection to use the predefined processes of the back-end")]),t._v("\np "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get the collection list to get easier access to the collection ids, via auto completion")]),t._v("\ncollections "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get the formats")]),t._v("\nformats "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" 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We need to rename at least the label of one dimension, ")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# because otherwise identity of the data cubes is assumed. The bands dimension consists ")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# only of one label, so we can rename this to be able to merge those data cubes.")]),t._v("\nmarch_renamed "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("rename_labels"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" march_reduced"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n dimension "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"bands"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n target "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" c"),e("span",{pre:!0,attrs:{class:"token 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"),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("255")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# export shall be format PNG")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# look at the format description")]),t._v("\nformats"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("output"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("PNG\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# store the results using the format and set the create options")]),t._v("\nresult "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token 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string"}},[t._v('"G"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("blue"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"B"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# create a job")]),t._v("\njob "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("graph "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" title "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"S1 Example R"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" description "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Getting Started example on openeo.org for R-client"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# then start the processing of the job and turn on logging (messages that are captured on the back-end during the process execution)")]),t._v("\nstart_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" log "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("TRUE")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Now the resulting PNG file of the RGB backscatter composite is stored as a PNG file in the current working directory. It looks like this:")]),t._v(" "),e("p",[e("img",{attrs:{src:a(193),alt:"RGB composite"}})]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("The "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client/blob/master/examples/S1-example.R",target:"_blank",rel:"noopener noreferrer"}},[t._v("source code"),e("OutboundLink")],1),t._v(" of the example can also be found on the R client repository on Github.")])]),t._v(" "),e("h2",{attrs:{id:"user-defined-functions"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-functions"}},[t._v("#")]),t._v(" User Defined Functions")]),t._v(" "),e("p",[t._v("If your use case can not be accomplished with the "),e("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("default processes")]),t._v(" of openEO, you can define a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#user-defined-function-udf"}},[t._v("user defined function")]),t._v(".")],1),t._v(" "),e("p",[t._v("In general the processing workflow works by uploading the Python or R script into the users file directory on the back-end and reference the script via its URL or by its relational name (e.g. "),e("code",[t._v("/scripts/script1.R")]),t._v(") in the function "),e("code",[t._v("run_udf")]),t._v(". The latter function is a predefined openEO process that the back-end might provide, if UDFs are supported.")]),t._v(" "),e("p",[t._v("Find out more about UDFs in the respective "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-udf",target:"_blank",rel:"noopener noreferrer"}},[t._v("Python UDF"),e("OutboundLink")],1),t._v(" and "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf",target:"_blank",rel:"noopener noreferrer"}},[t._v("R UDF"),e("OutboundLink")],1),t._v(" repositories with their documentation and examples.")])])}),[],!1,null,null,null);e.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[48],{193:function(t,e,a){t.exports=a.p+"assets/img/getting-started-result-example.7820ee84.jpg"},535:function(t,e,a){"use strict";a.r(e);var s=a(4),n=Object(s.a)({},(function(){var t=this,e=t._self._c;return e("ContentSlotsDistributor",{attrs:{"slot-key":t.$parent.slotKey}},[e("h1",{attrs:{id:"r-client"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#r-client"}},[t._v("#")]),t._v(" R Client")]),t._v(" "),e("h2",{attrs:{id:"useful-links"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#useful-links"}},[t._v("#")]),t._v(" Useful links")]),t._v(" "),e("ul",[e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Documentation"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/",target:"_blank",rel:"noopener noreferrer"}},[t._v("Vignettes"),e("OutboundLink")],1)]),t._v(" "),e("li",[e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client",target:"_blank",rel:"noopener noreferrer"}},[t._v("Code Repository"),e("OutboundLink")],1)]),t._v(" "),e("li",[t._v("for function documentation, use R's "),e("code",[t._v("?")]),t._v(" function or see the "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/index.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("online documentation"),e("OutboundLink")],1)])]),t._v(" "),e("h2",{attrs:{id:"installation"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#installation"}},[t._v("#")]),t._v(" Installation")]),t._v(" "),e("p",[t._v("Before you install the R client module into your R environment, please make sure that you have at least R version 3.6. Older versions might also work, but were not tested.")]),t._v(" "),e("p",[t._v("Stable releases can be installed from CRAN:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("install.packages"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"openeo"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Installing the development version")]),t._v(" "),e("p",[t._v("If you want to install the development version, you can install from GitHub. It may contain more features, but may also be unstable.")]),t._v(" "),e("p",[t._v("You need to have the package 'devtools' installed. If it is not installed use "),e("code",[t._v('install.packages("devtools")')]),t._v(".")]),t._v(" "),e("p",[t._v("Now you can use "),e("code",[t._v("install_github")]),t._v(" from the devtools package to install the development version:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("devtools"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("::")]),t._v("install_github"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("repo"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Open-EO/openeo-r-client"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dependencies"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token boolean"}},[t._v("TRUE")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" ref"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"develop"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("If this gives you an error, something went wrong with the installation so please check the requirements again.")])]),t._v(" "),e("p",[t._v("If you have troubles installing the package, feel free to "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" or leave an issue at the "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client/issues",target:"_blank",rel:"noopener noreferrer"}},[t._v("GitHub project"),e("OutboundLink")],1),t._v(".")],1),t._v(" "),e("p",[t._v("Now that the installation was successfully finished, we can load the package and connect to openEO compliant back-ends.\nIn the following chapters we quickly walk through the main features of the R client.")]),t._v(" "),e("h2",{attrs:{id:"exploring-a-back-end"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#exploring-a-back-end"}},[t._v("#")]),t._v(" Exploring a back-end")]),t._v(" "),e("p",[t._v("If you do not know an openEO back-end that you want to connect to yet, you can have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(", to find all known back-ends with information on their capabilities.")]),t._v(" "),e("p",[t._v("For this tutorial we will use the openEO instance of Google Earth Engine, which is available at "),e("code",[t._v("https://earthengine.openeo.org")]),t._v(".\nNote that the code snippets in this guide work the same way for the other back-ends listed in the openEO Hub. Just the collection identifier and band names might differ.")]),t._v(" "),e("p",[t._v("First we need to establish a connection to the back-end.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\ngee "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("host "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The object stored as variable "),e("code",[t._v("gee")]),t._v(" is a connection and resembles the "),e("code",[t._v("OpenEOClient")]),t._v(" - an object, that bundles all information and functions to interact with the openEO back-end. It can be used explicitly in all of the functions to determine which connection has to be used (usually parameter "),e("code",[t._v("con")]),t._v("). If only one connection is active, then you can omit the parameter, because the last active connection is always stored in a package environment and used if no specific connection was present (see "),e("code",[t._v("?active_connection")]),t._v(" in the package documentation).")]),t._v(" "),e("p",[t._v("The capabilities of the back-end and the collections are generally publicly available, unless the data collections are proprietary and licensing issues prevent the back-end provider from publishing the collection. For the publicly available information you do not need to have an account on the back-end for reading them.")]),t._v(" "),e("h3",{attrs:{id:"collections"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#collections"}},[t._v("#")]),t._v(" Collections")]),t._v(" "),e("p",[t._v("Collections represent the basic data the back-end provides (e.g. Sentinel 1 collection) and are therefore often used as input data for job executions ("),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#eo-data-collections"}},[t._v("more info on collections")]),t._v(").\nWith the following code snippet you can get all available collection names and their description. The collection list and its entries have their own implementations of the "),e("code",[t._v("print")]),t._v(" function. The collection list object is coerced into a "),e("code",[t._v("data.frame")]),t._v(" only for printing purposes and the collection for the collection some key information are printed.")],1),t._v(" "),e("p",[t._v("To get the collection list can be indexed by the collections ID to get the more details about the overview information. With the "),e("code",[t._v("describe_collection")]),t._v(" function you can get an even more detailed information about the collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get the collection list")]),t._v("\ncollections "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# print an overview of the available collections (printed as data.frame or tibble)")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# to print more of the reduced overview metadata")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("collections"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("`COPERNICUS"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("/")]),t._v("S2`"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# Dictionary of the full metadata of the "COPERNICUS/S2" collection (dict)')]),t._v("\ns2 "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" describe_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S2"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the collection entry from the list, e.g. collections$`COPERNICUS/S2`")]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("s2"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("In general all metadata objects are based on lists, so you can use "),e("code",[t._v("str()")]),t._v(" to get the structure of the list and address fields by the "),e("code",[t._v("$")]),t._v(" operator.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("If the package is used with RStudio the metadata can also be nicely rendered as a web page in the viewer panel by running "),e("code",[t._v('collection_viewer(x="COPERNICUS/S2")')]),t._v(".")])]),t._v(" "),e("h3",{attrs:{id:"processes"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[t._v("#")]),t._v(" Processes")]),t._v(" "),e("p",[t._v("Processes in openEO are tasks that can be applied to (EO) data.\nThe input of a process might be the output of another process, so that several connected processes form 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punctuation"}},[t._v("(")]),t._v("names"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v('# print metadata of the process with ID "load_collection"')]),t._v("\nprint"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("processes"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("list_processes()")]),t._v(" method returns a list of process metadata objects that the back-end provides.\nEach process list entry is a more complex list object (called 'ProcessInfo') and contains the process identifier and additional metadata about the process, such as expected arguments and return types.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("As for the collection, processes can also be rendered as a web page in the viewer panel, if RStudio is used. In order to open the viewer use "),e("code",[t._v("process_viewer()")]),t._v(" with either a particular process ("),e("code",[t._v('process_viewer("load_collection")')]),t._v(") or you can pass on all processes ("),e("code",[t._v("process_viewer(processes)")]),t._v("). When all processes are chosen, there is also a search bar and a category tree.")])]),t._v(" "),e("p",[t._v("For other graphical overviews of the openEO processes, there is an "),e("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[t._v("online documentation")]),t._v(" for general process descriptions and the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" for back-end specific process descriptions.")],1),t._v(" "),e("h2",{attrs:{id:"authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#authentication"}},[t._v("#")]),t._v(" Authentication")]),t._v(" "),e("p",[t._v("In the code snippets above, authentication is usually not necessary, since we only fetch general information about the back-end.\nTo run your own jobs at the back-end or to access job results, you need to authenticate at the back-end.")]),t._v(" "),e("p",[t._v("Depending on the back-end, there might be two different approaches to authenticate.\nYou need to inform yourself at your back-end provider of choice, which authentication approach you have to carry out and how to obtain login credentials for yourself and your 'openeo' installation in R.")]),t._v(" "),e("p",[t._v("You can also have a look at the "),e("a",{attrs:{href:"https://hub.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[t._v("openEO Hub"),e("OutboundLink")],1),t._v(" to see the available authentication types of the back-ends.\nFor Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Recommendation")]),t._v(" "),e("p",[t._v("The Google Earth Engine implementation for openEO only supports Basic authentication, but generally the preferred authentication method is "),e("a",{attrs:{href:"#openid-connect-authentication"}},[t._v("OpenID Connect")]),t._v(" due to better security mechanisms implemented in the OpenID Connect protocol.")])]),t._v(" "),e("h3",{attrs:{id:"basic-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#basic-authentication"}},[t._v("#")]),t._v(" Basic Authentication")]),t._v(" "),e("p",[t._v("The Basic authentication method is a common way of authenticate HTTP requests given username and password.")]),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via Basic authentication:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("login_type"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"basic"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n user"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"user"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n password"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"password"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("TIP")]),t._v(" "),e("p",[t._v("You can get username and password here: "),e("a",{attrs:{href:"https://github.com/Open-EO/openeo-earthengine-driver#demo",target:"_blank",rel:"noopener noreferrer"}},[t._v("https://github.com/Open-EO/openeo-earthengine-driver#demo"),e("OutboundLink")],1)])]),t._v(" "),e("p",[t._v("After successfully calling the "),e("code",[t._v("login")]),t._v(' method with the "basic" login type, you are logged into the back-end with your account.\nThis means, that every call that comes after that via the connection variable is executed by your user account.')]),t._v(" "),e("h3",{attrs:{id:"openid-connect-authentication"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#openid-connect-authentication"}},[t._v("#")]),t._v(" OpenID Connect Authentication")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("For Google Earth Engine, only "),e("a",{attrs:{href:"#basic-authentication"}},[t._v("Basic Authentication")]),t._v(" is supported at the moment.")])]),t._v(" "),e("p",[t._v("The OIDC ("),e("a",{attrs:{href:"https://openid.net/connect/",target:"_blank",rel:"noopener noreferrer"}},[t._v("OpenID Connect"),e("OutboundLink")],1),t._v(") authentication can be used to authenticate via an external service.")]),t._v(" "),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication if the back-end supports the simplified authentication method:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("login"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The following code snippet shows how to log in via OIDC authentication if the simplified authentication method doesn't work and you need to provide a client ID and secret:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# get supported OIDC providers which the back-end supports")]),t._v("\noidc_providers "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_oidc_providers"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nlogin"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("provider "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" oidc_providers"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("some_provider"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n config "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n client_id"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"..."')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n secret "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"..."')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("Calling this method opens your system web browser, with which you can authenticate yourself on the back-end authentication system.