xskillscore.Contingency¶
-
class
xskillscore.
Contingency
(observations, forecasts, observation_category_edges, forecast_category_edges, dim)¶ Class for contingency based skill scores
- Parameters
observations (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.
forecasts (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.
observation_category_edges (array_like) – Bin edges for categorising observations. Bins include the left most edge, but not the right.
forecast_category_edges (array_like) – Bin edges for categorising forecasts. Bins include the left most edge, but not the right.
dim (str, list) – The dimension(s) over which to compute the contingency table
- Returns
- Return type
Examples
>>> a = xr.DataArray(np.random.normal(size=(3,3)), ... coords=[('x', np.arange(3)), ('y', np.arange(3))]).to_dataset(name='test1') >>> b = xr.DataArray(np.random.normal(size=(3,3)), ... coords=[('x', np.arange(3)), ('y', np.arange(3))]).to_dataset(name='test1') >>> a['test2'] = xr.DataArray(np.random.normal(size=(3,3)), ... coords=[('x', np.arange(3)), ('y', np.arange(3))]) >>> b['test2'] = xr.DataArray(np.random.normal(size=(3,3)), ... coords=[('x', np.arange(3)), ('y', np.arange(3))]) >>> category_edges_a = np.linspace(-2,2,5) >>> category_edges_b = np.linspace(-3,3,5) >>> Contingency(a, b, category_edges_a, category_edges_b, dim=['x','y']) <xskillscore.Contingency> Dimensions: (forecasts_category: 4, observations_category: 4) Coordinates: observations_category_bounds (observations_category) <U12 '(-2.0, -1.0]'... forecasts_category_bounds (forecasts_category) <U12 '(-3.0, -1.5]' ..... * observations_category (observations_category) int64 1 2 3 4 * forecasts_category (forecasts_category) int64 1 2 3 4 Data variables: test2 (observations_category, forecasts_category) int64 ... test1 (observations_category, forecasts_category) int64 ...
References
http://www.cawcr.gov.au/projects/verification/
-
__init__
(observations, forecasts, observation_category_edges, forecast_category_edges, dim)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(observations, forecasts, …)Initialize self.
accuracy
()Returns the accuracy score(s) for a contingency table with K categories
bias_score
([yes_category])Returns the bias score(s) for dichotomous contingency data
correct_negatives
([yes_category])Returns the number of correct negatives (true negatives) for dichotomous contingency data.
equit_threat_score
([yes_category])Returns the equitable threat score(s) for dichotomous contingency data.
false_alarm_rate
([yes_category])Returns the false alarm rate(s) (probability of false detection) for dichotomous contingency data.
false_alarm_ratio
([yes_category])Returns the false alarm ratio(s) for dichotomous contingency data.
false_alarms
([yes_category])Returns the number of false alarms (false positives) for dichotomous contingency data.
Returns Gerrity equitable score for a contingency table with K categories.
Returns the Heidke skill score(s) for a contingency table with K categories
hit_rate
([yes_category])Returns the hit rate(s) (probability of detection) for dichotomous contingency data.
hits
([yes_category])Returns the number of hits (true positives) for dichotomous contingency data.
misses
([yes_category])Returns the number of misses (false negatives) for dichotomous contingency data.
odds_ratio
([yes_category])Returns the odds ratio(s) for dichotomous contingency data
odds_ratio_skill_score
([yes_category])Returns the odds ratio skill score(s) for dichotomous contingency data
Returns the Peirce skill score(s) (Hanssen and Kuipers discriminantor true skill statistic) for a contingency table with K categories.
success_ratio
([yes_category])Returns the success ratio(s) for dichotomous contingency data.
threat_score
([yes_category])Returns the threat score(s) for dichotomous contingency data.
Attributes
dichotomous
forecast_category_edges
forecasts
observation_category_edges
observations