API Reference
This page provides an auto-generated summary of xskillscore
’s API.
For more details and examples, refer to the relevant chapters in the main part of the
documentation.
Deterministic Metrics
Correlation Metrics
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Effective sample size for temporally correlated data. |
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Pearson's correlation coefficient. |
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2-tailed p-value associated with pearson's correlation coefficient. |
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2-tailed p-value associated with Pearson's correlation coefficient, accounting for autocorrelation. |
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Slope of linear fit. |
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Spearman's correlation coefficient. |
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2-tailed p-value associated with Spearman's correlation coefficient. |
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2-tailed p-value associated with Spearman rank correlation coefficient, accounting for autocorrelation. |
Distance Metrics
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Mean Absolute Error. |
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Mean Absolute Percentage Error. |
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Mean Error. |
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Median Absolute Error. |
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Mean Squared Error. |
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R^2 (coefficient of determination) score. |
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Root Mean Squared Error. |
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Symmetric Mean Absolute Percentage Error. |
Probabilistic Metrics
Currently, most of our probabilistic metrics are ported over from
properscoring to work with
xarray
DataArrays and Datasets.
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Calculate Brier score (BS). |
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Continuous Ranked Probability Score with the ensemble distribution. |
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Continuous Ranked Probability Score with a Gaussian distribution. |
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Continuous Ranked Probability Score with numerical integration of the normal distribution. |
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Returns the data required to construct the discrimination diagram for an event; |
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Returns the rank histogram (Talagrand diagram) along the specified dimensions. |
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Returns the data required to construct the reliability diagram for an event; |
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Computes the relative operating characteristic for a range of thresholds. |
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Calculate Ranked Probability Score. |
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Calculate the Brier scores of an ensemble for exceeding given thresholds. |
Contingency-based Metrics
These metrics rely upon the construction of a Contingency
object. The user calls the
individual methods to access metrics based on the table.
|
Class for contingency based skill scores |
Contingency table
Dichotomous-Only (yes/no) Metrics
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Returns the bias score(s) for dichotomous contingency data |
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Returns the number of correct negatives (true negatives) for dichotomous contingency data. |
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Returns the equitable threat score(s) for dichotomous contingency data. |
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Returns the false alarm rate(s) (probability of false detection) for dichotomous contingency data. |
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Returns the false alarm ratio(s) for dichotomous contingency data. |
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Returns the number of false alarms (false positives) for dichotomous contingency data. |
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Returns the hit rate(s) (probability of detection) for dichotomous contingency data. |
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Returns the number of hits (true positives) for dichotomous contingency data. |
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Returns the number of misses (false negatives) for dichotomous contingency data. |
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Returns the odds ratio(s) for dichotomous contingency data |
Returns the odds ratio skill score(s) for dichotomous contingency data |
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Returns the success ratio(s) for dichotomous contingency data. |
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Returns the threat score(s) for dichotomous contingency data. |
Multi-Category Metrics
Returns the accuracy score(s) for a contingency table with K categories |
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Returns Gerrity equitable score for a contingency table with K categories. |
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Returns the Heidke skill score(s) for a contingency table with K categories |
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Returns the Peirce skill score(s) (Hanssen and Kuipers discriminantor true skill statistic) for a contingency table with K categories. |
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Computes the relative operating characteristic for a range of thresholds. |
Comparative
Tests to compare whether one forecast is significantly better than another one.
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Returns the Jolliffe and Ebert significance test. |
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Returns the Delsole and Tippett sign test over the given time dimension. |
Resampling
Functions for resampling from a dataset with or without replacement that create a new
iteration
dimension.
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Resample over |
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Resample over |