\nAfter that the website will give you the instructions to go back to the R client, where your connection has logged your account in.\nThis means, that every call that comes after that via the connection variable is executed by your user account.")]),t._v(" "),e("h2",{attrs:{id:"creating-a-user-defined-process"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#creating-a-user-defined-process"}},[t._v("#")]),t._v(" Creating a (user-defined) process")]),t._v(" "),e("p",[t._v("Now that we know how to discover the back-end and how to authenticate, lets continue by creating a new batch job to process some data.")]),t._v(" "),e("p",[t._v("First we need to create a process builder object that carries all the available predefined openEO processes of the connected back-end as attached R functions with the parameters stated in the process metadata.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("p "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" processes"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The functions of the builder return process nodes, which represent a particular result in the workflow. As one of the first steps we need to select the source data collection.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("datacube "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("load_collection"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("\n id "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"COPERNICUS/S1_GRD"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n spatial_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("list"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("west "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" south "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.06")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" east "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("16.65")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" north "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token number"}},[t._v("48.35")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n temporal_extent"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-03-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"2017-04-01"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("\n bands"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("c"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VV"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"VH"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("This results in a process node that represents a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#spatial-datacubes"}},[e("code",[t._v("datacube")])]),t._v(' and contains the "COPERNICUS/S1_GRD" data restricted to the given spatial extent, the given temporal extent and the given bands .')],1),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Sample Data Retrieval")]),t._v(" "),e("p",[t._v("In order to get a better understanding about the processing mechanisms and the data structures used in openEO Platform, it helps to check the actual data from time to time.\nThe function "),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/reference/get_sample.html",target:"_blank",rel:"noopener noreferrer"}},[e("code",[t._v("get_sample")]),e("OutboundLink")],1),t._v(" aids the user in downloading data for a very small spatial extent. It is automatically loaded into R so that you can directly inspect it with "),e("code",[t._v("stars")]),t._v('.\nRead the vignette on "'),e("a",{attrs:{href:"https://open-eo.github.io/openeo-r-client/articles/sample_data.html",target:"_blank",rel:"noopener noreferrer"}},[t._v("Sample Data Retrieval"),e("OutboundLink")],1),t._v('" for more details.')])]),t._v(" "),e("p",[t._v("Having the input data ready, we want to apply a process on the datacube.\nTherefore, we can call the process directly on the datacube object, which then returns a datacube with the process applied.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("min_reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token keyword"}},[t._v("function")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("context"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("{")]),t._v(" \n return"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("min"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" data"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("}")]),t._v("\n\nreduced "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("reduce_dimension"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" datacube"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" reducer "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" min_reducer"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" dimension"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"t"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The datacube is now reduced by the time dimension named "),e("code",[t._v("t")]),t._v(', by taking the minimum value of the timeseries values. Now the datacube has no time dimension left. Other so called "reducer" processes exist, e.g. for computing maximum and mean values.')]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("Everything applied to the datacube at this point is neither executed locally on your machine nor executed on the back-end.\nIt just defines the input data and process chain the back-end needs to apply when it sends the datacube to the back-end and executes it there.\nHow this can be done is the topic of the next chapter.")])]),t._v(" "),e("p",[t._v("After applying all processes you want to execute, we need to tell the back-end to export the datacube, for example as GeoTiff:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("formats "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_file_formats"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nresult "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" p"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("save_result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("data "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" reduced"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" format "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" formats"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("output"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("`GTIFF"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("-")]),t._v("ZIP`"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The first line retrieves the back-ends offered input and output formats. The second line creates the result node, which stores the data as a zipped GeoTiff.")]),t._v(" "),e("h2",{attrs:{id:"batch-job-management"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#batch-job-management"}},[t._v("#")]),t._v(" Batch Job Management")]),t._v(" "),e("p",[t._v("After you have finished working on your (user-defined) process, we can now send it to the back-end and start the execution.\nIn openEO, an execution of a (user-defined) process is called a "),e("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html#data-processing-modes"}},[t._v("(batch) job")]),t._v(".\nTherefore, we need to create a job at the back-end using our datacube, giving it the title "),e("code",[t._v("Example Title")]),t._v(".")],1),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" create_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("graph"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v("result"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("title "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"Example Title"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("The "),e("code",[t._v("create_job")]),t._v(" method sends all necessary information to the back-end and creates a new job, which gets returned.\nAfter this, the job is just created, but has not started the execution at the back-end yet.\nIt needs to be queued for processing explicitly:")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("start_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("After the job was executed, status updates can be fetched by using the "),e("code",[t._v("list_jobs()")]),t._v(" function. This function returns a list of job descriptions, which can be indexed with the jobs ID to limit the search results. But remember that only "),e("code",[t._v("list_jobs()")]),t._v(" refreshes this list. So, to monitor a job you have to iteratively call the job ("),e("code",[t._v("describe_job()")]),t._v(") or the job list "),e("code",[t._v("list_jobs()")]),t._v(".")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("jobs "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" list_jobs"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\njobs "),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# printed as a tibble or data.frame, but the object is a list")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# or use the job id (in this example 'cZ2ND0Z5nhBFNQFq') as index to get a particular job overview")]),t._v("\njobs"),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("$")]),t._v("cZ2ND0Z5nhBFNQFq\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# alternatively request detailed information about the job")]),t._v("\ndescribe_job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("p",[t._v("When the job is finished, calling "),e("code",[t._v("download_results()")]),t._v(" will download the results of a job. Using "),e("code",[t._v("list_results()")]),t._v(" will return an overview about the created files and their download link or it states the error message, in case of an error.")]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# list the processed results")]),t._v("\nlist_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# download all the files into a folder on the file system")]),t._v("\ndownload_results"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("job "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" job"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v(" folder "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"/some/folder/on/filesystem"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n")])])]),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Note")]),t._v(" "),e("p",[t._v("The printing behavior and the actual data structure might differ!")])]),t._v(" "),e("p",[t._v("Now you know the general workflow of job executions.")]),t._v(" "),e("h2",{attrs:{id:"full-example"}},[e("a",{staticClass:"header-anchor",attrs:{href:"#full-example"}},[t._v("#")]),t._v(" Full Example")]),t._v(" "),e("p",[t._v("In this chapter we will show a full example of an earth observation use case using the R client and the Google Earth Engine back-end.\nInstead of batch job processing, we compute the image synchronously. Synchronous processing means the result is directly returned in the response, which usually works only for smaller amounts of data.")]),t._v(" "),e("div",{staticClass:"custom-block tip"},[e("p",{staticClass:"custom-block-title"},[t._v("Use Case")]),t._v(" "),e("p",[t._v("We want to produce a monthly RGB composite of Sentinel 1 backscatter data over the area of Vienna, Austria for three\nmonths in 2017. This can be used for classification and crop monitoring.")])]),t._v(" "),e("p",[t._v("In the following code example, we use inline code comments to describe what we are doing.")]),t._v(" "),e("div",{staticClass:"custom-block warning"},[e("p",{staticClass:"custom-block-title"},[t._v("WARNING")]),t._v(" "),e("p",[t._v("The username and password in the example above work at the time of writing, but may be invalid at the time you read this. Please "),e("RouterLink",{attrs:{to:"/contact.html"}},[t._v("contact us")]),t._v(" for credentials.")],1)]),t._v(" "),e("div",{staticClass:"language-r extra-class"},[e("pre",{pre:!0,attrs:{class:"language-r"}},[e("code",[t._v("library"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("openeo"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\nlibrary"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("tibble"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(")")]),t._v("\n\nuser "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"group7"')]),t._v("\npwd "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"test123"')]),t._v("\n\n"),e("span",{pre:!0,attrs:{class:"token comment"}},[t._v("# connect to the back-end and login either via explicit call of login, or use your credentials in the connect function")]),t._v("\ngee "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" connect"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v("(")]),t._v("host "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"https://earthengine.openeo.org"')]),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("user "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" user"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("password "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" pwd"),e("span",{pre:!0,attrs:{class:"token punctuation"}},[t._v(",")]),t._v("login_type "),e("span",{pre:!0,attrs:{class:"token operator"}},[t._v("=")]),t._v(" "),e("span",{pre:!0,attrs:{class:"token string"}},[t._v('"basic"')]),e("span",{pre:!0,attrs:{class:"token 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Keep in mind that you don't need to implement all endpoints in the first iteration and that you can specify in the Capabilities, which endpoints you are supporting.")],1),e._v(" "),t("p",[e._v("For example, you could start by implementing the following endpoints in the first iteration:")]),e._v(" "),t("ul",[t("li",[e._v("Well-Known Document: "),t("code",[e._v("GET /.well-known/openeo")])]),e._v(" "),t("li",[e._v("Capabilities: "),t("code",[e._v("GET /")]),e._v(" and "),t("code",[e._v("GET /file_formats")])]),e._v(" "),t("li",[e._v("Data discovery: "),t("code",[e._v("GET /collections")]),e._v(" and "),t("code",[e._v("GET /collections/{collection_id}")])]),e._v(" "),t("li",[e._v("Process discovery: "),t("code",[e._v("GET /processes")])]),e._v(" "),t("li",[e._v("Data processing: "),t("code",[e._v("POST /result")])]),e._v(" "),t("li",[e._v("Authentication (if required): "),t("code",[e._v("GET /credentials/basic")])])]),e._v(" "),t("p",[e._v("A more detailed recommendation about the endpoints and processes to implement can be found in the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/"}},[e._v("API and Processes profiles")]),e._v(".")],1),e._v(" "),t("p",[e._v("Afterwards you can already start experimenting with your first process graphs and process EO data with our client libraries on your back-end.")]),e._v(" "),t("p",[t("em",[e._v("More information will follow soon, for example about back-end compliance testing.")])])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[78],{520:function(e,t,o){"use strict";o.r(t);var r=o(4),n=Object(r.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"getting-started-for-service-back-end-providers"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#getting-started-for-service-back-end-providers"}},[e._v("#")]),e._v(" Getting started for service (back-end) providers")]),e._v(" "),t("p",[e._v("As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a standardized interface you may want to implement a driver for openEO.")]),e._v(" "),t("p",[e._v("First of all, you should go through the list of "),t("a",{attrs:{href:"https://github.com/Open-EO",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO repositories"),t("OutboundLink")],1),e._v(' and check whether there is already a back-end driver that suits your needs. In this case you don\'t need to develop your own driver, but "only" need to ingest your data, adopt your required processes and set up the infrastructure. Please follow the documentation for the individual driver you want to use.')]),e._v(" "),t("p",[e._v("If your preferred technology has no back-end driver yet, you may consider writing your own driver. All software written for openEO should follow the "),t("RouterLink",{attrs:{to:"/documentation/software-guidelines.html"}},[e._v("software development guidelines")]),e._v(".")],1),e._v(" "),t("p",[e._v("You certainly need to understand the "),t("RouterLink",{attrs:{to:"/documentation/1.0/glossary.html"}},[e._v("glossary")]),e._v(", the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/arch.html"}},[e._v("architecture")]),e._v(" of openEO and the concepts behind "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#section/Processes"}},[e._v("processes")]),e._v(" and "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#section/Processes/Process-Graphs"}},[e._v("process graphs")]),e._v(". This helps you read and understand the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html"}},[e._v("API specification")]),e._v(". Technical API related documents like "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#section/Cross-Origin-Resource-Sharing-(CORS)"}},[e._v("CORS")]),e._v(" and "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#section/API-Principles/Error-Handling"}},[e._v("error handing")]),e._v(" should be read, too.")],1),e._v(" "),t("p",[e._v("If you do not want to start from scratch, you could try to generate a server stub from the "),t("a",{attrs:{href:"https://www.openapis.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenAPI 3.0"),t("OutboundLink")],1),e._v("-based "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html"}},[e._v("API specification")]),e._v(" with the "),t("a",{attrs:{href:"https://github.com/OpenAPITools/openapi-generator",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenAPI Generator"),t("OutboundLink")],1),e._v(".\nIf you are using Python to implement your driver you may reuse some common modules of the existing driver implementations:")],1),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python Driver Commons"),t("OutboundLink")],1)])]),e._v(" "),t("p",[e._v("You can implement a back-end in iterations. It is recommended to start by implementing the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Capabilities"}},[e._v("Capabilities")]),e._v(" microservice. "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/EO-Data-Discovery"}},[e._v("EO Data Discovery")]),e._v(", "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Process-Discovery"}},[e._v("Process Discovery")]),e._v(" are important for the client libraries to be available, too. Afterwards you should implement "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Batch-Jobs"}},[e._v("Batch Job Management")]),e._v(" or "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#/paths/~1result/post"}},[e._v("synchronous data processing")]),e._v(". All other microservices can be added later and are not strictly required to run openEO services. Keep in mind that you don't need to implement all endpoints in the first iteration and that you can specify in the Capabilities, which endpoints you are supporting.")],1),e._v(" "),t("p",[e._v("For example, you could start by implementing the following endpoints in the first iteration:")]),e._v(" "),t("ul",[t("li",[e._v("Well-Known Document: "),t("code",[e._v("GET /.well-known/openeo")])]),e._v(" "),t("li",[e._v("Capabilities: "),t("code",[e._v("GET /")]),e._v(" and "),t("code",[e._v("GET /file_formats")])]),e._v(" "),t("li",[e._v("Data discovery: "),t("code",[e._v("GET /collections")]),e._v(" and "),t("code",[e._v("GET /collections/{collection_id}")])]),e._v(" "),t("li",[e._v("Process discovery: "),t("code",[e._v("GET /processes")])]),e._v(" "),t("li",[e._v("Data processing: "),t("code",[e._v("POST /result")])]),e._v(" "),t("li",[e._v("Authentication (if required): "),t("code",[e._v("GET /credentials/basic")])])]),e._v(" "),t("p",[e._v("A more detailed recommendation about the endpoints and processes to implement can be found in the "),t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/"}},[e._v("API and Processes profiles")]),e._v(".")],1),e._v(" "),t("p",[e._v("Afterwards you can already start experimenting with your first process graphs and process EO data with our client libraries on your back-end.")]),e._v(" "),t("p",[t("em",[e._v("More information will follow soon, for example about back-end compliance testing.")])])