Inspect Model Parameters

Once we have reviewed the structure of our APSIM model—including the model paths and component names—we are in a good position to explore model internals more deeply. This tutorial introduces the inspect_model_parameters() method, which provides a unified way to extract parameters from a variety of APSIM model components.

Note

The inspect_model_parameters() method simplifies parameter inspection by consolidating functionality that was previously spread across multiple methods such as:

examine_management_info
read_cultivar_params
Various model-specific inspectors

inspect_model_parameters function signature

inspect_model_parameters(
    model_type: str,
    simulations: Union[str, list],
    model_name: str,
    parameters: Union[str, set, list, tuple] = 'all',
    **kwargs
    ) -> Union[dict, list, pd.DataFrame, Any]

model_type(str) see more details here: The type or class of the model to inspect.
Examples:: Shorthand: 'Clock', 'Weather' Fully qualified: 'Models.Clock', 'Models.Climate.Weather'
simulations (str or list):: One or more simulation names from your APSIM file to query. defaults to all
model_name: (str): The instance name of the model within the simulation. Example: If model_type = Solute, this could be 'NO3', 'NH4', or 'Urea'. if the model was renamed, the new name is the model_name

parameters (str, set, list, tuple, optional): Specific parameter(s) to retrieve. Defaults to 'all', which returns all available attributes. Common examples for layered models like Solute: Depth, InitialValues, SoluteBD, Thickness.

**kwargs: Reserved for future use (currently unused).

Returns

Model Type	Return Format
Weather	File `path(s)` as string(s)
Clock	Dictionary with `Start` and `End` datetimes
Manager	`Dictionary` of script parameters
Soil models	`pandas.DataFrame` with layered data
Report	`Dictionary` with VariableNames and EventNames
Cultivar	`Dictionary` of parameter strings

Let’s take a look at how it works.

from apsimNGpy.core import ApsimModel
model_instance = ApsimModel('Maize')

Inspect full soil Organic profile:

model_instance.inspect_model_parameters('Organic', simulations='Simulation',
 model_name='Organic')

# output

   CNR  Carbon      Depth  FBiom  ...         FOM  Nitrogen  SoilCNRatio  Thickness
12.0    1.20      0-150   0.04  ...  347.129032     0.100         12.0      150.0
12.0    0.96    150-300   0.02  ...  270.344362     0.080         12.0      150.0
12.0    0.60    300-600   0.02  ...  163.972144     0.050         12.0      300.0
12.0    0.30    600-900   0.02  ...   99.454133     0.025         12.0      300.0
12.0    0.18   900-1200   0.01  ...   60.321981     0.015         12.0      300.0
12.0    0.12  1200-1500   0.01  ...   36.587131     0.010         12.0      300.0
12.0    0.12  1500-1800   0.01  ...   22.191217     0.010         12.0      300.0
[7 rows x 9 columns]

Inspect soil Physical profile:

model_instance.inspect_model_parameters('Physical', simulations='Simulation',
 model_name='Physical')

# output

    AirDry        BD       DUL  ...        SWmm Thickness  ThicknessCumulative
0.130250  1.010565  0.521000  ...   78.150033     150.0                150.0
0.198689  1.071456  0.496723  ...   74.508522     150.0                300.0
0.280000  1.093939  0.488438  ...  146.531282     300.0                600.0
0.280000  1.158613  0.480297  ...  144.089091     300.0                900.0
0.280000  1.173012  0.471584  ...  141.475079     300.0               1200.0
0.280000  1.162873  0.457071  ...  137.121171     300.0               1500.0
0.280000  1.187495  0.452332  ...  135.699528     300.0               1800.0
[7 rows x 17 columns]

Inspect soil Chemical profile:

model_instance.inspect_model_parameters('Chemical', simulations='Simulation',
 model_name='Chemical')

# output

       Depth   PH  Thickness
    0-150  8.0      150.0
  150-300  8.0      150.0
  300-600  8.0      300.0
  600-900  8.0      300.0
 900-1200  8.0      300.0
1200-1500  8.0      300.0
1500-1800  8.0      300.0

Tip

Inspect one or more specific parameters. This can be achievement by key word argument parameters. This argument accepts both strings and lists or tuple. Please see the examples below.

model_instance.inspect_model_parameters('Organic',
   simulations='Simulation',
   model_name='Organic', parameters='Carbon')

#output

  Carbon
  1.20
  0.96
  0.60
  0.30
  0.18
  0.12
  0.12

Tip

only few selected parameters 'Carbon', 'CNR'

model_instance.inspect_model_parameters('Organic',
 simulations='Simulation',
  model_name='Organic',
  parameters=['Carbon', 'CNR'])

# output

   Carbon   CNR
  1.20  12.0
  0.96  12.0
  0.60  12.0
  0.30  12.0
  0.18  12.0
  0.12  12.0
  0.12  12.0

Inspect Report model attributes.

