PythonCaller

Executes a Python script to manipulate the feature.

When a specialized task is required, such as custom statistical analysis of an attribute, but Workbench does not provide a transformer suited to the task, a Python script can perform specialized and complex operations on a feature's geometry, attributes, and coordinate system.

Note: Using Python to perform arbitrary operations on features is a powerful aspect of Workbench. However, the logic introduced into a workspace is less visible and can therefore be more difficult to maintain than logic built using Workbench’s built-in transformers. It is recommended that other transformers be used when possible instead of Python scripts.

Access to a feature’s attributes, geometry and coordinate system information is provided via the FME Objects Python API.

Interface Paradigm

The PythonCaller can interface with a Python script in two different ways: by function or by a class:

• Use the Function Interface when you intend to process a single feature at a time.
• Use the Class Interface for more flexibility.

The Class Interface is useful when you want to operate on a group of features together, such as collecting all the features received and then outputting them in a specific sort order. Another common use case is to accumulate all the features, perform an operation on the whole set, and then output all of the features with a calculated value as a new attribute.

Function Interface Example

The PythonCaller will call the Python function with exactly one argument: an FMEFeature object.

The function will be called with each FMEFeature that comes into the input port. This feature will then continue through the workspace pipeline via the output port.

The function’s return value will be ignored by the PythonCaller. Any raised exception will terminate the translation. Any raised FMEException will be logged as an ERROR and will terminate the translation.

The example below adds a string attribute to each feature and sets it to the current time:

import fmeobjects

import time

def timestampFeature(feature):

curTime = time.ctime(time.time())

feature.setAttribute("timestamp", curTime)

Class Interface Example

The PythonCaller will call two methods on the class: input() and close(). The input() method will be called for each FMEFeature that comes into the input port. When no more FMEFeatures remain, the close() method will be called. Features that need to continue through the workspace for further processing must be explicitly written out using the pyoutput() method.

The class interface can operate on a group of features, instead of processing incoming features one at a time. This is done by storing incoming features in a list, then processing them all at once before being output.

The example below calculates the total area of all the features processed and then outputs all the features with a new attribute containing the total area:

import fmeobjects

class FeatureProcessor(object):

def __init__(self):

self.featureList = []

self.totalArea = 0.0

def input(self,feature):

self.featureList.append(feature)

self.totalArea += feature.getGeometry().getArea()

def close(self):

 for feature in self.featureList:

 feature.setAttribute("total_area", self.totalArea)

 self.pyoutput(feature)

Script Editing

A PythonCaller transformer can call scripts that are stored in the transformer itself or scripts that are stored globally for the entire workspace:

• To store a Python script with a specific PythonCaller transformer, use the Python Script parameter in the transformer.
• To store a Python script globally, click the Advanced Workspace Parameter in the Navigator, and double-click Startup Python Script. Storing scripts globally has the advantage of keeping the Python logic centralized, which makes editing and maintenance easier. This is useful when there are multiple PythonCaller transformers throughout the workspace that use the same script. For more information, see Startup and Shutdown Python Scripts in the FME Workbench help.

FME can access .py modules that are stored on the file system, including modules in external Python libraries. Use the Python "import" command to load these modules. FME will search both the standard Python module locations and the workspace location to find the module to be imported.

Parameters

Dependencies

Specifying a Python Interpreter

An FME installation includes a Python version 2.7 and Python version 3.5 interpreter. The default Python interpreter used for Python processing is the Python 2.7 interpreter. The FME Objects Python API supports Python 2.7, Python 3.4, and Python 3.5.

The Python interpreter used by FME to execute Python scripts is controlled by the Python Compatibility workspace parameter and the Preferred Python Interpreter setting under Tools > FME Options > Translation.

Python Compatibility specifies the version of Python with which Python scripts are compatible. FME loads the Preferred Python Interpreter if it is compatible with the Python Compatibility. If not, FME loads an appropriate Python interpreter matching Python Compatibility.

For more information, see the FME Workbench help.

Installing Python Packages

If you would like to install a third-party package for use by Python in FME, see Installing Python Packages to FME Desktop in the FME Workbench help.

Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Transformer Categories

Workflows

FME Licensing Level

FME Professional edition and above

Search FME Knowledge Center

Search for samples and information about this transformer on the FME Knowledge Center.