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/79.db0b8d8b.js b/assets/js/79.9744d354.js similarity index 99% rename from assets/js/79.db0b8d8b.js rename to assets/js/79.9744d354.js index ac068ad2a..51f9ea54e 100644 --- a/assets/js/79.db0b8d8b.js +++ b/assets/js/79.9744d354.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[79],{520:function(e,a,t){"use strict";t.r(a);var o=t(4),s=Object(o.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"performance-guide-for-openeo-backends"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-guide-for-openeo-backends"}},[e._v("#")]),e._v(" Performance guide for openEO backends")]),e._v(" "),a("h2",{attrs:{id:"openeo-api-vision-on-performance-scalability"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#openeo-api-vision-on-performance-scalability"}},[e._v("#")]),e._v(" openEO API vision on performance & scalability")]),e._v(" "),a("p",[e._v("Given that the openEO API only defines a web service, it can in no way ensure the performance or scalability of an\nimplementation. What it can do however, is avoiding API definitions that prevent an implementation from being efficient.\nWhen openEO was designed, performance was one of the key design drivers, so here we try to explain how that is achieved.")]),e._v(" "),a("h3",{attrs:{id:"bringing-the-processing-to-the-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#bringing-the-processing-to-the-data"}},[e._v("#")]),e._v(" Bringing the processing to the data")]),e._v(" "),a("p",[e._v("Two evolutions created the need for an API designed for performance and scalability: EO programmes like Copernicus that\npushed data volumes into the petabyte range, and a move towards ever larger cloud infrastructure and HPC processing capacity\nto analyze these volumes of data. OpenEO supports this by defining data access and processing into a single specification.\nAs a result, the openEO process graph allows the backend to choose any data access pattern that is optimal for\nthe processing that is to be executed, and the dataset that is to be read.")]),e._v(" "),a("p",[e._v("A popular example of such a case, is infrastructures that store the data on the same machines that do the processing.\nIn such a case, an openEO backend can choose to load and process the data directly on the machine that has the data stored.\nAnother example is adjusting and aligning the data chunks for the processing to the internal layout of the file format that\nstores the EO data. IO performance optimizations like this are only possible if the processing engine has deep knowledge\nof the data organization from storage system over networks to file formats.")]),e._v(" "),a("h3",{attrs:{id:"datacube-processing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#datacube-processing"}},[e._v("#")]),e._v(" Datacube processing")]),e._v(" "),a("p",[e._v("The datacube view that openEO uses as a model to represent the data as it is transformed by various processes also has\nimportant performance and scalability implications.")]),e._v(" "),a("p",[e._v("The easiest way to understand this is to contrast it with a more traditional 'product-based' view of building workflows.\nIn a product or file based workflow, a process operates on a set of input files and generates a set of output files. Many\nEO workflows have been written like this, but the consequence is that every process spends time on reading date into memory, and\nwriting it back to disk. Persistent storage is often the slowest component in a processing system, and thus these workflows\nspend a lot of time on IO. In the openEO specification, a process transforms one datacube into another datacube.\nBackend implementations are encouraged to avoid writing data to disk in between processing steps whenever possible. By\nkeeping the datacubes into memory, this avoids those costly IO operations.")]),e._v(" "),a("p",[e._v("Here it is important to note that openEO does not enforce or define how the datacube should look like on the backend. The\ndatacube can be a set of files, or arrays in memory distributed over a cluster. These choices are left to the\nbackend implementor, this guide only tries to highlight the possibilities.")]),e._v(" "),a("p",[e._v("For scalability, the openEO processes clearly define along which set of dimensions of the datacube they operate. When\na user writes a process graph, it should never instruct the backend to apply a black box algorithm or function on the\nentire datacube. For most algorithms, this is not necessary, and loading the complete datacube of a Copernicus mission at once\nis simply not possible. Hence, users run '"),a("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/api/reference.html#section/Processes/Process-Graphs",target:"_blank",rel:"noopener noreferrer"}},[e._v("user-defined (child) processes"),a("OutboundLink")],1),e._v("' over a 1-dimensional array, or even multidimensional arrays or 'chunks'\nof the datacube. Based on this information, the backend is able to define both a data access and processing strategy that is\noptimal for the given process graph.")]),e._v(" "),a("h2",{attrs:{id:"process-graph-execution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-execution"}},[e._v("#")]),e._v(" Process graph execution")]),e._v(" "),a("p",[e._v("Here we go a bit more into detail about how a backend evaluates a process graph. Again, this is not normative or\nmandated by the specification, but rather an explanation of one way to achieve optimal performance.")]),e._v(" "),a("p",[e._v("In general, process graphs are first analyzed as a whole before the actual processing starts. The analysis phase serves\nto reveal the optimal processing strategy and parameters.")]),e._v(" "),a("p",[e._v("These are a few examples of things that can be derived from a process graph and subsequent optimizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("Masking:")]),e._v(" when a raster dataset is masked with another raster or polygons, then often the loading of the datacube to\nwhich the mask is applied can be limited to unmasked values.")]),e._v(" "),a("li",[a("strong",[e._v("Vector filtering:")]),e._v(" various operations (aggregate_spatial, filter_spatial, mask_polygon) can restrict the datacube to a\nset of polygons, resulting in a rather sparse cube. Loading and processing of sparse cubes can be rather different from dense data cubes.")]),e._v(" "),a("li",[a("strong",[e._v("Resampling:")]),e._v(" resampling operations can allow data to be loaded from overviews rather than original resolution. Applying\nresampling and reprojection at load time can also be faster and save memory.")]),e._v(" "),a("li",[a("strong",[e._v("Multitemporal processing:")]),e._v(" many EO algorithms work over the temporal dimension rather than spatial dimensions. The\ntype of algorithm can be inferred from the process graph, allowing to adjust the processing strategy accordingly.")])]),e._v(" "),a("h2",{attrs:{id:"performance-faq"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-faq"}},[e._v("#")]),e._v(" Performance FAQ")]),e._v(" "),a("h3",{attrs:{id:"i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[e._v("#")]),e._v(" I have a highly optimized workflow, can openEO expose it?")]),e._v(" "),a("p",[e._v("Basically openEO can expose anything as a custom 'process'. If your algorithm can not be expressed as an openEO process graph,\nthen you can just let your backend advertise your custom process. By doing this, you still benefit from a lot of the standardized\nfeatures in the openEO API, and most tools for openEO will also support working with custom processes. This is fairly similar\nto exposing your process in other standards such as OGC Processes.")]),e._v(" "),a("p",[e._v("We do expect however that it is much more likely that your workflow can still reuse a few standardized processes. For instance,\nif it can be run on a geographical bounding box, the filter_bbox process would be a standardized way to specify that. Or\nperhaps it can operate on any set of Sentinel-2 products, in which case you might fit in a load_collection to let your users\ncustomize the input data. So usually, while you might start from a fully custom process, you'll notice that openEO offers\nways to gradually standardize your workflow further in a stepwise manner.")]),e._v(" "),a("h3",{attrs:{id:"can-openeo-be-as-fast-as-a-hand-written-workflow"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#can-openeo-be-as-fast-as-a-hand-written-workflow"}},[e._v("#")]),e._v(" Can openEO be as fast as a hand-written workflow?")]),e._v(" "),a("p",[e._v("This question depends on which backend implementation you compare to which workflow, so there is no generic answer.\nWe do note that for writing non-trivial workflows in a cloud environment, you require a combination of algorithmic programming skills\nand cloud engineering that usually requires a team of skilled persons spending (in total) multiple person months to years on the same workflow.\nSo if you know that many workflow patterns in the operational openEO backends have already been highly optimized, you may\nwant to consider if the potential of reducing processing cost with a few percentages justifies the effort.")]),e._v(" "),a("p",[e._v("Also consider that next to the openEO API, there's also a community of open source backend implementations. So if you have\nthe skills to optimize processing pipelines to perfection, why don't you consider contributing to a backend that matches your\npreferred technology stack?")])])}),[],!1,null,null,null);a.default=s.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[79],{522:function(e,a,t){"use strict";t.r(a);var o=t(4),s=Object(o.a)({},(function(){var e=this,a=e._self._c;return a("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[a("h1",{attrs:{id:"performance-guide-for-openeo-backends"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-guide-for-openeo-backends"}},[e._v("#")]),e._v(" Performance guide for openEO backends")]),e._v(" "),a("h2",{attrs:{id:"openeo-api-vision-on-performance-scalability"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#openeo-api-vision-on-performance-scalability"}},[e._v("#")]),e._v(" openEO API vision on performance & scalability")]),e._v(" "),a("p",[e._v("Given that the openEO API only defines a web service, it can in no way ensure the performance or scalability of an\nimplementation. What it can do however, is avoiding API definitions that prevent an implementation from being efficient.\nWhen openEO was designed, performance was one of the key design drivers, so here we try to explain how that is achieved.")]),e._v(" "),a("h3",{attrs:{id:"bringing-the-processing-to-the-data"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#bringing-the-processing-to-the-data"}},[e._v("#")]),e._v(" Bringing the processing to the data")]),e._v(" "),a("p",[e._v("Two evolutions created the need for an API designed for performance and scalability: EO programmes like Copernicus that\npushed data volumes into the petabyte range, and a move towards ever larger cloud infrastructure and HPC processing capacity\nto analyze these volumes of data. OpenEO supports this by defining data access and processing into a single specification.\nAs a result, the openEO process graph allows the backend to choose any data access pattern that is optimal for\nthe processing that is to be executed, and the dataset that is to be read.")]),e._v(" "),a("p",[e._v("A popular example of such a case, is infrastructures that store the data on the same machines that do the processing.\nIn such a case, an openEO backend can choose to load and process the data directly on the machine that has the data stored.\nAnother example is adjusting and aligning the data chunks for the processing to the internal layout of the file format that\nstores the EO data. IO performance optimizations like this are only possible if the processing engine has deep knowledge\nof the data organization from storage system over networks to file formats.")]),e._v(" "),a("h3",{attrs:{id:"datacube-processing"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#datacube-processing"}},[e._v("#")]),e._v(" Datacube processing")]),e._v(" "),a("p",[e._v("The datacube view that openEO uses as a model to represent the data as it is transformed by various processes also has\nimportant performance and scalability implications.")]),e._v(" "),a("p",[e._v("The easiest way to understand this is to contrast it with a more traditional 'product-based' view of building workflows.\nIn a product or file based workflow, a process operates on a set of input files and generates a set of output files. Many\nEO workflows have been written like this, but the consequence is that every process spends time on reading date into memory, and\nwriting it back to disk. Persistent storage is often the slowest component in a processing system, and thus these workflows\nspend a lot of time on IO. In the openEO specification, a process transforms one datacube into another datacube.\nBackend implementations are encouraged to avoid writing data to disk in between processing steps whenever possible. By\nkeeping the datacubes into memory, this avoids those costly IO operations.")]),e._v(" "),a("p",[e._v("Here it is important to note that openEO does not enforce or define how the datacube should look like on the backend. The\ndatacube can be a set of files, or arrays in memory distributed over a cluster. These choices are left to the\nbackend implementor, this guide only tries to highlight the possibilities.")]),e._v(" "),a("p",[e._v("For scalability, the openEO processes clearly define along which set of dimensions of the datacube they operate. When\na user writes a process graph, it should never instruct the backend to apply a black box algorithm or function on the\nentire datacube. For most algorithms, this is not necessary, and loading the complete datacube of a Copernicus mission at once\nis simply not possible. Hence, users run '"),a("a",{attrs:{href:"https://openeo.org/documentation/1.0/developers/api/reference.html#section/Processes/Process-Graphs",target:"_blank",rel:"noopener noreferrer"}},[e._v("user-defined (child) processes"),a("OutboundLink")],1),e._v("' over a 1-dimensional array, or even multidimensional arrays or 'chunks'\nof the datacube. Based on this information, the backend is able to define both a data access and processing strategy that is\noptimal for the given process graph.")]),e._v(" "),a("h2",{attrs:{id:"process-graph-execution"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-execution"}},[e._v("#")]),e._v(" Process graph execution")]),e._v(" "),a("p",[e._v("Here we go a bit more into detail about how a backend evaluates a process graph. Again, this is not normative or\nmandated by the specification, but rather an explanation of one way to achieve optimal performance.")]),e._v(" "),a("p",[e._v("In general, process graphs are first analyzed as a whole before the actual processing starts. The analysis phase serves\nto reveal the optimal processing strategy and parameters.")]),e._v(" "),a("p",[e._v("These are a few examples of things that can be derived from a process graph and subsequent optimizations:")]),e._v(" "),a("ul",[a("li",[a("strong",[e._v("Masking:")]),e._v(" when a raster dataset is masked with another raster or polygons, then often the loading of the datacube to\nwhich the mask is applied can be limited to unmasked values.")]),e._v(" "),a("li",[a("strong",[e._v("Vector filtering:")]),e._v(" various operations (aggregate_spatial, filter_spatial, mask_polygon) can restrict the datacube to a\nset of polygons, resulting in a rather sparse cube. Loading and processing of sparse cubes can be rather different from dense data cubes.")]),e._v(" "),a("li",[a("strong",[e._v("Resampling:")]),e._v(" resampling operations can allow data to be loaded from overviews rather than original resolution. Applying\nresampling and reprojection at load time can also be faster and save memory.")]),e._v(" "),a("li",[a("strong",[e._v("Multitemporal processing:")]),e._v(" many EO algorithms work over the temporal dimension rather than spatial dimensions. The\ntype of algorithm can be inferred from the process graph, allowing to adjust the processing strategy accordingly.")])]),e._v(" "),a("h2",{attrs:{id:"performance-faq"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#performance-faq"}},[e._v("#")]),e._v(" Performance FAQ")]),e._v(" "),a("h3",{attrs:{id:"i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#i-have-a-highly-optimized-workflow-can-openeo-expose-it"}},[e._v("#")]),e._v(" I have a highly optimized workflow, can openEO expose it?")]),e._v(" "),a("p",[e._v("Basically openEO can expose anything as a custom 'process'. If your algorithm can not be expressed as an openEO process graph,\nthen you can just let your backend advertise your custom process. By doing this, you still benefit from a lot of the standardized\nfeatures in the openEO API, and most tools for openEO will also support working with custom processes. This is fairly similar\nto exposing your process in other standards such as OGC Processes.")]),e._v(" "),a("p",[e._v("We do expect however that it is much more likely that your workflow can still reuse a few standardized processes. For instance,\nif it can be run on a geographical bounding box, the filter_bbox process would be a standardized way to specify that. Or\nperhaps it can operate on any set of Sentinel-2 products, in which case you might fit in a load_collection to let your users\ncustomize the input data. So usually, while you might start from a fully custom process, you'll notice that openEO offers\nways to gradually standardize your workflow further in a stepwise manner.")]),e._v(" "),a("h3",{attrs:{id:"can-openeo-be-as-fast-as-a-hand-written-workflow"}},[a("a",{staticClass:"header-anchor",attrs:{href:"#can-openeo-be-as-fast-as-a-hand-written-workflow"}},[e._v("#")]),e._v(" Can openEO be as fast as a hand-written workflow?")]),e._v(" "),a("p",[e._v("This question depends on which backend implementation you compare to which workflow, so there is no generic answer.\nWe do note that for writing non-trivial workflows in a cloud environment, you require a combination of algorithmic programming skills\nand cloud engineering that usually requires a team of skilled persons spending (in total) multiple person months to years on the same workflow.\nSo if you know that many workflow patterns in the operational openEO backends have already been highly optimized, you may\nwant to consider if the potential of reducing processing cost with a few percentages justifies the effort.")]),e._v(" "),a("p",[e._v("Also consider that next to the openEO API, there's also a community of open source backend implementations. So if you have\nthe skills to optimize processing pipelines to perfection, why don't you consider contributing to a backend that matches your\npreferred technology stack?")])])