fameio

FAME-Io

Python scripts for FAME models, generation of protobuf input files and conversion of protobuf output files. Please visit the FAME-Wiki to get an explanation of FAME and its components.

Installation

We recommend installing fameio using PyPI:

pip install fameio

You may also use pipx. For detailed information please refer to the official pipx documentation.

pipx install fameio

fameio is currently developed and tested for Python 3.8 or higher. See the pyproject.toml for a complete listing of dependencies.

Usage

FAME-Io currently offers two main scripts makeFameRunConfig and convertFameResults. Both are automatically installed with the package. The first one creates a protobuf file for FAME applications using YAML definition files and CSV files. The latter one reads output files from FAME applications in protobuf format and converts them to CSV files.

You may use the example data provided for the AMIRIS model which can be used to simulate electricity markets in Germany, Austria, and a simple proof-of-concept model.

Make a FAME run configuration

Digests configuration files in YAML format, combines them with CSV data files and creates a single input file for FAME applications in protobuf format. Call structure:

makeFameRunConfig -f <path/to/scenario.yaml>

You may also specify any of the following arguments:

Command	Action
-l or --log	Sets the logging level. Default is info. Options are debug, info, warning, warn, error, critical.
-lf or --logfile	Sets the logging file. Default is None. If None is provided, all logs get only printed to the console.
-o or --output	Sets the path of the compiled protobuf output file. Default is config.pb.
-enc or --encoding	Sets the encoding of all yaml files to the given one (e.g. 'utf8' or 'cp1252'. Default is None, i.e. your operating system's standard.

This could look as follows:

makeFameRunConfig -f <path/to/scenario.yaml> -l debug -lf <path/to/scenario.log> -o <path/to/config.pb>

You may also call the configuration builder from any Python script with

from fameio.scripts.make_config import Options, run as make_config

make_config({Options.FILE: "path/to/scenario.yaml", })

Similar to the console call you may also specify custom run config arguments and add it in a dictionary to the function call.

from fameio.scripts.make_config import Options, run as make_config

run_config = {Options.FILE: "path/to/scenario.yaml",
              Options.LOG_LEVEL: "info",
              Options.OUTPUT: "output.pb",
              Options.LOG_FILE: "scenario.log",
              }

make_config(run_config)

You can also use the associated argument parser, to extract the run_config dynamically from a string:

from fameio.scripts.make_config import Options, run as make_config
from fameio.source.cli.make_config import handle_args

my_defaults = {Options.FILE: "path/to/scenario.yaml",
                 Options.LOG_LEVEL: "info",
                 Options.OUTPUT: "output.pb",
                 Options.LOG_FILE: "scenario.log",
                 }
my_arg_string = ['-f', 'my/other/scenario.yaml', '-l', 'error']

run_config = handle_args(my_arg_string, my_defaults)
make_config(run_config)

Scenario YAML

The "scenario.yaml" file contains all configuration options for a FAME-based simulation. It consists of the sections Schema, GeneralProperties, Agents and Contracts, and the optional section StringSets. All of them are described below.

Schema

The Schema describes a model's components such as its types of agents, their inputs, what data they exchange, etc. It is also used to validate the model inputs provided in the scenario.yaml. Since the Schema is valid until the model itself is changed, it is recommended to defined it in a separate file and include the file here.

Currently, the schema specifies:

• which type of Agents can be created
• what type of input attributes an Agent uses
• what type of Products an Agent can send in Contracts, and
• the names of the Java packages for the classes corresponding to Agents, DataItems and Portables.

The Schema consists of the sections JavaPackages and AgentTypes.

JavaPackages

This section defines the name of the Java packages in which the model code is located. A similar data set was formerly specified in the fameSetup.yaml, but is now specified in the schema. Each of the three sections Agents, DataItems, and Portables contain a list of fully qualified java package names of your model's classes. Package names can occur in multiple lists and may overlap. It is not necessary (but possible) to specify the nearest enclosing package for each Agent, DataItem or Portable. Specifying any super-package will also work. Also, package names occur on multiple lists for Agent, DataItem or Portable.

