zhaoyuanjuns/moonbit_extractor/calibration

README
U
ActivityModel
U
AuditReport
U
BalanceAudit
U
BenchmarkCase
U
BenchmarkResult
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BinodalCurve
U
BoundaryIntersection
U
BoundaryProjection
U
CartesianPoint
U
Concentration
U
ContactPattern
U
CountercurrentResult
U
CountercurrentStage
U
CrossValidation
U
CrosscurrentLeachingResult
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CrosscurrentResult
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CrosscurrentStage
U
CsvOptions
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DesignPoint
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Duration
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EfficiencyCalibration
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EfficiencyStageResult
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EfficiencyStep
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EquilibriumCurve
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EquilibriumPoint
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EquilibriumTable
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ExtractionError
U
ExtractionStep
U
Extrapolation
U
Finding
U
LeachingStage
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LeachingStageResult
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LedgerEntry
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LeverResult
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LinearTable
U
Mass
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MassFlow
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MassUnit
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NumericSummary
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Observation
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OperatingWindow
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PhaseSplit
U
Point
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Range
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RecoveryEnvelope
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RegressionModel
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SafetyFactor
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ScenarioResult
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ScenarioSummary
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SearchResult
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SensitivityPoint
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Severity
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SingleStageResult
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SingleStageScenario
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SolventDesign
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StageCountResult
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Statistics
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SvgCanvas
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TernaryComposition
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TieLine
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TieLineTable
U
TimeUnit
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Violation
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activity_factor
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analysis_balance_report
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apply_safety_factor
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approximately_equal
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audit_balances
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average_recovery
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balance_report
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bounded_minimize
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calibrate_efficiency
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cartesian_grid
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casebook
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centroid
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compare_contact_patterns
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concentration_from_fraction
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contains
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countercurrent_constant_k
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crosscurrent_constant_k
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crosscurrent_equal_solvent
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crosscurrent_equal_wash
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crosscurrent_leach
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distribution_coefficient
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extraction_factor
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fit_linear
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fit_polynomial
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from_cartesian
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grid_minimize
V
is_finite
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latin_hypercube
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leave_one_out
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ledger_entry
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ledger_passes
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lever_rule
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linspace
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map_unit_triangle
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maximum_residual
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mccabe_thiele_stages
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minimum_countercurrent_stages
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mix_point
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operating_line
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pareto_front
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parse_equilibrium_csv
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parse_tie_lines_csv
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polygon_area
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quantile
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recommended_safety_factor
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recovery_fraction
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run_casebook
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run_single_stage_scenario
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run_single_stage_scenarios
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sample_curve
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selectivity
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shortcut_actual_stages
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single_stage_constant_k
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single_stage_leach
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single_stage_recovery_envelope
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single_stage_sensitivity
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single_stage_table
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solvent_for_countercurrent
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solvent_for_crosscurrent
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solvent_for_single_stage
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split_on_tie_line
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stages_with_efficiency_profile
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stages_with_uniform_efficiency
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summarize
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summarize_scenarios
V
sweep_distribution
V
sweep_solvent_flow
V
ternary_triangle
V
to_cartesian
V
validate_curve
V
wash_for_target

zhaoyuanjuns/moonbit_extractor/calibration does not have a README file

BootstrapFit

pub(all) struct BootstrapFit {
  replicate : Int
  model : RegressionModel
  prediction_at_reference : Double
  residual_scale : Double
  accepted : Bool
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

Resampling and uncertainty summaries for calibration models.

CoefficientSummary

source

pub(all) struct CoefficientSummary {
  index : Int
  mean : Double
  standard_deviation : Double
  minimum : Double
  maximum : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

CrossValidation

source

pub(all) struct CrossValidation {
  folds : Int
  mean_squared_error : Double
  worst_squared_error : Double
  predictions : Array[Double]
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

EfficiencyCalibration

source

pub(all) struct EfficiencyCalibration {
  model : RegressionModel
  predicted_efficiencies : Array[Double]
  clamped_count : Int
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

FitUncertainty

source

pub(all) struct FitUncertainty {
  reference_x : Double
  mean_prediction : Double
  standard_deviation : Double
  lower_prediction : Double
  upper_prediction : Double
  relative_uncertainty : Double
  replicates : Int
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

MeasuredPoint

source

pub(all) struct MeasuredPoint {
  run_id : String
  x : Double
  y : Double
  standard_uncertainty : Double
  replicate : Int
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

A measured point with an explicit run identifier and optional uncertainty.

