F(x) Tables With Extra Input

The plethora of f(x) nodes all have a similar role as the Calculator List node. But where the Calculator List node shines when the calculations are simple, the f(x) nodes are better suited for more advanced calculations since the code is kept in a separate python file. You can place this python file anywhere, but it might be a good idea to keep it in the same folder as your workflow or in a subfolder to that folder.

The script file

When writing a “function” (it is actually a python class) you need to inherit from one of the two classes TableWrapper or TablesWrapper. The first one is easier and should be your default choice.

The TableWrapper provides access to the input and output table with self.in_table and self.out_table respectively. These variables are of type table.File, so use the Table API to read/write the data. For example:

from sympathy.api import table_wrapper

class MyCalculation(table_wrapper.TableWrapper):
    def execute(self):
        spam = self.in_table.get_column_to_array('spam')

        # My super advanced calculation that totally couldn't be
        # done in the :ref:`Calculator List` node:
        more_spam = spam + 1

        self.out_table.set_column_from_array('more spam', more_spam)

When working with a list of tables you can use the same base class as above and your code will conveniently be executed once for each table. Alternatively you can use the class sympathy.api.table_wrapper.TablesWrapper (note the s). This class will give access to the entire list all at once. This is useful for when you need your code to be aware of several different tables from the list at once, or if you need to output a list with a different number of tables than the input. If you don’t need any of these features you’re better off using the TableWrapper base class.

When using the TablesWrapper base class you should access the input and output data with self.in_table_list and self.out_table_list respectively (which are of the type table.FileList). An example function:

from sympathy.api import table_wrapper

class MyCalculation(table_wrapper.TablesWrapper):
    def execute(self):
        for in_table in self.in_table_list:
            # Only input tables with a column named 'spam' will yield an
            # output table.
            if 'spam' not in in_table.column_names():
                continue
            spam = in_table.get_column_to_array('spam')

            # My super advanced calculation that totally couldn't be
            # done in the :ref:`Calculator List` node:
            more_spam = spam + 1

            out_table = self.out_table_list.create()
            out_table.set_column_from_array('more spam', more_spam)
            self.out_table_list.append(out_table)

Three of the f(x) nodes (F(x) Table With Extra Input, F(x) Tables With Extra Input, F(x) Tables With Extras Input) have an extra table input port. You can access the extra table(s) as self.extra_table.

Testing your script

When writing an f(x) script it can be convenient to be able to run the script from inside Spyder without switching to Sympathy. To do this you should first export all the data that the f(x) node receives on its input port as a sydata file. Then add a code block like this to the end of your script file:

if __name__ == "__main__":
    from sympathy.api import table
    input_filename = r"/path/to/input_file.sydata"

    with table.File(filename=input_filename, mode='r') as input_file:
        output_file = table.File()
        MyCalculation(input_file, output_file).execute()

You can now step through the code, set up break points and inspect variables as you run the script. Note that this will only work if the script is meant to be run with the Clean output option selected.

Configuration

When Clean output is enabled (the default) the output table will be empty when the functions are run.

When the Clean output setting is disabled the entire input table will get copied to the output before running the functions in the file. This is useful when your functions should only add a few columns to a data table, but in this case you must make sure that the output has the same number of rows as the input.

By default (pass-through disabled) only the functions that you have manually selected in the configuration will be run when you execute the node, but with the pass-through setting enabled the node will run all the functions in the selected file. This can be convenient in some situations when new functions are added often.

class node_table_function_selector.FunctionSelectorTablesWithExtra(*args, **kwargs)

Apply functions to a list of Tables. Also passes an extra auxiliary Table to the functions.

Inputs:

port1 : Datasource

Path to Python file with scripted functions.

extra : Table

Extra Table with eg. specification data.

port2 : Tables

Tables with data to apply functions to.

Outputs:

port3 : Tables

Tables with the results from the applied functions

Configuration:

Clean output

If disabled the incoming data will be copied to the output before running the nodes.

Put results in common outputs : checkbox

Use this checkbox if you want to gather all the results generated from an incoming Table into a common output. This requires that the results will all have the same length. An exception will be raised if the lengths of the outgoing results differ. It is used only when clean output is active. Otherwise it will be disabled and can be considered as checked.

Select functions

Choose one or many of the listed functions to apply to the content of the incoming Table.

Enable pass-through

If disabled only selected functions are run. Enable this to override the functions selection and run all functions in the python file.

Ref. nodes:

F(x) Table