}),[],!1,null,null,null);a.default=s.exports}}]); \ No newline at end of file diff --git a/assets/js/80.34938e44.js b/assets/js/80.4db57826.js similarity index 99% rename from assets/js/80.34938e44.js rename to assets/js/80.4db57826.js index a2029eb0f..fdbf491e4 100644 --- a/assets/js/80.34938e44.js +++ b/assets/js/80.4db57826.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{522:function(e,o,t){"use strict";t.r(o);var a=t(4),r=Object(a.a)({},(function(){var e=this,o=e._self._c;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"getting-started-with-openeo-and-xarray-and-dask"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#getting-started-with-openeo-and-xarray-and-dask"}},[e._v("#")]),e._v(" Getting started with openEO and Xarray and Dask")]),e._v(" "),o("p",[e._v("As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a\nstandardized interface you may want to implement a driver for openEO.")]),e._v(" "),o("p",[e._v("First of all, you should read carefully the "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/backends/getting-started.html"}},[e._v("getting started guide for service providers")]),e._v(".")],1),e._v(" "),o("div",{staticClass:"custom-block tip"},[o("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),o("p",[e._v("The Xarray-Dask implementation for openEO is not a full-fledged out-of-the-box openEO back-end,\nbut can be part of the infrastructure for the data management and processing part.\nIn detail it can be used as data source for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/EO-Data-Discovery"}},[e._v("EO Data Discovery")]),e._v(" and e.g.\nin combination with a Dask cluster as processing back-end for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Data-Processing"}},[e._v("Data Processing")]),e._v(".\nIn any case, a "),o("a",{attrs:{href:"#http-rest-interface"}},[e._v("HTTP REST interface must be available in front of process implementations to properly answer openEO requests")]),e._v(".")],1)]),e._v(" "),o("p",[e._v("There are two main components involved with openEO and Xarray:")]),e._v(" "),o("ol",[o("li",[o("a",{attrs:{href:"#process-graph-parser-for-python"}},[e._v("Process Graph Parser for Python")])]),e._v(" "),o("li",[o("a",{attrs:{href:"#python-processes-for-openeo"}},[e._v("Python Processes for openEO")])])]),e._v(" "),o("h2",{attrs:{id:"process-graph-parser-for-python"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-parser-for-python"}},[e._v("#")]),e._v(" Process Graph Parser for Python")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-pg-parser-networkx"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This pg-parser parses OpenEO process graphs from raw JSON into fully traversible networkx graph objects.")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" can be imported from the pg-parser and includes "),o("code",[e._v("Process")]),e._v(" objects, that include a")]),e._v(" "),o("ul",[o("li",[e._v("spec: Process definition (e.g. https://github.com/Open-EO/openeo-processes)")]),e._v(" "),o("li",[e._v("implementation: Callable process implementation (https://github.com/Open-EO/openeo-processes-dask/tree/main/openeo_processes_dask/process_implementations)")]),e._v(" "),o("li",[e._v("namespace")])]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" automatically maps from the name of a process to the "),o("code",[e._v("spec")]),e._v(" and to the "),o("code",[e._v("implementation")]),e._v(".\nEvery "),o("code",[e._v("Process")]),e._v(" in the "),o("code",[e._v("ProcessRegistry")]),e._v(" requires a "),o("code",[e._v("spec")]),e._v(", while "),o("code",[e._v("implementation")]),e._v(" and "),o("code",[e._v("namespace")]),e._v(" are optional.")]),e._v(" "),o("p",[e._v("An example on how to use the pg-parser can be found "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx/blob/main/examples/01_minibackend_demo.ipynb",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"python-processes-for-openeo"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#python-processes-for-openeo"}},[e._v("#")]),e._v(" Python Processes for openEO")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This package includes the implementations of openEO processes, using Xarray and Dask. Currently, the "),o("code",[e._v("load_collection")]),e._v(" and "),o("code",[e._v("save_result")]),e._v(" process are not included as these implementations can differ widely for different backends.")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("specs")]),e._v(" can be found in the "),o("code",[e._v("openeo-processes-dask")]),e._v(" as a submodule. That way, the specification and the implementation are stored close to each other.")]),e._v(" "),o("h2",{attrs:{id:"the-load-collection-and-save-result-process"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#the-load-collection-and-save-result-process"}},[e._v("#")]),e._v(" The load_collection and save_result process")]),e._v(" "),o("p",[e._v("As mentioned before, the "),o("code",[e._v("load_collection")]),e._v(" and "),o("code",[e._v("save_result")]),e._v(" processes are back-end-specific and therefore not included in "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(". The "),o("a",{attrs:{href:"https://processes.openeo.org/#load_collection",target:"_blank",rel:"noopener noreferrer"}},[e._v("load_collection"),o("OutboundLink")],1),e._v(" process should return a "),o("code",[e._v("raster-cube")]),e._v(" object - to be compliant with the "),o("code",[e._v("openeo-processes-dask")]),e._v(" implementations, this should be realized by a "),o("code",[e._v("xarray.DataArray")]),e._v(" loaded with "),o("code",[e._v("dask")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"connection-to-odc-and-stac"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#connection-to-odc-and-stac"}},[e._v("#")]),e._v(" Connection to ODC and STAC")]),e._v(" "),o("p",[e._v("For testing purposes with "),o("code",[e._v("DataArrays")]),e._v(" - which can be loaded from one file - the "),o("code",[e._v("xarray.open_dataarray()")]),e._v(" function can be used to implement a basic version of "),o("code",[e._v("load_collection")]),e._v(".")]),e._v(" "),o("p",[e._v("Large data sets can be organised as "),o("code",[e._v("opendatacube Products")]),e._v(" or as "),o("code",[e._v("STAC Collections")]),e._v(".")]),e._v(" "),o("ul",[o("li",[o("p",[o("code",[e._v("opendatacube Products")]),e._v(": The implementation of "),o("code",[e._v("load_collection")]),e._v(" can include the "),o("code",[e._v("opendatacube")]),e._v(" function "),o("code",[e._v("datacube.Datacube.load()")]),e._v(". It is recommended to use the "),o("code",[e._v("dask_chunks")]),e._v(" parameter, when loading the data. The function returns a "),o("code",[e._v("xarray DataSet")]),e._v(", in order to be compliant with "),o("code",[e._v("openeo-processes-dask")]),e._v(", it can be converted to a "),o("code",[e._v("DataArray")]),e._v(" using the "),o("code",[e._v("Dataset.to_array(dim='bands')")]),e._v(" function. A sample "),o("code",[e._v("load_collection")]),e._v(" process using OpenDatacube "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo_odc_driver/blob/c197387c10f8fef7d5573270a35961a278a18e1d/openeo_odc_driver/processing.py#L38",target:"_blank",rel:"noopener noreferrer"}},[e._v("can be found here"),o("OutboundLink")],1),e._v(".")])]),e._v(" "),o("li",[o("p",[o("code",[e._v("STAC Collections")]),e._v(": Alternatively, the "),o("code",[e._v("load_collection")]),e._v(" process can be implemented using the "),o("code",[e._v("odc.stac.load()")]),e._v(" function. To make use of "),o("code",[e._v("dask")]),e._v(", the "),o("code",[e._v("chunks")]),e._v(" parameter must be set. Just as in the previous case, the resulting "),o("code",[e._v("xarray DataSet")]),e._v(" can be converted to a "),o("code",[e._v("DataArray")]),e._v(" with "),o("code",[e._v("Dataset.to_array(dim='bands')")]),e._v(". A similar implementation is the one of the "),o("code",[e._v("load_stac")]),e._v(" process "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask/blob/9267e4ccffbbbf755cb7b8a43ba80d9483398314/openeo_processes_dask/process_implementations/cubes/load.py#L83",target:"_blank",rel:"noopener noreferrer"}},[e._v("available here"),o("OutboundLink")],1),e._v(".")])])]),e._v(" "),o("h2",{attrs:{id:"openeo-client-side-processing"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#openeo-client-side-processing"}},[e._v("#")]),e._v(" openEO Client Side Processing")]),e._v(" "),o("p",[e._v("The client-side processing functionality allows to test and use openEO with its processes locally, i.e. without any connection to an openEO back-end.\nIt relies on the projects "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-pg-parser-networkx"),o("OutboundLink")],1),e._v(", which provides an openEO process graph parsing tool, and "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(", which provides an Xarray and Dask implementation of most openEO processes.")]),e._v(" "),o("p",[e._v("You can find more information and usage examples in the openEO Python client documentation "),o("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/cookbook/localprocessing.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("available here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"adding-a-new-process"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#adding-a-new-process"}},[e._v("#")]),e._v(" Adding a new process")]),e._v(" "),o("p",[e._v("To add a new process, there are changes required in the "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("ol",[o("li",[e._v("Add the process spec")]),e._v(" "),o("li",[e._v("Add the process implementation")])]),e._v(" "),o("p",[e._v("The HTTP rest interface should have a "),o("code",[e._v("processes")]),e._v(" endpoint that reflects the process specs from "),o("code",[e._v("openeo-processes-dask")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"add-the-process-spec"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-the-process-spec"}},[e._v("#")]),e._v(" Add the process spec")]),e._v(" "),o("p",[e._v("Currently, "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(" includes the process definitions as a "),o("code",[e._v("submodule")]),e._v(" in the "),o("code",[e._v("openeo-processes-dask/specs")]),e._v(". The submodule can be found under https://github.com/eodcgmbh/openeo-processes, which is a fork from https://github.com/Open-EO/openeo-processes to reflect which processes (with their implementations) are actually available in "),o("code",[e._v("openeo-processes-dask")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"add-the-process-implementation"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-the-process-implementation"}},[e._v("#")]),e._v(" Add the process implementation")]),e._v(" "),o("ol",[o("li",[e._v("Select a process from "),o("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("processes.openeo.org"),o("OutboundLink")],1),e._v(" which does not yet have an\nimplementation in "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[e._v("Clone "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(", checkout a new branch, and start implementing the missing process. Make sure you properly handle all parameters defined for this process. Add a test for your process in "),o("code",[e._v("openeo-processes-dask/tests")]),e._v(" ideally using dask. The "),o("code",[e._v("create_fake_rastercube")]),e._v(" from the "),o("code",[e._v("openeo-processes-dask/tests/mockdata")]),e._v(" can be used for testing, with the "),o("code",[e._v("backend")]),e._v(" parameter set to "),o("code",[e._v("numpy")]),e._v(" or "),o("code",[e._v("dask")]),e._v(".")]),e._v(" "),o("li",[e._v("Push your code and open a PR.")])]),e._v(" "),o("h2",{attrs:{id:"http-rest-interface"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#http-rest-interface"}},[e._v("#")]),e._v(" HTTP REST Interface")]),e._v(" "),o("p",[e._v("The next step would be to set up a HTTP REST interface (i.e. an implementation of the openEO HTTP API) for the new openEO environment.\nIt must be available in front of the process implementations to properly answer openEO client requests.\nCurrently, the "),o("a",{attrs:{href:"https://openeo.eodc.eu/v1.0",target:"_blank",rel:"noopener noreferrer"}},[e._v("EODC"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://openeo.eurac.edu/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Eurac Research"),o("OutboundLink")],1),e._v(" back-ends use Xarray and Dask and thus\nare the first implementations of back-ends to look at.")]),e._v(" "),o("ul",[o("li",[e._v("EODC is using a Python implementation, the "),o("a",{attrs:{href:"https://github.com/eodcgmbh/openeo-fastapi",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-fastapi"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[e._v("Eurac Research relies on a Java based implementation, the "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-spring-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-spring-driver"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("If you have any questions, please "),o("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact us")]),e._v(".")],1)])}),[],!1,null,null,null);o.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[80],{521:function(e,o,t){"use strict";t.r(o);var a=t(4),r=Object(a.a)({},(function(){var e=this,o=e._self._c;return o("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[o("h1",{attrs:{id:"getting-started-with-openeo-and-xarray-and-dask"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#getting-started-with-openeo-and-xarray-and-dask"}},[e._v("#")]),e._v(" Getting started with openEO and Xarray and Dask")]),e._v(" "),o("p",[e._v("As a back-end provider who wants to provide its datasets, processes and infrastructure to a broader audience through a\nstandardized interface you may want to implement a driver for openEO.")]),e._v(" "),o("p",[e._v("First of all, you should read carefully the "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/backends/getting-started.html"}},[e._v("getting started guide for service providers")]),e._v(".")],1),e._v(" "),o("div",{staticClass:"custom-block tip"},[o("p",{staticClass:"custom-block-title"},[e._v("Note")]),e._v(" "),o("p",[e._v("The Xarray-Dask implementation for openEO is not a full-fledged out-of-the-box openEO back-end,\nbut can be part of the infrastructure for the data management and processing part.\nIn detail it can be used as data source for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/EO-Data-Discovery"}},[e._v("EO Data Discovery")]),e._v(" and e.g.\nin combination with a Dask cluster as processing back-end for "),o("RouterLink",{attrs:{to:"/documentation/1.0/developers/api/reference.html#tag/Data-Processing"}},[e._v("Data Processing")]),e._v(".\nIn any case, a "),o("a",{attrs:{href:"#http-rest-interface"}},[e._v("HTTP REST interface must be available in front of process implementations to properly answer openEO requests")]),e._v(".")],1)]),e._v(" "),o("p",[e._v("There are two main components involved with openEO and Xarray:")]),e._v(" "),o("ol",[o("li",[o("a",{attrs:{href:"#process-graph-parser-for-python"}},[e._v("Process Graph Parser for Python")])]),e._v(" "),o("li",[o("a",{attrs:{href:"#python-processes-for-openeo"}},[e._v("Python Processes for openEO")])])]),e._v(" "),o("h2",{attrs:{id:"process-graph-parser-for-python"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#process-graph-parser-for-python"}},[e._v("#")]),e._v(" Process Graph Parser for Python")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-pg-parser-networkx"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This pg-parser parses OpenEO process graphs from raw JSON into fully traversible networkx graph objects.")]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" can be imported from the pg-parser and includes "),o("code",[e._v("Process")]),e._v(" objects, that include a")]),e._v(" "),o("ul",[o("li",[e._v("spec: Process definition (e.g. https://github.com/Open-EO/openeo-processes)")]),e._v(" "),o("li",[e._v("implementation: Callable process implementation (https://github.com/Open-EO/openeo-processes-dask/tree/main/openeo_processes_dask/process_implementations)")]),e._v(" "),o("li",[e._v("namespace")])]),e._v(" "),o("p",[e._v("The "),o("code",[e._v("ProcessRegistry")]),e._v(" automatically maps from the name of a process to the "),o("code",[e._v("spec")]),e._v(" and to the "),o("code",[e._v("implementation")]),e._v(".\nEvery "),o("code",[e._v("Process")]),e._v(" in the "),o("code",[e._v("ProcessRegistry")]),e._v(" requires a "),o("code",[e._v("spec")]),e._v(", while "),o("code",[e._v("implementation")]),e._v(" and "),o("code",[e._v("namespace")]),e._v(" are optional.")]),e._v(" "),o("p",[e._v("An example on how to use the pg-parser can be found "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-pg-parser-networkx/blob/main/examples/01_minibackend_demo.ipynb",target:"_blank",rel:"noopener noreferrer"}},[e._v("here"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("h2",{attrs:{id:"python-processes-for-openeo"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#python-processes-for-openeo"}},[e._v("#")]),e._v(" Python Processes for openEO")]),e._v(" "),o("ul",[o("li",[e._v("Repository: "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("This package includes the implementations of openEO processes, using Xarray and Dask. 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That way, the specification and the implementation are stored close to each other.")]),e._v(" "),o("h2",{attrs:{id:"the-load-collection-and-save-result-process"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#the-load-collection-and-save-result-process"}},[e._v("#")]),e._v(" The load_collection and save_result process")]),e._v(" "),o("p",[e._v("As mentioned before, the "),o("code",[e._v("load_collection")]),e._v(" and "),o("code",[e._v("save_result")]),e._v(" processes are back-end-specific and therefore not included in "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(". The "),o("a",{attrs:{href:"https://processes.openeo.org/#load_collection",target:"_blank",rel:"noopener noreferrer"}},[e._v("load_collection"),o("OutboundLink")],1),e._v(" process should return a "),o("code",[e._v("raster-cube")]),e._v(" object - to be compliant with the "),o("code",[e._v("openeo-processes-dask")]),e._v(" implementations, this should be realized by a "),o("code",[e._v("xarray.DataArray")]),e._v(" loaded with "),o("code",[e._v("dask")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"connection-to-odc-and-stac"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#connection-to-odc-and-stac"}},[e._v("#")]),e._v(" Connection to ODC and STAC")]),e._v(" "),o("p",[e._v("For testing purposes with "),o("code",[e._v("DataArrays")]),e._v(" - which can be loaded from one file - the "),o("code",[e._v("xarray.open_dataarray()")]),e._v(" function can be used to implement a basic version of "),o("code",[e._v("load_collection")]),e._v(".")]),e._v(" "),o("p",[e._v("Large data sets can be organised as "),o("code",[e._v("opendatacube Products")]),e._v(" or as "),o("code",[e._v("STAC Collections")]),e._v(".")]),e._v(" "),o("ul",[o("li",[o("p",[o("code",[e._v("opendatacube Products")]),e._v(": The implementation of "),o("code",[e._v("load_collection")]),e._v(" can include the "),o("code",[e._v("opendatacube")]),e._v(" function "),o("code",[e._v("datacube.Datacube.load()")]),e._v(". 