MeasuredPoint::new

source

fn MeasuredPoint::new(run_id~ : String, x~ : Double, y~ : Double, standard_uncertainty? : Double, replicate? : Int) -> MeasuredPoint raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

MeasuredPoint::weight

source

fn MeasuredPoint::weight(self : MeasuredPoint) -> Double

ModelComparison

source

pub(all) struct ModelComparison {
  first_rmse : Double
  second_rmse : Double
  first_mae : Double
  second_mae : Double
  preferred : String
  improvement_fraction : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

Compare two fitted models on the same measured points.

Observation

source

pub(all) struct Observation {
  x : Double
  y : Double
  weight : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

Small, dependency-free regression primitives for measured equilibrium data. The implementation deliberately keeps the normal equations visible so that a process engineer can audit the fit and its residuals without a black box.

Observation::new

source

fn Observation::new(x~ : Double, y~ : Double, weight? : Double) -> Observation raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

PredictionInterval

source

pub(all) struct PredictionInterval {
  x : Double
  prediction : Double
  lower : Double
  upper : Double
  half_width : Double
  confidence_level : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

RegressionDiagnostics

source

pub(all) struct RegressionDiagnostics {
  count : Int
  residuals : Array[ResidualRecord]
  mean_absolute_error : Double
  median_absolute_error : Double
  root_mean_squared_error : Double
  maximum_absolute_error : Double
  bias : Double
  r_squared : Double
  outlier_count : Int
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

RegressionModel

source

pub(all) struct RegressionModel {
  coefficients : Array[Double]
  degree : Int
  observations : Int
  weighted_mean_y : Double
  residual_sum_squares : Double
  total_sum_squares : Double
  r_squared : Double
  root_mean_squared_error : Double
  max_absolute_error : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

RegressionModel::coefficient

source

fn RegressionModel::coefficient(self : RegressionModel, index : Int) -> Double raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

RegressionModel::predict

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fn RegressionModel::predict(self : RegressionModel, x : Double) -> Double

RegressionModel::residual

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fn RegressionModel::residual(self : RegressionModel, observation : Observation) -> Double

RegressionModel::summary

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fn RegressionModel::summary(self : RegressionModel) -> String

ResidualRecord

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pub(all) struct ResidualRecord {
  run_id : String
  x : Double
  observed : Double
  predicted : Double
  residual : Double
  standardized_residual : Double
  leverage : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

ResponseSurfaceModel

source

pub(all) struct ResponseSurfaceModel {
  coefficients : Array[Double]
  observations : Int
  residual_sum_squares : Double
  r_squared : Double
  first_center : Double
  second_center : Double
  first_scale : Double
  second_scale : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

ResponseSurfaceModel::coefficient

source

fn ResponseSurfaceModel::coefficient(self : ResponseSurfaceModel, index : Int) -> Double raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

ResponseSurfaceModel::gradient

source

fn ResponseSurfaceModel::gradient(self : ResponseSurfaceModel, first : Double, second : Double) -> (Double, Double)

ResponseSurfaceModel::grid

source

fn ResponseSurfaceModel::grid(self : ResponseSurfaceModel, first_lower~ : Double, first_upper~ : Double, second_lower~ : Double, second_upper~ : Double, count~ : Int) -> Array[SurfaceGridPoint] raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

ResponseSurfaceModel::predict

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fn ResponseSurfaceModel::predict(self : ResponseSurfaceModel, first : Double, second : Double) -> Double

RobustFit

source

pub(all) struct RobustFit {
  model : RegressionModel
  iterations : Int
  removed_points : Int
  diagnostics : RegressionDiagnostics
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

A robust fit result after iteratively down-weighting large residuals.

SurfaceGridPoint

source

pub(all) struct SurfaceGridPoint {
  first : Double
  second : Double
  response : Double
  first_gradient : Double
  second_gradient : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

SurfaceObservation

source

pub(all) struct SurfaceObservation {
  first : Double
  second : Double
  response : Double
  weight : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

A response-surface observation for two process variables.