For example, for a project with all its

• Agent-derived java classes located in packages below the package named "agents",
• DataItem implementation classes in a subpackage named "msg",
• Portable implementation classes in a subpackages named "portableItems" and "otherPortables",

the corresponding section in the schema would look like this:

JavaPackages:
  Agents:
    - "agents"
  DataItems:
    - "msg"
  Portables:
    - "portableItems"
    - "otherPortables"

AgentTypes

Here, each type of agent that can be created in your FAME-based application is listed, its attributes and its available Products for Contracts. The structure of this section

AgentTypes:
  MyAgentType:
    Attributes:
      MyAttribute:
        ...
      MyOtherAttribute:
        ...
    Products: [ 'Product1', 'Product2', 'Product3' ]
  MyOtherAgentWithoutProductsOrAttributes:

• MyAgentType Java's simple class name of the Agent type
• Attributes indicates that beginning of the attribute definition section for this Agent type
• MyAttribute Name of an attribute as specified in the corresponding Java source code of this Agent type (annotated with "@Input")
• MyOtherAttribute Name of another attribute derived from Java source code
• Products list of Products that this Agent can send in Contracts; derived from Java source code of this Agent type (annotated with "@Product")
• MyOtherAgentWithoutProductsOrAttributes an Agent type that requires neither Attributes nor Products

Both Attributes and Products are optional - there could be useful Agents that require neither of them. In the above example attribute definition was not shown (indicated by ...). The next example provides details on how to define an attribute:

MySimpleAttribute:
  AttributeType: enum
  Mandatory: true
  List: false
  Values: [ 'AllowedValue1', 'AllowedValue2' ]
  Default: 'AllowedValue1'
  Help: 'My help text'

MyComplexAttribute:
  AttributeType: block
  NestedAttributes:
    InnerAttributeA:
      AttributeType: integer
    InnerAttributeB:
      AttributeType: double

• MySimpleAttribute, MyDoubleList, MyComplexAttribute Names of the attributes as specified in the Java enum annotated with "@Input"
• AttributeType (required) data type of the attribute; see options in table below
• Mandatory (optional - true by default) if true: the attribute is required for this agent and validation will fail if the attribute is missing in the scenario and no default is provided
• List (optional - false by default)
  • AttributeType: time_series cannot be true
  • AttributeType: block
    • if true: any nested element in the scenario must be part of a list element and thus can appear multiple times
    • if false: any nested element in the scenario can only appear once
  • any other AttributeType: the attribute is interpreted as list, i.e. multiple values can be assigned to this attribute in the scenario
• NestedAttributes (required only if AttributeType: block, otherwise disallowed) starts an inner Attribute definition block - defined Attributes are sub-elements of MyComplexAttribute
• Values (optional - None by default): if present defines a list of allowed values for this attribute
• Default (optional - None by default): if present defines a default value to be used in case the scenario does not specify it
• Help (optional - None by default): if present defines a help text to you attribute

AttributeType	value
integer	a 32-bit integer value
double	a 64-bit floating-point value (integers also allowed)
long	a 64-bit integer value
time_stamp	either a FAME time stamp string or 64-bit integer value
string	any string
string_set	a string from a set of allowed Values defined in StringSet section in scenario
enum	a string from a set of allowed Values defined in schema
time_series	either a path to a .csv-file or a single 64-bit floating-point value; does not support List: true
block	this attribute has no value of its own but hosts a group of nested Attributes; implies NestedAttributes to be defined

GeneralProperties

Specifies FAME-specific properties of the simulation. Structure:

GeneralProperties:
  RunId: 1
  Simulation:
    StartTime: 2011-12-31_23:58:00
    StopTime: 2012-12-30_23:58:00
    RandomSeed: 1
  Output:
    Interval: 100
    Process: 0

Parameters:

• RunId an ID that can be given to the simulation; use at your discretion
• StartTime time stamp in the format YYYY-MM-DD_hh:mm:ss; first moment of the simulation.
• StopTime time stamp in the format YYYY-MM-DD_hh:mm:ss; last moment of the simulation - i.e. simulation terminates after passing that time stamp
• RandomSeed seed to initialise random number generation; each value leads to a unique series of random numbers.

