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    AggregateBin

    source
    pub(all) struct AggregateBin {
      index : Int
      lower : Double
      upper : Double
      count : Int
      mean : Double
      minimum : Double
      maximum : Double
      standard_deviation : Double
    } derive(Eq, ToJson, 
    Debug    )

    ColumnStatistics

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    pub(all) struct ColumnStatistics {
      count : Int
      minimum : Double
      maximum : Double
      mean : Double
      standard_deviation : Double
      missing_count : Int
    } derive(Eq, ToJson, 
    Debug    )

    CsvOptions

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    pub(all) struct CsvOptions {
      delimiter : String
      has_header : Bool
      comment_prefix : String
    } derive(Eq, 
    Debug    )

    CsvOptions::default

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    fn CsvOptions::default() -> CsvOptions

    DataQuality

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    pub(all) enum DataQuality {
      Accepted
      Suspect(reason~ : String)
      Rejected(reason~ : String)
    } derive(Eq, ToJson, 
    Debug    )

    DataRule

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    pub(all) enum DataRule {
      XRange(lower~ : Double, upper~ : Double)
      YRange(lower~ : Double, upper~ : Double)
      XStrictlyIncreasing
      NoRejectedRows
      MaximumAbsoluteSlope(value~ : Double)
      MaximumRelativeJump(value~ : Double)
      MinimumRows(count~ : Int)
    } derive(Eq, ToJson, 
    Debug    )

    A reusable validation rule for measured equilibrium data.

    FeatureVector

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    pub(all) struct FeatureVector {
      id : String
      features : Array[Double]
      target : Double
    } derive(Eq, ToJson, 
    Debug    )

    GroupSummary

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    pub(all) struct GroupSummary {
      key : String
      count : Int
      mean : Double
      weighted_mean : Double
      minimum : Double
      maximum : Double
      total : Double
    } derive(Eq, ToJson, 
    Debug    )

    Histogram

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    pub(all) struct Histogram {
      bins : Array[AggregateBin]
      total_count : Int
      underflow : Int
      overflow : Int
    } derive(Eq, ToJson, 
    Debug    )

    MeasurementRow

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    pub(all) struct MeasurementRow {
      id : String
      x : Double
      y : Double
      temperature_kelvin : Double
      pressure_pascal : Double
      replicate : Int
      quality : DataQuality
    } derive(Eq, ToJson, 
    Debug    )

    A row of measured process data with optional operating metadata.

    MeasurementRow::is_usable

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    fn MeasurementRow::is_usable(self : MeasurementRow) -> Bool

    MeasurementRow::mark

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    MeasurementRow::new

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    fn MeasurementRow::new(id~ : String, x~ : Double, y~ : Double, temperature_kelvin~ : Double, pressure_pascal~ : Double, replicate? : Int) -> MeasurementRow raise 
    ExtractionError

    MeasurementRow::relative_volatility

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    fn MeasurementRow::relative_volatility(self : MeasurementRow) -> Double

    MeasurementRow::temperature_celsius

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    fn MeasurementRow::temperature_celsius(self : MeasurementRow) -> Double

    MeasurementTable

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    pub(all) struct MeasurementTable {
      name : String
      rows : Array[MeasurementRow]
      x_label : String
      y_label : String
    } derive(Eq, ToJson, 
    Debug    )

    MeasurementTable::accepted_count

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    fn MeasurementTable::accepted_count(self : MeasurementTable) -> Int

    MeasurementTable::correlation

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    MeasurementTable::deduplicate_x

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    MeasurementTable::filter_x

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    MeasurementTable::mark_outliers

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    MeasurementTable::moving_average

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    MeasurementTable::name

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    fn MeasurementTable::name(self : MeasurementTable) -> String

    MeasurementTable::new

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    fn MeasurementTable::new(name~ : String, rows~ : ArrayView[MeasurementRow], x_label? : String, y_label? : String) -> MeasurementTable raise 
    ExtractionError

    MeasurementTable::quality_report

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    fn MeasurementTable::quality_report(self : MeasurementTable) -> TableQuality

    MeasurementTable::rejected_count

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    fn MeasurementTable::rejected_count(self : MeasurementTable) -> Int

    MeasurementTable::rows

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    MeasurementTable::sorted_by_x

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    MeasurementTable::to_csv

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    fn MeasurementTable::to_csv(self : MeasurementTable) -> String

    MeasurementTable::usable_rows

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    MeasurementTable::x_statistics

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    MeasurementTable::x_values

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    fn MeasurementTable::x_values(self : MeasurementTable) -> Array[Double]

