swirlspy.ver package¶

Submodules¶

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

swirlspy.ver.metric.accuracy(cont)¶

Calculates accuracy

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	accuracy – accuracy = (hit+corrneg)/total total = hit+miss+false_alarm+corrneg
Return type:	float

swirlspy.ver.metric.csi(cont)¶

Calculates Critical Success Index

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	critical success index – csi = hit/(hit+false_alarm+miss)
Return type:	float

swirlspy.ver.metric.ets(cont)¶

Returns Equitable Threat Score[0 = no skill][1 = perfect]

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	equitable threat score – ets = (hit - hit_expected)/(hit+miss+false_alarm-hit_expected) where hit_expected = hit+miss)*(hit+false_alarm)/total total = hit + miss + false_alarm + corrneg
Return type:	float

swirlspy.ver.metric.far(cont)¶

Calculates False Alarm Ratio

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	false alarm ratio – far = false_alarm/(hit+false_alarm)
Return type:	float

swirlspy.ver.metric.freq_bias(cont)¶

Calculates Frequency Bias

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	frequency bias – freq_bias = (hit+false_alarm)/(hit+miss)
Return type:	float or int

swirlspy.ver.metric.hss(cont)¶

Calculates Heidke Skill Score [0 = no skill][1=perfect]

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	Heidke Success Score
Return type:	int or float hss = (hit+corrneg-hit_corrneg_expected)/(total-hit_corrneg_expected) hit_corrneg_expected = ((hit+miss)(hit+false_alarm) + (false_alarm+corrneg) (miss+corrneg)) / total

swirlspy.ver.metric.pod(cont)¶

Calculates Probability of Detection

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	probability of detection – pod = hit/(hit+miss)
Return type:	float

swirlspy.ver.metric.pofd(cont)¶

Calculates Probability of False Detection

Parameters:	cont (tuple cont = (hit, miss, false_alarm, corrneg) hit (number of hits) = cont[0] miss (number of misses) = cont[1] false_alarm (number of false alarms) = cont[2] corrneg (number of correct negatives) = cont[3]) –
Returns:	probability of false detection – pofd = false_alarm/(false_alarm + corrneg)
Return type:	float