| Package |   |
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Tools for input preparation and output digestion of FAME models
FAME-Io compiles input for FAME models in protobuf format and extracts model outputs to human-readable files. Please visit the FAME-Wiki to get an explanation of FAME and its components.
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.
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.
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.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)
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.
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:
The Schema consists of the sections JavaPackages and AgentTypes.
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
the corresponding section in the schema would look like this:
JavaPackages:
Agents:
- "agents"
DataItems:
- "msg"
Portables:
- "portableItems"
- "otherPortables"
One can leave out the DataItems specifications, but Agents and Portables are required and must not be empty.
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' ]
Outputs: [ 'Column1', 'Column2', 'Column3' ]
Metadata:
Some: "Dict with Metadata that you would like to add"
MyOtherAgentWithoutProductsOrAttributes:
MyAgentType Java's simple class name of the Agent typeAttributes indicates that beginning of the attribute definition section for this Agent typeMyAttribute 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 codeProducts list or dictionary of Products that this Agent can send in Contracts; derived from Java source code of this
Agent type (annotated with "@Product")Outputs list or dictionary of Output columns that this Agent can write to; derived from Java source code of this
Agent type (annotated with "@Output")Metadata dictionary with any content that is assigned to this Agent type as additional informationMyOtherAgentWithoutProductsOrAttributes an Agent type that requires neither Attributes nor ProductsAttributes, Products, Outputs and Metadata are optional - there may be useful Agents that require none of them. Products and Outputs can both be lists of Strings, or dictionaries with additional Metadata. For example, you could write the above in the following way:
Products:
Product1:
Metadata:
Any: "information you would like to add to Product1 using a dictionary form"
Product2:
Product3:
Outputs:
Column1:
Column2:
ThisEntry: "is ignored, as it is not below the keyword: 'Metadata'"
Metadata:
My: "Metadata"
That: "will be saved to Column2"
Column3:
Here, "Product1" and "Column2" have additional, optional Metadata assigned to them (using the keyword "Metadata"). The other Products and Columns have no metadata assigned to them - which is also ok.
In the AgentType definition example above attribute definition was not shown explicitly (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'
Metadata:
Go: "here"
MyComplexAttribute:
AttributeType: block
NestedAttributes:
InnerAttributeA:
AttributeType: integer
Values:
1:
Metadata:
Explain: "1 is a allowed value"
2:
Metadata:
Comment: "2 is also allowed, but consider using 1"
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 belowMandatory (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 providedList (optional - false by default)
AttributeType: time_series cannot be trueAttributeType: block
NestedAttributes (required only if AttributeType: block, otherwise disallowed) starts an inner Attribute
definition block - defined Attributes are sub-elements of MyComplexAttributeValues (optional - None by default):
Metadata keywordDefault (optional - None by default):
Values in case those are definedHelp (optional - None by default): if present, defines a help text for your AttributeMetadata (optional - None by default): if present, defines additional metadata assigned to the 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 |
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
Parameters:
RunId an ID that can be given to the simulation; use at your discretionStartTime 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 stampRandomSeed seed to initialise random number generation; each value leads to a unique series of random numbers.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"
Metadata:
Can: "also be assigned"
- Type: MyAgentWithoutInputs
Id: 2
Agent Parameters:
Type Mandatory; Java's simple class name of the agent to be createdId 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 aboveMetadata Optional; can be assigned to each instance of an Agent, as well as to each of its AttributesThe 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.
Metadata can be assigned to any value, list item, or superstructure.