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The submodule can be found under https://github.com/eodcgmbh/openeo-processes, which is a fork from https://github.com/Open-EO/openeo-processes to reflect which processes (with their implementations) are actually available in "),o("code",[e._v("openeo-processes-dask")]),e._v(".")]),e._v(" "),o("h3",{attrs:{id:"add-the-process-implementation"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#add-the-process-implementation"}},[e._v("#")]),e._v(" Add the process implementation")]),e._v(" "),o("ol",[o("li",[e._v("Select a process from "),o("a",{attrs:{href:"https://processes.openeo.org/",target:"_blank",rel:"noopener noreferrer"}},[e._v("processes.openeo.org"),o("OutboundLink")],1),e._v(" which does not yet have an\nimplementation in "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[e._v("Clone "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-processes-dask",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-processes-dask"),o("OutboundLink")],1),e._v(", checkout a new branch, and start implementing the missing process. Make sure you properly handle all parameters defined for this process. Add a test for your process in "),o("code",[e._v("openeo-processes-dask/tests")]),e._v(" ideally using dask. The "),o("code",[e._v("create_fake_rastercube")]),e._v(" from the "),o("code",[e._v("openeo-processes-dask/tests/mockdata")]),e._v(" can be used for testing, with the "),o("code",[e._v("backend")]),e._v(" parameter set to "),o("code",[e._v("numpy")]),e._v(" or "),o("code",[e._v("dask")]),e._v(".")]),e._v(" "),o("li",[e._v("Push your code and open a PR.")])]),e._v(" "),o("h2",{attrs:{id:"http-rest-interface"}},[o("a",{staticClass:"header-anchor",attrs:{href:"#http-rest-interface"}},[e._v("#")]),e._v(" HTTP REST Interface")]),e._v(" "),o("p",[e._v("The next step would be to set up a HTTP REST interface (i.e. an implementation of the openEO HTTP API) for the new openEO environment.\nIt must be available in front of the process implementations to properly answer openEO client requests.\nCurrently, the "),o("a",{attrs:{href:"https://openeo.eodc.eu/v1.0",target:"_blank",rel:"noopener noreferrer"}},[e._v("EODC"),o("OutboundLink")],1),e._v(" and "),o("a",{attrs:{href:"https://openeo.eurac.edu/",target:"_blank",rel:"noopener noreferrer"}},[e._v("Eurac Research"),o("OutboundLink")],1),e._v(" back-ends use Xarray and Dask and thus\nare the first implementations of back-ends to look at.")]),e._v(" "),o("ul",[o("li",[e._v("EODC is using a Python implementation, the "),o("a",{attrs:{href:"https://github.com/eodcgmbh/openeo-fastapi",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-fastapi"),o("OutboundLink")],1),e._v(".")]),e._v(" "),o("li",[e._v("Eurac Research relies on a Java based implementation, the "),o("a",{attrs:{href:"https://github.com/Open-EO/openeo-spring-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("openeo-spring-driver"),o("OutboundLink")],1)])]),e._v(" "),o("p",[e._v("If you have any questions, please "),o("RouterLink",{attrs:{to:"/contact.html"}},[e._v("contact us")]),e._v(".")],1)])}),[],!1,null,null,null);o.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/83.2fdd1946.js b/assets/js/83.776adc2c.js similarity index 93% rename from assets/js/83.2fdd1946.js rename to assets/js/83.776adc2c.js index 30d63cd1f..e814ad25d 100644 --- a/assets/js/83.2fdd1946.js +++ b/assets/js/83.776adc2c.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[83],{526:function(e,t,o){"use strict";o.r(t);var s=o(4),n=Object(s.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"openeo-profiles"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#openeo-profiles"}},[e._v("#")]),e._v(" openEO Profiles")]),e._v(" "),t("p",[e._v("We've defined requirements grouped into a set of profiles for the openEO API and the openEO Processes. This helps to classify the implementation status of an openEO instance and gives recommendations to implementors which functionality to prioritize.")]),e._v(" "),t("ul",[t("li",[t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/api.html"}},[e._v("API Profiles")])],1),e._v(" "),t("li",[t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/processes.html"}},[e._v("Process Profiles")])],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[83],{537:function(e,t,o){"use strict";o.r(t);var s=o(4),n=Object(s.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"openeo-profiles"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#openeo-profiles"}},[e._v("#")]),e._v(" openEO Profiles")]),e._v(" "),t("p",[e._v("We've defined requirements grouped into a set of profiles for the openEO API and the openEO Processes. This helps to classify the implementation status of an openEO instance and gives recommendations to implementors which functionality to prioritize.")]),e._v(" "),t("ul",[t("li",[t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/api.html"}},[e._v("API Profiles")])],1),e._v(" "),t("li",[t("RouterLink",{attrs:{to:"/documentation/1.0/developers/profiles/processes.html"}},[e._v("Process Profiles")])],1)])])}),[],!1,null,null,null);t.default=n.exports}}]); \ No newline at end of file diff --git a/assets/js/84.45a846f1.js b/assets/js/84.2d980e77.js similarity index 99% rename from assets/js/84.45a846f1.js rename to assets/js/84.2d980e77.js index 5f3f4cff5..badf2ddef 100644 --- a/assets/js/84.45a846f1.js +++ b/assets/js/84.2d980e77.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[84],{527:function(e,t,a){"use strict";a.r(t);var o=a(4),r=Object(o.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"glossary"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#glossary"}},[e._v("#")]),e._v(" Glossary")]),e._v(" "),t("p",[e._v("This glossary introduces the major technical terms used in the openEO project.")]),e._v(" "),t("h2",{attrs:{id:"general-terms"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#general-terms"}},[e._v("#")]),e._v(" General terms")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("EO")]),e._v(": Earth observation")]),e._v(" "),t("li",[t("strong",[e._v("API")]),e._v(": application programming interface ("),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Application_programming_interface",target:"_blank",rel:"noopener noreferrer"}},[e._v("wikipedia"),t("OutboundLink")],1),e._v("); a communication protocol between client and back-end")]),e._v(" "),t("li",[t("strong",[e._v("client")]),e._v(": software tool or environment that end-users directly interact with, e.g. R (RStudio), Python (Jupyter notebook), and JavaScript (web browser); R and Python are two major data science platforms; JavaScript is a major language for web development")]),e._v(" "),t("li",[t("strong",[e._v("(cloud) back-end")]),e._v(": server; computer infrastructure (one or more physical computers or virtual machines) used for storing EO data and processing it")]),e._v(" "),t("li",[t("strong",[e._v("big Earth observation cloud back-end")]),e._v(": server infrastructure where industry and researchers analyse large amounts of EO data")])]),e._v(" "),t("h2",{attrs:{id:"processes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[e._v("#")]),e._v(" Processes")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("process")]),e._v(" is an operation that performs a specific task on a set of parameters and returns a result. An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("pre-defined process")]),e._v(" is a process provided by the "),t("em",[e._v("back-end")]),e._v(", often one of the "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[e._v("ones centrally defined by openEO")]),e._v(".")],1),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("user-defined process")]),e._v(" is a process defined by the "),t("em",[e._v("user")]),e._v(". It can directly be part of another process graph or be stored as custom process on a back-end. Internally it is a "),t("em",[e._v("process graph")]),e._v(" with optional additional metadata.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("process graph")]),e._v(" chains specific process calls from the set of pre-defined and user-defined processes together. A process graph itself is a (user-defined) process again. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually.")]),e._v(" "),t("h2",{attrs:{id:"eo-data-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),t("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),t("strong",[e._v("granule")]),e._v(" (sometimes also called "),t("em",[e._v("item")]),e._v(" or "),t("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),t("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),t("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),t("li",[t("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),t("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),t("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),t("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) datacube. All further processing is then applied to the datacube on the back-end.")]),e._v(" "),t("h2",{attrs:{id:"spatial-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#spatial-datacubes"}},[e._v("#")]),e._v(" Spatial datacubes")]),e._v(" "),t("p",[e._v("A spatiotemporal datacube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as spatial datacubes in short.\nSpecial cases are raster and "),t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("vector datacubes"),t("OutboundLink")],1),e._v(".\nLearn more about datacubes in the "),t("a",{attrs:{href:"https://openeo.org/documentation/1.0/datacubes.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("datacube documentation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"vector-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#vector-data"}},[e._v("#")]),e._v(" Vector data")]),e._v(" "),t("p",[e._v("In general, "),t("strong",[e._v("vector data")]),e._v(' represent specific things (also called "features") in a space, e.g. on the surface of the Earth.')]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("coordinate")]),e._v(" represents a specific point in space.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("feature")]),e._v(' is a thing that usually has a geometry (e.g. the outline of an agricultural field, a forest or an urban area) and it may have additional properties assigned (e.g. a name, a color or a population).\nIn rare cases features may not have a geometry, which is often referred to as an "empty geometry".')]),e._v(" "),t("p",[t("strong",[e._v("Geometries")]),e._v(" consist of one or more coordinates that may be connected and then form a specific type of geometry, e.g. two points can be connected to a straight line and four straight lines can be connected to rectangle.")]),e._v(" "),t("p",[e._v("Commonly used types of geometries are:")]),e._v(" "),t("ul",[t("li",[e._v("Point")]),e._v(" "),t("li",[e._v("LineString (connected straight line pieces)")]),e._v(" "),t("li",[e._v("Polygon (connected straight line pieces forming a closed ring, possibly with holes - for example a triangle or rectangle)")])]),e._v(" "),t("p",[e._v('Multiple geometries of the same type can be combined into a group of geometries, e.g. a so-called "multi point" or "multi polygon".')]),e._v(" "),t("p",[e._v("Features and geometries are specified by the OGC in the "),t("a",{attrs:{href:"https://www.ogc.org/standards/sfa",target:"_blank",rel:"noopener noreferrer"}},[e._v("Simple Feature Access specification"),t("OutboundLink")],1),e._v(" (and ISO 19125). See the specification for more details.")]),e._v(" "),t("h2",{attrs:{id:"user-defined-function-udf"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),t("p",[e._v("The abbreviation "),t("strong",[e._v("UDF")]),e._v(" stands for "),t("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),t("RouterLink",{attrs:{to:"/documentation/1.0/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),t("h2",{attrs:{id:"data-processing-modes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),t("p",[e._v("Processes can run in three different ways:")]),e._v(" "),t("ol",[t("li",[e._v("Results can be pre-computed by creating a "),t("em",[t("strong",[e._v("batch job")])]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until explicitly put into the processing queue. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible although log files are generated for them. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),t("li",[e._v("A more dynamic way of processing and accessing data is to create a "),t("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),t("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),t("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),t("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),t("OutboundLink")],1),e._v(" (Web Coverage Service) or "),t("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),t("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),t("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),t("li",[e._v("Processes can also be executed "),t("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously). Results are delivered with the request itself and no job is created. Only lightweight computations, for example previews, should be executed using this approach as timeouts are to be expected for "),t("a",{attrs:{href:"https://www.pubnub.com/blog/2014-12-01-http-long-polling/",target:"_blank",rel:"noopener noreferrer"}},[e._v("long-polling HTTP requests"),t("OutboundLink")],1),e._v(".")])])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[84],{526:function(e,t,a){"use strict";a.r(t);var o=a(4),r=Object(o.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"glossary"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#glossary"}},[e._v("#")]),e._v(" Glossary")]),e._v(" "),t("p",[e._v("This glossary introduces the major technical terms used in the openEO project.")]),e._v(" "),t("h2",{attrs:{id:"general-terms"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#general-terms"}},[e._v("#")]),e._v(" General terms")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("EO")]),e._v(": Earth observation")]),e._v(" "),t("li",[t("strong",[e._v("API")]),e._v(": application programming interface ("),t("a",{attrs:{href:"https://en.wikipedia.org/wiki/Application_programming_interface",target:"_blank",rel:"noopener noreferrer"}},[e._v("wikipedia"),t("OutboundLink")],1),e._v("); a communication protocol between client and back-end")]),e._v(" "),t("li",[t("strong",[e._v("client")]),e._v(": software tool or environment that end-users directly interact with, e.g. R (RStudio), Python (Jupyter notebook), and JavaScript (web browser); R and Python are two major data science platforms; JavaScript is a major language for web development")]),e._v(" "),t("li",[t("strong",[e._v("(cloud) back-end")]),e._v(": server; computer infrastructure (one or more physical computers or virtual machines) used for storing EO data and processing it")]),e._v(" "),t("li",[t("strong",[e._v("big Earth observation cloud back-end")]),e._v(": server infrastructure where industry and researchers analyse large amounts of EO data")])]),e._v(" "),t("h2",{attrs:{id:"processes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#processes"}},[e._v("#")]),e._v(" Processes")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("process")]),e._v(" is an operation that performs a specific task on a set of parameters and returns a result. An example is computing a statistical operation, such as mean or median, on selected EO data. A process is similar to a function or method in programming languages.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("pre-defined process")]),e._v(" is a process provided by the "),t("em",[e._v("back-end")]),e._v(", often one of the "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html"}},[e._v("ones centrally defined by openEO")]),e._v(".")],1),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("user-defined process")]),e._v(" is a process defined by the "),t("em",[e._v("user")]),e._v(". It can directly be part of another process graph or be stored as custom process on a back-end. Internally it is a "),t("em",[e._v("process graph")]),e._v(" with optional additional metadata.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("process graph")]),e._v(" chains specific process calls from the set of pre-defined and user-defined processes together. A process graph itself is a (user-defined) process again. Similarly to scripts in the context of programming, process graphs organize and automate the execution of one or more processes that could alternatively be executed individually.")]),e._v(" "),t("h2",{attrs:{id:"eo-data-collections"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#eo-data-collections"}},[e._v("#")]),e._v(" EO data (Collections)")]),e._v(" "),t("p",[e._v("In our domain, different terms are used to describe EO data(sets). Within openEO, a "),t("strong",[e._v("granule")]),e._v(" (sometimes also called "),t("em",[e._v("item")]),e._v(" or "),t("em",[e._v("asset")]),e._v(" in the specification) typically refers to a limited area and a single overpass leading to a very short observation period (seconds) or a temporal aggregation of such data (e.g. for 16-day MODIS composites). A "),t("strong",[e._v("collection")]),e._v(" is a sequence of granules sharing the same product specification. It typically corresponds to the series of products derived from data acquired by a sensor on board a satellite and having the same mode of operation.")]),e._v(" "),t("p",[e._v("The "),t("a",{attrs:{href:"http://ceos.org/ourwork/workinggroups/wgiss/access/opensearch/",target:"_blank",rel:"noopener noreferrer"}},[e._v("CEOS OpenSearch Best Practice Document v1.2"),t("OutboundLink")],1),e._v(" lists the following synonyms used by other organizations:")]),e._v(" "),t("ul",[t("li",[t("strong",[e._v("granule")]),e._v(": dataset (ESA, ISO 19115), granule (NASA), product (ESA, CNES), scene (JAXA)")]),e._v(" "),t("li",[t("strong",[e._v("collection")]),e._v(": dataset series (ESA, ISO 19115), collection (CNES, NASA), dataset (JAXA), product (JAXA)")])]),e._v(" "),t("p",[e._v("In openEO, a back-end offers a set of collections to be processed. All collections can be requested using a client and are described using the "),t("a",{attrs:{href:"https://github.com/radiantearth/stac-spec",target:"_blank",rel:"noopener noreferrer"}},[e._v("STAC (SpatioTemporal Asset Catalog) metadata specification"),t("OutboundLink")],1),e._v(" as STAC collections. A user can load (a subset of) a collection using a special process, which returns a (spatial) datacube. All further processing is then applied to the datacube on the back-end.")]),e._v(" "),t("h2",{attrs:{id:"spatial-datacubes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#spatial-datacubes"}},[e._v("#")]),e._v(" Spatial datacubes")]),e._v(" "),t("p",[e._v("A spatiotemporal datacube is a multidimensional array with one or more spatial or temporal dimensions.\nIn the EO domain, it is common to be implicit about the temporal dimension and just refer to them as spatial datacubes in short.\nSpecial cases are raster and "),t("a",{attrs:{href:"https://r-spatial.org/r/2022/09/12/vdc.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("vector datacubes"),t("OutboundLink")],1),e._v(".\nLearn more about datacubes in the "),t("a",{attrs:{href:"https://openeo.org/documentation/1.0/datacubes.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("datacube documentation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h2",{attrs:{id:"vector-data"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#vector-data"}},[e._v("#")]),e._v(" Vector data")]),e._v(" "),t("p",[e._v("In general, "),t("strong",[e._v("vector data")]),e._v(' represent specific things (also called "features") in a space, e.g. on the surface of the Earth.')]