GNATS simulation

para-atm includes capabilities to facilitate running the GNATS simulation from within Python. These capabilities are provided by the paraatm.io.gnats module. The following functionality is provided:

  • Boilerplate code to automatically start and stop the Java virtual machine, and to prevent it from being started multiple times
  • Behind-the-scenes path handling, so that the GNATS simulation does not need to be run from within the GNATS installation directory
  • A utility function to retrieve GNATS constants from the Java environment
  • Return trajectory results directly as a pandas DataFrame
  • Both high-level and low-level interfaces for defining simulations

The functions for interfacing with GNATS are subject to change. Currently, the code has been tested with GNATS beta1.10 on Ubuntu Linux.

Two mechanisms are provided for interfacing with the GNATS simulation. The first is GNATS basic interface, which provides a simple interface for running a simulation from given TRX and MFL files without having to write any of the code to drive the simulation. The second option is to use GNATS wrapper interface, which provides more control over the simulation but requires writing more code.

GNATS basic interface

The GNATS basic interface makes it possible to run a GNATS simulation without having to write any of the GNATS driver code. The simulation is specified by providing the TRX and MFL files. The trajectory results are returned as a simulation output in the form of a DataFrame.

The basic interface is implemented through the GnatsBasicSimulation class. The following is a complete example, which recreates the DEMO_Gate_To_Gate_Simulation_SFO_PHX_beta1.9.py from the GNATS samples directory.

1
2
3
4
5
6
7
8
9
 import os
 from paraatm.io.gnats import GnatsBasicSimulation, GnatsEnvironment

 GnatsEnvironment.start_jvm()
 trx_file = os.path.join(GnatsEnvironment.share_dir, "tg/trx/TRX_DEMO_SFO_PHX_GateToGate_geo.trx")
 mfl_file = os.path.join(GnatsEnvironment.share_dir, "tg/trx/TRX_DEMO_SFO_PHX_mfl.trx")

 simulation = GnatsBasicSimulation(trx_file, mfl_file, 22000, 30)
 df = simulation()['trajectory']

Lines 5 and 6 specify the locations of the TRX and MFL files that will be used. In this example, these files are referenced within the GNATS installation directory, by accessing the GnatsEnvironment.share_dir variable, which stores the location of the “share” directory based on GNATS_HOME. Of course, the user is free to specify TRX and MFL files that are not located within the GNATS installation as well. Note that line 4, which manually starts the JVM, is necessary so that GnatsEnvironment.share_dir is available for use on lines 5 and 6.

Line 8 creates an instance of the simulation class by specifying the input files as well as the simulation propagation time and time step. Line 9 executes the simulation and stores the trajectory results in the variable df. More information about the simulation results is given below in Running the GNATS simulation.

If more control over the simulation is needed, the user can use the GNATS wrapper interface (the basic simulation interface itself is implemented in terms of the wrapper interface).

GNATS wrapper interface

The GNATS wrapper interface is provided for users that need more control over the simulation (e.g., to customize the simulation inputs or to pause the simulation as it runs). para-atm provides a base class that the user can derive from, which automates some of the steps of interfacing with GNATS.

Creating a GNATS simulation

The GNATS wrapper interface is used by writing a class that derives from the GnatsSimulationWrapper class. This is best understood through an example. The complete code for the following example is available at tests/gnats_gate_to_gate.py, and it is based on DEMO_Gate_To_Gate_Simulation_SFO_PHX_beta1.9.py from the GNATS samples directory.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
 from paraatm.io.gnats import GnatsSimulationWrapper, GnatsEnvironment

 class GateToGate(GnatsSimulationWrapper):
     def simulation(self):

         GNATS_SIMULATION_STATUS_PAUSE = GnatsEnvironment.get_gnats_constant('GNATS_SIMULATION_STATUS_PAUSE')
         GNATS_SIMULATION_STATUS_ENDED = GnatsEnvironment.get_gnats_constant('GNATS_SIMULATION_STATUS_ENDED')

         DIR_share = GnatsEnvironment.share_dir

         simulationInterface = GnatsEnvironment.simulationInterface
         environmentInterface = GnatsEnvironment.environmentInterface
         aircraftInterface = GnatsEnvironment.aircraftInterface
         # ...

