src.pydasa.elements.specs.numerical#
Numerical perspective for variable representation.
This module defines the NumericalSpecs class representing the computational value ranges and discretization properties of a variable.
- Classes:
BoundsSpecs: Value bounds in original units StandardizedSpecs: Standardized value specifications with discretization NumericalSpecs: Numerical variable specifications (combines BoundsSpecs and StandardizedSpecs)
IMPORTANT: Based on the theory from:
# H.Gorter, Dimensionalanalyse: Eine Theoririe der physikalischen Dimensionen mit Anwendungen
Classes#
Value bounds in original units: min, max, mean, deviation, setpoint. |
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Standardized value specifications: ranges, statistics, and discretization. |
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Numerical perspective: computational value ranges. Answers the question: "What VALUES can this variable take?" |
Module Contents#
- class src.pydasa.elements.specs.numerical.BoundsSpecs#
Value bounds in original units: min, max, mean, deviation, setpoint.
This class represents the value ranges and statistical measures in the original (non-standardized) units of a variable.
- property setpoint: float | None#
setpoint Get specific value/point system for original units.
- Returns:
Specific value/point system for original units.
- Return type:
Optional[str]
- property min: float | None#
min Get minimum range value.
- Returns:
Minimum range value.
- Return type:
Optional[float]
- property max: float | None#
max Get the maximum range value.
- Returns:
Maximum range value.
- Return type:
Optional[float]
- property mean: float | None#
mean Get the Variable average value.
- Returns:
Variable average value.
- Return type:
Optional[float]
- property median: float | None#
median Get the median value.
- Returns:
Median value.
- Return type:
Optional[float]
- property dev: float | None#
dev Get the Variable standardized deviation.
- Returns:
Variable standardized deviation.
- Return type:
Optional[float]
- clear()#
clear() Reset bounds attributes to default values.
Resets all value ranges in original units.
- Return type:
None
- class src.pydasa.elements.specs.numerical.StandardizedSpecs#
Standardized value specifications: ranges, statistics, and discretization.
This class represents value ranges and statistical measures in standardized units, plus discretization properties for numerical analysis (step size, range array).
- property std_setpoint: float | None#
std_setpoint Get specific value/point system for standardized units.
- Returns:
Specific value/point system for standardized units.
- Return type:
Optional[str]
- property std_min: float | None#
std_min Get the standardized minimum range value.
- Returns:
standardized minimum range value.
- Return type:
Optional[float]
- property std_max: float | None#
std_max Get the standardized maximum range value.
- Returns:
standardized maximum range value.
- Return type:
Optional[float]
- property std_mean: float | None#
std_mean Get standardized mean value.
- Returns:
standardized mean.
- Return type:
Optional[float]
- property std_median: float | None#
std_median Get standardized median value.
- Returns:
Standardized median.
- Return type:
Optional[float]
- property std_dev: float | None#
std_dev Get standardized standardized deviation.
- Returns:
Standardized standardized deviation.
- Return type:
Optional[float]
- clear()#
clear() Reset standardized attributes to default values.
Resets all value ranges in standardized units.
- Return type:
None
- class src.pydasa.elements.specs.numerical.NumericalSpecs#
Bases:
BoundsSpecs,StandardizedSpecsNumerical perspective: computational value ranges. Answers the question: “What VALUES can this variable take?”
- This perspective combines:
BoundsSpecs: Value ranges in original units (min, max, mean, deviation, setpoint)
StandardizedSpecs: Value ranges in standardized units
Discretization properties (step, range array)
- This perspective focuses on:
Concrete bounds (minimum, maximum)
Central tendency (mean value)
Variation (standard deviation)
Discretization for simulations (step size, range arrays)
Unit conversions (original <-> standardized)
Variable dependencies (calculated variables)
- # From BoundsSpecs
_setpoint (Optional[float]): Specific value/point in original units. _min (Optional[float]): Minimum value in original units. _max (Optional[float]): Maximum value in original units. _mean (Optional[float]): Mean value in original units. _median (Optional[float]): Median value in original units. _dev (Optional[float]): Standard deviation in original units.
- Type:
original units
- # From StandardizedSpecs
_std_setpoint (Optional[float]): Specific value/point in standardized units. _std_min (Optional[float]): Minimum value in standardized units. _std_max (Optional[float]): Maximum value in standardized units. _std_mean (Optional[float]): Mean value in standardized units. _std_median (Optional[float]): Median value in standardized units. _std_dev (Optional[float]): Standard deviation in standardized units.
- Type:
standardized units
- # Discretization properties
_step (Optional[float]): Step size for simulations. _data (NDArray[np.float64]): Range for numerical analysis in standardized units.
- property step: float | None#
step Get standardized step size.
- Returns:
Step size (always standardized).
- Return type:
Optional[float]
- property data: numpy.typing.NDArray[numpy.float64]#
data Get standardized data array.
- Returns:
Data array for range (always standardized).
- Return type:
NDArray[np.float64]
- generate_data()#
generate_data() Generate standardized data array from min, max, using step value.
- Raises:
ValueError – If needed values are missing.
- Return type:
None
- clear()#
clear() Reset numerical attributes to default values.
Resets all value ranges, discretization, and step size by calling parent clear() methods.
- Return type:
None