SurfaceObservation::new

source

fn SurfaceObservation::new(first~ : Double, second~ : Double, response~ : Double, weight? : Double) -> SurfaceObservation raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

SurfaceResidual

source

pub(all) struct SurfaceResidual {
  first : Double
  second : Double
  observed : Double
  predicted : Double
  residual : Double
} derive(Eq, ToJson, @moonbitlang/core/debug.Debug
Debug)

accepted_bootstrap_fits

source

fn accepted_bootstrap_fits(fits : ArrayView[BootstrapFit]) -> Array[BootstrapFit]

bootstrap_acceptance_rate

source

fn bootstrap_acceptance_rate(fits : ArrayView[BootstrapFit]) -> Double

bootstrap_fits

source

fn bootstrap_fits(observations : ArrayView[Observation], degree : Int, replicates : Int, reference_x : Double, maximum_rmse : Double) -> Array[BootstrapFit] raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

bootstrap_sample

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fn bootstrap_sample(observations : ArrayView[Observation], replicate : Int) -> Array[Observation] raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

calibrate_efficiency

source

fn calibrate_efficiency(ideal_stages : ArrayView[Double], actual_stages : ArrayView[Double]) -> EfficiencyCalibration raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Calibrate stage efficiency from measured ideal/actual stage pairs. Efficiencies are physically constrained to (0, 1] after prediction.

calibration_quality_label

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fn calibration_quality_label(rmse : Double, relative_uncertainty : Double) -> String

compare_fit_predictions

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fn compare_fit_predictions(first : RegressionModel, second : RegressionModel, xs : ArrayView[Double]) -> Array[Double]

compare_models

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fn compare_models(first : RegressionModel, second : RegressionModel, points : ArrayView[MeasuredPoint]) -> ModelComparison raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

cross_validated_rmse

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fn cross_validated_rmse(observations : ArrayView[Observation], degree : Int, folds : Int) -> Double raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

diagnose

source

fn diagnose(model : RegressionModel, points : ArrayView[MeasuredPoint], outlier_threshold? : Double) -> RegressionDiagnostics raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Build residual and leverage diagnostics for an existing polynomial model.

fit_linear

source

fn fit_linear(observations : ArrayView[Observation]) -> RegressionModel raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

fit_polynomial

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fn fit_polynomial(observations : ArrayView[Observation], degree : Int) -> RegressionModel raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Fit y = c0 + c1*x + ... + cn*x^n by weighted least squares.

fit_response_surface

source

fn fit_response_surface(observations : ArrayView[SurfaceObservation]) -> ResponseSurfaceModel raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

fit_uncertainty_markdown

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fn fit_uncertainty_markdown(uncertainty : FitUncertainty, label : String) -> String

learning_curve

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fn learning_curve(observations : ArrayView[Observation], degree : Int, minimum_size : Int) -> Array[Double] raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

leave_one_out

source

fn leave_one_out(observations : ArrayView[Observation], degree : Int) -> CrossValidation raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Leave-one-out validation is useful for small laboratory equilibrium tables.

maximum_prediction_difference

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fn maximum_prediction_difference(first : RegressionModel, second : RegressionModel, xs : ArrayView[Double]) -> Double

model_prediction_band

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fn model_prediction_band(model : RegressionModel, observations : ArrayView[Observation], xs : ArrayView[Double], confidence_multiplier : Double) -> Array[(Double, Double, Double)]

model_sensitivity

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fn model_sensitivity(model : RegressionModel, x : Double, relative_step? : Double) -> Double raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Calculate a normalized sensitivity of a fitted correlation at one point.

prediction_grid

source

fn prediction_grid(model : RegressionModel, lower~ : Double, upper~ : Double, count~ : Int) -> Array[PredictionInterval] raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Evaluate a model on a regular grid for plotting or process screening.

prediction_interval

source

fn prediction_interval(model : RegressionModel, points : ArrayView[MeasuredPoint], x : Double, confidence_level? : Double) -> PredictionInterval raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Estimate a prediction interval using model residual dispersion.