Parameters in section Output are deprecated and will be removed in FAME-Io v3.0

• Interval number of simulation ticks in between write-to-disk events; may be used for performance optimisations;
• Process id of process that performs write-to-disk operations; leave at 0 to be compatible with single-processes;

Agents

Specifies all Agents to be created in the simulation in a list. Each Agent has its own entry. Structure:

Agents:
  - Type: MyAgentWithInputs
    Id: 1
    Attributes:
      MyEnum: SAME_SHARES
      MyInteger: 2
      MyDouble: 4.2
      MyTimeSeries: "./path/to/time_series.csv"

  - Type: MyAgentWithoutInputs
    Id: 2

Agent Parameters:

• Type Mandatory; Java's simple class name of the agent to be created
• Id Mandatory; simulation-unique id of this agent; if two agents have the same ID, the configuration process will stop.
• Attributes Optional; if the agent has any attributes, specify them here in the format "AttributeName: value"; please see attribute table above

The specified Attributes for each agent must match the specified Attributes options in the linked Schema (see above). For better structure and readability of the scenario.yaml, Attributes may also be specified in a nested way as demonstrated below.

Agents:
  - Type: MyAgentWithInputs
    Id: 1
    Attributes:
      Parent:
        MyEnum: SAME_SHARES
        MyInteger: 2
      Parent2:
        MyDouble: 4.2
        Child:
          MyTimeSeries: "./path/to/time_series.csv"

In case Attributes are defined with List: true option, lists are assigned to an Attribute or Group:

Attributes:
  MyDoubleList: [ 5.2, 4.5, 7, 9.9 ]
  MyListGroup:
    - IntValueA: 5
      IntValueB: 42
    - IntValueA: 7
      IntValueB: 100

Here, MyDoubleList and MyListGroup need to specify List: true in the corresponding Schema. The shorter []-notation was used to assign a list of floating-point values to MyDoubleList. Nested items IntValueA and IntValueB of MyListGroup are assigned within a list, allowing the specification of these nested items several times.

Contracts

Specifies all Contracts, i.e. repetitive bilateral transactions in between agents. Contracts are given as a list. We recommend moving Contracts to separate files and to use the !include command to integrate them in the scenario.

Contracts:
  - SenderId: 1
    ReceiverId: 2
    ProductName: ProductOfAgent_1
    FirstDeliveryTime: -25
    DeliveryIntervalInSteps: 3600

  - SenderId: 2
    ReceiverId: 1
    ProductName: ProductOfAgent_2
    FirstDeliveryTime: -22
    DeliveryIntervalInSteps: 3600
    Attributes:
      ProductAppendix: value
      TimeOffset: 42

Contract Parameters:

• SenderId unique ID of agent sending the product
• ReceiverId unique ID of agent receiving the product
• ProductName name of the product to be sent
• FirstDeliveryTime first time of delivery in the format "seconds after the January 1st 2000, 00:00:00"
• DeliveryIntervalInSteps delay time in between deliveries in seconds
• Attributes can be set to include additional information as int, float, enum, or dict data types

Definition of Multiple Similar Contracts

Often, scenarios contain multiple agents of similar type that also have similar chains of contracts. Therefore, FAME-Io supports a compact definition of multiple similar contracts. SenderId and ReceiverId can both be lists and support One-to-N, N-to-One and N-to-N relations like in the following example:

Contracts:
  # effectively 3 similar contracts (0 -> 11), (0 -> 12), (0 -> 13)
  # with otherwise identical ProductName, FirstDeliveryTime & DeliveryIntervalInSteps
  - SenderId: 0
    ReceiverId: [ 11, 12, 13 ]
    ProductName: MyOtherProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

  # effectively 3 similar contracts (1 -> 10), (2 -> 10), (3 -> 10)
  # with otherwise identical ProductName, FirstDeliveryTime & DeliveryIntervalInSteps
  - SenderId: [ 1, 2, 3 ]
    ReceiverId: 10
    ProductName: MyProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

  # effectively 3 similar contracts (1 -> 11), (2 -> 12), (3 -> 13)
  # with otherwise identical ProductName, FirstDeliveryTime & DeliveryIntervalInSteps
  - SenderId: [ 1, 2, 3 ]
    ReceiverId: [ 11, 12, 13 ]
    ProductName: MyThirdProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

Combined with YAML anchors complex contract chains can be easily reduced to a minimum of required configuration. The following example is equivalent to the previous one and allows a quick extension of contracts to a new couple of agents e.g. (4;14):

Groups:
  - &agentList1: [ 1,2,3 ]
  - &agentList2: [ 11,12,13 ]

Contracts:
  - SenderId: 0
    ReceiverId: *agentList2
    ProductName: MyOtherProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

  - SenderId: *agentList1
    ReceiverId: 10
    ProductName: MyProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

  - SenderId: *agentList1
    ReceiverId: *agentList2
    ProductName: MyThirdProduct
    FirstDeliveryTime: 100
    DeliveryIntervalInSteps: 3600

StringSets

This optional section defines values of type string_set. In contrast to enum values, which are statically defined in the Schema, string_set values can be dynamically defined in this section. If an agent attribute is of type string_set and the attribute is set in the scenario, then

1. the section StringSets in the scenario must contain an entry named exactly like the attribute, and
2. the attribute value must be contained in the string set's Values declaration.

For instance:

In schema:

AgentTypes:
  FuelsMarket:
    Attributes:
      FuelType:
        AttributeType: string_set

In scenario:

StringSets:
  FuelType:
    Values: ['OIL', 'HARD_COAL', 'LIGNITE']

Agents:
 - Type: FuelsMarket
   Id: 1
   Attributes:
     FuelType: OIL

Important: If different types of Agents shall refer to the same StringSet, their attributes in schema must have the exact same name.

CSV files

TIME_SERIES inputs are not directly fed into the Scenario YAML file. Instead, TIME_SERIES reference a CSV file that can be stored some place else. These CSV files follow a specific structure:

• They should contain exactly two columns - any other columns are ignored. A warning is raised if more than two non-empty columns are detected.
• The first column must be a time stamp in form YYYY-MM-DD_hh:mm:ss
• The second column must be a numerical value (either integer or floating-point)
• The separator of the two columns is a semicolon
• The data must not have headers, except for comments marked with #

You may add comments using #. Exemplary content of a valid CSV file:

# If you want an optional header, you must use a comment
2012-01-01_00:00:00;400
2013-01-01_00:00:00;720.5
2014-01-01_00:00:00;650
2015-01-01_00:00:00;99.27772
2016-01-01_00:00:00;42  # optional comment on this particular data point
2017-01-01_00:00:00;0.1

Please refer also to the detailed article about TimeStamps in the FAME-Wiki.

Split and join multiple YAML files

The user may include other YAML files into a YAML file to divide the content across files as convenient. We explicitly recommend using this feature for the Schema and Contracts sections. Otherwise, the scenario.yaml may become crowded.