To assign Metadata to a primitive value, create a dictionary from it, set the actual value with the inner
keyword Value and add the keyword Metadata like this:
ValueWithoutMetadata: 1
SameValueWithMetadata:
Value: 1
Metadata: # describe `SameValueWithMetadata` herein
You can assign Metadata to a list of primitive values using the keyword Values like this:
ValueListWithoutMetadata: [ 1,2,3 ]
SameValueListWithListMetadata:
Values: [ 1,2,3 ]
Metadata: # describe the whole list of values with Metadata here
or specify Metadata for each (or just some) value individually, like this:
ValueListWithoutMetadata: [ 1,2,3 ]
SameValueListWithMetadataAtEachElement:
- Value: 1
Metadata: # describe this specific value "1" with Metadata here
- Value: 2 # this value has no Metadata attached, but you can still use the keyword `Value`
- 3 # or use in the actual directly since this value has no Metadata anyway
or assign Metadata to both the list and any of its list entries, like this:
ValueListWithoutMetadata: [ 1,2,3 ]
SameValueListWithAllMetadata:
Metadata: # Recommendation: place the Metadata of the list first if the list of values is extensive, as in this case
Values:
- Value: 1
Metadata: # describe this specific value "1" with Metadata here
- Value: 2
Metadata: # describe this specific value "2" with Metadata here
- Value: 3
Metadata: # describe this specific value "3" with Metadata here
You can assign Metadata directly to a nested element by adding the Metadata keyword:
NestedItemWithoutMetadata:
A: 1
B: 2
SameNestedItemWithMetadata:
A: 1
B: 2
Metadata: # These Metadata describe `SameNestedItemWithMetadata`
Similar to lists of values, you can assign Metadata to a list of nested elements using the Values keyword, like this:
ListOfNestedItemsWithoutMetadata:
- A: 1
B: 10
- A: 2
B: 20
SameListOfNestedItemsWithGeneralMetadata:
Values:
- A: 1
B: 10
- A: 2
B: 20
Metadata: # These Metadata describe `SameListOfNestedItemsWithGeneralMetadata` as a whole
and, similar to nested elements, you can assign Metadata directly to any list element, like this:
ListOfNestedItemsWithoutMetadata:
- A: 1
B: 10
- A: 2
B: 20
SameListOfNestedItemsWithGeneralMetadata:
- A: 1
B: 10
Metadata: # These Metadata describe the first list item
- A: 2
B: 20
Metadata: # These Metadata describe the second list item
Again, you may apply both variants and apply Metadata to the list and each of its items if you wish.
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
Metadata:
Some: "additional information can go here"
- 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 productReceiverId unique ID of agent receiving the productProductName name of the product to be sentFirstDeliveryTime first time of delivery in the format "seconds after the January 1st 2000, 00:00:00"DeliveryIntervalInSteps delay time in between deliveries in secondsMetadata can be assigned to add further helpful information about a ContractAttributes can be set to include additional information as int, float, enum, or dict data typesOften, 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
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
StringSets in the scenario must contain an entry named exactly like the attribute, andValues 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.
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:
YYYY-MM-DD_hh:mm:ss or
a FAME-Timestamp integer value#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.
For large CSV files (with more than 20,000 rows) we recommend using the integer representation of FAME-Timestamps in the
first column (instead of text representation) to improve conversion speed.
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.
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
a.yaml
folder/b.yaml
folder/c.yaml
folder/deeper_folder/d.yaml
the following !include commands work
ToBe: !include "folder/b.yaml"
OrNot: !include "folder/deeper_folder/d.yaml"
ThatIs: !include "c.yaml"
TheQuestion: !include "deeper_folder/d.yaml"
Provided that
Or: maybe
not: "?"
the resulting file would look like this:
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:
!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
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:
Set1:
Subset1:
Key: Value
Set2:
OtherKey: OtherValue
- Type: MyAgentWithInputs
Id: 1
Attributes: !include_node [ file_to_be_included.yaml, Set1:Subset1 ]
Compiling "including_file.yaml" results in
- Type: MyAgentWithInputs
Id: 1
Attributes:
Key: Value
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:
Contracts:
- ContractA
- ContractB
Contracts:
- ContractC
- ContractD
- ContractE
Contracts: [!include "my_contract*.yaml", "Contracts"]
results in
Contracts:
- ContractA
- ContractB
- ContractC
- ContractD
- ContractE
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.
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 in addition to the outputs (default=False). |
-mt or --merge-times | Option to merge TimeSteps of a certain range of steps in the output files to associate multiple time steps with a common logical time in your simulation and reduce number of lines in output files |
The option --merge-times requires exactly three integer arguments separated by spaces:
| Position | Name | Meaning |
|---|---|---|
| First | Focal point | TimeStep on which steps-before earlier and steps-after later TimeSteps are merged on |
| Second | Steps before | Range of TimeSteps before the focal-point they get merged to, must be Zero or positive |
| Third | Steps after | Range of TimeSteps after the focal-point they get merged to, must be Zero or positive |
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 0 1799 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.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)
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}
}
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 fame@dlr.de. 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 fame@dlr.de for working together on the code in common projects or towards common publications and thus further develop FAME-Io.
FAQs
Tools for input preparation and output digestion of FAME models
We found that fameio demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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