residual_scale

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fn residual_scale(model : RegressionModel, observations : ArrayView[Observation]) -> Double

response_surface_residuals

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fn response_surface_residuals(model : ResponseSurfaceModel, observations : ArrayView[SurfaceObservation]) -> Array[SurfaceResidual]

robust_polynomial_fit

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fn robust_polynomial_fit(points : ArrayView[MeasuredPoint], degree : Int, maximum_iterations? : Int, cutoff? : Double) -> RobustFit raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Iteratively reweighted least squares for noisy equilibrium measurements.

select_surface_optimum

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fn select_surface_optimum(model : ResponseSurfaceModel, observations : ArrayView[SurfaceObservation], maximize? : Bool) -> SurfaceObservation raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

summarize_coefficients

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fn summarize_coefficients(fits : ArrayView[BootstrapFit], coefficient_count : Int) -> Array[CoefficientSummary]

summarize_fit_uncertainty

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fn summarize_fit_uncertainty(fits : ArrayView[BootstrapFit], reference_x : Double, lower_probability : Double, upper_probability : Double) -> FitUncertainty raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

surface_rmse

source

fn surface_rmse(model : ResponseSurfaceModel, observations : ArrayView[SurfaceObservation]) -> Double raise @zhaoyuanjuns/moonbit_extractor/domain.ExtractionError
ExtractionError

Source Files

Site source Report issue Packages Build queue Skills Statistics

#BootstrapFit

#CoefficientSummary

#CrossValidation

#EfficiencyCalibration

#FitUncertainty

#MeasuredPoint

#MeasuredPoint::new

#MeasuredPoint::weight

#ModelComparison

#Observation

#Observation::new

#PredictionInterval

#RegressionDiagnostics

#RegressionModel

#RegressionModel::coefficient

#RegressionModel::predict

#RegressionModel::residual

#RegressionModel::summary

#ResidualRecord

#ResponseSurfaceModel

#ResponseSurfaceModel::coefficient

#ResponseSurfaceModel::gradient

#ResponseSurfaceModel::grid

#ResponseSurfaceModel::predict

#RobustFit

#SurfaceGridPoint

#SurfaceObservation

#SurfaceObservation::new

#SurfaceResidual

#accepted_bootstrap_fits

#bootstrap_acceptance_rate

#bootstrap_fits

#bootstrap_sample

#calibrate_efficiency

#calibration_quality_label

#compare_fit_predictions

#compare_models

#cross_validated_rmse

#diagnose

#fit_linear

#fit_polynomial

#fit_response_surface

#fit_uncertainty_markdown

#learning_curve

#leave_one_out

#maximum_prediction_difference

#model_prediction_band

#model_sensitivity

#prediction_grid

#prediction_interval

#residual_scale

#response_surface_residuals

#robust_polynomial_fit

#select_surface_optimum

#summarize_coefficients

#summarize_fit_uncertainty

#surface_rmse

Source Files

BootstrapFit

CoefficientSummary

CrossValidation

EfficiencyCalibration

FitUncertainty

MeasuredPoint

MeasuredPoint::new

MeasuredPoint::weight

ModelComparison

Observation

Observation::new

PredictionInterval

RegressionDiagnostics

RegressionModel

RegressionModel::coefficient

RegressionModel::predict

RegressionModel::residual

RegressionModel::summary

ResidualRecord

ResponseSurfaceModel

ResponseSurfaceModel::coefficient

ResponseSurfaceModel::gradient

ResponseSurfaceModel::grid

ResponseSurfaceModel::predict

RobustFit

SurfaceGridPoint

SurfaceObservation

SurfaceObservation::new

SurfaceResidual

accepted_bootstrap_fits

bootstrap_acceptance_rate

bootstrap_fits

bootstrap_sample

calibrate_efficiency

calibration_quality_label

compare_fit_predictions

compare_models

cross_validated_rmse

diagnose

fit_linear

fit_polynomial

fit_response_surface

fit_uncertainty_markdown

learning_curve

leave_one_out

maximum_prediction_difference

model_prediction_band

model_sensitivity

prediction_grid

prediction_interval

residual_scale

response_surface_residuals

robust_polynomial_fit

select_surface_optimum

summarize_coefficients

summarize_fit_uncertainty

surface_rmse