Scaling

Fields

formula

string, v3_linear | v3_power | v3_logarithmic | v3_exponential

parameters

list

dimensions

list, see Dimension

scaled_mki_set

list

formula

For NMDv4 this is the Scaling.formula. For NMDv3 use the following mapping from NMD_ProfielSets_Versies.SchalingsFormuleID:

1. -> v3_linear

2. -> v3_power

3. -> v3_logarithmic

4. -> v3_exponential

parameters

For the NMDv4 this is the Scaling.parameters. For NMDv3 the parameters are built as follows:

parameters = [
  NMD_ProfielSets_Versies.SchalingsFormuleA1,
  NMD_ProfielSets_Versies.SchalingsFormuleB1,
  NMD_ProfielSets_Versies.SchalingsFormuleC,
]

dimensions

The list of dimensions. See Dimension.

scaled_mki_set

For each dimension a few values are taken as input parameters for the calculation of the MKI with scaling:

• minimum

• lower-midpoint - midway between the minimum and the inspected value

• inspected_value

• upper-midpoint - midway between the inspected value and the maximum value

• maximum

Make sure to maintain the ordering of the dimensions as you defined them in the list of dimensions. So in the case of two dimensions, the dimension you listed first in the field dimensions will be called dimension 1 and the second dimension will be called dimension 2.

input_values

Every combination of the dimension values must be taken. In the case of 1 dimension this will result in 5 scaled_mki_sets. In the case of 2 dimensions this will result in 25 scaled_mki_sets:

• minimum 1, minimum 2

• minimum 1, lower-midpoint 2

• minimum 1, inspected_value 2

…

• maximum 1, upper-midpoint 2

• maximum 1, maximum_value 2

In the case of 2 dimensions, this list can be generated using the following pseudocode.

dimension1 = (
  dimension1.minimum,
  dimension1.lower_midpoint,
  dimension1.inspected_value,
  dimension1.upper_midpoint,
  dimension1.maximum,
)
dimension2 = (
  dimension2.min,
  dimension2.lower_midpoint,
  dimension2.inspected_value,
  dimension2.upper_midpoint,
  dimension2.maximum,
)
for input_dimension1 in dimension1:
  for input_dimension2 in dimension2:
    print {
      "input_values": [input_dimension1, input_dimension2],
      "mki": calculate_mki(input_values),
      "mki_net": calculate_mki_net(input_values)
    }

mki

The total MKI of the profile with the input_values as the inputs for scaling and including the supplement (toeslag) for category 3.

mki_net

The total MKI of the profile with the input_values as the inputs for scaling, but excluding the supplement (toeslag) for category 3.

Example

{
  "formula": "v3_linear",
  "parameters": [1.1, 0, 0.1],
  "dimensions": [], // not actually empty
  "scaled_mki_set": [
    {
      "input_values": [0.5],
      "mki": 0.5,
      "mki_net": 0.4,
    },
    {
      "input_values": [0.75],
      "mki": 0.75,
      "mki_net": 0.6
    },
    {
      "input_values": [1.0],
      "mki": 1.0,
      "mki_net": 0.85
    },
    {
      "input_values": [1.5],
      "mki": 1.5,
      "mki_net": 1.4
    },
    {
      "input_values": [2.0],
      "mki": 2.0,
      "mki_net": 1.8
    }
  ]
}