FME Transformers: 2024.1

Categories
Geometries

Geometries

Spatial Analysis

Spatial Analysis

Categories
Geometries

Geometries

Spatial Analysis

Spatial Analysis

AffineWarper

Performs warping operations on the spatial coordinates of features. It is used to adjust a set of observed input features according to a spatial transform defined by a set of control vectors. This transformer computes an affine (scale, rotation, and offset) transformation based on Control vector features and applies this transformation to the Observed features to generate output, and produces good corrections when the entire set of Observed data requires a single transformation.

Each Control feature represents a control vector (a 2-point line feature) whose start point is at some location in the original Observed data space, and whose end point is at the corresponding location in the desired output data space. Each control vector represents the correction required to go from the observed vertex to the desired vertex, in terms of direction and distance. Control vectors with only one point are interpreted as a requirement that this location not change from the observed dataset to the reference dataset. This is often referred to as a tie point.

Control vectors can be stored in a dataset of any format and read into FME, or generated directly in an FME workspace, which is often the case when the vectors are not known in advance and must be generated from incoming data.

Usage Notes

  • The RubberSheeter transformer provides similar functionality but applies a different transformation to each Observed vertex, depending on its distance to nearby Control vectors. This makes the RubberSheeter more appropriate for cases when the distortions in the data are not constant.
  • Please also see the Affiner, which performs an affine transformation on the coordinates of the feature.

Configuration

Input Ports

Two sets of features must be routed into this transformer.

Features that enter the Control port represent the control features used to compute the corrections.

Features that enter the Observed port are the features that will be corrected.

Output Ports

The modified Observed features are output via the Corrected port.

Parameters

Group By

The default behavior is to use the entire set of input features as the group. This option allows you to select attributes that define which groups to form. Each set of features that have the same value for all of these attributes will be processed as an independent group.

Complete Groups

 

Select the point in processing at which groups are processed:

When All Features Received

This is the default behavior. Processing will only occur in this transformer once all input is present.

When Group Changes (Advanced)

This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger processing on the currently accumulating group. This may improve overall speed (particularly with multiple, equally-sized groups), but could cause undesired behavior if input groups are not truly ordered.

There are two typical reasons for using When Group Changes (Advanced) . The first is incoming data that is intended to be processed in groups (and is already so ordered). In this case, the structure dictates Group By usage - not performance considerations.

The second possible reason is potential performance gains.

Performance gains are most likely when the data is already sorted (or read using a SQL ORDER BY statement) since less work is required of FME. If the data needs ordering, it can be sorted in the workspace (though the added processing overhead may negate any gains).

Sorting becomes more difficult according to the number of data streams. Multiple streams of data could be almost impossible to sort into the correct order, since all features matching a Group By value need to arrive before any features (of any feature type or dataset) belonging to the next group. In this case, using Group By with When All Features Received may be the equivalent and simpler approach.

Note  Multiple feature types and features from multiple datasets will not generally naturally occur in the correct order.

As with many scenarios, testing different approaches in your workspace with your data is the only definitive way to identify performance gains.

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

String Functions

These functions manipulate and format strings.

Special Characters

 A set of control characters is available in the Text Editor.

Math Functions

Math functions are available in both editors.
Date/Time Functions Date and time functions are available in the Text Editor.

Math Operators

These operators are available in the Arithmetic Editor.

FME Feature Functions

These return primarily feature-specific values.

FME Parameters

FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters Create your own editable parameters.

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

  • Add a row
  • Remove a row
  • Move current row up one
  • Move current row down one
  • Move current row to top
  • Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

  • Cut a row - delete and copy to clipboard
  • Copy a row to the clipboard
  • Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

FME Community

The FME Community is the place for demos, how-tos, articles, FAQs, and more. Get answers to your questions, learn from other users, and suggest, vote, and comment on new features.

Search for all results about the AffineWarper on the FME Community.