traval

Contents:

  • Getting Started
    • Installation
    • Usage
  • Examples
    • Example 1: Applying an automatic error detection algorithm to a time series
      • Data
      • The error detection algorithm and the RuleSet object
      • The Detector object
      • Binary Classification statistics and the confusion matrix
      • Receiver Operator Characteristic plot
    • Example 2: Applying an error detection algorithm to a full dataset
      • Load data
      • Helper functions for obtaining additional timeseries
      • Define error detection algorithm
      • Error detection
      • Calculate statistics
        • ROC-plot
    • Error detection rules included in traval
      • rule_ufunc_threshold: float threshold
      • rule_ufunc_threshold: threshold series
      • rule_diff_ufunc_threshold
      • rule_other_ufunc_threshold
      • rule_max_gradient
      • rule_spike_detection
      • rule_offset_detection
      • rule_outside_n_sigma
      • rule_diff_outside_of_n_sigma
      • rule_outside_bandwidth
      • rule_shift_to_manual_obs
      • rule_compare_to_manual_obs
      • rule_combine_corrections_or
      • rule_combine_corrections_and
      • rule_funcdict
      • rule_keep_comments
  • API-docs
    • Detector
      • Detector
        • Detector._validate_input_series()
        • Detector.apply_ruleset()
        • Detector.confusion_matrix()
        • Detector.get_comment_series()
        • Detector.get_corrections_comparison()
        • Detector.get_corrections_dataframe()
        • Detector.get_final_result()
        • Detector.get_indices()
        • Detector.get_results_dataframe()
        • Detector.get_series()
        • Detector.plot_overview()
        • Detector.reset()
        • Detector.set_truth()
        • Detector.stats_per_comment()
        • Detector.uniqueness()
    • RuleSet
      • RuleSet
        • RuleSet.add_rule()
        • RuleSet.del_rule()
        • RuleSet.from_json()
        • RuleSet.from_pickle()
        • RuleSet.get_resolved_ruleset()
        • RuleSet.to_dataframe()
        • RuleSet.to_json()
        • RuleSet.to_pickle()
        • RuleSet.update_rule()
      • RuleSetEncoder
        • RuleSetEncoder.default()
    • Rule Library
      • rule_combine_corrections_and()
      • rule_combine_corrections_or()
      • rule_combine_nan_and()
      • rule_combine_nan_or()
      • rule_diff_outside_of_n_sigma()
      • rule_diff_ufunc_threshold()
      • rule_flat_signal()
      • rule_funcdict()
      • rule_hardmax()
      • rule_hardmin()
      • rule_keep_comments()
      • rule_max_gradient()
      • rule_offset_detection()
      • rule_other_ufunc_threshold()
      • rule_outside_bandwidth()
      • rule_outside_n_sigma()
      • rule_pastas_outside_pi()
      • rule_shift_to_manual_obs()
      • rule_spike_detection()
      • rule_ufunc_threshold()
    • Time Series Comparison
      • DateTimeIndexComparison
        • DateTimeIndexComparison.idx_in_both()
        • DateTimeIndexComparison.idx_in_idx1()
        • DateTimeIndexComparison.idx_in_idx2()
      • SeriesComparison
        • SeriesComparison.compare_by_comment()
        • SeriesComparison.comparison_series()
      • SeriesComparisonRelative
        • SeriesComparisonRelative.compare_to_base_by_comment()
    • Time series Utilities
      • CorrectionCode
      • bandwidth_moving_avg_n_sigma()
      • corrections_as_float()
      • corrections_as_nan()
      • create_synthetic_raw_time_series()
      • diff_with_gap_awareness()
      • get_correction_status_name()
      • get_empty_corrections_df()
      • interpolate_series_to_new_index()
      • mask_corrections_above_below()
      • mask_corrections_above_threshold()
      • mask_corrections_below_threshold()
      • mask_corrections_equal_value()
      • mask_corrections_modified_value()
      • mask_corrections_no_comparison_value()
      • mask_corrections_not_equal_value()
      • resample_short_series_to_long_series()
      • spike_finder()
      • unique_nans_in_series()
    • Binary Classification
      • BinaryClassifier
        • BinaryClassifier.accuracy
        • BinaryClassifier.confusion_matrix()
        • BinaryClassifier.false_discovery_rate
        • BinaryClassifier.false_negative_rate
        • BinaryClassifier.false_omission_rate
        • BinaryClassifier.false_positive_rate
        • BinaryClassifier.from_confusion_matrix()
        • BinaryClassifier.from_series_comparison_relative()
        • BinaryClassifier.get_all_statistics()
        • BinaryClassifier.informedness
        • BinaryClassifier.matthews_correlation_coefficient
        • BinaryClassifier.mcc
        • BinaryClassifier.negative_predictive_value
        • BinaryClassifier.positive_predictive_value
        • BinaryClassifier.prevalence
        • BinaryClassifier.sensitivity
        • BinaryClassifier.specificity
        • BinaryClassifier.true_negative_rate
        • BinaryClassifier.true_positive_rate
    • Plots
      • ComparisonPlots
        • ComparisonPlots.plot_relative_comparison()
        • ComparisonPlots.plot_series_comparison()
        • ComparisonPlots.reset_color_dict()
        • ComparisonPlots.update_color_dict()
      • det_plot()
      • roc_plot()
traval
  • Overview: module code

All modules for which code is available

  • traval.binary_classifier
  • traval.detector
  • traval.plots
  • traval.rulelib
  • traval.ruleset
  • traval.ts_comparison
  • traval.ts_utils

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