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("coordinate")]),e._v(" represents a specific point in space.")]),e._v(" "),t("p",[e._v("A "),t("strong",[e._v("feature")]),e._v(' is a thing that usually has a geometry (e.g. the outline of an agricultural field, a forest or an urban area) and it may have additional properties assigned (e.g. a name, a color or a population).\nIn rare cases features may not have a geometry, which is often referred to as an "empty geometry".')]),e._v(" "),t("p",[t("strong",[e._v("Geometries")]),e._v(" consist of one or more coordinates that may be connected and then form a specific type of geometry, e.g. two points can be connected to a straight line and four straight lines can be connected to rectangle.")]),e._v(" "),t("p",[e._v("Commonly used types of geometries are:")]),e._v(" "),t("ul",[t("li",[e._v("Point")]),e._v(" "),t("li",[e._v("LineString (connected straight line pieces)")]),e._v(" "),t("li",[e._v("Polygon (connected straight line pieces forming a closed ring, possibly with holes - for example a triangle or rectangle)")])]),e._v(" "),t("p",[e._v('Multiple geometries of the same type can be combined into a group of geometries, e.g. a so-called "multi point" or "multi polygon".')]),e._v(" "),t("p",[e._v("Features and geometries are specified by the OGC in the "),t("a",{attrs:{href:"https://www.ogc.org/standards/sfa",target:"_blank",rel:"noopener noreferrer"}},[e._v("Simple Feature Access specification"),t("OutboundLink")],1),e._v(" (and ISO 19125). See the specification for more details.")]),e._v(" "),t("h2",{attrs:{id:"user-defined-function-udf"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#user-defined-function-udf"}},[e._v("#")]),e._v(" User-defined function (UDF)")]),e._v(" "),t("p",[e._v("The abbreviation "),t("strong",[e._v("UDF")]),e._v(" stands for "),t("strong",[e._v("user-defined function")]),e._v(". With this concept, users are able to upload custom code and have it executed e.g. for every pixel of a scene, or applied to a particular dimension or set of dimensions, allowing custom server-side calculations. See the section on "),t("RouterLink",{attrs:{to:"/documentation/1.0/udfs.html"}},[e._v("UDFs")]),e._v(" for more information.")],1),e._v(" "),t("h2",{attrs:{id:"data-processing-modes"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#data-processing-modes"}},[e._v("#")]),e._v(" Data Processing modes")]),e._v(" "),t("p",[e._v("Processes can run in three different ways:")]),e._v(" "),t("ol",[t("li",[e._v("Results can be pre-computed by creating a "),t("em",[t("strong",[e._v("batch job")])]),e._v(". They are submitted to the back-end's processing system, but will remain inactive until explicitly put into the processing queue. They will run only once and store results after execution. Results can be downloaded. Batch jobs are typically time consuming and user interaction is not possible although log files are generated for them. This is the only mode that allows to get an estimate about time, volume and costs beforehand.")]),e._v(" "),t("li",[e._v("A more dynamic way of processing and accessing data is to create a "),t("strong",[e._v("secondary web service")]),e._v(". They allow web-based access using different protocols such as "),t("a",{attrs:{href:"http://www.opengeospatial.org/standards/wms",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WMS"),t("OutboundLink")],1),e._v(" (Open Geospatial Consortium Web Map Service), "),t("a",{attrs:{href:"http://www.opengeospatial.org/standards/wcs",target:"_blank",rel:"noopener noreferrer"}},[e._v("OGC WCS"),t("OutboundLink")],1),e._v(" (Web Coverage Service) or "),t("a",{attrs:{href:"https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames",target:"_blank",rel:"noopener noreferrer"}},[e._v("XYZ tiles"),t("OutboundLink")],1),e._v(". These protocols usually allow users to change the viewing extent or level of detail (zoom level). Therefore, computations often run "),t("em",[e._v("on demand")]),e._v(" so that the requested data is calculated during the request. Back-ends should make sure to cache processed data to avoid additional/high costs and reduce waiting times for the user.")]),e._v(" "),t("li",[e._v("Processes can also be executed "),t("strong",[e._v("on-demand")]),e._v(" (i.e. synchronously). Results are delivered with the request itself and no job is created. 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")]),e._v("\n\t"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// You need to replace `Python` and `3.7` with the respective runtime identifier and version as returned in UDF discovery")]),e._v("\n\t"),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("return")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token keyword"}},[e._v("this")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),t("span",{pre:!0,attrs:{class:"token function"}},[e._v("run_udf")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("data"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"udf.py"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"Python"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("}")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n\n"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// Call the UDF as part of the reduce_dimension process to reduce the bands to a single value")]),e._v("\ndatacube "),t("span",{pre:!0,attrs:{class:"token operator"}},[e._v("=")]),e._v(" builder"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(".")]),t("span",{pre:!0,attrs:{class:"token function"}},[e._v("reduce_dimension")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v("(")]),e._v("datacube"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" udfProcess"),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(",")]),e._v(" "),t("span",{pre:!0,attrs:{class:"token string"}},[e._v('"bands"')]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(")")]),t("span",{pre:!0,attrs:{class:"token punctuation"}},[e._v(";")]),e._v("\n\n"),t("span",{pre:!0,attrs:{class:"token comment"}},[e._v("// Further process the data cube or save it using save_result...")]),e._v("\n")])])]),t("h3",{attrs:{id:"python"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#python"}},[e._v("#")]),e._v(" Python")]),e._v(" "),t("p",[e._v("The Python client has "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-python-client/udf.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("separate documentation on UDFs"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("h3",{attrs:{id:"qgis"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#qgis"}},[e._v("#")]),e._v(" QGIS")]),e._v(" "),t("p",[e._v("QGIS doesn't natively support running UDFs yet, but you can use copy and paste user-defined processes that include UDFs.")]),e._v(" "),t("h3",{attrs:{id:"r"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#r"}},[e._v("#")]),e._v(" R")]),e._v(" "),t("p",[e._v("There's no dedicated UDF documentation for R yet, but you can have a look at the "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf#usage",target:"_blank",rel:"noopener noreferrer"}},[e._v("getting started guide and examples in the UDF server repository"),t("OutboundLink")],1),e._v(" for now.")]),e._v(" "),t("h3",{attrs:{id:"web-editor"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#web-editor"}},[e._v("#")]),e._v(" Web Editor")]),e._v(" "),t("p",[e._v("Running UDFs in the Web Editor just requires you to drag the "),t("code",[e._v("run_udf")]),e._v(" process into the model builder. Afterwards you can edit the process parameters to your needs. You have to set the environment / programming language to run the UDF with and then you can either directly program a UDF in the code editor or select an UDF file you have previously uploaded to the user workspace.")]),e._v(" "),t("h2",{attrs:{id:"back-ends"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#back-ends"}},[e._v("#")]),e._v(" Back-ends")]),e._v(" "),t("p",[e._v("If you'd like to offer user-defined functions for your users, you need to implement either the process "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html#run_udf"}},[t("code",[e._v("run_udf")])]),e._v(" or "),t("RouterLink",{attrs:{to:"/documentation/1.0/processes.html#run_udf_externally"}},[t("code",[e._v("run_udf_externally")])]),e._v(" (or both). Both usually require you to access UDF servers via the "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-udf/api_docs/",target:"_blank",rel:"noopener noreferrer"}},[e._v("UDF API"),t("OutboundLink")],1),e._v(". For the process "),t("code",[e._v("run_udf")]),e._v(" you may bypass the API and exchange data directly between the openEO back-end and the openEO UDF servers for performance reasons.")],1),e._v(" "),t("p",[t("code",[e._v("run_udf")]),e._v(" allows users to run their code on your infrastructure. This can be done using one of the UDF server implementations in "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-udf",target:"_blank",rel:"noopener noreferrer"}},[e._v("R"),t("OutboundLink")],1),e._v(". 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This is not yet an out-of-the-box solution for running UDFs securely and fast.")])])}),[],!1,null,null,null);t.default=a.exports}}]); \ No newline at end of file diff --git a/assets/js/88.92a50275.js b/assets/js/88.98123f03.js similarity index 97% rename from assets/js/88.92a50275.js rename to assets/js/88.98123f03.js index 2b10bb7af..b414210ba 100644 --- a/assets/js/88.92a50275.js +++ b/assets/js/88.98123f03.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{536:function(e,t,o){"use strict";o.r(t);var n=o(4),r=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"contributor-code-of-conduct"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#contributor-code-of-conduct"}},[e._v("#")]),e._v(" Contributor Code of Conduct")]),e._v(" "),t("p",[e._v("As contributors and maintainers of this project, we pledge to respect all people who\ncontribute through reporting issues, posting feature requests, updating documentation,\nsubmitting pull requests or patches, and other activities.")]),e._v(" "),t("p",[e._v("We are committed to making participation in this project a harassment-free experience for\neveryone, regardless of level of experience, gender, gender identity and expression,\nsexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.")]),e._v(" "),t("p",[e._v("Examples of unacceptable behavior by participants include the use of sexual language or\nimagery, derogatory comments or personal attacks, trolling, public or private harassment,\ninsults, or other unprofessional conduct.")]),e._v(" "),t("p",[e._v("Project maintainers have the right and responsibility to remove, edit, or reject comments,\ncommits, code, wiki edits, issues, and other contributions that are not aligned to this\nCode of Conduct. Project maintainers who do not follow the Code of Conduct may be removed\nfrom the project team.")]),e._v(" "),t("p",[e._v("Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by\nopening an issue or contacting one or more of the project maintainers.")]),e._v(" "),t("p",[e._v("This Code of Conduct is adapted from the "),t("a",{attrs:{href:"http://contributor-covenant.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Contributor Covenant"),t("OutboundLink")],1),e._v(", version 1.0.0, available at\n"),t("a",{attrs:{href:"http://contributor-covenant.org/version/1/0/0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://contributor-covenant.org/version/1/0/0/"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[88],{534:function(e,t,o){"use strict";o.r(t);var n=o(4),r=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"contributor-code-of-conduct"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#contributor-code-of-conduct"}},[e._v("#")]),e._v(" Contributor Code of Conduct")]),e._v(" "),t("p",[e._v("As contributors and maintainers of this project, we pledge to respect all people who\ncontribute through reporting issues, posting feature requests, updating documentation,\nsubmitting pull requests or patches, and other activities.")]),e._v(" "),t("p",[e._v("We are committed to making participation in this project a harassment-free experience for\neveryone, regardless of level of experience, gender, gender identity and expression,\nsexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.")]),e._v(" "),t("p",[e._v("Examples of unacceptable behavior by participants include the use of sexual language or\nimagery, derogatory comments or personal attacks, trolling, public or private harassment,\ninsults, or other unprofessional conduct.")]),e._v(" "),t("p",[e._v("Project maintainers have the right and responsibility to remove, edit, or reject comments,\ncommits, code, wiki edits, issues, and other contributions that are not aligned to this\nCode of Conduct. Project maintainers who do not follow the Code of Conduct may be removed\nfrom the project team.")]),e._v(" "),t("p",[e._v("Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by\nopening an issue or contacting one or more of the project maintainers.")]),e._v(" "),t("p",[e._v("This Code of Conduct is adapted from the "),t("a",{attrs:{href:"http://contributor-covenant.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Contributor Covenant"),t("OutboundLink")],1),e._v(", version 1.0.0, available at\n"),t("a",{attrs:{href:"http://contributor-covenant.org/version/1/0/0/",target:"_blank",rel:"noopener noreferrer"}},[e._v("http://contributor-covenant.org/version/1/0/0/"),t("OutboundLink")],1),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/89.144159fa.js b/assets/js/89.eb69d8d4.js similarity index 99% rename from assets/js/89.144159fa.js rename to assets/js/89.eb69d8d4.js index 92627ba6a..840f8af7f 100644 --- a/assets/js/89.144159fa.js +++ b/assets/js/89.eb69d8d4.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[89],{537:function(e,t,o){"use strict";o.r(t);var n=o(4),r=Object(n.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h1",{attrs:{id:"software-development-guidelines"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#software-development-guidelines"}},[e._v("#")]),e._v(" Software Development Guidelines")]),e._v(" "),t("p",[e._v("This document describes guidelines for software developers, written for the "),t("a",{attrs:{href:"http://openeo.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO"),t("OutboundLink")],1),e._v(" project.\nSince the openEO infrastructure encompasses several programming languages and software environments, this document does not prescribe particular tools or platforms but rather focuses on general principles and methods behind them.")]),e._v(" "),t("ol",[t("li",[e._v("License: all software developed in the openEO project and published on the "),t("a",{attrs:{href:"http://github.com/open-eo/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO GitHub"),t("OutboundLink")],1),e._v(" organisation shall be licensed under the "),t("a",{attrs:{href:"https://opensource.org/licenses/Apache-2.0",target:"_blank",rel:"noopener noreferrer"}},[e._v("Apache 2.0 license"),t("OutboundLink")],1),e._v(". If software repositories deviate from this, or contain code or other artifacts that deviates from this, this shall be described in the "),t("code",[e._v("README.md")]),e._v(" file.")]),e._v(" "),t("li",[e._v("Location: Official openEO software is developed under the "),t("a",{attrs:{href:"https://github.com/open-EO/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO GitHub organisation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("li",[e._v("Proof-of-concept versus sustainable: each repository shall indicate its status: either "),t("em",[e._v("proof-of-concept")]),e._v(", or "),t("em",[e._v("sustainable")]),e._v(". Proof-of-concept code is meant to work but comes without quality assurance. 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Tools such as "),t("a",{attrs:{href:"https://codecov.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("codecov.io"),t("OutboundLink")],1),e._v(" to automatically indicate the amount of code covered by tests, and code that is not covered by tests shall be used in combination with a continuous integration framework.")]),e._v(" "),t("h2",{attrs:{id:"continuous-integration"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#continuous-integration"}},[e._v("#")]),e._v(" Continuous integration")]),e._v(" "),t("p",[e._v("Repositories containing running software shall use an appropriate continuous integration platform, such as GitHub Actions or similar, to show whether the current build passes all checks. 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If software repositories deviate from this, or contain code or other artifacts that deviates from this, this shall be described in the "),t("code",[e._v("README.md")]),e._v(" file.")]),e._v(" "),t("li",[e._v("Location: Official openEO software is developed under the "),t("a",{attrs:{href:"https://github.com/open-EO/",target:"_blank",rel:"noopener noreferrer"}},[e._v("openEO GitHub organisation"),t("OutboundLink")],1),e._v(".")]),e._v(" "),t("li",[e._v("Proof-of-concept versus sustainable: each repository shall indicate its status: either "),t("em",[e._v("proof-of-concept")]),e._v(", or "),t("em",[e._v("sustainable")]),e._v(". Proof-of-concept code is meant to work but comes without quality assurance. Software repositories with proof-of-concept developments shall clearly say so in the first paragraph of the "),t("code",[e._v("README.md")]),e._v(" file.")]),e._v(" "),t("li",[e._v("Sustainable code should undergo standard "),t("a",{attrs:{href:"#software-quality-guidelines"}},[e._v("quality checks")]),e._v(", and point out its "),t("a",{attrs:{href:"#software-documentation-guidelines"}},[e._v("documentation")]),e._v(".")]),e._v(" "),t("li",[e._v("Sustainable code shall undergo "),t("a",{attrs:{href:"#software-review"}},[e._v("code review")]),e._v("; no direct commits to master; any commit shall come in the form of a PR, commit after review.")]),e._v(" "),t("li",[e._v("Sustainable code shall be written in a "),t("a",{attrs:{href:"#test-driven-development"}},[e._v("Test-driven manner")]),e._v(", and repositories shall at the top of their "),t("code",[e._v("README.md")]),e._v(" give indication of the degree to which code is covered by tests.")]),e._v(" "),t("li",[t("a",{attrs:{href:"#continuous-integration"}},[e._v("Continuous integration")]),e._v(" shall be used to indicate code currently passes its test on CI platforms.")]),e._v(" "),t("li",[e._v("A "),t("RouterLink",{attrs:{to:"/documentation/code-of-conduct.html"}},[e._v("Code of conduct")]),e._v(" describes the rules and constraints to developers and contributors.")],1),e._v(" "),t("li",[e._v("Version numbers of sustainable software releases shall follow "),t("a",{attrs:{href:"http://semver.