Hint

Report attributes are returned in two categories;

‘EventNames’: used for triggering recording or reporting events.
‘VariableNames’: actual variable paths.

       model_instance.inspect_model_parameters('Report',
        simulations='Simulation',
         model_name='Report')

# output

{'EventNames': ['[Maize].Harvesting'],
'VariableNames': ['[Clock].Today',
'[Maize].Phenology.CurrentStageName',
'[Maize].AboveGround.Wt',
'[Maize].AboveGround.N',
'[Maize].Grain.Total.Wt*10 as Yield',
'[Maize].Grain.Wt',
'[Maize].Grain.Size',
'[Maize].Grain.NumberFunction',
'[Maize].Grain.Total.Wt',
'[Maize].Grain.N',
'[Maize].Total.Wt']}

model_instance.inspect_model_parameters('Report',
 simulations='Simulation',
 model_name='Report',
 parameters='EventNames')

# output

{'EventNames': ['[Maize].Harvesting']}

Inspect Weather path

Hint

The returned weather file is a path for weather data

model_instance.inspect_model_parameters('Weather', simulations='Simulation',
  model_name='Weather')

# output
'%root%/Examples/WeatherFiles/AU_Dalby.met'

Inspect Manager script parameters.

Tip

These scripts are from the Manager Module. You need to know the exact name of the script hence you may want to inspect the whole Manager Models in the simulations file. Please use inspect_model(model_type='Manager', fullpath=False) to make a selection:

model_instance.inspect_model_parameters('Manager',
simulations='Simulation', model_name='Sow using a variable rule')

# output

{'Crop': 'Maize',
'StartDate': '1-nov',
'EndDate': '10-jan',
'MinESW': '100.0',
'MinRain': '25.0',
'RainDays': '7',
'CultivarName': 'Dekalb_XL82',
'SowingDepth': '30.0',
'RowSpacing': '750.0',
'Population': '10'}

Tip

Script Manager parameters can vary significantly between different scripts. To understand what parameters are available in a given context, it’s best to inspect them using the method above. In the following example, we demonstrate how to inspect the value of a specific parameter—Population

model_instance.inspect_model_parameters('Manager',
simulations='Simulation', model_name='Sow using a variable rule',
parameters='Population')

# Output

{'Population': '10'}

Inspect Cultivar parameters:

model_instance.inspect_model_parameters('Cultivar',
simulations='Simulation', model_name='B_110') # lists all path specifications for B_110 parameters abd their values

# output

{'[Phenology].Juvenile.Target.FixedValue': '210',
'[Phenology].Photosensitive.Target.XYPairs.X': '0, 12.5, 24',
'[Phenology].Photosensitive.Target.XYPairs.Y': '0, 0, 0',
'[Phenology].FlagLeafToFlowering.Target.FixedValue': '1',
'[Phenology].FloweringToGrainFilling.Target.FixedValue': '170',
'[Phenology].GrainFilling.Target.FixedValue': '730',
'[Phenology].Maturing.Target.FixedValue': '1',
'[Phenology].MaturityToHarvestRipe.Target.FixedValue': '100',
'[Rachis].DMDemands.Structural.DMDemandFunction.MaximumOrganWt.FixedValue': '36'}

model_instance.inspect_model_parameters('Cultivar', simulations='Simulation',
 model_name='B_110', parameters='[Phenology].Juvenile.Target.FixedValue')

# output

{'[Phenology].Juvenile.Target.FixedValue': '210'}

Caution

Please note that cultivar parameters are represented with an equal operator before the values, here they are returned as key value pairs with parameters as the keys

Inspect SurfaceOrganicMatter module. the surface organic matter parameters are not layered as Organic, Physical and Water or Chemical:

model_instance.inspect_model_parameters('Models.Surface.SurfaceOrganicMatter',
simulations='Simulation', model_name='SurfaceOrganicMatter')

# output

{'NH4': 0.0,
 'InitialResidueMass': 500.0,
 'StandingWt': 0.0,
 'Cover': 0.0,
 'LabileP': 0.0,
 'LyingWt': 0.0,
 'InitialCNR': 100.0,
 'P': 0.0,
 'InitialCPR': 0.0,
 'SurfOM': <System.Collections.Generic.List[SurfOrganicMatterType] object at 0x000001DABDBB58C0>,
 'C': 0.0,
 'N': 0.0,
 'NO3': 0.0}

model_instance.inspect_model_parameters('Models.Surface.SurfaceOrganicMatter', simulations='Simulation',
 model_name='SurfaceOrganicMatter', parameters={'InitialCNR', 'InitialResidueMass'})

# output
{'InitialCNR': 100.0, 'InitialResidueMass': 500.0}

Caution

If there are more than one simulation, using inspect_model_parameters() without specifying the simulation name will return a nested dictionary.