    MeasurementTable::y_statistics

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    MeasurementTable::y_values

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    fn MeasurementTable::y_values(self : MeasurementTable) -> Array[Double]

    QualityControlSummary

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    pub(all) struct QualityControlSummary {
      results : Array[RuleResult]
      passed : Bool
      total_violations : Int
      failed_rules : Int
    } derive(Eq, ToJson, 
    Debug    )

    QualityControlSummary::to_markdown

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    fn QualityControlSummary::to_markdown(self : QualityControlSummary) -> String

    RuleResult

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    pub(all) struct RuleResult {
      rule_name : String
      passed : Bool
      violations : Int
      first_row : Int
      message : String
    } derive(Eq, ToJson, 
    Debug    )

    TableQuality

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    pub(all) struct TableQuality {
      rows : Int
      usable_rows : Int
      rejected_rows : Int
      duplicate_x_groups : Int
      monotone_x : Bool
      finite_values : Bool
      message : String
    } derive(Eq, ToJson, 
    Debug    )

    TimePoint

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    pub(all) struct TimePoint {
      timestamp : Double
      value : Double
      quality : DataQuality
    } derive(Eq, ToJson, 
    Debug    )

    Time-series utilities for sampled process measurements.

    TimeSeries

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    pub(all) struct TimeSeries {
      name : String
      unit : String
      points : Array[TimePoint]
      sampling_interval : Double
    } derive(Eq, ToJson, 
    Debug    )

    TimeSeries::linear_trend

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    fn TimeSeries::linear_trend(self : TimeSeries) -> TrendResult

    TimeSeries::mark_quality

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    fn TimeSeries::mark_quality(self : TimeSeries, predicate : (Double) -> DataQuality) -> TimeSeries

    TimeSeries::maximum

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    fn TimeSeries::maximum(self : TimeSeries) -> Double

    TimeSeries::mean

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    fn TimeSeries::mean(self : TimeSeries) -> Double

    TimeSeries::minimum

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    fn TimeSeries::minimum(self : TimeSeries) -> Double

    TimeSeries::new

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    fn TimeSeries::new(name~ : String, unit~ : String, timestamps : ArrayView[Double], values : ArrayView[Double], interval? : Double) -> TimeSeries raise 
    ExtractionError

    TimeSeries::slice

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    TimeSeries::standard_deviation

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    fn TimeSeries::standard_deviation(self : TimeSeries) -> Double

    TimeSeries::timestamps

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    fn TimeSeries::timestamps(self : TimeSeries) -> Array[Double]

    TimeSeries::valid_points

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    fn TimeSeries::valid_points(self : TimeSeries) -> Array[TimePoint]

    TimeSeries::values

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    fn TimeSeries::values(self : TimeSeries) -> Array[Double]

    TimeSeries::variance

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    fn TimeSeries::variance(self : TimeSeries) -> Double

    TrendResult

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    pub(all) struct TrendResult {
      slope : Double
      intercept : Double
      r_squared : Double
      first_value : Double
      last_value : Double
      direction : String
    } derive(Eq, ToJson, 
    Debug    )

    WindowSummary

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    pub(all) struct WindowSummary {
      start : Double
      end : Double
      count : Int
      mean : Double
      minimum : Double
      maximum : Double
      standard_deviation : Double
    } derive(Eq, ToJson, 
    Debug    )

    accepted_only

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    aggregate_correlation

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    fn aggregate_correlation(first : ArrayView[Double], second : ArrayView[Double]) -> Double raise 
    ExtractionError

    aggregate_covariance

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    fn aggregate_covariance(first : ArrayView[Double], second : ArrayView[Double]) -> Double raise 
    ExtractionError

    aggregate_cv

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    fn aggregate_cv(values : ArrayView[Double]) -> Double

    aggregate_mad

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    fn aggregate_mad(values : ArrayView[Double]) -> Double

    aggregate_maximum

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    fn aggregate_maximum(values : ArrayView[Double]) -> Double

    aggregate_mean

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    fn aggregate_mean(values : ArrayView[Double]) -> Double

    aggregate_median

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    fn aggregate_median(values : ArrayView[Double]) -> Double

    aggregate_minimum

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    fn aggregate_minimum(values : ArrayView[Double]) -> Double

    aggregate_range

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    fn aggregate_range(values : ArrayView[Double]) -> (Double, Double)

    aggregate_rank

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    fn aggregate_rank(values : ArrayView[Double]) -> Array[Int]