Command: !Include

To hint YAML to load the content of another file use !include "path/relative/to/including/yaml/file.yml". You can concatenate !include commands and can use !include in the included file as well. The path to the included file is always relative to the file using the !include command. So with the following file structure

file-structure

a.yaml
folder/b.yaml
folder/c.yaml
folder/deeper_folder/d.yaml

the following !include commands work

in a.yaml

ToBe: !include "folder/b.yaml"
OrNot: !include "folder/deeper_folder/d.yaml"

in b.yaml

ThatIs: !include "c.yaml"
TheQuestion: !include "deeper_folder/d.yaml"

Provided that

in c.yaml

Or: maybe

d.yaml

not: "?"

the resulting file would look like this:

THe Joined file a.yaml

ToBe:
  ThatIs:
    Or: maybe
  TheQuestion:
    not: "?"
OrNot:
  not: "?"

You may also specify absolute file paths if preferred by starting with a "/".

When specifying only a file path, the complete content of the file is assigned to the given key. You always need a key to assign the !include command to. However, you cannot combine the value returned from !include with other values in the same key. Thus, the following combinations do not work:

caveats.yml

!include "file.yaml" # no key assigned

Key:
  Some: OtherItem
  !include "file.yaml" # cannot join with other named items

List:
  - an: entry
  !include "file.yaml" # cannot directly join with list items, even if !include returns a list

Integrate specific nodes of YAML files

Instead of including all content in the included file, you may also pick a specific node within that file. For this use !include [<relative/path/to/file.yaml>, Path:To:Field:In:Yaml]. Here, : is used in the node-specifying string to select a sequence of nodes to follow - with custom depth. Consider the following two files:

file_to_be_included.yaml

Set1:
  Subset1:
    Key: Value
Set2:
  OtherKey: OtherValue

including_file.yaml

- Type: MyAgentWithInputs
  Id: 1
  Attributes: !include_node [ file_to_be_included.yaml, Set1:Subset1 ]

Compiling "including_file.yaml" results in

resulting_file.yaml

- Type: MyAgentWithInputs
  Id: 1
  Attributes:
    Key: Value

Load multiple files

Using wildcards in the given path (e.g. "path/to/many/*.yaml") will lead to loading multiple files and assigning their content to the same key. You can make use of this feature with or without specifying a node selector. However, the elements to be joined across multiple files must be lists. These lists are then concatenated into a single list and then assigned to the key in the file calling !include. This feature is especially useful for Contracts: You can split the Contracts list into several files and place them in a separate folder. Then use !include to re-integrate them into your configuration. An example:

my_contract1.yaml

Contracts:
 - ContractA
 - ContractB

my_contract2.yaml

Contracts:
 - ContractC
 - ContractD
 - ContractE

including_file.yaml

Contracts: [!include "my_contract*.yaml", "Contracts"]

results in

result.yaml

Contracts:
 - ContractA
 - ContractB
 - ContractC
 - ContractD
 - ContractE

Ignoring files

Files that have their name start with "IGNORE_" are not included with the !include command. You will see a debug output to notify you that the file was ignored. Use this to temporarily take files out ouf your configuration without deleting or moving them.

Read FAME results

Takes an output file in protobuf format of FAME-based applications and converts it into files in CSV format. An individual file for each type of Agent is created in a folder named after the protobuf input file. Call structure:

convertFameResults -f <./path/to/protobuf_file.pb>

You may also specify any of the following arguments:

Command	Action
-l or --log	Sets the logging level. Default is WARNING. Options are DEBUG, INFO, WARNING, ERROR, CRITICAL.
-lf or --logfile	Sets the logging file. Default is None. If None is provided, all logs get only printed to the console.
-a or --agents	If specified, only a subset of agents is extracted from the protobuf file. Default is to extract all agents.
-o or --output	Sets the path to where the generated output files are written to. If not specified, the folder's name is derived from the input file's name. Folder will be created if it does not exist.
-se or --single-export	Enables export of individual agents to individual files, when present. If not present (the default) one file per AgentType is created.
-m or --memory-saving	When specified, reduces memory usage profile at the cost of runtime. Use only when necessary.
-cc or --complex-column	Defines how to deal with complex indexed output columns (if any). IGNORE ignores complex columns. SPLIT creates a separate file for each complex indexed output column.
-t or --time	Option to define conversion of time steps to given format (default=UTC) by -t/--time {UTC, INT, FAME}
--input-recovery or --no-input-recovery	If True, all input data are recovered as well as the outputs (default=False).