In this example, Line 3 defines the GateToGate class as a subclass of GnatsSimulationWrapper class. Then, the simulation() method is defined. This is where the user’s code for setting up and running the GNATS simulation should go. Notice that lines 6 and 7 use the get_gnats_constant() method to retrieve specific constants from the Java environment, which are used later in the simulation code.

Line 9 gets a reference to the location of the “share” directory used by GNATS. Lines 11-13 retrieve references to the interface objects, which are available through GnatsEnvironment. The bulk of the remaining code follows the example file that is included with GNATS.

As compared to the GNATS sample file, some key differences in this implementation are:

  • from GNATS_Python_Header_standalone import * is not used (in general, import * is not advisable)
  • Cleanup calls for gnatsStandalone.stop() and shutdownJVM() are not needed, as they are automatically handled
  • GNATS constants are retrieved using the utility function get_gnats_constant(), as opposed to importing the constants from GNATS_Python_Header_standalone.py, where each constant is manually defined

Running the GNATS simulation

Once the user-defined class deriving from GnatsSimulationWrapper has been created, the simulation is executed by creating an instance of the class and calling its __call__() method. This method will handle various setup behind the scenes, such as starting the JVM, creating the GNATSStandalone instance, and preparing the current working directory. Once the simulation is prepared, the user’s simulation() method is called automatically. The output file is automatically created by communicating with the user-defined write_output() method, and the trajectory results are stored as a DataFrame in the 'trajectory' key of the returned dictionary.

For example, the GateToGate simulation class defined above could be invoked as:

1
2
g2g_sim = GateToGate()
df = g2g_sim()['trajectory']

Here, line 1 creates an instance of the GateToGate class. Line 2 executes the simulation, passing no arguments (note that the () operator invokes the __call__ method). The return value of g2g_sim() is a dictionary, and we retrieve the value of the 'trajectory' key, which is a DataFrame that stores the resulting trajectory data. Note that line 2 is just shorthand for:

results = g2g_sim()
df = results['trajectory']

Additional keyword arguments provided to __call__() are passed on to simulation(). This makes it possible to create a simulation instance that accepts parameter values. For example:

1
2
3
4
5
6
7
class MySim(GnatsSimulationWrapper):
    def simulation(self, my_parameter):
        # .. Perform simulation using the value of my_parameter

my_sim = MySim()
df1 = my_sim(my_parameter=1)['trajectory']
df2 = my_sim(my_parameter=2)['trajectory']

Here, the user-defined simulation() method on line 2 is defined to accept an argument, my_parameter. Once the simulation class is instantiated, repeated calls can be made using different parameter values, as shown on lines 6 and 7.

If the simulation method itself returns values, __call__() stores these in the 'sim_results' key of the dictionary that it returns. For example:

1
2
3
4
5
6
7
class MySimWithReturnVals(GnatsSimulationWrapper):
    def simulation(self):
        # .. Perform simulation
        return some_data

my_sim = MySimWithReturnVals()
some_data = my_sim(return_df=False)['sim_results']

In this example, the call to my_sim() on line 7 uses the return_df=False option to suppress storing the trajectory results. However, this is not required, and both trajectory results and custom return values can be returned if needed.