Inspect simulation Clock. Only two attributes are inspected Start and End dates, and they are are returned as python datetime objects

Example:

model_instance.inspect_model_parameters('Clock', simulations='Simulation',
  model_name='Clock')

{'End': datetime.datetime(2000, 12, 31, 0, 0),
'Start': datetime.datetime(1990, 1, 1, 0, 0)}

model_instance.inspect_model_parameters('Clock', simulations='Simulation',
model_name='Clock', parameters='End')

# Output

datetime.datetime(2000, 12, 31, 0, 0)

Extract Start year only. let’s see with start year as an example:

model_instance.inspect_model_parameters('Clock', simulations='Simulation',
model_name='Clock', parameters='Start').year

# output
1990

Extract End year only:

model_instance.inspect_model_parameters('Clock', simulations='Simulation',
model_name='Clock', parameters='End').year
2000

For this model_type, argument values to parameters can be start_date, end, End, Start, end_date, start. All will return the same thing, respectively. Example:

model_instance.inspect_model_parameters('Clock', simulations='Simulation',
model_name='Clock', parameters='end_date')

# output

datetime.datetime(2000, 12, 31, 0, 0)

# Inspect Solute models with Urea as an example. Others Solutes include NO3, NH4

model_instance.inspect_model_parameters('Solute', simulations='Simulation', model_name='Urea')

output

       Depth  InitialValues  SoluteBD  Thickness
    0-150            0.0  1.010565      150.0
  150-300            0.0  1.071456      150.0
  300-600            0.0  1.093939      300.0
  600-900            0.0  1.158613      300.0
 900-1200            0.0  1.173012      300.0
1200-1500            0.0  1.162873      300.0
1500-1800            0.0  1.187495      300.0

Inspect NH4 ``InitialValues``For layered properties,

Hint

All are returned as pandas even if one parameter is specified.

model_instance.inspect_model_parameters('Solute', simulations='Simulation',
 model_name='NH4',
 parameters='InitialValues')

output

    InitialValues
          0.1
          0.1
          0.1
          0.1
          0.1
          0.1
          0.1

Tip

The primary limitation of inspect_model_parameters is its verbosity—it often requires passing model_type, model_name and simulations or navigating deeply nested structures.

The inspect_model_parameters_by_path() method addresses this verbosity problem by allowing users to simply specify the path to the model component and (optionally) the parameters to inspect. This makes the API more concise and user-friendly.

As with inspect_model_parameters(), the parameters argument is optional—if not provided, the method will attempt to extract all available parameters from the model at the given path.

Inspect SurfaceOrganicMatter module parameters

model = ApsimModel('Maize')
model.inspect_model_parameters_by_path('.Simulations.Simulation.Field.SurfaceOrganicMatter')
# output

{'InitialCPR': 0.0,
  'InitialCNR': 100.0,
  'NH4': 0.0,
  'NO3': 0.0,
  'Cover': 0.0,
  'LabileP': 0.0,
  'N': 0.0,
  'SurfOM': <System.Collections.Generic.List[SurfOrganicMatterType] object at 0x1ae5c10c0>,
  'InitialResidueMass': 500.0,
  'LyingWt': 0.0,
  'StandingWt': 0.0,
  'C': 0.0,
  'P': 0.0}

Inspect surface organic matter module parameters by selecting a few parameters

model.inspect_model_parameters_by_path('.Simulations.Simulation.Field.SurfaceOrganicMatter',
 parameters = 'InitialCNR')
# output

{'InitialCNR': 100.0}

Inspect Sow using a variable rule manager module parameters

model.inspect_model_parameters_by_path('.Simulations.Simulation.Field.Sow using a variable rule')

Inspect Sow using a variable rule manager module parameters by selecting a few parameters

model.inspect_model_parameters_by_path('.Simulations.Simulation.Field.Sow using a variable rule',
   parameters= 'Population')

# output
{'Population': '10'}