swirlspy.ver package¶
Submodules¶
swirlspy.ver.crosstab module¶

swirlspy.ver.crosstab.
contingency
(threshold, forecast, observed)¶ Generates contingency table statistics for traditional binary verification
 Parameters
threshold (float) – Threshold value of variable for verification.
forecast (xarray) – An xarray containing forecasted values of variable
observed (xarray) – An xarray containing observed values of variable
 Returns
contingency – tuple structure is (hit, miss, false_alarm, corrneg)
 Return type
tuple
Notes
 hit: int
number of hits
 miss: int
number of misses
 false_alarm: int
number of false alarms
 corrneg: int
number of correct negatives
swirlspy.ver.metric module¶

swirlspy.ver.metric.
FSS
(threshold, forecast, observed, winsize)¶ Generates FSS
 Parameters
threshold (float) – Threshold value of variable for verification.
forecast (numpy array) – An xarray containing forecasted values of variable
observed (numpy array) – An xarray containing observed values of variable
winsize (int) – Window size
 Returns
fss – FSS score
 Return type
float

swirlspy.ver.metric.
accuracy
(cont)¶ Calculates accuracy
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
accuracy – Accuracy.
 Return type
float

swirlspy.ver.metric.
brier_skill_score
(forecast, observation)¶ Function to calculate the Brier Skill Score. This function is a wrapper about sklearn.metrics.brier_score_loss.
 Parameters
forecast (xarray.DataArray) – xarray populated with forecasted probabilities ~ (0, 1).
observation (xarray.DataArray) – xarray containing binary observation data.
 Returns
bss – Brier skill score.
 Return type
float

swirlspy.ver.metric.
csi
(cont)¶ Calculates Critical Success Index
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
critical success index – Critical success index.
 Return type
float

swirlspy.ver.metric.
ets
(cont)¶ Returns Equitable Threat Score[0 = no skill][1 = perfect]
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
equitable threat score – Equitable threat score.
 Return type
float

swirlspy.ver.metric.
f1Score
(forecast, observation, average='binary')¶ Computes the f1 score.
The f1_score can be seen as a weighted average of the precision and recall. The f1 score reaches its best value at 1 and its worst at 0. The formula for the f1 score is:
\[f1 = 2 * \frac{precision * recall}{precision + recall}\] Parameters
forecast (xarray.DataArray) – xarray populated with binary forecast data.
observation (xarray.DataArray) – xarray containing binary observation data.
average (string) –
This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
 ’binary’:
Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.
 ’micro’:
Calculate metrics globally by counting the total true positives, false negatives and false positives.
 ’macro’:
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
 ’weighted’:
Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an Fscore that is not between precision and recall.
 ’samples’:
Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).
 Returns
f1 – The f1 score.
 Return type
float or array of float, shape = [n_unique_labels]

swirlspy.ver.metric.
far
(cont)¶ Calculates False Alarm Ratio
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
false alarm ratio – False alarm ratio.
 Return type
float

swirlspy.ver.metric.
freq_bias
(cont)¶ Calculates Frequency Bias
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
frequency bias – Frequency bias.
 Return type
float or int

swirlspy.ver.metric.
hss
(cont)¶ Calculates Heidke Skill Score [0 = no skill][1=perfect]
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
Heidke Success Score – The Heidke Success Score
 Return type
int or float

swirlspy.ver.metric.
pod
(cont)¶ Calculates Probability of Detection
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
probability of detection – Probability of detection.
 Return type
float

swirlspy.ver.metric.
pofd
(cont)¶ Calculates Probability of False Detection
 Parameters
cont (tuple) – contingency tuple (refer to swirlspy.ver.crosstab)
 Returns
probability of false detection – The probability of false detection.
 Return type
float

swirlspy.ver.metric.
precision_recall
(forecast, observation, average=None)¶ A function to compute precisionrecall pairs for different probability thresholds. This function is a wrapper around sklearn.metrics.precision_recall_curve.
 Parameters
forecast (xarray.DataArray) – xarray populated with forecasted probabilities ~ (0, 1).
observation (xarray.DataArray) – xarray containing binary observation data.
average (list of strings) –
Decides the averaging in calculating the average precision score. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
 ’micro’:
Calculate metrics globally by considering each element of the label indicator matrix as a label.
 ’macro’:
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
 ’weighted’:
Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).
 ’samples’:
Calculate metrics for each instance, and find their average.
 ’binary’:
Only report results for the class specified by pos_label. This is applicable only if targets both forecast and observation arrays are binary.
 Returns
precision_recall_data – Contains precision and recall data as keys “precision” and “recall” required for plotting precision and recall curve. The average precision score, area under the curve and thresholds are included as keys “ap” , “auc”, and “thresholds” respectively.
 Return type
dict

swirlspy.ver.metric.
reliability
(forecast, observation, normalize=False, n_bins=5, strategy='uniform')¶ Function to generate data required for the plotting of reliability diagrams. Forecast and observation xarrays are considered to be geographically similar. This function is a wrapper around sklearn.calibration.calibration_curve.
 Parameters
forecast (xarray.DataArray) – xarray populated with forecasted probabilities ~ (0, 1).
observation (xarray.DataArray) – xarray containing binary observation data.
normalize (bool) – Whether y_prob needs to be normalized into the bin [0, 1], i.e. is not a proper probability. If True, the smallest value in y_prob is mapped onto 0 and the largest one onto 1. Defaults to False.
n_bins (int) – Number of bins. A bigger number requires more data. Bins with no data points (i.e. without corresponding values in y_prob) will not be returned, thus there may be fewer than n_bins in the return value.
strategy (str) – Strategy used to define the widths of the bins. Options are ‘uniform’ and ‘quantile’. Defaults to ‘uniform’.
 Returns
reliability_data – Contains data useful for plotting reliability diagrams. Data vars are: observed_rf (observed relative frequency) and nforecasts (number of forecasts). Climatology is included as an attribute.
 Return type
xarray.Dataset

swirlspy.ver.metric.
roc
(forecast, observation, average=None)¶ Function to compute Receiver Operating Characteristic. This function is a wrapper around sklearn.metrics.roc_curve and sklearn.metrics.roc_auc_score.
 Parameters
forecast (xarray.DataArray) – xarray populated with forecasted probabilities ~ (0, 1).
observation (xarray.DataArray) – xarray containing binary observation data.
average (string, [None, ‘micro’, ‘macro’ (default), ‘samples’, ‘weighted’]) –
If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
 ’micro’:
Calculate metrics globally by considering each element of the label indicator matrix as a label.
 ’macro’:
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
 ’weighted’:
Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).
 ’samples’:
Calculate metrics for each instance, and find their average.
Will be ignored when observation is binary.
 Returns
roc_data – Contains Probability of Detection and Probability of False Detection as keys “pod” and “pofd” required for plotting ROC curves. Area under the ROC curve and thresholds are included as keys “auc” and “thresholds” respectively.
 Return type
dict