    aggregate_replicates

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    aggregate_sort_indices

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    fn aggregate_sort_indices(values : ArrayView[Double]) -> Array[Int]

    aggregate_standard_deviation

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    fn aggregate_standard_deviation(values : ArrayView[Double]) -> Double

    aggregate_trimmed_mean

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    fn aggregate_trimmed_mean(values : ArrayView[Double], trim_fraction : Double) -> Double raise 
    ExtractionError

    aggregate_variance

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    fn aggregate_variance(values : ArrayView[Double]) -> Double

    aggregate_winsorized_mean

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    fn aggregate_winsorized_mean(values : ArrayView[Double], fraction : Double) -> Double raise 
    ExtractionError

    aggregate_z_scores

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    fn aggregate_z_scores(values : ArrayView[Double]) -> Array[Double]

    autocorrelation

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    fn autocorrelation(series : TimeSeries, lag : Int) -> Double raise 
    ExtractionError

    bin_index

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    fn bin_index(value : Double, lower : Double, upper : Double, bins : Int) -> Int

    cumulative_counts

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    fn cumulative_counts(histogram : Histogram) -> Array[Int]

    detect_plateau

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    fn detect_plateau(values : ArrayView[Double], tolerance~ : Double) -> Array[Bool]

    detect_spikes

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    fn detect_spikes(series : TimeSeries, threshold : Double) -> Array[TimePoint]

    detect_step_changes

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    fn detect_step_changes(series : TimeSeries, threshold : Double) -> Array[TimePoint]

    feature_matrix

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    fn feature_matrix(table : MeasurementTable) -> Array[Array[Double]]

    first_difference

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    fn first_difference(values : ArrayView[Double]) -> Array[Double]

    group_by_label

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    group_summary_csv

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    fn group_summary_csv(groups : ArrayView[GroupSummary]) -> String

    group_summary_markdown

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    fn group_summary_markdown(groups : ArrayView[GroupSummary]) -> String

    histogram

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    fn histogram(values : ArrayView[Double], lower : Double, upper : Double, bins : Int) -> Histogram raise 
    ExtractionError

    histogram_density

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    fn histogram_density(histogram : Histogram) -> Array[Double]

    histogram_quantile

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    fn histogram_quantile(histogram : Histogram, probability : Double) -> Double raise 
    ExtractionError

    mark_quality_by_rules

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    merge_tables

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    monotone_direction

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    fn monotone_direction(values : ArrayView[Double]) -> Int

    moving_average

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    moving_sum

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    normalize_column

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    fn normalize_column(values : ArrayView[Double]) -> Array[Double] raise 
    ExtractionError

    Normalize a measured column to the unit interval.

    parse_equilibrium_csv

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    Parse a two-column equilibrium table (x,y) from CSV text.

    parse_tie_lines_csv

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    Parse tie lines with columns r_carrier,r_solute,r_solvent,e_carrier,e_solute,e_solvent.

    quality_control

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    relative_difference

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    fn relative_difference(values : ArrayView[Double]) -> Array[Double]

    resample_linear

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    fn resample_linear(table : MeasurementTable, lower~ : Double, upper~ : Double, count~ : Int) -> Array[MeasurementRow] raise 
    ExtractionError

    Resample a monotone table onto evenly spaced x coordinates.

    resample_uniform

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    fn resample_uniform(series : TimeSeries, interval : Double) -> TimeSeries raise 
    ExtractionError

    rescale_column

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    fn rescale_column(values : ArrayView[Double], source_lower~ : Double, source_upper~ : Double, target_lower~ : Double, target_upper~ : Double) -> Array[Double] raise 
    ExtractionError

    rolling_summaries

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    rolling_variance

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    fn rolling_variance(values : ArrayView[Double], window~ : Int) -> Array[Double] raise 
    ExtractionError

    standardize_column

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    fn standardize_column(values : ArrayView[Double]) -> Array[Double] raise 
    ExtractionError

    table_features

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    fn table_features(table : MeasurementTable) -> Array[FeatureVector]

    time_series_csv

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    fn time_series_csv(series : TimeSeries) -> String

    time_series_markdown

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    fn time_series_markdown(series : TimeSeries) -> String

    validate_rule

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    validate_rules

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    weighted_group_by_label

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    fn weighted_group_by_label(labels : ArrayView[String], values : ArrayView[Double], weights : ArrayView[Double]) -> Array[GroupSummary] raise 
    ExtractionError

    winsorize_column

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    fn winsorize_column(values : ArrayView[Double], lower_probability~ : Double, upper_probability~ : Double) -> Array[Double] raise 
    ExtractionError

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