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("Semantic Versioning 2.0.0"),t("OutboundLink")],1),e._v(".")])]),e._v(" "),t("h2",{attrs:{id:"software-quality-guidelines"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#software-quality-guidelines"}},[e._v("#")]),e._v(" Software quality guidelines")]),e._v(" "),t("ul",[t("li",[e._v("software shall be written in such a way that another person can understand its intention")]),e._v(" "),t("li",[e._v("comment lines shall be used sparsely, but effectively")]),e._v(" "),t("li",[e._v("reuse of unstable or esoteric libraries shall be avoided")])]),e._v(" "),t("h2",{attrs:{id:"software-documentation-guidelines"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#software-documentation-guidelines"}},[e._v("#")]),e._v(" Software documentation guidelines")]),e._v(" "),t("p",[e._v("Software documentation shall include:")]),e._v(" "),t("ul",[t("li",[e._v("installation instructions")]),e._v(" "),t("li",[e._v("usage instructions")]),e._v(" "),t("li",[e._v("explain in detail the intention of the software")]),e._v(" "),t("li",[e._v("pointers to reference documents explaining overarching concepts")])]),e._v(" "),t("p",[e._v("Each repository's "),t("code",[e._v("README.md")]),e._v(" shall point to the documentation.")]),e._v(" "),t("p",[e._v("Reference documentation shall be written using well-defined reference documentation language, such as "),t("a",{attrs:{href:"https://tools.ietf.org/html/rfc2119",target:"_blank",rel:"noopener noreferrer"}},[e._v("RFC2119"),t("OutboundLink")],1),e._v(" or "),t("a",{attrs:{href:"http://arc42.org",target:"_blank",rel:"noopener noreferrer"}},[e._v("arc42"),t("OutboundLink")],1),e._v(", and refer to the definitions used.")]),e._v(" "),t("h2",{attrs:{id:"software-review"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#software-review"}},[e._v("#")]),e._v(" Software review")]),e._v(" "),t("ul",[t("li",[e._v("sustainable software development shall take place by always having two persons involved in a change to the master branch: individuals push to branches, pull request indicate readiness to be taken up in the master branch, a second developer reviews the pull request before merging it into the master branch.")]),e._v(" "),t("li",[e._v("software review discussions shall be intelligible for external developers, and serve as implicit documentation of development decisions taken")])]),e._v(" "),t("h2",{attrs:{id:"test-driven-development"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#test-driven-development"}},[e._v("#")]),e._v(" Test-driven development")]),e._v(" "),t("p",[e._v("Software shall be developed in a test-driven fashion, meaning that while the code is written, tests are developed that verify, to a reasonable extent, the correctness of the code. Tools such as "),t("a",{attrs:{href:"https://codecov.io/",target:"_blank",rel:"noopener noreferrer"}},[e._v("codecov.io"),t("OutboundLink")],1),e._v(" to automatically indicate the amount of code covered by tests, and code that is not covered by tests shall be used in combination with a continuous integration framework.")]),e._v(" "),t("h2",{attrs:{id:"continuous-integration"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#continuous-integration"}},[e._v("#")]),e._v(" Continuous integration")]),e._v(" "),t("p",[e._v("Repositories containing running software shall use an appropriate continuous integration platform, such as GitHub Actions or similar, to show whether the current build passes all checks. This helps understand contributors that the software passes tests on an independent platform, and may give insights in the way the software is compiled, deployed and tested.")]),e._v(" "),t("h2",{attrs:{id:"additional-guidelines"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#additional-guidelines"}},[e._v("#")]),e._v(" Additional guidelines")]),e._v(" "),t("p",[e._v("There is specific guideline for "),t("a",{attrs:{href:e.$site.themeConfig.docPath+"developers/clients/library-guidelines.html"}},[e._v("client library development")]),e._v(".")])])}),[],!1,null,null,null);t.default=r.exports}}]); \ No newline at end of file diff --git a/assets/js/93.7e1b8d20.js b/assets/js/93.98abe5ea.js similarity index 99% rename from assets/js/93.7e1b8d20.js rename to assets/js/93.98abe5ea.js index 6bc30d82f..9bf7f357c 100644 --- a/assets/js/93.7e1b8d20.js +++ b/assets/js/93.98abe5ea.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[93],{542:function(e,t,n){"use strict";n.r(t);var a=n(4),r=Object(a.a)({},(function(){var e=this,t=e._self._c;return t("ContentSlotsDistributor",{attrs:{"slot-key":e.$parent.slotKey}},[t("h2",{attrs:{id:"the-problem"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#the-problem"}},[e._v("#")]),e._v(" The problem")]),e._v(" "),t("p",[e._v("Earth Observation data are becoming too large to be downloaded\nlocally for analysis. Also, the way they are organised (as tiles,\nor "),t("em",[e._v("granules")]),e._v(": files containing the imagery for a small part of\nthe Earth and a single observation date) makes it unnecessary\ncomplicated to analyse them. The solution to this is to store these\ndata in the cloud, on compute back-ends, process them there, and\nbrowse the results or download resulting figures or numbers. But\nhow do we do that?")]),e._v(" "),t("p",[e._v("The aim of openEO is to develop an open API to connect R, python\nand javascript clients to big Earth observation cloud back-ends in\na simple and unified way.")]),e._v(" "),t("p",[e._v("With such an API,")]),e._v(" "),t("ul",[t("li",[e._v("each client can work with every back-end, and")]),e._v(" "),t("li",[e._v("it becomes possible to compare back-ends in terms of capacity,\ncost, and results (validation, reproducibility)")])]),e._v(" "),t("h2",{attrs:{id:"why-an-api"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#why-an-api"}},[e._v("#")]),e._v(" Why an API?")]),e._v(" "),t("p",[e._v("An API is an application programming interface. It "),t("em",[e._v("defines")]),e._v("\na "),t("em",[e._v("language")]),e._v(" that two computers (a client and a server) use to\ncommunicate.")]),e._v(" "),t("p",[e._v("The following figure shows how many interfaces are needed to be able\nto compare back-ends from different clients, without an openEO API:")]),e._v(" "),t("p",[t("img",{attrs:{src:"/images/api.png",alt:"Current situation, without openEO API"}})]),e._v(" "),t("p",[e._v("With an openEO API (dark blue), the situation becomes much easier:")]),e._v(" "),t("p",[t("img",{attrs:{src:"/images/api2.png",alt:"Situation in the future, with openEO API"}})]),e._v(" "),t("p",[e._v("However, existing back-ends need to be taught to work with the new\nAPI, and clients that interact with back-ends need to be developed.")]),e._v(" "),t("p",[e._v("The task of the openEO project is to design, develop, and evaluate\nan API for cloud-based Earth Observation data processing.")]),e._v(" "),t("h2",{attrs:{id:"first-results"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#first-results"}},[e._v("#")]),e._v(" First results")]),e._v(" "),t("p",[e._v("The openEO project started in Oct 2017. Now, within 6 months, we\nfinished the first proof of concept, and demonstrate it. Three use\ncases were selected for this, three clients were developed pretty\nmuch from scratch (Python, R, and JavaScript), and\nseven back-ends were built or interfaced. Full information is available\nfrom the projects "),t("a",{attrs:{href:"https://github.com/Open-EO/",target:"_blank",rel:"noopener noreferrer"}},[e._v("github organisation"),t("OutboundLink")],1),e._v(",\nand we point here to the")]),e._v(" "),t("ul",[t("li",[e._v("swagger-2.0 complient "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-api",target:"_blank",rel:"noopener noreferrer"}},[e._v("API"),t("OutboundLink")],1),e._v(" and its "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-api/",target:"_blank",rel:"noopener noreferrer"}},[e._v("documentation"),t("OutboundLink")],1)])]),e._v(" "),t("h2",{attrs:{id:"the-three-use-cases"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#the-three-use-cases"}},[e._v("#")]),e._v(" The three use cases")]),e._v(" "),t("p",[e._v("The three use cases comprise")]),e._v(" "),t("ol",[t("li",[e._v("Derive minimum NDVI measurements over pixel time series of Sentinel 2 imagery")]),e._v(" "),t("li",[e._v("Create a monthly aggregated Sentinel 1 product from a custom Python script")]),e._v(" "),t("li",[e._v("Compute time series of zonal (regional) statistics of Sentinel 2 imagery over user-uploaded polygons")])]),e._v(" "),t("p",[e._v("The full description, including the consecutive interaction steps of the API, is found on the")]),e._v(" "),t("ul",[t("li",[e._v("proof-of-concept "),t("a",{attrs:{href:"https://open-eo.github.io/openeo-api/poc/index.html",target:"_blank",rel:"noopener noreferrer"}},[e._v("use cases"),t("OutboundLink")],1),e._v(" site")])]),e._v(" "),t("h2",{attrs:{id:"links-to-the-client-and-back-end-implementations"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#links-to-the-client-and-back-end-implementations"}},[e._v("#")]),e._v(" Links to the client and back-end implementations")]),e._v(" "),t("p",[e._v("Clients:")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python client"),t("OutboundLink")],1),e._v("; "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-python-client/blob/master/examples/notebooks/Compositing.ipynb",target:"_blank",rel:"noopener noreferrer"}},[e._v("Example notebook"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-client",target:"_blank",rel:"noopener noreferrer"}},[e._v("R client"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-js-client",target:"_blank",rel:"noopener noreferrer"}},[e._v("Javascript client"),t("OutboundLink")],1),e._v(" and "),t("a",{attrs:{href:"https://github.com/Open-EO/openeo-web-editor",target:"_blank",rel:"noopener noreferrer"}},[e._v("JavaScript web editor"),t("OutboundLink")],1)])]),e._v(" "),t("p",[e._v("Back-ends")]),e._v(" "),t("ul",[t("li",[t("a",{attrs:{href:"https://open-eo.github.io/openeo-grassgis-driver/",target:"_blank",rel:"noopener noreferrer"}},[e._v("GRASS GIS driver"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-wcps-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("WCPS driver"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-openshift-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("OpenShift driver"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-geopyspark-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("Python GeoPySpark/GeoTrellis driver"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-sentinelhub-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("Sentinel Hub driver"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-earthengine-driver",target:"_blank",rel:"noopener noreferrer"}},[e._v("Google Earth Engine back-end"),t("OutboundLink")],1)]),e._v(" "),t("li",[t("a",{attrs:{href:"https://github.com/Open-EO/openeo-r-backend",target:"_blank",rel:"noopener noreferrer"}},[e._v("R back-end"),t("OutboundLink")],1),e._v(" (developed for testing purposes)")])]),e._v(" "),t("p",[e._v('With all this, you can install your own back-end of choice, install\na client, and start analysing the data! (but do read "Next steps",\nbelow). Alternatively, we show a couple of videos of screen casts\nmade while testing the various clients and back-ends.')]),e._v(" "),t("h2",{attrs:{id:"proof-of-concept-videos"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#proof-of-concept-videos"}},[e._v("#")]),e._v(" Proof-of-concept videos")]),e._v(" "),t("h3",{attrs:{id:"r-client-and-wcps-back-end-use-case-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#r-client-and-wcps-back-end-use-case-1"}},[e._v("#")]),e._v(" R client and WCPS back-end, use case 1")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/NoD0nVGM3ww",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"grass-gis-back-end-use-cases-1-2-and-3"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#grass-gis-back-end-use-cases-1-2-and-3"}},[e._v("#")]),e._v(" Grass GIS back-end, use cases 1, 2 and 3")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/NgF1WgCtSiM",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"r-client-and-r-back-end-use-case-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#r-client-and-r-back-end-use-case-1"}},[e._v("#")]),e._v(" R client and R back-end, use-case 1")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/Yb_QflO-ulE",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"r-client-and-r-back-end-use-case-3"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#r-client-and-r-back-end-use-case-3"}},[e._v("#")]),e._v(" R client and R back-end, use case 3")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/LYnad6KC_CU",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"openeo-web-editor-js-client-with-three-back-ends-use-case-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#openeo-web-editor-js-client-with-three-back-ends-use-case-1"}},[e._v("#")]),e._v(" openEO Web Editor (JS client) with three back-ends (use case 1)")]),e._v(" "),t("p",[e._v("Demonstrates use of back-ends Sentinel Hub, WCPS EURAC, and OpenShift EODC")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/zDaQkw0NhpY",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"openeo-web-editor-js-client-with-r-back-end-use-case-3"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#openeo-web-editor-js-client-with-r-back-end-use-case-3"}},[e._v("#")]),e._v(" openEO Web Editor (JS client) with R back-end (use case 3)")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/XsPbKypUuIE",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"python-client-with-geopyspark-back-end-use-case-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#python-client-with-geopyspark-back-end-use-case-1"}},[e._v("#")]),e._v(" Python client with GeoPySpark back-end (use case 1)")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/qtIp9OC0qHY",frameborder:"0",allowfullscreen:""}}),e._v(" "),t("h3",{attrs:{id:"openeo-web-editor-js-client-with-google-earth-engine-back-end-use-case-1"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#openeo-web-editor-js-client-with-google-earth-engine-back-end-use-case-1"}},[e._v("#")]),e._v(" openEO Web Editor (JS client) with Google Earth Engine back-end (use case 1)")]),e._v(" "),t("iframe",{attrs:{width:"560",height:"315",src:"https://www.youtube.com/embed/LmUlmsIGJrs",frameborder:"0",allowfullscreen:""}}),e._v(" 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Also, the way they are organised (as tiles,\nor "),t("em",[e._v("granules")]),e._v(": files containing the imagery for a small part of\nthe Earth and a single observation date) makes it unnecessary\ncomplicated to analyse them. The solution to this is to store these\ndata in the cloud, on compute back-ends, process them there, and\nbrowse the results or download resulting figures or numbers. But\nhow do we do that?")]),e._v(" "),t("p",[e._v("The aim of openEO is to develop an open API to connect R, python\nand javascript clients to big Earth observation cloud back-ends in\na simple and unified way.")]),e._v(" "),t("p",[e._v("With such an API,")]),e._v(" "),t("ul",[t("li",[e._v("each client can work with every back-end, and")]),e._v(" "),t("li",[e._v("it becomes possible to compare back-ends in terms of capacity,\ncost, and results (validation, reproducibility)")])]),e._v(" "),t("h2",{attrs:{id:"why-an-api"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#why-an-api"}},[e._v("#")]),e._v(" Why an API?")]),e._v(" "),t("p",[e._v("An API is an application programming interface. It "),t("em",[e._v("defines")]),e._v("\na "),t("em",[e._v("language")]),e._v(" that two computers (a client and a server) use to\ncommunicate.")]),e._v(" "),t("p",[e._v("The following figure shows how many interfaces are needed to be able\nto compare back-ends from different clients, without an openEO API:")]),e._v(" "),t("p",[t("img",{attrs:{src:"/images/api.png",alt:"Current situation, without openEO API"}})]),e._v(" "),t("p",[e._v("With an openEO API (dark blue), the situation becomes much easier:")]),e._v(" "),t("p",[t("img",{attrs:{src:"/images/api2.png",alt:"Situation in the future, with openEO API"}})]),e._v(" "),t("p",[e._v("However, existing back-ends need to be taught to work with the new\nAPI, and clients that interact with back-ends need to be developed.")]),e._v(" "),t("p",[e._v("The task of the openEO project is to design, develop, and evaluate\nan API for cloud-based Earth Observation data processing.")]),e._v(" "),t("h2",{attrs:{id:"first-results"}},[t("a",{staticClass:"header-anchor",attrs:{href:"#first-results"}},[e._v("#")]),e._v(" First results")]),e._v(" "),t("p",[e._v("The openEO project started in Oct 2017. Now, within 6 months, we\nfinished the first proof of concept, and demonstrate it. Three use\ncases were selected for this, three clients were developed pretty\nmuch from scratch (Python, R, and JavaScript), and\nseven back-ends were built or interfaced. 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useful if it is being used, and it needs testing by a wide audience. 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- + diff --git a/documentation/1.0/developers/profiles/api.html b/documentation/1.0/developers/profiles/api.html index bfc49268b..a03eb90cd 100644 --- a/documentation/1.0/developers/profiles/api.html +++ b/documentation/1.0/developers/profiles/api.html @@ -8,8 +8,8 @@ - - + +
openEO
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- + diff --git a/documentation/1.0/javascript/index.html b/documentation/1.0/javascript/index.html index ed0276c6d..3b23eb85d 100644 --- a/documentation/1.0/javascript/index.html +++ b/documentation/1.0/javascript/index.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/1.0/processes.html b/documentation/1.0/processes.html index db1c564ae..42823b582 100644 --- a/documentation/1.0/processes.html +++ b/documentation/1.0/processes.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/1.0/python/client-side-processing.html b/documentation/1.0/python/client-side-processing.html index f241c5657..a28ca312e 100644 --- a/documentation/1.0/python/client-side-processing.html +++ b/documentation/1.0/python/client-side-processing.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/1.0/python/index.html b/documentation/1.0/python/index.html index 223ce1250..68c9c37b3 100644 --- a/documentation/1.0/python/index.html +++ b/documentation/1.0/python/index.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/1.0/udfs.html b/documentation/1.0/udfs.html index 7378f293e..9f202738f 100644 --- a/documentation/1.0/udfs.html +++ b/documentation/1.0/udfs.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/code-of-conduct.html b/documentation/code-of-conduct.html index 65b7efa35..d1ff21c9e 100644 --- a/documentation/code-of-conduct.html +++ b/documentation/code-of-conduct.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/documentation/software-guidelines.html b/documentation/software-guidelines.html index a6eca7ee2..ddce27efb 100644 --- a/documentation/software-guidelines.html +++ b/documentation/software-guidelines.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/events/igarrs2021.html b/events/igarrs2021.html index 4b5037b03..d212292a8 100644 --- a/events/igarrs2021.html +++ b/events/igarrs2021.html @@ -8,8 +8,8 @@ - - + + - + diff --git a/news/2017-12-05-welcome.html b/news/2017-12-05-welcome.html index 1c511bba8..0414eb5cc 100644 --- a/news/2017-12-05-welcome.html +++ b/news/2017-12-05-welcome.html @@ -8,8 +8,8 @@ - - + + - + diff --git a/news/2017-12-18-VITO_meeting.html b/news/2017-12-18-VITO_meeting.html index 7aa8830c2..a24c59321 100644 --- a/news/2017-12-18-VITO_meeting.html +++ b/news/2017-12-18-VITO_meeting.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/news/2018-01-31-WWU_meeting.html b/news/2018-01-31-WWU_meeting.html index 3c411e42a..9cc1e4712 100644 --- a/news/2018-01-31-WWU_meeting.html +++ b/news/2018-01-31-WWU_meeting.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/news/2018-03-15-poc.html b/news/2018-03-15-poc.html index a944257a5..bc69d2e9d 100644 --- a/news/2018-03-15-poc.html +++ b/news/2018-03-15-poc.html @@ -8,8 +8,8 @@ - - + +
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- + diff --git a/news/2018-05-23-user-requirements-survey-launched.html b/news/2018-05-23-user-requirements-survey-launched.html index 5c7550bb5..12f43d511 100644 --- a/news/2018-05-23-user-requirements-survey-launched.html +++ b/news/2018-05-23-user-requirements-survey-launched.html @@ -8,8 +8,8 @@ - - + +
openEO