The API

class paraatm.io.gnats.GnatsBasicSimulation(trx_file, mfl_file, propagation_time, time_step)

Simple interface for running a GNATS simulation from TRX and MFL files

If more control is needed, create a subclass of GnatsSimulationWrapper

__init__(trx_file, mfl_file, propagation_time, time_step)

Define basic simulation

Parameters:
  • trx_file (str) –
  • mfl_file (str) –
  • propagation_time (int) – Total flight propagation time in seconds
  • time_step (int) – Time step in seconds
__call__(output_file=None, return_df=True, **kwargs)

Execute GNATS simulation and write output to specified file

Parameters:
  • output_file (str) – Output file to write to. If not provided, a temporary file is used
  • return_df (bool) – Whether to read the output into a DataFrame and return it
  • **kwargs – Extra keyword arguments to pass to simulation call
Returns:

A dictionary with the following keys:
’trajectory’ (if return_df==True)

DataFrame with trajectory results

’sim_results’

Return value from child simulation method
Return type:

dict
class paraatm.io.gnats.GnatsSimulationWrapper

Parent class for creating a GNATS simulation instance

Users should implement the following methods in the derived class:

simulation
This method runs the actual GNATS simulation. If the simulation code needs to access data files relative to the original working directory, use the GnatsEnvironment.build_path() method, which will produce an appropriate path to work around the fact that GNATS simulation occurs in the GNATS_HOME directory.
write_output
This method writes output to the specified filename.
cleanup
Cleanup code that will be called after simulation and write_output. Having cleanup code in a separate method makes it possible for cleanup to occur after write_output. The cleanup code should not stop the GNATS standalone server or the JVM, as this is handled by the GnatsEnvironment class.

Once an instance of the class is created, the simulation is run by calling the instance as a function, which will go to the __call__() method. This will call the user’s simulation method, with additional pre- and post-processing steps. The JVM will be started automatically if it is not already running.

simulation()

Users must implement this method in the derived class

Assume that the jvm is already started and that it will be shutdown by the parent class.

The function may accept parameter values, which must be provided as keyword arguments when invoking __call__().

write_output(filename)

Users must implement this method in the derived class

It will be called after the simulation method and should issue the commands necessary to write the output to the specified file.

__call__(output_file=None, return_df=True, **kwargs)

Execute GNATS simulation and write output to specified file

Parameters:
  • output_file (str) – Output file to write to. If not provided, a temporary file is used
  • return_df (bool) – Whether to read the output into a DataFrame and return it
  • **kwargs – Extra keyword arguments to pass to simulation call
Returns:

A dictionary with the following keys:
’trajectory’ (if return_df==True)

DataFrame with trajectory results

’sim_results’

Return value from child simulation method
Return type:

dict
class paraatm.io.gnats.GnatsEnvironment

Class that provides static methods to start and stop the JVM for GNATS

classmethod start_jvm(gnats_home=None)

Start java virtual machine and GNATS standalone server

This function is called automatically by GnatsSimulationWrapper, so normally there is no need for the user to call it directly.

If the JVM is already running, this will do nothing. If the JVM has already been stopped, this will raise an error, since it cannot be restarted.

This function takes care of setting the Java classpath, changing directories, starting the JVM, and starting the GNATS standalone server.

References to gnatsStandalone as well as other interface objects, which are normally available via the GNATS header file, are stored as attributes of the class.

Path issues with GNATS are handled behind the scenes by setting the classpath and changing directories prior to starting the JVM. The original directory is remembered, and it is restored after the JVM is stopped.

Parameters:gnats_home (str, optional) – Path to GNATS home directory. If not provided, the GNATS_HOME environment variable will be used.
classmethod stop_jvm()

Stop java virtual machine and GNATS server

This also moves back to the original directory that was set prior to starting the JVM

If this function is not called manually, it will be called automatically at exit to make sure that the JVM is properly shutdown. Multiple calls are OK.

classmethod get_gnats_standalone()

Retrieve reference to GNATSStandalone class instance

classmethod get_gnats_constant(name, classname='Constants')

Return the variable that stores the named GNATS constant

Parameters:
  • name (str) – Name of GNATS constant to retrieve
  • classname (str) – Name of the Java class under which the constant is defined (refer to the GNATS Python header file)
classmethod build_path(filename)

Return a path to filename that behaves as if original directory is current working directory

This will internally convert relative paths to be relative to the original working directory (otherwise, GNATS considers GNATS_HOME to be the working directory).