Provide an option to initialize FLORIS with preset defaults and no input files

[rafmudaf]

When using FLORIS, we generally get an input file from somewhere (probably the examples/ directory in the repository) and modify it to fit our needs. We use the new input file to initialize FlorisModel, and then we generally start changing the inputs programmatically. For example, for anything but a trivial study, we'll always change the layout and wind conditions. Consider the characteristic workflow from Example 10:

fmodel.set(layout_x=layout_x, layout_y=layout_y)

# Define a simple inflow with just 1 wind speed
wd_array = np.arange(0, 360, 4.0)
ws_array = 8.0 * np.ones_like(wd_array)
turbulence_intensities = 0.06 * np.ones_like(wd_array)
fmodel.set(
    wind_directions=wd_array, wind_speeds=ws_array, turbulence_intensities=turbulence_intensities
)

Therefore, we could classify the inputs to FLORIS as:

• Tier 1: Layout and wind condition
• Tier 2: Turbine definition, primarily the power and Ct curves
• Tier 3: Tunable settings like wake model parameters

Where FLORIS is used within another software, the FLORIS inputs have to be copied, supplied by the user, or somehow retrieved in order for FLORIS to function as a library (see flasc1, flasc2, WAVES). Instead, I propose to provide a method for using FLORIS as a library within a calling code with a logical set of default inputs (i.e. one of the existing example input files + NREL 5 MW turbine). Possible solutions for this are:

• Modify FlorisModel to load a predefined input file if selected: FlorisModel(configuration="defaults")
• Modify FlorisModel to use a predefined input set if no configuration is given: FlorisModel() See Provide an option to initialize FLORIS with preset defaults and no input files #1037 (comment)
• Provide a mechanism for retrieving an input set from FLORIS and to then use for initialization:

from floris import FlorisModel, default_inputs
fmodel = FlorisModel(configuration=default_inputs)

[misi9170]

I really like this idea. Although setting up a default with an empty configuration (fmodel = FlorisModel()) would be very clean, that is maybe a bit dangerous in that it really isn't clear what is being loaded. I quite like the first option: FlorisModel(configuration="defaults") (or, simply FlorisModel("defaults")) because it saves the user having to import a dictionary, but it's still fairly explicit. Internally, we could still import the default_inputs from a special file.

[rafmudaf]

Thanks for the feedback, this is very helpful. I agree that the empty initializer is too vague and will probably lead to unintended errors. After sitting on it for a few days, I think a combination of the other two options would be useful.

## Option 1; both forms are equivalent since it's the only argument
fmodel = FlorisModel(configuration="defaults")
fmodel = FlorisModel("defaults")

## Option 2
from floris import FlorisModel
default_inputs = FlorisModel.get_default_inputs()    # static method; returns a deep copy of the default input dict

# modify as needed
default_inputs["..."] = {"asdf": [i for i in range(10), "fdsa": [i for i in range(10)]}

# init
fmodel = FlorisModel(configuration=default_inputs)

[rafmudaf]

Another consideration here is that this might lead to an unclear state of FLORIS if users are not required to look at an input file. On one hand, it's on you if you don't read the docs to see what options are available in the inputs and what are their defaults. On the other hand, you don't know what you don't know when you're getting started. Considering this, it could be helpful to add an option to print both a condensed form of the current configuration options and a long form with all configuration options.

fmodel = FlorisModel("defaults")
fmodel.configs_short()
> Python version: 3.10
> FLORIS version: 4.2
> Configuration summary:
> solver:
>   type: turbine_grid
>   turbine_grid_points: 3
> 
> farm:
>   turbine_count: 3
>   turbine_type:
>   - nrel_5MW
> 
> flow_field:
>   findex_count: 20
> 
> wake:
>   model_strings:
>     combination_model: sosfs
>     deflection_model: gauss
>     turbulence_model: crespo_hernandez
>     velocity_model: gauss
> 
>   enable_secondary_steering: true
>   enable_yaw_added_recovery: true
>   enable_active_wake_mixing: false
>   enable_transverse_velocities: true



fmodel.configs_full()
> Python version: 3.10
> FLORIS version: 4.2
> Date: 25 Nov 2024
> Time: 10:00 AM
> Configuration:
> name: GCH
> description: Three turbines using Gauss Curl Hybrid model
> floris_version: v4
> 
> logging:
>   console:
>     enable: true
>     level: WARNING
>   file:
>     enable: false
>     level: WARNING
> 
> solver:
>   type: turbine_grid
>   turbine_grid_points: 3
> 
> farm:
>   layout_x:
>   - 0.0
>   - 630.0
>   - 1260.0
>   layout_y:
>   - 0.0
>   - 0.0
>   - 0.0
>   turbine_type:
>   - nrel_5MW
> 
> flow_field:
>   air_density: 1.225
>   reference_wind_height: -1
>   turbulence_intensities:
>   - 0.06
>   wind_directions:
>   - 270.0
>   wind_shear: 0.12
>   wind_speeds:
>   - 8.0
> 
> wake:
>   model_strings:
>     combination_model: sosfs
>     deflection_model: gauss
>     turbulence_model: crespo_hernandez
>     velocity_model: gauss
> 
>   enable_secondary_steering: true
>   enable_yaw_added_recovery: true
>   enable_active_wake_mixing: false
>   enable_transverse_velocities: true
> 
>   wake_deflection_parameters:
>     gauss:
>       ad: 0.0
>       alpha: 0.58
>       bd: 0.0
>       beta: 0.077
>       dm: 1.0
>       ka: 0.38
>       kb: 0.004
>   wake_velocity_parameters:
>     gauss:
>       alpha: 0.58
>       beta: 0.077
>       ka: 0.38
>       kb: 0.004
>   wake_turbulence_parameters:
>     crespo_hernandez:
>       initial: 0.1
>       constant: 0.5
>       ai: 0.8
>       downstream: -0.32