Additionally, you may merge TimeSteps of a certain range of steps in the output files to i) associate multiple time steps with a common logical time in your simulation ii) reduce number of lines in output files

For this, add the option merge-times and specify the arguments as follows:

Command	Action
-fp or --focal-point	TimeStep on which steps-before earlier and steps-after later TimeSteps are merged on
-sb or --steps-before	Range of TimeSteps before the focal-point they get merged to
-sa or --steps-after	Range of TimeSteps after the focal-point they get merged to

This could look as follows:

convertFameResults -f <./path/to/protobuf_file.pb> -l debug -lf <path/to/output.log> -a AgentType1 AgentType2 -o myCsvFolder -m -cc SPLIT merge-times -fp 0 -sb 1799 -sa 1800

Make sure that in the range of time steps you specify for merging there is only one value per column in the merged time range. If multiple values per column are merged values will get concatenated and might yield unexpected results.

You may also call the conversion script from any Python script with:

from fameio.scripts.convert_results import Options, run as convert_results

convert_results({Options.FILE: "./path/to/protobuf_file.pb"})

Similar to the console call you may also specify custom run config arguments and add it in a dictionary to the function call.

from fameio.scripts.convert_results import Options, run as convert_results

run_config = {Options.FILE: "./path/to/protobuf_file.pb",
              Options.LOG_LEVEL: "info",
              Options.LOG_FILE: "scenario.log",
              Options.OUTPUT: "Output",
              Options.AGENT_LIST: ['AgentType1', 'AgentType2'],
              Options.MEMORY_SAVING: False,
              Options.SINGLE_AGENT_EXPORT: False,
              Options.RESOLVE_COMPLEX_FIELD: "SPLIT",
              Options.TIME: "INT",
              Options.TIME_MERGING: {},
              }

convert_results(run_config)

You can also use the associated argument parser, to extract the run_config dynamically from a string:

from fameio.scripts.convert_results import Options, run as convert_results
from fameio.source.cli.convert_results import handle_args

my_defaults = {Options.FILE: "./path/to/protobuf_file.pb",
               Options.LOG_LEVEL: "info",
               Options.LOG_FILE: "scenario.log",
               Options.OUTPUT: "Output",
               Options.AGENT_LIST: ['AgentType1', 'AgentType2'],
               Options.MEMORY_SAVING: False,
               Options.SINGLE_AGENT_EXPORT: False,
               Options.RESOLVE_COMPLEX_FIELD: "SPLIT",
               Options.TIME: "INT",
               Options.TIME_MERGING: {},
               }
my_arg_string = ['-f', 'my/other/scenario.yaml', '-l', 'error']

run_config = handle_args(my_arg_string, my_defaults)
convert_results(run_config)

Cite FAME-Io

If you use FAME-Io for academic work, please cite as follows.

Bibtex entry:

@article{fameio2023joss,
  author  = {Felix Nitsch and Christoph Schimeczek and Ulrich Frey and Benjamin Fuchs},
  title   = {FAME-Io: Configuration tools for complex agent-based simulations},
  journal = {Journal of Open Source Software},
  year    = {2023},
  doi     = {doi: https://doi.org/10.21105/joss.04958}
}

Available Support

This is a purely scientific project by (at the moment) one research group. Thus, there is no paid technical support available. However, we will give our best to answer your questions and provide support.

If you experience any trouble with FAME-Io, you may contact the developers via [e-mail]. Please report bugs and make feature requests by filing issues following the provided templates (see also Contribute). For substantial enhancements, we recommend that you contact us via [e-mail] for working together on the code in common projects or towards common publications and thus further develop FAME-Io.