  • User Requirements Survey for openEO launched

User Requirements Survey for openEO launched

Written on May 23, 2018 by Sarah Carter.

This survey (opens new window) aims to gather a detailed view of external user needs in relation EO data access and analysis. This will enable the openEO project team to better identify current needs of different users and the potential for openEO to be useful to them and to overcome any barriers they have in their work streams. Ultimately, this will steer the openEO API in a way which meets user needs including being compatible with additional EO service providers and their data infrastructures. Additional processes and use cases will also be added where necessary with the aim of benefiting more potential users.

The survey is open to all, so please fill it in by 18 June 2018, and share within your relevant networks. It should take no longer than 10 minutes to complete.

https://ec.europa.eu/eusurvey/runner/openEO_UserSurvey (opens new window)

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openEO

  • H2020 EO Big Data Hackathon Nov 7-8

H2020 EO Big Data Hackathon Nov 7-8

Written on October 7, 2019 by Edzer Pebesma.

openEO will participate, along with the four other H2020 projects funded under the same call, in the H2020 EO Big Data Hackathon (opens new window), which is open for participation.

The hackaton will take place on Nov 7 and 8, 2019, in Frascati, Italy.

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openEO

Lessons learned while developing the openEO API

Written on July 31, 2020 by Matthias Mohr.

With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could address a lot of problems that came up, some issues remained that we couldn't solve. This article explains some of the lessons learned and thus also gives an honest overview about current limitations of the openEO API.

# 1. Collection Names

The naming of data sets (collections) is different across providers. We thought about recommending to follow lists like the CEOS Mission Index (opens new window), but then the question is what data does it really refer to? It lists Sentinel-2 A and Sentinel-2 B, but would that be Level 1C or 2A or something completely different? Therefore, openEO decided to not enforce any naming scheme as data sets throughout the affiliated providers were mostly differently pre-processed anyway. To improve the situation, openEO allows to define process parameters. This allows to define algorithms independently of data set names, which can later be assigned when executing the process. Also, openEO recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm.

Some more background information can be found in the corresponding GitHub issue (opens new window). In general, many limitations (including the following) concern the data holdings of the providers and may hopefully be solved by initiatives that work on Analysis Ready Data (ARD).

# 2. Other Names

Similarly, the naming of bands (and potentially other dimension labels, too) are inconsistent across providers, we faced names such as B2, B02, 2 or blue for Sentinel-2. openEO doesn't enforce any naming scheme, but has found ways to improve the situation for users. First, process parameters can be used (see above). Second, bands can also be specified using the common names as specified by STAC (opens new window) or wavelength ranges. openEO recommends to use common names whenever available.

For example, to avoid specific naming you could filter for Sentinel-2 (A and B) band 2 as follows:

  • by common name: filter_bands(data = cube, bands = ['blue']) (blue maps to the wavelength range 0.45 - 0.50 according to STAC)
  • by wavelength, specified in μm: filter_bands(data = cube, wavelength = [[0.49, 0.50]]) (wavelength is 0.4966 for Sentinel-2A and 0.4921 for Sentinel-2B)

In general, metadata is aligned for usage in openEO processes by using the STAC specification (opens new window), which improves interoperability.

# 3. Chunked Collections

Some providers (for example Google Earth Engine (opens new window)) expose data sets such as Sentinel-2 L2A as one large collection. Other providers expose the data sets split into groups, e.g. chunked by CRS and resolution. EURAC has collections such as S2_32632_60m_L2A or S2_32635_10m_L2A for example. There are valid reasons (opens new window) for any of the implementations, but to explain them is too much for this article. Unfortunately, those differences decrease the interoperability and in the end the re-usability of the processes. openEO aims to make algorithms re-usable and thus recommends to split processing instructions into two parts: data loading/pre-processing and the actual algorithm. This allows greater re-usability of the algorithms.

# 4. What to specify?

The API has a focus on Service and Data Discovery, Data Processing and Result Retrieval/Publishing. Some more customer-oriented tasks such as user registration and payments are not handled through the openEO API. These aspects are too different across providers and can be handled more efficiently through external interfaces. Most providers already offer such services independently of the openEO API implementation anyway to offer their pre-openEO services.

Still, the openEO API uses OpenID Connect as main Authentication and Authorization mechanism (see also point 5). OpenID Connect (opens new window) has a User Registration extension draft (opens new window) that can be used by back-ends and may be adopted in the future. We closely follow upcoming specifications and standards and are happy to adopt anything useful.

# File Formats

Similarly, file formats (opens new window) and their options (opens new window) are often very different between processing software. That is why GDAL exists. To improve interoperability, the openEO API just recommends to align them with GDAL, which is used in most relevant software anyway. Unfortunately, this comes with some small inconveniences. For example, most people would probably expect to just specify "GeoTiff" as file format name, but GDAL has named it "GTiff". Another idea was to use media types (opens new window) such as application/json, but then we realized that these are often to broad or not even defined. For example, openEO had to push the OGC (opens new window) to specify an official media type for GeoTiff. But the same issue exists for other file formats, too.

# 5. Authentication

One of the most complex issues we faced during the API development was actually not EO or data processing related. It was the Authentication and Authorization mechanism. We quickly decided to use one of the well-established standards available for security reasons. Unfortunately, most of them don't seem to cater very well for our use case where you have a number of different clients and back-ends communicating with each other. Usually in the Web you have multiple clients communicating with a single back-end (1:n), but in openEO we have a n:m relation. There are CLI tools, web clients and more that need to authenticate against a growing set of independent providers. Therefore, you can't easily ship clients with security measures like Client IDs (and Client Secrets) as required by OpenID Connect (opens new window) (based on OAuth 2 (opens new window)). Users need to get those information by themselves and provide it to the clients, which is inconvenient and probably confusing to many non-developers. This issue is probably the thing I dislike most in the current API specification and thus would love to hear from you whether there are good solutions out there that we missed.

# 6. Debugging Experience

The logging and debugging experience varies a lot between cloud processing and the "traditional workflow" (you download data and compute locally with your preferred tools). While it's "free" on a local machine to just do "trial and error" to check whether something is running, cloud processing usually comes with a cost and you likely don't want to work with "trial and error". Also you don't directly have access to the hardware and software and thus it is harder to investigate problems. openEO took several measures to mitigate these issues, but it is still a different experience for users. I guess that is a long learning process until it is adopted by users and may need additional effort to implement tools to improve the situation. While many users are getting used to it in situations where a streamlined experience is somewhat possible as only few clients communicate with a single provider (e.g. Google Earth Engine), the issue increases in openEO due to the different infrastructure and software used on back-end side.

# 7. Defining interoperable Processes

Defining a common set of pre-defined processes was a very long and challenging task.

  1. First, we had to come up with a useful schema that allowed us to describe processes in JSON. We got inspired by different other standards such as WPS (opens new window) and OpenAPI (opens new window).
  2. Second, we had to find what processes are required and which granularity they should have. That quickly led to over a hundred processes (opens new window), mainly for data cube and math operations. And that was only for mostly raster operations. We still have a big hole in the processes regarding vector related operations, but we aim to close that in the next year(s).
  3. Third, we had to figure out what common processing softwares offered and how to come up with process definitions than can be implemented with those softwares.
  4. Last, we had to specify it in a way that is unambiguous and easy to understand by users.

All of these tasks took much more time than initially expected. For example, one of the partners reported that the granularity of the processes is not really suited for the underlying processing software. While we couldn't really change the granularity, it was at least not an issue to make openEO work for them. Fortunately, the process part of the openEO API is very flexible and allows providers to change the processes according to their needs or define their own processes. Due to the fact that the actual parameters and return values are described in JSON Schema, clients and back-ends can easily re-use existing JSON Schema implementations and adopt to changes in the process specifications. Thus, if a back-end doesn't support parts of a process, it can just be changed/removed and a client can understand that and cater for it.

# Conclusion

This was a deep dive into some of my challenges during the last years. I hope it is useful for others and I'm hoping to get into discussions with developers facing similar issues. Maybe with joint forces we can solve some of these for a better cloud processing experience in the future. If you have anything to say, please contact me via e-mail, GitHub issues (API (opens new window), Processes (opens new window)) or Twitter (opens new window). You can also contact openEO.

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openEO

Stable release of the openEO API and Processes (v1.0.0)

Written on July 31, 2020 by Matthias Mohr.

After two release candidates in early 2020, the openEO Consortium released the first stable version of its openEO API (opens new window) and their set of common processes (opens new window). This blog post gives an overview of the most notable changes since version 0.4 and provides an outlook on the next months' project activity.

For those who are new to openEO, we have developed an open API to connect R, Python, JavaScript and other clients to big Earth observation cloud back-ends in a simple and unified way. The idea is that this will make cloud-based processing of large satellite image collections, or other data cubes such as weather model data easier for data scientists. The entire development process has been open, and all software developed in this project is open source and available on GitHub (opens new window). For more information about openEO, please read page about the project first.

# Processes

The most important change was introduced just recently in openEO API version 1.0.0 RC2. Until then, openEO process graphs allowed users to express processing algorithms and execute them on a back-end using pre-defined processes exposed by a back-end provider. While this still exists, a user can now also promote a process graph to a full openEO process description, following the same schema as the pre-defined processes: it must include the processing instructions and metadata like an identifier, parameters, return values, examples etc. This allows user-defined processes to be stored and used like pre-defined processes, enabling users to extend the processing capabilities of a back-end. Users can now encapsulate algorithms in separate user-defined processes and re-use these in other user-defined processes. This also allows the exchange of processes (algorithms) between users and back-ends, and creates the possibility for a repository of user-contributed processes that other users can benefit from. A place for finding and sharing such processes is (planned to be) the openEO Hub (opens new window).

Through user-defined processes (and user defined functions) users may also be able to substitute processes that back-ends have not implemented. In fact, several pre-defined processes are defined with alternative processing instructions that use other openEO pre-defined processes to solve the task. For example, the pre-defined process normalized_ difference can be emulated with a combination of the processes add, subtract and divide. So if a back-end is missing the normalized_difference process, a user can just download it (opens new window) and use an openEO client to push it to the back-end where it then acts as drop-in replacement for the missing pre-defined process. In the future we plan to allow just using the URL of the process, so that no download/upload would be required.

As mentioned above, openEO offers a set of common processes (opens new window) to improve interoperability and portability between back-ends. This is not a complete list and is expected to be extended by the openEO Consortium. More than that, we would like to see that processes arise from the community and that we can take over and standardize whatever comes up and is useful for the different domains working with openEO. We can then simply use the user-defined processes as they share the same schema as the pre-defined processes.

The pre-defined openEO processes are released separately from the API to ensure a faster release cycle than we plan for the API. We want to be able to quickly adapt to community needs with additional processes. Future version of the processes are valid with any future API version through the specification of the process metadata standard in openEO.

The pre-defined processes itself also got an update just after the API release, but the changes (opens new window) are relatively small compared to the API. The focus was to clarify unspecified and ambiguous parts of the specification. Some processes such as apply_neighborhood and constant were added for the use cases. In collaboration with Julia Wagemann (opens new window) from the ECMWF (opens new window) we started defining processes such as aggregate_temporal_frequency, anomaly and climatological_normal for climatology and meteorology use-cases. Unfortunately, this effort could not be finished yet due to the COVID-19 situation. We want to continue to broaden the processes to be useful for more domains apart from core Earth Observation.

# STAC and OGC APIs

The openEO API now supports STAC (API) versions 0.9 and 1.x, which implements OGC API - Features. Thus, a potential integration of the openEO API with upcoming OGC API standards should be easy to establish.

# User-defined Functions (UDFs)

Note: Don't get confused, user-defined processes and user-defined functions describe different things.

Although already foreseen in API version 0.4.0, the openEO UDF API has evolved and is better integrated in the openEO API. It now allows to run Docker containers or custom scripts in potentially any programming language. The UDF API itself is not part of the openEO API, but is a separate specification to describe the data exchange between a back-end implementing the openEO API and a UDF API instance. The UDF API is in release candidate phase and we'll likely post a separate blog post focussing solely on them. Currently, implementations in R and Python are available. It is foreseen that users could also host their own UDF API instances and call them from remote back-ends.

# Other improvements

Other notable changes and additions:

  • Support for importing user-provided data sets has been added.
  • Providers can now expose their terms of service and privacy policies.
  • The WebSocket-based Subscription API for notifications and monitoring introduced in version 0.4 has been replaced by a logging mechanism in the HTTP API. This makes implementing the API easier for clients and back-ends, but doesn't work in real-time any longer.

The full set of changes can be found in the API's change log (opens new window).

# Limitations

With openEO, we are digging into new grounds regarding the interoperability of big Earth observation clouds. While we could solve a lot of issues that came up, there were some issues that we couldn't solve as part of the API and processes. We want to be honest and give some insights into the lessons learned, so I have posted these as a separate blog post.

# Next steps

The API's development went through several iterations and is working well for our broad range of use cases, which we'll report soon. Implementations by several organizations for several API versions provide evidence that the API and the processes are useful for a wide range of use cases. Client and back-end developers will now finish their implementations and we hope to have the first operational services this year. We will follow up with new blog posts once other releases are available. If you are interested in trying out openEO, please contact us to get more information.

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openEO

  • openEO Virtual User Workshop

openEO Virtual User Workshop

Written on September 2, 2020 by Matthias Schramm.

# UPDATE:

Presentations and Notebooks of the user workshop can be downloaded here (opens new window).

openEO hosts a (virtual) user workshop on 19 October 2020 (10:30 - 16:30 CEST in two parts) and encourages interested parties to register for this event at https://ec.europa.eu/eusurvey/runner/openEOUserWorkshop (opens new window).

Needed preparation: Manuals for installing the various client APIs, recommended for testing openEO in guided sessions, can be found in the User Documentation under Getting Started.

User questionnaire: Please help us also by spending some minutes for this user questionnaire (opens new window).

Access details: Please click the session topics below to connect to the corresponding sessions in Microsoft Teams.

Agenda:

Part 1
No. Start Duration Topic Remark
1.1 10:30h 30min Introduction into openEO + technical overview
1.2 11:00h 20min Overview backends
1.3 11:20h 20min Overview processes + documentation
1.4 11:40h 5min Installation of openEO
1.5 11:45h 15min Q/A: Backends, Installation, Q/A Chat Questionaire
Break for users to install openEO, partners stay available in chat for answering questions on that in bilateral talks
Part 2
No. Start Duration Topic Remark
2.1 14:00h 5min Welcome back / Ways for users to approach to openEO
Live Demontrations, Part 1
2.2a 14:05h 20min Python Client Parallel session
2.2b 14:05h 20min JavaScript Client + mobile application
14:25h 5min Short break / Switching sessions
Live Demontrations, Part 2
2.3a 14:30h 20min Web Editor + QGIS Parallel session
2.3b 14:30h 20min R Client
14:50h 5min Short break / Switching sessions
Guided Sessions and Q/A
2.4a 14:55h 60min Guided room for users to test the R Client Parallel session
2.4b 14:55h 60min Guided room for users to test the Python Client
2.4c 14:55h 60min Guided room for users to test other clients
2.5a 15:55h 15min Q/A and Discussions: R Client Parallel session
2.5b 15:55h 15min Q/A and Discussions: Python
2.5c 15:55h 15min Q/A and Discussions: Other clients
Wrap up
2.6 16:10h 20min Q/A; Discussion on user needs, future work

Background: openEO is a user-driven open source API, which grants a uniform communication between the Earth Observation (EO) data user community and diverse EO cloud service providers. Thus, it aims for a cross-platform interoperability to ease a comparison, the cooperation and the switch between them. The project consortium (H2020 Grant No 776242) released a stable version of the openEO API, covering standardised online processing of all aspects of an EO data life cycle on diverse cloud platforms.

In this User Workshop, the audience is provided with insight into the functionalities of the openEO API. In various live demonstration and training sessions, real demonstration cases will be explained from the user perspective. Possible applications of the openEO API will be presented for different access levels – from the use of programming languages to browser based model builders or mobile applications. Target audience for this event are programmers, decision makers, as well as third-party cloud EO service providers.

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  • MOOC Cubes and Clouds - open for registration

MOOC Cubes and Clouds - open for registration

Written on March 22, 2024 by Peter Zellner.

The latest EO college course “Cubes and Clouds” explores the newest concepts in Earth Observation through engaging lectures, videos, animated content, and hands-on exercises!

openEO plays a main role in the course due to its standardized API for homogenizing cloud processing. It allows for portablility of the created workflow between different cloud providers, reproducibility and sharing of the workflow which is enriched with valuable metadata. The idea behind and need for openEO are tought in a dedicated lecture. All hands-on exercises use openEO, guiding participants from basic processes to carrying out end-to-end EO workflows and sharing their results!

You can subscribe to the course for free and learn about cloud native EO at Cubes & Clouds – EO College (eo-college.org) (opens new window).

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